COMPETENCE IN NEW PRODUCT DEVELOPMENT …

THE ROLE OF ALLIANCE MARKET ORIENTATION and ALLIANCE

COMPETENCE IN NEW PRODUCT DEVELOPMENT PERFORMANCE

by

Pelin Bicen, B.S., M.B.A.

Dissertation In

MARKETING

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Chair: Dr. Shelby D. Hunt

Committee Member: Dr. Donna F. Davis

Committee Member: Dr. Roy D. Howell

Committee Member: Dr. G. Tyge Payne

Fred Hartmeister Dean of the Graduate School

May, 2009

© Copyright 2009 by Pelin Bicen

All rights reserved.

Dedicated to

my dearest parents, Ali Bicen and Nuray Bicen, my little sister, Tulin, and

my lovely husband, Fatih, for being always there for me.

Texas Tech University, Pelin Bicen, May 2009

ii

ACKNOWLEDGEMENTS

I find myself thinking where to begin. I do know that I began my dissertation with the

intention of crafting something interesting and important, of making a significant

contribution to advancement of marketing knowledge. Such an endeavor is never a one-

person task. I will mention several people in the following paragraphs. These people have

made a considerable difference in my life, and provided boundless support and

encouragement in the realization of this dissertation and getting my degree. For that I am

greatly appreciative.

First, I am very indebted to my chair, Dr. Shelby D. Hunt, for his support and

commitment to ensure that this dissertation has a significant theoretical contribution to

marketing discipline. Dr. Hunt has always been a great mentor, a primary source of

unceasing encouragement, and intellectual motivation without which I could not

complete my dissertation. I find myself very lucky to find the privilege and honor to work

with him. I have also considerably benefited from help, support, and guidance of the rest

of my dissertation committee members. I am very thankful to Dr. Donna Davis, Dr. Roy

Howell, and Dr. Tyge Payne. Their specialized knowledge has guided me to refine and

revise this research tremendously. Dr. Davis is an expert in business-to-business

marketing. She has helped me in finding the most current B-B literature, and having a

strong theoretical foundation in my dissertation. Her positive and constructive attitude

and insightful comments have always encouraged me to look forward. Dr. Howell is a

leading scholar in psychometry and structural equation modeling. His continuous

enthusiasm and insightful questions and comments in measurement issues helped this

dissertation to be rigid and solid. I am very thankful to him motivating me to learn more


iii

about SEM and psychometry. Dr. Payne has contributed to this dissertation with his

challenging and interesting questions. His thoughtful comments really helped me to think

about my questions from the management perspective which redirected this research

towards a more strategic and managerial focus.

Dr. Debbie Laverie deserves special thanks for her boundless support and

encouragement during my PhD program. I find myself very lucky to have the privilege to

have her as a department chair, mentor, and co-author. Her innovative, professional, and

visionary style has motivated me to become a better marketing scholar. Without her, it

would not be possible for me to gain many skills that are the bread and butter of being a

great scholar. I wish to thank Professors Bob McDonald, James Wilcox, Dennis Arnett,

Dale Duhan, Tillmann Wagner, Shannon Rinaldo, Mayukh Dass, and Gomes-Casseres,

for their help and concern in the different stages of my doctoral program and dissertation.

I would like to thank Ms. Sherry Fowler for her thoughtful help and boundless

encouragement to make me think positively in every challenge.

I also would like to thank wholeheartedly a very special colleague of mine in the

doctoral program, Naveen Gudigantala. His endless support, positive thinking, and

encouragement have motivated me to become a better person. He has assisted me in work

and play, sharing good and bad times together. With his friendship, the PhD did not seem

that difficult. I am very indebted to him for having faith in me.

I also want to acknowledge the financial support from the Society of Marketing

Advances and Texas Tech University; and data support from the Institute for the Study of

Business Markets (ISBM) at Penn State University. Their recognition means a lot for me

and my scholarly career. My special thanks go to Professors Gary Lilien and Ralph Oliva


iv

at Penn State for their support and encouragement to doctoral students. With their help

and faith in doctoral students, we will keep advancing marketing science and practice.

My special thanks are extended to my lovely husband, Fatih. He has provided endless

sacrifices to help me through the doctoral program. He shared my every struggle and joy

in my PhD program. During the time we were in different countries; he always made me

feel close to him. His unselfish love, trust, and faith in me have given me tremendous

strength and perseverance to overcome the difficulties and challenges. Without him, I

would complete neither this dissertation nor this program.

Finally, I am extremely grateful to my dearest family for their love and endless

support. My parents extended their passion and motivation for education and science to

me. They were the strongest support for my education. They have provided me values

and wisdom about life. They gave me the freedom to be who I am. They have always

trusted me and my choices. This freedom and trust have motivated me to achieve high

standards in my life. My special thanks also go to my little sister, Tulin, for her concern

for my well-being. Her messages and phone calls still warm my heart. Her wishes for me

to succeed and be happy are the most valuable gift of my life.

Looking back over the last five years in Texas Tech, I realize how lucky I am to be a

part of Texas Tech Community. I have a lot of fond memories of my time here and

consider Lubbock my second home. I met the nicest people in Lubbock. I made strong

and sincere friendships. I love beautiful weather of Lubbock. I love Red Raiders. I love

the calm and pleasant atmosphere in here. I consider this dissertation the fruit of my

cherished days at Lubbock, and will proudly wear the Red Raider.


v

TABLE of CONTENTS

ACKNOWLEDGEMENTS .............................................................................................ĐĐ

ABSTRACT...................................................................................................................VĐĐĐ

LIST OF TABLES ........................................................................................................... X

LIST OF FIGURES .......................................................................................................XĐĐ

CHAPTER 1: INTRODUCTION.................................................................................... 1

1.1. OVERVIEW ...................................................................................................................... 1 1.2. PURPOSE AND THE SCOPE OF THE STUDY....................................................................... 5 1.3. OVERVĐEW OF THE CONCEPTUAL FRAMEWORK............................................................. 6 1.4. OVERVIEW OF THE RESEARCH HYPOTHESES ............................................................... 15 1.5. OVERVIEW OF THE RIVAL MODELS .............................................................................. 25 1.6. OVERVĐEW OF THE RESEARCH DESĐGN ........................................................................ 27 1.7. OVERVIEW OF THE DATA ANALYSIS AND MEASUREMENT .......................................... 28 1.8. OVERVĐEW OF THE THEORETĐCAL AND MANAGERĐAL CONTRĐBUTĐONS ..................... 29 1.9. STUDY OUTLINE .......................................................................................................... 33

CHAPTER 2: LITERATURE REVIEW ..................................................................... 39

2.1. LITERATURE REVIEW ON INTER-ORGANIZATIONAL NEW PRODUCT DEVELOPMENT.. 39 2.1.1. Defining New Product Innovation .........................................................................................40 2.1.2. Measurement of New Product Performance..........................................................................44 2.1.3. Inter-organizational New Product Development...................................................................48

2.2. LITERATURE REVIEW ON ALLIANCE MARKET ORIENTATION...................................... 55 2.2.1. Definition and Measurement of Market Orientation .............................................................56 2.2.2. Alliance Market Orientation..................................................................................................65

2.3. LITERATURE REVIEW ON ALLIANCE COMPETENCE ..................................................... 71

CHAPTER 3: THEORETICAL FRAMEWORK AND HYPOTHESES ................. 78

3.1. THEORETICAL FRAMEWORK......................................................................................... 78 3.1.1. A Resource-Based View of the Firm......................................................................................78 3.1.2. A Competence-Based View of the Firm .................................................................................83 3.1.3. The Resource-Advantage Theory of Competition ......................................................................86

3.1.2.1. R-A Theory and New Product Alliance Performance ................................................................ 94 3.2. HYPOTHESES............................................................................................................... 101 3.2.1. Alliance Market Orientation................................................................................................101 3.2.2. Goal Congruence and Complementary Resources ..............................................................104 3.2.3. Trust and Commitment ........................................................................................................107 3.2.4. Joint Alliance Competence ..................................................................................................110 3.2.5. Joint Top Management Support ..........................................................................................116 3.2.6. New Product Creativity .......................................................................................................117

3.3. SUMMARY OF THE HYPOTHESES....................................................................................... 120


vi

CHAPTER 4: METHODOLOGY .............................................................................. 121

4.1. SAMPLING ISSUES ............................................................................................................. 121 4.1.1. Sample Frame..........................................................................................................................121 4.1.2. Data Sources............................................................................................................................124 4.1.3. Identification of Key Informants ..............................................................................................126

4.2. DATA COLLECTION METHODS ........................................................................................... 128 4.2.1. Field interviews and pretest.....................................................................................................129 4.2.2. Final field study .......................................................................................................................131 4.2.3. Non-response bias....................................................................................................................136 4.2.4. Common Method Bias..............................................................................................................138 4.2.5. Characteristics of the Key Respondents and Their Firms........................................................139

4.3. QUESTIONNAIRE DESIGN AND MEASUREMENTS .............................................................. 142 4.3.1. Dependent Variable.............................................................................................................142 4.3.2. Independent Variables.........................................................................................................143 4.3.3. Control variables.................................................................................................................152

CHAPTER 5: MEASURE DEVELOPMENT ........................................................... 154

5.1 GENERAL MEASURE DEVELOPMENT PROCEDURES........................................................... 154

5.2. DEVELOPMENT OF ALLIANCE MARKET ORIENTATION MEASURES ................................. 156 5.2.1 Pretest Evaluation of Alliance Market Orientation Measures..................................................156

5.3. FINAL FIELD STUDY VALIDATION OF ALLIANCE MARKET ORIENTATION MEASURE...... 159 5.3.1. Coefficient Alpha and Item-to-total Correlation......................................................................159 5.3.2. Examining Dimensionality of Alliance Market Orientation.....................................................166

5.4. MEASURE DEVELOPMENT FOR OTHER MAJOR CONSTRUCTS .......................................... 170 5.4.1. Testing the Measure Characteristics in the Pretest .................................................................170 5.4.2. Testing the Measure Characteristics in the Final Field Study.................................................171 5.4.3 Confirming the Measurement Properties in the Respecified Model .........................................172

CHAPTER 6: RESULTS OF HYPOTHESIS TESTING ......................................... 185

6.1 REVIEW AND REVISION OF HYPOTHESIZED RELATIONSHIPS ............................................ 185

6.2. EXAMINATION OF CORRELATION MATRIX ....................................................................... 188

6.3. EXAMINATION OF ASSUMPTION VIOLATIONS .................................................................. 190

6.4. FINDING THE BEST MODEL ............................................................................................... 193

6.5. MAIN EFFECTS ESTIMATION RESULTS ............................................................................. 199 6.5.1. Alliance Market Orientation ....................................................................................................199 6.5.2. Goal Congruence and Complementary Resources ..................................................................200 6.5.3 Trust and Commitment..............................................................................................................201 6.5.4 Joint Alliance Competence .......................................................................................................201 6.5.5 Joint Top Management Support................................................................................................202 6.5.6. New Product Novelty and New Product Meaningfulness ........................................................202 6.5.7. Control Variables ....................................................................................................................203 6.5.8. Testing Alliance Market Orientation as a Key Mediator .........................................................203


vii

CHAPTER 7: CONCLUSION..................................................................................... 208

7.1 EVALUATĐNG ACHĐEVEMENT OF THE RESEACH OBJECTĐVES ............................................ 209

7.2 DĐSCUSSĐON AND THEORETĐCAL CONTRĐBUTĐON .............................................................. 211 7.2.1 Alliance Market Orientation .....................................................................................................212 7.2.2 Joint Alliance Competence .......................................................................................................217 7.2.3 New Product Creativity.............................................................................................................218 7.2.4 Control Variables .....................................................................................................................220 7.2.5 Theoretical Contribution ..........................................................................................................221

7.3 MANAGERĐAL CONTRĐBUTĐON ........................................................................................... 223

7.4 LĐMĐTATĐONS ...................................................................................................................... 225

7.5 FUTURE RESEARCH ISSUES ................................................................................................ 227

REFERENCES.............................................................................................................. 230

APPENDICES ............................................................................................................... 249


viii

ABSTRACT

This dissertation seeks to enhance understanding of successful inter-organizational

innovation activities by developing and testing a new theory framework for explaining

the role of alliance market orientation and alliance competence in the context of

alliance’s new product development (NPD) processes. Drawing on resource-based

theory, competence-based theory and, particularly, R-A theory, this dissertation

investigates how alliance market orientation and joint alliance competences of partnering

firms help them gain strategic advantages over competing dyads.

The main contribution of this study is to define alliance market orientation, develop a

measure for it, and test its role in the context of inter-organizational NPD performance.

This dissertation defines alliance market orientation as a capability that enables an

alliance (1) to jointly and systematically gather market intelligence (from competitor

analyses, studies of customer needs/preferences, and studies of the factors that influence

competitors’ and customers’ behaviors), (2) to inter-organizationally coordinate and

disseminate the knowledge gleaned from the market intelligence gathered, and (3) to

efficiently and effectively respond to the knowledge that is coordinated and disseminated.

Although the concept of inter-organizational market orientation has been discussed in the

marketing literature previously, it has been the focus of little conceptual development and

no empirical research. Given the increasing number of NPD alliances in recent years and

their high failure rates, it is important that we understand the role of alliance market

orientation in inter-organizational NPD activities.

A conceptual framework involving potential antecedents -- joint alliance competence,

goal congruence, complementary resources, trust and commitment -- is hypothesized to


ix

lead to alliance market orientation. It is also hypothesized that alliance market orientation

as a key mediating variable lead to new product outcomes. Based on R-A theory, alliance

market orientation is conceptualized as an idiosyncratic resource in this dissertation.

Therefore, it provides a strong ground for alliances’ sustainable competitive advantage in

some market segments and eventually improves their new product performances. As

another central variable in the model, alliance competence is postulated as it is led by top

management support. Alliance competence is also hypothesized to influence goal

congruence and complementary resources of alliances. The process is examined via

cross-sectional online-survey methodology. A total of 253 firms involved in dyadic new

product alliances in various high-tech industries participated in the study. Structural

equation modeling techniques were used to test the proposed model. The empirical

results point to the importance of alliance market orientation as a key mediating variable

to alliances’ new product performance. Joint alliance competence, goal congruence, trust,

and commitment lead to alliance new product outcomes only through alliance market

orientation. This study also found that top management support plays an important role in

building and maintaining firms’ alliance competences. Among the antecedents of alliance

market orientation, joint alliance competence is found to be the most influential one. It

means that in order for alliances to develop market orientation as a relationship property,

joint alliance competence seems to be the prerequisite. Finally, this study found that new

product novelty and new product meaningfulness are equally important in new product

performances of alliances. While the generalization of the empirical results may be

limited for several reasons, they nevertheless yield significant theoretical and managerial

implications for marketing strategy.


x

LIST OF TABLES

Table 1.1 Studies that Investigate Market Orientation from a Relationship Perspective 14

Table 2.2 Market Orientation Scales…………………………………………………….64

Table 4.1 Response Rate Summary ……………………………………………………133

Table 4.2 Reasons for non-participation to the survey ………………………………...134

Table 4.3 Reasons for not providing dyadic partner name……………………………..135

Table 4.4 Comparing Early and Late Respondents for Final Survey ………………….137

Table 4.5 Characteristics of the Final Field Survey ……………………………………141

Table 4.6 Job Titles for the Key Respondents………………………………………….141

Table 4.7 Industry Types for Participating Firms…………………………………. ......141

Table 5.1 Alliance Market Orientation Scale Development Process in the Pretest ........160

Table 5.2 Loadings from Exploratory Factor Analysis for the Final Measure of Creativity

in Pretest ......……………………………………………………………......163

Table 5.3 Confirmatory Factor Analysis on the Components of Alliance Market

Orientation Measures …………………………………………………........164

Table 5.4 Inter-Construct Correlations and Average Variance Extracted Values ……..164

Table 5.5 Alliance Market Orientation Scale Development Process Final Field Study 165

Table 5.6 Confirmatory Factor Analysis for the Final Measure of Alliance Market

Orientation in Final Field Study …..……………………………………….169

Table 5.7 Item and Scale Reliabilities for the Final Survey in Pretest ………………...174

Table 5.8 Descriptive Statistics for Composite Scales ………………………………...177

Table 5.9 Exploratory Factor Analysis for Major Constructs …………………………178

Table 5.10 Confirmatory Factor Analysis: Fit Indices for Measurement Model ...........180

Table 5.11 Confirmatory Factor Analysis for Major Constructs: Measurement Model..181

Table 5.12 Inter-Construct Correlations ……………………………………………….184

Table 6.1 Summary of the Hypothesis Testing ………………………………………...188

Table 6.2 Multiple Regression Results and Collinearity Diagnostics ………………... 192

Table 6.3 Fit indices of the Rival Models ……………………………………………...195

Table 6.4 Estimation Results from the Structural Equation Model of Alliance Market

Orientation Testing Main Effects Model (Model 1) ..……………………...196


xi


Orientation Testing Main Effects Model (Model 2)………….. ..………….197


Orientation Testing Main Effects Model (Model 3)………..........................199

Table 6.7 Test of Mediation, Nested Model Analysis …………………………………199

Table 6.8.Test of Mediation, Sobel Test Results………..... …………………………...206


xii

LIST OF FIGURES

Figure 1.1 An Alliance Market Orientation and Alliance Competence Model of New

Product Performance…..…………………………………………..................36

Figure 1.2 Rival Model of Alliance New Product Performance……………….…….......37

Figure 1.3 Rival Model of Alliance New Product Performance (AMO is a Second Order

Construct) …..……………………………………………………………….38

Figure 3.1 A Schematic of Resource-Advantage Theory of Competition………………99

Figure 3.2 Competitive Position Matrix………………………………………………..100

Figure 4.1 Procedure for Empirical Test ………………………………………………130

Figure 5.1 Measurement Model for Alliance Market Orientation ……………………..168

Figure 6.1 Structural Equation Model of Alliance Market Orientation and Alliance

Competence Model for New Product Performance ......…………………... 187

Figure 6.2 Structural Equation Model of Alliance Market Orientation and Alliance

Competence Model for New Product Performance...………………………207


1

CHAPTER 1: INTRODUCTION

1.1. Overview

New product development (NPD) is an engine of economic growth (Schumpeter

1934). It is frequently articulated in the marketing and management literatures that

product development is critical because new products are becoming the nexus of

competition of many organizations (Brown and Eisenhardt 1995; Clark and Fujimoto

1991; Hunt 2000). Since it is well recognized that new products are the life-blood of an

organization (Perks 2000), scholars have recently turned their attention toward

understanding the success factors of new product development. Researchers have

investigated such issues as the effect of organizations’ information processing activities

on new product development (Moorman 1995; Moorman and Miner 1997), the role of

firm size and incumbency in innovation management (Chandy and Tellis 1998), and the

performance of cross-functional new product development teams (Olson, Walker, and

Ruekert 1995; Song, Montoya-Weiss, and Schmidt 1997). Therefore, academic research

on new product development has largely concentrated on various intra-organizational

aspects of new product innovation (Rindfleisch and Moorman 2001).

One of the fundamental areas of research in the marketing field involves the concept

of exchange. More than two decades ago, Hunt (1983) concluded that “… the primary

focus of marketing is the exchange relationship” (p. 9). Many of the studies have

discussed the nature, antecedents, and consequences of various forms of exchange

relationships (Anderson and Narus 1990; Anderson and Weitz 1992; Bagozzi 1975;

Dwyer, Schuur, and Oh 1987; Frazier 1983). This exchange-based paradigm has

informed many inquiries in the domain of inter-organizational relationships such as the


2

relations between buyers and suppliers (Cannon and Homburg 2001; Cannon and Perrault

1999; Ganesan 1994; Jap 1999; Jap and Ganesan 2000; Kalwani and Narayandas 1995),

manufacturers and distributors (Anderson and Narus 1990; Anderson and Weitz 1992;

Morgan and Hunt 1994), service providers and clients (Heide and John 1988; Moorman,

Zaltman, and Desphande 1992), and strategic alliances (Kandemir, Yaprak, and Cavusgil

2006; Lambe, Spekman, and Hunt 2002; Luo, Rindfleisch, and Tse 2007; Rindfleisch

2000; Rindfleisch and Moorman 2001, 2003; Sivadas and Dyer 2000). In general terms,

inter-organizational relations are regarded as a basis for success in competitive

environments, and in turn are regarded as major determinants of competitive advantage

(Gulati 1998; Hunt 1997; Hunt and Morgan 1997; Jap 1999). Despite this wealth of inter-

organizational research, several aspects of inter-firm relations have eluded inquiry

(Rindfleisch and Moorman 2001). Three stand out.

First, while intra-organizational aspects of new product development and inter-

organizational relationships certainly warrant research attention, there is also an

important confluence between these two research streams (Rindfleisch 1998). It is known

that many organizations enter into business alliances to quicken the pace of innovation,

share risks, and gain access to resources (Sivadas and Dwyer 2000). A business alliance

is defined as collaborative efforts between two or more firms in which the firms pool

their resources in an effort to achieve mutually compatible goals that they could not

achieve easily alone (Lambe et al. 2002). Although it is well accepted that strategic

business partnerships are essential to being first to market with new products and services

(Hagedoorn 2002), there is a paucity of research on understanding the dynamics for


3

effective innovation management in an inter-organizational context (Erdem, Calantone,

and Droge 2006).

Second, over the last two decades, a movement toward thinking of marketing as an

organization-wide process rather than a function dominates both the marketing discipline

and the marketing practice (Moorman and Rust 1999). The most profound indication of

this change can be found in the market orientation (MO) literature (Gebhardt, Carpenter,

and Sherry 2006; Kohli and Jaworski 1990; Narver and Slater 1990). For example,

Jaworski and Kohli (1993) modeled market orientation as an organization-wide

generation of market intelligence, dissemination of the intelligence across departments,

and organization-wide responsiveness to it. Narver and Slater (1990) viewed market

orientation as an organizational culture that most effectively and efficiently creates the

behaviors for the creation of superior value for buyers. Day (1994) viewed market

orientation as a dynamic capability of an organization that involves market information

processing activities.

Briefly, market orientation has been discussed as a major prerequisite for being able

to create, measure, and deliver superior customer value, and in turn is regarded as a major

determinant of sustainable competitive advantage (Hunt and Morgan 1995). New product

development efforts have been discussed as the generation, dissemination, and utilization

of information (Moorman and Miner 1997). Therefore, it would seem relevant to consider

the market orientation of organizations as a potential predictor of the performance of their

new product development efforts. Much of the research in market orientation literature

that has focused on the relationship between market orientation and innovation mainly

found support for this relationship (Atuahene-Gima 1995; Atuahene-Gima and Ko 2001;


4

Han, Kim, and Srivasta 1998; Im and Workman 2004; Lukas and Ferrell 2000; Zhou,

Kim, and Tse 2005). However, academic research on market orientation has largely

concentrated on various intra-organizational aspects (Rindfleisch and Moorman 2001).

As noted by Hunt and Lambe (2000, p. 28, italics added),

Research on MO to date takes a single perspective… Because firms often create superior value for customers by collaborating with other organizations, firms that partner with other firms to compete must develop a strategy of MO that is inter-firm rather than intra-firm in nature. The antecedents, consequences, and measures of a business strategy of inter-firm MO are still lacking.

Third, many firms enter into business alliances and work across organizational

boundaries to co-develop new products and services, and it is steadily becoming a major

business model in a wide range of industries (Deck and Strom 2002). Nevertheless, many

of these alliances fail (Day 1995; Parkhe 1993; Varadarajan and Cunningham 1995).

Therefore, a rich literature has evolved on the question of what makes alliances succeed

(Lambe et al. 2002). One of the possible answers to this question is an alliance

competence, which is an organizational ability for finding, developing, and managing

alliances (Lambe et al. 2002, p. 143). According to Lambe et al. (2002), alliance

competence is a higher-level resource that contributes to competitive advantage in the

marketplace. Alliance competence can be viewed as a firm’s partnering ability.

Therefore, as noted by Sivadas and Dwyer (2000), one of the issues to examine is what

impact the partnering abilities of alliance partners has on cooperative market oriented

behaviors exhibited in the alliance.

In this dissertation, I seek to enhance marketing’s understanding of these issues by

examining the role of interfirm level market orientation in new product development

activities of alliances. For this reason, main objective of this study is to conceptualize a


5

new constuct, alliance market orientation, and find its possible antecedents and new

product related consequences. In addition to examining the role of alliance market

orientation in new product performance, I also investigate the relationship between joint

alliance competence (alliance competences of both firms) and alliance market orientation,

and their overall roles in an alliance’s new product development activities. Therefore,

this study explores four key issues:

1. What is an alliance market orientation? 2. What are the possible antecedents and new product related consequences of an

alliance market orientation? 3. What is the impact of joint alliance competence on an alliance market orientation? 4. How do an alliance market orientation and joint alliance competence help new

product alliances gain strategic advantages over competitors?

1.2. Purpose and the Scope of the Study

The purpose of this study is to address a gap in our understanding of the development

of market orientation of an alliance and its relationship with the joint alliance competence

of partner firms in new product development context. This study will attempt to replicate

and extend the article written by Lambe et al. (2002). Hubbard and Armstrong (1994)

defined a replication with extension as a duplication of a previously published emprical

research project that serves to investigate the generalizability of earlier research findings.

In an important call by Evanschitzky et al. (2007), authors mentioned that scientific

findings rest upon replication and, therefore, replications are needed especially for

important papers. Since Lambe et al.’s (2002) paper is a compelling paper in the alliance

literature, this study attempts to respond to this call.

In this study, as a first step, alliance market orientation will be conceptualized, and a

conceptual framework involving potential antecedents and new product related

consequences of alliance market orientation will be presented. Moreover, the relationship


6

between joint alliance competence and alliance market orientation will be discussed.

Although not exhaustive in its consideration of all potential antecedents and

consequences, the framework highlights key constructs from relationship marketing,

alliance, market orientation, and new product development literatures. The unit of

analysis thoughout this study will be a dyad. The focus will be on new product

development alliances, which are defined as formalized inter-organizational

arrangements between partnering firms to jointly generate, exchange, and utilize

information related to the research and development and marketing of new product

innovations (adapted from Rindfleisch and Moorman 2001, p. 1). The context is

relationships between financially independent new product alliance partners. Therefore,

vertically integrated hierarchical relationships will be beyond the scope of this study.

1.3. Overview of the Conceptual Framework

New product development (NPD) is important in society because it is an engine of

economic growth. In order for firms to have sustained competitive advantage in the long

run, firms should continously engage in creating new products and services (Hunt 2000;

Hunt and Morgan 1995; Schumpeter 1934). A recent BusinessWeek-BCG survey shows

that 72 percent of the senior executives in the survey named new product development

(NPD) as one of their top three priorities; however, survey results also show that almost

half of the respondents are dissatisfied with the returns on their investments in that area

(Business Week 2006a). Therefore, many organizations are entering into business

alliances to reduce the inherent risk associated with NPD and to overcome the intensified

competition and the rapid technological change (Doz and Hamel 1998; Heller and

Fujimoto 2004; Sivadas and Dwyer 2000; Varadarajan and Cunnigham 1995).


7

By entering strategic alliances to develop new products, alliance partners concern

themselves with obtaining information on the environment, engaging in a high degree of

inter-organizational information exchange, and making long-term decisions (Rindfleisch

and Moorman 2001; Sanzo, Santos, Vazquez, and Alvarez 2003). Indeed, market

orientation’s concept of inter-functional coordination in an organizational level appears to

have become a boundary-less activity and to have proliferated between inter-firm inter-

functional activities (Mason, Doyle, and Wong 2006). Therefore, it would seem relevant

to consider the market orientation of an alliance as a predictor of the performance of a

new product development effort. Indeed, research by Littler and Leverick (1995), Littler,

Leverick, and Bruce (1995) and Piercy and Cravens (1995) into collaborative NPD

projects shows that concentrating too much on ensuring inter-organizational harmony and

on preserving inter-relationships at the expense of a market focus may be harmful to new

product performance. Furthermore, Spekman, Isabella, and MacAvoy (1999, p. 26 italics

added) highlight that “there is a notable shift to a more market-focused view of alliance

activity.”

Recently, there has been a rising interest in examining market orientation from a

relationship perspective (Baker, Simpson, and Siguaw 1999; Blesa and Bigne 2005;

Langerak 2001; Leisen, Lilly, and Winsor 2002; Min and Mentzer 2000; Sanzo, Santos,

Vasquez, and Alvarez 2003; Siguaw, Simpson, and Baker 1998; Zhao and Cavusgil

2006). In all these studies, researchers investigate how the degree of market orientation of

one member of a distribution channel member influences the market orientation of

another channel member and/or whole value chain, and how their market orientation is

related to relevant channel characteristics (e.g., trust, commitment, satisfaction,


8

dependence). However, they do not examine market orientation as a relationship property

that could be developed at an inter-firm level. To my knowledge, there are only two

studies that investigate market orientation at the network level e.g., distribution network

and supply-chain network (Elg 2002; Grunert et al. 2005). However, both of these studies

discuss network level of market orientation only conceptually. Briefly, the marketing

literature has paid little attention to market orientation at an inter-organizational level.

The summaries of studies that investigate market orientation from a relationship

perspective can be found in the Table 1.1.Although anecdotal evidence and qualitative

analyses hint at possible outcomes, the lack of a systematic investigation makes it

difficult to properly assess the concept of alliance market orientation (AMO). Therefore,

main objective of this study is to take an initial step toward a systematic understanding of

the alliance market orientation concept, its antecedents, and its consequences in the new

product development context.

Although business alliances have been formed for distribution, product bundling,

marketing, and assorted other purposes, in this study I am interested in examining the

factors of alliance performance generalized to NPD goals (e.g., NP creativity and NP

performance). Therefore, using the two-firm alliance dyad as the referent unit of analysis,

I conceptualize an alliance market orientation that is an antecedent variable of NPD goals

(e.g., NP creativity and NP performance). I take as point of departures the definition of

market orientation by Kohli, Jaworski, and Kumar (1993) and Narver and Slater (1990).

Three closely related frameworks have been the foundation for much of market

orientation research: the behavioral perspective (Narver and Slater 1990), the process-

driven perspective (Kohli and Jaworski 1990; Kohli et al. 1993), and the systems


9

perspective (Becker and Homburg 1999). Although these perspectives have remarkable

differences, marketing researchers have noted that there is a fair amount of conceptual

and operational overlaps as well (Avlonitis and Gounaris 1997; Cadogan and

Diamantopoulos 1995; Helfert, Ritter, and Walter 2002). The underlying concepts and

activities that these three frameworks share can be given as the understanding of

customer wants, inter-departmental integration within the firm, and the importance of

decisive action in response to market opportunities (Noble, Sinha, and Kumar 2002). This

study adapts the definitions of Kohli, Jaworski, and Kumar (1993) and Narver and Slater

(1990) for two reasons. First, Kohli, Jaworski, and Kumar’s (1993) conceptualization of

market orientation expands the focus on market (e.g., external stakeholders) rather than

only customers, which is an essential ingredient of cooperative product development

efforts (Littler and Leverick 1995). Second, Narver and Slater’s (1990) conceptualization

of market orientation is based on three behavioral components (customer orientation,

competitor orientation, and inter-functional coordination), and their market orientation

conceptualization captures specific behavioral activities of an alliance (Littler et al. 1995;

Perks 2000; Spekman et al. 1999)

In this study, the conceptualization of an alliance market orientation does not strive to

re-define market orientation, but rather attempts to explicate the understanding of how

cooperative market oriented behavior of an alliance might be used to drive its new

product development strategy. In this study, alliance market orientation is conceptualized

as a collaborative effort. I define an alliance market orientation as a capability that

enables an alliance (1) to jointly and systematically gather market intelligence (from

competitor analyses, studies of customer needs/preferences, and studies of the factors that


10

influence competitors’ and customers’ behaviors), (2) to inter-organizationally coordinate

and disseminate the knowledge gleaned from the market intelligence gathered, and (3) to


Drawing on a resource-based of view of the firm (RBV), the competence-based view of

the firm, and, particularly, the resource-advantage theory of competition (R-A theory), an

alliance market orientation (AMO) is conceptualized as an idiosyncratic resource in this

dissertation.

In their seminal article, Hunt and Morgan (1995, p. 11) defined resources as “any

tangible or intangible entity available to the firm that enables it to produce efficiently

and/or effectively a market offering that has value for some market segment(s).” An RBV

of the firm argues that only resources that are rare, valuable, imperfectly imitable, and

non-substitutable can generate competitive advantage and superior financial performance

(Barney 1991; Day and Wensley 1988; Hunt and Lambe 2000; Wernerfelt 1984).

Research in this area has emphasized that resources and capabilities that lead to

competitive advantage are owned and controlled by a single firm. However, recently,

RBV researchers have recognized that these resources and capabilities may extend

beyond firm boundaries and be sources of inter-organizational competitive advantage

(Asanuma 1989; Bensaou and Anderson 1999; Das and Teng 2000; Day 1995; Dyer

1996; Dyer and Singh 1998; Jap 1999; Jap and Ganesan 2000; Lambe et al. 2002;

Varadarajan and Cunningham 1995). The R-A theory of competition views these

resources as a relational capital (Hunt 2000). Hunt discusses relational resources (capital)

in strategic alliances as they are radically heterogeneous and immobile, which means that

there is no marketplace where such resources can be traded. Marketing and management


11

scholars suggest that when partners are willing to make relationship-specific investments

by combining their resources in a unique way, their strategic advantages over competitors

that are unwilling to do so are increased. Therefore, idiosyncratic, inter-organizational

resources lead to competitive advantage (Dyer and Singh 1998). Lambe et al. (2002)

define alliance idiosyncratic resources as those that are (1) developed during the life of

the alliance, (2) unique to the alliance, and (3) facilitate the combining of the distinct

lower-order resources contributed by the partner firms (and, hence, are higher-order

resources).

Hunt and Morgan (1995, p.13) discuss market orientation as a firm resource. They

argue that “a market orientation is intangible, can’t be purchased in the marketplace, is

socially complex in its structure, has components that are highly interconnected, has mass

efficiencies, and is probably increasingly effective the longer it has in the place.”

Therefore, market orientation can be discussed as a source of sustainable competitive

advantage. Drawing on the R-A theory of competition, this study argues that alliance

market orientation is an idiosyncratic resource of an alliance that provides an alliance

comparative advantage in resources that will yield a marketplace position of competitive

advantage and, thereby, superior financial performance. Under compatible marketing

philosophies, alliance partners become more efficient (e.g., they reduce their costs) and

effective (e.g., they increase customer value) in their efforts toward a common goal (e.g.,

competitive advantage). According to the R-A theory of competition, the marketplace

position of a competitive advantage results from an alliance, relative to its competitors,

having a resource assortment that enables it to produce a market offering (in this study,


12

new product) for some market segment(s) that is perceived to have superior value and/or

is produced at lower costs (Hunt 2000).

Lambe et al. (2002) argue that some organizations are better than others in finding,

developing, and managing alliances. The authors define this organizational ability as

alliance competence. They argue that alliance competence promotes the acquisition and

creation of the complementary and idiosyncratic resources that facilitate competitive

advantage and superior financial performance. In addition, they show that an alliance

competence positively affects idiosyncratic resources because it helps partner firms to

manage an alliance in a way that allows them to combine their complementary resources

over time to create idiosyncratic resources. Since I argue that alliance market orientation

is an idiosyncratic resource, one of the objectives of this proposed study is to examine the

relationship between joint alliance competence and alliance market orientation, and their

overall effect on new product development outcomes. In a recent study, Sivadas and

Dwyer (2000) question the impact the partnering abilities of alliance partners has on

cooperative behaviors exhibited in the alliance. They conjecture that high partnering

capability of alliance partners would foster greater cooperative and coordinative

behavior. In this proposed study, one of the objectives is to test this claim empirically and

take an initial step toward a systematic understanding of the relationship between joint

alliance competence and alliance market oriented behaviors.

Briefly, this study examines the role of alliance market orientation and alliance

competence in the new product development efforts of two-firm alliances. A conceptual

framework (Figure 1.1), involving potential antecedents—relational factors (i.e., trust and

commitment), inter-organizational factors (i.e., goal congruence, and complementary


13

resources), and joint alliance competence—is hypothesized to lead to an alliance market

orientation that enables an alliance to achieve higher new product creativity, which in

turn leads to higher new product performance than either firm would have been able to

accomplish individually. In addition, since it is hypothesized that relational factors, joint

alliance competence, and inter-organizational factors have indirect effects on new

product outcomes only through alliance market orientation, I argue that an alliance

market orientation acts as a key mediating variable that influences new product

performance of an alliance.

In this study, new product development alliances will be evaluated after the fact by

respondents. The study is based on the assumption that alliance market orientation will

lead to alliance new product performance. It might be the case for the reverse that new

product performance could lead to inferences about an alliance’s market oriented

behaviors and joint alliance competence. I attempt to control for this by trying to

introduce variability in the new product development projects. Respondents will be asked

to think about all the new product development projects in which their alliance has been

involved, not just successful, unsuccessful, or typical projects. Therefore, the proposed

model will be a static model that provides a snap shot perspective of a partnership, in

contrast to a dynamic model or an inter-temporal perspective.


14

Table 1.1. Studies that investigate mark et orientation from a relationship

perspective

Authors Primary Focus Empirical/

Conceptual

Sample/unit of

analysis

Siguaw et al. (1998) Impact of supplier’s market orientation on distributor’s market orientation. Its effects on relationship variables from distributor’s perspective. Ramifications of market oriented-behaviors in a dyadic relationship.

Empirical. Supplier-distributor / Dyads

Baker et al. (1999) The effect of a supplier’s perceptions of a reseller’s market orientation on the supplier’s perception of relationship factors.

Empirical. Suppliers/ Dyads

Elg (2002) Discussion about the meaning and content of inter-firm market orientation in a distribution network (supplier, manufacturer, and distributor) and how it is influenced by different network and relationship characteristics.

Conceptual.

Elg (2001)

Inter-firm market orientation of retailer-supplier.

Conceptual

Elg (2005) Inter-firm market orientation in channels

Case Study Manufacturer-Retailer /Dyads

Min and Mentzer (2000)

The role of market orientation in supply chain management

Conceptual

Zhao and Cavusgil (2006)

Effects of Supplier’ market orientation on manufacturers trust and long term relationship orientation

Empirical

Supplier-manufacturer / Dyads

Blesa and Bigne (2005)

Effects of manufacturers’ market orientation on distributors’ dependence and satisfaction.

Empirical

Manufacturer and distributor/ Dyads

Sanzo et al. (2003) Buyers’ degree of market orientation and its effect on buyers’ satisfaction with the supplier.

Empirical

Buyer and Supplier/ single firm

Grunert et al. (2005) Market oriented behaviors of value chain

Qualitative study Food Industry- 5 markets

Mason et al. (2006) The role of market orientation in supply chain configuration

Qualitative study Field interviews with supply chain members

Leisen et al. (2002) The relationships between organizational culture, market orientation, and marketing effectiveness in the context of strategic marketing alliances.

Empirical strategic marketing alliances /single firm


15

1.4. Overview of the Research Hypotheses

In this section, the research hypotheses of the study will be discussed briefly. An

overview of the alliance new product development process is depicted in Figure 1.1.

Alliance Market Orientation

Firms engage in inter-organizational agreements in the context and for the purpose of

achieving innovative new products. The basic explanation for innovation collaborations

is that it allows partners to economize their research investment that is often necessary for

radical innovation process (Steinmueller 2002). Although collaborative innovation

activities are of substantial importance to firms who want to remain competitive against

rivals capable of generating and delivering innovative products, few of them are able to

succeed in alliance management and result in successful innovative products. According

to the latest Association of Strategic Alliance Professional’s (ASAP 2007) survey among

alliance managers and business/corporate development executives, the success rate of

alliances in terms of pre-defined alliance financial performance objectives is only 30

percent. When importance of the new product alliances in free market economy is taken

into account, finding an answer for high failure rate among NPD alliances is urgent.

In this study, I argue that what should be central to understanding NPD alliance

management is whatever distinguishes successful and effective NPD alliances from those

that are unsuccessful and ineffective – that is, whatever produces NPD alliance success

instead of failure. Although the alliance management and new product literatures provide

many contextual factors that lead to the success or failure of NPD alliances, this study

argues that the presence of alliance market orientation is central to the high new product

alliance performance. Alliance market orientation is central to the NPD alliance success


16

because (1) it encompasses the gathering information on market intelligence, the

dissemination of this intelligence between partner firms, and the response to that

intelligence in a concerted manner which is necessary in delivering creative and

meaningful products close to the customer constellations of attributes, (2) it requires

close customer linkages through which NPD alliances can provide an ability to be more

nimble and more responsive by developing innovative products relative to their non-

market oriented competitors, (3) it demands monitoring close and potential rivals by

which respective alliance can develop innovative products that are dramatically

differentiated from those of competitors, and (4) its basic core component is being inter-

organizationally coordinated which plays a key role in the dissemination of market

intelligence, developing quality of information sharing process, resolution of inter-

organizational problems and disagreements by means of non-routine ways, and

responsiveness to market intelligence in novel and meaningful ways. Therefore, when

alliance market orientation is present, it provides alliances efficiency, effectiveness, and

productivity. In essence, alliance market orientation leads directly to novel and

meaningful new products and high new product performance. Since alliance market

orientation is conceptualized as a key construct in this study, it is hypothesized as the key

mediating variable of the theoretical model. Therefore, this study proposes that

H1: Alliance market orientation has a positive effect on a) new product novelty, b) new

product meaningfulness, and c) new product performance.

Goal Congruence and Complementary Resources

Jap (1999) argued that partner firms are more likely to engage in the development of

strategic advantages when they have congruent goals, because, by definition, congruent


17

goals refer to the pursuit of similar directions. Heide and John (1988) and Erdem et al.

(2006) indicated that when alliance partners have congruent goals there is a strong

incentive to build close relationships, and develop idiosyncratic resources which are

unique to the alliance, develop during the life of an alliance, and facilitate the combining

of the distinct lower-order resources contributed by the partner firms (Lambe et al. 2002).

In this study, alliance market orientation is conceptualized as an idiosyncratic resource.

Therefore, I argue that congruent goals of an NPD alliance dyad facilitate their market

oriented behaviors.

H2: Goal congruence has a positive effect on alliance market orientation

One of the critical issues in new product development alliances is the identification of

the complementary resources that each partner brings and the synergy that these

complementary resources create (Percy and Cravens 2000). As highlighted by Perk

(2000), complementary resources can influence the partners’ approach towards

integrating market information in the new product co-development process. In addition,

Das and Teng (2000) asserted that partners’ resource alignment influences the alliance

performance through collective strengths and cooperative behavior. Therefore, it can be

argued that successful reception of complementary resources may lead to development of

creative new products through collaborative and cooperative market oriented behaviors of

alliances.

H3: Complementary resources has a positive effect on alliance market orientation.

Trust and Commitment

One of the most widely acknowledged social norms for coordination of inter-

organizational exchange is trust (Donney and Cannon 1997; Jap 1999; Morgan and Hunt


18

1994). Anderson and Weitz (1992) argued that when trust exists between partners, they

both believe that long-term idiosyncratic investments can be made with limited risk. Elg

(2002) and Jassawalla and Sashittal (1998) argued that if alliance partners trust each

other, they will be more willing to gather market information together, share this

information with each other, and respond to that information in a concerted manner.

Since alliance market orientation is conceptualized as an idiosyncratic resource in this

study, I argue that trust is a sine qua non of developing and managing market orientation

in NPD alliances. Therefore, this study proposes that

H4: Trust is has a positive effect on alliance market orientation.

Commitment is another cornerstone of strategic alliances (Spekman et al. 1999).

Morgan and Hunt (1994) conceptualized commitment as an exchange partner believing

that an ongoing relationship with another is so important as to warrant maximum efforts

at maintaining it. Grunert et al. (2005) found that balanced relationship which comes with

trust and commitment is an antecedent for market orientation in a value-chain. Likewise,

Erdem et al. (2006) and Perk (2000) indicated that commitment of NPD partners to the

relationship can create a willingness to share market intelligence that they gather jointly,

and to respond to that information in a concerted manner. Anderson and Weitz (1992)

equated commitment with long term orientation and argued that commitment to the

relationship results in partners working together to increase mutual benefits. Therefore,

this study posits that

H5: Commitment has a positive effect on alliance market orientation.

Trust and commitment are the sine qua non of strategic alliances, for, without them,

there can be no alliances (Spekman et al. 1999). In their seminal article, Morgan and


19

Hunt (1994) defined trust as the confidence of one party in an exchange partner’s

reliability and integrity. Sivadas and Dwyer (2000) argued that lack of trust is inherent

and the primary concern in NPD alliances, thereby, a major cause of alliance failure. The

relationship between trust and commitment has received consistent support from the

literature (e.g., Morgan and Hunt 1994). The social exchange and relationship marketing

literatures indicate that trusting partners will have the desire to commit themselves to

their relationships. In an NPD alliance context, trust promotes commitment because as

trust develops between alliance partners, firms are more willing to sacrifice short-term

advantages and commit to their relationships for the sake of achieving long term

competitive advantage (Jap 1995). Therefore, this study posits that

H6: Trust has a positive effect on commitment.

Joint Alliance Competence

A central question in the alliance literature is why some firms are much more

successful at forming and sustaining alliances that contribute to their long-term

competitive advantage than others (Day 1995; Eisenhardt and Schoonhoven 1996; Gulati

1998; Kandemir et al. 2006; Varadarajan and Cunningham 1995). By adapting some of

the insights and answers from the competitive advantage literature, competence-based

theory is an internal factors theory of business strategy that provides an explanation to

this phenomenon. Numerous scholarly works have been developing competence-based

theory in a systematic manner (Chandler 1990; Prahalad and Hamel 1990; Hunt 2000;

Reed and DeFlippi 1990; Teece and Pisano 1994; Sanchez, Heene, and Thomas 1996).

Based on the findings of this literature, competence has been defined as an organizational

capability to deploy tangible and intangible entities in a way that helps a firm to compete


20

in its competitive marketplaces (Sanchez et al. 1996). This study argues that alliance

competence is such an entity that provides firms the ability to find competent alliance

partners, and develop and manage the relationship with them in as efficient a manner as

possible. This study adapts Lambe et al.’s (2002, p. 143) definition of alliance

competence to explain the success factors of alliances in new product development

context. Alliance competence has been defined as an organizational ability for finding,

developing, and managing alliances. Drawing on the R-A theory of competition, alliance

competence is conceptualized as a higher-order resource which is the ability to integrate

lower order resources in a way that can not be duplicated by competitors (Hunt 2000).

Consistent with Lambe et al.’s (2002) conceptualization, alliance competence has three

dimensions: (1) alliance experience, (2) partner identification propensity, and (3) alliance

manager development capability. In this study, joint alliance competence indicates that

both alliance partners have organizational ability for finding, developing, and managing

alliances.

In strategic alliances, goal congruence of the partners enhances the consistency of

expectations and assured mutual gains (Erdem et al. 2006; Jap 1999). Experienced

alliance partners accumulate and leverage alliance management know-how associated

with their prior and ongoing alliance experiences (Anand and Khanna 2000; Kandemir et

al.2006). Such experiences are expected to improve the ability of firms to identify and

select partners that they have similar goals with (Jap 1995). At Cisco, one of the leading

technology companies in the world, the company uses a basic framework for deciding

with whom to embark on significant collaborative effort. Cisco’s one of the criteria for


21

picking partners is the compatible goals and shared vision of technology and market

development (Deck and Strom 2002).

Firms that are able to proactively scan for competent partners are likely to identify

partners with congruent goals and compatible strategies (Kandemir et al. 2006; Sarkar et

al. 2001; Weitz and Jap 1995). When XM Satellite Radio Holding allied with Samsung

Electronics Co. to develop a new satellite radio, Dan Murphy, senior vice-president for

sales and marketing at XM, mentioned that their good eyes for allies helped them to find

a partner which had similar goals (Business Week 2006b).

Alliances require ongoing management of the relationship within a clear strategic

framework (Doz and Hamel 1998). Since this a complicated process, alliance

management needs multifaceted individuals with characteristics that enhance the

alliance’s mission. One of the role responsibilities of these alliance managers is to

understand the compatibility of possible partners’ strategic intents (Spekman et al. 1999).

Therefore, competent alliance partners develop and train competent alliance managers

that will identify, select, and negotiate with other partners that have congruent goals.

Therefore, this study proposes that

H7: Joint alliance competence has a positive effect on goal congruence of the dyad.

The departure point for all partnerships is the exchange of complementary resources

(Hunt and Morgan 1994), because, no single company has the complete array of

resources to individually develop and deliver projects (Sarkar et al. 2001).

Complementary resources refer to the degree to which the partners are able to complete

each other’s performance by supplying distinct resources (Anderson and Narus 1990;

Dyer and Singh 1998; Jap 1995). Competent alliance partners have an ability to


22

proactively monitor for partnering opportunities, and therefore, they are able to identify

partners with unique complementary resources.

Experiences with previous alliance partners contribute to knowledge about how to

develop, manage, and use alliances (Lambe et al. 1997; Simonin 1997). Day (1995), and

Varadarajan and Cunningham (1995) argued that accumulated history of collaboration

with previous alliance partners improve firms’ abilities with respect to identification of

potential partners that have the complementary resources.

Alliance managers are the essential part of alliance development and alliance success.

Competent alliance partners assign strong alliance managers in the charge of managing

the relationship with other alliance partners. Firms with competent alliance managers

have the vision to maintain a broad perspective spanning inside and outside the company

and to serve as the driving force behind the alliance’s creation including systematic and

proactive scanning for and identification of potential alliance partners that have the

complementary resources (Lambe et al. 2002; Spekman et al. 1999). Therefore, this study

posits that

H8: Joint alliance competence has a positive effect on complementary resources.

It is widely accepted in the market orientation literature that use of market

information is critical in developing innovative products (Atuahene-Gima and Ko 2001;

Han et al. 1998; Im and Workman 2004; Lukas and Ferrell 2000; Zhou et al. 2005).

Notwithstanding the growth of new product development alliances, the suggested means

to improve the NPD process and more importantly to encompass a market dimension

remains focused on the tasks of management within the context of a single firm (Perk

2000). To my knowledge, this study represents the first attempt to understand the role of


23

market orientation of alliances in collaborative new product development process. In this

study, alliance market orientation is conceptualized as an idiosyncratic resource. It is

discussed in the NPD alliance literature that alliances risk becoming myopic and obsessed

with the relationship per se rather than needs of customers, competitive actions, and

realities of market environment (Jassawalla and Sashittal 1998; Littler et al. 1995; Perk

2000; Piercy and Cravens 1995; Rindfleich and Moorman 2001; Sivadas and Dwyer

2000; Spekman et al. 1999).

I argue that competent alliance partners having an accumulated history of

collaborative NPD projects, high partner identification propensity, and alliance manager

development capability, may focus on developing market focus. More specifically,

competent alliance partners can identify and select partners to develop mechanisms to

systematically gather information on market intelligence (e.g., customer needs,

competitor analyses, regulations) and coordinate inter-organizational activities to act in

response to that intelligence. In addition, Lambe et al. (2002) empirically showed that

joint alliance competence leads to idiosyncratic resources. Since this study conceptualizes

alliance market orientation as an idiosyncratic resource, consistent with Lambe et al.’s

(2002) findings, this study posits that

H9: Joint alliance competence has a positive effect on alliance market orientation

Joint top management support

Effective alliance management is an integral part of alliance development and,

thereby, alliance success (Spekman et al. 1999). It is maintained in the alliance literature

that effective alliance management demands top management support (Day 1995;

Eisenhardt and Schoonhoven 1996; Hunt and Morgan 1994; Gulati 1998; Mohr and


24

Spekman 1994; Varadarajan and Cunningham 1995). In addition, the competence

literature argues that since the strategic decisions of organizations are driven by top

management, competences are developed under the control of top management (Hamel

and Prahalad 1994; Lambe et al. 2002; Sanchez et al. 1996). In his seminal article

Gomes-Casseres (1994) mentioned that alliances require their top managements’

attention, especially in the stages of planning, partner search, negotiation, and

appointment and training of alliance and business development managers. Since the

management of an alliance is a challenging process, top management levels of competent

alliance partners have put alliance executives in charge of managing external strategic

relations. In this study, joint top management support indicates that both of the alliance

partners’ top managements invest time and necessary resources in developing and

improving their alliances. Therefore, this study posits that

H10: Joint top management support has a positive effect on joint alliance competence.

New Product Creativity

The Marketing Science Institute (2008-2010) has named innovation a top-tier priority

topic in general, and organizing for effective innovation and creating a culture of

innovation in specific. New product development is the most common form of

innovation. Although scholars and practitioners have been interested in understanding the

meaning of innovation and effective development of innovation management in

organizations, an examination of innovation literature clearly shows that there has not

been a consensus in the definition of innovation. Because of a lack of consistency in the

construct definition and, thereby, a lack of mature measurement for this construct, in this

study I will instead use new product creativity. New product creativity is conceptualized


25

in this study as the degree to which new products are perceived as representing unique

differences from competitors’ products in ways that are meaningful to target customers

(adapted from Im and Workman 2004, p. 115).

The NPD literature argues that NP creativity enhances competitive advantage of

organizations by providing unique and usefulness of the product (Song and Montoya-

Weiss 2001). Creative firms provide novel and meaningful products. By doing so, these

firms meet the changing needs and preferences of current and potential customers

(Atuahene-Gima and Ko 2001; Lukas and Ferrell 2000). Therefore, as it is argued by the

NPD literature creative products lead to high new product performance. Consistent with

the single firm focus of NP creativity, this study posits that NP alliances that develop

unique and meaningful products will have high new product performance. To my

knowledge, this study represents the first attempt to measure NP creativity-NP

performance relationship in a collaborative NP context. Therefore,

H11: New product novelty has a positive effect on new product performance

H12: New product meaningfulness has a positive effect on new product performance.

1.5. Overview of the Rival Models

Structural Equation Modeling (SEM) is being increasingly used in the social science

research (Bollen 1989; Bollen and Long 1992). Indeed, researchers agree that there are

several issues that must be considered in any SEM analysis. One of these issues is testing

multiple plausible rival models (Thompson 1998), so that stronger evidence supporting

the correct specification of a model can be adduced.

In the current study, I argue that there may be two plausible rival models. Note that

the proposed model posits that alliance market orientation is the key mediating variable


26

that influences new product outcomes of an alliance. It means that top management

support, goal congruence, complementary resources, trust, commitment, and joint

alliance competence -- all of which have been associated with new product outcomes in

the past research -- influence new product creativity and new product performance only

through the key mediating variable of alliance market orientation. In the original model

both joint alliance competence and alliance market orientation are second-order reflective

constructs. The reasons for this conceptualization are provided in Chapter 2 and 3.

One rival view would be one positing only direct paths from each of these constructs

to the new product outcomes. It is suggested by previous new product development

literature that top management support, goal congruence, complementary resources, trust,

and commitment can act as antecedents of new product outcomes (e.g., new product

creativity and new product performance). Examples include complementary resources

and new product performance (Harrigan and Newman 1990; Littler et al. 1995; Sivadas

and Dwyer 2000), goal congruence and new product performance (Erdem et al. 2006;

Swink 2000), trust and new product performance (Sivadas and Dwyer 2000, Song et al.

1997), commitment and new product performance (Cooper 1994; Cooper

and Kleinschmidt 1995; Littler et al. 1995), and top management support and new

product performance (Brown and Eisenhardt 1995; Cooper 1979; Erdem et al. 2006;

Heller and Fujimoto 2004; Jassawalla and Sashittal 1998; Littler et al. 1995; Swink

2000). This proposed rival model is depicted in Figure 1.2.

Second rival model can be one that conceptualizes alliance market orientation and

alliance competence as having distinct components and, therefore, treats each component


27

separately. Second rival model is shown in Figure 1.3. The model comparisons and

process of the selection of the best model are reported in Chapter 6.

1.6. Overview of the Research Design

In this study, the sample frame consists of two-firm alliances. Therefore, the unit of

analysis throughout this study is a dyad. The focus is on the new product development

alliances, and such consideration is expected to control for any extraneous effects due to

variations in alliance type (Eisenhardt and Schoonhoven 1996; Lambe et al. 2002;

Rothaermel and Deeds 2004). The context is relationships between financially

independent new product alliance partners. Therefore, vertically integrated hierarchical

relationships will be beyond the scope of this study. The respondents are the key

informants, who are highly knowledgable about the phenomena under this study (e.g.,

R&D managers, CMOs, COOs, CSOs, CTOs, and alliance/business development

professionals).

The research sample for this study were developed from three sources. First, in

accordance with the National Cooperative Research and Production Act of 1993, new

product alliances are required to file written notification with the Attorney General and

the FTC in order to minimize the threat of antitrust prosecution. These filings are

published in the Federal Register Index, and they provide information about the identity

and location of alliance partners, and formation date and objectives of that alliance. The

index starts from 1998, and it provides the daily news about new product alliances. The

second source is the Thompson Financial Security SDC Platinum Database. The SDC

Platinum Worldwide database provides detailed information on mergers, acquisitions,

joint ventures, and strategic alliances around the globe. Based on the sample requirements


28

of the study, only the U.S. base high-tech companies involved in strategic business

alliances were chosen. The joint ventures, mergers, and acquisitions were excluded in the

sample selection. Since these indexes do not include names of individual executives, I

gathered their names, phone numbers and e-mail addresses from company websites and

Lead411.com. The third data source was ISBM (Institute for Study of Business Markets)

at Penn State University. Selected member firms were contacted by ISBM representative

with a cover letter and research synopsis. They were informed about my upcoming phone

call and e-mail. The many of the firms contacted through ISBM are Fortune 500

companies.

Data collection process had three stages: (1) field interviews with three experienced

alliance consultants, (2) pretest and pretest interviews with 22 alliance executives, (3)

final online field survey with 253 business professionals. In the final field survey, I called

1025 executives, among those 326 accepted to participate to the study (32 %). After

outliers and missing data detection, 253 usable surveys left (78 %). The details about the

data collection procedure are provided in Chapter 4. The cover letters and online survey

invitation are shown in Appendices.

1.7. Overview of the Data Analysis and Measurement

In this study, the hypotheses were tested using structural equation modeling (SEM).

Using SEM methodology makes it possible to simultaneously test all the hypothesized

relationships among the constructs. A two-stage approach was employed to analyze the

data and test the hypotheses. This two-stage approach has two advantages (Anderson and

Gerbing 1988): (1) It requires a smaller sample size because of the reduced model at each

stage, and (2) it can avoid the potential confounding effect between the measurement


29

model and the structural model (Wei 2006). SEM analysis relies on a sample of at least

100 to be stable, and sample under 150 can have difficulty converging (Bollen 1989).

This dissertation has 253 responses, yielding an adequate sample for testing of

hypotheses and providing stable and reliable results.

To my knowledge, this study is the first attempt to develop a measure for alliance

market orientation. In order to develop better measures, many researchers have followed

the measure development procedure suggested by Churchill (1979). Basically, he

recommends the following sequential steps. (1) Specify the domain of construct based on

literature review, (2) generate a pool of items based on literature review and in-depth

interviews, (3) collect primary data, (4) purify measures, (4) assess reliability and

validity. These steps involve an iterative process. At each stage, the researcher evaluates

the results and decides whether to modify the measure and repeat the same procedure.

This study followed the above iterative steps through pretest and final field survey.

Chapter 5 explains how the alliance market orientation scale is developed. Other major

constructs were borrowed from previous literature. Therefore, their psychometric

properties were intensively investigated. Chapter 5 explains the overall measure

development and measurement model in detail.

1.8. Overview of the Theoretical and Managerial Contributions

A study by Hagedoorn (2002) on 40 years of data shows that there is a growth in the

number of new product development partnerships since 1960. However, the literature

review on new product development alliances includes only a few studies dealing directly

with new product alliance success (for details, see Erdem et al. 2006). Therefore, given

the growing popularity of NPD alliances and the importance of managing the innovation


30

process in inter-organizational context, developing an integrative theory addressing key

new product development alliance issues is timely and significant. The main objective of

this study is to identify ways in which NPD alliances can be made more successful.

Therefore, in this dissertation, I seek to enhance marketing’s understanding of these

issues by examining the role of the market orientation at the inter-firm level in new

product development activities of alliances. The main motive is to define alliance market

orientation, develop its measure and determine its possible antecedents and consequences

in new product development context.

Based on the empirical findings, this study shows that joint top management support

has a strong positive influence on joint alliance competence. In addition, firms with

alliance competences are more likely to find partners with complementary resources and

congruent goals. According to the results, goal congruency has a strong positive impact

on alliance market orientation. However, contrary to expectation, complementary

resources do not have a significant effect on alliances’ market oriented behaviors. One

reason for this counter-intuitive finding may be that alliances, no matter what their

purpose is (e.g., market oriented, non-market oriented), should have complementary

resources to initiate the alliances. Complementary resources are the primary motivation to

form all types of alliances. When the average score to this variable is examined in

Chapter 5, it is seen that it has the highest value with minimum variance among other

variables.

It is shown that joint alliance competence leads to the alliances’ market oriented

behaviors. In essence, when a firm with alliance competence partners with another firm

with alliance competence, it is highly likely that they will develop bifocal vision and be


31

more market focused than their rivals who do not have joint alliance competence. This

study argues and shows that in order for alliances to be successful, internal focus in terms

of developing trust and commitment is a necessary but not sufficient criteria. They also

need to develop a market focus which will help them to understand what customer wants

and what their competitors are doing. Empirical results show that relational factors lead

to alliances’ market oriented behaviors which will eventually boost new product

outcomes.

This study is first to posit alliance market orientation as a key inter-organizational

construct in alliance NPD performance. Although a number of researchers point to the

importance of market orientation in inter-organizational relationships (see Table 1.1),

there is no systematic work to date on market orientation and its role in “expanding the

size of the pie” between partner firms (Jap 1995). By conceptualizing alliance market

orientation as an idiosyncratic resource, this study also contributes to the knowledge of

the role of idiosyncratic resources in alliances. As a nonfungible resource, alliance market

orientation is an incentive and motivation to develop and maintain the relationships

between partner firms (Williamson 1985).

In terms of marketing practice, this dissertation should be of considerable interest to

practitioners involved in inter-organizational new product development activities.

Although it is well-known that many organizations enter into business alliances to hasten

the pace of innovation, overcome budgetary constraints, spread out risks, and gain access

to resources (Sivadas and Dwyer 2000), there is a paucity of research in this area. Many

alliance executives have noted that their alliances are tended to be unstable, and a large

number of them fail. Therefore, this study suggests a number of implications which


32

enable alliance participants to both increase the efficacy of their collaborative new

product development efforts and reduce their level of frustration. Three stand out.

First, it is well-known that NPD is a knowledge-based asset strategy. Therefore, it

includes knowing the market (e.g., customers, competitors, and regulations), sharing the

market-related information both intra- and inter-organization wide, and acting on it in a

collaborative manner. This study concludes that innovations developed in alliances

should have some commonalities with the innovations that are developed internally.

Having a market focus is one of these commonalities. Based on the results, some NPD

alliances fail because managerial efforts in alliance context are directed towards making

the relationship work at the expense of a market focus. As Perks (2000) claims, NPD

alliances risk becoming myopic and obsessed with the inter-organizational relationships

per se, rather than the realities of the markets, which are the bread and butter of an NPD

process. This study recommends that alliances should integrate their internal processes

(e.g., being in contact with alliance partner, coordinating NPD activities, regular reviews

of alliance’s pipeline products) with their external factors (e.g., customer needs/wants,

competitors’ actions, market regulations). As Day (1994) points out, new product

development is a spanning process should be informed by both external and internal

activities. Consistent with Day, this study shows that new product alliances should not

only incorporate inter-organizational factors to their new product development processes,

but also, more importantly, the realities of market.

Second, this study posits that alliances should focus on ways to improve their market

focus in their innovation projects, and consequently, to enhance their project

performance. Engaging in relationship-based governance structures (e.g., trust and


33

commitment), adequate top management support, working with competent alliance

partners, and having congruent goals and complementary resources are some mechanisms

that organizations should pursue to enhance their market orientation to achieve high co-

development performance.

Finally, this study further recommends that, although congruent goals,

complementary resources, top managerial support, alliance competence and relationship

factors directly influence alliance market orientation, these factors by themselves do not

have a direct effect on NPD related outcomes. Rather, the key/central to unique and

meaningful products and eventually high new product performance seems to be to have

market-orientation in an inter-organizational context. Briefly, this study suggests that

market-oriented alliances that generate, disseminate, and utilize market information faster

and better than their competitors will have a greater competitive advantage.

1.9. Study Outline

This dissertation is organized into seven chapters. A brief description of each chapter

follows. Chapter 1, the current chapter, notes the importance of inter-organizational new

product development activities in providing sustainable competitive advantage to partner

firms. As seen in this chapter, there is a need for understanding several aspects of inter-

organizational relations. This chapter summarizes these aspects and provides general

overview of the study, including research background, research questions, scope of the

study, conceptual framework, research hypotheses, rival models, research design and

sampling issues, data analysis, and theoretical and managerial contributions.

Chapter 2 is a review of the most relevant literature to the proposed research

questions which are provided in Chapter 1. Specifically, the background literature review


34

discusses certain aspects of inter-organizational new product development; introduce the

concept of alliance market orientation based on the recent intra- and inter-organizational

market orientation studies; and finally, examine the concept of alliance competence and

discuss each dimension of this construct. This discussion aims to provide the backdrop

upon which the theoretical framework of the study is structured.

Chapter 3 presents the theoretical framework of the study. In this chapter, first,

resource-based view of the firm (RBV), competence-based view of the firm (CBV) and

as an integrative theory of competition, the resource-advantage theory (R-A theory) of

competition is reviewed. In the second section of this chapter, I provide the research

hypotheses to be tested in an empirical study. The theoretical rationale for specific

relationships between selected variables is articulated in detail.

Chapter 4 describes the research methodology, which focuses on the empirical

validation of the hypothesized relationships in the model. This chapter begins by

discussing sampling issues related to sampling frame, identification of key respondents,

unit of analysis, data collection methods, characteristics of firms and key respondents,

and questionnaire design and measurement.

Chapter 5 details the procedures and results used for development of the alliance

market orientation measure. It explains how the initial measures are tested for their

validity and reliability based on the iterative procedure recommended by Churchill

(1979). Then the chapter describes the procedures and results from testing the respecified

measurement model in order to develop the final measures.

Chapter 6 provides the results from hypotheses testing. It begins by reviewing the

hypotheses. After explaining the issues related to assumption violation, it reveals


35

methods and results of testing main effects in a structural equation model. It concludes

with mediation tests of alliance market orientation. According to the mediation analyses,

alliance market orientation is, indeed, the key mediator in alliance new product

performance model.

The final chapter, Chapter 7, presents a discussion of the results and implications of

this study. It reviews and evaluates the original objectives of this study, and it provides

the details of theoretical and managerial implications to be drawn from the empirical

results. It is followed by study limitations. The dissertation ends with suggestions for

future research.


36


37


38


39

CHAPTER 2: LITERATURE REVIEW

This chapter aims to provide a theoretical justification for the selection of the

variables applied in this study. Therefore, its development is geared to present and

discuss the most relevant literature to the proposed research questions highlighted in the

first chapter. Briefly, the background literature review (1) discusses certain aspects of

inter-organizational new product development, then (2) provides an overview of alliance

market orientation, before (3) discussing the concept of alliance competence.

2.1. Literature Review on Inter-Organizational New Product Development

The Marketing Science Institute (MSI) (2008-2010) has named innovation a top-tier

priority topic. New product development has been considered as the most common form

of innovation (Hlavacek and Thompson 1973; Wei 2006).

Littler and Leverick (1995) have pointed to the growing complexity and costliness of

developing new products; the shortening product life cycles; the uncertainty in research

and development; the internationalization of industries; the increasingly rapid pace of

technological change; and the apparent convergence of markets and technologies.

Therefore, working across corporate boundaries to co-develop products has been

promoted as a means by which some of these problematic aspects of the new product

development process can be lessened (Deck and Strom 2002; Littler et al. 1995).

Nevertheless, many of these collaborative new product development projects fail. Some

statistics put the failure1 rate of NP alliances as high as 70 percent (Parkhe 1993).

Therefore, the attempt to identify ways in which inter-organizational NPD can be made

1 In this study, new product alliance failure has been conceptualized as an inter-organizational product development project that does not meet an NPD alliance’s financial and overall performance objectives.


40

more successful is timely and significant. As a testimony of the importance of innovation

management in an inter-organizational context, Institute for the Study of Business

Markets (ISBM) has named “managing product development in the context of complex

product development alliances” as a research priority.

Since the main concern in this study is to understand the high performance factors in

NPD alliances’ innovation projects, I, first, review the different definitions of innovation.

Following this, in the second part I will review the inter-organizational NPD literature.

2.1.1. Defining New Product Innovation

Academics and practitioners believe that they have begun to understand the

management of innovations in organizations. However, an examination of the literature

in innovation management shows that there has been a lack of consistency in defining

innovation (Garcia and Calantone 2002). This lack of consistency in defining and

operationalizing innovation has resulted in interchangeable use of the construct

“innovation” to define innovation types. Some of the innovation types that have been

used in the innovation literature can be identified as radical, incremental, really new,

discontinuous, and imitative innovations, as well as for architectural, modular,

improving, and evolutionary innovations (for detailed review, Garcia and Calantone

2002). The following review of different definitions of innovation and innovation types

by different groups of researchers will demonstrate this view.

Utterback (1971) mentions that the definition of innovation should be distinct from

invention. Therefore, first, he defines invention as an original solution resulting from the

synthesis of information about a need or want and information about the technical means

with which the need or want may be met. He adds that an invention should be followed


41

by entrepreneurial action before it has significance in economic terms. Therefore, he

defines innovation as an invention which has reached the market introduction in the case

of a new product, or first use in a production process, in the case of a process innovation.

Booz, Allen, and Hamilton (1982) define innovation as a product and/or process

which is new to both the market and the firm. In their six dimensional scale, they view

interaction between the firm and the marketplace as a dynamic process. The basic

dimensions include: (1) cost reductions i.e., new products that provide a similar

performance at lower cost, (2) re-positioning i.e., new products that are targeted to new

market segments, (3) improvements in/revisions to existing products such as new

products that improve performance and replace existing products, (4) additions to

existing product lines i.e., new products that complement a firm’s existing product line,

(5) new product line i.e., new products that allow a firm to enter an established market for

the first time, and (6) new to the world product i.e., new products that create an entirely

new market.

Ettlie, Bridges, and O’Keefe (1984, p.683) assert that “one of the theoretical

typologies that have emerged in the literature on organizational innovation is the

dichotomy of radical versus incremental innovation.” Authors mention that these two

types of innovation differ in “whether or not the innovation incorporates technology that

is clear, risky departure from existing practice.” In addition, they suggest that if a

technology is new to the markets and new to the organizations, or if it needs both

“process” and “output change”, “the magnitude or cost of change required by the

organization may be sufficient to warrant the designation of a rare and radical, as

opposed to incremental innovation.”


42

Van de Ven (1986, p.590) refers innovation as “the development and implementation

of new ideas by people who over time engage in transactions with others within an

institutional context.” As it is clear from the definition, the term “innovation” consists of

four major factors: ideas, people, transactions, and institutional context. The author also

mentions that this definition is sufficiently general to apply to a wide variety of technical,

product, process, and administrative kinds of innovations.

Henderson and Clark (1990) demonstrate that incremental and radical innovation

typologies have become a tradition in categorization of innovation. Authors have pointed

to this “incomplete and potentially misleading” categorization of innovation by

explicating that sometimes a minor product improvement can cause disastrous effects on

industry incumbents. Therefore, they view the product as a system and a set of

components. They classify innovation into two dimensions: (1) components and (2)

linkage among components. Based on these two dimensions, authors define four types of

innovation: radical, incremental, modular, and architectural. Radical innovation is

defined as “a new dominant design and a new set of design concepts embodied in

components that are linked together in a new architecture” (p. 11). Incremental

innovation is defined as the product “improvement that occurs in individual components,

but the underlying core design concepts, and the links, between them remain same” (p.

11). A modular innovation is defined as the “innovation that changes only the core design

concepts of a technology and innovation that changes only the relationships between

them,” (p. 12). Finally, architectural innovation is the “innovation that changes a

product’s architecture but leaves the components, and the core design concepts that they

embody, unchanged” (p. 12).


43

Chandy and Tellis (1998, p.475) define innovation as “the propensity of a firm to

introduce new products that (1) incorporate substantially different technology from

existing products and (2) can fulfill key customer needs better than existing products.”

This definition focuses on two basic factors: technology and key customer needs. Authors

identify two levels (low and high) for each factor and mention that these levels for each

factor lead to four types of product innovations: incremental innovations, market

breakthroughs, technological breakthroughs, and radical innovations. Briefly,

incremental innovations refer relatively minor changes in technology and provide

relatively low incremental customer benefits per dollar. Market breakthroughs focus on

core technology that is similar to existing products but provide substantially higher

customer benefits per dollar; whereas, technological breakthroughs adopt a substantially

different technology than existing products but do not provide superior customer benefits

per dollar. In contrast to the previous three types of innovation, radical innovations

involve substantially new technology and provide substantially greater customer benefits

per dollar, relative to existing products.

McDermott and O’Connor (2001, p. 424) define innovation as “a new technology or

combination of technologies that offer worthwhile benefits.” According to authors major

innovations need new skills, levels of market understanding, leaps in new processing

abilities, and systems throughout the organization. They assert that “the newly developed

product or process is distinct from current and existing activities within the firm that the

process of bringing the product to market may not be closely parallel that of any existing

products within the firm” (p. 424-425). Consistent with Green and Aiman-Smith (1995),

the authors used four basic dimensions in the operationalization of innovation: (1)


44

technological uncertainty, (2) technical inexperience, (3) business inexperience, and (4)

technology cost.

In this study, consistent with Garcia and Calantone (2002), I will use the innovation

definition of 1991 OECD study. Garcia and Calantone (2002) have reviewed the

innovation literature and concluded that OECD definition of innovation best captures the

essence of innovation from overall perspective. OECD (p.305) defines innovation as “an

iterative process initiated by the perception of a new market and/or new service

opportunity for a technology-based invention which leads to development, production,

and marketing tasks striving for the commercial success of the invention.” Therefore, this

definition has two basic factors: (1) innovation process includes both the technological

development of an invention and its market introduction to end-users through adoption

and diffusion, and (2) the innovation process is iterative meaning that it includes both the

first introduction and the re-introduction of an improved innovation.

2.1.2. Measurement of New Product Performance

In this study, proposed model examines the antecedents and consequences of new

product creativity. Previous studies have examined the creativity in new products

including its significant antecedents; however, they overlook its effect on new product

performance (e.g., Moorman 1995; Moorman and Miner 1997; Rindfleisch and Moorman

2001; Sethi et al. 2001). Investigating the effects of new product creativity on new

product performance is crucial because, the ability to transform the positional advantages

of new product creativity into new product performance determines a firm’s competitive

advantage, thereby, survival and growth in rapidly changing competitive markets (Day

and Wensley 1988; Hunt 2000; Im 1998). Since new product performance has been


45

measured in many ways, I will briefly review the literature on new product performance

and its measurement in this section of the study. Then, I will discuss which measure I will

use in this study to measure new product performance.

Although new product performance has been discussed as a determinant of a firm’s

competitive position, new product development researchers have indicated that literature

has been using different performance measures to measure the new product development

activities (e.g., Griffin and Page 1993; Song and Parry 1997; Wind and Mahajan 1997).

According to these researchers, using different measures make it difficult to draw

generalizations across investigations, to compare findings across researchers and research

projects, and to more effectively benchmark the performance of organizations.

Previous new product development studies measure performance by using a single

measure which is the financial profitability of a firm (e.g., Cooper 1979). As Narver and

Slater (1990) mentioned, for businesses the overriding objective is profitability.

However, many researcher have highlighted that new product performance should be

measured by using multiple measures (e.g., Griffin and Page 1993).

Cooper (1984) discussed that new product performance measure can have three

dimensions: the impact on or importance of the program to sales and profits, the success

rate of the programs that the firm develops, and the performance of the program relative

to competitor’s in terms of profits and costs. Deshpande et al. (1993) used four different

dimensions to measure new product performance which are relative profits, relative firm

size, relative market share, and relative firm growth. Kleinschmidt and Cooper (1991)

suggested that new product performance can be measured by using five dimensions that


46

include overall success, market share, met performance objectives, return on investment,

and new opportunities.

In their comprehensive study, Griffin and Page (1993) concluded that the concept of

new product development performance has many dimensions and each may be measured

in a variety of ways. Generally, authors compared and contrasted the measures used by

academics and companies to evaluate new product development performance.

Furthermore, they indicated that managers and academics tend to focus on rather

different sets of new product development performance measures. Academics tend to

focus on product development performance at the firm level; whereas, managers tend to

measure performance in terms of individual product performance. Specifically, authors

report that there are five dimensions of new product performance: measures of firm

benefits, firm-level measures (e.g., percentage of sales by new products), product-level

measures (e.g., development cost, launched on time, product performance level, met

quality guidelines, spend to market), financial performance measures (e.g., break-even

time, attain margin goals, attain profitability goals, IRR/ROI), and customer acceptance

measures (e.g., customer acceptance, customer satisfaction, met revenue goals, revenue

growth, met market share goals, and met unit sales goals). Griffin and Page (1996)

iterated their previous study and concluded that there are three dimensions in new

product performance measure: customer-based, financial-based, and technical-based.

In their meta-analysis study, Montoya-Weiss and Calantone (1994) articulated that

new product development performance can be measured by three criteria: financial

performance (e.g., profit, sales, payback period, and costs), market share performance,

and technical performance (e.g., meeting the firm-based objectives set up for technical


47

advancement). Furthermore, Gatignon and Xuereb (1997) used several criteria to measure

new product performance including relative ROI (e.g., firm’s other products,

competitor’s products), product advantage (e.g., overall advantage, design, and quality),

radicalness of the product, innovation relative cost (e. g., marketing costs,

manufacturing/operations, research and development, and overall costs), and innovation

similarity with other firms’ products. Derozier (2003) measured performance which is the

consequent of new product creativity based on several dimensions: customer-based (e.g.,

customer attraction, customer satisfaction, value for customers), growth, market share,

new product success rate, and financial-based (e.g., ROA, ROS). Song and Parry (1997)

used four criteria to measure new product performance: overall profitability, relative sales

volume, relative to firm’s other products new product profitability, relative to firm’s

objectives new product profitability. Han et al. (1998) investigated the effect of market

orientation on performance. Furthermore, they use both objective and subjective

measures to evaluate performance. Objective measures include income growth and ROA,

and subjective measures include relative growth and profitability. In their inter-

organizational new product development study, Sivadas and Dwyer (2000) measured

performance by using a five item scale that evaluates the new product quality, time taken,

market share, speed to market, and meeting of target costs. They also used additional

items to measure the financial performance and market share for the respective new

product.

Consistent with the previous study, I tend to use multiple measures to evaluate inter-

organizational new product performance. I adopted Im and Workman’s (2004) new

product performance scale to identify the dimensions. I use three dimensions to measure


48

new product performance including customer and market measure (e.g., sales and market

share), financial measures (e.g., ROI and profitability), and overall measure (e.g.,

customer satisfaction and overall performance). Since Im and Workman’s study is in

firm-level, I modified their scale for alliance context (e.g., dyadic level). This study use

subjective measures of new product performance. In their comprehensive analysis, Song

and Parry (1997) concluded that there is a high correlation between subjective measures

of product performance and objective measures of product performance (e.g., perceived

level of success achieved by the new product in the marketplace in terms of relative

profitability and sales volume). New product development researchers have further

indicated that due to inaccurate and rarely available financial data, subjective

performance measures are preferred (e.g., Gatignon and Xuereb 1997; Han et al. 1998;

Olson et al. 1995; Song and Parry 1997).

2.1.3. Inter-organizational New Product Development

In the Schumpeterian world of innovation, it is widely accepted that new product

development provides firms with sustainable competitive advantage (Day 1994; Sorescu,

Prabhu, and Chandy 2003). Therefore, marketing and management scholars have turned

their attention to understand the success factors in developing new products. Many of

these studies have examined the new product development process of an organization that

generates product and process innovations via in-house R&D department (Rindfleisch

2000). However, as recently noted by Hagedoorn (2002), there has been a growth in the

number of inter-organizational R&D partnerships since 1960. Although the inter-firm

partnerships seem to flourish, a literature review in co-development alliances identifies


49

few scholarly studies dealing directly with inter-organizational new product development

activities (Erdem et al. 2006). In this section, I will review these scholarly studies.

Kotabe and Swan (1995) focused on the role of strategic alliances in high technology

new product development activities. The authors examined 905 new product innovations

to understand the strategic alliance factors on new product innovativeness. Their findings

showed that intra-organizational rather than inter-organizational new product

development activities, horizontal alliances rather than vertical alliances, small-sized

firms rather than large-sized firms, biochemical industries, inter-industrial co-operations,

and European firms are the strongest contributors to the level of product innovativeness.

Littler and Leverick’s (1995) study focused on collaborative product development

activities. The authors’ objective was to understand the factors that affect the outcomes of

inter-organizational co-development activities. Authors’ findings showed that there are

six groups of factors that determine the successful outcomes of co-development

activities: (1) selecting a partner, (2) establishing the formal ground rules, (3) setting up a

task force, (4) managing the new product development process, (5) ensuring mutual

understanding and equality, and (6) maintaining a market focus such as market trends,

competitors’ actions, and environmental factors.

Littler et al. (1995) conducted a study to identify the risks and benefits of

collaborative new product development projects. They also identified the key success

factors for collaborative new product activities. They argued that main benefits for

collaborating on product development projects include satisfying customer needs, taking

advantage of market opportunities for which the firm lacks necessary skills and technical

expertise, and responding to changes in technology. However, authors also asserted that


50

there are several challenges that partner firms face during the collaboration process such

as gaining inside knowledge of its partner’s unique skills and expertise, less direct control

over product development, divergent management styles, and budgeting processes of the

collaborating firms. Finally, authors discussed the factors that enhance the performance

of the collaborative activities such as (1) the presence of a product or collaboration

champion, (2) creating an equal benefit, (3) trust between partners, (4) flexibility in

alliance management, (5) fit with existing businesses, and (6) selection of a partner.

Millson, Raj, and Wilerman (1996) agreed that partnering arrangements in new

product development enable organizations to accomplish their innovative objectives.

According to them, collaborative new product development is not a discrete event but a

maturation process that involves several phases including awareness, exploration,

commitment, and dissolution. They also discussed that there are some issues that need

consideration during the partnering process including identifying both the originating

firm’s needs and the partner’s needs, understanding the likely consequences of the

relative size and capabilities of partners, and selecting the appropriate alliance strategy.

Jassawalla and Sashittal (1998) compared integration with collaboration in new

product development process. Authors argued that compared the integration collaboration

is described as a more complex and higher intensity cross-functional linkage. Their

conceptualization of collaboration included informal, cooperative relationships that build

a shared vision and mutual understanding among participants in the NPD process. They

also noted that successful collaboration requires participants who contribute an openness

to change, a willingness to cooperate, a high level of trust, a priority that senior


51

management gives to NPD and the level of autonomy afforded to participants in the NPD

process.

Perks (2000) suggested that in order for inter-organizational co-development

activities to have successful performance results, market information such as customer,

competitor, and other macro-scale variables should be integrated into the inter-firm NPD

processes. She claimed that this is a critical determinant of NPD success. In her study,

she investigated processes to integrate market information in the context of NPD

collaboration in inter-organizational context.

Rindfleisch (2000) discussed the role of trust in horizontal and vertical new product

alliances. He examined the effect of relationship form on organizational trust by

comparing to horizontal with vertical alliances. The results of his study showed that

participants in vertical NP alliances display higher levels of inter-organizational trust than

participants in horizontal NP alliances. In addition, the findings also indicated that while

organizational trust enhances cooperative activities in vertical alliances, it does not have

an enhancing effect on cooperation in horizontal NP alliances.

Sivadas and Dwyer (2000) developed a new concept called cooperative competency

which has three dimensions: (1) trust, (2) cooperation, and (3) communication. They

drew on and adapted key issues from the concepts of mutual adjustment, absorptive

capacity, and relational capability. Their empirical results indicated that cooperative

competency is a key mediating factor which leads to new product performance,

regardless of whether it is an intra- or inter-firm new product development activity.

Furthermore, the findings also showed that formalized and clannish administration,


52

mutual dependence, and institutional support are important factors in developing

cooperative competency in inter-organizational context.

Rindfleisch and Moorman (2001) examined how organizations acquire and utilize

information in inter-organizational new product development context. They suggested

that horizontal alliances have lower levels of relational embeddedness and higher levels

of knowledge redundancy than vertical alliances. Their findings indicated that while

embeddedness enhances both the acquisition and utilization of information in NP

alliances, redundancy diminishes information acquisition but enhances information

utilization. Overall, their findings challenged with the strength-of-ties literature.

Deck and Strom (2002) argued that working across corporate boundaries to co-

develop products and services is becoming the operative model for R&D in a wide range

of industries. However, they also claimed that companies rarely consider making co-

development as an integral part of their business models. In that manner, authors

suggested a three level co-development model including: (1) a strategy for development

chain design, (2) process and governance structures that define how partners should work

together, and (3) information technology that effectively supports collaborative product

development.

Hagedoorn (2002) examined 40 years of data on strategic new product development

alliances. His findings indicated that there is a clear pattern of growth in the newly made

R&D partnerships since 1960; and this growth is largely caused by an overwhelming

increase in the absolute numbers of contractual partnerships. In addition, the findings also

showed that partnering in NPD is dominated by companies from the world’s most


53

developed economies, and most interestingly, there is a trend towards the domesticized

nature of NPD partnering by US companies.

Rindfleisch and Moorman (2003) examined the longitudinal effect of co-development

activities on a firm’s level customer orientation. Their findings showed that firms

engaged in cooperative alliances with competitors will become less customer-oriented

over time. In contrast, findings do not indicate the same type of decrease for firms

engaged in channel-dominated NPD alliances. Moreover, authors observed that firms in

competitor-dominated alliances with weak relational ties with their collaborators exhibit a

greater decrease in customer orientation compared with firms with strong ties with their

collaborators. Finally, authors concluded that firms that collaborate with competitors in

alliances with a third party monitor experience a smaller decrease in customer orientation

than firms in alliances without such a monitor.

Heller and Fujimoto (2004, p.35) examined the horizontal NP alliances and suggest

that “ongoing close interaction of horizontal alliance partners at multiple hierarchical

levels can be used to facilitate the mutual accumulation of superior organizational

capabilities within the alliance firms.” They argued that in order for this pattern of

cooperation to function effectively three conditions should me met: Alliance partners

must (1) co-exist as separate learning organizations, (2) be able to evaluate accurately a

partner’s relative organizational capability strengths and weaknesses, and (3) have the

motivation and ability to facilitate a partner’s inter-firm learning.

Cloodt, Hagedoorn, and Roijakkers (2006) demonstrated the growing interest in

strategic new product development alliances in international computer industry. Their

focus was on major structural transformations in the computer industry, the role of


54

leading companies and changing patterns in inter-firm R&D networks. Their findings

presented that during the 1980s and 1990s the computer industry shifted from vertical to

horizontal integration, causing inter-firm collaboration to become more important than

the system of control through integrated production. They argued that during those years

new product alliances increased computer companies’ access to new technologies and

enable them to exploit these technologies more efficiently.

Erdem et al. (2006) investigated the partner selection processes in new product

development alliances. They asserted that co-development activities enhance competitive

advantage of partner firms. Their findings suggest that there are three aspects of partner

selection in co-development alliances: (1) Technological alignment, (2) strategic

alignment, and (3) relational alignment. They argued that the later phases are as

important as the initial phase in ensuring the transfer and integration of critical know-how

and in creating product value through collaboration.

Knudsen (2007) investigated the role of different types of inter-organizational

relationships in NPD performance. The underlying premise in his study was that not only

the type of inter-firm relationships but also the combination of the relationships are

important for NPD performance. The findings indicated that varying needs for external

knowledge, and thus types of relationships, are observed depending on the particular

stages in the NPD process, the character of the knowledge base of the firm, and industrial

conditions.

Luo et al. (2007) examined the impact of competitor-based alliances on partner firms’

financial performance. Their findings indicated that intensity of a firm’s alliances with its

competitors has an inverted U-shaped influence on return on equity. The authors also find


55

that a firm’s competitor orientation can have such a moderating effect on this curvilinear

relationship. Specifically, authors’ findings indicated that competitor-oriented strategies

of firms strengthen the curvilinear effect; whereas competitor-oriented objectives of firms

weaken this relationship.

In the current study, by adapting from Rindfleisch and Moorman (2001), new product

alliances are defined as formalized inter-organizational arrangements between partnering

firms to jointly generate, exchange, and utilize information related to the research and

development, and marketing of new product innovations.

2.2. Literature Review on Alliance Market Orientation

The marketing concept is a cornerstone of modern marketing thought. It articulates

that to achieve a sustained competitive advantage, firms should identify and satisfy

customer needs more efficiently and effectively than their competitors (Day 1994; Kirca

et al. 2005). Market orientation is a central component of the marketing concept

(Deshpande and Farley 1998), and it has become increasingly important to the study and

practice of marketing and management (Gebhardt et al. 2006). Previous research has

studied market orientation in two distinct research streams: (1) Defining and developing

measures of a firm’s market orientation and (2) identifying antecedents and consequences

of a firm’s greater market orientation (Gebhardt et al. 2006; Kirca et al. 2005). Thus, I

can argue that the dominant paradigm of market orientation is of a firm generating

market intelligence to meet its customer needs. However, to a large extent, competition

takes place increasingly between networks of firms (Achrol and Kotler 1999; Morgan and

Hunt 1994; Thorelli 1986). As it is mentioned by Morgan and Hunt (1994, p.20), “to be

an effective competitor (in the global economy) requires one to be a trusted cooperator


56

(in some network).” Therefore, it can be argued that to effectively and efficiently

compete with other networks, involved firms in a network should develop a superior

capability to understand and meet their customer needs. This would, in turn, make it

relevant to study market orientation at an inter-organizational level (Elg 2002). Indeed,

there has been a rising interest in examining market orientation from a relationship

perspective (see Table 1.1 for details).

The main objective of this study is to develop a concept of market orientation at an

inter-organizational level, in general, and examine this concept in a dyadic new product

alliance context, in specific. Therefore, in the following sections, first, I review the

market orientation literature relating to assessment of different definitions and

measurement of market orientation. Second, I will introduce the concept of alliance

market orientation.

2.2.1. Definition and Measurement of Market Orientation

Definition of Market Orientation

Studies that define market orientation include those of Kohli and Jaworski (1990),

Narver and Slater (1990), Jaworski and Kohli (1993), Kohli et al. (1993), Day (1994),

Hunt and Morgan (1995), Siguaw, Brown, and Widing (1997), Wrenn (1997), Deshpande

and Farley (1998), Becker and Homburg (1999), and Narver, Slater, and MacLahlan

(2004). In this section, I review some of the widely used market orientation definitions

and measurements.

Narver and Slater (1990) conceptualized market orientation as a business culture that

most efficiently and effectively creates superior value for its customers and, thus,

continuous superior business performance for the business. They argued that this business


57

culture will produce the necessary behaviors. Three behavioral components of market

orientation include: (1) Customer orientation, (2) competitor orientation, and (3) inter-

functional coordination. Customer orientation means that a firm has a sufficient

understanding of its target market to be able to create superior value for them

continuously. Competitor orientation is that a firm understands the short-term strengths

and weaknesses and long-term capabilities and strategies of both the key current and key

potential competitors. Finally, inter-functional coordination is the coordinated utilization

of company resources in creating superior value for target customers. Authors argued that

effective inter-functional coordination requires, among other things, an alignment of the

functional areas’ incentives and the creation of interdependency among business

functions so that each function perceives its own advantage in cooperating closely with

the others. In addition, authors mentioned that market orientation should have a long-term

focus. Authors suggested that profitability is an overriding objective in a market

orientation. Therefore, they separated long term focus perspective of market orientation

and profitability objective from three behavioral components of market orientation.

Jaworski and Kohli (1993) defined market orientation as a composed set of three

activities: (1) Organization-wide generation of market intelligence pertaining to current

and future customer needs, (2) dissemination of the intelligence across departments, and

(3) organization-wide responsiveness to it. Authors’ conceptualization of market

orientation has a process-driven perspective. Their definition focused on specific

behaviors. Briefly, intelligence generation pertains to customer needs/preferences and it

includes an analysis of how those needs and preferences may be affected by exogenous

factors such as government regulation, technology, competitors, and other environmental


58

forces. Intelligence dissemination is the communication and dissemination of market

information to relevant departments and individuals in the organization. Responsiveness

is the action taken in response to intelligence that is generated and disseminated.

Becker and Homburg (1999) introduced a new perspective of market orientation.

They referred to this perspective as systems-based perspective. They conceptualized

systems-based perspective of market orientation as market-oriented management in terms

of the degree to which management systems are designed in such a way as to promote a

business organization’s orientation towards its customers and competitors. They argued

that management systems here refer to systems which support the performance of basic

management functions such as planning, organizing, and controlling.

Day (1994) proposed that market orientation represents superior skills in

understanding and satisfying customers. Indeed, he argued that market orientation has

three principal features: (1) Market orientation is a set of beliefs that puts customer’s

interest first, (2) market orientation is the ability of organizations to generate,

disseminate, and use superior information about customers and competitors, and (3)

market orientation is the coordinated application of inter-functional resources to the

creation of superior customer value. Moreover, he asserted that market-driven

organizations are superior in their market-sensing and customer linking capabilities. He

further argued that when these two capabilities are deeply embedded within routine

organizational processes, all functional activities and organizational processes will be

better directed toward anticipating and responding to changing market requirements

ahead of competitors.


59

Hunt and Morgan (1995) argued that market orientation can be a resource. They

conceptualized market orientation as (1) the systematic gathering of information on

customers and competitors, both present and potential, (2) the systematic analysis of

information for the purpose of developing market knowledge, and (3) the systematic use

of such knowledge to guide strategy recognition, understanding, creation, selection,

implementation, and modification.

Deshpande and Farley (1998) compared three market orientation scales including

Narver and Slater (1990), Kohli and Jaworski (1990), and Deshpande, Farley, and

Webster (1993). Their findings showed that these three scales are syntactically similar,

reliable, and valid. Moreover, they argued that they are not only numerous but rather

redundant which makes the task of using all three scales somewhat tedious to the

respondent. Therefore, they suggested that an attempt to synthesize the scales themselves

could be useful. In their final analysis, they conceptualized market orientation as the set

of cross-functional processes and activities directed at creating and satisfying customers

through continuous needs-assessment.

Slater et al. (2004) discussed the current use of market orientation and indicated that

the disagreement about the relationship between market orientation and marketplace

innovation is due to a too narrow understanding of market orientation. They argued that

market orientation conceives this concept as only responsive market orientation.

Therefore, the authors proposed a second dimension of market orientation and name it as

proactive market orientation which is a business attempt to discover, to understand, and

to satisfy the latent needs of customers.


60

In summary, marketing and management literatures use two conceptualizations of

market orientation extensively. Kohli and Jaworski (1990) emphasized the process-driven

perspective; whereas Narver and Slater (1990) focused on behavioral perspective.

Although these two perspectives have remarkable differences, scholars agree that there is

a fair amount of overlap as well. For instance, Narver and Slater (1990) are consistent

with Kohli and Jaworski (1990) in that they define customer and competitor orientation

as information acquisition and dissemination behaviors that are required to understand

what existing and potential customers’ value and what the current and potential

competitors’ strategies are (Deshpande and Farley 1998). Since both conceptualizations’

main aim is to capture the creating and delivering superior value to customers, both of

them emphasize the role of coordination across departments (i.e., inter-functional

coordination vs. organization-wide responsiveness). Moreover, Cadogan and

Diamantopoulos (1995) argued that the process-driven and behavioral perspectives have

conceptual and operational overlaps in nearly all dimensions. Avlonitis and Gounaris

(1997) argued that discussing these two perspectives as they are conceptually different

from each other should be avoided. Consistent with the literature, I argue in this study

that both definitions are very useful in studying market orientation from different angles.

Therefore, in this dissertation I adapted both definitions to study market orientation in

inter-firm level. I can provide two reasons for this selection: (1) Different from Narver

and Slater’s (1990) conceptualization, Kohli and Jaworki’s (1990) definition captures not

only current and potential customers’ needs/preferences, but also an analysis of these

needs and preferences that may be affected by exogenous factors such as government

regulations, technology, competitors, and other environmental factors. Since one of the


61

reasons of strategic alliance formation is environmental uncertainty and turbulence, I

argue that macro perspective should be included in the conceptualization of market

orientation in inter-firm level, and (2) Narver and Slater’s (1990) definition and

measurement base on three behavioral components. When NPD alliances’ specific

activities are taken into account, it can be observed that behavioral perspective fits to

their strategies better than process-driven perspective. Therefore, in this study,

conceptualization and operationalization of AMO includes both perspectives.

Measurement of Alliance Market Orientation

Besides the debate concerning the definition of market orientation, another debate has

devoted considerable attention to the development of a valid and reliable measurement

scale for market orientation. Some of the developed scales that measure market

orientation include those Narver and Slater (1990), Jaworski and Kohli (1993), Kohli et

al. (1993), Matsuno, Mentzer, and Rentz (2000), Deshpande and Farley (1998), Deng and

Dart (1994), Harris (2002), Becker and Homburg (1999), and Grewal and Tansujah

(2001). The details about these market orientation scales can be found in the Table 2.2.

Another question regarding a market orientation scale is whether it should be treated

as first or second order construct. Several studies in the market orientation literature use

different representations. Consistent with Matsuno, Mentzer, and Rentz (2000), Matsuno,

Mentzer, and Ozsomer (2002) treated market orientation as a second order construct.

They aggregated the scale to have three indicators (e.g., information generation,

information dissemination, and information responsiveness). In their rationalization this

aggregation is justified because (1) the validity of the second order market orientation

scale has been established (e.g., Matsuno et al. 2000), (2) given the sample size, it


62

maximizes the degrees of freedom in estimating the path coefficients between market

orientation and its outcomes, and (3) it reduces levels of random error while accounting

for measurement error and retaining three dimensional scale of market orientation.

Furthermore, Zhou, Yim, and Tse (2005) treated market orientation as a second order

construct. They reported that because of the complexity of the causal model, it is better to

represent market orientation as second order latent factor. They also indicated that given

the measurement validity of the overall market orientation scale, this technique could

reduce model the complexity and be used for SEM and hypotheses testing.

Although many studies have represented market orientation scale as a second order

construct such as presented as above, some of the studies have used market orientation as

a first order construct. For example, in their conceptualization, Narver and Slater (1990)

treated market orientation as a first order construct which has three dimensions: customer

orientation, competitor orientation, and inter-functional coordination. Lukas and Ferrell

(2000) used Narver and Slater (1990) scale and concluded that each dimension has

different effects on proposed outcomes (e.g., number of line extensions, number of me-

too products, and number of new to the world products). Consistently, Han et al. (1998)

adapted Narver and Slater’d (1990) scale and found that each of these three dimensions

has different effects on the consequences (e.g., organizational innovation). Im and

Workman (2004) used Narver and Slater’s (1990) scale and concluded that customer

orientation, competitor orientation, and inter-functional coordination have different

effects on new product and marketing program creativity. Specifically, they found that

although customer orientation enhances new product and marketing program

meaningfulness, it does negatively affect the novelty dimension of creativity.


63

Current study follows the former tradition where market orientation is conceptualized

as a second-order reflective construct. In essence, alliance market orientation is

conceptualized as it is having three dimensions -- inter-organizational customer

orientation, inter-organizational competitor orientation, and inter-organizational

coordination -- and the correlation between these dimensions can be explained by the

higher order latent construct called alliance market orientation. I compare the original

model, where alliance market orientation is a second order reflective construct, with a

rival model, where each dimension of alliance market orientation is treated separately, in

Chapter 6. Consistent with the expectation, higher order model is a better model both

conceptually and empirically.


64

Table 2.2 Market Orientation Scales

Authors

Objective of the study

Definition of market orientation

Number of

scale items

Kohli and

Jaworski (199

3)

To investigate the antecedents and consequences of

market orientation and to develop

a valid m

easure of

market orientation.

Organization-wide generation of market intelligence pertaining to current

and future needs of custom

ers, dissemination of intelligence within the

organization

, and respo

nsiveness to it.

32 items

Narver and

Slater (199

0)

To develop a valid measure of market orientation and

test the impact of market orientation on business

performance.

Organization culture that m

ost effectively and efficiently creates the

necessary behaviors for the creation of superior value for buyers and, thus,

continuous superior performance for the business. It consists three

behavioral com

ponents (customer orientation, com

petitor orientation and

inter-functional coo

rdination) and two decision

criteria (long tern focus and

profitability)

15 items

Matsuno,

Mentzer, and

Rentz (20

00)

Develop

ing an improved m

arket orientation scale bu

ilt

on M

ARKOR.

Kohli and Jaw

orski’s (199

0) definition of m

arket orientation

22 items

Kohli,

Jaworski, and

Kum

ar (19

93)

Develop

ing a valid measure of market orientation

Kohli and Jaw

orski’s (199

0) definition of m

arket orientation

20 items

Deshpande and

Farley (199

8)

To synthesize and retest the market orientation scales

of Narver and Slater (199

0), D

eshpande, F

arley, and

Webster (1990

), and Kohli et al. (19

93)

The set of cross functional processes and activities directed at creating and

satisfying customers through continuous needs-assessm

ent.

10 items

Deng and

Dart (1994)

Develop

ing a valid measure of market orientation

The generation of app

ropriate m

arket intelligence pertaining to current and

future customer needs and the relative abilities of com

petitive entities to

satisfy these needs; the integration and dissemination of such intelligence

across departm

ents; and the coordinated design and execution of the

organization

's strategic respo

nse to m

arket op

portunities

33 items

Harris (200

2)

Generating a multi-perspective, m

ulti-informant

approach for m

easuring m

arket orientation.

Extent to which an organization

is perceived to act in a coordinated,

custom

er, and com

petitor oriented fashion

. 46

items

Becker and

Hom

burg (19

99)

Develop

a scale m

easuring the extent of m

arket

orientation an organization’s managem

ent system

. The degree to which the different m

anagem

ent system

s of an organization

are designed in a market oriented way. T

hey subsum

e custom

er and

competitor orientation under the construct of m

arket orientation.

60 items

Grewal and

Tansujah (200

1)

To investigate the role of market orientation and

strategic flexibility in helping Thai firm

s manage the

recent Asian crisis.

Kohli and Jaw

orski’s (199

0) definition of m

arket orientation

18 items


65

2.2.2. Alliance Market Orientation

Market orientation is discussed as a major prerequisite for being able to create

superior customer value, which in turn is regarded as a major determinant of competitive

advantage (Grunert et al. 2005). Notwithstanding the importance of competitive

advantage for a firm’s survival, suggested means to improve the firm’s competitiveness

and more particularly to encompass a market orientation dimension remain focused

primarily on the tasks and quality of management within the context of a single firm

(Perks 2000). Therefore, it can be argued that the dominant paradigm in the market

orientation literature has been intra-organizational.

However, recently there has been a rising interest in looking at market orientation

from a relationship perspective. As recently noted by Rindfleisch and Moorman (2001, p.

14), “…researchers could explore the relationship among alliance participation,

information exchange, and market orientation…” Likewise, Hunt and Lambe (2000, p.

28) suggested that “…firms that partner with other firms to compete must develop a

strategy of MO that is inter-firm rather than intra-firm in nature. The antecedents,

consequences, and measures of a business strategy of inter-firm MO are still lacking.”

In this study, the main objective is to attempt to develop a new concept --alliance

market orientation (AMO) -- and examine its meaning, antecedents, and consequences in

the context of dyadic new product alliances. In this section of the study, first, I review

some of the scholarly studies that examine market orientation from a relationship

perspective. Then, I position the current study in this literature. For that purpose, I define

alliance market orientation and discuss its importance in new product development

alliances’ performances.


66

To my knowledge, Siguaw et al. (1998) are the first to recognize the significance of

market orientation in inter-organizational settings. They suggested that a strategy for

easing the tensions facing suppliers and distributors in their channel relationships may be

the adoption of market oriented behaviors. Therefore, they developed a causal model

indicating that a supplier’s market orientation has a likely effect on both the distributor’s

market orientation and other channel relationship factors. Their main findings indicated

that, from a distributor’s perspective, a supplier’s market oriented behaviors directly or

indirectly influence all the channel relationship factors including the distributor’s market

orientation, trust, cooperative norms, commitment, and satisfaction with financial

performance.

Baker et al. (1999) investigated the effects of market orientation in a channel context.

They proposed that there is a relationship between a supplier’s perception of a reseller’s

market orientation and the supplier’s perceptions of certain key relationship marketing

constructs including commitment, trust, cooperative norms, and satisfaction. They

provided support for each of their proposed relationships.

Helfert et al. (2002) explored the notion of market orientation with a focus on inter-

organizational relationships. They argued that overall market orientation of firms needs to

be translated into a relationship level in order to be effective. Therefore, they identified

four main relationship management task bundles that have to be performed by the

companies: Inter-organizational exchange, inter-organizational coordination, conflict

resolution, and adaptation. Inter-organizational exchange activities serve to settle needs

and requirements of the partners in a relationship. Inter-organizational coordination refers

to the synchronization of the relationship partners’ actions. Conflict resolution extends


67

the notion of coordination and it requires a timely action to the conflict as well as the

readiness for compromises and a sense of justice. Finally, adaptation activities are the

ones which are necessary in customer relationships in order to meet the special needs or

capabilities of a partner. They suggested that in addition to a translation of market

orientation into a relationship management, informational, physical, human, and financial

resources should be present in market-oriented firms.

Elg (2002) focused on the importance of recognizing market orientation on a network

level and as an inter-firm phenomenon. He defined inter-firm market orientation as “the

activities that two or more independent companies carry out together to make a network

or an individual relationship more sensitive to the demands of the market” (p. 634).

Furthermore, he limited the focus of his study to distribution systems. The reason behind

it is that, he argued, a distribution system usually has an identifiable set of customers in

common that the channel members cooperate in order to serve as effectively as possible.

In his conceptual framework, he discussed the inter-organizational antecedents that

influence the degree of inter-firm market orientation. Three broad sets of antecedents are:

(1) Network structure-- relationship pattern, the power and influence structure, and the

division of labor, (2) relationship characteristics-- trust, cooperation, and conflicts, and

(3) individual firm characteristics-- firm level market orientation.

Leisen et al. (2002) explored the relationships between organizational culture, market

orientation, and marketing effectiveness in the context of strategic marketing alliances.

Their findings suggested that organizational culture influences marketing effectiveness.

They also found that increased internal culture enhances an internal market effectiveness

dimensions; whereas increased external culture enhances an external market effectiveness


68

dimension. They further found the same internal/external alignment when examining the

relationship between market orientation and market effectiveness.

Sanzo et al. (2003) examined the effect of a firm level market orientation on its

relational characteristics. Therefore, they attempted to verify a model in which a firm’s

cultural market orientation serves as an antecedent for its degree of satisfaction with its

main supplier. The main findings of their study indicated there is an indirect influence

that the buyer firm’s cultural market orientation exerts on the level of satisfaction with its

main supplier through effective communication, trust, and perceived conflict.

Blesa and Bigne (2005) investigated the relationship between a manufacturer’s

market orientation and a distributor’s dependence and satisfaction with the relationship.

The main findings claimed that all aspects of manufacturers’ market orientation have a

positive effect on distributor’s satisfaction, except response implementation and the

influence of distributor dependence; therefore, their primary result was that adoption of

market orientation is justified in practice by increased dependence and satisfaction among

distributors.

Grunert et al. (2005) examined the concept of value chain market orientation. They

defined it as “chain members’ generation intelligence pertaining to current and future

end-user needs, dissemination of this intelligence across chain members, chain wide

responsiveness to it” (p. 430). Intelligence generation refers to the sum of activities of all

chain members pertaining to information on end-users. Dissemination includes all

exchanges of information on end-users between chain members. Finally, responsiveness

refers to the actions of the chain members toward the creation of superior value for the

end-users. They also noted that these market oriented activities need not be evenly


69

distributed across the chain. According to their findings, degree of market orientation of

value chains found to be related to (1) degree of heterogeneity and dynamism of end-

users served, (2) nature of chain relationships, and (3) regulations and prevailing mental

models of decision makers.

Mason et al. (2006) explored and described the market oriented supply chain. They

defined market oriented supply chain as the demand-driven supply chain which places

consumer demand at the center of supply chain strategy. They focused on the need to

gather, distribute, and utilize market information throughout an organization and with

strategic partnering firms. Their main objective was to develop a conceptual framework

through generating insights into how and why market oriented firms are able to organize

their supply chain configuration.

Zhao and Cavusgil (2006) explored the role of market orientation in inter-firm

relationships. The main objective of their study was to examine the impact of supplier’s

market orientation on supplier-manufacturer relationships. Therefore, they developed a

model for the study of the impact that supplier’s market orientation has on

manufacturer’s trust in the supplier. Their findings provided empirical evidence that

supplier’s market orientation positively influences manufacturer’s trust in supplier, which

in turn leads to manufacturer’s long term orientation toward the supplier.

In summary, the literature about the role of market orientation in inter-organizational

relationships regards inter-firm market orientation as the sum of the market orientation of

each of the collaborating companies; therefore, it can be argued that they do not consider

market orientation as a relationship property that could be developed and examined at an

inter-organizational level.


70

In this study, the main objective is to explore the marketing understanding of these

issues by examining market orientation at an inter-firm level. The scope of this study is

limited to new product alliances. One reason for this selection is the importance of

market orientation in firms’ innovation activities. It has been suggested that market

orientation is a mechanism for providing innovative ideas and motivation to respond the

environment (Hurley and Hult 1998). The incorporation of a customer focus into the

NPD process is a key determinant in successful new product introductions (Cooper and

Klenschmidt 1995). However, it is also well known that collaboration in innovation

activities with firms from similar or competing product sectors may offer significant

advantages over intra-firm new product development. Therefore, it would be relevant to

examine the role of inter-firm level market orientation in predicting the performance of

collaborative new product development efforts. Indeed, some of the scholarly studies

pointed to the danger of managerial effort being directed towards making the relationship

work at the expense of a market focus (Littler and Leverick 1995 ; Littler et al. 1995, p.

31; Luo et al. 2007; Perks 2000, p. 180.; Piercy and Cravens 1995; Rindfleisch and

Moorman 2001, 2003; Spekman et al. 1999). These studies claimed that new product

alliances risk becoming short-sighted with the relationships per se rather than the realities

of market (Perks 2000, p. 180). As Spekman et al. (1999, p.29) highlighted, “there is a

notable shift to a more market-focused view of alliance activity…moving into the twenty-

first century these businesses can not afford such singular focus.” Therefore, the main

objective of this study is to improve the marketing understanding of the role of inter-firm

level market orientation in new product alliances’ new product development

performance.


71

In this study, alliance market orientation refers to a capability that enables an alliance

(1) to jointly and systematically gather market intelligence (from competitor analyses,

studies of customer needs/preferences, and studies of the factors that influence

competitors’ and customers’ behaviors), (2) to inter-organizationally coordinate and

disseminate the knowledge gleaned from the market intelligence gathered, and (3) to

efficiently and effectively respond to the knowledge coordinated and disseminated. It is

proposed in this study that a new product development alliance that embraces joint and

concerted market oriented behaviors will be more likely to have higher new product

performance relative to other strategic alliances which do not show this joint behavior.

2.3. Literature Review on Alliance Competence

Competence-based theory focuses on firms’ internal factors to explain their

successful business strategies (Hunt and Lambe 2000). The importance of internal factors

is underlined by Penrose (1959, p. 24-25; italics in original) as “it is never resources

themselves that are the inputs to the production process, but only the services that the

resources can render.” Resources are defined by Hunt (2000, p.34) as “any tangible or

intangible entity available to the firm that enables it to produce efficiently and/or

effectively market offering that has value for some market segments.” Specifically,

competence-based theory explains how firms develop business strategies to effectively

and efficiently use their resources (Rumelt 1984), thereby, what they could do

particularly well relative to their competitors. The details about the competence-based

theory will be provided in Chapter 3. In this section, first, I will review various

definitions of “competence” in competence-based theory literature. Following this, I will


72

define alliance competence, explore its dimensions, and investigate its role in alliance

performance.

Sanchez et al. (1996) defined competence as a firm capability to deploy tangible and

intangible entities in a way that helps a firm compete in its marketplace. For Prahalad and

Hamel (1990), a firm should be viewed as a collection of various competences because

sustainable competitive advantage derives from an ability to build the core competences

that drives innovative products more efficiently and effectively than competitors. They

argued that for a competence to be core, it should (1) provide access to a wide variety of

markets, (2) make a significant contribution to customers’ perceptions of benefits, and (3)

be difficult for rivals to imitate. Likewise, Day (1994) defined core competences as

distinctive capabilities of a firm that span and support multiple lines of business. He

further highlighted that these competences must be managed with special care through

the focused commitment of resources, assignment of dedicated people, and continued

efforts to learn, supported by dramatic goals for improvement.

Hamel and Prahalad (1994b, p.124) asserted that any company which does not adjust

its business strategies to “the future will watch its skills, capabilities, and resources

become progressively less attuned to industry realities.” The gap between the pace of

industry change and the pace of company change gives rise to the need for organizational

transformation. They further reported that this organizational transformation must be

driven by a point of view about the future of the industry, and to create this future

perspective requires industry foresight. Briefly, industry foresight involves “anticipating

the future by asking what types of benefits firms should provide their customers in the

next five to fifteen years and what new competences should be acquired or built to offer


73

such benefits” (Hunt 2000 p. 83). Therefore, industry foresight is “based on deep insights

into trends in technology, demographics, regulations, and lifestyles which can be

harnessed to re-write industry rules and create new competitive spaces” (Hamel and

Prahalad 1994b, p. 128). As Hunt (2000) argued, without resource leveraging which

focuses on resource effectiveness, industry foresight will not enable a company to

increase its competitive advantage.

Proactive innovation is the bread and butter of sustainable competitive advantage.

Hunt (2000, p.87) defined proactive innovation as innovation by firms that “…is not

prompted by specific pressures from specific competitors.” Indeed, it is genuinely

entrepreneurial in the sense of targeting new opportunities and subsequently developing

new market offerings. Accelerated pace of proactive innovation requires firms to be

driven by this environmental change and also to drive this change. Teece and Pisano

(1994) claimed that driving the environment and being driven by it stresses the

importance of dynamic capabilities. They defined dynamic capabilities as the subset of

the competences which allow firms to develop new products and/or processes and

respond to changing markets. Similarly, Hunt (2000, p.87) defined these capabilities as

renewal competences. He asserted that renewal competences prompt proactive innovation

by enabling firms to (1) anticipate potential market segments (unmet; changing; and/or

new needs, wants, and desires); (2) envision market offerings that might be attractive to

such segments; (3) foresee the need to acquire, develop, or create the required resources,

including competences, to produce the envisioned market offerings.”

As mentioned earlier, competent firms could run their business particularly well,

relative to their competitors (Selznick 1957). By the same token, it can be argued that


74

there are some firms that enable to use their alliances to achieve their strategic goals

better than their competitors do. Furthermore, as Day (1995, p.299) noted a recurring

finding from alliance performance analyses is that some firms consistently have much

higher success rates than others. Lambe et al. (2002) named this concept as alliance

competence. They defined it as an organizational ability for finding, developing, and

managing alliances. They further argued that alliance competence should enhance the

ability of firms to use alliances as a strategic option for pooling and deploying partner

firms’ basic resources to compete in their marketplace. They proposed that there are three

dimensions of alliance competence: (1) Alliance experience, (2) alliance manager

development capability, and (3) partner identification propensity. In the rest of this

section, I will briefly examine these three dimensions.

Alliance experience

DuPont is a well-known company for its ability to collaborate with other firms to

innovate and commercialize new products that have unique properties. As Day (1995,

p.299) argued, “they …have a deep base of experience in partnership that is woven into a

core competency that enables them to outperform rivals in many aspects of alliance

management.”

Firms like DuPont seek more alliance partners and learn from their own mistakes. We

argue that firms with alliance competence have more tendencies to go out and seek more

alliance partners, because they are aware of the value of partnering in the highly

competitive business environment. As Spekman et al. (1999) noted firms with alliance

competence will invest in their people by training them and developing alliance

management programs to motivate them to engage more in future alliance initiatives.


75

Kandemir et al. (2006) also highlighted that since alliance management is a complex

process and the detailed interactions between partners can not be completely expressed in

a formal contract, it is important for partnering firms to have prior alliance experience. In

a survey done by Littler et al. (1995), the findings suggested that firms with partnering

abilities and skills are more likely to engage in new initiatives and have tendencies to

build more experience out of it. Alliance experience enables partnering firms to build

distinctive capabilities by motivating them to pursue activities to accumulate and

leverage alliance management know-how associated with its prior and ongoing

experiences (Anand and Khanna 2000). Therefore, we argue that alliance competence is a

higher order resource which motivates companies to seek for new alliance partners and

engage in more alliance activities and gain more experience out of it.

Partner identification propensity

In a survey done by Littler et al. (1995) with firms involved in new product

development alliances, the first factor revealed as a basis for success is the partner

selection. Having good eyes for allies can reposition firms in competitive markets and

maintain their competitive advantages (Kandemir et al. 2006; Gulati 1999). In other

saying, sustainability and viability of a strategic business alliance is to a great extent,

determined by partner selection process (Erdem et al. 2006; Gleister 1996). In a study

done by Dev, Klein, and Fisher (1996), a number one failure is attributed to inappropriate

partner selection. Erdem et al. (2006) stated that choosing the right partner may reduce

the clash between the logic of alliances and logic of new product development. Kandemir

et al. (2006) defined the term alliance scanning as the extent to which a firm proactively

monitors for and identifies partnering opportunities. Day (1995) argued that a firm that is


76

adept at identifying, consummating, and managing strategic alliances probably has a first

mover advantage in bringing the best candidate into a relationship. Many scholars have

noted that firms that can scan, monitor, and partner with competent firms enhance their

chances of alliance success (Dyer and Singh 1998; Hunt 1997; Lambe et al. 2002;

Sivadas and Dwyer 2000). Therefore, this study proposes that firms with alliance

competence will have more tendencies to recognize the best partner for the possible

alliance relationship, thereby, have partner identification propensity.

Alliance manager development capability

Alliance management requires constant attention, because it is a complicated and

complex process. Several Fortune 100 companies which have strategic alliances with

other firms have created a position called Director of Strategic Alliances whose job is to

identify and evaluate alliance potentials and possibilities (Dyer and Singh 1998; Sivadas

and Dwyer 2000). Spekman et al. (1999) argued that alliance managers are an integral

part of an alliance development and alliance performance. However, these alliance

managers’ development in part depends on their company seeing and valuing strategic

alliance activities and the related skills and competences. Spekman et al. (1999) argued

that firms with alliance competence train and develop alliance managers who will

negotiate, structure, and run alliances in ways that allow such firms to (1) secure

attractive alliance partners, (2) minimize the chances of such alliance mismanagement as

poor conflict resolution, and (3) work with their partner firms to successfully combine

and synthesize their complementary resources over time into idiosyncratic resources that

lead to competitive advantage. Therefore, as an evidence of the importance of alliance

managers in strategic alliance management, this study proposes that more of alliance


77

competence of a firm will motivate them to invest in alliance management position, train

alliance managers, and empower them.

In summary, though alliance competence has been conceptualized as a second order

formative construct by Lambe et al. (2002), this study examines alliance competence as a

second order reflective construct. The reasoning is that alliance competence has three

dimensions: alliance experience, partner identification propensity, and alliance manager

development capability. In our view, these dimensions are conceptually related, likely to

have same antecedents and consequences, and interchangeable. Indeed, it is not difficult

to think of a firm with long years of alliance experience, knows whom to partner with,

and understand the value of alliance manager position and provide the necessary

resources to its people for the effective and efficient alliance management. Clearly,

alliance competence act as if it is the cause of these three dimensions rather then the

result of them. Therefore, we argue that strong positive inter-correlation between these

dimensions can be explained by a second order construct called alliance competence. In

addition to conceptual reasoning, the empirical investigation of alliance competence as a

second order reflective construct is provided in Chapter 6.

In summary, this chapter discusses the relevant literature on inter-organizational new

product development, alliance market orientation, and alliance competence. The next

chapter provides the theoretical framework which will be studied and tested in this study.


78

CHAPTER 3: THEORETICAL FRAMEWORK and HYPOTHESES

Figure 1.1 depicts the theoretical framework that will be studied and tested in this

study. The theoretical framework for this study draws on resource-based view (RBV),

competence-based view, and as an integrative theory, the resource-advantage (R-A)

theory of competition. The first section briefly discusses these three theories. Since a

comprehensive review of each theory is beyond the scope of this study, I discuss only the

portions that aim to explain and elaborate the relationships in the proposed model.

Following this, the second section focuses on developing an alliance market orientation

and alliance competence model of alliance new product performance, and it proposes

several research hypotheses to test the theory of alliance new product performance

suggested by the model. In this section, first, I examine the antecedents of alliance market

orientation (e.g., alliance competence, goal congruence, complementary resources, trust

and commitment) and the role of alliance market orientation as an antecedent to new

product related outcomes. In specific, I focus on two outcomes which have received

considerable amount of attention in the marketing literature: (1) new product creativity

(e.g., new product novelty and new product meaningfulness) and (2) new product

performance. Then, the role of top management support in alliance competence will be

discussed. Finally, I will investigate new product creativity as a force which drives an

alliance to have high new product performance.

3.1.Theoretical Framework

3.1.1. A Resource-Based View of the Firm

What are the sources of a firm’s sustained competitive advantage? This question has

motivated many scholars in the field of strategic management and marketing to find out


79

the factors that lead firms to have high returns over a long period of time. The SWOT

framework has been used to structure this research stream since 1960s (Andrews 1971;

Ansoff 1965; Barney 1991). This framework suggests that firms strive to match between

their internal resources with environmental opportunities, while decreasing the risk

created by external environment and avoiding their internal weaknesses (Barney 1991 p.

99; Hunt 2000). Therefore, one group of research is representing the industry-based view

of the firm, has suggested that firms can have a sustained competitive advantage as long

as they focus on the external environment by changing the structure of the chosen

industry (e.g., exit/entry barriers and relative bargaining power) (Montgomery and Porter

1991; Porter 1980). According to Porter (1980, 1985), a firm’s internal factors come into

play only after it implements the strategies that exploit external opportunities, while

neutralizing external threats. The basic logic in the industry-based view of the firm

dictates that firms have similar resources (resource homogeneity) to implement their

strategies; or otherwise, should the heterogeneity of resources exist among firms, these

resources can be easily bought and sold in the marketplace (Barney 1991). However, the

assumptions of industry-based view of the firm have been questioned by many business

strategy scholars (for a review, see Hunt 2000, p. 74-75). These scholars have rather

argued that there is a link between firms’ internal characteristics and their performance;

and they further discuss the role of resource-heterogeneity and resource immobility as

possible sources of sustained competitive advantage (Barney 1991; Conner 1991; Penrose

1959; Rumelt 1984; Wernerfelt 1984).

Analyzing firms in terms of their resource endowments has a long tradition in

marketing and management (Barney 1991; Conner 1991; Day and Wensley 1988; Day


80

and Netungandi 1992; Rumelt 1984; Wernerfelt 1984). The idea of analyzing firms as

bundles of resources goes back to the seminal work of Penrose (1959). Works drawing on

Penrose (1959) to develop a resource-based perspective have concluded that a firm’s

ability to attain high performance and competitive advantage depends on its ability to

deploy and combine resources that are important to the production process. As

Wernerfelt (1984, p.171) argues “[f]or the firm, resources and products are the two sides

of the same coin.”

Consistent with evolutionary theory in economics, the resource-based view of the

firm argues that a firm’s above normal returns “…can not be assessed by employing a

static view of competition, in which the focus is on how price is determined for products

and processes that are unchanging” (Conner 1991, p. 127). Likewise, adapting key

concepts from Schumpeterian’s vision of the dynamics of competition, resource-based

view asserts that if firms do not have heterogeneity in resources, they will have little

incentive for investing in exploration activities (Rumelt 1984). However, unlike

Schumpeter (1950), resource-based view ejects the necessity of pre-existing monopolistic

earnings to support such initiatives and does not find inconsistent with the view that less

than revolutionary innovations, well protected by ‘resource barriers’ (Wernerfelt 1974)

can yield above-normal returns (Conner 1991). Furthermore, the resource-based view

agrees with Chicago view, and disagrees with Bain-type industrial organization

economics view, that a firm’s above normal returns result from acquiring, integrating,

and deploying valuable (Barney 1991; Conner 1991), unique (Barney 1991), non-

tradable, inimitable (Barney 1991; Dierickx and Cool 1989), and causally ambiguous


81

(Lippman and Rumelt 1982; Reed and DeFillippi 1990) resources, rather than the

industry structure.

Briefly, two prominent research streams have different perspectives regarding the

sources of sustainable competitive advantage. The first research stream, the industry-

based view of the firm, has focused on the industry as the relevant unit of analysis;

whereas, in the second research stream, the resource-based view of the firm, the relevant

unit of analysis has been viewed as a single firm. As mentioned by Hunt (2000, p.85),

“Idiosyncratic firm factors, not industry factors, explain most of the variance in the firm

performance. Industry is the ‘tail’ of competition; the firm is the ‘dog’.” Dyer and Singh

(1998, p.660) argued that both of these research streams overlook the competition among

networks in their calculation of sustainable competitive advantage, “the (dis)advantages

of an individual firm are often linked to the (dis)advantages of the network of

relationships in which the firm is embedded.” They further assert that inter-organizational

relationships can be a source of competitive advantage which would make the dyad as a

relevant unit of analysis. They concluded their analysis with

A firm’s critical resources may extend beyond firm boundaries…firms who combine resources in unique ways may realize an advantage over competing firms who are unable or unwilling to do so. Thus, idiosyncratic inter-firm linkages may be a source of relational rents and competitive advantage…Indeed, the ‘explosion in alliances’ during the past decade suggests that a pair or network of firms is an increasingly important unit of analysis and, therefore, deserves more study. (p. 660-661; italics in original).

In their seminal article, Dyer and Singh (1998) argued that partner firms can

develop sustainable competitive advantages only by moving away from arm’s-length

exchanges and specializing their relationships through investments in idiosyncratic

resources, substantial knowledge exchange, combination of scarce complementary

resources, and more effective governance mechanisms. They further asserted that since


82

idiosyncratic resources generated by partner firms have causal ambiguities, time

compression diseconomies, interconnectedness, scarcity, and indivisibility, they are

difficult to duplicate precisely. Although they explicated what the isolating mechanisms

that intend to preserve the inimitability of these partnering behaviors are, they did not

provide a systematic framework that explains how the dynamic aspects of inter-

organizational resource bundles interrelate to one another so as to develop competitive

advantage (Jap 1995). This study aims to respond to that call.

In this study, I am interested in examining how collaborative and coordinated

processes between new product development alliance partners can generate competitive

advantages. When the failure rate of co-development alliances is taken into account,

understanding the factors that lead to alliance new product performance is timely and

significant. Therefore, I argue that market-focused new product development alliances

will have greater potential to develop creative products which would turn into high new

product performance than their non-market oriented competitors. In this study, I propose

that alliance market orientation is an idiosyncratic resource. I argue that alliance market

orientation as an idiosyncratic resource is inimitable in its nature, valuable, rare, socially

complex, non-tradable, and causally ambiguous. Jap (1995) discussed the ultimate form

of inimitability in which partner firms themselves may not completely know how they

manage the collaborative process to achieve strategic outcomes successfully. However, I

also argue that partner firms can still form similar arrangements with alternative firms,

meaning that the general co-development process should not change significantly from

one partner to the other (Day 1995; Jap 1995). However, the specifics of inter-

organizational structures, relational factors, each partner’s goals, complementary skills,


83

capabilities, and top management supportive mechanisms will vary, making the alliance

market orientation rare, valuable, non-substitutable, and most importantly, difficult to

imitate precisely. Therefore, I propose in this study that successful NPD alliances are

distinguished from unsuccessful ones by their customer and competitor oriented

behaviors, and inter-organizational coordination efforts. Alliance market orientation as a

working across organizational boundaries activity, I argue, is an idiosyncratic resource of

an alliance that leads to the development of creative new products which lead to

differential new product performance for the partner firms.

3.1.2. A Competence-Based View of the Firm

Why do some companies start with comparable resource bundles, but perform so

differently? As mentioned before, the resource-based view (RBV) of the firm asserts that

resource immobility and heterogeneity explains the performance differences. According

to RBV scholars, resource heterogeneity, which is the uniqueness of a firm’s resource

assortment, generates high performance only if the firm’s resources are rare, durable,

non-tradable, valuable, and inimitable (Barney 1991; Conner 1991; Lippman and Rumelt

1982; Peteraf 1993; Reed and De Fillippi 1990; Rumelt 1984; Wernerfelt 1984).

Although the resource-based view provides an explanation and understanding for

differential firm performances, many questions remain. For example, how do firms

decide how to use their resources? How do firms determine their business strategies

based on the existing resources that enable them to compete in the marketplace more

effectively and efficiently than their competitors?

Another “internal factors” theory of business strategy provides a map that explains

and enhances our understanding in a firm’s sustainable competitive advantage (Hunt


84

2000). This view of business strategy is called a competence-based view of the firm. A

competence has been defined by Sanchez et al. (1996) as an organizational ability to

deploy tangible and intangible entities in a way that helps a firm compete in the

marketplace. Day (1994) referred competences as the glue that brings the resource

endowments together and enables them to be deployed advantageously.

Briefly, although RBV has been used frequently in the business strategy literature as

an explanation for a firm’s high performance in the marketplace, competence-based view

“provides a complementary explanation” of high firm performance because it explains

how firms develop strategies to effectively and efficiently deploy resources (Lambe et al.

2002). In other words, this view provides the bridge between resources and strategy

(Lewis and Gregory 1996). Although firms may have many competences to perform their

value chain activities, some of them support a market position that is valuable and

difficult to match (Day 1994). These competences are called distinctive competences of

a firm, and they enable it to coordinate diverse production skills and integrate multiple

streams of technologies (Prahalad and Hamel 1990). Prahalad (1993) suggested that

distinctive competences have basically three features: (1) they are a significant source of

competitive differentiation, (2) they cover both current and future range of business, and

(3) they are socially complex and ambiguous, therefore, they are hard to duplicate by

competitors. He further asserts that since competence permeates the whole organization

(Prahalad 1993), it is tacit which means that they are uncodified and involve learning by

doing that is accumulated by experience (Hunt 2000). Furthermore, the role of top-

management in developing and managing a firm’s distinctive competences has also been

discussed in the competence-based view strategy literature (Day 1994; Hamel and


85

Prahalad 1991, 1995, Prahalad and Hamel 1990; Prahalad 1993). It has been argued that

“the role of top management…is essentially one of energizing the whole organization…it

involves developing a shared mindset and shared goals, and developing strategies for

acquiring competency” (Prahalad 1993, p. 42).

Distinctive competences can explain why some firms are simply better than their

competitors at doing things (Sanchez et al. 1996). Firms can have many competences

some of which have been discussed in the business strategy literature, such as

entrepreneurial competence (Foss 1993), R&D and production competences (Prahalad

and Hamel 1990), marketing competence (Day 1992; Day and Nedungandi 1992),

customer linking and channel bonding competences (Day 1994), and learning

competences (Dickson 1996). Competences can be considered as resources because, they

are socially complex, inter-connected, “tangible and/or intangible entities”, are “available

to the firm” and, most importantly, they “enable a firm to produce efficiently and/or

effectively a market offering that has value for some market segment(s)” (Hunt 2000, p.

34). Day (1994, p.39) further supported this claim that “[e]very business acquires

capabilities that enable it to carry out the activities necessary to move its products or

services through the value chain.” In their article, Hunt and Morgan (1995) argued that

competences are higher-order resources. Furthermore, Hunt (2000) recognizeed that the

firm’s ability to combine lower order resources in a way that is difficult to duplicate

precisely by competitors is called a higher-order resource.

In this study, I am interested in examining why some alliances are better than their

competitors at developing new products. Adapting a key concept from Lambe et al.

(2002), I argue that since these alliances are more competent, they are better at partnering


86

and, therefore, able to invest in idiosyncratic resources which will be difficult to imitate

by competitors. Lambe et al. define alliance competence as an organizational ability for

finding, developing, and managing alliances. As mentioned by Spekman et al. (1999), an

alliance competence increases customer benefits, and its advantages are based on an

ability to leverage both the firm’s and its partners’ resources. Consistent with Hunt’s

(2000) conceptualization of competence, I agree with Lambe et al. that alliance

competence is a higher-order resource, and it has three dimensions: alliance experience,

partner identification propensity, and alliance manager development capability. I propose

that the specifics of alliance competence will vary making it difficult to imitate precisely

and, therefore, it will facilitate idiosyncratic resources which will eventually contribute to

alliances’ competitive advantage.

3.1.3. The Resource-Advantage Theory of Competition

Firms compete. And, in today’s complex business environment, firms collaborate to

compete. Some of these collaborations are able to survive in this competitive

environment, some of them are not. Why? Why is there performance diversity among

alliances? Why only some alliances are able to develop creative new products, why not

others? These business phenomena call for an explanation.

As Hunt (2002, p.195) articulated, “Any construction that purports to be theory must

be capable of explaining and predicting real-world phenomena.” Therefore, when the

above questions are taken into account, one should expect that a theory of competition

should satisfactorily explain the phenomenon of alliance performance diversity. As an

interdisciplinary theory of competition, resource-advantage theory (R-A theory)

developed by Hunt (2000) and Hunt and Morgan (1995, 1996, 1997) responds to this call.


87

The R-A theory of competition “draws on – shares affinities with – diverse theories,

research programs, and traditions” (Hunt 2000, p. 17). Since a comprehensive review of

each theory is beyond the scope of this study, I will briefly discuss the theories and their

affinities with R-A theory.

First, in the neo-classical tradition, economies are viewed through the lens of

mechanistic Newtonian physics. In this tradition, firms are conceptualized as profit

maximizing entities in static equilibrium. The principles of classical Newtonian physics

(e.g., determinism, equilibrium, static linear relationships) have influenced the way that

traditional economists model the economy. However, this tradition has been challenged

by evolutionary economics based on the fact that the premises of the neo-classical

economics are not able to explain and predict well enough the real world economic

conditions (Hunt 2000). Indeed, evolutionary economics models the economy on

Darwinian evolution theory. When the complex environment of organizations -- channel

partners, competitors, customers, government, and other macro environmental variables -

- is taken into account, it would be relevant to consider a firm’s environment as its

“ecosystem” (Moore 1993). In contrast with neo-classical tradition, evolutionary

economics view economies through the lens of biological metaphors (Hodgson 1993;

Hunt 2000). Therefore, competition is not consummatory and equilibrium provoking,

rather it is disequilibrium provoking and process oriented (Dosi and Nelson 1994).

Hodgson (1993, p.201) recognized that for a theory of competition to reflect the process

of natural selection, it must have durable and heritable units of selection and,

furthermore, this process should involve “a variety of forms from which to select.” In

other words, a selection process should involve “the struggle results in the survival of the


88

‘fitter,’ not necessarily the ‘fittest’” (Hunt 2000, p. 21). R-A theory shares affinities with

evolutionary economics: (1) R-A theory is argued to be an evolutionary, phylogenetic,

non-consummatory, disequilibrium provoking, theory of competition, and (2) R-A theory

accommodates, complements, and incorporates the evolutionary, competence perspective

of he firm.

Second, R-A theory draws on and shares many affinities with Austrian economics.

Four of them are: (1) “R-A theory views competition as a process, not a state”, this stems

from the premise that firms’ primary objective is to seek superior financial performance.

Since all firms can not be simultaneously superior and rivals have an urge to achieve

better financial results than an existing firm, any temporary equilibrium has always a

tendency to be unstable, (2) “R-A theory views competition as a knowledge discovery

process” which means that firms learn through competition as a result of feedback from

their financial performances, (3) “R-A theory does hold that the value of market offering

as perceived by a market segment is central to understanding how the process of

competition actually works”, and finally, (4) R-A theory does not restrict resources to a

firm’s tangible factors of production, rather it defines resources as “the tangible and

intangible entities available to the firm that enable it to produce efficiently and/or

effectively a market offering that has value for some market segment(s)” (Hunt 2000, p.

30-34).

Third, R-A theory draws on and shares affinities with heterogeneous demand theory.

Three of them are: (1) “R-A theory agrees that demand in the overwhelming majority of

industries is substantially heterogeneous”, (2) “R-A theory agrees that…a market

offering is a distinct entity that is composed of a bundle of attributes, which maybe


89

tangible or intangible, objective or subjective, and which may be viewed by some

potential buyer(s) as a want satisfier”, and (3) “R-A theory attributes heterogeneous

supply to the fact that many intra-industry firm resources are heterogeneous and

imperfectly mobile,” here heterogeneity means each firm in the marketplace has at least

some resources that are unique to it, and immobility refers that some of these resources

can not be acquired and duplicated precisely. R-A theory argues that heterogeneous

resources are valuable and, thereby, lead to competitive advantage in the marketplace

only if these resources are immobile (Hunt, p. 52-54).

Fourth, R-A theory draws more on and shares more affinities with differential

advantage theory than any other research tradition. I will attempt to summarize four of

them which are related to the subject of matter of this study. (1) R-A theory maintains

that competition is a dynamic process, and the propulsion mechanism is the urge to

achieve superior financial performance. It also believes that firms have known resources

in an unknown environment which makes the profit maximization is a utopian objective,

(2) R-A theory denies that perfect competition of neo-classical economy does represent

the appropriate welfare ideal. Instead, the appropriate welfare ideal must accommodate

competition-induced innovation which is the engine of economic growth, (3) R-A theory

agrees that competition involves both proactive and reactive actions, (4) R-A theory

believes that dynamic process of competition involves the struggle among competitors

for advantages. It further asserts that firms struggle with each other for comparative

advantage in resources which will yield marketplace positions of competitive advantage,

and thereby, superior financial performance.


90

Fifth, when the affinities that R-A theory shares with resource-based theory taken into

account, R-A theory especially agrees with a resource-based view (RBV) of the firm.

Some of the affinities that R-A theory shares with an RBV of the firm can be summarized

as followings: (1) R-A theory adapts the view that firms are combiners of heterogeneous,

imperfectly mobile resources that are historically situated in space and time. By

heterogeneity, R-A theory means that some of the resources that a firm has are unique to

that firm and this explains why only some firms are profitable, not others. But, why only

some of the profitable firms are able to sustain their success in the long term? Consistent

with an RBV of the firm, R-A theory asserts that the successful firms that are able to

sustain their performance have not only heterogeneous resources, but also these resources

are not able to be duplicated precisely by competitor firms, (2) both an RBV of the firm

and R-A theory agree that firms and their resources are historically situated entities. In

other words, these two theories question whether the firm is a production function,

thereby, should be represented as a mathematical abstraction. Indeed, R-A theory, as a

general theory of competition, incorporates the production function, mathematical

abstraction of the firm in neo-classical theory as a special case.

Sixth, R-A theory agrees with competence based theory from many perspectives.

However, I will discuss only some of them that explain and elaborate the relationships in

the proposed model of this study. They can be articulated as followings: (1) Consistent

with competence based theory, R-A theory agrees that competition is an ongoing and

dynamic process. Urge to achieve superior financial performance is the major driver of

the dynamic nature of competition. Since simultaneous firm superiority in financial

performance is not likely, for R-A theory, competition among firms stimulates the


91

proactive and reactive innovations. In other words, R-A theory agrees that firms are not

only respond to their environments in the sense that they are striving for finding the best

“fit” between their existing resources and market opportunities, but they are also

changing the environment in the Schumpeterian sense, (2) both R-A theory and

competence based theory argue that organizational learning is endogenous to the process

of competition. Specifically, R-A theory emphasizes the contribution of competition to

organizational learning. R-A theory asserts that “firms learn by competing as a result of

feedback from relative financial performance signaling relative marketplace position,

which in turn signals relative resources” (p. 89), and (3) R-A theory and competence

based theory, both, agree that firms and firm behaviors should be investigated in macro

organizational system context. Briefly, R-A theory posits that “societal resources, societal

institutions, competitors, suppliers, consumers, and public policy shape – but do not

determine—firms and firm actions, and thereby, influence how well the process of

competition works” (p. 89).

Finally, R-A theory draws on and shares affinities with different forms of institutional

theory. In this section, I will briefly discuss R-A theory’s affinities with institutional

economics, transaction cost economics, and economic sociology: (1) As to the

institutional economics, both theories share the view that competition is a disequilibrium

provoking and dynamic process. Furthermore, both theories oppose with the neo-classical

view that resources can be only tangible and physical (e.g., labor, capital, land). Rather,

specific competences of specific firms and organizational capabilities should also be

counted as resources. Indeed, R-A theory agrees that both resource allocation and

resource creation are important factors in economic growth. (2) As to the transactional


92

cost economics, both theories agree that “many resources are firm specific and that such

firm-specific resources are important for explaining economic phenomena” (p. 100).

Specifically, R-A theory emphasizes the importance of firm-specific assets in

competition process. However, apart from transaction-cost economics, R-A theory agrees

that imperfect information makes the maximizing orientation of organizations impossible.

Rather, it is the quest for superior performance, that is, more than, better than, that

ensures that R-A competition is dynamic. Finally, (3) as to the economic sociology, both

theories explain, predict, and enhance our understanding in how societal institutions that

promote trust contribute to wealth creation. Furthermore, both theories share the view

that embedded social relations constitute a resource only contingently. Indeed, for R-A

theory, all entities constitute resources only contingently. R-A theory is moderately

socialized (for review see Hunt 2000, p. 102), therefore, it agrees that changing

environments shape the firms, firms shape the environments in a proactive or reactive

way, and firms reshape themselves through their renewal competences.

In summary, R-A theory is disequilibrium provoking, evolutionary, never-ending,

process theory of competition. It views (1) innovation and organizational learning as

natural outcomes of the process of competition, (2) firms and consumers as having costly

and imperfect information, and (3) macro-environmental factors (e.g., institutions, public

policy, customers, suppliers, competitors) as affecting economic performance. As

discussed above, R-A theory draws from and shares affinities with several research

traditions and theories. However, it is not a composite of these theories. Rather, it draws

only on those perspectives of the traditions that fit. In R-A theory, firms and their

resources are hereditary units of selection, and it is the competition process which selects


93

firms and resources. R-A theory defines competition as the constant struggle among firms

for comparative advantage in resources that will yield marketplace positions of

competitive advantage for some market segment(s) and, thereby, superior financial

performance. R-A theory emphasizes the importance of market segments due to

differential consumers’ tastes and preferences. It also stresses the importance of

comparative (dis)advantage in resources, and respective marketplace positions of

competitive (dis)advantage. Figure 3.1 displays the dynamic nature of R-A competition;

whereas, Figure 3.2 shows the competitive position matrix. R-A theory views firm as

combiners of heterogeneous and imperfectly mobile resources, under conditions of

imperfect and costly information, toward the primary objective superior financial

performance. Due to the heterogeneity and immobility of the resources, R-A theory

focuses on comparative advantage in resources among organizations. Some firms will

have comparative advantage in resources that are available to them and lead them to

effectively and efficiently produce particular market offering(s) that have value for

particular market segment(s). As shown in Figure 3.1 and Figure 3.2., when firms have

comparative (dis)advantage in resources, they will occupy marketplace positions of

competitive (dis)advantage that will result superior (inferior) financial performance.

Furthermore, how well the process of competition fosters the firm productivity and

economic growth is significantly influenced by various environmental factors (e.g., the

societal resources, the societal institutions, competitors and suppliers, consumers, and

public policy decisions). Specifically, the concept of marketplace positional advantage

means that some firms are occupying one of three cells (cell 2, 3, and 6). Briefly, Figure

3.2 displays nine possible competitive marketplace positions based on two dimensions


94

and three levels for each dimension. According to the figure, one can say that depends on

the level of a firm’s relative resource-produced value for some segments and its level in

relative resource costs for producing such value, it will either occupy advantageous,

disadvantageous, or indeterminate position, which would in turn affect its financial

position (e.g., superior, inferior, parity). In the following section, I use R-A theory to

provide a theoretical foundation for the proposed model of new product alliance

performance.

3.1.2.1. R-A Theory and New Product Alliance Performance

Innovative products are an important engine of economic growth (Sorescu, Chandy,

and Prabhu 2003). However, there are inherent risks associated with firm-level

innovation activities (e.g., expensive R&D activities, resource constraints). Therefore,

firms consider entering into strategic business alliances to develop new products.

Interestingly, only some of these collaborative new product development activities are

able to be successful in the sense that they are able to meet the overall performance

objectives of the respective NPD alliance. But, why? Why are only some of the alliances

able to develop creative products that meet consumer needs and preferences? Why is

there financial performance diversity among NPD alliances? Why are only few NPD

alliances able to sustain their performances in the long term? This study attempts to

answer these questions by conceptualizing a new construct called alliance market

orientation. I argue that market oriented alliances that focus on market factors (e.g.,

consumers, competitors, regulations) as well as internal processes of inter-organizational

collaboration (e.g., building interpersonal relationships, ensuring good communications)

are more likely to develop creative new products and, therefore, have greater new product


95

performance than their competitors who do not have such a market focus. In this study, I

propose a model of NPD alliance performance which incorporates alliance market

orientation as a key mediating variable. I argue that as an interdisciplinary, integrative

theory of competition, R-A theory can provide a theoretical foundation for this theoretical

model. Reasoning follows.

First, consistent with a resource-based view of the firm, R-A theory broadens the

concept of resources. Resources are defined as “the tangible and intangible entities

available to the firm that enable it to produce efficiently and/or effectively a market

offering that has value for some market segment(s)” (Hunt 2000, p. 128). In R-A theory,

resources are categorized as financial (e.g., cash reserves and access to financial

markets), physical (e.g., plant, raw materials, and equipment), legal (e.g., trademarks and

licenses), human (e.g., the skills and knowledge of individual employees), organizational

(e.g., controls, routines, cultures, and competences), informational (e.g., knowledge about

market segments, competitors, and technology), and relational (e.g., relationships with

competitors, suppliers, and customers).

In this study, I propose that alliance market orientation is both an informational and

relational resource of an alliance: (1) It is an informational resource, because alliance

market orientation is conceptualized in this study as an alliance’s knowledge based-asset

strategy. Indeed, it is about gathering market intelligence, disseminating this intelligence

through inter-organizational coordination, and efficiently and effectively responding to

the intelligence coordinated and disseminated. R-A theory posits that investment on

informational resources will be undertaken by firms when they expect that they will

contribute to their competitive advantage. In the market orientation literature, it is


96

articulated that “companies that are better equipped to respond to market requirements

and anticipate changing conditions are expected to enjoy long-run competitive advantage

and superior profitability” (Day 1994, p. 37). Briefly, alliances that focus on markets will

be investing on their informational resources. Since there are differences in the history of

alliances with respect to the investments in market related informational resources, it is

expected that these alliance resources will be some way unique to them (Hunt 2000).

Furthermore, since these informational resources are tacit, complex, socially created,

embedded so deeply into the nature of alliances, they can not be directly observed,

thereby, sold and/or bought in the marketplace. (2) R-A theory asserts that “the stock of

relational resources of a firm…includes its stock of relationships with…customers,

suppliers, competitors, governmental agencies, and unions” (Hunt 2000, p. 188). I argue

that alliance market orientation is a relational resource because the process of

collaboration across organizational boundaries can contribute to alliance partners’ ability

to efficiently and/or effectively produce a market offering (e.g., creative new product)

that has value for some market segment(s). Briefly, alliance market orientation as a

relational resource is heterogeneous and immobile. “There is no—can be no—central

marketplace where such entities…are traded” (p. 188). Briefly, since alliance market

orientation is argued to be a relational and informational resource, it will yield a

marketplace position of competitive advantage, and thereby, superior financial

performance in the long run.

Second, alliance market orientation is conceptualized in this study as an idiosyncratic

resource. Idiosyncratic, inter-firm relationships specific, resources uniquely support the

alliance partners’ relationships (Teece 1987; Williamson 1985). These resources are


97

discussed as tangible (e.g., joint manufacturing facility) or intangible (e.g., efficient

collaboration process), and nonfungible. Nonfungible nature of alliance market

orientation means that it is not “easily transferable to other relationships; therefore [it]

lose[s] [its] value in the event that the relationship is terminated” (Jap 1999, p. 464).

Lambe et al. (2002) define idiosyncratic resources as those that are (1) developed during

the life of the alliance, (2) unique to the alliance, and (3) higher-order resources. R-A

theory supports the view that alliance market orientation is a collaborative effort, and

therefore, “makes possible the integration of the partner firms’ individual resources, that

is, allow alliances to extract the competitive advantage potential from the combination of

the partner firms’ respective resources” (Hunt 2000, p. 144; italics in original). Due to

unique, rare, causally ambiguous, highly interconnected, tacit, and time compressed

nature of alliance market orientation, I argue, competitors will fail to acquire, duplicate,

or find its substitutes in the marketplace. Therefore, sustainable competitive advantage is

expected to be the direct payoff of an alliance’s market orientation efforts.

Third, I argue in this study that alliance competence is a renewal competence.

Renewal competences are viewed by R-A theory as abilities that enable

firms to (1) anticipate potential market segments (unmet; changing; and/or new needs; wants; and desires); (2) envision market offerings that might be attractive to such segments; and (3) foresee the need to acquire, develop, or create the required resources, including competences, to produce the envisioned market offerings” (Hunt 2000, p. 87-88).

As it is argued by R-A theory, renewal competences are expected to enable firms to

improve their industry foresight and, thereby, anticipate market requirements ahead of

competitors. Therefore, as it continues, competent firms foresee the need to acquire,

develop, or create the required resources to produce the envisioned market offerings. By


98

the same token, an alliance competence as a renewal competence enables partner firms in

an alliance to understand the evolving needs/preferences of customers before competitors

and energize their alliance to respond them. Furthermore, an alliance competence enables

partner firms to connect their inter-organizational processes to the external environment.

In other words, since an alliance competence can be a renewal competence in R-A

competition, it can enable alliance partners to focus on markets in efficient and/or

effective a manner to produce market offerings that have value for some market

segment(s). Therefore, I argue that an alliance competence not only leads partner firms to

acquire complementary resource from firms that have congruent goals but also enables

them to develop idiosyncratic resources such as alliance market orientation to have a

marketplace of competitive advantage and, thereby superior financial performance in the

long term. In summary, the theoretical framework in this study draws on a resource-based

view of the firm, a competence-based view of the firm, and as an integrative theory of

competition, the R-A theory. My main objective in this section is to provide a brief

summary of each theory and explain how they provide a theoretical foundation to the

relationships that are examined in this study. In the following section, I will elaborate the

relationships between variables. I further provide the research hypotheses and rationales

behind them.


99

Figure 3.1. A Schematic of Resource-Advantage Theory of Competition

Read: Competition is the disequilibrating, ongoing process that consists of the constant struggle among firms for a comparative advantage in resources that will yield a marketplace position of competitive advantage . and, thereby, superior financial performance Firms learn through competition as a result of feedback from relative financial performance “signaling” relative market position, which, in turn signals relative resources.

Source: Hunt and Morgan (1997)

Societal Resources Societal Institutions

Competitors - Suppliers

Consumers

Public Policy

Resources

Market Position

Fina ncial Performance

• Comparative Advantage • Parity

• Comparative Disadvantage

• Competitive Advanta ge • Parity

• Competitive Disadvantage

• Superior • Parity

• Inferior


100

Read: The marketplace position of competitive advantage identified as Cell 3A in each segment results from the firm, relative to its competitors, having a resource assortment that enables it to produce an offering that (a) is perceived to be of superior value by consumers in that segment and (b) is produced at lower costs than rivals. * Each competitive position matrix constitutes a different market segment (denoted as segment A, segment B, …). Source: Adapted from Hunt and Morgan (1997).

Figure 3.2. Competitive Position Matrix

Segment DSegment DSegment DSegment DSegment DSegment DSegment C

Segment BIntermediatePosition

1D 2D 3D

CompetitiveAdvantage



6A5A4D

ParityPosition

CompetitiveDisadvantage


7D


IntermediatePosition

8A 9A


1C 2C 3C




6A5A4C

ParityPosition



7C



8A 9A


1B 2B 3B




6A5A4B

ParityPosition



7B



8A 9A

IndeterminatePosition

1A 2A 3A




6A5A4A

ParityPosition



7A



8A 9A

Lower Parity Superior

Relative Resource-Produced Value(Effectiveness)

Lower

Parity

Higher

RelativeResourceCost(Efficiency)

Segment A

Segment DSegment DSegment DSegment DSegment DSegment DSegment C

Segment BIntermediatePosition

1D 2D 3D




6A5A4D

ParityPosition



7D



8A 9A


1C 2C 3C




6A5A4C

ParityPosition



7C



8A 9A


1B 2B 3B




6A5A4B

ParityPosition



7B



8A 9A


1A 2A 3A




6A5A4A

ParityPosition



7A



8A 9A

Lower Parity Superior

Relative Resource-Produced Value(Effectiveness)

Lower

Parity

Higher

RelativeResourceCost(Efficiency)

Segment A


101

3.2. Hypotheses


More than half a century ago, Drucker (1954) articulated that organizations have two

main responsibilities: marketing and innovation. In other words, market information (e.g.,

customers, competitors, and regulations) enables firms to perform better through the

development of new products which are perceived as unique and meaningful by some

market segment(s). Similarly, Cooper (1979) posited that in order for firms to develop

new creative products and, consequently have high new product performance, they have

to understand the needs and wants of current and potential customers and satisfy them

faster and better than their competitors. By using Day and Wensley’s (1988) source-

position-performance (SPS) framework, it can be argued that market orientation as an

organizational resource enables firms to enhance their positional advantage through the

development of creative new products, which would in turn result in improving new

product performance. Slater and Narver (1995) and Gatignon and Xuereb (1997) agreed

that since market oriented firms focus on gathering market intelligence, they can position

themselves to respond to continually evolving customer needs through the addition of

unique and meaningful products. Market oriented firms not only listen to their current

and potential customers’ expressed needs but also commit to understand their latent needs

(Slater and Narver 1999; Von Hippel 1988). With a strong commitment to serving its

customers, a market oriented firm is willing to satisfy customers’ expressed and latent

needs through developing creative products.

Although new product innovations are important for a firm’s survival, “one of the

most important lessons executives have learned about innovation in the past few years is


102

that companies should not go it alone…companies are drawing business partners…into

innovation networks” (BusinessWeek 2007, p. 58). More than a decade ago, Powell

(1996) pointed out that the locus of innovation is not the individual firms; it is rather the

network of firms. Consistently, Dyer and Singh (1998) suggested that a firm’s alliance

partners are the most important source of new and creative ideas which result in

innovations. However, Perk (2000) and Piercy and Cravens (1995) noted that although

many firms ally with other firms to engage in new product development projects, many

of them fail. According to Lerner et al.’s (2003) study, only seven percent of innovation

alliances in biotech-biopharma alliances result in an approved drug by FDA. When

considering only alliances that progressed through the phase I and phase II of clinical

trials, the reported alliance success rate is about 26 percent. In essence, these reports

indicate that the majority of alliances will not result in commercialized products. As

reasoning, Perk (2000) and Piercy and Cravens (1995) proposed that failed alliances do

not have a mind set for effective integration of market information into their NPD

processes which is seen as a critical determinant of high NPD performance. Similarly,

Spekman et al. (1999) argued that there should be a shift to a more market-focused view

of alliance activity. Littler et al. (1995) claimed that NPD alliances should respond to key

customer needs and take advantage of market opportunities. Indeed, they found in their

empirical study that the effect of focusing on the internal processes of collaboration at the

expense of market factors may have a bearing on the eventual performance of the product

being developed. Market focus view perspective of innovation alliances is also

emphasized by Steinmueller (2002, p.30) by “…a new theory about the relation between


103

knowledge generation and its use in the innovation process has emerged that stresses the

systemic nature of innovation processes.”

Consistent with Spekman et al. (1999), I argue in this study that firms who ally with

other firms to develop creative products may not accomplish their objectives without

integrating market information into their processes. Indeed, “these businesses can not

afford such a singular focus” (p. 26). In this study, I argue that there is an entity called

“alliance market orientation” which leads new product alliances to develop and maintain

market oriented behaviors to increase the likelihood of their high new product

performance.

Alliance market orientation has been conceptualized in this study as an idiosyncratic

resource, and defined as a capability that enables an alliance (1) to jointly and

systematically gather market intelligence (from competitor analyses, studies of customer

needs/preferences, and studies of the factors that influence competitors’ and customers’

behaviors), (2) to inter-organizationally coordinate and disseminate the knowledge

gleaned from the market intelligence gathered, and (3) to efficiently and effectively

respond to the knowledge that is coordinated and disseminated. In this study, alliance

market orientation is conceptualized as a higher-order construct having three dimensions:

inter-organizational customer orientation, inter-organizational competitor orientation, and

inter-organizational coordination. Inter-organizational customer orientation is

conceptualized as an alliance partner firms’ joint efforts to understand its target market to

be able to create superior value for them. Inter-organizational competitor orientation is

conceptualized as an alliance partner firms’ concerted actions to understand the strengths

and strategies of current and potential key competitors and respond to them. Finally,


104

inter-organizational coordination is conceptualized as the extent to which alliance

partners share market intelligence, synchronize their NPD activities, conduct joint NPD

tasks, and respond to each other’s needs and requests to create superior customer value.

According to the R-A theory of competition, alliance market orientation’s value is

derived from its being unique, rare, causally ambiguous, highly interconnected, and tacit.

This does not mean that members of the dyad can not form similar inter-organizational

arrangements with other firms. However, the specifics of each firm’s alliance

competence, top management support, complementary resources, congruent goals, and

relational factors will vary, making this idiosyncratic resource virtually impossible to

duplicate precisely. Therefore, sustainable competitive advantage is expected to be the

direct payoff of an idiosyncratic nature of alliance’s market orientation efforts. Briefly,

alliance market orientation enables partner firms to understand the market factors that

may affect collaborative new product development process, to achieve high new product

performance through unique and meaningful products. Thus, this study hypothesizes that

H1: Alliance market orientation has a positive effect on a) new product novelty, b)

new product meaningfulness, and c) new product performance.

3.2.2. Goal Congruence and Complementary Resources

Goal congruence is the extent to which partner firms perceive the possibility of

simultaneous goal achievement (Jap 1999; John and Reve 1982). As Spekman et al.

(1999) suggested, alliance partners must share mutually achievable goals. One of the

most important goals is to meet the needs of the consumers better than competitors. They

further highlight that problems and conflicts will arise between partner firms when their

goals are misaligned. Hamel et al. (2002) stressed that for a successful relationship; the


105

partners’ strategic goals should converge while their competitive goals diverge. In their

empirical study, Littler et al. (1995) found that goal congruence and mutual benefit are

the one of the main factors for success in new product collaborations. Likewise, Day

(1995) questioned why so many alliances have disappointment and frustration in their

collaborations. He figures out that one of the main reasons for alliance failure is the

conflict in partners’ objectives. Erdem et al. (2006, p. 337) highlighted that “high goal

correspondence enhance(s) the consistency of expectations and assured mutual gains.”

Similarly, Perks (2000) suggested that goal correspondence plays an important role in

NPD alliances in determining the approach towards integrating market information in the

collaborative new product development process. In her compelling piece, Jap (1999, p.

465) found an empirical support for her claim that as goals between partner firms become

increasingly aligned, “there is a strong incentive to form a close relationship that exploits

the distinctive aspects of the dyad’s environment… as well as to make the necessary

investments to exploit this joint potential.” She further argues that goal congruence

between partner firms motivate them to develop and maintain idiosyncratic resources. In

the current study, alliance market orientation is conceptualized and discussed as a

nonfungible and idiosyncratic resource. Therefore, this study hypothesizes that

H2: Goal congruence has a positive effect on alliance market orientation.

One of the main motives of a firm entering into a strategic business alliance is to

collaborate with partners whose resources enable it to accomplish what it can not do

alone (Day 1995). Varadarajan and Cunnigham (1995) defined complementary resources

as nonredundant distinctive competences brought by each member to the partnership.

They highlight that an alliance can achieve a competitive advantage by pooling their


106

complementary resources. However, Asanuma (1989) claimed that complementary

resources by themselves are not sufficient to achieve competitive advantage; rather they

should be combined in a unique way. Similarly, Das and Teng (2000) suggested that

complementary alignments are the most widely acknowledged type of alignment in

alliances. Consistent with Asanuma (1989), authors argued that complementary resources

have a positive effect on alliance success only though creating valuable idiosyncratic

resources. Perk (2000) suggested that complementary resources of alliance partners can

influence their approach towards generating, integrating, and acting on market

information in the collaborative new product development. She also proposes that having

complementarities in resources can enable partner firms to focus on the realities of

markets. Knudsen (2007) pointed out that complementary resources foster learning from

partners through the combination of different and complementary skills. He also argues

that complementary resources are likely to trigger new ideas that challenge existing

knowledge and understanding. Therefore, as he claims, a successful combination of

complementary resources may lead to creation of new products through collaboration and

cooperation. Jap (1999, p. 465) posited that complementary resources, “the degree to

which the firms are able to fill out, or complete, each other’s performance by supplying

distinctive capabilities, knowledge, and resources,” are essential to successful

collaboration and cooperation process. She found an empirical support that

complementary resources lead to competitive advantage of the dyad only through

idiosyncratic resources. Alliance market orientation has been conceptualized in this study

as an idiosyncratic resource. Therefore, consistent with the previous findings,

H3: Complementary resources have a positive effect on alliance market orientation


107

3.2.3. Trust and Commitment

In order to increase their competitiveness in the marketplace, strategic business

alliances may prefer to have a market focus. Indeed, Littler et al. (1995) pointed to the

danger of alliance management being directed toward making the relationship work at the

expense of a market focus. Perk (2002) also highlighted the importance of being market

oriented in an alliance context. In their comprehensive study, Gebhardt et al. (2006)

found that there are five core values that drive market orientation in organizations:

market as the raison d’etre, collaboration, respect, keeping promises, openness, and trust.

They suggest that combination of these values creates an environment that is supportive

of collaboration, leveraging the capabilities of all members, creating a shared

understanding of the problems, allowing for the creation of more effective solutions to

problems, and assisting in effective implementation of solutions through tighter

collaboration. By the same token, I argue that trust is an important value that can foster

market oriented behaviors of an alliance. Sivadas and Dwyer (2000) developed a

construct, “cooperative competency”, and test it into an NPD alliance context. They

conceptualize it as having three dimensions: trust, coordination, and communication.

They argue that trust provides a support to collaborative activities of an alliance. Elg

(2002) proposed that alliance partners who have previously demonstrated reliability and

integrity, thereby, commitment in to the relationship will be less hesitant to share

information. He further argues that these alliance partners will be more willing to

generate market intelligence together and act on the gathered and shared intelligence in a

concerted manner. Briefly, he posits that high degree of trust and commitment will make

the alliance partners more willing to engage in joint activities that will eventually


108

increase the level of respective alliance’s market orientation. Similarly, Bensaou and

Anderson (1999) asserted that when partner firms trust in each other, and, are thereby

committed to the relationship, they are more willing to risk making idiosyncratic

investments. Jap (1999) also discussed the role of trust in inter-firm cooperation. She

argues that when trust exists between partners, they are more willing to share

information, and tend to approach the relationship with a problem solving orientation.

Furthermore, she discusses that idiosyncratic investments are nonfungible, meaning that

they lose their value in the event that the relationship is terminated. In her empirical

findings, trust facilitates these idiosyncratic investments and provides assurance of

reduced opportunistic behavior and competitive advantage from the joint efforts. Grunert

et al. (2006) found that trust and commitment provide a balanced relationship among

partner firms and lead to market orientated behaviors of a value chain. Likewise, Erdem

et al. (2006) and Perk (2000) stated that commitment of alliance partners into the

relationship can foster their willingness to gather market information, share this

information in order to increase the common understanding, and respond to that

information in a concerted manner. Morgan and Hunt (1994, p. 26) concluded that

“cooperative behaviors is the only outcome posited to be influenced directly by both

relationship commitment and trust.” Alliance market orientation is conceptualized in this

study as a cooperative market focused behaviors of an alliance. Therefore, consistent with

the previous literature, this study posits that

H4: Trust has a positive effect on alliance market orientation.



109

Rapid and radical technological developments in the competitive markets,

globalization of business, and the shortening product life cycles are forcing firms to seek

more creative and flexible means for meeting competition (Donney and Cannon 1997;

Wind and Mahajan 1997). Many firms have responded to these challenges by entering

into alliances. As pointed out by Donney and Cannon (1997, p. 35), “such collaborative

relationships rely on relational forms of exchange characterized by high levels of trust.”

Trust is the “the firm’s belief that another company will perform actions that will result in

positive outcomes for the firm, as well as not to take unexpected actions that would result

in negative outcomes for the firm” (Anderson and Narus 1990, p. 45). Donney and

Cannon (1997) conceptualized trust based on two dimensions: credibility of exchange

partner and benevolence. First dimension refers as an expectancy that the partner’s word

or written statement can be relied on; whereas, second dimension refers to the extent to

which one partner is genuinely interested in the other partner’s welfare and motivated to

seek joint gain. Morgan and Hunt (1994) pointed out that trust is central to all relational

exchanges (e.g., relationship with the customers, competitors, and suppliers). This study

adapts Morgan and Hunt’s (1994, p. 23) definition of trust “which exists when one party

has confidence in an exchange partner’s reliability and integrity.”

Commitment is another sine qua non of collaborative relationships for without it there

can be no exchange relationships. Dwyer et al. (1987) discussed about commitment as it

happens at the most advanced phase of the exchange relationships. They define

commitment as it refers to an implicit or explicit pledge of relational continuity between

exchange partners. By taking the vagueness of the notion of commitment, the authors

consider three measurable criteria of commitment: inputs, durability, and consistency.


110

Morgan and Hunt (1994) posited that commitment, like trust, is central to all relational

exchanges. The current study adapts Morgan and Hunt’s (1994) conceptualization of

commitment. Drawing on the conceptualizations of commitment in social exchange, they

define commitment as “an exchange partner believing that the ongoing relationship with

another is so important as to warrant maximum efforts at maintaining it” (p. 23).

Dwyer et al. (1987, p. 19) reported that commitment occurs only after “the exchange

partners have achieved a level of satisfaction from the exchange process.” In other words,

since “commitment entails vulnerability, parties will seek only trustworthy partners”

(Morgan and Hunt 1994, p. 24). Spekman et al. (1999) suggested that commitment to an

exchange relationship draws from both a sense of trust and a belief that the exchange

relationship has merit and warrants support. Therefore, consistent with the previous

findings, this study posits that


3.2.4. Joint Alliance Competence

Some firms, like Eli Lilly., are better at finding partners that have congruent goals

and complementary resources, developing and managing relationships with them than

other firms. Drawing on the competence-based literature and the R-A theory of

competition, Lambe et al. (2002) proposed that there is such an entity called alliance

competence which explains why some alliances are simply better than others. In their

conceptualization, they posit that alliance competence has three dimensions: (1) alliance

experience, (2) partner identification propensity, and (3) alliance manager development

capability.


111

In alliance formation, one of the main concerns is partner firms’ goal convergence

(Hamel, Doz, and Prahalad 2002). Goal convergence/congruence is defined by Jap (1995,

p. 38) as “the extent to which firms perceive the possibility of simultaneous goal

accomplishments.” Millson et al. (1996) pointed out that before firms consider allying

with other firms, they need to be keenly aware of prospective partners’ resources, goals,

and motives. Similarly, Littler and Leverick (1995) stressed that goal congruence

assessment of collaborating organizations has been frequently discussed as an issue

meriting significant consideration at the outset of any collaboration. I argue in this study

that firms with alliance competence are able to identify, select, and negotiate with other

firms that have compatible goals. Reasoning follows.

First, experiences with alliances provide firms a guide about how to successfully form

and manage alliances (Day 1995; Lambe et al. 2002; Spekman et al. 1999). In the

formation stage, due to accumulated alliance-related knowledge, firms are able to find

partners with high goal correspondence which enhances the consistency of expectations

and assure mutual gains (Erdem et al. 2006). Second, firms that have an ability to

proactively scan for, and identify, promising alliances may be able to find partners that

have congruent and compatible goals (Kandemir et al. 2006; Sarkar et al. 2001). Third,

alliances require constant attention (Doz and Hamel 1998, p. 261). Competent firms give

their alliances plenty of attention by appointing competent alliance managers. To

effectively and/ or efficiently monitor and manage the relationships with alliance

partners, many Fortune 100 companies assign a “Strategic Alliance Manager” position

(Sivadas and Dwyer 2000). One of the role and responsibilities of alliance manager is


112

being visionary. In other words, alliance managers are responsible for understanding the

partner firms’ compatibility of goals and strategic intents (Spekman et al. 1999). Thus,

H7: Joint alliance competence has a positive effect on goal congruence.

Complementary resources are the raison d’etre of strategic alliances and the resultant

interdependence is the foundation for alliance development and high performance

(Spekman et al. 1999). Drawing on Anderson and Narus’s (1990) conceptualization, Jap

(1995, p. 39) defined complementary resources as “the degree to which the firms are able

to ‘fill out or complete’ each other’s performance by supplying distinct capabilities,

knowledge, and resources that enhance the likelihood of goal achievement.” Dyer and

Singh (1998) pointed out that there are several challenges faced by firms attempting to

have competitive advantages through complementary resources. Some firms are able to

overcome these difficulties, some of them are not. This study posits that firms with

alliance competence tend to recognize the valuable complementary resources in potential

alliance partners. The reasoning follows.

First, it is articulated by Dyer and Singh (1998) that firms that have alliance

management experience have a tendency to find potential partners and value their

complementary resources. Previous research indicates that firms that have prior alliance

experience results in more opportunities to find partners that have complementary

resource endowments (Day 1995; Gulati 1995; Spekman et al. 1999). Second, a major

criterion for partner selection in alliance formation is to select a partner who has

complementary skills (Spekman et al. 1999). By definition, competent alliance firms have

outstanding partner identification skills, which means that these firms proactively scan

for, and identify, promising alliance partners (Lambe et al. 2002). Since this ability


113

implies proactiveness, the alliance partners can achieve the first mover advantages in

bringing the best possible candidates into their relationships (Day 1995; Kandemir et al.

2006; Sarkar et al. 2001; Varadarajan and Cunnigham 1995; Weitz and Jap 1995).

Therefore, “firms that are able to proactively scan for partnering opportunities may be

able to identify partners with complementary resources and strategic compatibilities”

(Kandemir et al. 2006, p. 327). Third, the role of alliance managers in alliance

management is central. Firms such as Boeing, Hewlett Packard, Xerox, and Microsoft

have appointed a Director of Strategic Alliances. The role of these individuals is to

identify, select, and evaluate potential alliance partners as well as monitor and coordinate

the alliance activities (Dyer and Singh 1998). Alliance managers have the responsibility

of enhancing the alliance’s mission (Spekman et al. 190). One of the main missions is to

find compatible alliance partners that have complementary resources.

Briefly, alliance competence has been conceptualized in this study as a renewal

competence .Hunt (2000) discussed renewal competences as resources that enable firms

to foresee the need to acquire, develop, and create the required resources to produce the

envisioned market offering. By the same logic, alliance competence enables partner firms

to acquire required resources (e.g., complementary resources) by allying with other

competent firms. Thus, this study posits that alliance competence provides firms to

identify and access benefits of strategic resource complementary.


This study posits that joint alliance competence should have a direct, positive effect

on the dimensions of alliance market orientation. Several reasons of this direct

relationship have eluded inquiry. Two stand out.


114

First, alliance competence has been discussed in this study as a distinct competence.

Day (1994, p. 39) argued that distinct competences enable firms to “support a market

position that is valuable and difficult to match.” He further explores that distinct

competences should be managed through training and giving empowerment to competent

managers, learning from past mistakes, and striving for the resources that enable them to

improve their abilities. According to the R-A theory of competition, distinct resources

have a potential to contribute to firms’ ability to produce market offerings that are close

to consumers’ constellations of attributes as efficient and/or effective a manner as

possible (Hunt 2000; Hunt and Morgan 1995). Market orientation is such an

organizational ability which enables firms to maintain superior customer value through

generating, disseminating, and responding to market intelligence. With a strong

commitment to serving its customers, market orientation enables firms to direct their

resources necessary to fulfill their customer needs through developing new creative

products (Slater and Narver 1995; Zhou, Yim, and Tse 2005). By definition, distinct

resources can contribute to this organizational ability to produce unique and meaningful

products that meet customer needs. By the same token, alliance competence as a distinct

resource can contribute to an alliance ability to serve its customers better than the

competitors by enabling them to develop creative new products which eventually lead to

high new product performance.

Second, alliance competence has been conceptualized in this study as a renewal

competence which enables firms to have strong market sensing, to envision market

offerings that might be perceived as unique and meaningful by customers, and to foresee

the need to acquire, develop, or create the required resources to produce the envisioned


115

market offerings (Hunt 2000). Today, many firms realize that due to the global scope of

competition, rapid pace of technological change, and pressure for shortening product

development periods, they are likely to find themselves in lacking in resources to

efficiently and/or effectively compete in multiple national markets (Varadarajan and

Cunnigham 1995). For example, Boeing realized that real technological innovation would

be only possible if it shared the risk with partners (BusinessWeek 2007). Competent

firms, like Boeing, will see where the market leads. Therefore, they will be willing to

acquire complementary resources from their alliance partners and/or create (in)tangible

idiosyncratic resources with them to produce meaningful and unique new products.

Alliance market orientation is proposed as an idiosyncratic resource. Because it is

developed during the life of an alliance, unique to the alliance, and higher order resource

(Lambe et al. 2002). Therefore, alliance competence, as a renewal competence, can

motivate alliance partners to know their markets, share the information gathered from

these markets, and act on it through joint development of market offerings that might be

perceived as unique and meaningful by some market segment(s) (Jap 1999). Eventually,

alliance market orientation as an intangible resource can lead an alliance to have a

marketplace position of competitive advantage relative to its competitors and thereby,

superior financial performance. Spekman et al. (1999) identified that the underlying

premise of competent alliances is value creation and providing unique valuable solutions

to the marketplace which, I argue, can be achieved through joint market oriented

behaviors of an alliance. In their study, Sivadas and Dwyer (2000) suggested that there is

an issue in alliance studies to be examined which is what impact the partnering

capabilities of the alliance partners has on their cooperation abilities. They conjecture that


116

high partnering capability (e.g., selecting the right kind of partners and providing

appropriate managerial support) would foster greater cooperation competence. To my

knowledge, this is the first study to empirically test this claim. Therefore, this study

hypothesizes the following

H9: Joint alliance competence has a positive effect on alliance market orientation.

3.2.5. Joint Top Management Support

Cisco consistently has much higher success in collaborating with other firms than

their competitors. Indeed, strategic business alliances are central to the Cisco’s business

strategy. Firms, like Cisco, “see and value alliance activities and the related skills and

competences” (Spekman et al. 1999, p. 207). They develop an organizational culture that

supports alliance activities and allying behavior.

Essentially, senior management is the one who is in control and must have accurate

foresight, and, a clear cut and compelling view of the company’s future. They must

manage their firms’ alliance activities with special care through the focused commitment

of resources, assignment of dedicated alliance managers, continued efforts to work with

partners that have congruent goals and complementary resources (Day 1994; Deck and

Strom 2002; Dyer and Singh 1998; Eisenhardt and Schoonhoven 1996; Hamel and

Prahalad 1994; Lambe et al. 2002; Parkhe 1993; Spekman et al. 1998). As Sivadas and

Dwyer (2000) mentioned, several Fortune 100 companies’ top managements create a

position called “Director of Strategic Alliances” whose job is to scan and identify

potential alliance partners, negotiate with them, and manage the alliance activities. A

recurring finding from analyses of alliance performance further shows that successful

alliances are managed by senior managers who create a climate by clarifying the


117

responsibilities and contribution of the partners involved in the alliance (Day 1995;

Sivadas and Dwyer 2000).

Finding, developing and managing alliances can be a firm’s core competence.

Because it (1) provides access to a wide variety of markets through allying with different

firms, (2) makes a significant contribution to customers’ perceptions of benefits by

enabling firms to develop valuable market offerings, and (3) be difficult for competitors

to duplicate. As highlighted by Lambe et al. (2002, p.147), since “the strategic direction

of organizations is driven by senior management, competences are developed or

maintained only under the urging of senior management.” For example, Toshiba Corp.

has over the years made strategic business alliances a cornerstone of its corporate

strategy. As Fumio Sato, the President and CEO of Toshiba Corporation, articulated,

having a strategic business alliance is the only viable strategy for companies with global

ambitions (Varadarajan and Cunnigham 1995). Since the scope of this study is dyadic

new product development alliances, I am actually interested in two-firm alliances.

Therefore, joint top management support indicates that both of the alliance partners’ top

managements support the alliance’s new product activities. Therefore, I have the

following,

H10: Joint top management has a positive effect on joint alliance competence.

3.2.6. New Product Creativity

Why do so many of the new products fail? Marketing researchers have dedicated

considerable efforts to understand the main factors that drive successful new products.

One of the possible answers for this question has been found as creative ideas and their

manifestations as products and processes (Cooper 1979; Kleinschmidt and Cooper 1991;


118

Montoya-Weisss and Calantone 1994; Im and Workman 2004; Sethi and Smith 2001).

Sethi and Smith (2001, p. 74) defined new product creativity as “the extent to which the

product is different from competing alternatives in a way that is valued by customers.”

As the authors point out, “meaningful uniqueness” provides the foundation for new

product creativity. They further assert that new product creativity is the main determinant

of high new product performance. Similarly, Cooper (1979) probed the question of what

makes a successful new product. In his empirical study, the findings showed that one of

the major factors which differentiate successful products from unsuccessful ones is

product uniqueness and superiority. He also highlighted that new creative products

should not only have unique features, but also be meaningful to customers by meeting

their needs. Kotabe and Swan (1995, p. 622) defined creative new products as “the extent

to which the new product changes the customer’s habits or usage patterns.” And, they

discussed the role of new product creativity in alliance performance. This study adapts Im

and Workman’s (2004) definition of new product creativity. They defined it as the degree

to which new products are perceived to represent unique differences from competitors’

products in ways meaningful to target customers. New product creativity enables a firm

to differentiate its products (Cooper 1979; Im 1999; Kleinschmidt and Cooper 1991;

Song and Parry 1997). Gatignon and Xuereb (1997) indicated that product differentiation

through new product creativity and the ability to meet customer needs lead to high new

product performance. Brown and Eisenhardt (1995) argued that unique benefits of

products facilitate their financial performance.

In the R-A theory of competition, new product creativity can be considered as a

resource (Hunt 2000; Hunt and Morgan 1995, 1997). Because new product creativity is


119

an intangible entity available to the firm that enables it to efficiently and/or effectively

produce a market offering that has value for some market segments. Therefore, the

comparative advantage in unique and meaningful resources will provide a firm to hold a

marketplace of competitive advantage which in turn leads to a superior financial

performance. Since the combination of uniqueness and meaningfulness in resources can’t

be sold and/or bought in the marketplace, is complex, has interconnectedness and mass

efficiencies, it can not be duplicated precisely by competitors, therefore it can be an

important source of sustainable competitive advantage, thereby, superior financial

performance. New product performance has been conceptualized in this study as the

extent to which a new product alliance meets market (e.g., sales, market share), financial

(e.g., return on investment, profits) and overall new product development (customer

satisfaction, overall performance) objectives. Therefore, this study postulates that new

product creativity is a major determinant of a firm’s high new product performance.

H11: New product novelty has a positive effect on new product performance.



120

3.3. Summary of the Hypotheses

H1: Alliance market has a positive effect on a) new product novelty b) new product

meaningfulness c) new product performance.

H2: Goal congruence has a positive effect on alliance market orientation.

H3: Complementary resources have a positive effect on alliance market orientation.

H4: Trust has a positive effect on alliance market orientation.



H7: Joint alliance competence has a positive effect on goal congruence.


H9: Joint alliance competence has a positive effect on alliance market orientation.

H10: Joint top management support has a positive effect on joint alliance competence.

H11: New product novelty has a positive effect on new product performance.



121

CHAPTER 4: METHODOLOGY

The main objective of this study is to identify ways in which new product

development alliances can be made more successful. Therefore, this study aims to model

the effects of alliance market orientation and joint alliance competence of partner firms

on alliance new product performance. Based on my research objectives, I selected the

survey method to collect data, because this approach may enhance the external validity,

reliability, and applicability of results from empirical studies (Cook and Campbell 1979;

Lyon, Lumpkin, and Dess 2000).

This chapter first explicates the issues related to methodology that have the potential

to influence the empirical results. It begins with a discussion of sampling issues, followed

by a discussion of the data collection process that includes a series of pretests and final

field survey. In the final section, I briefly discuss the questionnaire design and outline the

key constructs.

4.1. Sampling Issues

I considered three basic issues in the sampling process. The first issue is the

description of the research setting and the sample frame. The second sampling issue is

about the data sources from which dyadic new product development alliances can be

developed. The final issue concerns the identification of key informants within the

participating firms to report on inter-organizational dynamics and new product

development process.

4.1.1. Sample Frame

Alliances can be formed for various reasons such as exploring a new market,

developing a new product and/or service, co-production, strategic purchasing, joint


122

marketing, exchange of know-how/technology, and, a burgeoning channel of distribution

(Lambe et al. 2002; Spekman et al. 1999; Varadarajan and Cunningham 1995). In this

study, the focus will be on developing a new product in an inter-organizational context,

and such consideration is expected to control for any error variance arising from the

different alliance types (Eisenhardt and Schoonhoven 1996; Lambe et al. 2002). New

product alliances are defined as “formalized inter-organizational arrangements between

partnering firms to jointly generate, exchange, and utilize information related to the

research and development and marketing of new product innovations” (adapted from

Rindfleisch and Moorman 2001, p.1). The scope of the study includes relationships

between financially independent new product alliance partners. Thus, vertically

integrated hierarchical relationships will not be considered in the scope of this study.

The unit of analysis throughout the study will be dyadic new product alliances. There

are two reasons for this unit of analysis selection: (1) The main goal of this study is to

understand inter-organizational relationship dynamics in new product alliances; and a

relationship requires at least two parties (Jap 1995) and (2) this study aims to develop a

concept called alliance market orientation which is the joint and concerted market

oriented behaviors of an alliance, and it further strives for exploring the effect of alliance

market orientation and joint alliance competence on joint new product development

performance. Therefore, the focus of the conceptul model is on joint, mutually shared

new product related activities and outcomes. Since alliance market orientation as a

relational property is mutually determined by each partner’s behavior in the relationship,

dyadic approach represents more complete picture than a focal firm approach (Anderson

et al. 2006). Although the unit of analysis is dyad and measures are based on mutual and


123

joint understanding of the inter-organizational relationship between partners, due to

logistical challenges, most of the data is collected from one partner in the respective

alliances. Since the number of matched data was not sufficient for the analysis, one side’s

responses regarding the relationship were taken in to account. The details about the data

collection process will be provided in the next section.

The final data for the study is collected through a large scale cross-sectional survey of

U.S. base alliances who have previously participated in joint new product development

activities. There are two reasons for selecting U.S. base new product development

alliances: (1) Previous research suggests that international alliances may be

systematically different than domestic alliances (e.g., Harrigan 1985; Kogut and Singh

1988; Parkhe 1993; Rindfleisch 1995; Saxton 1997). Therefore, consistent with

Rindfleisch (1995), I included firms that are either US companies or a domestic division

of a multinational organization, (2) as Saxton (1997) mentions, finding contact

information of foreign firms represents a barrier to participation in the study.

Furthermore, cross-sectional data collection approach is chosen for the current study.

Although the effectiveness of the static model regarding the causality may be challenged,

it is adopted due to the exploratory nature of the current study (Wei, 2006). In addition,

Rindfleisch, Malter, Ganesan, and Moorman (2007) study show that benefits of

longitudinal data collection appear to be considerably overstated.

New products have been discussed in many different contexts, such as new services,

new processes, new business models, new applications, and new physical products. A

study of new products would ideally need broad sample to provide generalizable results.

However, broad sampling for generalizability demands an enormous amount of research


124

and labor work, making it favorable to use smaller and limited sample (Im 1999). In this

research, in order to control the error variance arising from different contexts of new

products, sample frame is limited to high-tech firms. The main reason for high-tech firm-

based alliances was to be chosen is that because they have exhibited a tendency to engage

more in research and development activities and, thus, develop creative and meaningful

products. According to 2007 Technology barometer study from Price Water House

Coopers and BSI Global research Inc., high tech companies truly believe in the concept

of partnership and, they also think that co-development alliances will be one of the prime

drivers of future growth for their organizations. According to the study, more than 70

percent of high-tech companies are involved in new product alliances. The high-tech

firms surveyed include following fields: biosciences (biotechnology, biopharma,

pharmaceuticals), semiconductors, medical equipments, electronics, software and

hardware, and energy. This sample frame that limits the sample based the firm types is

consistent with other NPD research (Im 1999; Noordweir et al. 1990).

4.1.2. Data Sources

The second sampling issue is the data sources. The list of contacts for the research

sample are developed from three sources: First, pursuant to Section 6(a) of the National

Cooperative Research and Production Act of 1993, new product alliances are required to

file written notification simultaneously with the Attorney General and the Federal Trade

Commission in order to minimize the threat of antitrust prosecution. These filings are

published in the Federal Register Index, and they provide information about the identity

and location of alliance partners, and formation date and objectives of that alliance. The

index starts from 1998, and under the “anti-trust divison” section of Federal Register


125

Content, it provides the daily news about new product alliances. For this study, I

examined all of the new product alliances filed during the period January 2, 1998 to

December 30, 2006. Consistent with Rindfleisch (1998), the reason for the time period

selection is that there is generally a two year gap between new product alliance formation

and tangible product development outcomes (Peck 1986; Rosenfeld 1996).

The second source was the Thompson Financial Security SDC Platinum Database.

The SDC Platinum Worldwide database provides detailed information on mergers,

acquisitions, joint ventures, and strategic alliances around the globe. Based on the sample

requirements of the study, only the U.S. base high tech companies who are involved in

strategic business alliances were chosen. The joint ventures, mergers, and acquisitions

were excluded in the sample selection. Since both Federal Register Index and SDC

Platinum databases do not include names of individual executives, I checked each firm’s

website thoroughly to find the key respondent’s contact information. Since the survey

was conducted electronically, the link to the online survey and the cover letter were sent

to executives’ e-mail addresses. Lead411.com was the main source for the executives’ e-

mail and phone numbers. Cover letter and questionnaire can be found in the Appendices.

The third data source was ISBM (Institute for Study of Business Markets) at Penn

State University. Selected member firms were contacted by ISBM representative with a

cover letter and research synopsis. Executives were informed about my upcoming phone

call and e-mail. The many of the firms contacted through ISBM are Fortune 500

companies.


126

4.1.3. Identification of Key Informants

The third sampling issue is the identification of key informants. Key informant

approach enables researchers to gather information about a firm by collecting data from

executives within that firm who are highly knowledgeable about the phenomenon under

investigation (Campbell 1955; Philips 1981). This study focuses on inter-organizational

dynamics and joint new product development process. Therefore, finding key informants

who are involved in inter-organizational new product development activities and know

inter-organizational dynamics is of primary importance. The key informant approach was

tested by John and Reve (1982), and their results validate the approach and demonstrate

the utility of key informants in inter-organizational studies. Consistently, after John and

Reeve (1982), many inter-organizational studies have used the key informant approach

successfully (Anderson and Narus 1990; Anderson et al. 2006; Im and Workman 2004;

Kandemir et al. 2006; Lambe et al. 2002; Littler et al. 1995; Lusch and Brown 1996;

Morgan and Hunt 1994; Rinsfleisch and Moorman 2001; Sivadas and Dwyer 2000). As

suggested by Campbell (1955), key informants should meet two criteria: (1) they should

occupy roles that make them knowledgeable about the related phenomena, and (2) they

should be capable of communicating with researchers.

As a first criterion, consistent with the other inter-organizational new product

development studies (e.g., Link and Bauer 1989; Rindfliech and Moorman 2001; Sivadas

and Dwyer 2000), I choose executives who are involved in the new product alliance

formation and management as key informants. The reason for this selection is that these

executives have high levels of knowledge about their firms, inter-organizational new

product development activities, and inter-organizational dynamics (Kotabe and Swan


127

1995; Link and Bauer 1989; Rindfleisch and Moorman 2001; Sivadas and Dwyer 2000).

Similarly, these professionals are critical in building inter-organizational relationships,

solving alliance related problems, and managing the alliance development (Lambe et al.

2002; Spekman et al. 1999; Wittman 2001). As suggested by Rindfleisch (1998), in order

to minimize the concerns about the quality of key informant data, partner firms in each

alliance were pre-contacted by phone to verify the name, position, e-mail address of the

key informants, and to obtain cooperation by stressing the importance of the study.

The field and pilot interviews suggest that R&D managers, business/corporate

development executives, marketing managers, alliance managers, CEOs, CSOs, and

CTOs are suitable for sources of inter-organizational new product development

information. The pretest reveals that these executives are highly involved in co-

development projects – providing mean score of 5.22 for involvement and 6.12 for

knowledge on a seven point scale. The final field study supports these results – providing

mean score of 5.31 for involvement and 6.35 for knowledge on a seven point scale. In

both pretest and final field study, executives have at least 16 years of work experience in

co-development projects.

As a second criterion, key informants should be able to communicate with

researchers. It means that informants should be able to understand the wordings and

meanings of the questions in the questionnaire. Therefore, I interviewed with three highly

knowledgeable alliance consultants in the field study to collect verbal protocols to

confirm that key informants in the main study will not have difficulties in understanding

the language of the questionnaire. After their suggestions, some questions are made more

clear and precise. In addition, in order to lessen the confusion, executives were provided


128

with definitions of some concepts (e.g., alliance, customers, alliance types, new product,

and market) based on the suggestions. The second wave interviews with 16 pretest survey

respondents confirmed that questions and items were clear to the respondents, and survey

was in a language they understood. This study uses a single key informant from each

firm. Although some researchers have raised concerns about the reliability of the single

informants, other researchers support the view that careful selection of single informants

along with the use of consistent scales provides reliable and valid results (John and Reeve

1982; Im and Workman 2004; Im 1999). In this study, each key informant in the final

study was called and interviewed with about the study, when they are not reached they

were sent an e-mail about the details of the study and purpose of the research. When they

thought that they were not right for the study, they forwarded me to an executive who can

be helpful and responsive.

Collecting data from new product alliance partner firms can be considered as a

lengthy and labor intensive process. As an incentive to participate, each firm was offered

an executive summary, customized summary report of the results, and a detailed

comparison of their respective alliance’s new product development data benchmarked

against all other alliances in the study.

4.2. Data collection methods

This section provides an overview of the data collection method used in this study.

The first part reviews the field interviews and pretest, while the second part details the

final field survey administration. Figure 4.1 provides the flow of the data collection.


129

4.2.1. Field interviews and pretest

A series of field interviews, pretest, and pretest interviews were conducted in order to

develop reliable and valid measures and ensure that the survey administration is

appropriate for the key informants. Field interviews were conducted in February 2008.

Specifically, I contacted with three executives involved in alliance consulting for over 15

years each. During the interviews, alliance consultants were asked to provide comments

about the measure items, survey instructions, and survey format to help refining the

questionnaire. Their comments were evaluated to assess adequacy of each question’s

wording, clarity, scope, and relevance. Since alliance market orientation, main

contribution of the study, is a newly developed concept, they were also asked to comment

on the concept and its role in inter-organizational new product development activities of

firms. They also suggested that due to the heavy travel schedule of executives,

conducting an online survey would be a better idea to increase the response rate. Based

on field interviews, survey items were modified and survey format was refined.

One of the field interviewees offered to provide his help to gather pretest contacts. He

contacted some of his colleagues, who are eligible for this study, through LinkedIn.

Pretest and pretest interviews were performed during the months of March and April.

Among 40 managers contacted, 16 of them were willing to provide their thoughts to the

survey (40% response rate). Since the unit of analysis in the survey is dyad, each of these

16 respondents was called and asked whether they can provide their alliance partner’s

name. Among 16 respondents, 10 of them provided their partner name. 10 partners were

contacted via phone and told that we were forwarded to them by their R&D partner. After

the phone conversation, 6 of the partners were willing to help in the survey (60 %


130

partner-base dyadic response rate, 15 % overall dyadic response rate, 44 % -- (22/ 40+10)

overall response rate). Based on the suggestion of field interviewees, pretest was

conducted via online survey. After pretest respondents completed the survey, I conducted

telephone interviews with each of the 22 (16+6) executives to discuss their experiences

with the questionnaire. The interviewees were asked about the inappropriate wordings,

suggestions for instructions as well as the format and layout of the survey. In addition to

finalizing the measures and determining their level of detail, relevance, and clarity, the

pretest served to test the process to be used in contacting prospective respondents. All the

respondents were interested in and supportive of the research. Based on their feedback,

clear definition of the key terms, such as alliance, customer, competitor, market, new

product, and environment, were provided in the survey.

Figure 4.1 Procedure for Empirical Test

Final Field Study (N= 1015 -- database)

(N= 726 -- effective sample size)

Field Interviews (N=3)

Pretest (N= 22)

Response rate 44 %

Response rate 35.1 % -- overall 78.2 % -- potential

Follow-up Interviews (N=22)


131

4.2.2. Final field study

Based on the feedback from field interviews and pretest, surveys were sent to a large

sample via e-mail. I obtained the names of the firms to make up the sample from the

Thompson Financial Security SDC Platinum Alliance Database and Federal Register

Index. After careful search about each firm (e.g., active, still an independent firm),

database included 962 firms who have been a part of dyadic new product alliance. The

names of the executives and their contact information were generated from firms’

websites and Lead411.com.

In order to increase the response rate, I called each executive to ask for cooperation.

To those who are being reached, the purpose of the dissertation and the nature of the

request was explained in detail. Their questions about the study were answered on the

phone and later on. Executives who thought that they are not eligible for the study

forwarded me to another executive who is more knowledgable about the phenomenon. I

left a voicemail to each executive who were not able to be reached. In the voicemail, the

purpose of the study, how their contact info was generated, my contact information, and

details about the request were provided. They were also notified about the forthcoming

personalized e-mail. After each call, an e-mail was sent to each executive’s e-mail. A

detailed cover letter was attached to the e-mail. In order to increase the response rate,

follow-up reminder e-mails were sent to the non-respondents two weeks after the initial

e-mail. I then called and sent e-mails to each remaining non-respondent twice to

maximize the response rate. Appendices include the cover letter, initial message text,

questionnaire, and the follow-up message text.


132

Though those phone calls and sending each executive a personalized message were

labor intensive and expensive to administer, since each executive was personally

contacted and given the freedom to contact me about any issue, they contributed

significantly to the enhanced response rate and also increased the quality of the sample.

Response rates for the pretest and final field study are provided in Table 4.1.

Field survey was conducted between May 2008 and November 2008. Among 962

companies, 289 executives called me or sent me an e-mail by saying that they were not

right for the study. The general reasons for rejection are provided in the Table 4.2.

Among the rest of the 673 executives, 284 accepted to participate to the study. Among

those, 41 respondents dropped out from the study due to unexpected reasons, yielding a

sample size of 243. Among these executives, 20 of them were unusable for the final

analysis due to severe incomplete information and misunderstanding of the survey

questions, yielding a usable sample size of 223. Since the unit of analysis is dyad in this

study, each executive who completed the survey was requested to provide their NPD

alliance partner’s contact info. Among 223 executives, 53 accepted our request and

provided their partner’s name. The reasons for not providing partner name can be found

at Table 4.3. 53 partner companies were contacted via phone and e-mail and told that we

were forwarded to them by their NPD partner. 42 partner firms accepted to provide their

insights to the study, and 34 out of 42 completed the survey. Due to severe incomplete

information, 4 of the executives’ insights were deleted, yielding a sample size of 30

matched dyad. Since 30 is a very small sample size for SEM analysis, we decided to

include these 30 executives’ insights to the initial 223 firms, yielding a final usable

sample size of 253 (223+30) (see Table 4.1. for the response rate summary).


133

Response rates are compared favorably with those reported in other studies (e.g.,

Littler et al. 1995; Park 1993; Rindfleisch and Moorman 2001; Sivadas and Dwyer 2000).

Though response rate is favorable with respective to previous research, non-response bias

can still be an issue. In the next session, the non-response bias issue will be discussed.

Table 4.1. Response Rate Summary Pretest Final Field Study (main

survey) Precontacted 40 962

# of rejections * 6 289

Effective sample size (Partner 1)

34 673

# of executives accepted to participate to the survey

18 284

# of executives completed the survey

16 243

Not usable (Partner 1)** 0 20

# of usable surveys (partner 1)

16 223

# of executives provided dyadic partner name ***

10 53

# of dyadic partner accepted to participate to the

survey

10 42

# of dyadic partner completed the survey

6 34

Not usable (Partner 2)** 0 4

# of usable surveys (partner 2)

6 30

Total # of usable surveys & usable rate ****

Overall response rate: (16 +6)/ (34+10) = 50 % Potential response rate: (16+6)/ (18+10)= 79 %

Overall response rate: (223 + 30)/ (673+53)

=35 % Potential response

rate: (223 +30)/ (284+42)

= 78 % * Executives either called or sent me an e-mail about their reason of non-participating to the survey. The reasons for non-participation are shown in Table 4.2. ** Responses with too many missing values were considered not usable. *** Reasons for not providing the partner name can be found in Table 4.3. **** Since the matched dyadic sample size is small (N=30), we decided to combine the survey responses (N= 225+30=255). Necessary caution is taken in the analysis phase.


134

Table 4.2. Reasons for non-participation to the survey (N=289)

Reasons: Number of respondents

It violates the anti-trust laws and regulations, and contractual provisions

67

Very busy with several alliance related projects 45

Our alliances have not yielded any marketed product, so their success can not be truly judged

33

We don’t currently make the alliance information available to the public

31

Our company’s partnership likely would not be appropriate for survey

18

Company policy for competitive reasons 17

Our alliance fell a part, thereby, we do not have a product, so to say, performance outcomes

12

Our company has not been involved in any NPD alliance 12

Alliance is going through significant changes 11

Our alliance is at the critical phase of the product introduction and solely focused on this effort.

8

Company’s legal department does not allow us to complete the survey

7

We filed bankruptcy and has ceased operations 6

Our alliance filed an S-1 and we are going public. 5

We are acquired by our partner. 5

Questions are difficult to answer and it takes considerable time to complete it.

5

We do not have sufficient resources to respond to the large number of requests

4

Alliance structure prevents us from providing details about the alliance

4


135

Table 4.3. Reasons for not providing partner name (N=172)

Reason: Number of respondents:

Non closure agreements (NDA) take place in the alliance 34

Governmental anti-trust regulations do not allow us 27

Confidential information is requested in the survey 18

We do not want word of our perception to reach them 12

Data is very sensitive 11

Existence of the alliance has not publicly disclosed 11

We do not have the partner’s permission 10

Project is at sensitive stage 9

Partner would not be open discussing the alliance; their corporate culture will not allow it.

7

We have a policy not to provide partner’s contact information 6

They are our competitor as well as partner 6

Numerous change in the partner’s management 4

It would not be professional to disclose our partner 3

Business is in stealth mode 3

Business ethic does not allow us. 3

Alliance was a total failure, we do not have positive outcomes

3

It will potentially damage the relationship with our partner 3

Partner is a new start-up company and wishes remain anonymous

2

Military defense related alliance, thereby, confidential information.

1


136

4.2.3. Non-response bias

One major disadvantage of survey study is the non-response bias. Though the

response rate in this study is comparable to other inter-organizational co-development

studies, since non-response bias can hinder researchers from generalizing the results to

population, I check the existence of non-response bias in this section.

As Armstrong and Overtoon (1977) recommended, potential non-response bias was

examined through an extrapolation method of comparing early with late respondents. I

used two weeks as a dividing point between early and late respondents because the

reminder e-mails were sent to non-respondents two weeks after the initial e-mails. Major

characteristics of the late respondents were compared to early respondents based on the

assumption that late respondents are more similar to non-respondents. This method

examines whether late respondents are statistically distinct from early respondents with

respect to the major characteristics. Table 4.4 shows the major characteristics and t-test

results. The table includes respondents’ characteristics and evaluation of major

constructs. The results from the t-tests suggest that there is no significant difference

between early and late respondents. As a result, I conclude that non-response bias does

not influence the findings in this study.


137

Table 4.4. Comparing Early and Late Respondents for Final Survey

Variables Early Respondents (N= 209)

Late Respondents (N=43)

t-value

p-value

Manager’s involvement in the formation of the alliance

5.19 5.53 -.99 .32

Manager’s knowledge about the alliance’s NPD project

6.25 6.50 -1.42 .16

Manager’s total years of experience in the NPD projects

16.16 15.07 .79 .43

Manager’s total number of years in the current firm

7.68 6.74 .77 .44

Joint Alliance Experience (Composite Scale)

14.94 14.07 1.49 .14

Joint Alliance Partnership Propensity (Composite Scale)

16.29 16.04 .43 .67

Joint Alliance Manager Development Capability (Composite Scale)

11.37 12.25 -1.37 .18

Goal Congruence (Composite Scale) 16.51 16.25 .46 .65

Complementary Resources (Composite Scale)

18.13 17.83 .51 .61

Commitment (Composite Scale) 16.22 15.74 .90 .37

Inter-organizational Customer Orientation (Composite Scale)

16.79 15.81 1.54 .13

Inter-organizational Competitor Orientation (Composite Scale)

13.79 14.30 -.71 .47

Inter-organizational Coordination (Composite Scale)

16.58 16.90 .53 .60

New Product Creativity (Composite Scale)

15.91 15.23 1.19 .23

New Product Meaningfulness (Composite Scale)

17.45 17.18 .48 .63

New Product Performance (Composite Scale)

16.12 15.65 .92 .36

Joint Top Management Support (Composite Scale)

17.80 17.41 .56 .57

Trust (Composite Scale)

17.37 17.04 .52 .61

Control Variable: Market Density (Composite Scale)

16.42 16.70 -.46 .65

Control Variable: Technology Density (Composite Scale)

16.00 15.74 .37 .71

Control Variable: Relationship History 2.79 2.55 .83 .41


138

4.2.4. Common Method Bias

This study employed cross sectional design field survey as the primary data collection

method. Since the responses are self reported and collected through the same survey

during the same period of time with cross-sectional design, common method variance

which is about the measurement method rather than the constructs themselves may cause

systematic measurement error (Podsakoff and Organ 1986; Podsakoff, MacKenzie, Lee,

and Podsakoff 2003). Since common method variance may bias the true relationships

between constructs, I check the existence of common method bias in this section. In order

to test the presence of common method bias, Harman’s one factor test and CFA were

performed. First, all variables of interest were entered into an EFA, using unrotated

principal component factor analysis (PCA), PCA with varimax rotation, and principal

axis analysis with varimax rotation to determine the eigenvectors and eigenvalues. All

these tests revealed the presence of 21 distinct factors with eigenvalue greater than 1,

rather than a single dominant factor. The 29 factors together accounted for 99 % of the

total variance; the first factor did not account for a majority of the variance (34 %). Thus,

no general factor is present. Second, the CFA showed that the single factor model did not

fit the data well. 2χ (3569, N=253) = 18706.23, p= .0000, RMSEA=.13, GFI=. 36,

PGFI= .33, AGFI= .35. Therefore, I conclude that test results do not suggest that

common method variance is of great concern and therefore, it is highly unlikely to

confound the findings and their interpretations.


139

4.2.5. Characteristics of the Key Respondents and Their Firms

This section briefly summarizes key respondents’ major characteristics and their

companies. Table 4.5 provides the descriptive statistics for the major characteristics in the

final survey. The participating firms have a wide range in terms of employee size (from

12 to 10,000+) and revenue amount (500 K to 100 B+). Key respondents were highly

involved in the formation of their respective alliances (mean of 5.30). Furthermore,

executives were highly knowledgeable about the new products developed as a result of

their firm’s participation in the respective co-development project (mean of 6.33). Total

years of experience that executives had in new product development projects in general

are high (mean of 18.14 years). In addition, the total number of years that they worked

for their present company is also high (mean of 8.52 years).

As it is mentioned before, key informants were chosen on a non-random basis. They

are chosen because they have specialized knowledge about the research phenomenon.

Table 4.6 summarizes the job titles of the key respondents. Most of the respondents were

alliance managers/business and corporate development managers (67 or 26.2 %) and

R&D/NPD managers (64 or 25.1 %). Chief Scientific (CSO) and Chief Technology

officers (CTO) were the third in the list (36 or 14.1 %). Chief Marketing Officers (CMO)

were the fourth highest position in the list (32 or 12.5%). Finally, Chief Executive

Officers (CEO) and Chief Operating Officers (COO) were included in our list as fifth and

sixth executive positions (32 or 12.5% ; 22 or 8.6%). Few positions entered by executives

are included in the “others” in the table 4.6 (e.g., market driven innovation manager, VP

of finance, new technologies manager, scientific affairs, technology leader, VP of

strategic marketing and collaborations, and chief business officer). Table 4.7 reports the


140

types of business of the participating firms. As noted earlier, a majority of the

participating firms were involved in high-tech business. The major types of business in

the sample include biosciences (e.g., biotechnology, biopharmaceuticals,

pharmaceuticals) (98 or 38.4 %), semiconductors (59 or 23.1 %), electronics (35 or 13.7

%), hardware and software (26 or 10.2 %), energy (25 or 9.8 %), and medical equipments

(12 or 4.7 %). The industry types provide evidence that the sample includes a wide

variety of businesses and, thereby, research findings can be generalizable to most high-

tech industries.


141

Table 4.5 Characteristics of the Final Field Survey

Mean Standard Deviation

Involvement in the formation of the respective NPD alliance (over 7)

5.30 1.97

Knowledge about the new products developed as a result of alliance involvement (over 7)

6.33 0.97

Total years of experience in the new product development projects

18.14 8.08

Total number of years executives have worked in their present company

8.52 7.16

Table 4.6 Job Titles for the Key Respondents

Number Percent

Business development managers 25 9.8 Corporate development managers 22 8.6 Alliance managers 20 7.8 R&D manager 42 16.5 NPD manager 22 8.6 Chief Scientific Officer (CSO) 16 6.3

Chief Technology Officer (CTO) 20 7.8

Chief Executive Officer (CEO) 27 10.6

Chief Marketing Officer (CMO) 32 12.5

Chief Operating Officer (CMO) 22 8.6

Others 7 2.7

Table 4.7 Industry Types for Participating Firms*

Number Percent

Biosciences (e.g., biotechnology, biopharma, and pharma)

98 38.4 %

Semiconductors 59 23.1 %

Electronics 35 13.7 %

Hardware and Software 26 10.2 % Energy 25 9.8 % Medical equipments 12 4.7 % * Some firms have operations in more than one industry. Therefore, their primary industry is taken into consideration.


142

4.3. Questionnaire Design and Measurements

Based on the feedback from field and pretest interviews, final field survey was

applied in an online environment. Executives, who accepted to participate to the study,

were sent the online-survey link via e-mail. In the survey, after a brief introduction of the

objective of the study, since the unit of analysis is dyad the survey instructed respective

firm to dyadic relationship as a reference point for completing the items and questions. In

addition, since the dependent variable is new product performance, firms were notified

that their alliance should have the NPD performance results at hand.

All the constructs were measured at a dyadic level. Furthermore, all the constructs

were measured by multi-items. Many of the hypothesized constructs have well-

established measures that have been used in various inter-organizational contexts.

Therefore, several measures needed little or no modification. Since some measures are

context specific (e.g., new product creativity, new product performance), they were

appropriately modified to fit the alliance context.

Given the rather novel nature of this study, one new scale was required for the key

construct alliance market orientation (AMO). In this new measure development, the

procedure developed by Churchill (1979) was followed. The details about the AMO

measurement development and pretest results for other major constructs can be found in

Chapter 5. In the next section, I will briefly outline the key constructs.

4.3.1. Dependent Variable

New Product Performance

I adapt the Im and Workman’s (2004) new product performance scale. They used

multiple subjective measures of new product performance including market measures


143

(e.g., sales, market share), financial measures (e.g., return on investment, profits), and

overall assessment measures (e.g., customer satisfaction, overall performance).

Furthermore, the authors adapted the market and financial measures from the work of

Song and Parry (1997), and overall measure from the works of Kleinschmidt and Cooper

(1991) and Page (1993).

4.3.2. Independent Variables

Joint Top Management Support

This study adopts Lambe et al.’s (2002) joint senior management commitment scale.

Since the context in the current study is the market oriented behaviors of new product

alliances, and top management support is one of the central concepts in the market

orientation literature, I change the name of the scale to top management support. The

measure taps the degree to which top management in both of the partner firms support the

use of new product alliance to achieve new product related objectives. Since Lambe et al.

(2002) use the scale in an alliance context, the scale can be considered as reliable.

Therefore, I use the scale with no modification. It is measured by four items: (1) We both

have senior-management level commitment to the use of alliances to achieve strategic

goals, (2) Top management in both firms believe that alliances play a role in the future

success of each firm, (3) When the situation calls for it, top-level management in both our

respective firms support the use of alliances, and (4) Top-management in both companies

encourage the use of alliances to achieve strategic goals.

Joint Alliance Competence

Joint alliance competence (JAC) is conceptualized as a second order reflective

construct having three dimensions (e.g., joint alliance experience, joint alliance partner


144

idetification propensity, and joint alliance manager development capability). The scale

items were borrowed from Lambe et al. (2002) Although JAC was conceptualized and

measured as a formative construct in Lambe et al. (2002), this study conceptualizes joint

alliance competence as a reflective construct. The reasoning follows.

First, we argue that the dimensions of alliance competence are manifestations of it in

the sense that they are each determined by it. In essence, it is that changes in the alliance

competence measure would produce changes in the measures, not the other way around.

Second, we argue that alliance experience, alliance partner identification propensity, and

alliance manager development capability share a strong common theme and each

dimension captures the essence of the domain of alliance competence. Therefore, they are

expected to covary with each other. Third, since we argue that these three dimensions are

being sampled from the same conceptual domain and conceptually interchangeable, they

are expected to have the same antecedents and/or consequences. For example, it is not

difficult to imagine a firm with long term of alliance experience, know whom to partner

with under what conditions, and know the value of training alliance managers whose sole

responsibility will be alliance management. Alliance experience, alliance partner

identification propensity, and alliance manager development capability are all

conceptually similar and tap the nature of alliance competence. Empirically, since there is

strict epistemic base in reflective measures, interpretational confounding can be assessed

and dealt with if found to be a problem (Howell, Breiwick, and Wilcox 2007a, b). In sum,

since alliance competence makes more sense as a reflective construct both conceptually

and empirically, this study conceptualizes alliance competence as a higher order

reflective construct.


145

Dimensions of Alliance Competence

Joint alliance experience is a reflective measure and it measures the degree to which

the partners have participated in alliances in the past. It is measured by three items: (1)

We both have a deep base of partnership experience, (2) we each have participated in

many alliances, and (3) individually we have been partners in a substantial number of

alliances. Joint partner identification propensity is a reflective measure and it measures

the extent to which the partner firms actively search for companies that they can ally with

to gain competitive advantage. It is measured by four items: (1) We each actively search

for promising alliance partners, (2) alliances that can help our business are sought out by

both of us, (3) we each are constantly seeking partnering opportunities, and (4) we both

are always looking for firms that we can partner with to jointly develop competitive

advantage. Finally, joint alliance manager development capability is a reflective measure

and it measures the extent to which the partners can develop managers to successfully run

alliances. It is measured by four items: (1) We both have programs to develop capable

alliance managers, (2) we each understand how to produce effective alliance managers,

(3) we both effectively train competent alliance managers, and (4) we each know how to

identify effective alliance managers. Since Lambe et al. (2002) conceptualize joint

alliance competence components as key concepts for all kinds of alliances’ success; this

study adopts their scale with no modification.

Alliance Market Orientation

Alliance market orientation is developed specifically for this study. As indicated

earlier, although market orientation has been discussed in inter-organizational context in

the previous studies, to my knowledge, no empirical study has attempted to develop a


146

measure for market orientation as a relational property. Therefore, this study will be the

first to measure market orientation in an alliance context. Drawing on Kohli et al. (1993)

and Narver and Slater (1990), alliance market orientation is defined as a capability that

enables an alliance (1) to jointly and systematically gather market intelligence (from

competitor analyses, studies of customer needs/preferences, and studies of the factors that

influence competitors’ and customers’ behaviors), (2) to inter-organizationally coordinate

and disseminate the knowledge gleaned from the market intelligence gathered, and (3) to


The scale is conceptualized as a higher order reflective construct having three

dimensions: interorganizational-customer orientation (IoCustor), inter-organizational

competitor orientation (IoCompor), and inter-organizational coordination (IoCoor).

Alliance market orientation measure is developed based on in-depth interviews with

managers and in-depth literature review. These there dimensions are samples from the

same conceptual domain: inter-organizational market oriented behaviors of alliances.

Since they tap the nature of alliance market orientation and underlie the same latent

construct, they are conceptually interchangeable. By definition (nominal meaning),

market oriented alliances are expected to be customer-centered, competitor oriented, and

inter-organizationally coordinated. That means, these three dimensions are the

manifestations of this construct in the sense that they are each determined by it. Changes

in alliance market orientation are expected to reflect on these three dimensions. Due to

the common domain, these three dimensions are expected to covary with each other. For

example, Microsoft-SAP alliance is a very promising innovation alliance. In their alliance

mission statement they indicate that their sole purpose is to serve end-users better than


147

their competitors do (BusinessWeek 2007a). Therefore, they are more aware of the

customer needs, scan federal regulations that affect their market, monitor competitors’ act

and their projects, and provide all types of opportunities to both sides to organize and

synchronize their daily NPD routines. Being customer oriented comes with being

competitor oriented, and being inter-organizationally coordinated and vice versa. One can

not claim that gathering market information but not disseminating it and acting on it

jointly will lead to success.

In sum, since alliance market orientation makes more sense as a reflective construct

both conceptually and empirically, this study conceptualizes alliance market orientation

as a second order reflective construct.

Dimensions of Alliance Market Orientation

IoCustor is conceptualized as an alliance’s joint effort to understand its target market

to be able to create superior value for them. Based on extensive literature review and

exploratory interviews with alliance consultants, we decided that Narver and Slater’s

customer orientation scale (1990) and Kohli et al.’ market orientation scale in general

(1993) reflect the meaning of alliance’s customer oriented behaviors. We adopted some

of their items and modified them in the alliance context. It is measured by six items: (1)

Our alliance’s business objectives are driven by customer satisfaction, (2) In our alliance,

we get together frequently to monitor our level of commitment and orientation to serving

customers’ needs, (3) Our alliance’s strategy for competitive advantage is based on our

joint understanding of customer needs, (4) Our alliance’s business strategies are driven

by our beliefs about how we can jointly create greater value for customers, (5) In our

alliance, we get together frequently to measure customer satisfaction, and (6) In our


148

alliance, we get together periodically to review the likely effect of changes in our

business environment (e.g., regulation, competition) on customers.

Based on the same reason noted above, IoCompor scale is adapted from Narver and

Slater (1990) and Kohli et al. (1993) and modified it for the alliance context. It is

conceptualized as an alliance’s concerted action to understand the strengths, weaknesses,

capabilities, and strategies of current and potential key competitors. It is measured by five

items: (1) Senior managers in our firm meet frequently with their counterparts in our

partner’s firm to discuss competitors’ strengths and strategies, (2) In our alliance, we

jointly target customers where we have an opportunity for competitive advantage, (3) In

our alliance, we jointly respond to competitive actions that threaten us, (4) In our

alliance, we frequently share information with each other concerning competitors’

strategies, and (5) In our alliance, we don’t work together to generate intelligence on

competition R .

Finally, based on extensive literature review and exploratory interviews with alliance

consultants, we decided that items from Kohli et al.’s market orientation scale (1993),

Narver and Slater’s inter-organizational scale (1990), Sivadas and Dwyer’s cooperative

competency scale (2000), Kandemir et al.’s alliance orientation scale (2006), and Ravani

and Kumar’s interaction orientation scale (2008) reflect the meaning of IOCorr. The

items adopted from these sources are modified for alliance context. IOCorr is

conceptualized as the extent to which alliance partners share market intelligence,

synchronize their NPD activities, conduct joint NPD tasks, and respond to each other’s

needs and requests. It is measured by six items: (1) Senior managers in both firms

Tunderstand how people across organizations can contribute to creating customer value,


149

(2) In our alliance, people in both organizations work hard to jointly solve our alliance’s

problems, (3) In our alliance, activities involved in the innovation process are well-

coordinated, (4) In our alliance, the different job activities related to new product

development activity fit together very well, (5) In our alliance, we have inter-

organizational meetings frequently to discuss market trends and developments, and (6) In

our alliance, people who have to work together are responsive to their co-workers’ needs

and requests.

Goal Congruence

Goal congruence scale is adapted from Jap (1999). The concept has been

conceptualized as the extent to which firms perceive the possibility of common goal

accomplishment. Jap (1999) used this construct in a study in which main objective is to

understand the pie expansion efforts of manufacturer-buyer dyads. Since the measure is

established and has been used in an inter-organizational context, especially for a dyadic

study, scale can be considered as reliable. Therefore, I adapt Jap’s (1999) scale and

modify it for an alliance context. Goal congruence scale is measured by four items: (1)

Our firm and our partner’s firm have different goals R , (2) Our firm and our partner’s firm

have compatible goals, (3) Our firm and our partner’s firm support each other’s

objectives, and (4) Our firm and our partner’s firm share the same goals in the

relationship.

Complementary resources

Consistent with Lamb et al. (2002) and Lambe, Spekman, and Hunt (2001),

complementary resources concept draws on Jap’s (1999) study. This concept has been

conceptualized in alliance studies as non-redundant distinctive competencies brought by


150

each member to the partnership (e.g., Day 1995; Dyer and Singh 1998; Varadarajan and

Cunningham 1995). Since the measure is well-established, especially in strategic alliance

context, I adapt Lambe et al. (2002) scale with no modification. This scale is measured by

three items: (1) We both contribute different resources to the relationship that help us

achieve mutual goals, (2) We have complementary strengths that are useful to our

relationship, and (3) We each have separate abilities that, when combined together,

enable us to achieve goals beyond our individual reach.

Trust

The concept of trust has been widely used in many inter-organizational studies.

Spekman et al. (1999) mention that trust is the sine qua non of strategic business alliances

for without it, there can be no alliance. Although many studies conceptualize and

measure trust (for detail see Donney and Cannon 1997), this study will adapt Morgan and

Hunt’s (1994) scale. Because their scale captures the essence of trust between new

product alliance partners better than the other trust scales. Morgan and Hunt (1994)

conceptualize trust as existing when one party has confidence in an exchange partner’s

reliability and integrity. The confidence on the part of the trusting party results from he

firm belief that the trustworthy party is reliable and has integrity, which are associated

with such qualities as consistent, competent, honest, fair, responsible, helpful, and

benevolent. Since the Dyadic Trust Scale of Larzelere and Houston (1980) taps these

dimensions of trust, Morgan and Hunt (1994) adapts their items. The current study adapts

Morgan and Hunt’s (1994) trust scale with no modification. Therefore, it is measured by

seven items: In our relationship, both our alliance partner and we (1) are honest, (2) can


151

be counted on to do what is right, (3) are faithful, (4) have confidence in each other, (5)

have high integrity, (6) are reliable, and (7) are trustworthy.

Commitment

Commitment has been discussed as another sine qua non of strategic business

alliances (Spekman et al. 1999). Although many studies have developed commitment

scale (e.g., Moorman et al. 1992), this study adapts Morgan and Hunt’s (1994)

commitment scale. Because their scale explains why alliances invest on idiosyncratic

resources in the long term better than other established scales. They conceptualize

commitment as an exchange partner believing that on going relationships with another is

so important as to warrant maximum efforts at maintaining it. They develop commitment

scale based on Meyer and Allen (1984) and Mowday, Steers, and Porter (1979). The

current study adapts Morgan and Hunt’s (1994) commitment scale with no modification.

Therefore, it is measured by seven items: Both our alliance partner and we view our

relationship as something (1) to be committed to, (2) important to our firms, (3) of

significance R , (4) our firms intend to maintain indefinitely, (5) much like being family,

(6) our firms really care about, and (7) deserving our firms’ maximum efforts to maintain.

New Product Creativity

New product creativity has been considered as one of the main factors that drive new

product performance. New product development literature has paid considerable attention

to explain new product performance through new product creativity (e.g., Cooper 1979;

Im and Workman 2004; Kleinschmidt and Cooper 1991; Sethi and Smith 2001; Song and

Parry 1997). Although there have been many new product creativity conceptualizations,

this study adapts Im and Workman’s (2004) recent conceptualization of new product


152

creativity which is defined as the degree to which new products are perceived to represent

unique differences from competitors’ products in ways meaningful to target customers.

Im and Workman’s (2004) conceptualization draws on Andrews and Smith’s (1996)

study in which authors conceptualize creativity for marketing programs of mature

products. Since, Andrews and Smith’s (1996) creativity scale can capture the meaning of

new product creativity in an alliance context; this study adapts their creativity scale and

extends it into inter-organizational new product development context. Im and Workman

(2004) measured new product creativity with two different constructs: new product

novelty and new product meaningfulness. Based on their empirical analysis and results,

this study treats new product creativity as a concept which has two dimensions (e.g. new

product novelty and new product meaningfulness). Therefore, novelty is measured by

seven items: New products generated by your alliance have tended to be (1) exciting, (2)

fresh, (3) unconventional, (4) novel, (5) unusual, (6) unique, and (7) original.

Meaningfulness is measured by five items: (1) Relevant, (2) suitable, (3) appropriate, (4)

useful, and (5) meaningful.

4.3.3. Control variables

To prevent model misspecification error and to control the potential confounding

effects two control variables are included in this study: market density and technology

density. Market density is defined as the potential demand for the new product in the

target market (Han et al. 1998; Im and Workman 2004; Song and Parry 1997). It was

added to model to control the environmental impact on new product performance. It is

measured by four items: (1) There are many potential customers for the product that our


153

alliance develops that provides a mass-marketing opportunity, (2) potential customers

have a great need for the product that our alliance develops, (3) the dollar size of the

market (either existing or potential) for the product that our alliance develops is large,

and (4) the market for the product that our alliance develops is growing very quickly.

Technology density is defined as a rapid rate of technological change in the targeted

market. It also aims to control the effect of macro environmental factor on new product

performance (Im and Workman 2004; Jaworksi and Kohli 1993; Narver and Slater 1990).

It is measured by four items: (1) The technology in our alliance’s market is changing

rapidly, (2) technological changes provide big opportunities in our alliance’s market, (3)

a large number of new product ideas have been made possible through technological

breakthroughs in our alliance’s market, and (4) technological developments in our

alliance’s market are minor R .


154

CHAPTER 5: MEASURE DEVELOPMENT

The development of valid and reliable measures is a first major step before hypothesis

testing. One of the main objectives of this dissertation is to develop a measure for alliance

market orientation concept. Although inter-organizational market orientation concept has

been the subject of little conceptual development, to our knowledge, there is no large

scale empirical study for the alliance market orientation measure.

The first section of this chapter discusses the measurement development procedures

adopted in this research before going into detail about three dimensions of alliance

market orientation: (1) inter-organizational customer orientation, (2) inter-organizational

competitor orientation, and (3) inter-organizational coordination. Then, I review the

measurement model for the other major constructs on the basis of an empirical analysis of

the collected data. For most of the other constructs, existing measures were tested for

validity and reliability prior to their adoption in the study. Next, I discuss the results of

the confirmatory factor analysis and tests of convergent and discriminant validity.

5.1 General Measure Development Procedures

Measure development has been a frequently discussed topic in the marketing literature.

The development of valid and reliable measures for constructs is a necessary first step in

theory testing. In order to develop better measures, many researchers have followed the

measure development procedure suggested by Churchill (1979). Basically, he

recommends the following sequential steps: (1) Specify the domain of construct based on

extensive literature review, (2) generate a pool of items based on literature review and

interviews, (3) collect primary data, (4) purify measures based on empirical results, (4)

assess reliability and validity. These steps have an iterative process. At each stage, the


155

researcher evaluates the results and decides whether to modify the measure and repeat the

same procedure. This study follows the above iterative steps through pretest and final

field survey. The next section explains how the measures for alliance market orientation

components have been developed. First, based on a thorough literature review, alliance

market orientation was conceptualized to specify the construct domain and to generate

items for its components. Alliance market orientation (AMO) is conceptualized as it has

three components (inter-organizational customer orientation--IoCustor, inter-

organizational competitor orientation--IoCompor, and inter-organizational coordination--

IoCoor). In the thorough research, items for each facet were generated. Second, in order

to establish face and content validity, a list of AMO items with the definitions was

submitted to a panel of experienced academic researchers and practitioners. Through

revision and modification, AMO scale with three dimensions (IoCustor, IoCompor, and

IoCoor) was well refined. Third, pretest data were then collected from twenty-two

executives. After follow-up interviews were conducted with each pretest survey

respondent in order to confirm the adequacy of each measurement instrument, measure

items were purified using a comprehensive analysis by using coefficient alpha, item-to-

total correlation, and exploratory factor analysis. We checked the pretest results and made

decisions about the final version of the scales based on the reliability and validity

assessments. The desirable psychometric properties of the AMO scale were achieved

through this empirical development process. Finally, the final field study was conducted

using the refined measure. Data from 253 usable surveys were collected to test the

reliability and validity of the alliance market orientation components.


156

5.2. Development of Alliance Market Orientation Measures

The previous inter-organizational relationship literature has discussed market

orientation as a firm’s property rather than a relationship property of alliances. They have

examined how a firm’s market oriented behaviors enhance the relationship quality

between partners. Few studies have discussed the term “inter-organizational alliance

market orientation,” and they urged researchers to examine this concept in detail and

develop a measure for it. In order to develop a valid and reliable AMO measure, based on

the pretest results, some initial measure items that were considered inappropriate were

deleted from the further analysis. Item deletion decision is made in the light of theoretical

and empirical perspectives (Anderson and Gerbing 1988). An item was deleted if it was

not statistically appropriate as a measure and if its deletion was not detrimental to the

conceptual domain of the latent construct (Im 1999).

5.2.1 Pretest Evaluation of Alliance Market Orientation Measures

In the pretest, a total of seventeen scale items collected from literature review and in-

depth interviews with executives were tested for reliability and validity based on internal

consistency (e.g., Cronbach alpha, item-to-total correlation and qualitative responses

from the follow-up interviews with twenty-two executives). These tests indicated the

existence and importance of three commonly perceived components of alliance market

orientation – inter-organizational customer orientation, inter-organizational competitor

orientation, and inter-organizational coordination. First, coefficient alpha was examined

for all three dimensions of alliance market orientation to examine internal consistency.

The results show that all dimensions of alliance market orientation have acceptable

internal consistency as reflected in a high coefficient alpha for each dimension (.90 for


157

IoCustor, .92 for IoCompor, and .85 for IoCoor). Second, consistent with Im (1999) and

Bunn (1993), the difference between two item-to-total correlations -- between dimension

item-to-total correlation and within-dimension item-total correlation -- was examined for

all three dimensions. The difference between these two item-to-total correlation values

has to be large enough to have scales with high-discriminant and convergent validity.

Items are retained that satisfied the following criteria: (1) high within-dimension item-to-

total correlations, (2) low between-dimension item-to-total correlations, and (3) within-

dimension item-to-total correlations are sufficiently greater than between dimension

item-to-total correlations. Table 5.1 summarizes the results from this analysis.

After the analysis, I contacted twenty-two pretest respondents for further insights.

According to Gatewood and Field (1998), qualitative feedback from survey respondents

can be used as a criterion for adding and dropping scale items. In the follow-up

interviews, each executive was asked to evaluate the appropriateness of the each item,

how it reflects the underlying construct, and whether it applies in business setting. Items

related to inter-organizational gatherings and meetings were considered irrelevant by the

executives. The general reaction to these items was that due to heavy daily schedules of

the firm representatives (e.g. alliance manager, business/corporate development

manager), they do not physically get together to discuss the market trends and customer

satisfaction. They rather communicate via e-mails and teleconferences. They also noted

that they generally organize biannual meetings with their partner to discuss the overall

market trends and competitive moves. Therefore, they recommended me to exclude these

items from the measurement. The scale items that more than five respondents suggest

deleting were considered for possible exclusion in the next step (Im 1999).


158

An exploratory factor analysis was also performed prior to the confirmatory factor

analysis. EFA is considered for recovering an underlying measurement model that can be

evaluated by confirmatory factor analysis later on. Factor analysis using Promax rotation

was employed to test the appropriateness of each item. Three factor solution is derived

based on the eigenvalue rule (eigenvalue >1) and the scree plot. After the third factor on,

each successive factor was accounting for smaller and smaller amounts of the total

variance. Three factors explain a substantial amount of variance (97 %). The factor

structure is shown in Table 5.2. According to the exploratory factor analysis results, the

loading of IoCompor Item5 was low. It may be due to the reverse coding of this item.

Therefore, we decided to exclude this item in the confirmatory factor analysis. Based on

the item-to-total correlation analysis and follow-up interviews and the exploratory factor

analysis, we decided to delete a total of six, thus retaining 11 scale items for the final

analysis (3 for the IoCustor, 4 for the IoCompor, and 4 for the IoCoor). The deleted items

are marked with a “*” in Table 5.1 and Table 5.2. Next, Lisrel 8 was used to conduct the

confirmatory factor analysis to examine the fit of the measurement model. The

measurement model examines relationship of the 11 observed indicators to their

underlying dimensions. In addition, it also assesses the convergent validity of the

constructs by evaluating the significance of the estimated indicators’ coefficients. The

results in Table 5.3 show that all indicators loaded positively and significantly on the

suggested dimensions, confirming that there is a good convergent validity of all 11

indicators. Squared multiple correlations are high; a substantial amount of variance is

explained by the underlying latent constructs. Table 5.4 shows the average variance

extracted (AVE) of latent variables and correlations between them. Since AVEs of latent


159

variables are higher than the squared correlations between each latent variable, IoCustor,

IoCompor, and IOCoor are distinct and show discriminant validity. The χ 2 statistics

shows an overall model fit assessment by testing the hypotheses that all the differences

between the observed and implied covariances are zero simultaneously. The

measurement model passes the χ 2 goodness of fit tests ( χ 2 (41) = 42.75 (p >.1)). As

indicated by the goodness of fit indices in Table 5.3, measurement model fits the data

very well (RMSEA= .04; NNFI=.94; CFI=.96; IFI=.96). Finally, we reviewed the results

and decided the refined measure with 11 items should be used for the final field survey.

5.3. Final Field Study Validation of Alliance Market Orientation Measure

The total of 253 responses that were collected from executives was analyzed for

measure validation. This section discusses the measure validation process that assessed

the validity and reliability of the alliance market orientation measure by using item-to-

total correlation, coefficient alpha and measurement model in CFA analysis.

5.3.1. Coefficient Alpha and Item-to-total Correlation

Coefficient alpha and item-to-total correlation were conducted to evaluate the

reliability of the alliance market orientation measure in the final field study. Table 5.5

summarizes the item-to-total correlation within and between dimensions, and coefficient

alpha values. The measures have high internal consistency, as indicated by coefficient

alphas .92 (for IoCustor), .92 (IoCompor), and .88 (IoCoor). For all measure items for

alliance market orientation, the within dimension item-to-total correlations are

substantially higher than the between-dimension item-to-total correlations, thus providing

an evidence of discriminant validity of the measures (e.g., minimum difference was .13

item 1 for IoCoor).


160

Table 5.1 Alliance Market Orientation Scale Development

Process in the Pretest (N=22)

Between Dim. Item/Total Correlation

Within Dim. Item/Total Correlation

Difference # of interview response for deletion

Inter-Organizational Customer Orientation (α =.90)

IOCompor: .34

.48

Our alliance’s business objectives are driven by customer satisfaction IOCoor: .25

.82

.57

0

IOCompor: .37 .34

In our alliance, we get together frequently to monitor our level of commitment and orientation to serving customers’ needs

IOCoor: .60

.71

.11

11 *

IOCompor: .54

.23 Our alliance’s strategy for competitive advantage is based on our joint understanding of customer needs

IOCoor: .15

.77

.62

0

IOCompor: .42

.21

Our alliance’s business strategies are driven by our beliefs about how we can jointly create greater value for customers

IOCoor: .08

.63

.55

0

IOCompor: .55 .20 In our alliance, we get together frequently to measure customer satisfaction

IOCoor: .60

.75

.15

13*

IOCompor: .53

.22

In our alliance, we get together periodically to review the likely effect of changes in our business environment (e.g., regulation, competition) on customers

IOCoor: .48

.75

.27

8*


161






Inter-Organizational Competitor Orientation (α =.92)

IOCustor: .62

.84 .22

Senior managers in our firm meet frequently with their counterparts in our partner’s firm to discuss competitors’ strengths and strategies

IOCoor: .51 .33

2

IOCustor: .42

. 42 In our alliance, we jointly target customers where we have an opportunity for competitive advantage

IOCoor: .26

.84

. 58

0

IOCustor: .48

.33 In our alliance, we jointly respond to competitive actions that threaten us

IOCoor: .40

.81

.41

1

IOCustor: .63

.89 .26

In our alliance, we frequently share information with each other concerning competitors’ strategies

IOCoor: .36 .53

2

IOCustor: .31

. 19 In our alliance, we don’t work together to generate intelligence on competition R

IOCoor: .31

.58

. 19

0

Inter-Organizational Coordination (α =.85)

IOCustor: .34

.35 .01

Senior managers in both firms understand how people across organizations can contribute to creating customer value

IOCoor: .13 .29

6*


162






IOCustor: .34

.35 .01

Senior managers in both firms understand how people across organizations can contribute to creating customer value

IOCompor: .13 .29

6*

IOCustor: .45

.76 .31 In our alliance, people in both organizations work hard to jointly solve our alliance’s problems

IOCompor: .47 .39

0

IOCustor: .42 .77 .25 In our alliance, activities involved in the innovation process are well-coordinated

IOCompor: .36 .31

0

IOCustor: .43

.85 .42 In our alliance, the different job activities related to new product development activity fit together very well

IOCompor: .25 .60

1

IOCustor: .35 .55 .20 In our alliance, we have inter-organizational meetings frequently to discuss market trends and developments

IOCompor: .35 .20

8*

IOCustor: .28 .79 .51 In our alliance, people who have to work together are responsive to their co-workers’ needs and requests

IOCompor: .50 .29

0

R Reverse coded item; * Item for deletion


163

Table 5.2 Loadings from Exploratory Factor Analysis for the

Final Measure of Alliance Market Orientation in Pretest (N=22)

Inter-organizational Coordination

Inter-organizational Competitor Orientation

Inter-organizational Customer Orientation

Our alliance’s business objectives are driven by customer satisfaction.

.26

.35

.92

Our alliance’s strategy for competitive advantage is based on our joint understanding of customer needs.

.17

.55

.92

Our alliance’s business strategies are driven by our beliefs about how we can jointly create greater value for customers.

.05

.36

.91

Senior managers in our firm meet frequently with their counterparts in our partner’s firm to discuss competitors’ strengths and strategies.

.53

.85

.49

In our alliance, we jointly target customers where we have an opportunity for competitive advantage.

.25

.87

.30

In our alliance, we jointly respond to competitive actions that threaten us.

.47

.93

.35

In our alliance, we frequently share information with each other concerning competitors’ strategies

.37

.94

.56

In our alliance, we don’t work together to generate intelligence

on competitionR

.22

.54*

.24

In our alliance, people in both organizations work hard to jointly solve our alliance’s problems

.91

.49

.18

In our alliance, activities involved in the innovation process are well-coordinated

.85

.36

.17

In our alliance, the different job activities related to new product development activity fit together very well

.89

.25

.17

In our alliance, people who have to work together are responsive to their co-workers’ needs and requests

.92

.44

.05

Variance Explained: 2.67 (38.2%) 1.72 (28.7%) 2.08 (30%)


164

Table 5.3 Confirmatory Factor Analysis on the

Components of Alliance Market Orientation Measures (N=22)

Coefficients Estimate (S.E.) Squared Multiple Correlation

λ 1 (IOCustor - X1) .90 (.17) * .81

λ 2 (IOCustor- X2) .94 (.17) * .88

λ 3 (IOCustor- X3) .90 (.17) * .80

λ 4 (IOCompor- X4)

.86 (.18) * .74

λ 5 (IOCompor- X5)

.85 (.18) * .72

λ 6 (IOCompor- X6)

.91 (.17) * .84

λ 7 (IOCompor- X7)

.95 (.16) * .91

λ 8 (IOCoor- X8) .92 (.17) * .85

λ 9 (IOCoor- X9) .84 (.18) * .71

λ 10 (IOCoor- X10) .86 (.18) * .74

λ 11 (IOCoor- X11) .91 (.17) * .83

Fit Indices

Chi square 42.75

d.f. (p value) 41 (.40)

RMSEA .04

CFI .96

NNFI .94

IFI .96

* Significant at .05 level

Table 5.4 Inter-Construct Correlations and Average

Variance Extracted Values (N=22)

IOCustor IOCompor IoCoor

IOCustor .89*

IOCompor .56 (.21) .87* IoCoor .18 (.21) .50 (.20) .88*

*Square root of the AVE values of each latent construct.

All square correlations are less that the values on the diagonal, indicating that discriminant validity exist (.e.g, .56 2 =.32 which is less that .87)


165

Table 5.5 Alliance Market Orientation Scale Development Process Final Field

Study (N=253)



Difference

Inter-organizational Customer Orientation (α =.92)

IOCompor: .42 .39 Our alliance’s business objectives are driven by customer satisfaction. IOCoor: .57

.81 .24

IOCompor: .51 .35 Our alliance’s strategy for competitive advantage is based on our joint understanding of customer needs.

IOCoor: .62

.86 .24

IOCompor: .50 .27 Our alliance’s business strategies are driven by our beliefs about how we can jointly create greater value for customers.

IOCoor: .60

.87 .17

Inter-organizationalCompetitor Orientation (α =.92)

IOCustor: .46

.30


IOCoor: .45

.76 .31

IOCustor: .47 .35 In our alliance, we jointly target customers where we have an opportunity for competitive advantage.

IOCoor: .42

.82 .40

IOCustor: .45 .42 In our alliance, we jointly respond to competitive actions that threaten us.

IOCoor: .43 .87

.44

IOCustor: .46 .36 In our alliance, we frequently share information with each other concerning competitors’ strategies

IOCoor: .42 .82

.40

Inter-organizational Coordination (α =.88)

IOCustor: .64 .13 In our alliance, people in both organizations work hard to jointly solve our alliance’s problems IOCompor: .53

.77

.24

IOCustor: .55 .24 In our alliance, activities involved in the innovation process are well-coordinated

IOCompor .41 .79

.38

IOCustor: .46

.26 In our alliance, the different job activities related to new product development activity fit together very well IOCompor: .29

.72

.43

IOCustor: .58 .16 In our alliance, people who have to work together are responsive to their co-workers’ needs and requests

IOCompor: .47

.74

.27


166

5.3.2. Examining Dimensionality of Alliance Market Orientation

In this section, the reliability and validity of the alliance market orientation measure

will be examined by using confirmatory factor analysis before testing the structural

model (Anderson and Gerbing 1988). The measurement model in Figure 5.1 shows that

alliance market orientation has three dimensions.

Since the main aim of measure development procedure is to develop a reliable and

valid measure of alliance market orientation that captures three distinct dimensions --

IoCustor, IoCompor, and IoCoor – it is necessary to determine whether each scale item

assesses the three distinct dimensions correctly, thus confirming discriminant validity. As

previously done, scale items were kept only if they had a high correlation with the

dimension that they belonged and they were highly distinctive from the other dimensions.

These scale items have high convergent validity with other items in their own dimension

and high discriminant validity against the items in the other dimensions. In order to verify

whether three underlying dimensions converge into one dimension, this study adopts the

method suggested by Bollen and Grandjean (1981) that examines the difference between

a measurement model with perfect correlation and the other with freely estimated

correlation.

In model 1 in Table 5.6, the three distinct latent dimensions are assumed to be

independent from each other in that the correlation between the three latent variables is

freely estimated ( 12φ , 23φ , 13φ =free) representing the two factor model. In contrast to

model 1, model 2 assumes that the three latent dimensions are perfectly correlated ( 12φ =

23φ = 13φ =1) in that there exists only one dimension. These two models are compared in


167

terms of overall fit as well as component fit in order to examine the dimensionality of

alliance market orientation.

As recommended by Bollen (1989), the estimation results for the two models are

compared for alliance market orientation in terms of 2χ statistics, fit indices, and

component fit. First, 2χ difference tests were used to compare the overall fits between

models 1 and 2. The test statistics, T= (N-1) F ML has an asymptotic 2χ distribution with

df= q (q+1)/2 –t. 2χ tests for two models are significant at less than the .05 level,

indicating that the model do not fit the data well. However, since 2χ test is sensitive to

sample size further examination of the model fit is needed. Using 2χ difference test

between the two models, model 1 improves the fit significantly over model 2, favoring

the three-dimensional model over a unidimensional model ( 2χ difference= 2χ restricted -

2χ edunrestrict = 758.52 -81.27 = 676.25 3 d.f.). Second, fit indices are compared between

rival models. All the baseline comparison indices – NFI, IFI, and RFI – show that three

dimensional model fits the data better than the unidimensional model does. In addition,

RMSEA indicates that fit for the three dimensional model is better than that for the

unidimensional model. Based on Browne and Cudeck’s cutoff standard, the RMSEA for

model 1 indicate a reasonably good fit, while this for model 2 indicates unacceptable fit.

In sum, the overall fit measures -- 2χ difference test, fit indices -- suggest that model

1 fits the data better, favoring the three dimensional model over unidimensional model.

Next, ML estimation results are reviewed to examine component fits. The results indicate

that all the coefficients (e.g., factor loadings) for measurement models are significant for

alliance market orientation. Thus, comparisons of the component fits of the models do


168

not provide strong grounds to support one model over the other. However, SMCs from

three dimensional model are higher than those from the unidimensional model, meaning

that three latent constructs in three dimensional model, compared to one latent construct

in unidimensional model, explain more variance in the observed variables.

The estimation results of correlations show that the three dimensions in model 1 are

somewhat correlated ( 12φ = .55, 23φ = .73, 13φ = .56). Even though some variances (.55 2 =

.30, .73 2 =.53, 56 2 = .31) are shared by the three dimensions, most of the variances (1-

.3=.7, 1-.53=.47, 1-.31=.69) still remain unshared by these dimensions. These

unexplained variances suggest that the three dimensions are not identical, favoring the

three dimensional model over uni-dimensional model.


169

Table 5.6 Confirmatory Factor Analysis for the Final Measure of Alliance

Market Orientation in Final Field Study (N=253)

Model 1 (Three

dimensional model)

Model 2

(Unidimensional model)

Estimate

(s.e)

SMC Estimate

(s.e)

SMC

λ 1 (IOCustor X1) .84 * (.05) .71 .76* (.05) .58

λ 2 (IOCustor X2) .92* (.05) .84 .83* (.05) .70

λ 3 (IOCustor X3) .93* (.05) .86 .82* (.05) .68

λ 4 (IOCompor X4) .79* (05) .63 .65* (.05) .42

λ 5 (IOCompor X5) .86* (.05) .74 .66* (.06) .43

λ 6 (IOCompor X6) .92* (.05) .85 .65* (.06) .43

λ 7 (IOCompor X7) .88* (.05) .77 .65* (.06) .43

λ 8 (IOCoor X8) .87* (.05) .76 .80* (.05) .64

λ 9 (IOCoor X9) .81* (.05) .66 .70* (.06) .49

λ 10 (IOCoor X10) .73* (.6) .54 .60* (.06) .36

λ 11 (IOCoor X11) .83* (.05) .68 .74* (.05) .55

Correlation:

12φ = .55

23φ = .56

13φ = .73

Fixed at 1

Fit Indices Chi-square 81.27 (< .001) 758.52 (<.001)

d.f. (p value) 41 44

NFI .98 .81

RFI .97 .76

IFI .99 .81

RMSEA .066 .30

* significant at .05 level


170

5.4. Measure Development for Other Major Constructs

For the other major constructs in the suggested model in Figure 1.1 (i.e., independent

variables, dependent variables and control variables), this study uses the existing measure

instruments gathered from the literature. Some measure items are modified to fit into the

context of the study, and purified by dropping inappropriate items. The measures of

major constructs are tested for their validity and reliability at two stages. First, constructs

are refined and revised using coefficient alpha and item-to-total correlations in the pretest

before they are included in the main survey. Next, the final field study confirms the

reliability and validity of the revised measures by using a series of factor analyses.

5.4.1. Testing the Measure Characteristics in the Pretest

The measurement characteristics of the major constructs are tested by using data

collected from twenty two pretest respondents. Based on Anderson and Gerbing (1988),

the decision to retain or delete items is made both on theoretical and empirical

considerations. Although empirical findings suggest dropping some items, due to

theoretical considerations those items are kept for the final study.

Construct reliabilities are tested based on internal consistency and item-to-total

correlation. Table 5.7 reports the item-to-total correlations for major constructs. The

items marked with (*) have low item-to-total correlations, therefore they are deleted from

the final survey. Second, internal consistency (e.g., coefficient alpha) is examined as the

overall scale reliability. Table 5.7 also reports the Cronbach alphas. The coefficient

alphas after deleting items are reported in parentheses. The results show that reliabilities

are ranging from .64 to .97. Compared to other measures complementary resources has

relatively low coefficient alpha (.64) due to inclusion of items with low-item-total


171

correlations. The decision to keep items with low item-to-total correlations was made

based on the idea that these items enhance the internal consistency of the measure by

addressing important aspect of the latent construct. Briefly, only those items that are least

likely to contribute, theoretically and empirically, by measuring the underlying constructs

are deleted.

5.4.2. Testing the Measure Characteristics in the Final Field Study

After the measures for major constructs are refined and revised in the pretest, a series

of factor analyses (e.g., EFA and CFA) was performed on the 253 responses collected in

the final field study. First, descriptive statistics for scales are reported in Table 5.8. Even

though some measures of major constructs are somewhat contaminated by the non-

normality (e.g., skewness and kurtosis), all the measures provide enough variances to test

the measurement model and evaluate the constructs’ reliabilities and validities. Second,

exploratory factor analysis is performed on major constructs. Table 5.9 reports the results

from exploratory factor analysis using principal component extraction method with

Varimax rotation in the final field study. The results from exploratory factor analysis

show that most of the indicators are distinctively loaded on the underlying latent

constructs, thus reflecting good reliabilities for most scale items. There are only three

items that have low loading problem -- NPN-2 from new product novelty, Comm-4 from

commitment, and TMS-5 from top management support. These three items with

relatively low reliabilities are excluded from the final measures since they do not assess

the significant aspect of the underlying construct (thus reducing the coefficient alphas).


172

5.4.3 Confirming the Measurement Properties in the Respecified Model

Based on the measurement purification and refinement procedures, measurement

model for major constructs is respecified prior to testing the structural model in Figure

1.1 for the final analysis. Respecified model is tested by series of confirmatory factor

analyses. Although exploratory factor analysis give a glimpse of idea about the

measurement model at the beginning, the conduct of confirmatory factor analysis is better

at later stages. It is recommended that estimation of the confirmatory measurement model

should come before the simultaneous estimation of the measurement and structural sub-

models (Anderson and Gerbing 1988; Churchill 1979). These two wave approach helps

to assure the measurement model and thereby, avoid interpretational confounding and

possible interaction of the measurement and structural model.

Table 5.10 summarizes the overall fit of the measurement model. Even though chi

square test is significant at .05 level, as indicated by fit indices, measurement model fits

data well. RMSEA also indicates acceptable fit for the measurement model. Then,

component fit is examined for each indicator. Table 5.11 provides ML estimation results

for coefficients and squared multiple correlations (SMCs). The SEM approach using

confirmatory factor analysis complements traditional methods (e.g., coefficient alpha,

item-to-total correlation and exploratory factor analysis) of evaluating reliability and

validity. The measurement model examines the relationship of indicators with their

underlying latent constructs, and provides a confirmatory assessment of convergent

validity by evaluating the significance of the parameter coefficients. The results show

that all indicators have significant relationships with their underlying latent constructs,

thus indicating good component fit for the proposed measurement model. Briefly, the


173

results imply that there is a good convergent validity for all indicators (Bagozzi and Yi

1988). The SMCs can also be used to assess the reliability measure for each indicator

because it is the shared variance between the indicator and its subjective underlying latent

construct. In addition, all error variance for indicators are significant, thus indicating that

measurement model with error term specification fits data well.


174

Table 5.7 Item and Scale Reliabilities for the Final Survey in Pretest (N=22)

Item # Item Item/total

correlation Trust α = .89 In our relationship, both our alliance partner and we

Trust-1 … are honest .65

Trust-2 … can be counted on what is right .80

Trust-3 … are faithful .72

Trust-4 … have confidence in each other .56

Trust-5 … have high integrity .67

Trust-6 … are reliable .61

Trust-7 … are trustworthy .82

Commitment α = .85 (.86) Both our alliance partner and we view our relationship as something

Comm-1 … be committed to .52

Comm-2 … important to our firms .74

Comm-3 … of significance .65

Comm-4 … our firms intend to maintain indefinitely .63

Comm-5 … much like being family .45 *

Comm-6 ... our firms really care about .66

Comm-7 … deserving our firms’ maximum efforts to maintain .72

Complementary Resources α = .64 Both our alliance partner and we

CR-1 … contribute different resources to the relationship that help us achieve mutual goals

.37

CR-2 … have complementary strengths that are useful to our relationship .58

CR-3 … have separate abilities that, when combined together, enable us to achieve goals beyond our individual reach

.40

Goal Congruence α = .87 (.91) Our alliance partner and we

GC-1 … have different goals .51 *

GC-2 … have compatible goals .82

GC-3 … support each other’s objectives .79

GC-4 R … share the same goals in the relationship .80

Top Management Support α = .74 (.76) Top management in both firms

TMS-1 …believe that alliances play a role in the future success of each firm .54

TMS-2 …are committed to the use of alliances to achieve strategic goals .68

TMS-3 …support the use of alliances when situations call for them. .56

TMS-4 …tell their employees that this alliance’s survival depends on its adapting to market trends.

.32 *

TMS-5 …believe that serving customers is the most important thing this alliance does.

.43


175


Item # Item Item/total

correlation

Alliance Experience α = .94

AllExp-1 Both our alliance partner and we have a deep base of partnership experience.

.84

AllExp-2 Our alliance partner and we each have participated in many alliances.

.88

AllExp-3 Individually our alliance partner and we have been partners in a substantial number of alliances.

.90

Alliance Partnership Propensity α = .88

AllPP-1 Our alliance partner and we each actively search for promising alliance partners.

.71

AllPP-2 Alliances that can help our business are sought out by both our alliance partner and us.

.74

AllPP-3 Our alliance partner and we each are constantly seeking partnering opportunities.

.84

AllPP-4 Both our alliance partner and we are always looking for firms that we can partner with to jointly develop competitive advantage.

.67

Alliance Manager Development Capability α = .97

AllMDC-1 Both our alliance partner and we have programs to develop capable alliance managers

.90

AllMDC-2 Our alliance partner and we each understand how to produce effective alliance managers.

.96

AllMDC-3 Both our alliance partner and we effectively train competent alliance managers.

.94

AllMDC-4 Our alliance partner and we each know how to identify effective alliance managers.

.93

New Product Novelty α = .86 ( .87) Please rate the degree to which the new product(s)

generated by your alliance have tended to be

NPN-1 … exciting .39 *

NPN-2 … fresh .62

NPN-3 … unconventional .52

NPN-4 … novel .75

NPN-5 … unusual .72

NPN-6 … unique .78 NPN-7 … original .58

New Product Meaningfulness α = .88

NPM-1 … relevant .66 NPM-2 .. . suitable .78 NPM-3 … appropriate .75 NPM-4 … useful .82

NPM-5 … meaningful .60


176


Item # Item Item/total correlation

New Product Performance α = .82 Please indicate the extent to which the new products

generated by your alliance have tended to be successful in terms of

NPP-1 … sales .70 NPP-2 … market share .65

NPP-3 … return on investment .62 NPP-4 … profits .50 NPP-5 … customer satisfaction .50 NPP-6 … overall performance .53 Market Density α = .79 (.90)

MD-1 There are many potential customers for the product that our alliance provides a mass marketing opportunity.

.24 *

MD-2 Potential customers have a great need for the product that our alliance develops.

.79

MD-3 The dollar size of the market (either existing or potential) for the product that our alliance develops is large.

.66

MD-4 The market for the product that our alliance develops is growing very quickly.

.79

Technology Density α = .96

TD-1 The technology in our alliance’s market is changing rapidly

.92

TD-2 Technological changes provide big opportunities in our alliance’s market

.94

TD-3 A large number of new product ideas have been made possible through technological breakthroughs in our alliance’s market

.87

TD-4 Technological developments in our alliance’s market are minor

.89

* Item is deleted. Item is retained only if deletion of an item reduces coefficient α .

** Number in parenthesis stands for coefficient α after deletion of low item-to-total correlation items.

Table 5.12 shows the inter-correlations between all of the constructs in the study.

Since alliance market orientation and alliance competence are second order reflective

constructs, they are also included in the matrix to examine their relationship with other

constructs. According to the correlation matrix, all the hypothesized relations in the

model are held. In sum, the structural equation modeling method confirms that all

measures for the major constructs in the specified model have good reliability and

validity, thus providing desirable psychometric properties of the measures.


177

Table 5.8 Descriptive Statistics for Major Scales )(a (N=253)

Mean (st.d.) Skewness Kurtosis

Trust 5.71 (1.04) -.84 .90

Commitment 5.54 (1.05) -.65 .62

Top Management

Support

5.92 (1.07) -1.1 1.26

Goal Congruence 5.47 (1.13) -.64 -.06

Complementary

Resources

6.02 (0.99) -1.23 2.13

Joint Alliance

Experience

4.91 (1.25) -.18 -.53

Joint Alliance

Partnership

Identification

Propensity

5.46 (1.11) -.55 -.015

Joint Alliance

Manager

Development

Capability

3.85 (1.47) .10 -.80

New Product Novelty 5.33 (1.05) -.31 -.04

New Product

Meaningfulness

5.83 (1.00) -.81 .62

New Product

Performance

5.32 (1.01) -.55 .81

Market Density 5.50 (1.24) -.78 .33

Technology Density 5.39 (1.34) -1.04 .85

Int. Org. Customer

Orientation

5.54 (1.14) -.70 .07

Int. Org. Competitor

Orientation

4.64 (1.45) -.46 -.56

Int. Org.

Coordination

5.37 (1.07) -.60 .29

All values are significant at p< .05 level.


178

Table 5.9 Exploratory Factor Analysis for Major Constructs (N=253)

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13

Trust

Trust-1 .85 .42 .35 .33 .19 .23 .37 .31 .23 .24 .44 .38 .44

Trust-2 .87 .39 .32 .40 .20 .18 .34 .25 .27 .12 .37 .39 .34

Trust-3 .85 .34 .39 .44 .22 .17 .34 .22 .21 .13 .42 .38 .31

Trust-4 .79 .46 .44 .37 .26 .22 .42 .33 .32 .17 .45 .48 .37

Trust-5 .85 .41 .40 .40 .23 .24 .39 .30 .27 .18 .50 .45 .45

Trust-6 .85 .42 .35 .39 .29 .20 .39 .31 .30 .20 .36 .47 .37

Trust-7 .91 .38 .35 .37 .27 .24 .38 .33 .24 .15 .43 .42 .41

Commitment

Comm-1 .52 .42 .41 .78 .33 .23 .43 .34 .37 .26 .31 .44 .46

Comm-2 .39 .42 .37 .91 .29 .23 .33 .29 .34 .22 .38 .45 .46

Comm-3 .43 .40 .43 .88 .24 .28 .40 .26 .32 .26 .45 .49 .44

Comm-4 .37 .33 .25 .58

*

18 .18 .33 .16 .23 .14 .23 .31 .32

Comm-6 .43 .44 .44 .75 .38 .28 .37 .34 .37 .23 .37 .44 .47

Comm-7 .38 .40 .36 .73 .33 .29 .30 .27 .30 .29 .34 .42 .45

Goal Congruence

GC-2 .44 .47 .47 .42 .38 .25 .45 .34 .44 .30 .31 .86 .44

GC-3 .48 .46 .42 .47 .34 .26 .44 .31 .44 .26 .40 .87 .47

GC-4 .42 .44 .43 .43 .27 .24 .44 .28 .38 .26 .37 .86 .43

GC-2 .44 .47 .47 .42 .38 .25 .45 .34 .44 .30 .31 .86 .44

GC-3 .48 .46 .42 .47 .34 .26 .44 .31 .44 .26 .40 .87 .47

GC-4 .42 .44 .43 .43 .27 .24 .44 .28 .38 .26 .37 .86 .43

Complementary Resources

CR-1 .45 .38 .40 .40 .14 .20 .31 .28 .21 .22 .84 .38 .41

CR-2 .44 .29 .34 .35 .14 .27 .28 .31 .24 .25 .88 .31 .41

CR-3 .44 .32 .36 .34 .13 .23 .26 .31 .19 .23 .84 .35 .41

Alliance Experience

AllE-1 .33 .48 .42 .37 .37 .25 .41 .48 .91 .25 .27 .41 .45

AllE-2 .27 .43 .42 .32 .43 .27 .38 .54 .92 .23 .20 .43 .47

AllE-3 .26 .44 43 .32 .37 .28 .39 .55 .93 .30 .23 .44 .44

Alliance Partnership Propensity AllP-1 .35 .51 .45 .29 .35 .21 .41 .90 .52 .31 .31 .31 .55

AllP-2 .38 .53 .52 .32 .37 .22 .42 .89 .50 .29 .35 .33 .58

AllP-3 .29 .43 46 .27 .35 .27 .43 .88 .50 .34 .36 .34 .52

AllP-4 .21 .30 29 .18 .33 .25 .20 .70 .38 .15 .22 .23 .32


179


F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13

Alliance Manager Development Capability

AllMDC-1 .24 .33 .31 .29 .95 .20 .25 .37 .38 .22 .13 .34 .31

AllMDC-2 .26 .37 .34 .28 .96 .25 .27 .41 .38 .23 .14 .36 .30

AllMDC-3 .24 .35 .35 .30 .95 .23 .27 .38 .38 .22 .15 .33 .31

AllMDC-4 .32 .41 .38 .32 .91 .27 .34 .40 .40 .27 .18 .37 .35

Top Management Support

TMS-1 .39 .48 .47 .50 .34 .33 .40 .48 .46 .32 .44 .42 .82

TMS-2 .38 .38 .41 .44 .26 .31 .43 .50 .41 .34 .39 .43 .85

TMS-3 .49 .45 .48 .39 .30 .32 .52 .51 .43 .33 .46 .50 .83

TMS-5 .27 .37 .21 .34 .15 .20 .26 .22 .19 .07 .27 .30 .32

*

New Product Novelty

NPN-2 .23 .34 .53* .21 .15 .31 .34 .27 .42 .28 .36 .30 .29

NPN-3 .28 .49 .80 .35 .25 .30 .44 .38 .39 .29 .37 .30 .36

NPN-4 .36 .55 .86 .33 .31 .28 .49 .44 .39 .39 .39 .40 .42

NPN-5 .32 .52 .83 .36 .29 .24 .45 .38 .33 .26 .31 .40 .38

NPN-6 .37 .54 .88 .33 .29 .28 .54 .38 .34 .34 .35 .44 .43

NPN-7 .41 .55 .82 .34 .37 .32 .53 .43 .39 .32 .36 .49 .48

New Product Meaningfulness

NPM-1 .43 .56 .61 .61 .31 .28 .85 .41 .39 .41 .31 .46 .52

NPM-2 .33 .52 .51 .51 .23 .24 .87 .34 .33 .33 .27 .38 .44

NPM-3 .25 .33 .34 .34 .13 .19 .74 .25 .29 .28 .23 .32 .30

NPM-4 .43 .56 .56 .56 .28 .22 .86 .37 .41 .40 .31 .44 .38

NPM-5 .44 .53 .59 .59 .33 .24 .85 .37 .40 .43 .33 .49 .44


NPP-1 .38 .87 .54 .54 .29 .31 .51 .40 .45 .30 .28 .47 .44

NPP-2 .38 .84 .58 .58 .29 .31 .50 .40 .40 .40 .32 .36 .39

NPP-3 .42 .86 .51 .51 .32 .24 .49 .41 .36 .36 .32 .40 .39

NPP-4 .39 .87 .51 .51 .38 .24 .45 .47 .40 .33 .32 .39 .37

NPP-5 .42 .84 55 .55 .34 .31 .54 .39 .41 .28 .35 .48 .42

NPP-6 .49 .86 .62 .62 .34 .37 .59 .47 .48 .34 .39 .56 .50


180


F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13

Technology Density

TD-1 .20 .28 .25 .22 .25 .92 .22 .25 .26 .33 .21 .24 .31

TD-2 .23 .30 .27 .28 .23 .91 .23 .28 .26 .34 .23 .22 .36

TD-3 .27 .30 .31 .22 .18 .87 .25 .19 .23 .36 .26 .22 .30

TD-4 .25 .25 .34 .20 .19 .85 .20 .20 .23 .32 .24 .26 .28

Market Density

MD-2 .17 .35 .39 .23 .25 .34 .37 .23 .23 .88 .23 .28 .36

MD-3 .23 .37 .38 .24 .23 .34 .37 .27 .27 .86 .28 .31 .37

MD-4 .16 .32 .30 .24 .18 .35 .38 .24 .24 .82 .24 .26 .28

Table 5.10 Confirmatory Factor Analysis :

Fit Indices for Measurement Model (N=253)

Chi square 1867.22 (p <.001)

d.f. (p- value) 1352

NFI .96

RFI .96

IFI .99

CFI .99

RMSEA .039


181

Table 5.11 Confirmatory Factor Analysis for Major Constructs:

Measurement Model Loading of Measurement Model

Parameter Estimates * (s.e.)

SMC Measure item

Error Variance Estimate * (s.e.)

1λ : TMS ( 1ξ ) on TMS-1 .82 (.05) .68 TMS-1 .32 (.04)

2λ : TMS ( 1ξ ) on TMS-2 .86 (.05) .74 TMS-2 .26 (.03)

3λ : TMS ( 1ξ ) on TMS-3 .86 (.05) .73 TMS-3 .27 (.03)

4λ : AllE ( 2ξ ) on AllE-1 .90 (.05) .81 AllE-1 .19 (.02)

5λ : AllE ( 2ξ )on AllE-2 .94 (.05) .88 AllE-2 .12 (.02)

6λ : AllE ( 2ξ ) on AllE-3 .93 (.05) .87 AllE-3 .13 (.02)

7λ : AllP ( 3ξ ) on AllP-1 .92 (.05) .84 AllP-1 .16 (.02)




11λ : AllMDC ( 4ξ ) on

AllMDC-1

.95 (.05) .91 AllMDC-1 .09 (.01)


AllMDC-2

.96 (.05) .93 AllMDC-2 .07 (.01)


AllMDC-3

.95 (.05) .90 AllMDC-3 .10 (.01)


AllMDC-4

.90 (.05) .81 AllMDC-4 .19 (.02)

15λ : GC ( 5ξ ) on GC-2 .85 (.05) .72 GC-1 .28 (.03)

16λ : GC ( 5ξ ) on GC-3 .89 (.05) .79 GC-2 .21 (.03)

17λ : GC ( 5ξ ) on GC-4 .87 (.05) .76 GC-3 .24 (.03)

18λ : CR ( 6ξ ) on CR-1 .84 (.05) .71 CR-1 .29 (.04)

19λ : CR ( 6ξ ) on CR-2 .89 (.05) .79 CR-2 .21 (.03)

20λ : CR ( 6ξ ) on CR-3 .86 (.05) .73 CR-3 .27 (.03)


182

Table 5.11 Confirmatory Factor Analysis for Major Constructs: Measurement

Model Loading of Measurement Model Parameter Estimates*

(s.e.) SMC Measure item Error Variance

Estimate * (s.e.)

21λ : Trust ( 7ξ ) on Trust-1 .85 (.05) .72 Trust-1 .28 (.03)



24λ : Trust ( 7ξ )on Trust-4 .78 (.05) .61 Trust-4 .39 (.04)




28λ : Comm ( 8ξ ) on Comm-1 .78 (.05) .61 Comm-1 .39 (.04)





33λ : NPN ( 9ξ ) on NPN-3 .78 (.05) .61 NPN-3 .39 (.04)

34λ : NPN ( 9ξ ) on NPN-4 .87 (.05) .75 NPN-4 .25 (.03)

35λ : NPN ( 9ξ ) on NPN-5 .82 (.05) .67 NPN-5 .33 (.03)

36λ : NPN ( 9ξ ) on NPN-6 .89 (.05) .79 NPN-6 .21 (.03)

37λ : NPN ( 9ξ ) on NPN-7 .83 (.05) .69 NPN-7 .31 (.03)

38λ : NPM ( 10ξ ) on NPM-1 .88 (.05) .78 NPM-1 .22 (.03)

39λ : NPM ( 10ξ ) on NPM-2 .84 (.05) .70 NPM-2 .30 (.03)

40λ : NPM ( 10ξ ) on NPM-3 .68 (.06) .46 NPM-3 .54 (.05)

41λ : NPM ( 10ξ ) on NPM-4 .87 (.05) .75 NPM-4 .25 (.03)

42λ : NPM ( 10ξ ) on NPM-5 .86 (.05) .75 NPM-5 .25 (.03)


183

Table 5.11 Confirmatory Factor Analysis for Major Constructs: Measurement

Model Loading of Measurement

Model Parameter Estimates * (s.e.)

SMC Measure item Error Variance Estimate * (s.e.)

43λ : NPP ( 11ξ ) on NPP-1 .88 (.05) .77 NPP-1 .23 (.03)

44λ : NPP ( 11ξ ) on NPP-2 .83 (.05) .69 NPP-2 .31 (.03)

45λ : NPP ( 11ξ ) on NPP-3 .84 (.05) .71 NPP-3 .29 (.03)

46λ : NPP ( 11ξ ) on NPP- 4 .85 (.05) .72 NPP- 4 .28 (.03)

47λ : NPP ( 11ξ ) on NPP-5 .85 (.05) .73 NPP-5 .27 (.03)

48λ : NPP ( 11ξ ) on NPP-6 .88 (.05) .78 NPP-6 .22 (.02)

49λ : MD ( 12ξ ) on MD-2 .89 (.05) .80 MD-2 .20 (.03)

50λ : MD ( 12ξ ) on MD-3 .89 (.05) .79 MD-3 .21 (.03)

51λ : MD ( 12ξ ) on MD-4 .81 (.05) .66 MD-4 .34 (.04)

53λ : TD ( 13ξ ) on TD-1 .92 (.05) .85 TD-1 .15 (.02)

54λ : TD ( 13ξ ) on TD-2 .92 (.05) .85 TD-2 .15 (.02)

55λ : TD ( 13ξ ) on TD-3 .86 (.05) .74 TD-3 .26 (.03)

55λ: TD ( 13ξ ) on TD-4

.83 (.05) .70 TD-4 .30 (.03)

* All parameter estimates and error variances are significant at .05 level.


184

Table 5.12 Inter-Construct Correlations (N=253)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1. Alliance Competence

1

2. All Exp .71 1 3.All PP .77 .55 1

4. All MDC .52 .37 .40 1

5. Goal Congruence

.67 .48 .52 .35 1

6 Compl. Resources

.54 .39 .42 .28 .37 1

7. Commitment .25 .18 .19 .13* .17 .13* 1 8. AMO .88 .63 .68 .46 .72 .48 .42 1

9.Int Org Custor .72 .52 .56 .38 .59 .40 .34 .82 1

10.Int.Org. Compor

.51 .37 .40 .27 .42 .28 .24 .58 .48 1

11.Int.Org. Coor

.82 .59 .63 .43 .66 .45 .38 .92 .76 .54 1

12. NPN .63 .45 .49 .33 .50 .35 .29 .70 .58 .41 .65 1

13. NPM .65 .47 .50 .34 .52 .36 .30 .72 .59 .42 .67 .54 1

14. NPP .70 .50 .54 .37 .56 .45 .32 .78 .64 .45 .72 .64 .65 1

15.TMS .84 .60 .65 .44 .56 .45 .30 .78 .64 .45 .72 .58 .60 .64 1

16. Trust .47 .34 .36 .25 .32 .25 .53 .62 .51 .36 .57 .44 .45 .49 .56 1

17. Market Density

.38 .27 .29 .20 .26 .21 .12* .35 .29 .20 .32 .39 .44 .38 .45 .23 1

18. Technology Density

.34 .25 .27 .18 .23 .19 .14* .33 .27 .19 .31 .28 .24 .31 .41 .26 .40 1

* significant at p<.1 level , rest of the inter-construct correlations are significant at p<.05 level


185

CHAPTER 6: RESULTS OF HYPOTHESIS TESTING

The main objective of hypothesis testing is the selection of appropriate statistical

methods to test the main and mediating effect among the model constructs. The

appropriate methods provide statistical results that confirm the hypothesized causal

relationships among constructs in the model. This chapter begins with a discussion of

hypotheses. Next, the possible assumption violations for hypothesis testing are discussed.

This is followed by main and mediating effect tests. Finally, the chapter concludes with

an overview of the study findings.

6.1 Review and Revision of Hypothesized Relationships

The measure development procedure described in Chapter 5 suggests that study has

reliable and valid constructs. Figure 6.1 shows the operational model and hypothesized

relationships among the major constructs. In the model, alliance market orientation and

joint alliance competence are tested as second order reflective constructs. The reasons are

provided in Chapter 3 and Chapter 4. Based on these specifications, the hypotheses

suggested in Chapter 3 are summarized in Table 6.1 with their directions as well as the

results of testing the hypotheses.

Column 3 in Table 6.1 indicates the hypothesized directions for the causal

relationships between major models. As reviewed in Chapter 3, causal relation between

alliance market orientation and new product outcomes (e.g., new product novelty, new

product meaningfulness, and new product performance) are predicted to be positive

(H )(1 a - H )(1 c ). Likewise, it is postulated that the causal relationships between goal

congruence and alliance market orientation (H 2 ) and complementary resources and

alliance market orientation (H 3 ) are positive. Next, it is predicted that trust and


186

commitment influence alliance market orientation positively (see H 4 and H 5 ) and also

the causal relation between trust and commitment is postulated as positive (H 6 ). Then,

joint alliance competence is hypothesized to have a positive relation with goal

congruence (H 7 ) and complementary resources (H 8 ). Next, it is postulated that joint

alliance competence has a positive effect on alliance market orientation (H 9 ). It is also

hypothesized that joint top management support will have a positive causal relation with

joint alliance competence (H 10 ). New product novelty and new product meaningfulness

are predicted to have positive effects on new product performance (see H 11 and H 12 ).

Finally, it is postulated that market density has a positive effect on new product novelty

(H )(13 a ), new product meaningfulness (H )(13 b ), and new product performance (H )(13 c );

whereas, technology density has a negative effect on new product novelty (H )(14 a), new

product meaningfulness (H )(14 b ), and new product performance (H )(14 c) In the next

section, results of main effects testing between major constructs will be discussed.


187


188

Table 6.1 Summary of the Hypothesis Testing

From To Hypothesized

Direction

Final

Results

H1 (a): Alliance Market Orientation

New Product Novelty + + **

H1 (b): Alliance Market Orientation

New Product Meaningfulness + + **

H1 (c): Alliance Market Orientation

New Product Performance + + **

H2: Goal Congruence Alliance Market Orientation + + **

H3: Complementary Resources

Alliance Market Orientation + N.S. (+)

H4: Trust Alliance Market Orientation + + **

H5: Commitment Alliance Market Orientation + + **

H6: Trust Commitment + + **

H7: Joint Alliance Competence

Goal Congruence + + **


Complementary Resources + + **


Alliance Market Orientation + + **

H10: Top Management Support

Joint Alliance Competence + + **

H11: New Product Novelty New Product Performance + + **

H12: New Product Meaningfulness

New Product Performance + + *

* * significant at p<.05 level * significant at p< .1 level N.S. non significant (the sign of the resulted relationship is shown in the parenthesis)

6.2. Examination of Correlation Matrix

The structural equation modeling is chosen as an appropriate analysis method since the

model examines the causal ordering of antecedents and consequences of alliance market

orientation simultaneously. For the final analysis, the data collected from the final field

survey has a total of 253 surveys. Even though the appropriate sample size issue in

structural equation modeling is open for debate (see Bentler and Chou 1987; Boomsma

1982), it is generally agreed that sample size greater than 200 provides stable results

(Schumacker and Lomax 1996).


189

A correlation matrix for the composite scales of major constructs was examined to

provide insight into the data and relationships between major constructs. Table 5.12

shows the correlations between alliance market orientation, its antecedents, its

consequences, and control variables. The correlation table provides some insights into the

relationships between the constructs. First, from the results in Table 5.12 it appears that

all of the antecedents are significantly correlated which implies a potential problem for

multi-collinearity. Second, all three dimensions of alliance market orientation are

significantly correlated with each other showing that latent factor alliance market

orientation may be the common source for these inter-correlations. As we tested in

Chapter 5, each of these three dimensions is uni-dimensional and distinct from each

other. Similarly, alliance competence dimensions are significantly correlated with each

other indicating that alliance competence may be the common cause for these inter-

correlations. In order to understand whether alliance competence and alliance market

orientation behave as point variables2, I applied the formula provided by Burt (1976).

According to Burt, the covariances of indicators of a reflective latent construct with other

latents should be proportional to their epistemic correlations with that specific latent

construct. In our case, if alliance market orientation and alliance competence are

reflective constructs, their dimensions’ relationships with other latents should be

proportional to their epistemic correlations with their own construct ( )λ . According to

the inter-construct correlation matrix and structural model results, these proportions are

held. In sum, both alliance competence and alliance market orientation act as a unitary

entity (point variable) and it supports our argument that these two latents are reflective.

2 For detailed information about point variables, see Howell et al (2007a).


190

Finally, alliance market orientation and it consequences are significantly correlated with

each other. Overall, the signs of the correlation matrix appear to be consistent with the

hypothesized relationships in the main model.

6.3. Examination of Assumption Violations

Since this study is using maximum likelihood (ML) estimation technique in structural

equation modeling, there are assumptions that should be satisfied to have unbiased

results. In this section possible violation of assumptions was examined. First, as

previously reviewed in Chapter 5, a normality assumption should be met in order to have

an efficient and unbiased ML estimator. Descriptive statistics for the final sample in

Table 5.8 report that some scales for major constructs are non-normal (see skewness and

kurtosis values). For this study, non-normality is not a serious concern since major

constructs’ measures have already been tested for their measurement properties in the

pretest.

Second, since extreme observations may have a large impact on the research results,

outliers need to be identified. The eight possible extreme outliers that are most distant

from the sample mean were detected using several different methods – bivariate plots,

Cook’s distance and Student residual. I have contacted the eight executives for their

responses and they mentioned that they misunderstood the new product performance

question and willing to take the survey one more time. After they retook the survey, I

included their responses to the survey responses and check the influence of these eight

observations. Therefore, each of the dependent variable (new product novelty, new

product meaningfulness, and new product performance) was regressed on composite

scales of the thirteen antecedent variables. The regression without these eight


191

observations provided results consistent with the one including them. With this indication

that these eight observations did not influence the regression results significantly, all

observations were retained for the final. Table 6.2 shows that estimation results from

regressions with the three dependent variables. As it is known, regression method

excludes measurement errors; therefore, it provides less stable results than structural

equation model does. Details of the estimation results will be discussed in the next

section. Third, as mentioned before in correlation matrix section, since some antecedents

are highly correlated with each other, multicollinearity test should be conducted.

Multicollinearity is a major concern especially for the regression model, since it produces

inaccurate parameter estimates, standard error, and test statistics. The variance inflation

factor (VIF) ranging from 1.30 to 2.87 confirms that a multi-collinearity problem does

not exist with the regression model reported in Table 6.2.


192

Table 6.2 Multiple Regression Results and Collinearity Diagnostics (N=253)

Dependent Variable

New Product Novelty

New Product Meaningfulness


vif

Independent Variable

Coeff. (s.e.)

t-value Coefficient (s.e.)

t-value Coeff. (s.e.)

t-value

Constant 3.04 (1.94)

1.57 8.01 (1.83) 4.38 ** 4.57 (1.94)

2.36* 1.83

Alliance Experience

.07 (.09)

.72 .08 (.08) .94 .09 (.09)

.98 1.90

Alliance Partnership Propensity

.13 (.07)

.1.61 .02 (.07) .32 .11 (.07)

1.43 1.43

Alliance Manager Development Capability

.03 (.05)

.56 -.01 (.05) -.25 .02 (.05)

.46 2.19

Top Management Support

.05 (.11)

.42 .17(.11) 1.59 -.09 (.11)

-.79 2.17

Goal Congruence

.04 (.11)

.34 .08 (.10) .82 -.04 (.11)

-.38 2.19

Compl. Resources

.10 (.10)

.97 -.08 (.09) -.81 -.05 (.10)

-.50 1.62

Inter-Organizational Customer Orientation

.25 (.11)

2.22** .29 (.10) 2.69** .51 (.11)

4.46** 2.43

Inter -Organizational Competitor Orientation

-.01 (.05)

-.26 .04 (.05) .71 .05 (.05)

.97 1.52

Inter-Organizational Coordination

.34 (.10)

3.41** .20 (.09) 2.13** .45 (.10)

4.47** 2.87

Trust .00 (.04)

-.02 .07 (.04) 1.66* .05 (.04)

1.19 1.84

Commitment .06 (.07)

.81 .00 (.06) .04 .05 (.06)

.80 1.95

Market Density

.18 (.78)

2.23** .29 (.07) 3.93** .20 (.08)

2.55** 1.32

Technology Density

.03 (.05)

.63 -.04 (.05) -.84 .01 (.05)

.23 1.30

R 2 = .47 F=16.11**

R 2 = .45 F= 15.31**

R 2 =.57 F= 24.68**

* Significant at .10 level (two tailed) ** Significant at .05 level (two tailed)


193

6.4. Finding the Best Model

Prior to estimating the structural model, the measurement model was estimated in

Chapter 5. The estimations results from the measurement model confirm that all the

measure items are measuring their underlying latent constructs without any

interpretational confounding. Since there was no problem with interpretational

confounding, I performed simultaneous estimation of measurement and structural model

in Lisrel 8. The main effects model tests structural links among alliance market

orientation, its antecedents, its consequences, and control variables simultaneously with

indicators in the measurement model. Figure 6.1 shows that structural equation model

used to test the main effects. In the figure, I excluded the specification of indicators in the

measurement model in order to avoid complexity. Finding the best model involves a

comparison of three competing structural equation models. The first model is the original

model postulated (Figure 1.1). The second model treats all nine latent constructs as the

antecedent to new product outcomes (Figure 1.2). It assumes that there is no causal

relationships among these nine constructs, they rather have direct arrows toward new

product outcomes. The third model treats alliance market orientation’s and alliance

competence’s dimensions as separate constructs and tests their independent main effects

(Figure 1.3). Goodness of fit indices is reported in Table 6.3. As Model 1&2 compared,

chi-square difference test indicates that Model 1 is superior to Model 2. Based on the chi-

square difference test ( 22Modelχ - 2

1Modelχ = 3221.04- 2923.85= 297.19 (d.f.=24)), with

more degree of freedom Model 1’s chi square value is significantly reduced. In addition,

Model 2 does not provide a solid theoretical explanation to inter-organizational new

product performance like Model 1 does. In addition, when the significant paths are


194

examined in two rival models (Table 6.4 and 6.5), it is shown that Model 2 has only 10

significant paths (34 % of the hypotheses supported at p<.1 level); whereas, Model 1 has

15 significant paths (75 % of the hypotheses supported at p<.1 level). Moreover, no

additional explanatory power is gained from the additional 18 paths in Model 2. In fact,

Model 1 explains more variance in new product novelty and new product

meaningfulness. The rival’s SMCs are NPN=.49, NPM=43, and NPP=.64. Whereas with

less antecedents, Model 1’s SMCs are NPN=.52, NPM=56, and NPP =.64. Model 3 treats

alliance market orientation and alliance competence components as distinct and separate

constructs and examine their causal relationships with other latents. Based on the chi-

square difference test ( 23Modelχ - 2

1Modelχ = 2999.27- 2923.85= 75.42 (d.f.=22)), with

more degree of freedom, Model 1’s chi square value is significantly reduced. Compared

to Model 1, Model 3 has 30 out of 46 significant paths (65 % of the hypotheses are

supported at p<.1 level). In addition, by having more antecedents Model 3 does not have

more explanatory power than Model 1 does. In fact, alliance market orientation does

explain more variance in new product outcomes as a second order construct than its

components do alone. Model 3’s SMCs are NPN=.48, NPM=51, and NPP =.61. Whereas

Model 1’s SMCs are NPN =.52, NPM=.56, and NPP=.64. In sum, the Model 1 is chosen

as the basic model for testing main effects in this dissertation. In other words, Model 1

represents the real world in a better way than Model 2 and 3 do. Since, based on the

results, I did not respecify the model by including or excluding some paths, the modeling

approach stays confirmatory rather than exploratory (Anderson and Gerbing 1988).


195

Table 6.3 Fit indices of the Original and Rival Models (N=253)

Model 1

(proposed model)

Model 2 Model 3

Chi square 2923.85 3221.04 2999.27

d.f. (p value) 1983 (p<0.0) 1959 (p<0.0)

1961 (p<0.0)

NFI .96 .96 .96 RFI .96 .96 .96 IFI .99 .98 .99 RMSEA .037 .044 .041

Model 1&2 Model 1&3 Model 2&3

Chi square difference

(d.f. difference)

297.19 ** (d.f. 24)

75.42 ** (d.f. 22)

221.77 ** (d.f. 2)

** Significant at .05 level (a) Model 1: Original proposed theoretical model (alliance market orientation and alliance competence are second order reflective constructs) (b) Model 2: All nine antecedents lead to new product outcomes (see Figure 1.2) (c) Model 3: Alliance market orientation and alliance competence has three distinct and separate dimensions. (see Figure 1.3)


196


Orientation Testing Main Effects Model (Model 1—Proposed Model) (N=253)

Path Parameter

Estimate

(s.e.)

C.R. Path Parameter

Estimate

(s.e.)

C.R.

Coefficient

AMO -- NPN .64 (.09) 8.30 ** AC -- AMO .60 (.10) 6.12 **

AMO -- NPM .66 (.09) 9.17 ** TMS -- AC .84 (.07) 9.78 **

AMO -- NPP .55 (.12) 5.58 ** NPN -- NPP .14 (.08) 2.04 **

GC -- AMO .22 (.05) 3.56 ** NPM -- NPP .14 (.07) 1.86 *

CR -- AMO .01 (.04) .17 MD -- NPN .17 (.05) 2.78 **

Trust -- AMO .20 (.04) 3.76 ** MD -- NPM .24 (.06) 4.08 **

Comm -- AMO

.12 (.04) 2.69 ** MD -- NPP .05 (.05) .93

Trust -- Comm

.53 (.06) 7.93 ** TD -- NPN .00 (.05) .08

AC -- GC .67 (.11) 8.32 ** TD -- NPM -.07 (.05) -1.31

AC -- CR .54 (.10) 6.92 ** TD -- NPP .03 (.05) .62

SMC values

Alliance Competence

. 70 Goal Congruence

.45 New Product Meaningfulness

.56

Comp. resources

.29 Alliance Market Orientation

.87 New Product Novelty

.52

Commitment .28 New Product Performance

.64

Fit Indices

Chi-square 2923.85 d.f. (p value)

1983 (<.0.0) NFI .96

RFI .96 IFI .99 RMSEA .037

* Significant at p<.10 ** Significant at p<.05 (a) (CR) Critical Ratio (b) All path coefficients in measurement models and variances are significant at the .05 level.


197


Orientation Testing Main Effects Model (Model 2) (N=253)

Path Parameter

Estimate

(s.e.)

C.R. Path Parameter

Estimate (s.e.)

C.R.

Coefficient

AMO -- NPN .54 (.07) 7.45 ** AC -- NPN .21 (.06) 3.25 **

AMO -- NPM .65 (.08) 8.42 ** AC -- NPM .12 (.07) 1.81 *

AMO -- NPP .74 (.11) 6.48 ** AC -- NPP .20 (.07) 2.97 **

GC -- NPN -.01 (.07) -.19 TMS -- NPN .02 (.07) .25

GC -- NPM .01 (.07) .18 TMS -- NPM .13 (.08) 1.52

GC -- NPP -.03 (.07) -.46 TMS -- NPP -.09 (.07) -1.26

CR -- NPN .06 (.06) .89 MD -- NPN .14 (.05) 2.67 **

CR -- NPM -.10 (.07) -1.40 MD -- NPM .24 (.06) 4.12 **

CR -- NPP -.04 (.06) -.61 MD -- NPP .10 (.06) 1.74 *

TRUST -- NPN .00 (.06) -.02 TD -- NPN .00 (.05) -.02

TRUST -- NPM .12 (.07) 1.78 * TD -- NPM -.07 (.05) -1.41

TRUST -- NPP .06 (.06) 1.05 TD -- NPP .03 (.05) .59

COMM -- NPN .04 (.07) .58 NPN -- NPP .10 (.08) 1.18

COMM -- NPM .00 (.05) -.02 NPM -- NPP .08 (.08) .98

COMM -- NPP .05 (.07) .76

SMC values

New Product meaningfulness


.49 New Product Performance

.64

Fit Indices

Chi-square

3221.04 d.f. (p value) 1959 (<.0.0) NFI .96




198

Table 6.6 Estimation Results from the Structural Equation Model of Alliance

Market Orientation; Testing Main Effects Model (Model 3) (N=253)

Path Parameter

Estimate

(s.e.)

C.R. Path Parameter

Estimate

(s.e.)

C.R.

Coefficient

Int-Org Cust -- NPN

.20 (.07) 2.88 ** Comm – Int-Org Coor

.13 (.05) 2.57 **

Int-Org Cust -- NPM

.30 (.08) 3.90 ** Trust -- Comm

.49 (.06) 7.92 **

Int-Org Cust -- NPP

.23 (.07) 3.36** All Exp -- GC .34 (.06) 5.29 **

Int-Org Comp -- NPN

-.06 (.06)

-.98 All PP -- GC .12 (.06) 1.91 *

Int-Org Comp -- NPM

.01 (.06) .11 All MDC -- GC

.17 (.06) 3.08 **

Int-Org Comp -- NPP

.03 (.05) .58 All Exp -- CR .07 (.07) .99

Int-Org Coor -- NPN

.44 (.08) 5.67 ** All PP -- CR .34 (.07) 4.88 **

Int-Org Coor -- NPM

.41 (.08) 4.96 ** All MDC -- CR

.00 (.06) -.02

Int-Org Coor -- NPP

.33 (.09) 3.77 ** All Exp – Int-Org Cust

.10 (.05) 1.97 **

GC -- Int-Org Cust

.36 (.06) 5.95 ** All PP – Int-Org Cust

.26 (.06) 4.61 **

GC -- Int-Org Comp

.38 (.07) 5.36 ** All MDC – Int-Org Cust

.00 (.04) .03

GC -- Int-Org Coor

.30 (.05) 5.74 ** All Exp – Int-Org Comp

-.04 (.06) -.62

CR -- Int-Org Cust

.02 (.05) .37 All PP – Int-Org Comp

.11 (.06) 1.76 *

CR -- Int-Org Comp

.05 (.06) .84 All MDC – Int-OrgComp

.03 (.05) .59

CR -- Int-Org Coor

.15 (.05) 3.24 ** All Exp – Int-Org Coor

.10 (.05) 2.24 **

Trust -- Int-Org Cust

.12 (.06) 2.06** All PP – Int-Org Coor

.22 (.05) 4.48 **

Trust -- Int-Org Comp

.04 (.07) .54 All MDC – Int-Org Coor

.07 (.04) 1.78 *

Trust -- Int-Org Coor

.25 (.05) 4.78 ** MD -- NPN .16 (.05) 3.19 **

TMS -- All Exp .68 (.07) 9.53 ** MD -- NPM .25 (.05) 4.47 ** TMS -- All PP .79 (.07) 11.11 ** MD -- NPP .05 (.05) .92


199

Table 6.6 Estimation Results from the Structural Equation Model of Alliance

Market Orientation; Testing Main Effects Model (Model 3) (N=253)

Path Parameter

Estimate

(s.e.)

C.R. Path Parameter

Estimate

(s.e.)

C.R.

Coefficient

TMS -- All MDC .50 (.08) 6.66 ** TD -- NPN .01 (.05) .16 Comm -- Int-Org Cust

.15 (.06) 2.47 ** TD -- NPM -.07 (.05) -1.28

Comm -- Int-Org Comp

.10 (.07) 1.42 TD -- NPP .03 (.04) .59

SMC values

New Product meaningfulness


.48 New Product Performance

.61

Fit Indices

Chi-square

2999.27 d.f. (p value) 1961 (p<.00) NFI .96



6.5. Main Effects Estimation Results

The results of ML estimation of parameters and goodness of fit indices for the main

effects model are reported in Table 6.4. In the first stage, overall model fit is examined.

Although the chi-square statistics ( 21Modelχ = 2923.85, d.f. =1983) is significant, all base-

line fit indices (over .95) and RMSEA (value of .037) support the view that this model is

correctly specified. In the second stage, path coefficients are estimated using the ML

estimation method. The last column in Table 6.1 summarizes the estimation results.


It is predicted that new product outcomes are positively influenced by alliance market

orientation as a second order construct. First, H1a assume that alliance market orientation


200

is expected to have a positive impact on new product novelty. The results indicate that

( β =.64; s.e.= .09; C.R= 8.30) alliance market orientation significantly enhances new

product novelty.

Second, H1b predicts that alliance market orientation positively influences new product

meaningfulness. The results show that alliance market orientation has a strong positive

effect on new product meaningfulness ( β =.66; s.e.= .09; C.R= 9.17). Finally, in H1c,

positive causal relationship is hypothesized from alliance market orientation and alliance

new product performance. Consistent with the expectation, alliance market orientation

has a positive strong impact on alliance new product performance. In sum, H1a, H1b, and

H1c are confirmed.

6.5.2. Goal Congruence and Complementary Resources

In this stage, the causal relationships from goal congruence and complementary

resources to alliance market orientation are examined. Alliance market orientation is

expected to be positively influenced by goal congruence and complementary resources.

In H2, goal congruence is expected to influence alliance market orientation positively.

The estimation results reveal that goal congruence has a positive and strong impact on

alliance market orientation ( β =.22; s.e.=.05; C.R= 3.56). Thus, H2 is supported. Second,

H3 predicts that complementary resources will have positive causal relationship with

alliance market orientation. The estimation results reveal that complementary resources

do not have an impact on alliance market orientation ( β =.01; s.e.=.04; C.R= .17). Thus,

H3 is rejected.


201

6.5.3 Trust and Commitment

Alliance market orientation is assumed to be influenced by trust and commitment

positively. First, in H4, I examine the causal relation between trust and alliance market

orientation. It posits that trust has a positive effect on this construct. Consistent with

expectation, the hypothesized relationship is supported by the result (γ =.20; s.e.= .04;

C.R= 3.76) . Thus, finding confirms H4. Similarly, H5 postulates that commitment has a

positive impact on alliance market orientation. The estimation results show that

commitment positively influences alliance market orientation ( β =.12; s.e.=.04; C.R=

2.69). Thus, H5 is supported. Finally, H6 proposes that trust enhances commitment.

Consistent with the expectation, the causal relationship between trust and commitment is

significantly positive (γ = .53; s.e.= .06; C.R= 7.93), thus confirming H6.

6.5.4 Joint Alliance Competence

Alliance competence is assumed to influence alliance market orientation, goal

congruence, and complementary resources positively. First, H7 examines the influence of

alliance competence on goal congruence. The causal relationship posits from alliance

competence to goal congruence is found to be positive ( β =.67; s.e.=.11; C.R=8.32), thus

supporting H7. Second, I examine the causal relationship between alliance competence

and complementary resources. H8 postulates that alliance competence has a positive

impact on complementary resources. The estimation results show that alliance

competence positively and strongly influences complementary resources ( β =.54; s.e.

=.10; C.R= 6.92). Thus, H8 is supported. Third, in H9, positive relationship is expected

between alliance competence and alliance market orientation. Suggested path is


202

supported between alliance market orientation and alliance competence ( β =.60; s.e.

=.10; C.R= 6.12). Thus, H9 is supported.

6.5.5 Joint Top Management Support

Joint top management support refers to both alliance partners’ senior managements’

support in the alliances’ new product development activities and routines. It is

highlighted in academic and business journals that the strategic direction of organizations

is driven by senior management; competences are developed or maintained under the

urging of senior management. H10 proposes that joint top management support has a

positive impact on alliance competence. The estimation results indicate that joint top

management support has a significant and strong positive causal relationship with

alliance competence (γ = .84; s.e.=.07; C.R= 9.78), thus supporting H10.

6.5.6. New Product Novelty and New Product Meaningfulness

In the last stage, the causal relationships from new product novelty and new product

meaningfulness to new product performance are examined. New product performance is

expected to be positively influenced by the two dimensions of new product creativity.

In H11, new product novelty is expected to influence the new product performance

positively. The estimation results reveal that new product novelty has a positive impact

on new product performance ( β =.14; s.e.= .08; C.R= 2.04). Thus, H11 is confirmed.

H12 predicts that new product meaningfulness will have a positive causal relationship

with new product performance. Indeed, the estimation results confirm that new product

meaningfulness has a positive impact on new product performance ( β =.14; s.e.=.07;

C.R= 1.86), thus confirming H12 at p<.1 level.


203

6.5.7. Control Variables

Based on the field and pretest interviews and literature review, I included two control

variables that are believed to have an effect on the new product outcomes (e.g., new

product novelty, new product meaningfulness, and new product performance): market

density and technology density. The details about these constructs can be found in

measurement section, Chapter 5. Based on the measurement model, both constructs

exhibit good reliabilities and validities. I use market density and technology density to

control for the environmental impact on new product outcomes (Im and Workman 2004;

Jaworski and Kohli 1993; Narver and Slater 1990). Based on the empirical results, I find

that, in general, market density has a positive and strong relationship with the two

dimensions of new product creativity – new product novelty (γ =.17; s.e.= .05; C.R=

2.78) and new product meaningfulness (γ =.24; s.e.=.06; C.R= 4.08). However, the

causal relationship between market density and new product performance does not hold

(γ =.05; s.e.=.05; C.R= .93). In addition, the results show in general that technology

density does not influence any of the new product outcomes – new product novelty

(γ =.00; s.e.= .05; C.R= .08), new product meaningfulness (γ = -.07; s.e.= .05; C.R= -

1.31), and new product performance (γ =.03; s.e.= .05; C.R= .62). The above estimation

results for the main effects model summarized in Table 6.1, and significant paths from

the main effects model and their coefficients are illustrated in Figure 6.2.

6.5.8. Testing Alliance Market Orientation as a Key Mediator

The proposed model includes potential mediation effect of alliance market

orientation. Specifically, alliance market orientation may serve to mediate the impact of

alliance competence, goal congruence, complementary resources, trust, and commitment


204

on new product outcomes. I tested for mediation effects through two complementary

procedures (Klein et al. 2007; Subramani 2004). The first, which assesses the model fit

and chi square tests of competing models, compares the research model that proposes full

mediation against competing, partially mediated models and proposes both direct and

mediated effects. The second procedure employs mediation analysis technique, Sobel

test, and provides information on the significance of mediation effects.

In comparing the research model with full mediation to partially mediated models,

Lisrel 8 can be employed to statistically compare results. I compute three alternative,

partially mediated models by adding paths for each to the fully mediated model: Three

paths are added from alliance competence to new product outcomes (Model 1), three

paths for each are added from goal congruence and complementary resources to new

product outcomes (Model 2), and three paths for each are added from trust and

commitment to new product outcomes (Model 3). To understand the additional

contribution of paths, we examine the chi square differences. Since full mediation model

constrains the additional paths (set to zero), it is the nested model3 in model comparisons.

Model comparison results are reported in Table 6.7. Accordingly, the chi square statistics

do not get better by the inclusion of either of the direct paths. The results support the

mediation modeled in the causal relationships. In the second procedure, the path

coefficients and standard error of direct paths between (1) independent and mediating

variable and (2) mediating and dependent variables were used to test the mediation effect.

In order to test the effect of mediation on dependent variables (e.g, new product

3 Any model which requires that some function of its free parameters equals a constant, like zero, is nested in the identical model that has no such restriction (Bollen 1989). In the original model, the paths from three dimensions of alliance competence to new product outcomes are set to zero. In the rival model, these paths are set free to estimate. Therefore, the original model is the nested model and the other three models are the full models.


205

outcomes), I use Sobel test. Sobel test tests whether a mediator carries the influence of an

independent variable to a dependent variable. It basically measures whether the decrease

in independent variable’s effect on dependent variable is significant after the inclusion of

mediator. Table 6.8 summarizes the Sobel test results for each path. The resulting z

statistics indicate that alliance market orientation completely mediates the links between

alliance competence, goal congruence, trust, commitment and new product outcomes at

p< .05 level4. Column 3 in Table 6.8 reports the variance accounted for (vaf) values.

These values show the relative size of the mediating effects. As it is shown, mediating

effect sizes are more than .50 in all relationships. In other words, in all relationships,

mediating effect of alliance market orientation explains more than 50 percent of the

variance in the new product outcomes.

In general, the results support that alliance market orientation completely mediates the

effects of alliance competence, goal congruence, trust and commitment on new product

outcomes. In essence, alliance market orientation is, indeed, the key mediator in the

causal model. In conclusion, alliance competence, trust, commitment, and goal

congruence have significant positive impacts on new product outcomes only through

alliance market orientation.

4 In order to show that a latent (M) completely or partially mediates the relationship between A and B, the relationship between A and B should be significant to start with. Sobel test tests whether the strength of the direct relationship between A and B reduces significantly after the inclusion of the mediation variable. Since the direct relationship between complementary resources and new product outcomes are non-significant before the inclusion of alliance market orientation, it is not considered for the Sobel test.


206

Table 6.7 Test of Mediation, Nested Model Analysis (N=253)

Model 1

(nested

model)

Model 2

(full model)

Model 3

(full model)

Model 4

(full model)

Model 5

(full

model)

Chi square 2923.85 2922.64 2910.15 2920.20 2895.24

d.f. (p value)

1983 (<.001)

1980 (<.001)

1977 (<.001)

1977 (<.001)

1968 (<.001)

Model 1&2 Model 1&3 Model 1&4 Model 1&5

Chi square difference

(d.f. difference)

1.21 (3)

13.70 (6) 3.65 (6) 28.61 (15)

(a) Model 1: Original model with alliance market orientation is a key mediator (b) Model 2: Alliance competence has both direct and indirect paths to new product outcomes (c) Model 3: Goal congruence and complementary resources have both direct and indirect paths to new product outcomes. (d) Model 4: Trust and commitment have both direct and indirect paths to new product outcomes. (e) Model 5: All antecedents of AMO have direct and indirect effects on new product outcomes. *None of the chi square difference test is found to be significant at p<.01 level. Thus, adding additional paths to the original model does not improve the causal model.

Table 6.8.Test of Mediation, Sobel Test Results

Paths

z-

statistics* Vaf 1 Paths z- statistics* Vaf

AC – AMO-NPC 4.49 .81 Trust –AMO –NPC 3.71 .64 AC – AMO- NPM 4.63 .93 Trust – AMO- NPM 3.71 .95 AC – AMO- NPP 3.81 .81 Trust – AMO-NPP 3.27 .76 GC-AMO-NPC 3.78 .56 Comm –AMO- NPC 2.79 .79 GC-AMO-NPM 3.86 .61 Comm- AMO-NPM 2.79 .66 GC-AMO-NPP 3.34 .55 Comm-AMO-NPP 2.59 .73

* All z-statistics are significant at p< .05 level; 1 Vaf accounts for variance accounted for. It determines the relative size of the mediating effects. Vaf = (a *b )/ (a*b+c) {“a” is the path coefficient of the path from independent variable (Iv) to mediator and “b” is the path coefficient of the path from mediator (M) to dependent variable (Dv)


207


208

CHAPTER 7: CONCLUSION

This research is the first attempt to develop a construct called alliance market

orientation and find its antecedents and consequences in new product development

context. A fundemental premise of the model is that alliance market orientation is

influenced by relational factors, inter-organizational factors, and joint aliance

competence. It provides alliances a competive advantage by creating novel and

meaningful new products that meet their financial objectives and secure their market

success. The results of the present study suggest that joint alliance competence, inter-

organizational, and relational factors influence alliance market orientation. More

importantly, alliance market orientation strongly enhances the alliance’s new product

creativity and new product performance. Novelty and meaningfulness dimensions of new

product creativity are primary drivers of new product performance. Finally, the

environmental characteristics of market and technology density influence the new

product outcomes.

This research is the first to conceptualize market orientation as a relationship property

developed between the alliance partners. In addition, it is the first study to examine inter-

organizational market orientation as an idiosyncratic resource. The theoretical foundation of

the alliance market orientation concept draws on resource advantage theory (R-A Theory).

Therefore, the empirical findings of this research contribute to the development of R-A

theory where alliance market orientation functions as an idiosyncratic, renewable, and

nonfungible resource developed between alliance partners. This research helps advancing

the knowledge of the alliance market orientation in the alliances’ new product development

processes. Although the theoretical model can not be fully validated by the cross-sectional


209

study employed in this research, the significant relationships uncovered using the empirical

model expand the theory of market orientation in an inter-organizational context. This

research provides insightful suggestions and implications for managing inter-organizational

new product creativity. It demonstrates the importance of inter-organizational and relational

factors, and joint alliance competence in the inter-organizational new product development

process.

This chapter discusses the results from testing the empirical model of alliance market

orientation and alliance competence. I begin by discussing the results of the research in light

of the research objectives set up in the first chapter. The second section discusses the

contributions of this research to theory development. This is followed by a discussion of the

implications for managers. The chapter concludes with a discussion of the study limitations

and future research directions.

7.1 Evaluating Achievement of the Reseach Objectives

This section reviews how the research findings support the original research objectives

set up in chapter 1. The first objective of this study was to define and measure alliance

market orientation. The primary task was to develop a conceptual framework for alliance

market orientation. Based on extensive literature review and exploratory in-depth interviews,

alliance market orientation was defined as a capability that enables an alliance (1) to jointly

and systematically gather market intelligence (from competitor analyses, studies of customer

needs/preferences, and studies of the factors that influence competitors’ and customers’

behaviors), (2) to inter-organizationally coordinate and disseminate the knowledge gleaned

from the market intelligence gathered, and (3) to efficiently and effectively respond to the

knowledge that is coordinated and disseminated.


210

The definition of alliance market orientation was extensively tested by developing a valid

and reliable measure of alliance market orientation through a series of empirical tests. A

comparison of unidimensional and three dimensional measurement models in maximum

likelihood estimation support that three seperate dimensions of alliance market orientation

exist – inter-organizational customer orientation, inter-organization competitor orientation,

and inter-organizational coordination. Empirical results indicate that the three dimensions of

alliance market orientation are distinct. In addition, a comparison of the first-order and

second-order structural models5 demonstrates that the empirical results favor the second

order model over the first order model. The measure development process followed by the

measure validation process confirms that the empirical responses to measure items were

consistent with the hypothesized conceptual constructs. The empirical results support the

definition of alliance market orientation and verify the three distinct dimensions of alliance

market orientation.

The second objective was to develop and test an integrated model of alliance market

orientation that examine antecedents and consequences of the construct. The specified model

was tested using structural equation modeling methods. Alliance Market Orientation as a

higher order idiosyncratic resource has a positive effect on its consequences -- new product

novelty, new product meaningfulness, and new product performance. In addition, among its

antecedents, joint alliance competence, goal congruency, trust and commitment have positive

effects on alliance market orientation. Only non-significant effect is found in complementary

5 Since alliance market orientation has only three dimensions, it is not possible to compare the first and second order measurement models. This comparison needs at least four dimensions. A three-factor model with correlated factors will be statistically equivalent to a model with a second-order factor predicting the three first-order factors. This is because the degrees of freedom are not changing between the two models. Both models are saturated at the latent level. Therefore, in order to test whether alliance market orientation is a second order factor, it needs to be tested in the structural model and chi square difference test should be run to see which model (e.g. first order vs. second order) is better.


211

resources. It means that given the effects of joint alliance competence, goal congruency, trust

and commitment, complementary resources do not have significant additional effect on

alliance market orientation. The rationale behind these findings provided in the next section.

The third objective was to test the relationship between alliance competence and alliance

market orientation. Based on Lambe et al. (2002) study, this dissertation conceptualizes

alliance market orientation as an idiosyncratic resource and examines its relationship with

joint alliance competence. Consistent with the expectation, alliance competence was found to

be a strong antecedent of alliance market orientation.

Finally, the fourth objective was to investigate how alliance market orientation and

alliance competence help co-development alliances gain strategic advantages over

competition. Alliance market orientation was found to be a key mediator for alliance new

product performance. In order for joint alliance competence to enhance new product

creativity and new product performance, it first should be converted to alliances’ market-

oriented behaviors. Both joint alliance competence and alliance market orientation are

needed to enhance alliance new product creativity and performance, which will help new

product alliances gain competitive advantages over competitors.

Overall, the main research goal has been to provide theoretical and practical insights into

effective and efficient new product development alliance management. The empirical results

support that this goal was achieved. The next section discusses these insights and

implications drawn from an analysis of the results.

7.2 Discussion and Theoretical Contribution

The explicit goal of this research has been to provide a foundation for building a theory

of alliance market orientation in the new product development context. I achieved this goal


212

by providing empirical evidence for the structure of alliance market orientation as well as

evidence for its relationship with the antecedents and consequences represented in the causal

model. The results provide considerable support for the model of alliance market orientation,

thus providing insights into the theory of alliance market orientation. This following section

is based on the discussion of the major findings from the tests of the causal relationships in

the empirical model. Table 6.1 summarizes the results and Figure 6.2 gives the significant

paths in the model.

7.2.1 Alliance Market Orientation

The objective of this study is to take the first step toward conceptualizing and developing

a scale for alliance market orientation and use the scale to understand alliance market

orientation’s role in the alliance’s inter-organizational new product development projects.

While the marketing literature has extensively investigated the role of firms’ market

orientation in their new product development activities, there have been no empirical studies

of inter-organizational market orientation and its role in co-development projects. A number

of researchers such as Elg (2000), Perk (2000), Rindfleisch and Moorman (2001), Hunt and

Lambe (2002), Sivadas and Dwyer (2000), and Spekman et al. (1999) have recognized the

need for future research on alliance market orientation. Although the notion of alliance

market orientation was suggested by these researchers and many others, the concept

remained undeveloped. This dissertation addresses this need by defining alliance market

orientation as a capability that enables an alliance (1) to jointly and systematically gather

market intelligence (from competitor analyses, studies of customer needs/preferences, and

studies of the factors that influence competitors’ and customers’ behaviors) , (2) to inter-

organnizationally coordinate and disseminate the knowledge gleaned from the market


213

intelligence gathered, and (3) to efficiently and effectively respond to the knowledge that is

coordinated and disseminated.

Consequences of Alliance Market Orientation

This dissertation demonstrates that an increase in alliance market orientation results in

enhancement of novel and meaningful aspects of a new product developed by the alliance.

This is accomplished through closely monitoring what customers want and need from new

products, being keenly aware of competitors’ products and market trends, and increasing

inter-organizational coordination, integration, and synchronization of the alliance’s daily new

product activities. The result is the production of novel and meaningful products that are

close to the customers’ constellation of attributes. Post study interviews with managers

support this conclusion. They indicated that it is becoming harder to differentiate their

products in the highly competitive business environment. Although it is challenging to gather

market information in a joint and systematic matter, some alliances see the value of having

common understanding about the market, what customers want, how competitors act, and

what regulations affect them. These alliances reported that having common understanding

about their market helps them develop bifocal vision and aids them in creating novel and

meaningful products. This process assists them to understand the needs in the market and

satisfy those needs with radically innovative and relevant products.

In one example, a biotech and pharma company ally to develop a drug for cancer

market. Both pharma company and biotech company have their own departments to track

the oncology market trends, watch competitive cancer drugs, approved therapies, and

drugs in development. The pharma company’s market intelligence department is

generally larger than the biotech company’s. The crucial point is that although they have


214

their own departments for different purposes, they gather and disseminate this

intelligence across both organizations to develop a common understanding about the

oncology market. Based on common understanding, they agree on the potential

therapeutic applications for particular drug candidates and enabling technologies. These

drug candidates are generally the ones which are very novel and relevant to current

market’s needs. Since it takes 10-15 years to develop a drug, pass clinical stages, and get

approval from FDA, it is really important to have an agreement about the market and its

relevant size before hand.

This study also tested the causal relationship between alliances’ market-oriented

behaviors and their new product peformance. The results suggest that alliance market

orientation increases alliances’ new product peformance. This result has come with little

surprise because alliances are formed to succeed. There is an inherent risk in allying with

other firms to develop radically innovative products. The risk is not limited to financial

terms but also partnering firms’ know-how. Therefore, before firms enter into alliance

agreements there is a long due diligence process. The purpose of the due diligence is to

find a partner who shares the same goal and has a strong desire to succeed. Therefore,

market-oriented alliances invest resources and time to understand the consumer

constellation of attributes, know the competitors’ projects, and have the common

understanding on the cross-organizational new product development activities. SAP’s

succesful market-oriented alliance with Microsoft provides an evidence that alliances

focusing on what they can do to meet the market needs are more likely to come up with

novel and meaningful products that are more succcessful than their competitors’

(BusinessWeek 2006a).


215

Antecedents of Alliance Market Orientation

Alliance market orientation was expected to be enhanced by joint alliance

competence, goal congruence, complementary resources, trust, and commitment. Perhaps

one of the interesting findings of this study is that an increase in complementary

resources did not have an effect on alliance market orientation. This result may reflect the

necessity of complementary resources for all types of alliances. Post study interviews

with managers supported this finding. According to managers, the reason that they enter

into alliances is because they lack certain resources needed to be succesful in the

specified markets. According to one executive, searching for a relevant partner for the

long-term innovation alliance is a very intense experience. An extensive due diligence

process assists them to understand the level of complementarity between partners. Most

firms would prefer doing in-house R&D if they had all the necessary resources available.

Having complementary resources in the alliance is not only a pre-condition to develop

alliance market orientation, it is a precondition for all types of alliances. No matter what

the alliance wants to achieve, the participants should have complementary resources to

succeed.

Based on empirical findings, joint alliance competence is the most influential

antecedent of alliance market orientation. Partnering firms with alliance competence are

experienced, invest time and resources to develop competent alliance managers, and have

high partner identification propensity. Firms with alliance competence understand that

they can not develop unique and relevant products without understanding the market and

respond to it in a timely manner. Therefore, they choose a partner with whom they can

have clear communication in terms of developing market-oriented behaviors, which will


216

eventually lead to success. As Spekman et al. (1999) indicate, developing a market

focused view in an alliance needs experience, vast investment of resources, and trained

alliance managers. Alliance market orientation is initiated by the competent alliance

partners. Based on R-A theory, joint alliance competence is conceptualized as a renewal

competence in this study. Alliance competence as a renewal competence enables firms to

improve their industry foresight and anticipate market requirements ahead of competitors.

An alliance competence as a renewal competence enables partner firms in an alliance to

understand the evolving needs and preferences of customers before competitors and

energize their alliance to respond to them. Furthermore, an alliance competence enables

partner firms to connect their inter-organizational processes to the external environment.

Goal congruence is a strong predictor of alliance market orientation. Post study

interviews with alliance managers supported this conclusion. It seems that similar goals

assure the partners that the other will walk the same path. As one manager stated,

assuring market-oriented behaviors in an alliance environment is far more challenging

than developing market-oriented behaviors in a firm. It needs both sides to focus on one

objective. Goal congruence is important in determining the likelihood that partners will

develop alliance market-oriented behaviors necessary for long-term success.

This study also postulated that alliance market orientation is enhanced by relational

factors such as trust and commitment. In their seminal article, Morgan and Hunt (1994)

concluded that cooperation is the only common outcome of trust and commitment.

Alliance market orientation is an intensive cooperative behavior; and therefore, it needs

partners dedicated to working together to achieve mutual goals. Dedication comes from

trustworthy partners and their commitment to the relationship. This study indicates that


217

alliance partners who have previously demonstrated reliability and integrity, and thereby

commitment to the relationship, will be less hesitant to share market-related information.

With trust and commitment, alliance partners are more willing to generate market

intelligence together and act on the gathered and shared intelligence in a concerted

manner. A high degree of trust and commitment will make the alliance partners more

willing to engage in joint activities that will eventually increase the level of the alliance’s

market orientation.

7.2.2 Joint Alliance Competence

This study replicates and extends Lambe et al.’s (2002) alliance competence

model to new product development alliances. According to their findings, alliance

competence is a key antecedent to complementary resources, idiosyncratic resources, and

alliance success. They also found that top management support is critical in developing

alliance competence. This dissertation supports and extends their findings in a way that

alliance competence is critical to goal congruency and complementary resources. Firms

with alliance competence have an ability to find partners who have the same goal and the

resources that complement partnering firm’s resources. Alliance market orientation is

conceptualized as an idiosyncratic resource in this study. Research findings reveal that

joint alliance competence contributes to alliance market orientation and strongly

enhances it. Therefore, it also supports Lambe et al.’s (2002) result that alliance

competence is shown to have a significant effect on alliances’ idiosyncratic resource.

Competence-based theory literature cites that competencies and core capabilities are

developed and maintained only with the urging of top management. Since competencies

take a long time to develop and have an expensive and intensive development process,


218

they require the attention of top management. Innovation alliances are risky

collaborations and in the face of the inevitable ups and downs of an innovation alliance,

long term co-development alliance commitment is difficult without top management

support. As one of the executives said, it may be common sense to think that top

management supports the alliance formation and development. In fact, very few firms

have visionary top management executives who provide the financial and managerial

support to improve their innovation alliances and energize their executives. The same

respondent concluded, they always say it but very few of them actually execute it. This

study confirms that top management support strongly enhances the firms’ alliance

competence.

Contrary to Lambe et al.’s (2002) findings, this study’s results show that alliance

competence leads to new product alliance performance only through alliance market

orientation. It seems that joint alliance competence is the initiator and motivator of

market-oriented behaviors of an alliance that eventually lead to alliance’s high new

product performance. Using Day and Wensley’s (1988) source-position-performance

(SPS) framework, results confirm that joint alliance competence enables an alliance to

enhance their positional advantage through the development of alliance market-oriented

behaviors, which in turn result in novel and meaningful products that improve the co-

development performance.

7.2.3 New Product Creativity

This dissertation postulated that new product novelty and meaningfulness as the

components of new product creativity positively influence the alliance’s new product

performance. This research confirms the contention that multiple measure of performance


219

should be used for B-B research (Im 1999). Top-line market performance of the new

product can be represented by relative market share and sales; bottom-line financial

performance can be measured by relative ROI and profitability. Overall satisfaction and

customer satisfaction capture the qualitative dimension of new product performance in

terms of the new product’s ability to meet original objectives. Consistent with the

expectation, new product novelty and meaningfulness positively influence the new

product performance. This finding shows that new product performance in terms of top-

line, bottom-line, and qualitative objective outcomes is driven by the increases in novelty

and relevant attributes of new products. Interestingly, both novelty and meaningfulness of

new products have equal contributions to new product performance. It means that in high

technology markets customers not only expect novel and unique products, but also they

expect this uniqueness to make sense for them. As the head of GlaxoSmithKline’s

regional operations, Andrew Witty, discussed their recent NPD alliance with Academia

Sinica. He stated that “we are looking for an innovation which is relevant, so innovation

which can actually ultimately, in our view, help move forward meaningful drugs.” As

another example, new diet drugs use novel approaches to weight loss which customers

like to try; however, if these novel approaches do not make sense for customers, these

drugs will not be able to keep their performances high in the long term. As an example,

both Xenical and Meridia use novel approaches to reduce fat in patients with obesity

problem. Most patients find the idea behind these medicines as novel. However, since

these medicines make patients to lose muscle instead of fat, they are found to be

meaningless by patients. Although they have novel approaches for weight loss, Xenical

and Meridia’s performances suffer due to the meaningless of the their products.


220

7.2.4 Control Variables

This dissertation includes two control variables that are commonly believed to

influence the new product development activities of alliances in the high technology

industry. These are market density and technology density. Market density refers to the

potential demand for the new product developed by the alliance in the target market.

Technology density refers to the rapid rate of technological change in the market.

Contrary to expectation, technology density does not have any significant effect on new

product outcomes. It means that rapid technological change influences neither the novelty

and meaningfulness of the new products nor their performance in the market. It seems

that alliances are not especially motivated to develop unique and relevant products just

because the technology in the market changes fast. Irrespective of the density of

technology change, firms are motivated to innovate. Following research finding

completes this view. Market density is found to have a positive effect on both new

product novelty and meaningfulness. It reveals that it is the market and its needs that

determine the creativity of new products and not the technology density. If there is a

certain need in the market, alliances are motivated to provide the unique and relevant

products to the customers. With Astra-Zeneca and MAP Pharmaceuticals Alliance’s

innovative product UDB, the uniqueness and relevance of the product come from the

market density in the pediatric asthma market. Contrary to expectation, market density

does not have a direct effect on new product performance. It influences alliances’ new

product performance only through novel and meaningful products.


221

7.2.5 Theoretical Contribution

This dissertation advances our understanding of successful inter-organizational new

product development processes. To begin with, it points alliance market orientation as a

key mediating factor in alliances’ new product development efforts. New product

development has been considered as the efficient and effective management of

knowledge assests. As a knowledge-based strategy, market orientation has been the focal

point in intra-organizational new product development studies. Recently, B-B

researchers have started questioning the role of market orientation in relationship context,

but there was no systematic work to date on inter-organizational market orientation and

its role in expanding the size of the pie between partners. This study extends our

understanding of inter-organizational market orientation and provides powerful assurance

to the partners of successful pie expansion.

This dissertation contributues to the marketing and management literatures by

conceptualizing alliance market orientation, developing its measure, and testing its role in

inter-organizational new product performance. According to the findings, alliance market

orientation helps alliance partners to jointly and systematically understand market and

respond to its needs better than the competing alliances. As an idiosyncratic relationship

property, alliance market orientation is positively influenced by goal congruence of

partners, their joint alliance competence, trust, and commitment. Alliance market

orientation motivates partners to have a strong market focus and bifocal vision; therefore,

it is expected that these alliance partners already know how to develop and manage an

alliance and solve the inter-organizational coordination problems. Firms with alliance

competence are competent and confident about solving inter-organizational issues. They


222

are aware that it is hard to meet market needs without having market focus. This study

shows that in order for NPD alliances to succeed in the market place, they have to inform

NPD processes through both external and internal activities. NPD alliances should not

only incorporate inter-organizational factors to their new product development processes,

but more importantly, the realities of their market.

This dissertation also contributes to our knowledge of idiosyncratic resources and

their role in making inter-organizational relationships effective and efficient. This study

empirically shows that alliance market orientation is an idiosyncratic resource which

eventually results in differential new product performance of the partner firms. Although

partner firms can still form similar arrangements with alternative firms, the specifics of

alliance competence, relational factors, partners’ goals, their complementary capabilities,

and top management supportive mechanisms will vary; and that makes the alliance

market orientation rare, valuable, non-substitutable, and most importantly, difficult to

imitate precisely.

Last, but not the least, the findings of this study provides some empirical evidence for

R-A theory. R-A theory agrees that competition is a dynamic and ongoing process.

Therefore, it suggests that the marketplace position of competitive advantage results from

idiosyncratic resources of an alliance. This resource assortment enables alliances to

produce market offerings for some market segment(s) that is perceived to have superior

value (Hunt 2000). Alliance market orientation is such a valuable idiosyncratic resource

assortment available to the alliances.


223

7.3 Managerial Contribution

The National Cooperative Research and Production Act (NCRPA) of 1993 allowed

research, development, and production activities to be performed in collaboration with

intra- and inter-industry partners. Since then, the number of new product alliances has

grown. Many firms have been willing to collaborate with other firms to share R&D costs,

pool risks, and access to the complemetary resources. Although NPD partnerships have

been seen as a panacea for many firms’ NPD problems, according to the recent statistics

failure rate among NPD alliances is reported as high as 70 percent. Given the growing

popularity of NPD alliances and the importance of managing the innovation process in

inter-organizational context, this dissertation suggests a number of implications for

practitioners.

First, collaboration with other firms is not without risks. As one of the managers put

in the post study interviews, it may be risky to share sensitive information with a partner

in the long term. The current partner may collaborate with another firm who is a

competitor of the respective firm. Therefore, it is very important to ensure the trust

between partners and their commitment to the ongoing relationship. This dissertation

shows that although relational factors are the sine qua non of the alliances, focusing too

much on relational factors may be detrimental in the long term. NPD alliances are

involved in knowledge use and the management process. Therefore, it is crucial for NPD

alliances to understand the market needs and meet them faster and better than their

competing alliances. This study shows that relational factors are the prerequisite for

developing alliance market orientation. Since NPD activities are considered as a spanning

process informed by both external and internal activities, relational factors should be used


224

to form and manage alliances’ market-oriented behaviors. Focusing too much on the

ongoing relationship may mislead alliance partners and lead them to miss the market

opportunity.

Second, this study also recommends that, since alliance market orientation is an

intensive set of collaborative behaviors, firms with a lack of alliance competence should

consider building their alliance competence first. In addition, they should collaborate

with firms who also have an alliance competence. Competent alliance partners know how

to form, develop, and manage alliances. They have alliance-related experiences, alliance

manager training programs, skills and capabilities to identify the best possible partner.

Therefore, firms with less alliance competence should collaborate with firms with

alliance competence to learn the NPD alliance management from them and eventually get

enough competence to show market-oriented behaviors in their current and future

alliances.

Third, this dissertation shows that joint alliance competence, relational factors, and

goal congruence are the prerequisite factors for alliance market orientation. These factors

affect alliances’ new product outcomes only through alliance market orientation.

Collaborating with competent firms who have the same objective and have a trustworthy

relationship will lead to competitive advantage only if these alliances are able to combine

these factors in a unique and idiosyncratic fashion to develop alliance market orientation.

Fourth, this dissertation suggests that both new product novelty and meaningfulness

are important factors in new product performance. Indeed, both of them are required for

new products to succeed in the market place. Especially in the biotech and pharma


225

industries, customers are looking for new treatments; but at the same time, they want

these treatments to be meaningful and relevant to their problems.

Lastly, this dissertation shows that top management support is crucial in developing

and managing firms’ alliance competences. In this study, big pharma and semiconductor

companies indicated that for every alliance they are working with, senior managers

assign an alliance manager or business development executive. Eli Lilly in 1999

established the pharma industry’s first Office of Alliance Management. The head of

alliance managers reports to Lilly’s senior vice president of corporate strategy who

reports to the chairman and CEO. This means that alliance directors are assigned to high

responsibilities, and the top management closely monitors their activities and

performance. In firms like Eli Lilly, commitment to collaboration and building alliance

competence is a fundamental part of the overall strategy and it originates from the top

management level.

7.4 Limitations

As with any study, findings in this study must be evaluated in light of certain key

limitations. The first limitation in this study is related to the sample frame. This study

selected alliances from various high-tech industries. It excluded other industries that may

provide creative products such as chemicals, automative, and petroleum. In addition, the

sample comprised small to large companies. Since the unit of analysis is dyad in this

study and I could not gather the partner information from most of the alliances, I do not

have knowledge about the size of the alliance; and therefore, I could not incorporate

alliance size as a control variable in the structural model.


226

The second limitation concerns the type of alliances. This study collected data from

customer-supplier, competitor, and collaborator type of alliances. Since Lisrel does not

allow the test of ordinal control variables, the type of alliance could not be controlled for

when testing the model. Although dividing the data into alliance types and running the

same model for these types could produce interesting results, it decreased the sample

size of each group dramatically. Because Lisrel is sensitive to sample sizes, I could not

investigate the results based on different groups.

The third limitation is that findings may be limited due to the selection criteria of the

NPD alliances in the study. Even though the respondents were asked to select an NPD

partner that they have commercialized a product with regardless of their performance,

they would still select a succesful one. Since the measures of performance provided

enough variances to evaluate new product performance, I believe that results are

acceptable.

The fourth limitation is from incorporating only one partner’s responses in the

analysis process. This study attempted to collect data from both sides of the alliance. For

several reasons, firms were not willing to provide their partners’ name. Among 253 firms,

only 30 dyadic matched data was available, which was not a sufficent sample size to

implement comprehensive theory testing. The firms’ reasons of not providing partner

names are reported at Table 4.3. Although the unit of analysis is dyad, this study uses a

single informant approach. The common method bias test statistics showed that this bias

is not an issue. However, getting responses from both sides of the same relationship

could have provided more insights regarding their relationship.


227

The fifth limitation concerns the new products. Although the survey indicated that

selection of new product alliance was limited to one that has a new product, some

executives indicated that they have a product in the market; but they do not have concrete

new product performance results, which might force them to makes guesses about

performance of their new product. Although it is sometimes hard to get financial and

market performance results in the early stage of new product launch, this dissertation

attempted to solve this problem by using self-reported multiple measures of new product

performance (Im 1998). In addition, this study could not use objective performance

results. Although it is desirable to use objective measures, they are difficult to obtain and

even more difficult to interpret (Rindfleisch 1998). As shown in the literature (Song and

Parry 1997), managerial perceptions are generally consistent with objective measures.

Finally, the cross-sectional approach may not lead to effective conclusions regarding

causality. Lack of sufficient causality may make it difficult to generalize the results to all

industries.

7.5 Future Research Issues

This dissertation represents the first attempt to understand the role of alliance market

orientation in inter-organizational new product development activities; and hopefully, it

will stimulate future research in this domain. In this section, I briefly discuss promising

venues for future reserach, many that I plan to pursue in developing a programmatic

stream of research emanating from this dissertation.

As an immediate extension of my dissertation research, I will keep gathering dyadic

alliance information. I will investigate the alliance partner’s perceptional differences

about the same inter-organizational factors, and how these differences impact the new


228

product outcomes. Based on the preliminary results of 30 dyadic alliances, I expect to see

perceptional differences in certain constructs (e.g., competence, relational factor). As a

conjecture, I expect that as the perceptions diverge between partners, they will have hard

time showing alliance market-oriented behaviors, which will eventually harm new

product outcomes.

Another promising research direction may be understanding how different alliance

types develop their market-oriented behaviors, and how new product related outcomes

are influenced by them. I collected data from different types of alliances. In my

preliminary analysis, I realized that different alliance types have different attitudes

toward developing alliance market-oriented behaviors. For example, antecedents of

alliance market orientation have stronger impacts on alliance market orientation in

customer-supplier type of alliances than they have in competitor-based alliances. Since

vertical alliances serve the same goal and do not compete for the same markets, it may be

easier for them to share market intellingence. However, competitor-based alliances, such

as Microsoft-SAP, may show resistance to share sensitive market intelligence even

though that intelligence may serve to an immediate need.

The third research area can be a longitudinal study of alliance market orientation. In

the pretest and post study interviews, managers indicated that alliance market orientation

may take long time to develop, and there may be other variables contributing the

formation of alliance market orientation. An extension of this study, which conducts in-

depth interviews and focus groups with executives as a part of longitudinal study, may

provide deeper analysis and insights (Gebhart et al. 2006). It may be interesting to

understand how and under what circumstances alliances become market-oriented. What


229

are the main factors that contribute to the formation of alliance market orientation? How

do these factors and their impact evolve or change?

The fourth research direction may be the investigation of how a single firms’ market

orientation contributes to their inter-organizational market orientation. Is it the case that

market-oriented firms have more tendency to build market-oriented alliances? Can we

expect that market-oriented firms engage in a high degree of systematic and joint market

intelligence gathering, interorganizational information exchange, and joint

responsiveness? I plan to fully explore the relationship between firm level market

orientation and inter-organizational market orientation through a longitudinal study in

which I track NPD alliance participants from initial alliance formation to dissolution of

the alliance. As part of this longitudinal study, I also plan to analyze how this relationship

differs among different types of alliances.

Finally, as part of my future research portfolio, I believe that it would be interesting

to examine the market-oriented alliances’ evaluation by stockholders. Using a

combination of survey methodology and event study, stockholders’ value appreciation of

market-oriented alliances can be investigated.


230

REFERENCES

Achrol, Ravi S.and Philip Kotler (1999), “Marketing in the Network Economy,” Journal

of Marketing, Vol. 63, Fundamental Issues and Directions for Marketing (1999), pp.

146-163.

Anand, B. and T. Khanna (2000), “Do Firms Learn to Create Value? The Case of

Alliances,” Strategic Management Journal, 21 (Special Issue): 295-315.

Anderson, James C. and David W. Gerbing (1988), “Structural Equation Modeling in

Practice: A Review and Recommended Two-Step Approach,” Psychological Bulletin,

103 (3), 411-423.

. and Narus, James A. (1990), “A Model of Distributor Firm And Manufacturer

Firm Working Partnerships,” Journal of Marketing, 54 (January), 42-58.

of Business-to-Business Marketing, 13 (4), 29-67.

, Philip C. Zerrillo Sr. and L. Olivia Wang (2006), “Estimating Firm-Specific

and Relational Properties in Interoganizational Relationships in Marketing,” Journal

of Business to Business Marketing, 13 (4), 29-67. Anderson, Erin and Barton Weitz (1992), “The Use of Pledges to Build and Sustain

Commitment in Distribution Channels,” Journal of Marketing Research, 29

(February), 18-34.

Andrews, Jonlee ad Daniel C. Smith (1996), “In Search of the Marketing Imagination:

Factors Affecting the Creativity of Marketing Programs for Mature Products,”

Journal of Marketing Research, 33 (May), 174-187.

Andrews, Kenneth R. (1971). The Concept of Corporate Strategy, Homewood, IL:

Richard D. Irwin.

Armstrong, J. Scott and Terry S. Overton (1977), “Estimating Non Response Bias in Mail

Surveys,” Journal of Marketing Research, 14 (August), 396-402.

Asanuma, B. (1989), “Manufacturer- Supplier Relationships in Japan and the Concept of

Relation-Specific Skill,” Journal of the Japanese and International Economies, 3(1), 1-30.

Atuahene-Gima K. (1995), “An Exploratory Analysis of the Impact of Market

Orientation on New Product Performance: A Contingency Approach,” Journal of

Product Innovation Management, 12 (4), 275-293.


231

and Anthony Ko (2001), “Effects of Market and Entrepreneurship Orientation

Alignment on Product Innovation,” Organization Science, 12 (1), 54-74.

Avlonitis, G.J. and S.P. Gounaris (1997), “Market Orientation and Company

Performance: Industrial vs. Consumer Goods Companies,” Industrial Marketing

Management, Vol.26, 385-402.

Ayers Doug, Robert Dahlstrom, Steven J. Skinner (1997), “An Exploratory Investigation

of Organizational Antecedents to New Product Success,” Journal of Marketing

Research, 34 (February),107-116.

Bagozzi, Richard P. (1975), "Marketing as Exchange," Journal of Marketing, 39

(October), 32-39.

and Youjae Yi (1988), “On the Evaluation of Structural Equation Models,”

Journal of Academy of Marketing Science, 16, 74-94.

, , and Lynn W. Philips (1991), “Assessing Construct Validity in

Organizational Research.” Administrative Science Quarterly, 36, 421-458.

Baker, Thomas L., Penny M. Simpson, and Siguaw, Judy A (1999), “The Impact of

Suppliers’ Perceptions of Reseller Market Orientation on Key Relationship

Constructs,” Academy of Marketing Science Journal, 27 (1), 50-57.

Barney, J.B (1991), “Firm Resources and Sustained Competitive Advantage,” Journal of

Management, 17: 99-120.

Becker, J. and C. Homburg (1999), “Market-Oriented Management: A System- Based

Perspective,” Journal of Market Focused Management, 4, 17-41.

Bensaou M. and Erin Anderson (1999), “Buyer-Supplier Relations in Industrial Markets:

When Do Buyers Risk Making Idiosyncratic Investments?” Organization Science, 10

(4), 460-481.

Bentler, Peter M. and Chih-Ping Chou (1987), “Practical Issues in Structural Modeling,”

Sociological Methods and Research, 16, 78-117.

Bidault, Francis and Thomas Cummings (1994), “Innovating Through Alliances:

Expectations and Limitations,” R&D Management, 24 (1), 33-45.

Blesa, Andreu and Enrique Bigné (2005), “The Effect of Market Orientation on

Dependence and Satisfaction in Dyadic Relationships,” Market Intelligence and

Planning, 23 (3), 249-265.


232

Bollen, Kenneth (1989), Structural Equations with Latent Models. John Wiley & Sons.

and B. D. Grandjean (1981), “The Dimension(s) of Democracy: Further Issues in

the Measurement and Effects of Political Democracy,” American Sociological

Review, 46 (5), 651-659.

and J. Scott Long (1992), “Tests for Structural Equation Models: Introduction,”

Sociological Methods and Research, 21 (November), 123-131.

Boomsma, Anne (1982), “The Robustness of LISREL against Small Sample Sizes in

Factor Analysis Models,” in Systems under Indirect Observation: Causality,

Structure,Prediction (Part I), K.G. Joreskog & H. Wold, eds. Amsterdam: North-

Holland.

Booz, Allen, Hamilton (1982), New Products Management for the 1980’s. New York:

Booz Allen Hamilton.

Brown, Shona L. and Kathleen M. Eisenhardt (1995), “Product Development: Past

Research, Present Findings, and Future Directions,” Academy of Management

Review, 20 (2), 343-378.

Burt, R. S. (1976), “Interpretational Confounding of Unobserved Variables in Structural

Equation Models,” Sociological Methods and Research, 5, 3-52.

Business Week (2006a), “The World’s Most Innovative Companies,” (April 24), 63-74.

Business Week (2006b), “From Handshake to Stores in Just Nine Months,”(March 27), 74.

Business Week (2007), “ Most Innovative Companies,” (May 14), 52.

Cadogan, J.W. and A. Diamantopoulos (1995), “Narver and Slater, Kohli and Jaworski

and The Market Orientation Construct: Integration and Internalization,” Journal of

Strategic Marketing, 3, 41-60.

Campbell, Donald D. (1955), “The Informant in Quantitative Research,” American

Journal of Sociology, 60, 339-342.

Cannon Joseph P. and William D. Perrault (1999), “Buyer-Seller Relationships in

Business Markets,” Journal of Marketing Research, 36 (November), 439-460.

and Christian Homburg (2001), “Buyers-Supplier Relationships and Customer

Firm Costs,” Journal of Marketing, 65 (January), 29-43.


233

Cassel, C.M., P. Hackl, and A. H. Westlund (1999), “Robustness of Partial Least-Squares

Method for Estimating Latent Variable Quality Structures,” Journal of Applied

Statistics, 26, 435-446.

Chandler, Alfred D. (1990). Scale and Scope: The Dynamics of Industrial Capitalism.

Cambridge, MA: Harvard University Press.

Chandy, Rajesh K. and Gerard J.Tellis (1998), “Organizing for Radical Product

Innovation: The Overlooked Role of Willigness to Cannibalize,” Journal of

Marketing Research, 36 (November), 474-487.

Churchill, Gilbert A. Jr. (1979), “A Paradigm for Developing Better Measures of

Marketing Constructs,” Journal of Marketing Research, 16 (February), 64-73.

Clark, K.B. and T. Fujimoto (1991). Product Development Performance. Boston:

Harvard Business School Press.

Cloodt, M., J. Hagedoorn, and R. Roijakkers (2006), “Trends ad Patterns in Interfirm

R&D Networks in the Global Computer Industry: An Analysis of Major

Developments, 1970-1990,” Business History Review, 80, 725-746.

Conner, Kathleen (1991), “A Historical Comparison of Resource-Based Theory and Five

Schools of Thought with Industrial-Organization Economics: Do We Have a New

Theory of the Firm?” Journal of Management, 17 (March), 121-154

Cook, Thomas D. and Donald T. Campbell (1979), Quasi-Experimentation: Design and

Analysis Issues for Field Settings.Boston: Houghton Mifflin.

Cooper, Robert G. (1979), “The Dimensions of Industrial New Product Success and

Faliure,” Journal of Marketing, 43 (Summer), 93-103.

(1984), “How New Product Strategies Impact on Performance,” Journal of

Product Innovation Management, 1, 5-18.

(1994), “New Products: The Factors that Drive Success,” International

Marketing Review, 11 (1), 60-76.

and Kleinschmidt E.J. (1995), “Benchmarking the Firm's Critical Success Factors

in New Product Development,” Journal of Product Innovation Management, 12 (5),

374-391.

Damanpour, Robert F. (1991) “Organizational Innovation: A Meta Analysis of Effects of

Determinants and Moderators,” Academy of Management Journal, 34(3), 555.


234

Das, T.K. and Bing-Sheng Teng (2000), “A Resource-Based Theory of Strategic

Alliances,” Journal of Management, 26 (1), 31-61.

Day, George S. (1992), “Marketing’s Contribution to the Strategy Dialogue,” Journal of

the Academy of Marketing Science, 20 (Fall), 323-330.

(1994), “The Capabilities of Market-Driven Organizations.” Journal of

Marketing, 58 (4), 37-52.

(1995), “Advantageous Alliances,” Journal of the Academy of Marketing

Science, 23 (4), 297-300.

, and Robin Wensley (1988), “Assessing Advantage: A Framework for

Diagnosing Competitive Competitive Superiority,” Journal of Marketing Research,

25 (May), 186-192.

, and Prakesh Nedungandi (1994), “Managerial Representations of Competitive

Advantage,” Journal of Marketing, 58 (April), 31-44.

Deck, Mark and Mark Strom (2002), “Model of Co-Development Emerges,” Research

and Technology Management, May-June, 47-53.

Deng, S. and J. Dart (1994), “Measuring Market Orientation: A Multi-Factor, Multi-Item

Approach,” Journal of Marketing Management, 10 (8), 725-742.

Deshpande, Rohit and Gerald Zaltman (1982), “Factors Affecting the Use of Market

Research Information: A Path Analysis,” Journal of Marketing Research, 19

(February), 14-31.

John U. Farley, and Frederick E. Webster, Jr. (1993), “Corporate Culture,

Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad Analysis,”

Journal of Marketing, Vol. 57, No. 1 (Jan., 1993), pp. 23-37.

and (1998), “Measuring Market Orientation: A Generalization and

Synthesis,” Journal of Market Focused Management, 2, 213-32.

Derozier, Caroline (2003), “Marketing Creativity in New Product Development: The

Role of Market Orientation, Technology Orientation, and Interfunctional

Coordination,” unpublished PhD Dissertation. Texas Tech University.

Dev, C.S., Saul Klein, and Reed A. Fisher (1996), “A Market-Based Approach for

Partner Selection in Marketing Alliances,” Journal of Travel Research, 35 (1), 11-7.


235

Diamantopoulos, Adamantios and Heidi M. Winklhofer (2001), “Index Construction with

Formative Indicators: An Alternative to Scale Development,” Journal of Marketing

Research, 38 (May): 269-277.

Dickson, Peter R. (1996), “The Static and Dynamic Mechanics of Competitive Theory,”

Journal of Marketing, 60 (October), 102-106.

Dierickx, Ingemar and Karel Cool (1989), “Asset Stock Accumulation and Sustainability

of Competitive Advantage,” Management Science, 35 (December), 1504-11.

Donney, Patricia M. and Joseph P. Cannon (1997), “An Examination of the Nature of

Trust in Buyer-Seller Relationships,” Journal of Marketing, 61 (April), 35-51.

Dosi, Giovanni and Richard Nelson (1994), “An Introduction to Evolutionary Economic

Theories,” Journal of Evolutionary Economics, 4, 153-172.

Doz, Yves L. and Gary Hamel (1998), Alliance Advantage, Massachusetts: Harvard

Business School Press.

Drucker, Peter F. (1954). The Practice of Management. New York: Harper and Row

Publishers.

Dwyer, F. Robert, Paul H. Schurr, and Sejo Oh, (1987), "Developing Buyer-Seller

Relationships," Journal of Marketing, 51 (April), 11-27.

Dyer, Jeffrey H.(1996), “Specialized Supplier Networks as a Source of Competitive

Advantage: Evidence from the Auto Industry,” Strategic Management Journal, 17,

271-292.

. and Harbir Singh (1998), “The Relational View: Cooperative Strategy and

Sources of Interorganizational Competitive Advantage,” Academy of Management

Review, 23 (4), 660-679.

Elg, Ulf (2002), “Inter-Firm Market Orientation: Its Significance and Antecedents in

Distribution Networks,” Journal of Marketing Management, 18, 633-655.

Emden, Zeynep, Roger Calantone, and Cornelia Droge (2006), “Collaborating for New

Product Development: Selecting the Partner with Maximum Potential to Create

Value,” Journal of Product Innovation and Management, 23, 330-341.

Ettlie, John E., William P. Bridges, and Robert D. O’Keefe (1984), “Organization

Strategy and Structural Differences for Radical versus Incremental Innovation,”

Management Science, 30 (6), 682-695.


236

Evanschitzky H., C. Baumgarth, R. Hubbard, J. Scott Armstrong (2007), “Replication

Research's Disturbing Trend”, Journal of Business Research, 60 (2007), 411-415.

Farr, C.M. and W. A. Fischer (1992), “Managing International High Technology

Cooperative Projects,” R&D Management, 22 (1), 55-67.

Felton, Arthur P. (1959), “Making the Marketing Concept Work,” Harvard Business

Review, 37 (July-August), 55-65.

Foss, Nicolai (1993), “Theories of the Firm: Contractual and Competence Perspectives,”

Journal of Evolutionary Economics, 3, 127-144.

Frazier, Gary L. (1983), “Inter-organizational Exchange Behavior: A Broadened

Perspective,” Journal of Marketing, 47 (Fall), 68-78.

Ganesan, Shankar (1994), "Determinants of Long-term Orientation in Buyer-Seller

Relationships," Journal of Marketing, 58 (April), 1-19.

Garcia, Rosanna and Roger Calantone (2002), “A Critical Look at Technological

Innovation Typology and Innovation Terminology: Literature Review,” Journal of

Product Innovation, 19, 110-132.

Gatignon, Hubert and Jean-Mark Xureb (1997), “Strategic Orientation of the Firm and

New Product Performance,” Journal of Marketing Research, 34 (February), 77-90.

Gebhardt, Gary F., Gregory S. Carpenter, and John F. Sherry Jr. (2006) “Creating a

Market Orientation: A Longitudinal, Multi-firm, Grounded Analysis of Cultural

Transformation,” Journal of Marketing, 70 (October), 37-55.

Glaister, Keith W. (1996), “UK-Western European Strategic Alliances: Motives and

Selection Criteria,” Journal of Euromarketing, 5(4), 5-35.

Green, S.G. and M. Aiman-Smith L. (1995), “Assessing a Multidimensional Measure of

Radical Technological Innovation,” IEEE Trans Engineer Management,42,303-14

Grewal, Rajdeep and P. Tansujah (2001), “Building Organizational Capabilities for

Managing Economic Crisis: The Role of Market Orientation and Strategic

Flexibility,” Journal of Marketing, 65 (April), 67-80.

Griffin, Abbie and Albert L. Page (1993), “An Interim Report on Measuring Product

Development Success and Failure,” Journal of Product Innovation Management, 10,

291-308.


237

and John R. Hauser (1996), “Integrating R&D and Marketing: A Review and

Analysis of the Literature,” Journal of Product Innovation Management, 13 (3), 191-215.

Grunert, Klaus G., Lisbeth F. Jeppesen, Kristina R. Jespersen, Anne-Mette Sonne, Kare

Hansen, Torbjorn Trondsen, and James A. Young, “Market Orientation of Value

Chains,” European Journal of Marketing, 39 (5/6), 428-455.

Gulati, Ranjay (1995), “Social Structure and Alliance Formation Patterns: A Longitudinal

Analysis,” Administrative Science Quarterly, 40, 619-652.

(1999), “Network location and learning: the influence of network resources and

firm capabilities on alliance formation” Strategic Management Journal, 20 (5), 397-420.

Hagedoorn, John (2002), “Inter-firm R&D Partnerships: An Overview of Major Trends

and Patterns since 1960,” Research Policy, 31 (4), 477-492.

Hamel, Gary, Yves L. Doz, and C.K. Prahalad (2002), “Collaborate with Your

Competitors-and Win,” in Harvard Business Review on Strategic Alliances, 1-21.

Hamel, Gary and C.K. Prahalad (1994). Competing for the Future. Boston, MA: Harvard

Business School.

Han, Jin K., Namwoon Kim, and Rajendra K. Srivastava (1998), “Market Orientation and

Organizational Performance: Is Innovation a Missing Link?” Journal of Marketing,

62 (October), 30-45.

Harrigan, Katherine R.(1985), Strategies for Joint Ventures. Lexington, MA: D.C. Heath

and Company.

and W.H. Newman (1990), “Bases of Inter-Organizational Cooperation:

Propensity, Power, Persistence,” Journal of Management Studies, 27 (4), 417-434.

Harris, Lloyd C.(2002), “Measuring Market Orientation: Exploring a Market Oriented

Approach,” Journal of Market Focused Management, 5, 239-70.

Heide, Jan B (1994), “Interorganizational Governance in Marketing Channels,” Journal

of Marketing, 58 (January), 71-85.

and George John (1988), “The Role of Dependence Balancing in Safeguarding

Transaction-Specific Assets in Conventional Channels,” Journal of Marketing, 52

(January), 20-35.


238

and Anne S. Miner (1992), “The Shadow of the Future: Effects of Anticipated

Interaction and Frequency of Contact on Buyer-Seller Corporation,” Academy of

Management Journal, 35 (2), 265-291.

Helfert, Gabriele, and Thomas Ritter, and Achim Walter (2002), “Redefining Market

Orientation from A Relationship Perspective,” European Journal of Marketing, 36,

9/10, 1119-1139.

Heller, Daniel H. and Takahiro Fujimoto (2004), “Interfirm Learning in High-

Commitment Horizontal Alliances: Findings from Two Cases in the World Auto

Industry,” Annals of Business Administrative Science, 3 (3), 35-52.

Henderson, Rebecca M. and Kim B. Clark (1990), “Architectural Innovation: The

Reconfiguration of Existing,” Administrative Science Quarterly, 35 (1), 9-30.

Howell, Roy D. (1987), “Covariance Structure Modeling and Measurement Issues: A

Note on Interrelations Among a Channel Entity’s Power Sources,” Journal of

Marketing Research, 24 (February): 119-126.

________, E. Breivik, and J.Wilcox (2007a), “Reconsidering Formative Measurement,”

Psychological Methods, 12 (2), 205-218.

________, ________, and ________ (2007b), “Is Formative Measurement Really a

Measurement? Reply to Bollen (2007)and Bagozzi (2007),” Psychological Methods,

12 (2), 238-245.

Hubbard, Raymond and J. Scott Armstrong (1994), “Replications and Extensions in

Marketing: Rarely Published but Quite Contrary,” International Journal of Research

in Marketing, 11 (June), 233-248.

Hudgson, G.M. Economics and Evolution. Ann Arbor: University of Michigan Press.

Hunt, Shelby D. (1997), “Competing Through Relationships: Grounding Relationship

Marketing in Resource-Advantage Theory”, Journal Marketing Management, 13, 1-15.

________ , Richard D. Sparkman Jr., and James B. Wilcox (1982), “The Pretest in

Survey Research: Issues and Preliminary Findings,” Journal of Marketing Research,

14 (May), 269-273.

(2000), A General Theory of Competition, Thousand Oaks, CA: Sage.

and Robert M. Morgan (1995), “The Comparative Advantage Theory of

Competition, Journal of Marketing, 59 (April), 1-15.


239

and Robert M. Morgan (1995), “Resource-Advantage Theory: A Snake

Swallowing Its Tail or a General Theory of Competition?” Journal of Marketing 60

(October), 107-114.

and C. Jay Lambe (2000), “Marketing’s Contribution to Business Strategy:

Market Orientation, Relationship Marketing and Resource-Advantage Theory,”

International Journal of Management Reviews, 2 (1), 17-43.

Hurley, R.F. and G.T.M. Hult (1998), “Innovation, Market Orientation, and

Organizational Learning: An Integration and Empirical Examination,” Journal of

Marketing, 42 (July), 42-54.

Im, Subin (1998), “The Model of Effect of Creativity on New Product Success,”

unpublished PhD Dissertation. The University of North Carolina at Chapel Hill.

and John P. Workman (2004), “Market Orientation, Creativity, and New Product

Performance in High Technology Firms,” Journal of Marketing, 68 (April), 114-132.

Jap, Sandy D. (1995), “A Longitudinal Approach to the Effects of Partner Firm

Characteristics, The Environment, and Mutual Trust on Synergy,” unpublished PhD

Dissertation. University of Florida.

(1999), “Pie-Expansion Efforts: Collaboration Processes in Buyer-Supplier

Relationships,” Journal of Marketing Research, 36 (November), 461-475.

Jarvis, C. B., S.B.Mackenzie, and P. M.Podsakoff (2003), “A Critical Review of

Construct Indicators and Measurement Misspecification in Marketing and Consumer

Research,” Journal of Consumer Research, 30 (September), 119-218.

(1999), “Pie-Expansion Efforts: Collaboration Processes in Buyer-Supplier

Relationships,” Journal of Marketing Research, 36 (November), 461-475.

, and Shankar Ganesan (2000), “Control Mechanisms and the Relationship Life

Cycle: Implications for Safeguarding Specific Investments and Developing

Commitment,” Journal of Marketing Research, 37 (May), 227-245.

Jassawalla, Avan R. and Hemant C. Sashittal (1998), “An Examination of Collaboration

in High-Technology New Product Development Process,” Journal of Product

Innovation Management, 15, 237-254.

Jaworski, Bernard and Ajay K. Kohli (1993), “Market Orientation: Antecedents and

Consequences,” Journal of Marketing, 57 (July), 53-70.


240

John, George and Torger Reve (1982), “The Reliability and Validity of Key Informant

Data from Dyadic Relationships in Marketing Channels,” Journal of Marketing

Research, 19 (November), 517-524.

Kalwani, Manohar U. and Narakesari Narayandas (1995), “Long-Term Manufacturer-

Supplier Relationships: Do They Pay Off for Supplier Firms?”, Journal of Marketing,

59 (January), 1-16.

Kandemir, Destan, Atilla Yaprak, and Tamer Cavusgil (2006), “Alliance Orientation:

Conceptualization, Measurement, and Impact on Market Performance,” Journal of the

Academy of Marketing Science, 34 (Summer), 324-340.

Kim, Keysuk (2000), “On Inter-Firm Power, Channel Climate, and Solidarity in

Industrial Distributor-Supplier Dyads,” Journal of the Academy of Marketing Science,

28 (3), 388-405.

Kirca, Ahmet, Satish Jayachandran, William O. Bearden (2005), “Market Orientation: A

Meta Analytic Review and Assessment of Its Antecedents and Impact on

Performance,” Journal of Marketing, 69 (April), 24-41.

Klein, Richard, Arun Rai, and, and Detmar Straub (2007), “Competitive and Cooperative

Positioning in Supply Chain Logistic Relationships,” Decision Science, 38(4), 611-46

Kleinschmidt, E.J. and Robert G. Cooper (1991), “The Impact of Product Innovativeness

on Performance,” Journal of Product Innovation Management, 8 (August), 240-251.

Knudsen, M. Praest (2007), “The Relative Importance of Interfirm Relationships and

Knowledge Transfer for New Product Development Success,” Journal of Product

Innovation Management, 24, 117-138.

Kogut, Bruce and H. Singh (1988), “The Effect of National Culture on the Choice of

Entre Mode,” Journal of International Business Studies, 19 (3), 411-432.

Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Constructs,

Research Propositions, and Managerial Implications,” Journal of Marketing, 54

(April), 1-18.

, Bernard J. Jaworski, and Ajith Kumar (1993), “MARKOR: A Measure of

Market Orientation,” Journal of Marketing Research, 30 (4), 467- 477.

Kotabe, Masaaki and K. Scott Swan (1995), “The Role of Strategic Alliances in High-

Technology New Product Development,” Strategic Management Journal, 16,621-36.


241

Kumar, Nirmalya, Louis W. Stern, and James C. Anderson (1993), “Conducting Interfirm

Research Using Key Informants,” Academy of Management Journal, 36 (Dec), 1633-51.

Lado, Augustine A., Nancy Boyd, and P. Wright (1992), “A Competency-Based Model

of Sustainable Competitive Advantage,” Journal of Management, 18, 77-91.

Lambe, C. Jay, Robert E. Spekman, and Shelby D. Hunt (2002), “Alliance Competence,

Resources, and Alliance Success: Conceptualization, Measurement, and Initial Test,”

Journal of the Academy of Marketing Science, 30 (2), 141-158.

Langerak, Fred (2001), “Effects of Market Orientation on the Behaviors of Salespersons

and Purchasers, Channel Relationships, and Performance of Manufacturers,”

International Journal of Research in Marketing, 18 (3), 221-234.

Larzelere, Robert E. and Ted L. Huston (1980), “The Dyadic Trust Scale: Toward

Understanding Interpersonal Trust in Close Relationships,” Journal of Marriage and

the Family, 42 (August), 595-604.

Leisen, Birgit, Bryan Lilly, and Robert D. Winson (2002), “The Effects of Organizational

Culture and Market Orientation on the Effectiveness of Strategic Marketing

Alliances,” Journal of Services Marketing, 16 (3), 201-222.

Lewis, M. A. and M.J. Gregory (1996), “Developing and Applying a Process Approach

to Competence Analysis,” in Dynamics of Competence-Based Competition: Theory

and Practice in the New Strategic Management. Eds R. Sanches, A heene, and H.

Thomas. London: Elsevier, 141-164.

Link, Albert N. and Laura L. Bauer (1989), Cooperative Research in U.S.

Manufacturing: Assessing Policy Initiatives and Corporate Strategies. Lexington,

MA: Lexington Books.

Lippman, S. A. and R.P. Rumelt (1982), “Uncertain Inimitability,” Bell Journal of

Economics, 13, 418-438.

Littler, Dale and Fiona Leverick (1995), “Joint Ventures for Product Development:

Learning from Experience,” Long Range Planning, 28 (3), 58-67.

, , and Margaret Bruce (1995), “Factors Affecting the Process of

Collaborative Product Development: A Study of UK Manufacturers of Information

and Communications Technology Products,” Journal of Product and Innovation

Management, 12, 16-32.


242

and (2003), “Interfirm Cooperation and Customer Orientation,” Journal

of Marketing Research, 40 (November), 421-436.

Lukas, Bryan A. and O.C. Ferrell (2000), “The Effect of Market Orientation on Product

Innovation,” Journal of the Academy of Marketing Science, 28 (2), 239-247.

Luo, Xueming, Aric Rindfleisch, and David K. Tse (2007), “Working with Rivals: The

Impact of Competitor Alliances on Financial Performance,” Journal of Marketing

Research, 44 (February), 73-83.

Lyon, Douglas W., G.T. Lumpkin, and Gregory G. Dess (2000), “Enhancing

Entrepreneurial Orientation Research: Operationalizing and Measuring a Key

Strategic Decision Making Process,” Journal of Management, 26 (5), 1055-85.

Marsh, H.W., K. Hau, J.R. Balla, and D. Grayson (1998), “Is More Ever Too Much? The

Number of Indicators per Factor in Confirmatory Factor Analysis,” Multivariate

Behavioral Research, 33, 181-220.

Matsuno, Ken and John T. Mentzer (2000), “The Effects of Strategy Type on the Market

Orientation-Performance Relationship,” Journal of Marketing, 64 (October), 1-16.

, and Aysegul Ozsomer (2002). “The Effects of Entrepreneurial Proclivity

and Market Orientation on Business Performance,” Journal of Marketing, 66, 18-32.

Mason, Katy, Peter Doyle, and Veronica Wong (2006), “Market Orientation and Quasi-

Integration: Adding Value Through Relationships,” Industrial Marketing


McDermott, C.M. and Gina C. O’Connor (2002), “Managing Radical Innovation: An

Overview of Emergent Strategy Issues,” The Journal of Product Innovation


Menon, Geeta, Barbara Bickart, Seymour Sudman, and Johnny Blair (1995), “How Well

Do You Know Your Partner Strategies for Formulating Proxy-Reports and Their

Effects on Convergence to Self-Reports,” Journal of Marketing Research, 32 (1), 77-84.

Meyer, John P. and Natalie J. Allen (1984), “Testing the ‘Side-Bet Theory’ of

Organizational Commitment: Some Methodological Considerations,” Journal of

Applied Psychology, 69 (3), 372- 378.

Millson, Murray R., S. P. Raj, and David Milemon (1996), “Strategic Partnering for

Developing New Products,” Research Technology Management, 39 (3), 41-49.


243

Min, Soonhong and John T. Mentzer (2000), “The Role of Marketing in Supply Chain

Management,” International Journal of Physical Distribution and Logistics

Management, 30 (9), 765-774.

Mohr, Jakki, and Robert Spekman (1994), “Characteristics of Partnership Success:

Partnership Attributes, Communication Behavior, and Conflict Resolution

Techniques,” Strategic Management Journal, 15, 135-152.

Montoya-Weiss, Mitzi M. and Roger Calantone (1994), “Determinants of New Product

Performance: A Review and Meta-Analysis,” Journal of Product Innovation

Management, 11 (November), 397-417.

Montgomery, Cynthia A. and Michael Porter (1991). Strategy: Seeking and Securing

Competitive Advantage. Boston. Harvard Business School Publishing.

Moore, James (1993), “Predators and Prey: A New Ecology of Competition,” Harvard

Business Review, May-June, 75-86.

Moorman, Christine (1995), “Organizational Market Information Processes: Cultural

Antecedents and New Product Outcomes,” Journal of Marketing Research, 32

(August), 318-335.

, Gerald Zaltman, and Rohit Desphande (1992), “Relationships between

Providers and Users of Market Research: The Dynamics of Trust within and Between

Organizations,” Journal of Marketing Research, 39 (August), 314-328.

and Anne S. Miner (1997), “The Impact of Organizational Memory on New Product

Performance and Creativity,” Journal of Marketing Research, 34 (February), 91-106.

and Roland T. Rust (1999), “The Role of Marketing,” Journal of Marketing, 63

(Special Issue), 180-197.

Morgan, Robert M. and Shelby D. Hunt (1994), “The Commitment-Trust Theory of

Relationship Marketing,” Journal of Marketing, 58 (July), 20-38.

Mowday, Richard T., R. M. Steers, and L. W. Porter (1979), “The Measurement of

Organizational Commitment,” Journal of Vocational Behavior, 14, 224-247.

Narver, John C. and Stanley F. Slater (1990), "The Effect of a Market Orientation on

Business Profitability," Journal of Marketing, 54 (October), 20-35.


244

, Stanley F. Slater, Douglas L. MacLachlan (2004), “Responsive and Proactive

Market Orientation and New-Product Success,” Journal of Product Innovation

Management 21 (5), 334–347.

Noble, Charles H., Rajiv K. Sinha, and Ajith Kumar (2002), “Market Orientation and

Alternative Strategic Orientations: A Longitudinal Assessments of Performance

Implications,” Journal of Marketing, 66 (October), 25-39.

OECD (1991), The Nature of Innovation and the Evolution of the Productive System,

Technology, and Productivity-The Challenge for Economic Policy. Paris: OECD, 303-314.

Olson, Eric M., C. Orville Walker Jr., and Robert W. Ruekert (1995), “Organizing for

Effective New Product Development: The Moderating Role of Product

Innovativeness,” Journal of Marketing, 59 (1), 48-62.

Page, Albert L. (1993), “Assessing New Product Development Practices and

Performance: Establishing Crucial Norms,” Jourmal of Product Innovation

Management, 10 (4), 273-290.

Parkhe, Arvind (1993), “Strategic Alliance Structuring: A Game Theoretic and

Transcation Cost Examination of Interfirm Cooperation,” Academy of Management

Journal, 36 (4), 794-829.

Peck, Merton J. (1986), “Joint R&D: The Case of the Microelectronics and Computer

Technology Corporation,” Research Policy, 15, 219-231.

Penrose, Edith T. (1959). The Theory of the Growth of the Firm. London.: Basic

Blackwell and Mott.

Perks, Helen (2000), “Marketing Information Exchange Mechanisms in Collaborative

New Product Development,” Industrial Marketing Management, 29, 179-189.

Peteraf, Margaret (1993), “The Cornerstones of Competitive Advantage: A Resource-

Based View,” Strategic Management Journal, 14 (3). 179-191.

Philips, Lynn W. (1981), “Assessing Measurement Error in Key Informant Reports: A

Methodological Note on Organizational Analysis in Marketing,” Journal of

Marketing Research, 18 (November), 395-415.

Piercy, N. F. And D. W. Cravens (1995), “The Network Paradigm and the Marketing

Organisation- Developing a New Management Agenda,” European Journal of

Marketing, 29 (3), 7-34.


245

Podsakoff, P.M. and D. W. Organ (1986), “Self-reports in Organizational Research:

Problems and Prospects,” Journal of Management, 12, 532-545.

, S.B. MacKenzie, J. Lee, and N.P. Podsakoff (2003), “Common Method Biases

in Behavioral Research: A Critical Review of the Literature and Recommended

Remedies,” Journal of Applied Psychology, 88, 879-903.

Porter, Michael E. (1980). Competitive Advantage. New York: Free Press.

Prahalad, C.K. (1993), “The Role of Core Competences in the Corporation,” Research

Technology Management, 36 (6), 40-47.

. and Gary Hamel (1990), “The Core Competence of the Corporation,” Harvard

Business Review (March/April), 79-91.

Reed, Richard and Robert J. DeFlippi (1990), “Causal Ambiguity, Barriers to Imitation,

and Sustainable Competitive Advantage,” Academy of Management Review, 15

(January), 88-107.

Rindfleisch, Aric (1998), “Cooperative Research Ventures and New Product

Development,” unpublished PhD Dissertation. University of Wisconsin-Madison

(2000), “Organizational Trust and Interfirm Cooperation: An Examination of

Horizontal versus Vertical Alliances,” Marketing Letters,11(1),81-95.

and Christine Moorman (2001), “The Acquisition and Utilization of Information

in New Product Alliances: A Strength of Ties Perspective,” Journal of Marketing, 65

(April), 1-18.

and (2003), “Interfirm Cooperation and Customer Orientation,” Journal

of Marketing Research, 40 (November), 421-436.

, Alan J. Malter, Shankar Ganesan, and Christine Moorman, “Cross Sectional

versus Longitudinal Survey Research: Concepts, Findings, and Guidelines,” Journal

of Marketing Research, forthcoming.

Rosenfeld, Stuart A. (1996), “Does Cooperation Enhance Competitiveness? Accessing

the Impacts of Inter-Firm Collaboration,” Research Policy, 25, 247-263.

Rumelt, Richard P. (1984), “Toward a Strategic Theory of the Firm,” ed. R. Lamb, 556-

570. Competitive Strategic Management. Englewood Cliffs.NJ: Prentice Hall.

Sanchez, Ron, Aime Heene, and Howard Thomas (1996). Dynamics of Competence-

Based Competition. New York: Elsevier.


246

Sanzo, Maria José, Maria L. Santos, Rodolfo Vasquez, and Luis I. Alvarez (2003), “The

Effect of Market Orientation on Buyer-Seller Relationship Satisfaction,” Industrial

Marketing Management, 32, 327-345.

Sarkar, M.B., Raj Echambadi, S. Tamer Cavusgil, and Preet Aulakh (2001), “The

Influence of Complementarity, Compatibility, and Relationship Capital on Alliance

Performance,” Journal of Academy of Marketing Science, 29 (4). 358-373.

Schumacker, Randall E. and Richard G. Lomax (1996), A Beginner’s Guide to Structural

Equation Modelling. Mahwah, NJ: Lawrence Erlbaum Associates.

Schumpeter, Joseph A. (1934), The Theory of Economic Development, Fifth Edition.

New Brunswick, New Jersey: Transaction Publishers.

Sethi, Rajesh, Daniel C. Smith, and C. Whan Park (2001), “Cross-Functional Product

Development Teams, Creativity, and the Innovativeness of New Consumer Products,”

Journal of Marketing Research, 38 (February), 73-85.

Selznick, P. (1957). Leadership in Administration. New York: Humanities Press.

Siguaw, Judy A., Penny M. Simpson, and Thomas L. Baker (1998), “Effects of Supplier

Market Orientation on Distributor Market Orientation and the Channel Relationship:

The Distributor Perspective,” Journal of Marketing, 62 (July), 99-111.

, Gene Brown, and Robert E. Widing II (1994), “The Influence of the Market

Orientation of the Firm on Sales Force Behavior and Attitudes,” Journal of Marketing

Research, 31 (February), 106-16.

Simonin, Bernard L. (1997), “The Importance of Collaborative Know-How: An Emprical

Test of the Learning Organization,”Academy of Management Journal, 40(5), 1150-74

Sivadas, Eugene and F. Robert Dwyer (2000), “An Examination of Organizational

Factors Influencing New Product Success in Internal and Alliance-Based Processes,”

Journal of Marketing, 64 (January), 31-49.

Slater, Stanley F. and John F. Narver (1995), “Market Orientation and The Learning

Organization,” Journal of Marketing, 59 (July), 63-74.

Song, X. Michael and Mark E. Parry (1992), “The R&D-Marketing Interface in Japanese

High-Technology Firms,” Journal of Product Innovation Management,” 9 (2), 91-112.

and (1997), “The Determinants of Japanese New Product Success,”

Journal of Marketing Research, 34 (February), 64-76.


247

and M. Montoya-Weiss and J.B. Schmidt (1997), “Antecedents and

Consequences of Cross-Functional Cooperation: A Comparison of R&D,

Manufacturing, and Marketing Perspectives,” Journal of Product Innovation


and (2001), “The Effect of Perceived Technological Uncertainty on

Japanese New Product Development,” Academy of Management Journal, 44 (1), 61-80.

Sorescu, Alina B., Rajesh K.Chandy, and Jaideep C. Prabhu (2003), “Sources and

Financial Consequences of Radical Innovation,”Journal of Marketing, 67, 82-102.

Spekman, Robert E., Lynn A. Isabella, Thomas C. MacAvoy (1999). Alliance

Competence: Maximizing the Value of Your Partnerships. New York: John Wiley.

Steinmuller, W.E. “Collaborative Innovation,” in Networks, Alliances, Partnerships in

Innovation Process. Eds. J. de la Mothe and Albert N. Link. P. 29-43. MA: Kluwer.

Subramani, M.R. (2004), “How do Suppliers Benefit from Information Technology Use

in Supply Chain Relationships,” MIS Quarterly, 28, 45-74.

Swink, Morgan (2000), “Technological Innovativeness as a Moderator of New Product

Design Integration and Top Management Support,” Journal of Product Innovation

Management, 17 (3), 208-220.

Teece, David and Gary Pisano (1994), “The Dynamic Capabilities of Firms: An

Introduction,” Industrial and Corporate Change 3 (E), 537-556.

Thompson, Bruce (1998), “Ten Commandments of Good Structural Equation Modelling

Behavior: A User Friendly, Introductory Primer on SEM,” Annual Meeting of US

Department of Education OSEP Project Directors’ Conference, Washington D.C.

Thorelli, Hans B. (1986), “Networks: Between Markets and Hierarchies,” Strategic

Management Journal, 7 (1), 37-51

Utterback, James M. (1971) “The Process of Technological Innovation within the Firm,”

The Academy of Management Journal, 14 (1), 75-78.

Van De Ven, Andrew H. (1986), “Central Problems in the Management of Innovation,”

Management Science, 32 (5), 590.

Varadarajan, P. Rajan and Margaret H. Cunningham (1995), “Strategic Alliances: A

Synthesis of Conceptual Foundations,” Journal of Academy of Marketing Science, 23

(4), 282-297.


248

Von Hippel, Eric (1984), “Novel Product Concepts from Lead Users: Segmenting Users

by Experience,” Working Paper no. 84-109, Cambridge, MA: Marketing Science

Institute.

Webster, Frederick E. Jr. (1988), “Rediscovering the Marketing Concept,” Business

Horizons, 31 (May-June), 29-39.

Wei, Susan (2006), “Market Orientation and Successful New Product Innovation: The

Role of Competency Traps,” unpublished PhD Dissertation. University of North

Carolina at Chapel Hill.

Weitz, Barton A. and Sandy D. Jap (1995), “Relationship Marketing and Distribution

Channels,” Journal of the Academy of Marketing Science, 23 (4), 305-320.

Wernerfelt, Birger (1984), “A Resource-Based View of the Firm,” Strategic Management

Journal, 5 (2), 171-180.

Wilcox, James B., Roy D. Howell, and Einar Breivik (2008), “Questions about Formative

Measurement,” Journal of Business Research, 61 (12), 1219-1228.

Williamson, Oliver E. (1985), The Economic Institutions of Capitalism. NY: Free Press.

Wind, Jerry and Vijay Mahajan (1997), “Issues and Opportunities in New Product

Development: An Introduction to the Special Issue,” Journal of Marketing Research,

34 (1), 1-12.

Wittman, C. Michael (2001), “Business Alliance Success: The Influence of Alliance

Competence, Idiosyncratic Resources, and Relational Factors,” unpublished PhD

Dissertation. Texas Tech University.

Wrenn, Bruce (1997), “The Market Orientation Construct: Measurement and Scaling

Issues,” Journal of Marketing Theory and Practice, 5 (Summer), 31-54.

Zaltman Gerald, Robert Duncan, and Jonny Holbek (1973), Innovations and

Organizations New York: John Wiley & Sons.

Zhao, Yushan and S. Tamer Cavusgil (2006), “The Effects of Suppliers’ Market

Orientation on Manufacturer’s Trust,” Industrial Marketing Management,35, 405-14.

Zhou, Kevin Zheng, Chi Kin Yim, and David K. Tse (2005), “The Effects of Strategic

Orientations on Technology-and Market-Based Breakthrough Innovations,” Journal

of Marketing, 69 (April), 42-60.


249

APPENDICES

FINAL INSTRUMENT

page

I. Cover Letter .......................................................................................................... ...250

II. Questionnaire ............................................................................................................251

III. E-mail body text (initial e-mail) ...............................................................................261

IV. Reminder e-mail ........................................................................................................262

V. Scales .......................................................................................................................263


250

Texas Tech University New Product Development Alliance Research Project Pelin Bicen, Principal Researcher Shelby D. Hunt, Faculty Advisor

Dear Mr. Brown, You have been selected to participate in a scientific study aimed at better understanding the relationships and relationship outcomes between dyadic new product alliance partners. In this study, we seek to answer the question of what makes new product alliances succeed by examining the role of the alliance partners’ joint generation and integration of market information in new product development activities. In addition, we also investigate how alliance competence – an organizational ability for finding, developing, and managing alliances – influences alliances’ market oriented behaviors.

The survey is just asking for your opinions and feelings, and there is no right or wrong answers. We understand that time is very valuable in managers’ daily schedules. Therefore, we extensively pretested the survey with distinguished CEOs, CMOs, R&D managers, alliance managers, and business consultants. They informed us that survey questions are easy to answer and they should take no more than 10-15 minutes to complete. Since you are in a scientifically selected limited sample, your experience will be a tremendous help to the progress of this academic project. Since this is an academic research project, the researchers will be the only people who will see the individual responses. Therefore, information collected in the surveys, names of respondents, and company names will remain confidential and will not be reported in any presentations or

publications. Any information given will be averaged across the responses of many other firms so that no one’s individual answers can be determined.

As an (admittedly modest) incentive to participate, we will send you a confidential customized summary report. This report will include (1) a summary of the main study results and an immediate access to the conclusions and recommendations that emerge and (2) a detailed comparison of ABCD Technologies’s data benchmarked against all other firms participated in our study.

If you have any questions about this project, please contact Pelin Bicen at +1 806 789 3595, or via email at [email protected]. Your cooperation is greatly appreciated.

Sincerely, Shelby D. Hunt Pelin Bicen The Jerry S. Rawls and P.W. Horn Professor of Marketing PhD Candidate Rawls College of Business Rawls College of Business Texas Tech University Texas Tech University


251

TEXAS TECH UNIVERSITY NEW PRODUCT ALLIANCE STUDY

General Instructions

1. The main purpose of this study is to understand why some new product alliances have better performance than others. This is the pre-test of the final survey. Your valuable inputs, suggestions, and comments will be used to improve the quality of the survey instrument. 2. Some of the questions ask about events which you may not have been directly involved in. Please try to answer these questions to the best of your knowledge based on anything you have seen or heard. If you think that you don’t have sufficient knowledge of alliance management, please pass this survey along an executive in your firm who has experience and knowledge. 3. It is important to provide your best answers for every question in the survey, even if you are not certain about the exact answer. Your approximate answer is far more useful than an incomplete survey. 4. We expect that survey should take no more than 10-15 minutes to complete. In appreciation of your assistance, we will be glad to send you a customized summary report of the main results of the study. Our intent is to provide you practical information that will help your alliance to manage alliance activities more effectively and efficiently. You can register your request by filling out your name and address provided below. We are committed to providing you timely and actionable research results. 5. The information you provide will be kept strictly confidential. We will not report respondent or company names. The research results will be based on aggregate responses from the entire sample. Any information given will be averaged across the responses of many other companies so that no one’s individual answers can be determined. 6. Since you are considered part of a small, scientifically selected sample of professionals, your response is very important for the successful completion of this project and the validity of its findings.

If you have any questions about this project, please don’t hesitate to contact the principal researcher. The contact information is provided below. Your timely response is very critical to

this study’s success.

Thank you in advance for your help in making this project a success. Pelin Bicen

Doctoral Candidate Department of Marketing Rawls School of Business

Texas Tech University 79409-2101 Phone: (806) 789 3595 Fax: (806) 742 2199

e-mail: [email protected]


252

SECTION A: ALLIANCE NEW PRODUCT DEVELOPMENT INFORMATION

This first section examines ONE of your New Product Development (NPD) Alliances that you have a commercialized product with. Please list one of your NPD alliance partners below. This partner does not necessarily have to be your “most important” or “most favored” partner, although it can be. We would like to sample from a wide variety of alliance relationships so that comparisons can be made across different types. Our first question asks you to provide some information regarding the type of your

relationship to your alliance partner.

We are interested in the nature of your firm’s relationship with your alliance partner who

initially formed this alliance. Using the coding scheme listed directly below, please classify

your alliance partner by recording the appropriate code in the space next to the alliance

partner’s name. Code: Nature of the Relationship:

(A) …………………………………………. Our partner is one of our customers (B)………………………………………….. Our partner is one of our suppliers (C) …………………………………………. Our partner is one of our competitors (D) …………………………………………..Other This next question is interested in the extent of your firm’s history with your alliance

partner who initially founded this alliance. Using the scale listed directly below, please rate

the number of previous inter-organizational relationships your firm had with your alliance

partner prior to the formation of this alliance, by recording the appropriate number in the

third space for each participant.

Few relationships Many relationships

(1) (2) (3) (4) (5) (6) (7)

The final question in this section is interested in the governance mode of your alliance.

Using the coding scheme listed directly below, please classify your alliance type by

recording the appropriate code in the last space. To assist you in the question, definition of

each term is provided below.

Equity alliance: Cooperative contracts are supplemented by equity investments by one partner in

the other partner. Sometimes these investments are reciprocated.

Non-equity alliance: Cooperation between firms is managed directly through contracts, without cross-equity holdings or an independent firm being created.

Joint ventures: Arrangements where a new, separate entity is created by the combination of the

resources of the two parent companies.

Code: Alliance Type:

(A) Equity alliance (B) Joint ventures (C) Non-equity alliance

Alliance Partner Name: Relationship Type: Relationship History: Alliance Type:


253

SECTION B:

NEW PRODUCT DEVELOPMENT ALLIANCE

CHARACTERISTICS AND ACIVITIES

The questions in this section concern your alliance characteristics and activities related to the alliance discussed in Section A. Please rate the degree to which the following items accurately describe the aggregate level of

trust between your firm and your partner.

Strongly

disagree

Strongly

agree In our relationship, both our alliance partner and

we 1 2 3 4 5 6 7

…………are honest

…………can be counted on to do what is right

…………are faithful

…………have confidence in each other

…………have high integrity

…………are reliable

…………are trustworthy

Please rate the degree to which the following items accurately describe the aggregate level of

commitment of your firm and your partner to the relationship.

Strongly

disagree

Strongly

agree Both our alliance partner and we view our

relationship as something 1 2 3 4 5 6 7

…be committed to

…important to our firms

…of significance

…our firms really care about

…deserving our firms’ maximum efforts to maintain


254

Please rate the degree to which the following items accurately describe the complementary

resources that your firm and your partner have.

Strongly

disagree

Strongly

agree Both our alliance partner and we

1 2 3 4 5 6 7

…both contribute different resources to the relationship that help us achieve mutual goals.

… have complementary strengths that are useful to our relationship.

…each have separate abilities that, when combined together, enable us to achieve goals beyond our individual reach.

Please rate the degree to which the following items accurately describe the goal congruence

that your firm and your partner have.

Strongly

disagree

Strongly

agree Our firm and our partner’s firm

1 2 3 4 5 6 7

…. have compatible goals

.....support each other’s objectives

.... share the same goals in the relationship.

Please rate the degree to which the following items accurately describe the top management

support of your firm and your partner firm in activities of your alliance.

Strongly

disagree

Strongly

agree Top management in both firms

1 2 3 4 5 6 7

… believe that alliances play a role in the future success of each firm.

… are committed to the use of alliances to achieve strategic goals.

… support the use of alliances when situations call for them.


255

Please indicate the extent to which you agree/disagree with how effectively your alliance

supports the behaviors necessary to respond to customers’ needs.

Strongly

disagree Strongly

agree

1 2 3 4 5 6 7

Our alliance’s business objectives are driven by customer satisfaction.

Our alliance’s strategy for competitive advantage is based on our joint understanding of customer needs.

Our alliance’s business strategies are driven by our beliefs about how we can jointly create greater value for customers.

Please indicate the extent to which you agree/disagree with how effectively your alliance

supports the behaviors necessary to respond to competitors’ threads.

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7


In our alliance, we jointly target customers where we have an opportunity for competitive advantage.

In our alliance, we jointly respond to competitive actions that threaten us.

In our alliance, we frequently share information with each other concerning competitors’ strategies.


256

Please indicate the extent to which you agree/disagree with the interaction among your

partner and your firm, integration across firms, and efforts to achieve the same goal in the

inter-organizational new product development activities.

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

In our alliance, people in both organizations work together to solve our alliance’s problems.

In our alliance, activities involved in the new product development process are well-coordinated.

In our alliance, the different job activities related to new product development activity fit together very well.

In our alliance, people who have to work together are responsive to their co-workers’ needs and requests.

Please indicate the extent to which you agree/disagree with your partner’s and your firm’s

experiences in alliance activities.

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

Both our alliance partner and we have a deep base of partnership experience.

Our alliance partner and we each have participated in many alliances.

Individually our alliance partner and we have been partners in a substantial number of alliances.


257


partnership selection abilities.

Strongly

disagree Strongly

agree

1 2 3 4 5 6 7

Our alliance partner and we each actively search for promising alliance partners.

Alliances that can help our business are sought out by both our alliance partner and us.

Our alliance partner and we each are constantly seeking partnering opportunities.

Both our alliance partner and we are always looking for firms that we can partner with to jointly develop competitive advantage.


alliance manager development capabilities.

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

Both our alliance partner and we have programs to develop capable alliance managers.

Our alliance partner and we each understand how to produce effective alliance managers.

Both our alliance partner and we effectively train competent alliance managers.

Our alliance partner and we each know how to identify effective alliance managers.


258

In this section, we are interested in the degree to which the new products generated by your

alliance are perceived to represent unique differences from competitors’ products in ways

meaningful to target customers. Please rate the degree to which the new products generated

by your alliance have tended to be

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

Unconventional

Novel

Unusual

Unique

Original

Relevant

Suitable

Useful

Meaningful

Please indicate the extent to which the new products generated by your alliance have tended

to be successful in terms of

Strongly disagree Strongly agree

1 2 3 4 5 6 7

Sales

Market Share

Return on investment

Profits

Customer satisfaction

Overall performance


259

SECTION C: MARKET ENVIRONMENT INFORMATION

This section is concerned with the general information about your alliance’s market. Please indicate the extent to which you agree/disagree with the characteristics of your

alliance’s target market.

Strongly

disagree Strongly

agree

1 2 3 4 5 6 7

Potential customers have a great need for the product that our alliance develops.

The dollar size of the market (either existing or potential) for the product that our alliance develops is large.

The market for the product that our alliance develops is growing very quickly.

Please indicate the extent to which you agree/disagree with the rate of technology change in

your alliance’s market.

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

The technology in our alliance’s market is changing rapidly.

Technological changes provide big opportunities in our alliance’s market.

A large number of new product ideas have been made possible through technological breakthroughs in our alliance’s market.

Technological developments in our alliance’s market minor.


260

SECTION D: BACKGROUND INFORMATION

The questions in this final section concern your individual role in the new product development alliance. How involved were you personally in the formation of this business alliance?

1

2

3

4

5

6

7

Not at all involved

Highly involved

How knowledgeable are you about the new products developed as a result of your firm’s

participation in this new product development alliance?

1

2

3

4

5

6

7

Not at all knowledgeable

Highly knowledgeable

How many total years of experience do you have in new product development? …..Years

How many years have you worked for your present company? …….Years

Please check the job title that best describes your current position

Chief Executive Officer (CEO)

Chief Scientific Officer (CSO)

Chief Technology Officer (CTO)

Chief Operating Officer (COO)

Chief Marketing Offier (CMO)

Vice President of Marketing

Vice President of Corporate/Business Development

Alliance Manager/Executive

Vice President/ Director of Research Development

Vice President/ Director of Engineering

Vice President/Director of New Product Development

Business Consultant

Other (please specify)

We greatly appreciate your contribution to this scientific research.


261

From: Bicen, Pelin To: [email protected] Subject: Texas Tech University R&D Alliance Research Project Dear Mr. Brown, My name is Pelin Bicen and I am a Ph.D. candidate at the Marketing Department of Texas Tech University. I have recently called you and left a message to your voicemail about the Texas Tech University's (TTU) Research and Development Alliance research project which is also my Ph.D. dissertation. TTU NPD Alliance Research project is designed to better understand the underlying factors that promote new product development alliance success, and therefore it aims to assist companies in their successful alliance initiatives. In order to come up with reliable and valid empirical results, this academic study will use a rigorous statistical analysis therefore it requires a considerable sample size. Texas Tech University Rawls College of Business has Thompson Financial Securities Alliance Database (TFSD). The sample is scientifically selected from this database. ABCD Technologies is a very important part of this scientifically selected sample. Since this research involves distinguished executives and requires a key respondent from each selected company, your participation is very crucial for this academic research project. Therefore, we would kindly like to ask whether you can be willing to participate to this academic research project by filling up an online survey. The details of the project and the confidentiality issues are discussed in the cover letter in the attachments. Since this is an academic research, your inputs will be kept strictly confidential. Since your input is very important for the reliable empirical results, hopefully you will have a favorable decision regarding providing your insights to this academic project Mr. Brown. Your expertise and cooperation will help us to get valid and attainable results regarding successful alliance management, therefore, it will be greatly appreciated. With respect to your time, please choose one of the followings regarding this survey invitation: 1) Please send me an e-mail including the link for this academic project's websurvey. 2) Please don't send me any more e-mail regarding this academic project. Looking forward to your response at your earliest convenience, With best personal regards, Pelin Bicen.


262

From: Bicen, Pelin To: [email protected] Subject: Texas Tech University R&D Alliance Research Project (reminder) Dear Mr. Brown, My name is Pelin Bicen and I am a Ph.D. candidate at the Marketing Department of Texas Tech University. I sent you an e-mail about the Texas Tech University's (TTU) R&D Alliance Research project last week. I know that you have a busy daily schedule, but I would kindly like to ask whether you have had a chance to review my request regarding this academic study which is also my PhD dissertation. Please accept my sincere apologies for sending many e-mails; but, since I have not heard from you regarding whether to participate to our R&D alliance management study and I know how busy you are, I would kindly like to remind you. Since ABCD Technologies is a very important part of our scientifically selected sample, your input is very important for the reliable and actionable results. It is very challenging to collect data from executives; therefore, it is hard to come up with a reasonable and reliable sample. In this process, your input is very important. I am attaching my previous e-mail below and the cover letter in the attachments for further information. Since I have only contacted with a carefully selected sample of firms from the Thompson Financial Securities Alliance Database (TFSD), it is extremely important that your response be included if my results are to accurately reflect the opinions of firms in the high-tech industry. Therefore, may I kindly request you to let me know whether you may have 10 minutes to complete our online survey? Looking forward to hearing from you at your earliest convenience Mr. Brown. With my best personal regards, Pelin.


263

SCALES

Trust:

In our relationship, both our alliance partner and we:

... are honest.

... can be counted on what is right.

... are faithful.

... have confidence in each other.

... have high integrity.

... are reliable

... are trustworthy. Adapted from: Morgan and Hunt (1994) (7 point scale, anchors are strongly disagree

and strongly agree)

Commitment:

Both our alliance partner and we view our relationship as something:

... to be committed to

... important to our firms

... of significance

... our firms intend to maintain indefinitely *

... much like being family *

... our firms really care about.

... deserving our firms’ maximum efforts to maintain. Adapted from: Morgan and Hunt (1994) and Wittman (2001), (7 point scale, anchors are

strongly disagree and strongly agree)

Complementary Resources * :

We both contribute different resources to the relationship that help us achieve mutual goals. We have complementary strengths that are useful to our relationship. We each have separate abilities that, when combined together, enable us to achieve goals beyond our individual reach.

Adapted from: Lambe et al. (2002), (7 point scale, anchors are strongly disagree and

strongly agree)


264

Joint Top Management Support:

Top Management in both firms:

... believe that alliances play a role in the future success of each firm.

... are committed to the use of alliances to achieve strategic goals.

... support the use of alliances when situations call for them.

... tell their employees that this alliance’s survival depends on its adapting to market trends * …believe that serving customers is the most important thing this alliance does * .

Adapted from: Lambe et al. (2002) and Jaworski and Kohli (1993) (7 point scale,

anchors are strongly disagree and strongly agree)

Goal Congruence of the Dyad:

Our firm and our partner’s firm: ... have different goals R * ... have compatible goals. ... support each other’s objectives. ... share the same goals in the relationship.

Adapted from: Jap (1999). (7 point scale, anchors are strongly disagree and strongly

agree)

Alliance Market Orientation (new scale)

Customer orientation * : Our alliance’s business objectives are driven by customer satisfaction. In our alliance, we get together frequently to monitor our level of commitment and orientation to serving customers’ needs * Our alliance’s strategy for competitive advantage is based on our joint understanding of customer needs. Our alliance’s business strategies are driven by our beliefs about how we can jointly create greater value for customers. In our alliance, we get together frequently to measure customer satisfaction * In our alliance, we get together periodically to review the likely effect of changes in our business environment (e.g., regulation, competition) on customers *

(7 point scale, anchors are strongly disagree and strongly agree)


265

Competitor Orientation * : Senior managers in our firm meet frequently with their counterparts in our partner’s firm to discuss competitors’ strengths and strategies. In our alliance, we jointly target customers where we have an opportunity for competitive advantage. In our alliance, we jointly respond to competitive actions that threaten us. In our alliance, we frequently share information with each other concerning competitors’ strategies. In our alliance, we don’t work together to generate intelligence on competition R *

Inter-organizational coordination * : Senior managers in both firms understand how people across organizations can contribute to creating customer value * In our alliance, people in both organizations work hard to jointly solve our alliance’s problems. In our alliance, activities involved in the innovation process are well-coordinated. In our alliance, the different job activities related to new product development activity fit together very well. In our alliance, we have inter-organizational meetings frequently to discuss market trends and developments * In our alliance, people who have to work together are responsive to their co-workers’ needs and requests.

Joint Alliance Competence:

Alliance Experience-Based Knowledge

Both our alliance partner and we have a deep base of partnership experience Our alliance partner and we each have participated in many alliances. Individually our alliance partner and we have been partners in a substantial number of alliances.

Alliance Manager Development Capability Our alliance partner and we each actively search for promising alliance partners. Alliances that can help our business are sought out by both our alliance partner and us. Our alliance partner and we each are constantly seeking partnering opportunities. Both our alliance partner and we are always looking for firms that we can partner with to jointly develop competitive advantage.

Partner Identification Propensity Both our alliance partner and we have programs to develop capable alliance managers Our alliance partner and we each understand how to produce effective alliance managers. Both our alliance partner and we effectively train competent alliance managers. Our alliance partner and we each know how to identify effective alliance managers.

Adapted from: Lambe et al. (2002). (7 point scale, anchors are strongly disagree and

strongly agree)


266

NP Creativity:

In regard to new product creativity, please rate the degree to which the new products generated by your alliance have tended to be Novelty: Meaningfulness:

Exciting * Relevant Fresh * Suitable Unconventional Appropriate * Novel Useful Unusual Meaningful Original. Unique

Adapted from: Andrews and Smith (1996), Im and Workman (2004), Sethi et al. (2001),

(7 point, anchors are strongly disagree and strongly agree)

NP performance

The new products generated by your alliance have tended to be successful in terms of

Sales Market share Return on investment Profits Customer satisfaction Overall performance

Adapted from: Im and Workman (2004), (7 point scale, anchors are strongly disagree

and strongly agree)

Market Density

There are many potential customers for the product that our alliance provides a mass marketing opportunity. * Potential customers have a great need for the product that our alliance develops. The dollar size of the market (either existing or potential) for the product that our alliance develops is large. The market for the product that our alliance develops is growing very quickly.

Technology Density

The technology in our alliance’s market is changing rapidly Technological changes provide big opportunities in our alliance’s market A large number of new product ideas have been made possible through technological breakthroughs in our alliance’s market Technological developments in our alliance’s market are minor

Adapted from: Im and Workman (2004), (7 point scale, anchors are strongly disagree

and strongly agree)

* Note: Unless otherwise noted, “we” refer to the two partner firms. * Dropped items after purification and psychometric tests.

Documents

COMPETENCE IN NEW PRODUCT DEVELOPMENT …