The Impact of Product, Price, Promotion and Place Logistics on Customer Satisfaction and Share of Business

The Impact of Product, Price, Promotion and Place/Logistics on Customer Satisfaction and Share of Business

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

By

Rudolf Leuschner, M.A.

Graduate Program in Business Administration

The Ohio State University

2010

Dissertation Committee:

Professor Douglas M. Lambert, Advisor

Professor Keely L. Croxton

Professor A. Michael Knemeyer

UMI Number: 3438241

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2010

ii

Abstract

Customer service has been a topic in marketing and logistics research for many decades. Much

of the research was functionally focused and lacked the integration of logistics customer service

with the other components of the Marketing Mix (price, product and promotion). In addition

many prior studies focused only on a single industry and there is little replication and limited

possibilities for generalizability. This shortcoming is alleviated in this research by using a multi-

industry approach that allows for replication across the samples. The focus of this research was

on business-to business relationships in several industries, health care, electronics, plastics, and

sporting goods. The goals of the research were to test a general model that across multiple

samples and industries and to understand where differences occur. The outcome variables are

customer satisfaction and share of business. The results show that the impact of each

component of the Marketing Mix varies by sample. In no two samples do the same components

of the Marketing Mix show a significant impact on customer satisfaction. This does not diminish

the importance of the Marketing Mix, but it shows that a careful evaluation of individual

samples is necessary. The impact of customer satisfaction on share of business is significant in

most samples, but not all of them. As a result of this research future researchers should

investigate why differences occur between the samples. Managers should take away that they

must perform customer service studies in their own company and that the studies must be

repeated in regular intervals.

iii

Dedication

This dissertation is dedicated to my family who always supported me.

iv

Acknowledgments

My first thanks go to Dr. Douglas M. Lambert for all the advice, guidance, and support he

provided me with. His tireless pursuit of perfection is remarkable and hopefully my work is a

reflection of this spirit. He is the best editor I have had and he sets an example of

professionalism that we all should strive to achieve. In addition, I would like to thank him for

providing me with all the data for this dissertation.

I would like to also thank Dr. A. Michael Knemeyer for the insightful comments throughout the

writing of this dissertation. His great attitude and skill as a researcher have made positive

contributions to the quality of my work. Special thanks go to Dr. Keely Croxton for her counsel

regarding the dissertation. It is remarkable that she let me defend my dissertation within days

of her giving birth to her beautiful daughter. I would also like to thank Marley for not wanting to

come out during my defense, which would have caused an interesting situation.

Next, I would like to extend my gratitude to several special people in the Fisher College of

Business. I am grateful to Drs. Martha Cooper, Walter Zinn, John Saldanha, Rao Unnava,

Thomas Otter, Bob Leone, and Michael Browne for teaching the classes and seminars that

taught me so much. I am especially thankful to Shirley Gaddis who always took care of me. I

also appreciate the support from my friends Francois Charvet, Matias Enz, Steve Robeano,

Sebastián García-Dastugue, Chris Randall, Ned Sandlin, Tim Pettit, Ping Wang, Jason Miller, and

http://ar.linkedin.com/in/sebastiangd

v

Matt Schwieterman. The members of the Global Supply Chain Forum have also provided me

with valuable feedback on various stages of the dissertation.

Last but not least, I would like to thank my family because without them none of this would

have been possible. First, I have to acknowledge my mother Maria Leuschner and my father

Wolfgang Leuschner. Only with their sacrifice, devotion and support was I able to do all the

things I did. Next, I have to thank my grandparents Alexandru and Stella Ioanitescu who were

the first ones to show me what hard work, dedication and perseverance really mean. Then, I

also would like to specifically mention my uncle Emil, my other uncle Lothar and my aunt Heidi.

Finally, I thank all the special people in my life who have cheered me on and kept me going.

vi

Vita

2004 ........................................................ B.S. Business Administration, University of Nevada

2006 ........................................................ M.B.A., University of Nevada

2009 ........................................................ M.A. Business, The Ohio State University

Publications

Lambert, Douglas M., Rudolf Leuschner, and Dale S. Rogers, “Implementing and Sustaining the

Supply Chain Management Processes,” in Douglas M. Lambert (editor), Supply Chain

Management: Processes, Partnerships, Performance, Third Edition, Sarasota, FL: Supply Chain

Management Institute, 2008, pp. 235-254.

Carter, Craig R., Rudolf Leuschner, and Dale S. Rogers (2007) “A Citation Analysis of the Journal

of Supply Chain Management: An Examination of Social Networks,” The Journal of Supply Chain

Management, Vol. 43, No. 2, pp. 15–28.

Rogers, Dale S. and Rudolf Leuschner (2004) “Supply Chain Management: Retrospective and

Prospective,” Journal of Marketing Theory and Practice, Vol. 12, No. 4, pp. 60-65.

Fields of Study

Major Field: Business Administration

Area of Specialization: Logistics

Minor Field: Marketing

vii

Table of Contents

Abstract ............................................................................................................................................ ii

Dedication ....................................................................................................................................... iii

Acknowledgments........................................................................................................................... iv

Vita .................................................................................................................................................. vi

Table of Contents ........................................................................................................................... vii

List of Tables ................................................................................................................................... xi

List of Figures ................................................................................................................................ xvii

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

1.1. Background ...................................................................................................................... 2

1.2. Scope of the Research ..................................................................................................... 4

1.3. Objectives and Research Questions ................................................................................ 6

1.4. Research Hypotheses ...................................................................................................... 8

1.5. Methodology and Research Design ............................................................................... 10

1.6. Contributions and Future Research ............................................................................... 13

viii

1.7. Organization .................................................................................................................. 15

CHAPTER 2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT ....................................... 16

2.1. Introduction and Origins of Customer Service .............................................................. 17

2.2. Customer Service in Marketing and Logistics ................................................................ 25

2.3. Customer Satisfaction and Firm Performance .............................................................. 44

2.4. Hypothesis Development .............................................................................................. 52

2.5. Summary ........................................................................................................................ 58

CHAPTER 3. METHODOLOGY ..................................................................................................... 59

3.1. Data Collection .............................................................................................................. 60

3.2. Overview of the Samples ............................................................................................... 64

3.3. Data Analysis Preparation ............................................................................................. 69

3.4. Overview of Questions Used in the Samples ................................................................. 76

3.5. Structural Equation Modeling ....................................................................................... 83

3.6. Measurement Model Results for Sample A-1 ............................................................... 87



ix

3.9. Measurement Model Results for Sample B-1.............................................................. 101

3.10. Measurement Model Results for Sample B-2.............................................................. 106

3.11. Measurement Model Results for Sample C-1.............................................................. 110

3.12. Measurement Model Results for Sample D-1 ............................................................. 114



3.15. Summary ...................................................................................................................... 129

CHAPTER 4. RESULTS................................................................................................................ 130

4.1. Overview of the Results Evaluation ............................................................................. 131

4.2. A-1 Blood Banking Reagents Sample Results .............................................................. 132

4.3. A-2 Coagulation Reagents Sample Results .................................................................. 135

4.4. A-3 Coagulation Reagents Sample Results .................................................................. 137

4.5. B-1 Professional Video Tape Sample Results ............................................................... 139

4.6. B-2 Consumer Video Tape Sample Results .................................................................. 141

4.7. C-1 Plastics Resin Sample Results ................................................................................ 143

4.8. D-1 Golf Balls Sample Results ...................................................................................... 145

x

4.9. D-2 Golf Clubs Sample Results ..................................................................................... 147

4.10. D-3 Golf Shoes Sample Results .................................................................................... 149

4.11. Sample Comparison ..................................................................................................... 151

4.12. Summary ...................................................................................................................... 155

CHAPTER 5. CONCLUSIONS ...................................................................................................... 156

5.1. Summary of Research Purpose .................................................................................... 157

5.2. Review of Research Objectives and Hypotheses ......................................................... 158

5.3. Summary of Findings ................................................................................................... 161

5.4. Research Limitations ................................................................................................... 163

5.5. Opportunities for Future Research .............................................................................. 165

5.6. Implications for Theory ................................................................................................ 166

5.7. Implications for Practice .............................................................................................. 167

5.8. Overall Conclusions ..................................................................................................... 169

References ................................................................................................................................... 170

xi

List of Tables

Table 1: Sample Overview ............................................................................................................. 11

Table 2: Effective Sample Size ........................................................................................................ 12

Table 3: Summary of Results of Previous Customer Service Studies ............................................. 23

Table 4: Constructs of the Marketing Mix ..................................................................................... 43

Table 5: Outcome Variables in Customer Service Studies ............................................................. 51

Table 6: Overview of the Samples ................................................................................................. 64

Table 7: Number of Questions and Usable Cases .......................................................................... 71

Table 8: Summary of Composite Formation Methods................................................................... 74

Table 9: Product Attributes Across all Sample ............................................................................... 77

Table 10: Price Attributes Across all Samples ................................................................................ 80

Table 11: Promotion/Personal Selling Attributes Across all Samples ............................................ 80

Table 12: Place/Logistics Attributes Across all Samples ................................................................ 82

Table 13: A-1 Measurement Model Product Construct Loadings ................................................. 88

xii

Table 14: A-1 Measurement Model Price Construct Loadings ...................................................... 89

Table 15: A-1 Measurement Model Promotion Construct Loadings ............................................. 89

Table 16: A-1 Measurement Model Place Construct Loadings ...................................................... 90

Table 17: A-1 Discriminant Validity Test Results ........................................................................... 91





Table 22: A-2 Discriminant Validity Test Results ........................................................................... 96





Table 27: A-3 Discriminant Validity Test Results ......................................................................... 100

Table 28: B-1 Measurement Model Product Construct Loadings ................................................ 102

xiii

Table 29: B-1 Measurement Model Price Construct Loadings .................................................... 103

Table 30: B-1 Measurement Model Promotion Construct Loadings ........................................... 103

Table 31: B-1 Measurement Model Place Construct Loadings .................................................... 104

Table 32: B-1 Discriminant Validity Testing Results ..................................................................... 105

Table 33: B-2 Measurement Model Product Construct Loadings ................................................ 106

Table 34: B-2 Measurement Model Price Construct Loadings .................................................... 107

Table 35: B-2 Measurement Model Promotion Construct Loadings ........................................... 108

Table 36: B-2 Measurement Model Place Construct Loadings .................................................... 108

Table 37: B-2 Discriminant Validity Testing Results ..................................................................... 109

Table 38: C-1 Measurement Model Product Construct Loadings ................................................ 110

Table 39: C-1 Measurement Model Product Construct Loadings ................................................ 111

Table 40: C-1 Measurement Model Promotion Construct Loadings ........................................... 111

Table 41: C-1 Measurement Model Place Construct Loadings .................................................... 112

Table 42: C-1 Discriminant Validity Testing Results ..................................................................... 113

Table 43: D-1 Measurement Model Product Construct Loadings ............................................... 115

xiv

Table 44: D-1 Measurement Model Price Construct Loadings .................................................... 116

Table 45: D-1 Measurement Model Promotion Construct Loadings ........................................... 116

Table 46: D-1 Measurement Model Place Construct Loadings .................................................... 117

Table 47: D-1 Discriminant Validity Testing Results .................................................................... 118











Table 58: A-1 Blood Banking Results............................................................................................ 133

xv

Table 59: A-1 Blood Banking Results Alternative Model ............................................................. 134

Table 60: A-2 Coagulation Reagents Results ............................................................................... 135

Table 61: A-2 Coagulation Reagents Results Alternative Model ................................................. 136

Table 62: A-3 Coagulation Reagents Results ............................................................................... 137

Table 63: A-3 Coagulation Reagents Results Alternative Model ................................................. 138

Table 64: B-1 Professional Tape Results ...................................................................................... 140

Table 65: B-1 Professional Tape Results Alternative Model ........................................................ 140

Table 66: B-2 Consumer Tape Results ......................................................................................... 141

Table 67: B-2 Consumer Tape Results Alternative Model ........................................................... 142

Table 68: C-1 Plastic Resin Results ............................................................................................... 143

Table 69: C-1 Plastic Resin Results Alternative Model ................................................................. 144

Table 70: D-1 Golf Balls Results ................................................................................................... 145

Table 71: D-1 Golf Balls Results Alternative Model ..................................................................... 146

Table 72: D-2 Golf Clubs Results .................................................................................................. 147

Table 73: D-2 Golf Clubs Results Alternative Model .................................................................... 148

xvi

Table 74: D-3 Golf Shoes Results ................................................................................................. 149

Table 75: D-3 Golf Clubs Results Alternative Model .................................................................... 150

Table 76: Overall Impact of the Marketing Mix on Customer Satisfaction ................................. 152

Table 77: Overall Impact of Customer Satisfaction on Share of Business ................................... 153

Table 78: Overall Impact of Customer Satisfaction on Preferred Share of Business ................... 154

Table 79: Overall Impact of the Marketing Mix on Customer Satisfaction ................................. 161

Table 80: Overall Impact of Customer Satisfaction on Share of Business ................................... 162

xvii

List of Figures

Figure 1: Conceptual Model with Hypotheses ................................................................................. 9

Figure 2: A Model of Service Quality Improvement and Profitability ............................................ 48

Figure 3: Conceptual Model ........................................................................................................... 53

Figure 4: Conceptual Model with Hypotheses ............................................................................... 57

Figure 5: Structural Model and Hypotheses ................................................................................ 131

1

CHAPTER 1.

INTRODUCTION

Traditionally, logistics has been viewed as a cost center in companies and the function’s primary

contributions to the bottom line are cost and asset reductions. However, there is some

evidence that logistics attributes have a stronger and more consistent influence on customer

satisfaction and share of business than the factors that are commonly attributed to the

marketing function (Lambert and Harrington 1989; Sterling and Lambert 1988). By comparing

logistics attributes to other attributes, in multiple industries, it can be determined whether

logistics consistently has a stronger impact on customer satisfaction and share of business. In

this dissertation, data from several industries are used: health services, electronics, plastics, and

sporting goods. In order to gain a better understanding of the factors that contribute

consistently to business performance nine distinct samples are analyzed. The results suggest

that managers should look beyond the common belief that logistics can only contribute cost and

asset reductions.

2

1.1. Background

Customer service has been an important research stream in both the marketing and the logistics

areas. Early academic work in Marketing included a large number of areas that today would be

considered as logistics activities, like transportation and distribution (Shaw 1915). Over time

marketing and logistics became more specialized, just as businesses became more functionally

specialized. The problem with functional silos is that both Marketing and Logistics functions can

influence customer service but if their actions are not coordinated, it can lead to suboptimal

decisions. More general frameworks like the “Marketing Concept” and the “Marketing Mix”

incorporate elements from marketing and logistics.

The “Marketing Concept” is the philosophy that firms should analyze the needs of their

customers, then make decisions to satisfy those needs and do so better than the competition

(Kotler 1967). The “Marketing Mix” (Borden 1953) activities have often been conceptualized as

the “Four P’s” of marketing (McCarthy 1960), product, price, promotion, and place. Once the

channels of distribution have been selected, “place” generally occurs in the logistics function as

time and place utilities are created. For this reason, “place” is synonymous with logistics service

(Coyle, Bardi and Langley 1992, Stock and Lambert 2001). Marketing and logistics are involved

in the Marketing Mix, yet conflicting objectives can hinder effective integration of service

activities (Sterling and Lambert 1988; Sterling and Lambert 1989). Customer service is a

boundary-spanning activity that takes place within and beyond the firm (La Londe, Cooper and

Noordewier 1988; Rinehart, Cooper and Wagenheim 1989). Integration within the firm should

3

focus on marketing and logistics activities that interface with the customer (Rinehart, Cooper

and Wagenheim 1989).

A problem facing many manufacturing firms when marketing to downstream members of their

supply chain is the integration of logistics customer service with the other components of the

Marketing Mix: product, price, and promotion (Innis and La Londe 1994; Sterling and Lambert

1988; Sterling and Lambert 1989). How management allocates scarce resources to the

components of the Marketing Mix has a significant impact on the market share and profitability

of a company (Innis and La Londe 1994, Leuthesser and Kohli 1995; Sterling and Lambert 1988;

Sterling and Lambert 1989). Logistics scholars have attempted to understand how logistics

activities affect customer service, but often this happened without consideration of a broad set

of marketing activities (Innis and La Londe, 1994; Sterling and Lambert 1988; Sterling and

Lambert 1989). Although the link between marketing and logistics customer service has been

documented in several studies (Emerson and Grimm, 1996; Emerson and Grimm 1998; Innis and

La Londe 1994; Lambert and Harrington 1989; Sterling and Lambert 1988; Sterling and Lambert

1989) other studies focused exclusively on either logistics (Mentzer, Flint and Kent 1999;

Mentzer, Flint and Hult 2001; Stank, Goldsby and Vickery 1999; Stank, et al. 2003) or marketing

(Cronin and Taylor 1992; Parasurman, Zeithaml and Berry 1985; Parasurman, Zeithaml and Berry

1988; Parasurman, Zeithaml and Berry 1991).

4

1.2. Scope of the Research

The main goal for this research was to develop a model that shows the contribution of logistics

relative to the other components of the Marketing Mix across several industries. By comparing

the four components of the Marketing Mix in multiple industries, it can be determined whether

logistics consistently has a stronger impact on customer satisfaction and share of business. Such

a result could help change the common belief that logistics can only contribute cost and asset

reductions.

Many previous studies on customer service focused only on a single industry (Innis and La Londe

1994; Stank, Goldsby and Vickery, 1999; Sterling and Lambert 1988) and few were replication

studies (Lambert and Harrinton, 1990; Lambert, Lewis and Stock 1993; Stank, et al. 2003). As

such, no prior research can claim generalizability. Generalizability provides the confidence that

a theoretical model can be expanded beyond the situation in which it was developed. If a study

is conducted in one industry, then the results may be valid only for that industry. A customer

service model with a more universal application was called for in several previous studies (Davis-

Sramek, Mentzer and Stank 2008; Mentzer, Flint and Hult 2001; Stank, Goldsby and Vickery

1999; Stank, et al. 2003) and this research addresses that need.

By using a multi-industry approach with nine samples, a model can be developed on one sample

and then validated on the others (Hubbard and Armstrong 1994; Hubbard and Vetter 1996).

This approach yields stronger results because it minimizes the chance for misspecification of the

model (Ehrenberg 2004). The need for replication has been voiced several times in the past

(Furchtgott 1984, Lubin 1957, Sterling, Rosenbaum and Weinkam 1995). Often, researchers and

5

reviewers seem biased toward publishing research that reports significant results (Rosenthal

1979). This leads to a majority of published studies showing significant results, even if Type I

errors cause them. In addition, a large number of studies showing non-significant results are

not published.

Lindsay and Ehrenberg (1993) offer guidelines for designing replication studies. If a study is

performed for the first time, the result can be regarded as one-off. One can ask, under what

conditions, if any, will it hold again? Would the same result be obtained at a later point in time?

Would the result hold in a different situation? If the study is repeated, these questions can be

answered. It is important to note that replicated studies are not identical. First, identical

replication is virtually impossible and second pointless because that would mean the results

must be the same. If the same result is obtained even with varying conditions, researchers can

note these conditions and investigate why they did not change the result. For this research,

different products, industries and points in time are the major conditions that may influence the

outcomes. There are two types of replications, close and differentiated. Close replications

attempt to keep as many of the conditions constant as possible. An example would be the two

samples on coagulation reagents (see samples A-2 and A-3) used in this research. Differentiated

replications involve major differences, such as different industries, different products, and

different position in the supply chain. The other samples would fall into that category.

Generally with close replication one expects to see the same result, while under differentiated

replication, variations are more likely.

6

1.3. Objectives and Research Questions

One framework for determining and assessing all variables important for selecting and

evaluating suppliers is the framework first presented by Lambert and Zemke (1982). Since the

goal of this research is to develop a generalizable model, it is best to start with more variables

and reduce them as the need arises during scale purification. The variables that buyers in

companies use to select and evaluate suppliers can be summarized into one of the four

components of the Marketing Mix: product, price, promotion/personal selling and

place/logistics. The product construct was made up of attributes describing the performance of

the product. Price contained attributes regarding competitiveness of pricing and the

satisfaction with billing procedures. Promotion (including personal selling) had attributes

related to advertising efforts and salesperson performance. Place (logistics) evaluated different

aspects of logistics performance. The outcomes measured in this research are: customer

satisfaction and share of business. Customer satisfaction is the overall evaluation of satisfaction

with a supplier. Share of business denotes the percentage of business given to a supplier.

The suppliers with better products will usually be rewarded with higher customer satisfaction.

Lower prices also mean generally higher customer satisfaction. Satisfaction with the

salesperson also has an impact on overall satisfaction. Suppliers who can deliver their products

on time and without errors can create higher customer satisfaction as well. Each of these four

relationships was tested in this dissertation in addition to the impact that customer satisfaction

has on share of business. These relationships were tested on a range of samples from several

industries.

7

The specific research questions for this dissertation are:

1. What are multi-item scales to assess the performance of customer service elements in

business-to-business relationships across industries?

2. What is the relative importance of the components of the Marketing Mix on customer

satisfaction in business-to-business settings in several industries?

3. What is the influence of customer satisfaction on share of business?

8

1.4. Research Hypotheses

The four components of the marketing mix were all believed to influence the level of customer

satisfaction (Emerson and Grimm 1998; Innis and La Londe 1994; Lambert and Harrington 1989;

Sterling and Lambert 1988). Therefore, the first hypothesis was:

H1: All components of the Marketing Mix contribute to customer satisfaction

More specifically, the relationship between the individual components of the Marketing Mix

were analyzed. The nature of all the relationships is projected to be positive and significant.

H1a: Product has a significant impact on customer satisfaction.

H1b: Price has a significant impact on customer satisfaction.

H1c: Promotion/personal selling has a significant impact on customer satisfaction.

H1d: Place/logistics has a significant impact on customer satisfaction.

While customer satisfaction is an important construct in the literature, it does not directly

translate into profitability or market share. There is evidence that satisfaction can and should

be connected to “hard” financial measures (Rust, Zahorik and Keiningham 1996). Share of

business is a good indicator of the financial success of a business-to-business relationship.

Focusing on expanding business with current customers rather than attracting new customers

has been referred to as the “Leaky Bucket Theory” (Brown, et al. 2005; Dowling and Uncles

1997), which holds that over time, customers defect and business is lost just as water is lost

9

through the holes in a bucket. There are two ways to maintain the water level in the bucket: put

more water into the bucket or plug the holes. Defecting customers can either be replaced by

new customers or by increased volume from the remaining customers (Brown, et al. 2005;

Dowling and Uncles 1997). It is often suggested that it is a better strategy to increase the share

of business than to attract new customers (Fornell and Wernerfeld 1987; Rust, Zahorik and

Keiningham 1996). In order to understand the effect of customer satisfaction on share of

business, the direct link was tested. It is believed to be a generally positive relationship (Rust,

Zahorik and Keiningham 1996). In addition, indirect effects between the Marketing Mix and

share of business are assessed.

H2: Customer satisfaction has a significant impact on share of business.

All of these hypotheses were tested. The research model with the hypotheses is displayed in

Figure 1.

Figure 1: Research Model with Hypotheses

10

1.5. Methodology and Research Design

A database of nine customer satisfaction surveys was used for this dissertation. This section

begins with a description of the data collection methodology. Next, there is a brief overview of

the samples. Then, the hypothesis testing is described.

Questionnaire Design

The data were collected using mail questionnaires which are described in more detail in Chapter

3. The service attributes in each survey were identified during in-depth, personal interviews

with key decision-makers in the sponsoring organization and buyers in 20 to 32 of each

sponsor’s customer firms. Those who determined how much business was given to suppliers

were asked to review the attributes and (1) describe any that they used that were not on the list

and (2) evaluate the wording to determine if it was clear to them what each question meant.

Attributes were presented by product, price, promotion and place to make it easier for those

interviewed to identify attributes that they considered that were not included. The objective

was to compile a set of comprehensive and meaningful questions for the mail survey. The

attributes were randomized for the surveys.

Next, mail questionnaires were sent to representative decision-makers in firms served by major

suppliers in the respective industry. The sponsor of the research was not identified. The

questionnaires consisted of the following:

Part A: importance of attributes used to select and evaluate suppliers and the performance of the top three suppliers on those attributes.

11

Part B: measurement of overall performance.

Part C: expected performance levels.

Part D: meaningful demographic data.

Overview of the Samples

For this study, nine samples were used for which the data were collected using the methodology

that was described previously. An overview of the samples is presented in Table 1.

Industry Sample Name Sample Size Responses Response Rate

Health services A-1 (Blood Banking Reagents) 2,015 754 37.42%

Health services A-2 (Coagulation Reagents) 1,005 299 29.75%

Health services A-3 (Coagulation Reagents) 667 212 31.78%

Electronics B-1 (Professional Tape) 1,369 342 24.98%

Electronics B-2 (Consumer Blank Tape) 434 77 17.74%

Plastics C-1 (Commodity Resin) 1,854 540 29.13%

Sporting goods D-1 (Golf Balls) 1,012 134 13.24%

Sporting goods D-2 (Golf Clubs) 2,240 172 7.68%

Sporting goods D-3 (Golf Shoes) 1,001 95 9.49%

Table 1: Sample Overview

The nine samples involve five distinct industries. The overall response rates vary from 7.68 to

37.42 percent. While the response rates in the Sporting goods industry are fairly low, this is not

unusual due to the fact that surveys of retailers generally have lower response rates (Ellram, La

Londe and Weber 1999). The sample sizes are sufficiently large and non-response bias is

assessed in two ways (Armstrong and Overton 1979; Lambert and Harrington). Therefore,

reliable conclusions can be drawn (Boyer and Swink 2008). The number of responses in each

sample is adequate for the type of analysis that is performed because each respondent was

12

asked to evaluate up to three suppliers, thus effectively increasing the number of cases for the

analysis. In Table 2, the effective sample size and the number of attributes is shown.

Industry Sample Name Responses Usable Cases Attributes

Health services A-1 (Blood Banking Reagents) 753 1,400 88

Health services A-2 (Coagulation Reagents) 299 435 78

Health services A-3 (Coagulation Reagents) 205 279 71

Electronics B-1 (Professional Tape) 347 508 83

Electronics B-2 (Consumer Blank Tape) 113 229 69

Plastics C-1 (Commodity Resin) 534 759 91

Sporting goods D-1 (Golf Balls) 141 288 130

Sporting goods D-2 (Golf Clubs) 120 265 149

Sporting goods D-3 (Golf Shoes) 89 205 135

Table 2: Effective Sample Size

Hypothesis Testing

All of the main hypotheses were tested using structural equation modeling (Bagozzi and Yi 1988;

Bollen 1989). Each of the four components of the Marketing Mix was modeled as a latent

variable with multiple indicators. The structural regression equations were used to test the

hypotheses. The advantage of this approach is the ability to use multi-item constructs and

jointly estimate structural parameters that correspond to the research hypotheses. Each sample

was analyzed individually. Then, values of the latent variables from the individual samples were

compared. In the industries where multiple samples exist, exploratory model development and

confirmatory analysis were performed.

13

1.6. Contributions and Future Research

The customer satisfaction and revenue implications of superior logistics service should not be

ignored. But such an argument can only be made with solid evidence. If the place/logistics

construct has a consistently higher influence on customer satisfaction and share of business,

then that could provide the necessary evidence. Determining if this is indeed the case is the

main motivation for this dissertation.

The most important theoretical contribution is the extension of the frameworks integrating the

Marketing Mix variables into a customer service context (Innis and La Londe, 1994; Lambert and

Harrington 1989; Lambert, Lewis and Stock 1993; Sterling and Lambert 1988; Sterling and

Lambert 1989). This extends previous theory by including the outcome variables customer

satisfaction and share of business. By using multiple samples from multiple industries, a stable

model can be developed (Rentz 1987). The power of replication should not be underestimated

because theoretical models should be tested extensively before theory can be accepted as valid

(Ehrenberg 2004).

The strength of replication lies not only in attempting to obtain the same result in multiple

samples, but also to test the impact of the Marketing Mix on customer satisfaction and the

impact of customer satisfaction on share of business. Therefore it is not only important to

determine if any of the 4P’s is significant more often than the others, but also to determine if all

of them have an impact at all. As most of the replication takes the form of differential

replication, larger deviations in the results are expected (Lindsay and Ehrenberg 1993).

14

By providing a number of validated scales, the research also has the potential to provide

direction for those interested in building questionnaires for measuring customer service

attributes. Scale purification will provide guidance on which questions are most applicable in

each industry. The research evaluates the data collection approach recommended by Lambert

and Zemke (1982) for identifying service attributes that are important for customers. This will

help managers who want to identify a useful set of questions for customer service and customer

satisfaction attributes. The methodology to identify attributes is general enough that it can be

used in any industry and it provides an accurate representation of all important attributes.

Managers can use the results of this study as a starting point to determine which attributes

increase customer satisfaction and share of business in their firms.

15

1.7. Organization

In Chapter 2, the relevant literature is reviewed. It includes sections on the origins of customer

service, customer service in marketing and logistics, and customer satisfaction. The literature is

then used to build the research hypotheses. Chapter 3 contains a description of the

methodology and contains sections on data collection, questionnaire development, sample

description and measurement model development. The results are presented in Chapter 4.

Chapter 5 includes a summary of the research purpose, a review of research objectives and

hypotheses, a summary of findings, research limitations, opportunities for future research, and

implications for theory and practice.

16

CHAPTER 2.

LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

In this chapter, a review of the relevant literature in the customer service domain is provided.

The review is divided in the following subsections: introduction and origins of customer service,

customer service research in the fields of marketing and logistics, and customer satisfaction and

firm performance. Following the literature review, the conceptual model is presented and the

research hypotheses are developed. The chapter concludes with a summary.

17

2.1. Introduction and Origins of Customer Service

Customer service research has a long tradition in business and understanding how to better

serve customers has always been part of the Marketing domain. This section will include the

development of customer service as a field of study. All research in this section is based on data

from surveys or experiments. The main analysis technique is rank-ordered lists, although some

studies used analysis of variance and this is a major difference from later work in this area. Early

in the twentieth century, business activities were classified into “three great divisions” (Shaw

1915):

the activities of production, which change the form of materials the activities of distribution, which change the place and ownership of the commodities

thus produced the facilitating activities which aid and supplement the operations of production and

distribution

“The accepted system of distribution had been built up on the satisfying of staple needs” (Shaw

1915). The ability of manufacturers to produce to the demand of the market was reached

quickly and then emphasis shifted to aspects like distribution performance in order to reach an

expanded market base (Shaw 1915).

Later, distribution was separated into its transportation and storage functions and the

importance of service was recognized (Clark 1922). Service is, “as far as the purchaser is

concerned, a part of the product, a part of the thing which he is purchasing” (Clark 1922). Later,

the importance of physical distribution was once again highlighted by including a chapter on it in

a Marketing textbook (Clark 1924). This was the first use of the term physical distribution

(Bowersox, Smykay and LaLonde 1968) which would be replaced by the term logistics in 1985

18

when the National Council of Physical Distribution Management changed its name to the

Council of Logistics Management (Bowersox 2007).

Until the early 1950’s, commercial and academic interest in distribution was “traditionally

fragmentary and most often a secondary consideration” (Bowersox 1969). “Manufacturers all

too often fail to realize the marketing penalty they pay when even a small proportion of the

outlets normally handling their type of product does not have their brand in stock. Generally

speaking, all marketing, selling, and advertising effort which has been put behind the product

fails to the extent that potential buyers do not find it on hand when they are buying” (Brown

1955). In a survey of factors that affect industrial buying decisions, the important areas were

identified as product quality, delivery performance, quality of salesperson, price, and effective

communication (Klass 1969).

It was suggested that stockouts, excess delivery time, or excess variability of delivery time all can

result in lost sales (LeKashman and Stolle 1965). This concept was later expanded by specifying

six steps to help companies achieve cost reductions through improved customer service

(Hutchinson and Stolle 1968):

Define the elements of service Determine the customer’s viewpoint Design a competitive service package Develop a program to sell service Market-test the program Establish performance controls

In an experimental approach, factors that are considered when selecting a supplier were

identified (Dickson 1966). The subject was assigned to read one of four hypothetical situations,

19

put himself in the position of the purchasing manager, and rate the importance of 23 purchasing

factors. The ranking of the factors did vary by individual case situation but quality, delivery, and

past performance were always in the top five and the cumulative ranking of the top five factors

were quality, delivery, performance, warranties, and facilities. Analysis of variance on the factor

rating showed that there was general agreement on factors with high and low importance, but

not for factors between the extremes (Dickson 1966). This was one of the first studies that

highlighted the importance of good delivery systems in purchasing decisions.

Researchers in another study attempted to determine the relative importance of determinants

of industrial buyers’ vendor selection (Wind, Green and Robinson 1968). Subjects were asked to

consider a list of 10 vendor characteristics and assign 100 to the most important, zero to the

least important and proportional values to the remainder (Wind, Green and Robinson 1968).

These results indicated agreement among the buyers as to the ranking of characteristics and

quality/price ratio and delivery reliability were indicated as the two most important

characteristics. In contrast, reciprocity and personal benefits to the buyer were grouped at the

bottom. This study confirmed the importance of logistics aspects in customer service, which in

this case was conceptualized as delivery reliability.

A similar approach was used in a later study by examining the role of customer service in

business-to-business purchasing situations (Cunningham and Roberts 1974). Buyers were asked

to name the five most important service factors and to rank them in order. Service factors were

then compared by three criteria: times mentioned, times ranked in top 5, times ranked first.

The rankings from combined results were (1) delivery reliability, (2) technical advice, (3) test

20

facilities, and (4) replacement guarantee. It was also found that 80% of the buyers formed a

favorable impression of suppliers if they would meet the buyers’ need for quality, service, and

price (Cunningham and Roberts 1974).

The first research to examine the role of physical distribution in industrial purchasing decisions

was by Perreault and Russ (1976c) who studied the importance of physical distribution, the

determinants of its importance, and the determinants of buyer satisfaction regarding physical

distribution. Aggregate results indicated the five most important supplier characteristics were

quality, distribution service, price, supplier management, and distance. The results showed the

relative importance of supplier characteristics varied widely across the six product categories

(semiconductors, bearings, acid, sheet plastic, fasteners, and lubricants), but only quality and

distribution service were consistently ranked as first and second most important. The highest

satisfaction was with billing procedures, order methods, and accuracy in filling orders, while the

lowest satisfaction was with delivery time and delivery time variation (Perreault and Russ 1976c).

The major contribution of this study is that customer service was assessed across different

products and industries.

Gilmour et al. (1977) examined the service provided by major suppliers in the scientific

instrument and supplies industry in Australia. Each respondent was shown a list of 17 customer

service elements and asked to rank order the five most important. The average importance of

each of the nine most mentioned elements was noted for all customers, for all suppliers and for

five types of customer organizations. The five most important purchasing elements for all

customers were availability, after-sales service, delivery reliability, delivery time, and technical

21

competence of the representatives. There were some differences in the rankings depending on

the segment which indicates a possible benefit for applying different customer service policies in

different segments (Gilmour, et al. 1977). However, the importance of the elements across the

five customer groups was quite similar, which supports the conclusion that customer service is

perceived uniformly.

The relative importance of physical distribution aspects continued to be an area of interest.

Anderson, Jerman and Constantin (1978) used a mail survey to ask respondents to make 20

paired comparisons of goals for physical distribution. The comparisons then were converted to

an interval scale. The results of the rankings were (1) order cycle time reliability, (2) percent

orders filled, (3) minimum physical distribution cost, (4) minimum order cycle time, and (5)

minimum damage in transit. The importance of elements is the same whether the respondent

was from top or middle management (Anderson, Jerman and Constantin 1978).

A mail survey of manufacturers and wholesalers in the over-the-counter pharmaceutical

products industry compared manufacturer and wholesaler views of customer service (Levy

1978). The wholesaler questionnaire requested information on the wholesalers’ perceptions of

suppliers’ service performance. The manufacturers’ questionnaire requested information on

their perceptions of the importance of each service to wholesale customers. The results of the

rank ordering of the customer service elements in terms of perceived dollar value were (1) fill

rate, (2) terms of sale, (3) lead time, (4) order placement policy, and (5) consistent delivery (Levy

1978).

22

A survey of purchasing managers (located in two industrial areas in Brazil) on physical

distribution service added more findings to the body of knowledge about customer service (Luce

1982). Respondents were asked to rank order the five overall purchasing factors and the five

specific physical distribution elements which they perceived as most important. The overall

purchasing factors mentioned most often were: quality, price, physical distribution, location,

and minimum order size. The five specific physical distribution elements were: accuracy in filling

orders, average delivery time, rush services and billing, action on complaints, and order status

information (Luce 1982).

Jackson, Keith and Burdick (1986) studied the perceived relative importance of six physical

distribution service components. Purchasing agents from 25 large industrial manufacturing

firms were randomly assigned to one product type and one buy class condition. The elements

ranked in the following order: (1) consistent delivery, (2) in-stock, (3) lead time, (4) cooperation,

and (5) order processing information. The results supported earlier research which found order

cycle time and in-stock performance to be important physical distribution service elements. No

differences were found based on size of firm or industry type (Jackson, Keith and Burdick 1986).

The relationship between service level, the resulting customer satisfaction level, and the

customer’s purchase decision has implications for the entire firm (Mentzer, Gomes and Krapfel

1989). The previously cited research revealed that elements of logistics were among the most

important sub-factors of customer service. It is also apparent that purchasing managers ranked

elements of logistics customer service high. A summary of the results of these studies is shown

in Table 3.

23

Study Research Method Main Findings

(Dickson 1966)

Experiment: read one of four scenarios and rate the importance of 23 purchasing factors

The ranking of the factors did vary by individual case situation but quality, delivery, and past performance were always in the top five and the cumulative ranking of the top five factors were quality, delivery, performance, warranties, and facilities. Analysis of variance on the factor rating showed that there was general agreement on factors with high and low importance, but not for factors between the extremes.

(Wind, Green and Robinson 1968)

Experiment: assign between zero and 100 based on importance to a list of 10 vendor characteristics.

The results indicated agreement among the buyers as to the ranking of characteristics and quality/price ratio and delivery reliability were indicated as the two most important characteristics. In contrast, reciprocity and personal benefits to the buyer were grouped at the bottom.

(Cunningham and Roberts 1974)

Experiment: name the five most important service factors and to rank them in order.

The rankings were (1) delivery reliability, (2) technical advice, (3) test facilities, and (4) replacement guarantee. It was also found that 80% of the buyers formed a favorable impression of suppliers if they would meet the buyers’ need for quality, service, and price.

(Perreault and Russ 1976c)

Survey: Importance and satisfaction of supplier characteristics in six product categories (semiconductors, bearings, acid, sheet plastic, fasteners, and lubricants)

The top five important supplier characteristics were quality, distribution service, price, supplier management, and distance. The relative importance of supplier characteristics varied widely across product categories, but only quality and distribution service were consistently ranked as first and second. The highest satisfaction was with billing procedures, order methods, and accuracy in filling orders, while the lowest satisfaction was with delivery time and delivery time variation.

Continued Table 3: Summary of Results of Previous Customer Service Studies

24

Table 3 continued

Study Research Method Main Findings

(Gilmour, et al. 1977)

Interviews: rank order the five most important of 17 customer service elements.

The five most important purchasing elements for all customers were availability, after-sales service, delivery reliability, delivery time, and technical competence of the representatives. There was some difference of ranking by segment which indicates a possible benefit for applying different customer service policies in different segments.

(Anderson, Jerman and Constantin 1978)

Survey: sales representatives for motor and rail transportation; each respondent completed 20 paired comparisons of goals that were then ranked.

The top five were (1) order cycle time reliability, (2) percent orders filled, (3) minimum physical distribution cost, (4) minimum order cycle time, and (5) minimum damage in transit.

(Levy 1978) Survey: wholesalers’ perceptions of their suppliers’ service performance, and the manufacturers’ perception of the importance of each service to their wholesalers.

The results of the rank ordering of the customer service elements in terms of perceived dollar value were (1) fill rate, (2) terms of sale, (3) lead time, (4) order placement policy, and (5) consistent delivery.

(Jackson, Keith and Burdick 1986)

Experiment: purchasing agents from 25 large industrial manufacturing firms were randomly assigned to one product type and one buy class condition.

The perceived relative importance of six physical distribution service components was assessed. The importance varied across five product types and three buy classes. Overall, the elements ranked in the following order: (1) consistent delivery, (2) in-stock, (3) lead time, (4) cooperation, and (5) order processing information.

25

2.2. Customer Service in Marketing and Logistics

In this section, more recent customer service research is described. The following research has

been published from the mid 1980s onwards and there are differences from the previous

research. Previous research mostly used rank-ordered lists or analysis of variance compared to

regression and structural equation modeling. The second difference is the connection of

customer service elements to outcome variables like customer satisfaction. Customer service

research in the Marketing domain is related to the SERVQUAL framework. In the Logistics

domain research can be divided into studies that solely focus on logistics elements, the logistics

service quality scale, and studies comparing elements related to Marketing and Logistics.

SERVQUAL

For more than two decades, the definition and measurement of service quality has occupied a

prominent position in the Marketing literature. Unlike a physical product where one can often

quantify quality, measuring service quality is different because there are no objective metrics

(Parasurman, Zeithaml and Berry 1985). Exploratory research (Parasurman, Zeithaml and Berry

1985) initially offered support for the idea that service quality is an overall evaluation similar to

an attitude. The difficulty of measuring service quality as a psychometric concept was overcome

by measuring the customer’s expectations on service quality in a general service category and

concurrently the perceptions about the particular firm whose service quality was being assessed.

The difference between expectation scores and perception scores was conceptualized as

perceived service quality. The total SERVQUAL score for service quality was calculated by

26

averaging the difference scores. The findings show that regardless of the type of service,

customers used the same general criteria for making an evaluative judgment about service

quality. Based on those results, it was concluded that it was possible to construct one multi-

item scale that could be used to evaluate universal service quality (Parasurman, Zeithaml and

Berry 1985).

Following the exploratory research, a multi-item scale for surveys was developed (Parasurman,

Zeithaml and Berry 1988). Ten aspects of service quality and 97 individual items were tested.

Each item was converted into two statements, one to measure service expectations about firms

in general and another to measure perceptions about the service performance of a particular

firm. After scale purification, the refined scale had 22 items spread among five dimensions

(Parasurman, Zeithaml and Berry 1988):

Tangibles: Physical facilities, equipment, and appearance of personnel Reliability: Ability to perform the promised service dependably and accurately Responsiveness: Willingness to help customers and provide prompt service Assurance: Knowledge and courtesy of employees and their ability to inspire trust and

confidence Empathy: Caring, individualized attention the firm provides its customers

Despite the obvious popularity of SERVQUAL in literature (Carman 1990; Johnson, Dotson and

Dunlop 1988), several researchers questioned its usefulness in measuring service quality and

proposed alternative approaches (Babakus and Boller 1992; Brown, Churchill and Peter 1993;

Cronin and Taylor 1992; Teas 1993). There is little, if any, theoretical or empirical evidence to

support the relevance of the expected service-perceived service gap as a basis for measuring

service quality (Brown, Churchill and Peter 1993; Cronin and Taylor 1992; Teas 1993). It was

shown that this operationalization of service quality confounds satisfaction and attitude (Cronin

27

and Taylor 1992). In addition to theoretical issues, the usefulness of SERVQUAL for making

managerial decisions is doubtful because numerous aspects of service are not covered and some

of the SERVQUAL questions are vague in some contexts.

Although SERVQUAL was originally intended as a generic measure of service performance

(Parasurman, Zeithaml and Berry 1985), subsequent research has shown that the SERVQUAL

items must be customized to the specific situation to which it is applied (Donnelly, et al. 1995;

Finn and Lamb 1991; Reidenbach and Sandifer-Smallwood 1990). Other researchers argue that

it takes more than a simple adaptation of the SERVQUAL items to effectively address service

quality in some environments (Brown, Churchill and Peter 1993; Carman 1990; Finn and Lamb

1991). It is also important to note that managers and researchers are advised to carefully assess

which issues are important to service quality in a particular situation and to modify the

SERVQUAL scale accordingly or develop a proprietary scale (Parasurman, Zeithaml and Berry

1991):

By design, the iterative procedure retained only those items that are common and relevant to all service firms included in the study. However, by the same token, this procedure may have deleted certain "good" items relevant to some but not all firms. Therefore, while SERVQUAL can be used in its present form to assess and compare service quality across a wide variety of firms or units within a firm, appropriate adaptation of the instrument may be desirable when only a single service is investigated.

While many researchers acknowledge the theoretical validity of the individual items comprising

the SERVQUAL scale, the usability of the conceptualization has been challenged several times

(Babakus and Boller 1992; Carman 1990; Cronin and Taylor 1992; Finn and Lamb 1991). Some

empirical evidence suggests that the proposed delineation of the five components is not

28

consistent when used on different industries (Babakus and Boller 1992; Carman 1990; Cronin

and Taylor 1992; Finn and Lamb 1991). Another unclear issue is the application of SERVQUAL to

business-to-business relationships versus the business-to-consumer context in which it was

developed. Some of the SERVQUAL items did not load on the same constructs when compared

across different types of industries and different situations in subsequent research (Babakus and

Boller 1992; Carman 1990; Cronin and Taylor 1992; Finn and Lamb 1991). This suggests that the

dimensions of service quality may vary between different industries. An additional area of

concern is whether a generic conceptual scheme like that has merit at all. Using the same

questions to evaluate a situation-specific concept like service has not been a successful strategy

in previous research. It would have merit to use the same constructs because it enables

comparing results from several samples.

Simply measuring service quality alone is of limited interest and therefore service quality was

linked to outcome variables in the service marketing literature. While studies in Marketing

identified significant relationships among service quality, marketing variables and profitability

and market share (Buzzell and Gale 1987; Gale 1992; Phillips, Chang and Buzzell 1983), other

researchers have shown that the link between service quality and business performance is

neither straightforward nor simple (Greisig 1994; Rust and Zahorik 1993). Some researchers

have focused on intermediate links between service quality and profitability (Zeithaml, Berry

and Parasurman 1996). The findings offered empirical support for the notion that improving

service quality can increase favorable behavioral intentions on the part of a customer.

29

Cronin and Taylor (1992) developed a competing scale to SERVQUAL called SERVPERF. The main

difference between the two scales is that SERVQUAL is made up of the difference between

actual performance and expectations of performance and SERVPERF only contains actual

performance. It was found that the SERVPERF was an antecedent of customer satisfaction. In

addition, customer satisfaction exerted a stronger influence on purchase intentions than did

service quality (Cronin and Taylor 1992).

The SERVQUAL scale also had a direct influence on logistics research. Stank, Goldsby and

Vickery (1999) used the five dimensions of SERVQUAL to build two scales: operational and

relational service quality. Both dependability and accuracy relate to the consistent quality or

conformance quality aspect of operational performance. The other dimensions, responsiveness,

assurance, and empathy are all aspects of relational performance. Tangibles might also be

viewed as being related to relational performance, at least to some degree, as they encompass

the physical appearance of employees (Stank, Goldsby and Vickery 1999). The logistics

customer service research is reviewed in the next section.

Logistics Customer Service

In both marketing and logistics, the nature of interactions between buyers and service suppliers

has been identified as an important influence on buyer satisfaction and is a significant predictor

of the continuation of a successful business relationship (Daugherty, Stank and Ellinger 1998;

Innis and La Londe 1994; Leuthesser and Kohli 1995). Empirical research revealed that relational

behavior is an important complement to offering quality in determining customer satisfaction

(Leuthesser and Kohli 1995). It was found that service employees that engaged in deliberate

30

efforts to understand their customers’ unique business conditions cause higher levels of buyer

satisfaction (Leuthesser and Kohli 1995). Logistics studies have revealed that both operational

and relational performance relative to the logistics aspect of service quality had significant

positive links to customer satisfaction and repurchase intentions (Daugherty, Stank and Ellinger

1998; Innis and La Londe 1994; Stank, Goldsby and Vickery 1999). Operational elements are

aspects related to product availability, product condition, delivery reliability, and delivery speed.

Relational elements are aspects related to communications and responsiveness.

There are many definitions and descriptions in the literature of how logistics activities create

value for the customer. The most traditional are based on the creation of time and place

utilities (Perreault and Russ 1976). Another approach are the “Seven Rs” that describe the

attributes of the company’s product/service offering that lead to utility creation through

logistics value: the company’s ability to deliver the right product in the right amount at the right

place at the right time for the right customer in the right condition at the right price (Coyle,

Bardi and Langley 1992; Stock and Lambert 2001). This definition implies that a significant part

of the value of a product is created by logistics service. The service-dominant logic of Marketing

also provides evidence for that argument (Vargo and Lusch 2004). Logistics customer service is

often defined as a component of, or used as a substitute for, logistics value (Langley and

Holcomb, 1992). Customer service adds value through three components (La Londe and Zinszer

1976):

31

An activity to satisfy customers’ needs Performance measures to ensure customer satisfaction A philosophy of firm-wide commitment

In a subsequent book customer service was defined as “a process for providing significant value-

added benefits to the supply chain in a cost effective way” (La Londe, Cooper and Noordewier

1988). The supply chain view of this definition points to the notion that the benefits of good

service go beyond the four walls of a company.

Using a sample from the personal products industry, the link between logistics capabilities,

customer satisfaction, customer loyalty, and market share was investigated (Daugherty, Stank

and Ellinger 1998). The path from logistics service through satisfaction to loyalty to market

share is not linear as previously believed. The results of that study indicate that both satisfaction

and loyalty are required to influence market share positively, however this is not a

straightforward process. Positive market share benefits accrue only when firms create

customers that are not only satisfied but also committed to repurchasing from a vendor over

time (Daugherty, Stank and Ellinger 1998). This highlights the importance of measuring the

financial benefits of customer service.

In addition, research has revealed that the relationship between service quality and outcome

measures is complex. For example, Stank, Goldsby and Vickery (1999) found that:

The covariance between operational and relational performance, is supported by a significant positive value.

The relationship between operational performance and customer satisfaction is statistically significant.

Improvements in operational performance yield higher levels of customer satisfaction.

Improvements in relational performance only marginally affect customer satisfaction as evidenced by the weak statistical significance.

32

Customer satisfaction has a highly significant positive effect on customer loyalty.

One issue that was raised was the fact that there could be other constructs that affect customer

satisfaction and loyalty (Stank, Goldsby and Vickery 1999). Operational service performance and

relational service performance are not the only variables affecting customer satisfaction. If

service quality is operationalized just as operational and relational service performance (Stank,

Goldsby and Vickery 1999), then product, pricing and service expectations that are set by the

sales person are neglected. It is very likely, however that such attributes influence the outcome

variables like satisfaction and loyalty. Another limitation is the fact that financial implications

are omitted.

A subsequent study alleviated some of the issues by adding two more constructs: cost

performance and market share (Stank, et al. 2003). Unlike previous studies, the findings show

relational performance had a significant relationship with customer satisfaction, while the

operational performance-satisfaction and cost performance-satisfaction relationships were not

significant. It can be interpreted that operational performance and cost performance are order

qualifiers and relational performance elements are the main drivers of determining which

suppliers are excellent. The link between customer loyalty and market share was significant but

at a lower level than the satisfaction-loyalty link (Stank, et al. 2003).

Because logistics service can be used by managers as a differentiating competitive tool, it is

important to discern whether suppliers and customers have a similar understanding about

logistics service expectations. Data from qualitative interviews showed a close match between

the supplier's perception of what the customer expects and actual customer expectations (Davis

33

and Mentzer 2006). A gap in perceptions about what loyalty means to customers and suppliers

was revealed as well. The more powerful customers had a very behavioral view of loyalty, while

suppliers took a more affective perspective. This is likely due to asymmetry in the relationship

(large customers and small suppliers). The zone of tolerance concept seems to show its effect in

this circumstance as well. The concept portrays service performance as a range rather than a

distinct point. Service levels can vary within the zone without changes in customers’ satisfaction.

Larger customers have a narrower zone, and suppliers differentiate service offerings in order to

cater to the demands of more powerful customers (Davis and Mentzer 2006).

In a later article, the results of the previously described exploratory research were tested with a

survey (Davis-Sramek, Mentzer and Stank 2008). Relational- and operational order fulfillment

are based on the conceptualization of Stank, Goldsby and Vickery (1999). Relational order

fulfillment service was modeled as an antecedent to operational order fulfillment service. The

relational component of order fulfillment was conceptualized like the personnel contact quality

construct in Mentzer, Flint and Hult (2001), which referred to the customer orientation of the

supplier’s customer service contact people. Operational order fulfillment was conceptualized as

driving customer satisfaction. Satisfaction is the result of a cognitive evaluation based on total

purchase experience over time and more specifically it is affected by (1) general satisfaction, (2)

confirmation of expectations, and (3) the distance from the customer’s hypothetical ideal

product. The final outcome is measured as loyalty, which is conceptualized as the causal

relationship between two variables: affective commitment and purchase behavior (Davis-

Sramek, Mentzer and Stank 2008).

34

To examine the model, data were collected from retailers of consumer durable manufacturing

goods (Davis-Sramek, Mentzer and Stank 2008). The results show the existence of a complex,

mediating relationship between satisfaction, affective commitment, and purchase behavior.

Just satisfying customers may not be enough to influence future behavior; forging emotional

bonds and trust in the relationship stems from satisfying customers and consequently influences

purchase behavior. The results justify the importance of looking at the emotional and

behavioral components of loyalty not only as distinctly different constructs, but as a causal

relationship between affective commitment and purchase behavior (Davis-Sramek, Mentzer and

Stank 2008). However, affective commitment and purchase behavior were measured as

perceptions. It would be revealing to see if measuring actual purchase behavior would change

that result.

Logistics Service Quality

In this section the stream of research centered on the logistics service quality scale (LSQ) is

reviewed. The LSQ conceptualization refers to a distinct operationalization of logistics customer

service. The quality of all aspects of logistics service several articles were published. Two

articles involved literature reviews (Bienstock, Mentzer and Bird 1997, Mentzer, Gomes and

Krapfel 1989) and three are based on empirical research (Mentzer, Flint and Hult 2001, Mentzer,

Flint and Kent 1999, Rafiq and Jafaar, 2007).

Twenty-six elements of logistics and customer service reported in the literature were

synthesized in order to obtain a three-dimensional construct composed of availability,

35

timeliness, and quality (Mentzer, Gomes and Krapfel 1989). This structure was generally

supported by later empirical evidence, with adaptations based on qualitative research

(Bienstock, Mentzer and Bird 1997). A multi-item scale of service quality in the logistics context

was developed as an extension of physical distribution service (Mentzer, Flint and Kent 1999).

As a result, service is believed to have considerable value as a competitive advantage input to

strategic planning.

The research project was made up of a qualitative stage and a quantitative stage. In the

qualitative phase, 13 focus group interviews were conducted with customers. The general

topics covered four basic areas (Mentzer, Flint and Kent 1999):

The nature of the participants' work in relation to the sponsoring organization Evaluation of the working relationship with the sponsoring organization Assessment of sponsoring organization’s performance Perceptions of what the sponsoring organization does well or poorly

In the quantitative phase, surveys were mailed and the results were analyzed. Respondents

were divided into 10 separate data sets based on industry, one for scale purification, eight for

scale validation. Respondents who failed to indicate the segment to which they belonged were

excluded from the data analysis. Following the analysis, nine constructs were identified that

make up LSQ (Mentzer, Flint and Kent 1999):

Information Quality: Value of information provided by the supplier. Ordering Procedures: Efficiency and effectiveness of the order process of the supplier. Ordering Release Quantities: Product availability (customers should be the most

satisfied when they are able to obtain the quantities they desire). Timeliness: Whether orders arrive when promised, but more broadly, timeliness is

dependent on the length of time between order placement and receipt.

36

Order Accuracy: How closely actual shipments match orders upon arrival (having the right items, the correct number of items, and no substitutions).

Order Quality: How well products work in two ways: how well they conform to product specifications and how well they meet customers' needs.

Order Condition: Ensuring undamaged products. Order Discrepancy Handling: How well the suppliers deal with issues that the customer

experiences due to the supplier’s fault. Personnel Contact Quality: Whether the customer service employees are

knowledgeable, empathize with the customer’s situation, and help them resolve their problems.

In an attempt to connect customer service with customer satisfaction, the LSQ constructs were

split into two categories, order placement and order receipt (Mentzer, Flint and Hult 2001).

Personnel contact quality, order release quantities, information quality, and ordering

procedures fall into the order placement category. The others, order accuracy, order condition,

order quality, timeliness, and order discrepancy handling fall into the order receipt category.

The two categories then drive customer satisfaction as an outcome construct (Mentzer, Flint

and Hult 2001). The single outcome variable is one of the limitations of this study and “LSQ

must be linked to other customer outcome measures, such as loyalty, word of mouth, and price

sensitivity, as well as supplier outcome measures, such as revenues, market share, and

profitability” (Mentzer, Flint and Hult 2001).

An extension of this scale was used in a survey of logistics managers in the United Kingdom

about their perceptions of third-party logistics (3PL) providers (Rafiq and Jafaar 2007). This was

an independent validation of the LSQ scale. The researchers conceptualized the constructs for

information quality and ordering procedures differently and achieve improved reliability of both

constructs. In contrast to previous research (Mentzer, Flint and Hult 2001), it was found that

the components of LSQ do not contribute equally to customer satisfaction. The functional LSQ

37

elements (personnel contact quality, ordering procedures, order discrepancy handling, and

information quality) are perceived as more important than the technical ones (Rafiq and Jafaar

2007).

Integration of Marketing and Logistics Elements of Customer Service

Organizationally, customer service involves a variety of people at different levels within the

organization and from different functions. A survey from ICSA showed that 51.0% of customer

service respondents report to sales/marketing, 14% to administration, 13% to logistics and the

remaining 22% report to other functions (ICSA 1988). A logistics organizational study shows that

customer service reports to logistics in 56.1% of companies (Bowersox 1987). Customer service

is a pervasive, boundary-spanning activity that takes place from within and beyond the firm and

integration within the firm should focus on marketing and logistics activities as the primary

functions which interface with the customer (Rinehart, Cooper and Wagenheim 1989). However,

traditionally marketing and logistics have evolved separately within many corporations and

therefore this can pose some serious challenges because both functions influence the service

experience of the customer. If the functions do not coordinated their effort, it can deteriorate

the customer satisfaction.

One of the earliest comprehensive frameworks for assessing the importance of customer service

was the work of Sterling and Lambert (1988). They focused on the office systems and furniture

industry. Before this study, customer service research had several shortcomings (Sterling and

Lambert 1987):

38

Either multiple industries were examined as a homogeneous group or if a single industry was assessed, the findings were not generalizable beyond a specific firm.

A majority of the studies examined customer service in isolation from the rest of the marketing mix.

Others perceived no functional boundaries to customer service, and consequently included in the "customer service" component variables which in fact were related specifically to product, price or promotion. In such instances "customer satisfaction" has, in effect, been mistakenly replaced by "customer service" as the output of all marketing effort.

Several studies used a cross-sectional approach to assess customer service within an industry

(Daugherty, Stank and Ellinger 1998; Davis-Sramek, Mentzer and Stank 2008; Stank, Goldsby and

Vickery 1999; Stank, et al. 2003). LSQ could be considered a generalizable model, but it lacks a

holistic perspective because it excludes Marketing aspects and it does not use actionable

outcome variables like financial outcomes (Mentzer, Flint and Kent 1999; Mentzer, Flint and

Hult 2001). The second shortcoming is a more serious issue because often outcome variables

such as customer satisfaction are used. If only a subset of attributes affecting the outcome

variable is analyzed, the findings of the entire study are in jeopardy. Namely the effect of the

factors that are included in the study is grossly overestimated. The third shortcoming illustrates

another problem that can arise by not including the other parts of the marketing mix. Thus,

several research gaps existed 20 years ago (Sterling and Lambert 1987):

It is uncertain which of the marketing mix variables are more important, and how these variables interact in affecting sales.

Most empirically based studies were conducted using restricted data, such as advertising or price data exclusively, and were aimed at testing alternative model formulations and therefore, the generalizability of their findings is questionable.

Most users of share measures have failed to: Carefully explore the role of share in their marketing and corporate strategic models Assess its relative importance under different environmental scenarios Establish empirically for their own brands, the historical and projected relationship

between share and the effectiveness of their marketing strategy variables

39

With few exceptions, researchers have failed to obtain direct empirical evidence concerning the cause-effect relationship between share or other objectiveness, such as profits—and the effectiveness of marketing strategy variables.

The first research gap is still valid because no study is known to have systematically included the

marketing mix components and connected it to share of business or other measures in a single

structural equation model. The second research gap is not so much of an issue any more

because there were several surveys that focused at least on an industry level so that cross-

sectional data of several companies could be analyzed (Daugherty, Stank and Ellinger 1998;

Davis-Sramek, Mentzer and Stank 2008; Stank, Goldsby and Vickery 1999; Stank, et al. 2003).

The third and fourth research gaps are difficult to assess because causal relationships are

inherently difficult to prove with statistical methods.

A factor analysis was performed on the office systems and furniture industry data and eleven

functionally oriented factors were obtained (Sterling and Lambert 1987). Each of the four

marketing mix components was represented by two or three factors:

Product

Product Flexibility

Product Development

Product Breadth/Quality Price

Discount Structure

All Inclusive Bids Promotion

Personal Selling

Sales Assistance/Training Place/Logistics

Physical Distribution Information System Capability

Transportation Services

Order Completeness

40

Scales developed from these factors were then used for hypothesis testing. The results of the

stepwise regression show that the four components of the marketing mix did not contribute

equally to the share of business allocated to vendors by end users. Specifically place/logistics

factors, which were referred to as physical distribution/customer service, consistently

contributed more to the share of business. Price and promotion factors were inconsistent in

their ability to predict share of business. Therefore, concentrating on only one or a few

elements of the marketing mix would be dangerous, if a firm's objective was to gain market

share (Sterling and Lambert 1987). Improved performance on the components of the Marketing

Mix may lead to increased market penetration.

Customer service, one of the key elements provided by logistics, was seen to have a significant

and positive impact on customer satisfaction, cognitive attitudes, and repurchase intentions

(Innis and La Londe 1994). Customer satisfaction is one of the key objectives of the marketing

function in most firms and cross-functional coordination should be encouraged to allow

marketing and logistics to work together to provide the optimal marketing mix output to the

customer (Innis and La Londe 1994). It can be concluded that only with efficient cross-functional

integration can an optimal level of customer satisfaction be achieved. Innis and La Londe (1994)

proposed but never tested a model where sales were driven by product, price, and promotion as

well as physical distribution and customer service (logistics activities).

The marketing and logistics dimensions of customer service proposed by Mentzer, Gomes and

Krapfel (1989) were tested with a survey by Emerson and Grimm (1996). A factor analysis was

performed and several constructs were revealed (Emerson and Grimm 1996):

41

Quality Product support – customer service Availability Product support – sales Pricing policy Communication Delivery quality

Three logistics factors and four marketing factors were obtained (Emerson and Grimm 1996).

The availability construct (proposed by Mentzer, Gomes and Krapfel 1989) was confirmed

without any adaptation. While quality of physical distribution service was suggested as a

construct by Mentzer, Gomes and Krapfel (1989), one modification was the renaming of this

construct as delivery quality, to better represent the two indicators (Emerson and Grimm 1996).

Communication formed the third logistics construct.

Of the four marketing constructs, pricing policy loaded on one factor as expected. However, the

product support items for sales representatives and product support items for customer service

representatives loaded on two separate factors. Customer service representatives are viewed

differently from sales representatives by customers, because a customer service representative

might communicate with only a small subset of accounts and there were many customers in the

sample who have never had any interaction with a customer service representative and would

certainly view a customer service representative differently from their sales representative

(Emerson and Grimm 1996). The last marketing construct was named quality, but the measures

contained questions on warranty, which was interpreted as a result of quality products.

In a later study, the difference in importance between marketing and logistics elements of

customer service was assessed under different environmental conditions (Emerson and Grimm

42

1998). The informants were asked to distribute 100 points across eight attributes, with

assignment of more points indicating greater importance (Emerson and Grimm 1998). The eight

attributes included four from logistics (percentage of order filled, order cycle-time consistency,

accuracy of orders shipped, and order status information) and four from marketing (terms of

sale, competence of customer service representatives, overall product quality, and action on

complaints) (Emerson and Grimm 1998). In order to calculate a score, the total assigned to the

four items representing marketing customer service were subtracted from the sum of the four

items representing logistics customer service (Emerson and Grimm 1998). Several findings were

reported (Emerson and Grimm 1998):

The more indirect a channel (the higher the number of intermediaries), the more important logistics customer service becomes.

Larger customers will place more importance on marketing customer service. Smaller customers often perceive the level of logistics service to be lower than the level

of marketing service they receive. Firms experiencing high levels of supplier flexibility are obtaining high levels of logistics

service.

A summary of constructs of the Marketing Mix variables obtained in three previous research

studies is shown in Table 4. The selected studies used customer service elements that could be

classified into the Marketing Mix components. Only studies where constructs could be assigned

the categories were used. It is apparent that the constructs are different across multiple

industries (office systems and furniture, plastics, and large power tools). But it is seems that

each component of the Marketing Mix is important and at least one factor was obtained for

each component.

43

Marketing Mix Component

Study/ Industry

Product Price Promotion Place/Logistics

(Sterling and Lambert 1987) / Office Systems and Furniture

Product Flexibility Product Development Product Breadth/

Quality

Discount Structure All Inclusive Bids

Personal Selling Sales Assistance/

Training

Information System Capability

Order Completeness Transportation

Services

(Lambert and Harrington 1989) / Plastics

Product Quality Credit Discount Structure

Direct Mail Gifts, Entertainment,

and Trade Shows Sales Support Quality of Sales Force

Information System Capability

Lead Time Order Servicing

Product Availability

(Emerson and Grimm 1996) / Large Power Tools

Quality Product support –

customer service

Pricing policy Communication Product support –

sales

Availability Delivery quality

Table 4: Constructs of the Marketing Mix

44

2.3. Customer Satisfaction and Firm Performance

Customer Satisfaction has long been a central topic in Marketing research and practice. High

customer satisfaction ratings are widely believed to be the best indicator of a company's future

profits (Kotler 1991). Managers often use customer satisfaction as a criterion for diagnosing

product or service performance and often tie customer satisfaction ratings to employee

compensation. To encourage actions that will lead to an optimal level of satisfaction, it is

necessary to understand the link between the antecedents of satisfaction and satisfaction's

behavioral and economic consequences (Anderson and Sullivan 1993). Satisfaction was

conceptualized as a post-purchase evaluation of product quality given pre-purchase

expectations (Kotler 1991).

Using data from Sweden, several experimental findings of satisfaction research were tested

(Anderson and Sullivan 1993). Satisfaction was found to increase with both perceived quality

and disconfirmation. The term disconfirmation refers to the concept that a customer perceives

a change in satisfaction only when their expectations are not confirmed. Both positive and

negative disconfirmation increased with the ease of evaluating quality. When quality is

ambiguous or difficult to evaluate, then expectations will play a greater role in determining

satisfaction, and more importantly, quality which falls short of expectations was found to have a

greater impact on satisfaction and retention than quality which exceeds expectations.

Satisfaction was found to have a positive impact on repurchase intentions, and the elasticity of

45

repurchase intentions with respect to satisfaction is found to be lower for firms that provide

high satisfaction (Anderson and Sullivan 1993).

Using the same dataset as above, the link between customer satisfaction, market share, and

profitability was investigated (Anderson, Fornell and Lehmann 1994). Firms that achieve high

customer satisfaction enjoy superior economic returns, but the findings also indicate that

economic returns from improving customer satisfaction are not immediately realized. Because

efforts to increase current customers' satisfaction primarily affect future purchasing behavior,

the greater portion of any economic returns from improving customer satisfaction will be

realized in subsequent periods. Overall, customer satisfaction actually may fall as market share

increases. This may be because gains in market share may come from attracting customers with

preferences more distant from the target market. The firm may overextend its capabilities as

the number of customers and/or segments grows. In such a situation, even though the overall

level of customer satisfaction is falling, a firm's sales and profits may be increasing. It is

important to note that this may be a short-run versus long-run phenomenon. In the long run, it

is possible that customer satisfaction and market share go together, but there is evidence that

this is not always the case in the short run (Anderson, Fornell and Lehmann 1994). This effect

may also not be recognized in a cross-sectional research design, because changes cannot be

measured.

The American Customer Satisfaction Index (ACSI) is a customer-based measurement system for

evaluating and enhancing the performance of firms, industries, economic sectors, and national

46

economies (Fornell, et al. 1996). It was designed to be representative of the economy as a

whole and covers more than 200 firms, with 1994 sales in excess of $2.7 trillion competing in

over 40 industries in the seven major consumer sectors of the economy. On an annual basis,

the ACSI system estimates a firm-level customer satisfaction index for each company in the

sample and weights these firm-level indices to calculate industry, sector, and national indices.

Overall customer satisfaction (ACSI) has three antecedents: perceived quality, perceived value,

and customer expectations. The immediate consequences of increased customer satisfaction

are decreased customer complaints and increased customer loyalty (Fornell, et al. 1996).

Many studies up to that point used the standard customer service paradigm (Oliver 1980).

Those models reveal anomalies and omissions of previous approaches and to propose

extensions and new discoveries that address the limitations and exclusions in existing theory.

Fournier and Mick (1999) chose a different approach to explore and describe satisfaction by

using firsthand accounts of subjects. This approach enabled the development of a more realistic

perspective of satisfaction as it unfolds in the course of daily life. Satisfaction was investigated

through extensive and repeated in-home interviews that focused on consumers' purchase and

usage experiences with technological products. The study resulted in five main conclusions

(Fournier and Mick 1999):

Consumer product satisfaction is an active, dynamic process. The satisfaction process often has a strong social dimension. Meaning and emotion are integral components of satisfaction. The satisfaction process is context-dependent and contingent, encompassing multiple

paradigms, models, and modes.

47

Product satisfaction is invariably intertwined with life satisfaction and the quality of life itself.

In order to assess the value of customer satisfaction in financial terms, a mathematical model

approach was used (Rust and Zahorik 1993). The research is built on the premise of defensive

marketing (Fornell and Wernerfeld 1987), which in contrast to offensive marketing focuses more

on retaining customer rather than acquiring new ones. It is generally believed that it is more

costly to add a new customer than to keep an existing one (Brown et. al 2005). The authors

show how customer satisfaction can be linked to individual loyalty, aggregate customer

retention rate, market share, and profitability. However, this is not a straightforward process

(Rust and Zahorik 1993).

Quantifying the results of quality expenditures by linking service quality and customer

satisfaction to financial outcomes can show the effect service improvements have on the

bottom line. The return on quality (ROQ) approach is characterized by the following

assumptions (Rust, Zahorik and Keiningham 1996):

Quality is an investment. Quality efforts must be financially accountable. It is possible to spend too much on quality. Not all quality expenditures are equally valid.

The relationship between service quality improvement efforts and profitability is modeled as a

chain of effects that can be seen in Figure 2. The improvement effort, if successful, leads to an

improvement in service quality. Improved service quality typically results in increased customer

satisfaction. Increased customer satisfaction in turn leads to higher levels of customer retention,

48

and also positive word-of-mouth. Revenue and market share increases are driven by losing

fewer existing customers and gaining more new customers. The increased revenues lead to

greater profitability. The effect of word-of-mouth is very difficult to measure in a practical

business situation, however it is important and it has a real effect (Kumar, Petersen and Leone

2007).

Source: (Kumar, Petersen and Leone 2007)

Figure 2: A Model of Service Quality Improvement and Profitability

Analysis of repurchase intention as a function of overall satisfaction showed that disappointed

customers had only a 45% probability of returning, whereas the satisfied and very satisfied

customers had a probability of over 90% (Rust, Zahorik and Keiningham 1996). The ROQ

approach also considers a shift in market share as a result of the shift in satisfaction and given

49

the right data specific ROQ figures can be calculated. Clearly, these numbers have little meaning

without knowing the details of the particular situation, but it is important to note that there are

approaches to quantify the results of increased customer satisfaction.

A review of outcome measures for customer service in prior research revealed that several

outcome variables have been used. They can be classified into perceptual and financial

measures. In general perceptual measures are about the subject’s opinion, while financial

measures are about quantifiable data. It is also apparent that most studies either choose to use

one type or the other and only few combine both approaches.

The most popular perceptual outcome variable is customer satisfaction (see Table 5). Other

related variables are loyalty (Daugherty, Stank and Ellinger 1998; Davis-Sramek, Mentzer and

Stank 2008; Stank, Goldsby and Vickery 1999; Stank, et al. 2003; Zeithaml, Berry and

Parasurman 1996) and future purchase intentions/behavior (Cronin and Taylor 1992; Davis-

Sramek, Mentzer and Stank 2008). Often several outcome variables are used in order to provide

a more accurate picture of the results. It is noteworthy, especially with exclusively perceptual

outcome measures, that there might be some issues with common method variance. The

problem may occur because strictly perceptual measures could mask actual differences. Data

collected with the same method may limit variance, which makes it more difficult to recognize

differences between the subjects.

In the other category, quantifiable measures of financial performance were used as outcome

variables. Market share was the most popular measure used (see Table 5). Others used

50

measures of profitability to determine success (Anderson, Fornell and Lehmann 1994; Phillips,

Chang and Buzzell 1983; Rust, Zahorik and Keiningham 1996). Financial outcome measures,

such as profitability and market share are often measured as perceptions. Often, not even the

managers themselves have accurate profitability data on individual customers and as such

obtaining accurate data is difficult, if not impossible (Lambert and Pohlen 2001; Lambert and

Sterling 1987). Market share data can be obtained from databases covering an industry (Stank,

et al. 2003).

51

Study Perceptual Outcomes Financial Outcomes

(Phillips, Chang and Buzzell 1983)

Relative Market Position Relative Direct Costs Relative Prices Return on Investment

(Sterling and Lambert 1987)

Share of business

(Cronin and Taylor 1992)

Customer Satisfaction Purchase Intentions

(Anderson, Fornell and Lehmann 1994)

EXPECTt = f1(EXPECTt-1, QUAL t-1, E1t) SATt = f2(QUALt-1, PRICEt-1, EXPECTt-2, E2t)

PROFITt = f3(SATt-1, , E3t) where Eit = vector of other factors (e.g., environmental trends, firm-specific factors, error)

(Zeithaml, Berry and Parasurman 1996)

Behavioral Intentions: Loyalty Switch Pay More External Response Internal Response

(Rust, Zahorik and Keiningham 1996)

Customer Attitudes, Emotions and Perceptions

Customer Satisfaction

Profitability Market Share Return on Quality

(Daugherty, Stank and Ellinger 1998)

Customer Satisfaction Loyalty

Market Share

(Stank, Goldsby and Vickery 1999)

Customer Satisfaction Loyalty

(Mentzer, Flint and Hult 2001)


(Stank, et al. 2003) Customer Satisfaction Loyalty

Market Share

(Rafiq and Jafaar 2007)


(Davis-Sramek, Mentzer and Stank 2008)

Customer Satisfaction Loyalty:

Affective Commitment Purchase Behavior

Table 5: Outcome Variables in Customer Service Studies

52

2.4. Hypothesis Development

The literature presented in this chapter revealed that there was an opportunity to add new

insights to the customer service domain. Although this area has a long stream of excellent

research, there are still gaps that should be addressed. While there were several studies that

presented a framework for assessing customer service, many of those were focused exclusively

on logistics (Stank, Goldsby and Vickery 1999) or marketing (Parasurman, Zeithaml and Berry

1985). The research revealed that narrow approaches to customer service needed to be

extended to include additional variables (Mentzer, Flint and Kent 1999) or customized to

individual situations (Parasurman, Zeithaml and Berry 1991). The only framework for

determining and assessing all variables important for selecting and evaluating suppliers is the

framework first presented by Lambert and Zemke (1982). This approach has been refined over

time and repeated in various industries and provides the direction for future research (Sterling

and Lambert 1987; Lambert and Harrington 1989).

The conceptual model of this research is depicted in Figure 3. The variables that buyers in

companies use to select and evaluate suppliers can be summarized into one of the four

categories of the Marketing Mix: product, price, promotion and place. While there were several

studies that included fewer variables responsible for driving customer satisfaction (Mentzer,

Flint and Kent 1999; Stank, Goldsby and Vickery 1999), they later would be extended to include

additional variables (Stank, et al. 2003). Since the goal of this research was to develop a

53

generalizable model, it was determined that the best strategy was to start with more variables

and reduce them as the need arises during scale development.

Figure 3: Conceptual Model

The four components of the marketing mix are all believed to influence the level of customer

satisfaction. The product construct generally covers attributes like performance, reliability,

quality, and product development. The price attributes address billing, discounts and

competitiveness. The promotion/personal selling attributes deal with characteristics of the

sales representative. While advertising would certainly be part of the promotion construct in

business-to-consumer relationships, business-to-business relationships are different (Mudambi

2002). There may be some advertisements in trade magazines, but during data analysis it was

evident that they only played a minor role compared to characteristics of the sales

54

representative. In addition, training the sales force to deliver the branding message is of critical

importance (Lynch and de Chernatony 2004). Attributes in the place/logistics category are

focused on delivery issues like reliability, availability, accuracy, lead time and logistics service.

Several studies focused on marketing and logistics components of customer service (Emerson

and Grimm 1998; Innis and La Londe 1994; Lambert and Harrington 1989). The Marketing Mix

variables are believed to drive satisfaction (Innis and La Londe 1994), however empirical testing

of these relationships is still lacking. Therefore, the first hypothesis is:

H1: The Marketing Mix has a positive significant impact on customer

satisfaction

Beyond the general relationship between the overall Marketing Mix, the individual components

must be assessed as well. Because customers first perceive the benefits of the product they are

buying it is likely the primary source of satisfaction they derive from the purchase. There is

evidence that better products increase customer satisfaction, however this is not a linear

relationship (Maddox 1981; Swan and Combs 1976). In the samples that are analyzed in this

research, a basic level of performance is present because only existing suppliers are evaluated.

H1a: Product has a positive significant impact on customer satisfaction.

Pricing is an important factor in determining which products to procure, and it is not necessarily

the absolute level of pricing but price level in relation to other factors (Herrmann, et al. 2004;

Hoyer, Herrmann and Huber 2002). This notion of price fairness is captured in the way the

55

construct is measured in this study. Because respondents are asked to indicate their

perceptions on a seven-point scale relative pricing is measured. Pricing has shown a significant

impact on satisfaction in several previous studies (Herrmann, et al. 2004; Hoyer, Herrmann and

Huber 2002; Morganoski 1988; Voss, Parasuraman and Grewal 1998). However, most of the

studies were experiments and this study would strengthen the understanding of the hypothesis.

H1b: Price has a positive significant impact on customer satisfaction.

Previous research has shown that good salespeople can help increase satisfaction (Goof, et al.

1997; Grewal and Sharma 1991; Johnson, Barksdale and Boles 2001; Liu and Leach 2001). This

effect has been shown to take place with manufacturers and retailers, which is important for

this research because in some samples of this research manufacturers are surveyed, while in

others retailers are surveyed (Goof, et al. 1997). Salespeople also set the expectations regarding

the other three components of the Marketing Mix, and if that is done well, it should result in

higher satisfaction.

H1c: Promotion/personal selling has a positive significant impact on customer

satisfaction.

The three constructs that belong to the Marketing function have all received attention in the

Marketing literature. The contrast between Marketing and Logistics attributes has not been

thoroughly explored and only a few studies have evaluated constructs from both areas together

(see Table 5). Logistics attributes have been shown to have an impact on customer satisfaction

56

in some studies (Davis-Sramek, Mentzer and Stank 2008; Mentzer, Flint and Hult 2001; Stank,

Goldsby and Vickery 1999). However, in the presence of other constructs like pricing attributes,

this effect is not consistent (Stank, et al. 2003).

H1d: Place/logistics has a positive significant impact on customer satisfaction.

While customer satisfaction is an important construct in the literature, the downside is that it

does not directly translate into financial success of a company. There is some evidence in the

literature that satisfaction can and should be connected to hard financial measures (Heskett, et

al. 1994; Heskett, Sasser and Schlesinger 2003; Rust, Zahorik and Keiningham 1996). The link

between satisfaction and business outcomes is well-documented in the Service-Profit Chain

framework (Heskett, et al. 1994; Heskett, Sasser and Schlesinger 2003; Homburg, Wieseke and

Hoyer 2009). Share of business is a good indicator for determining financial success within a

business-to-business relationship and therefore a useful outcome variable for this research. If

the goal of management is to increase sales from existing customers then share of business

must be expanded. In addition, understanding how the direct effect of the Marketing Mix on

share of business differs from the effect on customer satisfaction provides additional insight.

The relationship between customer satisfaction and share of business is also useful in

determining the accuracy of satisfaction as a predictor of financial success. If satisfied

customers are contributing to larger share of business, then managers can focus their attention

on activities that have a significant impact on customer satisfaction that are identified in the first

hypothesis. It would seem that high customer satisfaction should result in a large share of

57

business, but there have been examples where this was not clear (Homburg, Wieseke and Hoyer

2009; Kamakura, et al. 2002). The last hypothesis deals with the relationship between these

variables directly:

H2: Customer satisfaction has a positive significant impact on share of

business.

These hypotheses are tested in the course of this research. Figure 4 shows the conceptual

model with the hypotheses. While some of the individual hypotheses have been addressed in

previous research, connecting these links in one model adds a more holistic perspective. This

perspective is useful when evaluating the model in several industries because it is able to show

differences between the samples. If consistent patterns emerge across the different samples

they could be regarded as generalizable. The main contribution of this research is the

replication of the model over the nine samples.

Figure 4: Conceptual Model with Hypotheses

58

2.5. Summary

In this chapter, the relevant literature that influenced this research was presented. Although an

extensive literature base already exists, there are gaps that this research addresses. The main

gaps that are being addressed are:

The effect of logistics attributes relative to the other components of the Marketing Mix is examined.

Both customer satisfaction and share of business are used as outcome variables. Differences between industries are examined.

The described hypotheses are believed to advance knowledge in the field and hopefully will spur

new research in this area. The next chapter contains a description of the methodology. In

Chapter 4, the results are presented and analyzed. Chapter 5 contains the conclusions.

59

CHAPTER 3.

METHODOLOGY

In this chapter the research methodology is described. First, the data collection and research

instrument are reviewed. Next, the data analysis preparation including non-response bias

testing, missing data mitigation and parceling technique are described. Then, scale

development is reviewed for each sample:

Health Services (A-1): blood banking reagents

Health Services (A-2): coagulation reagents

Health Services (A-3): coagulation reagents

Electronics (B-1): professional video tape

Electronics (B-2): consumer video tape

Plastics (C-1): commodity resin

Sporting Goods (D-1): golf balls

Sporting Goods (D-2): golf clubs

Sporting Goods (D-3): golf shoes

For each of these samples the measurement model part of the structural equation modeling is

described. Last, an overview of the questions used for each sample and a summary are

provided.

60

3.1. Data Collection

A dataset of nine customer satisfaction studies used to conduct this research. For the data

collection, the methodology proposed by Lambert and Zemke (1982) was followed on all

samples. This methodology has been validated in several articles (Sterling and Lambert 1988,

Lambert and Harrington 1989, Lambert, Lewis and Stock, 1993). The studies consisted of the

following steps: external audit, internal audit, evaluation of customer perceptions and

identification of opportunities. Because this research requires customer provided data, only the

external audit part of each dataset was used. In the next section, the steps that were used for

obtaining the data are described in detail.

Data Collection Methodology

The external audit part in the Lambert and Zemke (1982) methodology was used to identify

attributes for evaluating suppliers. There are two parts in the data collection procedure: in-

depth, personal interviews and a mail survey. For each of the nine samples used for this

research, interviews were conducted with key decision-makers in each of the sponsoring

manufacturer organizations and then in 20-32 customer firms for each product category.

Customer firms were selected on the basis of their geographic location, size, marketing needs

and the major suppliers they used. The objective was to obtain a perspective that was as broad

as possible in order to compile a comprehensive and meaningful set of questions for the mail

survey. Any attribute that was mentioned during an interview was used on the questionnaire

because the goal was to provide the sponsoring organization with a comprehensive

61

understanding of factors that customers use to select and retain suppliers. The interviews were

continued until a saturation point was reached and no new attributes could be identified. The

number of interviews conducted is consistent with previous research (Blair and Presser 1993).

In the second part, the mail survey, all potential respondents from industry databases were

contacted by telephone and asked to participate in the survey before the mail questionnaire

was sent to them. Pre-qualifying the respondents improved the overall response rate (Allen,

Schewe and Wijk 1980, Hornik 1982). It also ensured that the informant was responsible for

choosing supplier, was knowledgeable about the suppliers and could provide the information to

complete the questionnaire. During the telephone conversations, the name and position of the

decision-maker was determined and the mailing address was verified. Respondents were

offered a summary of the research findings in return for their participation in the survey.

Then, questionnaires were sent to the representatives identified during the telephone

interviews. The questionnaires were sent in three waves. In addition to the three waves of

regular questionnaires, a short version of the main questionnaire was sent to non-respondents

in order to assess non-response bias (Lambert and Harrington 1990).

62

Research Instrument

The questionnaires consisted of four parts:

Part A:

Importance of attributes used to select and evaluate suppliers.

Performance of the top three suppliers on those attributes. Part B: measurement of overall performance. Part C: expected performance levels. Part D: meaningful demographic data.

The main data sources for the analysis in this research are the first two parts of the

questionnaires. The questions in Part A fell into one of the following four categories: product,

price, promotion/personal selling, and place/logistics. The product questions generally covered

attributes like performance, reliability, quality and product development. The price questions

addressed billing, discounts and competitiveness. The promotion/personal selling questions

dealt with characteristics of the sales representative. Advertising in trade magazines was not an

important factor for purchasing managers based on the low importance scores in the

questionnaires. Questions in the place/logistics category focused on delivery issues like

reliability, availability, accuracy, and lead time.

In Part A of the questionnaire, there were two tasks that must be completed. The first task was

an evaluation of the importance of the attributes. The respondent was asked to circle on a scale

from one to seven the number which best expressed the importance of each attribute when

deciding how much business to give to each supplier. If an attribute was not used or possessed

very little weight in the evaluation of suppliers, the respondent was asked to circle the number

63

one (not important). A rating of seven (very important) should be reserved for those factors

that would cause the respondent to reevaluate the amount of business done with a supplier, or

cause the manager to drop the supplier in the event of inadequate performance.

The second task in Part A was to evaluate the current performance of three major suppliers.

Using the scale labeled “perceived performance”, the respondent was asked to insert a number

between one and seven which best expresses the perception of the supplier's current

performance under the appropriate supplier heading. A rating of one indicates poor

performance and seven should be used for excellent performance. If a service was not available

from a supplier, then the respondent should use “NA” (not available).

In Part B, the current performance of all three major suppliers was evaluated. The two most

important questions were about overall customer satisfaction and current and ideal share of

business for each of the three major suppliers. These measures are used directly in the

structural equation models. Another question in this section was whether the customer would

recommend each of the suppliers they evaluated. The next two questions are about problems,

whether any were reported and if they were solved satisfactory. The question after that

assessed the percentage of on-time shipments during a typical month. The last question in that

section referred to price differences. The lowest price supplier receives a score of 0 percent and

for the other two the price premium as a percentage is indicated.

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3.2. Overview of the Samples

For this study, nine independent samples were collected using the methodology described

above. An overview of the samples is presented in Table 6.

Industry Sample Name Sample Size Responses Response Rate

Health Services A-1 (Blood Banking Reagents) 2,015 754 37.42%

Health Services A-2 (Coagulation Reagents) 1,005 299 29.75%

Health Services A-3 (Coagulation Reagents) 667 212 31.78%

Electronics B-1 (Professional Tape) 1,369 342 24.98%

Electronics B-2 (Consumer Blank Tape) 434 77 17.74%

Plastics C-1 (Commodity Resin) 1,854 540 29.13%

Sporting Goods D-1 (Golf Balls) 1,012 134 13.24%

Sporting Goods D-2 (Golf Clubs) 2,240 172 7.68%

Sporting Goods D-3 (Golf Shoes) 1,001 95 9.49%

Table 6: Overview of the Samples

The nine samples represented four distinct industries. The overall response rates varied from

7.68 to 37.42 percent. While the response rates in the Golf industry are fairly low, this is not

unusual due to the fact that surveys of retailers generally have lower response rates (Ellram, La

Londe and Weber 1999). The sample sizes are sufficiently large. Non-response bias was

assessed in two ways (Armstrong and Overton 1979; Lambert and Harrington) and no significant

differences were identified indicating that non-response bias was not a problem. Therefore,

reliable conclusions can be drawn (Boyer and Swink 2008). The number of responses in each

sample is adequate for the type of analysis that was performed because each respondent was

asked to evaluate up to three suppliers, thus bringing the sample size into an acceptable level

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for each sample. It is often suggested that at least 200 observations are necessary to run a

structural equation model (Shah and Meyer Goldstein 2006). This is also dependent on the

specifics of the model in particular the number of latent variables. In this model, there are six

latent variables (LV) so in the smallest samples an adequate ratio of observations to LVs was

achieved. In the next sections, each of the samples is described.

Health Services Industry

The health services industry yielded three separate samples. For sample A-1, a total of 2,015

hospitals with on-site blood banks were surveyed. Each hospital had 100 or more beds, which

was believed to be the smallest sized hospital to have an on-site blood bank. Three separate

mailings, spaced at three to four week intervals, were conducted. A total of 753 usable surveys

were obtained, representing an overall response rate of 37.42 percent. In addition, 55

responses of the short version were obtained.

For sample A-2, a total of 1,005 of the largest hospitals in the continental United States having

on-site coagulation/hematology laboratories were surveyed. Also included in the survey were a

limited number of commercial laboratories (approximately 40) and other hospitals. Three

separate mailings, spaced at three to four week intervals, were conducted. The second mailing

replicated the initial mailing list; all respondents contacted in the first survey were polled a

second time. The final, third mailing was restricted to those institutions not responding to the

first two mailings. A total of 299 usable surveys were obtained, representing an overall

response rate of 29.75 percent. An additional 147 short surveys were obtained.

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For sample A-3, a total of 667 hospitals in the continental United States having on-site

coagulation/hematology laboratories were surveyed. Three separate mailings, spaced at three

to four week intervals, were conducted. The second mailing replicated the initial mailing list; all

respondents contacted in the first survey were polled a second time. The third mailing was

restricted to those institutions not responding to the first two mailings. A total of 212 usable

surveys were obtained representing an overall response rate of 31.78 percent. In addition to

the regular surveys, 100 short surveys were received.

Electronics Industry

The customers in the electronics industry sample B-1 were mainly TV Broadcasters, TV

Producers/Post Production and various distributors. Personal in-depth interviews were

conducted at 32 companies in order to develop and pre-test the survey instrument. The

questionnaire was sent to representatives from 1,369 firms. There were 342 completed

questionnaires that were returned after three mailings. The response rate was 24.98 percent.

In addition, a two-page short version of the questionnaire was sent to the remaining non-

respondents and 70 completed questionnaires were returned.

In sample B-2, vendors of blank video cassettes were surveyed. In order to develop and pre-test

the questionnaire in-depth personal interviews at 23 firms were conducted. The complete

questionnaire was sent to 434 recipients in three mailings. At various times during the mailing

of surveys, the recipients were contacted by phone in order to encourage participation. There

were 77 completed questionnaires which represents a response rate of 17.74 percent. In

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addition, a four-page version of the survey was sent to non-respondents and 36 additional

surveys were received.

Plastics Resin Industry

The customers in the plastics resin industry were molders and extruders that purchased plastic

resins from approximately 24 manufacturers and more than a dozen distributors. Most of the

manufacturers sold directly to large original equipment manufacturers and used distributors to

reach smaller volume molders and extruders as well as to provide fast turnaround on small

volume orders. The market was very competitive with many of the manufacturers being well

known Fortune 1000 companies. There were two large national distributors and many regional

and local distributors that also served the market.

For sample C-1 a random sample of 1,920 purchasers was selected from a mailing list of

approximately 8,000 firms. Phone calls to each of the 1,920 buyers confirmed that 1,858 of

them had responsibility for purchasing plastic resins. Next, the questionnaires were mailed to

each of the 1,858 buyers. There were 260 completed questionnaires returned from the first

mailing. Another round of phone calls was followed by a second mailing. As a result of both

mailings, the number of usable surveys was 540 which represented a 29.13 percent response

rate. In addition, 161 responses from the mailing of the short version of the questionnaire were

obtained.

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Sporting Goods Industry

The sporting goods industry yielded three samples. For sample D-1, 1012 sellers of golf balls

were surveyed. Of these sellers, 762 were “Green Grass” pro shops and 250 were retailers.

Overall, 134 responses for the complete survey and seven for the reduced version were received,

which represents a 13.93 percent response rate.

For sample D-2, 2240 sellers of golf clubs were surveyed. Of these, 1737 were “Green Grass”

pro shops and 504 were retailers. Overall, 172 responses for the complete survey and 50 for the

reduced version were received, which represents a 7.68 percent response rate.

For sample D-3, 1001 sellers of golf shoes were surveyed. Of these 751 were “Green Grass” pro

shops and 250 were retailers. Overall, 95 responses for the complete survey and eight for the

reduced version were received, which represents a 9.49 percent response rate.

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3.3. Data Analysis Preparation

Before the data analysis can begin, several issues must be resolved: non-response bias, missing

data, and parceling. Non-response bias is a concern in surveys because it can lead to an

incorrect sampling frame. If the sample that is taken from a population has different

characteristics, then the results of the survey cannot be generalized to the population. Some

respondents did not answer every question, so there were some missing data with which to deal.

Testing for non-response bias is described next, then missing data issues and parceling is

covered.

Non-Response Bias Testing

There are two popular methods of determining non-response bias in the literature. In one, early

and late respondents are compared (Armstrong and Overton 1977) and in the other a subset of

questions is sent to non-respondents and the results are compared to the full version of the

questionnaire (Lambert and Harrington 1990). In all of the surveys used for this research, a

short version of the survey was sent to non-respondents. In order to test for non-response bias,

an analysis of variance (ANOVA) was performed on each sample to test for differences between

the full version of the survey and the reduced version.

Questions from Part A for each of the three suppliers were used to determine the difference

between the three waves of the regular survey and the short version (Lambert and Harrington

1990). Based on the results of the ANOVA, most questions that were analyzed showed no

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difference in means between the four different mailings (three mailings of the regular

questionnaire and one mailing of the short questionnaire). On the few questions where

differences between the groups were detected, further analysis was performed with multiple

comparisons using Tukey’s method (Lambert and Harrington 1990). The results of that analysis

showed that differences did not consistently occur in the short version of the survey. Overall,

there was no evidence to indicate that non-response bias is a concern in any of the samples.

Missing Data Mitigation

There are several procedures that are commonly used to deal with missing data: pairwise

deletion (or available-case analysis), listwise deletion (or complete-case analysis), mean

substitution, and imputation methods like maximum likelihood and Bayesian (Kamakura and

Wedel 2000, Schafer and Graham 2002). When listwise deletion is used, only cases with

complete data are used. Pairwise deletion is a procedure where incomplete cases are only

removed if they are used in one calculation. Both methods have disadvantages: listwise

deletion severely reduces the sample size and pairwise deletion may yield a covariance matrix

that is not positive-definite (Little and Rubin 1987). Mean substitution is another simple

procedure which may be used to deal with missing data, where missing data is replaced by the

average score. An issue with mean substitution is that covariances are systematically

underestimated, but simulations have shown that for low levels of missing data (less than 10

percent) this procedure can yield equally good results compared to more sophisticated methods,

like maximum likelihood imputation (Kamakura and Wedel 2000).

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When the amount of missing data was assessed, it was evident that there were some

respondents who only answered a few questions. Those responses with less than 75 percent of

the questions answered were deemed unreliable and removed. In addition, there were a few

questions that had much fewer responses than other questions. Therefore, all questions where

at least 80 percent of data were present were deleted. The remaining amount of missing data

were less than 10 percent, and as such mean substitution would be adequate (Kamakura and

Wedel 2000). In order to minimize undervalued correlations, mean replacement was conducted

for suppliers A, B, and C separately. So, missing data for supplier A was replaced by the mean

score for all responses for supplier A. The same procedure was used for data on suppliers B and

C. Table 7 shows the number of attributes used and the number of usable cases for data

analysis.

Industry Sample Name Responses Usable Cases

Attributes

Health Services A-1 (Blood Banking Reagents) 753 1,400 88

Health Services A-2 (Coagulation Reagents) 299 435 78

Health Services A-3 (Coagulation Reagents) 205 279 71

Electronics B-1 (Professional Tape) 347 508 83

Electronics B-2 (Consumer Blank Tape) 113 229 69

Plastics C-1 (Commodity Resin) 534 759 91

Sporting Goods D-1 (Golf Balls) 141 288 130

Sporting Goods D-2 (Golf Clubs) 120 265 149

Sporting Goods D-3 (Golf Shoes) 89 205 135

Table 7: Number of Questions and Usable Cases

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Parceling of Items to Build Composite Scores

Parceling is a technique in which factors are estimated using composites of items instead of

individual items (Garver and Mentzer 1999, Little, et al. 2004). So, instead of estimating the

constructs with the ratings of the questions, the scores of several questions are combined into a

composite that is then used to estimate the construct. The first reference for the parceling

technique was in the Psychology literature (Cattell 1956) and this technique has since been used

in other areas such as education, psychology, and marketing (Bandalos and Finney 2001).

While there is debate about the use of parcels (Cattell and Burdsal Jr. 1975, Little, et al. 2004),

parcels built on items that have a unidimensional structure are shown to accurately reflect the

scale (Bandalos 2002). In this study, before the parcels were constructed, the unidimensionality

of the construct was tested with a confirmatory factor analysis and only items that had

sufficiently high loadings were used for the parcels. The concerns that are voiced about this

technique generally relate to the fact that modeled data should be as close to the response of

the individual as possible in order to avoid the potential imposition or arbitrary manufacturing

of a false structure (Little, et al. 2004). It is questionable that Likert-type scales by definition do

not impose an arbitrary structure on the data. However, there are several advantages as well.

From a psychometric perspective the advantages are: higher reliability, higher communality, a

larger ratio of common-to-unique factor variance, and a smaller likelihood of distributional

violations (Little, et al. 2004). Advantages from an estimation perspective are that models based

on parceled data are more parsimonious, have fewer chances for residuals to be correlated or

dual loadings to emerge, and lead to reductions in various sources of sampling error (Little, et al.

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2004). In this dataset multivariate normality is potentially enhanced because the data are

transformed from categorical to continuous, which is another reason why some researchers

choose to use parcels (Landis, Beal, and Tesluk 2000).

The main reason parcels are used in this research is because too many items load on each

construct. Ideally three to five manifest variables should be used to estimate a latent variable

(Bollen 1989). There are two options to estimate a construct with more variables. The focal

construct can be estimated as a second-order latent variable or several variables can be

combined into a composite. Second-order models use several first-order constructs that make

up the second-order construct (Bollen 1989). When parcels are used to combine several items

the parcels are then used to estimate the construct (Bandalos 2002, Garver and Mentzer 1999).

The parcels in this research are built as importance-weighted averages of the performance

scores. When the respondents answer the question about an attribute in the questionnaire,

they are required to perform two tasks, indicate the importance of that attribute in selecting

and evaluating suppliers and the performance of each major supplier. Since the parcel score

takes into account importance and performance ratings, a more precise understanding of how

the respondent rates a particular attribute is obtained. When respondents answer a question,

they are required to consider the importance and performance of the attribute, and therefore

just using one or the other may cause bias in the data. Another reason for importance-weighted

parcels is that second-order models would not take importance scores into account, and the

estimation procedure would assign weights to each manifest variable in a manner that would

not reflect how respondents would assign relative importance to them. It was shown that

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weighted composites are preferred to un-weighted or equal-weighted composites (Rozeboom

1979). Generally, parcels where individual items have different weights are closer to the true

structure of the latent variable they represent (Bollen 1991). In this case, weighing the items by

relative importance should reasonably lead to a closer assessment of the latent variables that

are used in this research.

There are two issues regarding how parcels are built, how the items are combined and which

items are combined (see Table 8). Regarding the calculation of composites, the simplest

method is to add all variables. This procedure requires that each parcel has the same number of

variables, otherwise parcels with more variables are systematically overemphasized. Another

method is to average the items. The downside is that all attributes are assumed to be equally

important, which is not always accurate. Therefore, the procedure used in this study is a

weighted average in which weights are based on relative importance.

Method Description

Single factor Pair off items with highest and lowest loadings as first composite based on a single-factor solution; continue pairing until items are exhausted

Correlational Pair off items with highest interrorrelation as first composite; continue pairing until items are exhausted

Random Randomly assign items to composites Content Create composites based on rational grouping (s) of items Exploratory factor analysis Create composites based on results from exploratory factor

analysis Empirically equivalent Create composites with equal means, variances, and reliabilities

Adapted from: Landis, Beal, and Tesluk 2000 Table 8: Summary of Composite Formation Methods

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For this study, questions on similar issues are combined to build the parcels (see content

method in Table 8) because it allows better conceptualization of the construct (Bagozzi and

Edwards 1998). More on the results of the parceling is provided in the next section of this

chapter. The parcel scores were calculated using Equation 1, which follows the standard

formula for weighted averages.

Equation 1: Calculation of Parcel Scores

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3.4. Overview of Questions Used in the Samples

In order to get a better understanding of which questions are used in each sample an overview

of the questions that are used across the nine samples is provided next. There are 56 questions

that are used for the product construct. The main reason why there are that many questions is

because most of them address very specific issues that are used to evaluate the product

construct. The questions and in which samples they are used is displayed in Table 9. For the

price construct, fewer questions are used (25). Overall, the constructs are much more

consistent across the nine samples. The main differences occur because questions in some

sample are more general while others use more specific questions. There are a few questions

that are used in only one sample. Table 10 provides an overview of the questions and in which

samples they are used. There are 18 questions that are used for the promotion/personal selling

construct across the nine samples. One difference is that some questions ask the respondent to

evaluate “quality of sales force” and others use “sales representative characteristics”. Both

terms have been used interchangeably. The questions and which ones are used in each sample

are shown in Table 11. There are 25 different questions that are used for the place/logistics

construct. Some constructs are adapted to reflect specific industry conditions. The questions

and in which samples they are used is displayed in Table 12.

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Questions A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3

Adequate availability of newly introduced products X Advance information (literature, specs, prices, etc.) on new product introductions X X X X Consistent development of new golf balls by supplier X Development of new products by supplier X X X X Frequency of new product introduction X Speed at which vendor responds to industry technical improvements X Supplier adequately tests new products before delivering to market X X X X

Appropriate range of sizes X Availability of men's and women's products X Availability of different widths X Supplier has complete assortment of footwear items X

Consistency of supplier's delivered product after initial evaluation of samples by my facility

X X X X

Durability of product X Overall quality of resin relative to price X Past experience with supplier's product X Product durability (tape continues play back without loss of video and audio quality): after multiple passes

X

Product durability (tape continues play back without loss of video and audio quality): after extensive shuttling

X

Product quality relative to price X Product reliability (consistent performance from shipment to shipment) X X X X Quality of product X Quality of product line above minimum standards X Supplier replaces entire allotment when there is evidence of defective product X X X X Supplier's resins are of consistent color X Supplier's resins are of consistent quality X Warranty program for footwear X

Continued Table 9: Product Attributes Across all Sample

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Continued

Table 9 continued


Prompt notification of technical analysis results X X Service support if salesperson is not available X X

Consistency of product performance X Consistent shaft quality & performance X Consistent sizing X Fit and comfort X Footwear consistent with most preferred styles and trends X Functionality X Level of product performance X Overall appearance of shoe X Performance of premium balls X Processability of resin X Product features (distance, spin rate) X Product stability (Shelf life): Antiserum X X X Product stability (Shelf life): Red cells X X X Sensitivity (specificity) of reagent X X X Supplier at leading edge of technology X Supplier's products are at the leading edge of technology X X Supplier's resins are of consistent melt flow X Waterproofing X

Adequate identification/labeling of package contents X X Availability of the following package: type-bag X Durability of packaging X Packaging: aids in consumer purchasing decision X Packaging: product/technical info on package X Packaging: visual appeal X Quality/durability of packaging X X Vendor's willingness to work with your firm to develop custom packaging configurations X

Ability of vendor to handle: consumer complaints X

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Ability of vendor to handle: defective product returns X Past experience with vendor's product X


Adequate advance notice of price changes provided X X X X X X X

Responsiveness of vendor to competitor's price reductions X

Supplier does not raise prices more than once per year X X X X

Supplier gives you an adequate period of price protection after a price increase is announced X X X X

Supplier gives you an adequate period of price protection after a price decrease is announced X X

Vendor gives you adequate price protection and/or markdown funds X

Integrity of suggested retail price X

Margin reflects selling effort X

Profit margin X

Simplicity of pricing program X

Prompt and comprehensive response to competitive bid quotations X X X X X X X

Supplier reacts quickly to competitive price reductions X X X X X X

Pre-book discount program X X

Quantity discount structure X

Quantity discount structure based on size of individual order X X X X X X

Quantity discount structure based on total annual purchases X X X X X X

Sales rep will give you volume price even if you are buying less X

Supplier combines purchases of different products in order to compute volume discount X X X X

Assurance that my target price at retail will equal that of my competitors X

Competitiveness of price X X X X X X X X

Low price X

Lowest price X X X

Realistic, consistent pricing policy by supplier over time X

Sales rep has authority to negotiate special prices X X X X X

Willingness of sales rep to be flexible in offering special/volume discounts, pricing, etc. X

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Table 10: Price Attributes Across all Samples

Questions/Parcels A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3

Ability of vendor to: provide unique promotions to your firm X

Customer service backup if salesperson is not available X

Number of sales calls you personally receive per year from: vendor's sales representatives

X

Sales force characteristics: follows up promptly X

Sales representative characteristics: accessibility X X X X X X X

Timely response to requests for assistance from supplier's sales representative X X X X X X X X X

Quality of sales force: knowledge of industry trends X

Quality of sales force: knowledge of merchandising techniques X X X X

Quality of sales force: knowledge of my business X

Quality of sales force: knowledge of my competitor's business X

Sales representative characteristics: industry knowledge X X X X X X X

Sales representative characteristics: product knowledge X X X X X X X X

Sales representative characteristics: technical knowledge X X X X X

Quality of sales force: adequate preparation for sales calls X X X X

Quality of sales force: prompt follow-up X X X X

Quality of sales force: understands logistics issues X

Sales representative characteristics: concern/empathy X X X X

Sales representative characteristics: honesty X X X X X X X X X Table 11: Promotion/Personal Selling Attributes Across all Samples

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Questions/Parcels A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3

Action on complaints related to order servicing and shipping X X X X X X

Advance notice of shipping delays X X

Assistance from supplier in handling carrier loss and damage claims X X X X X X

Prompt handling of claims due to overages, shortages or shipping errors X X X X X X X X

Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error

X X X

Ability of supplier to meet specific service and delivery needs X X X X X

Ability of supplier to respond to changes in requested delivery dates X X X X

Ability to expedite emergency orders X

Adequate availability (Supplier' ability to deliver) of new products at time of introduction X X X X X X

Availability of reorder product X X X

Availability of supplier to meet specific and/or unique customer service/delivery needs of individual customers

X

Supplier expedites emergency orders in a fast, responsive manner X X X X X

Supplier's adherence to special shipping instructions X X X X X X X X X

Consistent lead times (supplier consistently meets promised delivery date) X X X X X X X

Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking

X X X

Length of promised lead times (from order submission to delivery): Normal orders X X X X X X X X

Length of promised lead times (from order submission to delivery): Emergency orders X X X X

Length of promised lead times: ad or promotional orders X

Length of promised lead times: ASAP or emergency orders X

Ability of supplier to automatically backorder out-of-stock items X

Ability to meet/keep dates for pre-booked shipments X X X

Accuracy in filling orders (correct product is shipped) X X X X X X X X

Availability of inventory status information X

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Availability of status information on orders X X X X X X X X

Supplier ships complete orders and within specified windows (no incomplete or split shipments)

X X X

Table 12: Place/Logistics Attributes Across all Samples

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3.5. Structural Equation Modeling

The statistical analysis was performed using the two-step approach for structural equation

modeling (SEM), which combines the features of a factor analysis with a path analysis (Anderson

and Gerbing 1988, Bollen 1989). In the first step, the measurement model is established with a

confirmatory factor analysis. Then, hypotheses are tested with the structural model in the

second step. This analysis technique allows the researcher to examine the validity of the

measures and the relationship of the constructs at the same time.

Scale Development and Measurement Model

In SEM, before starting the hypothesis testing, the scales used to measure the constructs must

be developed and validated. For this dissertation, the recommendations for building scales by

Gerbing and Anderson (1988) are followed. A necessary condition for building a scale

estimating the constructs is that the measures must be acceptably unidimensional (Gerbing and

Anderson 1988). That is, each set of indicators has only one underlying trait or construct in

common. A confirmatory factor analysis (CFA) measurement model is used to establish

unidimensionality (Anderson and Gerbing 1988). The criteria for assessing the CFA are the

overall model fit and the validity of the components (Garver and Mentzer 1999). The criteria are

described next and the results from the samples are provided in the later sections.

The fit indices that are used to assess model fit are chi-square, Tucker-Lewis index (TLI),

comparative fit index (CFI), and root mean squared error (RMSEA), which have been shown to

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be reliable (Hu and Bentler 1999). The chi-square goodness-of-fit statistic is the probably most

commonly used measure of fit and a starting point for other fit indices; however one drawback

is the increased sensitivity to large sample sizes. Additionally, chi-square divided by degrees of

freedom is another revealing fit statistic. The TLI compares a proposed model's fit to a nested

null model and it measures parsimony by assessing the degrees of freedom from the proposed

model to the degrees of freedom of the null model (Tucker and Lewis 1973). A well-fitting

model should have a TLI of 0.95 or higher (Hu and Bentler 1999), but values above 0.90 are

acceptable. The CFI is a noncentrality parameter-based index to overcome the limitation of

sample size effects (Bentler 1990). A well-fitting model should have a CFI of 0.96 or higher (Hu

and Bentler 1999) , but values above 0.90 are acceptable. The RMSEA index measures the

discrepancy between the observed and estimated covariance matrices per degree of freedom

(Steiger and Lind 1980). Ideally RMSEA values are less than 0.60 (Hu and Bentler 1999), but

values between 0.60 and 0.80 are still acceptable (Browne and Cudek 1993). In addition to

good overall fit indices, an acceptable measurement of unidimensional constructs should reveal

relatively small standardized residuals of and modification indices (Garver and Mentzer 1999). A

large residual is larger than 2.58 (Medsker, Williams and Holahan 1994). A significant

modification index is larger than 7.88 (Jöreskog and Sorborn 1993).

In order to assess construct validity, several components of the model are evaluated: reliability,

convergent validity, and discriminant validity (Garver and Mentzer 1999). Reliability was

assessed using internal consistency method via Cronbach’s alpha (Cronbach 1951, Nunnally

1978). Typically, reliability coefficients of 0.70 or higher are considered adequate (Cronbach

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1951, Nunnally 1978). Additionally, composite reliability (CR) scores were calculated to assess

construct reliability (Bagozzi and Yi 1988). A CR greater than 0.70 would imply that the variance

captured by the factor is significantly more than the variance indicated by the error components

(Bagozzi and Yi 1988). In addition to CR, average variance extracted (AVE) was calculated

(Bagozzi and Yi 1988). An AVE of more than 0.50 implies that more variance is captured than

error (Bagozzi and Yi 1988). Convergent validity is the extent to which a latent variable

correlates to the items used to measure it. This is achieved by using manifest variables that load

highly on the latent variables (Dunn, Seaker and Waller 1994). Most factor loadings should be

above 0.60 and ideally above 0.70 (Chin 1998). Discriminant validity is established using CFA.

Measurement models are constructed for all possible pairs of the theoretical constructs. These

models were tested on each selected pair by fixing the correlation between the constructs at

1.00. A significant difference in chi-square values for the fixed and free solutions indicates the

distinctiveness of the two constructs (Bagozzi, Yi and Phillips 1991). In addition, the confidence

interval for each pair of constructs was set to be equal to plus or minus two standard errors of

the respective correlation coefficient.

The development of the scales for each of the constructs representing the Marketing Mix

variables was performed on sample A-1 (blood banking reagents). Next, validation of the scales

was performed on sample A-2 (coagulation reagents). For the remaining samples, slight

adaptations were necessary because of industry and product differences. The attributes that

are used to evaluate blood banking reagents, video tapes, plastic resin, golf balls, golf clubs, and

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golf shoes have to be different because the products are very different. Not all questions for

one survey were available in the others.

The outcome variables, customer satisfaction and share of business, are measured as single

indicators. This has the consequence that there is no specific error associated with those

variables. However, the informant was prequalified and it was ensured that the key informant

was answering the questionnaire (Phillips and Bagozzi 1986). Based on that, the measurement

of the outcome variables should be as precise as possible and measurement error is not likely to

improve the overall model. Specifically for share of business it is not possible build multi-item

scales, and as such, it is not possible but to use the single item scale.

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3.6. Measurement Model Results for Sample A-1

A CFA was run on sample A-1 (blood banking reagents) to develop the scales for the constructs

and to establish the measurement model. The overall fit of the measurement model was

adequate (chi-square = 632.172 /98 d.f.; TLI = 0.955; CFI = 0.968; RMSEA = 0.062) and with the

exception of the chi-square, the fit indexes were above the recommended thresholds. The chi-

square has the property that it increases with larger sample sizes and as such the high value

should not cause dismissal of the model (Hu and Bentler 1999). The model did not show any

high modification indexes or residuals. All of this evidence points to a well-fitting measurement

model. Next, the constructs are assessed.

The product construct has four parcels representing 11 questions. The questions and parcels

are shown in Table 13. The first parcel is made up of questions on new product development.

The second parcel has questions on reagent performance. The third parcel is based on

questions regarding reagent quality. The fourth parcel is about service regarding the product.

Overall, the construct exhibits good reliability with a Cronbach’s alpha of 0.866, a CR of 0.874,

and an AVE of 0.634. All the parcels have significant loadings and the standardized loadings are

well above the minimum recommended values.

88

Questions/Parcels Standard Loadings

Supplier adequately tests new products before delivering to market

0.821 Development of new products by supplier

Advance information (literature, specs, prices, etc.) on new product introductions

Product stability (Shelf life): Red cells

0.765 Product stability (Shelf life): Antiserum

Sensitivity (specificity) of reagent

Supplier replaces entire allotment when there is evidence of defective product

0.797 Consistency of supplier's delivered product after initial evaluation of samples by my facility

Product reliability (consistent performance from shipment to shipment)

Service support if salesperson is not available 0.800

Prompt notification of technical analysis results

Table 13: A-1 Measurement Model Product Construct Loadings

The price construct has four parcels representing 11 questions. The questions and parcels are

shown in Table 14. The first parcel has several questions on price changes. The second parcel

uses questions regarding price competition. The third parcel has questions regarding discounts.

The fourth parcel uses questions on price level. Overall, the construct exhibits good reliability

with a Cronbach’s alpha of 0.847, a CR of 0.840, and an AVE of 0.569. All the parcels have

significant loadings and the standardized loadings are well above the minimum recommended

values.

89


Adequate advance notice of price changes provided

0.770 Supplier gives you an adequate period of price protection after a price increase is announced

Supplier does not raise prices more than once per year

Supplier reacts quickly to competitive price reductions 0.796

Prompt and comprehensive response to competitive bid quotations

Supplier combines purchases of different products in order to compute volume discount

0.703 Quantity discount structure based on total annual purchases

Quantity discount structure based on size of individual order

Sales rep has authority to negotiate special prices 0.745

Competitiveness of price

Table 14: A-1 Measurement Model Price Construct Loadings

The promotion construct has four parcels representing seven questions. The questions and

parcels are shown in Table 15. The first parcel has questions regarding sales representative

accessibility. The second parcel uses questions regarding knowledge of the sales representative.

The third parcel is on questions regarding personal characteristics of the sales representative.

Overall, the construct exhibits excellent reliability with a Cronbach’s alpha of 0.909, a CR of

0.912, and an AVE of 0.776. All the parcels have significant loadings and the standardized

loadings are well above the minimum recommended values.


Sales representative characteristics: accessibility 0.942

Timely response to requests for assistance from supplier's sales representative

Sales representative characteristics: product knowledge

0.868 Sales representative characteristics: industry knowledge

Sales representative characteristics: technical knowledge

Sales representative characteristics: honesty 0.829

Sales representative characteristics: concern/empathy

Table 15: A-1 Measurement Model Promotion Construct Loadings

90

The place construct has four parcels representing 14 questions. The questions and parcels are

shown in Table 16. The first parcel summarizes various aspects of problem solving on shipping.

The second parcel has questions on delivery flexibility. The third parcel is about lead times. The

fourth parcel summarizes delivery quality. Overall, the construct exhibits excellent reliability


significant loadings and the standardized loadings are well above the minimum recommended

values as shown in Table 16.


Prompt handling of claims due to overages, shortages or shipping errors

0.861


Action on complaints related to order servicing and shipping

Assistance from supplier in handling carrier loss and damage claims

Ability of supplier to meet specific service and delivery needs

0.885

Adequate availability (Supplier' ability to deliver) of new products at time of introduction

Supplier's adherence to special shipping instructions

Ability of supplier to respond to changes in requested delivery dates

Supplier expedites emergency orders in a fast, responsive manner

Consistent lead times (supplier consistently meets promised delivery date)

0.796 Length of promised lead times (from order submission to delivery): Normal orders

Length of promised lead times (from order submission to delivery): Emergency orders

Availability of status information on orders 0.721

Accuracy in filling orders (correct reagent is shipped)

Table 16: A-1 Measurement Model Place Construct Loadings

All constructs in the measurement model for sample A-1 exhibit high reliability and seem to fit

the data well. Next, discriminant validity is tested. The results of the discriminant validity testing

are shown in Table 17. The first row of each cell contains the chi square value after fixing the

91

correlation between two constructs to 1.00. The second row shows the difference to the chi

square value of the original model of 632.2 and the difference in degrees of freedom from 98 in

parentheses. The last row displays the p-value of the chi square difference test. All of the fixed

correlations cause the fit of the model to increase significantly and none of the correlation

confidence intervals include 1.00. This evidence supports the conclusion that the constructs are

distinct from each other and thus discriminant validity is achieved.

Product Price Promotion

Price New chi square: 835.6 Difference: 203.4 (1) p < 0.01

Promotion New chi square: 805.6 Difference: 173.4 (1) p < 0.01

New chi square: 641.3 Difference: 9.1 (1) p < 0.01

Place New chi square: 984.9 Difference: 352.7 (1) p < 0.01

New chi square: 710.2 Difference: 78 (1) p < 0.01


Table 17: A-1 Discriminant Validity Test Results

92


As the second step in validating the constructs, the measurement model is tested on an

independent sample, which in this case is sample A-2 (coagulation reagents). The overall fit of

the measurement model was adequate (chi-square = 270.454 /98 d.f.; TLI = 0.950; CFI = 0.964;

RMSEA = 0.064) and the fit indices are above the recommended thresholds. The model did not

show any high modification indexes or residuals. All of this evidence points to a well-fitting

measurement model. Next, the constructs are assessed.

The product construct has the same four parcels as sample A-1. The questions and parcels are

shown in Table 18. Overall, the construct exhibits good reliability with a Cronbach’s alpha of

0.822, a CR of 0.813, and an AVE of 0.533. All the parcels have significant loadings and all but

one have standardized loadings that are well above the minimum recommended values as

shown in Table 18. The third parcel has a lower standardized loading but since it is only one

variable the overall construct exhibits acceptable reliability.

93



0.866 Development of new products by supplier








Service support if salesperson is not available 0.789

Prompt notification of technical analysis results


The price construct has the same four parcels as sample A-1. Overall, the construct exhibits

good reliability with a Cronbach’s alpha of 0.842, a CR of 0.854, and an AVE of 0.595. All the

parcels have significant loadings and the standardized loadings are well above the minimum

recommended values.



0.692 Supplier gives you an adequate period of price protection after a price increase is announced










94

The promotion construct has the same three parcels that are used for sample A-1. Overall, the

construct exhibits very good reliability with a Cronbach’s alpha of 0.874, a CR of 0.845, and an

AVE of 0.645. All the parcels have significant loadings and the standardized loadings are well

above the minimum recommended values as shown in Table 20.










The place construct has the same four parcels as in sample A-1. Overall, the construct exhibits

very good reliability with a Cronbach’s alpha of 0.882, a CR of 0.880, and an AVE of 0.648. All

the parcels have significant loadings and the standardized loadings are well above the minimum

recommended values.

95



0.686





0.896











All constructs in the measurement model for sample A-2 exhibit high reliability and seem to fit

the data well. Next, discriminant validity is tested. The results of the discriminant validity testing

are shown in Table 22. The first row of each cell contains the chi square value after fixing the







96









Following the validation of the measurement model in two separate samples, it can be

concluded that the measures for the Product, Price, Promotion, and Place constructs are

validated and reliable. Next, the measurement models in the remaining samples are assessed as

adaptations to the validated scales.

97


For sample A-3 slight adaptations had to be made because not all questions from the previous

surveys were available. The overall fit of the measurement model was adequate (chi-square =

198.088 /74 d.f.; TLI = 0.918; CFI = 0.942; RMSEA = 0.078) and the fit indexes are close to the

recommended thresholds. The model did not show any high modification indexes or residuals.

All of this evidence points to a well-fitting measurement model. Next, the constructs are

assessed.

The product construct has three of the four parcels from the previous two samples. The

questions and parcels are shown in Table 23. The construct has a Cronbach’s alpha of 0.774, a

CR of 0.785, and an AVE of 0.550. All the parcels have significant loadings and all have

standardized loadings that are above the minimum recommended values.



0. 703 Development of new products by supplier









98

The price construct has three of the four parcels that appeared in the previous two samples.

The questions and parcels are shown in Table 24. Overall, the construct exhibits good reliability


significant loadings and standardized loadings that are above the recommended values.










The promotion construct has the same three parcels that are used for sample A-1 and A-2.

Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.863, a CR of

0.865, and an AVE of 0.682. All the parcels have significant loadings and the standardized

loadings are well above the minimum recommended values.










99

The place construct has the same four parcels as in sample A-1 and A-2. The only adaptation is

the removal of one question due to missing data. Overall, the construct exhibits very good

reliability with a Cronbach’s alpha of 0.846, a CR of 0.854, and an AVE of 0.595. All the parcels

have significant loadings and the standardized loadings are well above the minimum

recommended values.



0.722 Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error



0.891











All constructs in the measurement model for sample A-3 exhibit adequate reliability and seem

to fit the data well. Next, discriminant validity is tested. The results of the discriminant validity

testing are shown in Table 27. The first row of each cell contains the chi square value after fixing

the correlation between two constructs to 1.00. The second row shows the difference to the chi



100












101

3.9. Measurement Model Results for Sample B-1

For sample B-1 adaptations had to be made because not all questions from the previous surveys

were available and other more specific questions were used. The largest differences were on

the product construct, which is not surprising since the video tape industry is very different and

the surveys were designed to be industry- specific. The overall fit of the measurement model

was good (chi-square = 236.847/ 86 d.f.; TLI = 0.964; CFI = 0.974; RMSEA = 0.059) and the fit

indexes are meet the recommended thresholds. The model did not show any high modification

indexes or residuals. All of this evidence points to a well-fitting measurement model. Next, the

constructs are assessed.

The product construct has three parcels which are mostly specific to the video tape industry.

The first parcel summarizes consistency of performance in different aspects. The second parcel

summarizes the level of performance on the same aspects as the first parcel. The third parcel

summarizes various aspects of product quality. The questions, parcels and standardized

loadings are shown in Table 28. Overall, the construct exhibits very good reliability with a

Cronbach’s alpha of 0.883, a CR of 0.829, and an AVE of 0.622. All the parcels have significant

loadings and all have large enough standardized loadings that are above the minimum

recommended values.

102


Consistency of product performance: electromagnetics

Consistency of product performance: audio playback levels

Consistency of product performance: bit error rate

Consistency of product performance: dropouts

Consistency of product performance: scratches

Consistency of product performance: edge damage

Consistency of product performance: slitting errors

Consistency of product performance: creases/cinches

Consistency of product performance: wind quality

0.685

Level of product performance: electromagnetics

0.718

Level of product performance: audio playback levels

Level of product performance: bit error rate

Level of product performance: dropouts

Level of product performance: scratches

Level of product performance: edge damage

Level of product performance: slitting errors

Level of product performance: creases/cinches

Level of product performance: wind quality

Vendor replaces entire allotment when there is evidence of defective product

0.939

Consistency of vendor's delivered product after initial evaluation of samples by my facility

Product durability (tape continues play back without loss of video and audio quality): after multiple passes

Product durability (tape continues play back without loss of video and audio quality): after extensive shuttling

Quality of product line above minimum standards Table 28: B-1 Measurement Model Product Construct Loadings

The price construct has the same four parcels as the previous samples, however there are slight

adaptations in the questions that make up the parcels, but overall they can be considered

equivalent. The questions and parcels are shown in Table 29. Overall, the construct exhibits

good reliability with a Cronbach’s alpha of 0.838, a CR of 0.824, and an AVE of 0.540. All the

parcels have significant loadings and all have large enough standardized loadings that are above

the minimum recommended values.

103


Adequate advance notice of price changes provided 0.718

Vendor gives you an adequate period of price protection after a price increase is announced

Vendor reacts quickly to competitive price reductions 0.750





Sales rep will give you volume price even if you are buying less

Competitiveness of price 0.819

Low price

Table 29: B-1 Measurement Model Price Construct Loadings

The promotion construct has the same three parcels that are used in the previous samples. The

questions, parcels, and standardized factor loadings are shown in Table 30. One question was

added to the first parcel. Overall, the construct exhibits very good reliability with a Cronbach’s

alpha of 0.870, a CR of 0.886, and an AVE of 0.722. All the parcels have significant loadings and

the standardized loadings are well above the minimum recommended values.


Sales force characteristics: accessibility

0.788 Sales force characteristics: follows up promptly


Sales force characteristics: product knowledge

0.861 Sales force characteristics: industry knowledge

Sales force characteristics: technical knowledge

Sales force characteristics: honesty 0.897

Sales force characteristics: concern/empathy

Table 30: B-1 Measurement Model Promotion Construct Loadings

104

The place construct has the same four parcels as in previous samples with some slight

adaptations. Overall, the construct exhibits very good reliability with a Cronbach’s alpha of

0.904, a CR of 0.912, and an AVE of 0.723. All the parcels have significant loadings and the

standardized loadings are well above the minimum recommended values.


Action on complaints related to order servicing and shipping 0.830


Vendor meets specific customer service and delivery needs of individual customers

0.899 Vendor’s adherence to special shipping instructions

Vendor’s ability to respond to changes in requested delivery dates

Vendor expedites emergency orders in a fast, responsive manner





Accuracy in filling orders

Table 31: B-1 Measurement Model Place Construct Loadings

All constructs in the measurement model for sample B-1 exhibit adequate reliability and fit the

data well. Next, discriminant validity is tested. The results of the discriminant validity testing are

shown in Table 32. The first row of each cell contains the chi square value after fixing the







105





Place New chi square: 376.8 Difference: 140 (1) p < 0.01

New chi square: 334.8 Difference: 98 (1) p < 0.01


Table 32: B-1 Discriminant Validity Testing Results

106

3.10. Measurement Model Results for Sample B-2

For sample B-2 adaptations had to be made because not all questions from sample B-1 were

available. The overall fit of the measurement model was good (chi-square = 163.050/ 61 d.f.; TLI

= 0.933; CFI = 0.955; RMSEA = 0.086) and the fit indexes are close to the recommended

thresholds. The model did not show any high modification indexes or residuals. All of this

evidence points to a well-fitting measurement model. Next, the constructs are assessed.

The product construct has three parcels which are mostly specific to the video tape industry.

The first parcel summarizes aspects of new product development. The second parcel

summarizes several aspects of packaging. The third parcel summarizes various aspects of

service related to the product. The questions, parcels and standardized loadings are shown in

Table 33. Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.883, a

CR of 0.829, and an AVE of 0.622. All the parcels have significant loadings and all have large

enough standardized loadings that are above the minimum recommended values.


Speed at which vendor responds to industry technical improvements 0.879

Adequate availability of newly introduced products

Quality/durability of packaging

0.652 Adequate identification/labeling of package contents

Vendor's willingness to work with your firm to develop custom packaging configurations

Ability of vendor to handle: defective product returns

0.933 Ability of vendor to handle: consumer complaints

Past experience with vendor's product

Table 33: B-2 Measurement Model Product Construct Loadings

107

The price construct has the three of the four parcels from previous samples. The questions and

parcels are shown in Table 34. Overall, the construct exhibits good reliability with a Cronbach’s

alpha of 0.905, a CR of 0.908, and an AVE of 0.767. All the parcels have significant loadings and

all have large enough standardized loadings that are above the minimum recommended values.


Prompt and comprehensive response to competitive bid quotations 0.866


Lowest price

0.931 Willingness of sales rep to be flexible in offering special/volume discounts, pricing, incentives and other offers

Assurance that my target price at retail will equal that of my competitors

Responsiveness of vendor to competitor's price reductions

0.828 Adequate advance notice of price changes

Vendor gives you adequate price protection and/or markdown funds

Table 34: B-2 Measurement Model Price Construct Loadings

The promotion construct has the same three parcels that are used in the previous samples, but

some questions were added. The questions, parcels, and standardized factor loadings are

shown in Table 35. Overall, the construct exhibits very good reliability with a Cronbach’s alpha

of 0.901, a CR of 0.858, and an AVE of 0.670. All the parcels have significant loadings and the

standardized loadings are well above the minimum recommended values.

108


Customer service backup if salesperson is not available

0.907 Ability of vendor to: provide unique promotions to your firm

Number of sales calls you personally receive per year from: vendor's sales representatives

Timely response to requests for assistance from vendor's sales rep

Quality of sales force: knowledge of merchandising techniques

0.795

Quality of sales force: product knowledge

Quality of sales force: knowledge of industry trends

Quality of sales force: knowledge of my business

Quality of sales force: knowledge of my competitor's business

Quality of sales force: adequate preparation for sales calls

0.746 Quality of sales force: honesty

Quality of sales force: understands logistics issues

Table 35: B-2 Measurement Model Promotion Construct Loadings

The place construct has the three of the four parcels from previous samples. The questions,

parcels and standard loadings are shown in Table 36 Overall, the construct exhibits very good


have significant loadings and the standardized loadings are well above the minimum

recommended values.


Ability to expedite emergency orders

0.873 Ability of vendor to meet specific and/or unique customer service and delivery needs

Vendor's adherence to your specific shipping instructions

Length of promised lead times: normal reorders

0.809 Length of promised lead times: ad or promotional orders

Length of promised lead times: ASAP or emergency orders


0.870 Prompt handling of claims due to: overages, shortages/pricing errors

Advance notice of shipping delays

Table 36: B-2 Measurement Model Place Construct Loadings

109

All constructs in the measurement model for sample B-2 exhibit adequate reliability and fit the













Place New chi square: 216 Difference: 52.9 (1) p < 0.01



Table 37: B-2 Discriminant Validity Testing Results

110

3.11. Measurement Model Results for Sample C-1

For sample C-1, adaptations had to be made because not all questions from previous samples

were available. The overall fit of the measurement model was good (chi-square = 330.718 / 74

d.f.; TLI = 0.922; CFI = 0.945; RMSEA = 0.075) and the fit indexes are close to the recommended

thresholds. The model did not show any high modification indexes or residuals. All of this

evidence points to a well-fitting measurement model. Next, the constructs are assessed.

The product construct has three parcels which are mostly specific to the plastics resin industry.

The first parcel summarizes different aspects of packaging. The second parcel summarizes the

level of performance of the supplier’s product. The third parcel summarizes various aspects of

product quality. The questions, parcels and standardized loadings are shown in Table 38.

Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.823, a CR of

0.821, and an AVE of 0.606. All the parcels have significant loadings and all have large enough



Adequate identification/labeling of package contents

0.690 Quality/durability of packaging materials (bag, box, drum)

Availability of the following package: type-bag

Processability of resin 0.781

Supplier's resins are of consistent melt flow

Supplier's resins are of consistent quality

0.856 Overall quality of resin relative to price

Supplier's resins are of consistent color

Table 38: C-1 Measurement Model Product Construct Loadings

111

The price construct has three parcels and there are slight adaptations in the questions from

previous samples. The questions and parcels are shown in Table 39. Overall, the construct

exhibits good reliability with a Cronbach’s alpha of 0.742, a CR of 0.760, and an AVE of 0.513. All

the parcels have significant loadings and all have large enough standardized loadings that are

above the minimum recommended values.


Realistic, consistent pricing policy by supplier over time 0.719


Adequate advance notice of price changes 0.688

Supplier gives you an adequate period of price protection after a price increase is announced

Prompt and comprehensive response to competitive bid quotations 0.741

Quantity discount structure

Table 39: C-1 Measurement Model Product Construct Loadings

For the promotion construct the parceling technique that is used in the previous samples, is not

employed because fewer questions are available. Thus, with only four questions not enough

parcels could be built and therefore individual items are used. The questions and standardized

factor loadings are shown in Table 40. Overall, the construct exhibits good reliability with a

Cronbach’s alpha of 0.835, a CR of 0.831, and an AVE of 0.622. All the items have significant

loadings and the standardized loadings are well above the minimum recommended values.


Timely response to requests for assistance from supplier's sales representative 0.660

Quality of sales force-technical knowledge 0.732

Quality of sales force-prompt follow-up 0.847

Quality of sales force-honesty 0.782 Table 40: C-1 Measurement Model Promotion Construct Loadings

112

The place construct has the three of the four parcels from previous samples. Overall, the

construct exhibits good reliability with a Cronbach’s alpha of 0.844, a CR of 0.846, and an AVE of

0.647. All the parcels have significant loadings and the standardized loadings are well above the

minimum recommended values as shown in Table 41.


Accuracy in filling orders (correct product is shipped)

0.793 Availability of status information on orders

Ability of supplier to automatically backorder out-of-stock items

Availability of inventory status information

Ability to expedite emergency orders in a fact responsive manner

0.789 Supplier's adherence to special shipping instructions

Availability of supplier to meet specific and/or unique customer service/delivery needs of individual customers

Action on complaints (e.g. order servicing, shipping, product, etc.)

0.831 Advance notice of shipping delays


Table 41: C-1 Measurement Model Place Construct Loadings

All constructs in the measurement model for sample C-1 exhibit acceptable reliability and fit the


shown in Table 42 . The first row of each cell contains the chi square value after fixing the







113


Price New chi square: 518.2 Difference: 188 (1) p < 0.01






Table 42: C-1 Discriminant Validity Testing Results

114

3.12. Measurement Model Results for Sample D-1

For sample D-1, adaptations had to be made because of industry differences for the golf ball

industry and the fact that retailers were surveyed. The overall fit of the measurement model

was good (chi-square = 204.435 / 103 d.f.; TLI = 0.962; CFI = 0.971; RMSEA = 0.059) and the fit

indexes are well beyond the recommended thresholds. The model did not show any high

modification indexes or residuals. All of this evidence points to a well-fitting measurement


The product construct has five parcels which are mostly specific to the golf ball industry. The

first parcel summarizes development of new products. The second parcel summarizes various

aspects of product packaging. The third and fourth parcels are about performance and quality

of the golf balls. The questions, parcels and standardized loadings are shown in Table 43.

Overall, the construct exhibits excellent reliability with a Cronbach’s alpha of 0.902, a CR of

0.910, and an AVE of 0.718. All the parcels have significant loadings and they have large enough


115


Consistent development of new golf balls by supplier

0.849 Frequency of new product introduction

Advance information (literature, specs, prices, etc.) on new product intros

Packaging: visual appeal

0.826 Packaging: aids in consumer purchasing decision

Packaging: product/technical info on package

Durability of packaging

Performance of premium balls

0.786 Supplier's products are at the leading edge of technology

Product features (distance, spin rate)

Past experience with supplier's product

0.919 Quality of product

Durability of product

Table 43: D-1 Measurement Model Product Construct Loadings

The price construct has the same four parcels as the previous samples, however there are slight


equivalent. The questions and parcels are shown in Table 44. The first parcel summarizes the

effect of pricing on the retailer. The second parcel is about price increases. The third parcel is

made up of questions on discounts. In the fourth parcel there are questions concerning the

price level. Overall, the construct exhibits good reliability with a Cronbach’s alpha of 0.859, a CR

of 0.850, and an AVE of 0.587. All the parcels have significant loadings and all have large enough


116


Profit margin 0.776

Simplicity of pricing program


0.844 Supplier gives you an adequate period of price protection after a price decrease is announced

Quantity discount structure based on total annual purchases

0.714 Quantity discount structure based on size of individual order

Pre-book discount program

Lowest price

0.725 Competitiveness of price

Sales rep has authority to negotiate special prices

Table 44: D-1 Measurement Model Price Construct Loadings


questions, parcels, and standardized factor loadings are shown in Table 45. Overall, the

construct exhibits excellent reliability with a Cronbach’s alpha of 0.908, a CR of 0.916, and an




Sales force characteristics: accessibility 0.818

Timely response to requests for assistance from supplier's sales rep



Sales force characteristics: knowledge of merchandising techniques

Sales force characteristics: honesty

0.914 Sales force characteristics: concern/empathy

Sales force characteristics: adequate preparation for sales calls

Table 45: D-1 Measurement Model Promotion Construct Loadings

117

The place construct uses the same four parcels as previous samples. Although some questions

are different it should not change the overall validity of the construct. Overall, the construct

exhibits excellent reliability with a Cronbach’s alpha of 0.900, a CR of 0.910, and an AVE of 0.705.

All the parcels have significant loadings and the standardized loadings are well above the





Ability to meet/keep dates for pre-booked shipments


Adequate availability (supplier's ability to deliver) of new products at time of introduction

0.848 Availability of reorder product



0.838 Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking

Length of promised lead times (from order submission to delivery): reorders

Prompt handling of claims due to overages, shortages or shipping errors 0.777


Table 46: D-1 Measurement Model Place Construct Loadings

All constructs in the measurement model for sample D-1 exhibit very good reliability and fit the




square value of the original model of 206.4 and the difference in degrees of freedom from 103

in parentheses. The last row displays the p-value of the chi square difference test. All of the

118

fixed correlations cause the fit of the model to increase significantly and none of the correlation










Table 47: D-1 Discriminant Validity Testing Results

119


For sample D-2, only slight adaptations had to be made from sample D-1. The overall fit of the

measurement model was excellent (chi-square = 144.763 / 74 d.f.; TLI = 0.949; CFI = 0.964;

RMSEA = 0.060) and the fit indexes are at the recommended thresholds. The model did not

show any high modification indexes or residuals. All of this evidence points to a well-fitting

measurement model. Next, the constructs are assessed.

The product construct has four parcels which are mostly specific to golf clubs. The first parcel

summarizes development of new products. The second parcel is about golf club performance.

The third parcel covers golf club quality. The questions, parcels and standardized loadings are

shown in Table 48. Overall, the construct exhibits good reliability with a Cronbach’s alpha of

0.819, a CR of 0.815, and an AVE of 0.595. All the parcels have significant loadings and all have

large enough standardized loadings.


Supplier adequately tests new products before delivering to market 0.731

Consistent development of new products by supplier

Consistent shaft quality & performance

0.775 Supplier at leading edge of technology

Supplier's products are at the leading edge of technology

Past experience with supplier's product

0.806 Quality of product

Product reliability (consistent performance from shipment to shipment) Table 48: D-2 Measurement Model Product Construct Loadings

120

The price construct has three of the four parcels from the previous samples. The only parcel

that was removed was the one about discounts. The questions and parcels are shown in Table

49. The first parcel summarizes the effect of pricing on the retailer. The second parcel is about

price changes. The third parcel is made up of questions concerning the price level. Overall, the

construct exhibits good reliability with a Cronbach’s alpha of 0.798, a CR of 0.804, and an AVE of

0.578. All the parcels have significant loadings and all have large enough standardized loadings

that are above the minimum recommended values.



Profit margin

Adequate advance notice of price changes provided 0.811


Lowest price

0.784 Competitiveness of price

Sales rep has authority to negotiate special prices







121











The place construct uses the four parcels from previous samples. The first parcel covers the

efficiency and effectiveness of delivery. The second parcel summarizes the flexibility of the

logistics system. The third parcel is about lead time. The last parcel covers problem solving. The



above the minimum recommended values as shown in Table 51.

122
























123









124


For sample D-3, adaptations had to be made because of industry differences for the golf shoe

industry and the fact that retailers were surveyed. The overall fit of the measurement model

was adequate (chi-square = 186.527/ 74 d.f.; TLI = 0.935; CFI = 0.955; RMSEA = 0.086) and the fit

indexes are close to the recommended thresholds. The model did not show any high

modification indexes or residuals. All of this evidence points to a well-fitting measurement


The product construct has three parcels which are mostly specific to the golf shoe industry. The

first parcel summarizes the assortment of products offered by the manufacturer. The second

parcel and third parcels are about golf shoe performance and quality. The questions, parcels

and standardized loadings are shown in Table 53. Overall, the construct exhibits very good


have significant loadings and they have large enough standardized loadings that are above the

minimum recommended values.

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Appropriate range of sizes

0.800 Availability of men's and women's products

Availability of different widths

Supplier has complete assortment of footwear items

Waterproofing

0.888

Functionality

Consistent sizing

Fit and comfort

Overall appearance of shoe

Footwear consistent with most preferred styles and trends

Product quality relative to price

0.905 Warranty program for footwear

Product reliability (consistent product performance from shipment to shipment

Table 53: D-3 Measurement Model Product Construct Loadings

The price construct has three of the four parcels from the previous samples, and there are slight


equivalent. The questions and parcels are shown in Table 54. The first parcel summarizes the

effect of price changes. The second parcel is made up of questions on discounts. In the third

parcel there are questions concerning the margin. Overall, the construct exhibits good reliability


significant loadings and all have large enough standardized loadings that are above the

minimum recommended values.

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0.758 Supplier gives you an adequate period of price protection after a price decrease is announced

Quantity discount structure based on total annual purchases

0.699 Quantity discount structure based on size of individual order

Pre-book discount program

Integrity of suggested retail price

0.765 Margin reflects selling effort

Profit margin

















The place construct uses the same four parcels as previous samples. Overall, the construct

exhibits excellent reliability with a Cronbach’s alpha of 0.918, a CR of 0.914, and an AVE of 0.780.

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All the parcels have significant loadings and the standardized loadings are well above the

























128









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3.15. Summary

This chapter contains a description of the research methodology. There are nine samples that

are used for this research and each one is analyzed separately. There is no evidence that non-

response bias is a problem in any of the samples. Because too many items loaded on each

latent variable representing the Marketing Mix variables, parcels were used to summarize

several items into one importance-weighted composite score. The composite scores were then

used to estimate the constructs. The same constructs were used in all samples, but the

questions that were used to estimate each construct were not the same across all samples. All

measurement models exhibit adequate fit indices and there is enough evidence that the

variables represent the constructs well. The results of the structural part of the model are

presented in the next chapter.

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CHAPTER 4.

RESULTS

In this chapter, the results of the research are described. The measurement model part was

described in Chapter 3. In this chapter, the structural relationships between the constructs are

evaluated. In order to test the hypotheses, the structural relationships between the latent

variables of the structural equation modeling (SEM) are evaluated. The remainder of the

chapter is comprised of the structural model results from the nine samples. In order to gain a

more holistic understanding of the differences between the samples, a comparison of the

results is provided. The chapter ends with a summary.

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4.1. Overview of the Results Evaluation

In order to evaluate the strength of a relationship between two constructs, the estimate,

standard error, critical ratio, and p-value are assessed. The estimate represents a regression

coefficient that describes the magnitude of the relationship between the two constructs

(Arbukle 2008). The standard error (S.E.) is the amount of variability that is associated with the

estimate. The critical ratio (C.R.) is related to the significance of the regression weight. A C.R.,

larger than 1.96, indicates that a path that is significant at the 0.05 level (Arbukle 2008). The p-

value (P) denotes the absolute level of significance and refers to the probability of obtaining an

estimate that is zero. For this research, p-values of less than 0.01 are considered highly

significant, those between 0.01 and 0.05 are significant, and those between 0.05 and 0.10 are

marginally significant. In order to compare the relative strength of all paths in the model,

standardized estimates are presented. All relationships shown in Figure 5 as arrows are

estimated simultaneously in the SEM model.

Figure 5: Structural Model and Hypotheses

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4.2. A-1 Blood Banking Reagents Sample Results

The results for the blood banking reagents sample are displayed in Table 58. The overall fit of

the structural model was good (TLI = 0.950; CFI = 0.962; RMSEA = 0.065) and the fit indexes

were above the recommended thresholds. The difference between the measurement model

and the structural model is not large enough to be significant and that points to a well-fitting

structural model.

The first hypothesis shows the impact of the Marketing Mix on customer satisfaction. Three out

of four paths are significant. Promotion/personal selling has the strongest impact on

satisfaction (H1c), followed by place/logistics (H1d) and then by price (H1b). Only the product

construct does not have significant impact on satisfaction (H1a). With three out of four

constructs having a significant impact on customer satisfaction, it can be concluded that

Hypothesis one is supported.

One explanation for why product does not have a significant impact on customer satisfaction is

that blood banking reagents are regulated commodities that do not vary significantly in

performance and quality among the different manufacturers. New blood banking reagents must

be approved by the food and drug administration (FDA). As such, reagents must at least fulfill a

minimum level of performance and quality. New products would have to go through the same

approval process for any supplier. This explanation seems to be supported by the fact that the

parcel describing product performance has the highest average rating and the lowest standard

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deviation of all parcels in the model, which indicates that the different blood banking reagents

perform similarly well at a high level.

The second hypothesis describes the impact of customer satisfaction on share of business and

the analysis shows a significant and positive effect. In addition, indirect effects of the price,

promotion/personal selling and place/logistics constructs can be observed as well. This means

that those constructs have a significant impact on satisfaction, which in turn has a significant

impact on share of business. So, going back to the premise of the research, logistics attributes

are important part of the Marketing Mix and their impact on business generation must not be

ignored. Satisfaction explains 38.1 percent of the variance in the model and share of business

explains 6.5 percent of the variance.

Structural Path Estimate S.E. C.R. P Standard Estimate

H1a: Product Satisfaction -3.240 2.717 -1.192 0.233 -0.091

H1b: Price Satisfaction 2.743 1.232 2.227 0.026 0.140

H1c: Promotion Satisfaction 6.257 0.498 12.554 <0.001 0.415

H1d: Place Satisfaction 5.421 2.063 2.628 0.009 0.209

H2: Satisfaction Share 0.481 0.049 9.810 <0.001 0.254

Table 58: A-1 Blood Banking Results

In addition to the impact of satisfaction on current share of business, a variation of the model

was evaluated with preferred share of business as an alternative outcome variable. Preferred

share of business is the percentage of business that a respondent would ideally want to award

to a supplier. Introducing preferred share as an alternative outcome variable enables the

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assessment whether ideal and preferred share show the same level of significance and explain

similar amounts of variance.

The significance and the magnitude of the variables generally remains the same, but the link

between satisfaction and preferred share of business is stronger in magnitude. Overall, the

model fit was comparable (TLI = 0.951; CFI = 0.963; RMSEA = 0.065). Preferred share of business

explains a larger percentage of the variance (10.7 percent) in the model than current share of

business (6.5 percent). While the estimates are not significantly different there is still an

indication that customer satisfaction is a better predictor of preferred share of business.






H2: Satisfaction Preferred Share 0.585 0.045 12.878 <0.001 0.326

Table 59: A-1 Blood Banking Results Alternative Model

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4.3. A-2 Coagulation Reagents Sample Results

The next sample that is assessed is the coagulation reagents sample. The overall fit of the

structural model was good (TLI = 0.941; CFI = 0.955; RMSEA = 0.066). The difference between

blood banking and coagulation reagents largely refers to the type of test that is performed in

the laboratory. Blood banking reagents are necessary for every surgery, but coagulation tests

are not and they may be used less frequently. The pattern of results for the coagulation

reagents sample is different than in the blood banking sample and they are shown in Table 60.

The relationship between the promotion/personal selling construct and customer satisfaction is

significant (H1c), as it was in the blood banking sample. The effect of product attributes on

customer satisfaction is significant (H1a), and it was not significant in the blood banking sample.

There is a significant impact of the product construct on customer satisfaction. Overall, two of

the four components of the Marketing Mix have a significant relationship with customer

satisfaction. The impact of customer satisfaction on share of business is marginally significant.

Therefore, hypothesis two is not supported and no indirect effects are supported either.


H1a: Product Satisfaction 20.304 7.116 2.853 0.004 0.558




H2: Satisfaction Share 0.163 0.094 1.733 0.083 0.083

Table 60: A-2 Coagulation Reagents Results

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was evaluated with preferred share of business as an alternative outcome variable. The

significance and the magnitude of the variables generally remains the same, but the link

between satisfaction and preferred share of business is now significant. Overall, the model fit

was comparable (TLI = 0.944; CFI = 0.957; RMSEA = 0.065). Preferred share of business explains

a larger percentage of the variance (3.6 percent) in the model than current share of business

(0.7 percent). The differences are statistically significant and there is an indication that

customer satisfaction is a better predictor of preferred share of business. Medical laboratories

must go through a difficult procedure in order to switch suppliers. They must run parallel tests

with both the new and the old reagent for a week and compare results to ensure the tests are

interpreted correctly (Ezzelle, et al. 2008). This creates significant extra work for the staff and

may cause customers to keep a current supplier even if the performance is subpar.






H2: Satisfaction Preferred Share 0.368 0.091 4.049 *** 0.191

Table 61: A-2 Coagulation Reagents Results Alternative Model

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4.4. A-3 Coagulation Reagents Sample Results

The second survey on the coagulation sample was performed three years after A-2 and used the

same sampling frame. The overall fit of the structural model was good (TLI = 0.914; CFI = 0.935;

RMSEA = 0.080). The results are different than in the first survey on coagulation reagents. The

results are shown in Table 62. One component of the Marketing Mix, the price construct,

showed a significant impact on customer satisfaction (H1b). The product construct, which was

highly significant in the earlier sample, is not significant (H1a). This could be because some

suppliers who previously had worse performance improved and large differences exist no longer.

The opposite also could be possible, where some high-performing suppliers became worse.

Overall, hypothesis one has minimal support. The impact of customer satisfaction on share of

business is not significant and hypothesis two is not supported.



H1b: Price Satisfaction 21.862 4.353 5.023 *** 0.489

H1c: Promotion Satisfaction -1.590 5.120 -0.311 0.756 -0.041



Table 62: A-3 Coagulation Reagents Results



significance and the magnitude of the variables generally remains the same, but the magnitude

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of the relationship between customer satisfaction and share of business increased and moved

closer to significance. However, the impact of customer satisfaction on preferred share of

business is still not significant. Overall, the fit of the alternative model shown in Table 63 was

slightly better (TLI = 0.917; CFI = 0.938; RMSEA = 0.078). Preferred share of business explains a

larger percentage of the variance (0.9 percent) in the model than current share of business (0.4

percent), but it is at a very low level in both models. Although the explanatory power of

customer satisfaction decreases from 15.3 percent with current share of business to 12.8

percent with preferred share of business.



H1b: Price Satisfaction 19.295 4.121 4.682 <0.001 0.447

H1c: Promotion Satisfaction 0.251 4.971 0.05 0.96 0.007


H2: Satisfaction Preferred Share 0.135 0.087 1.547 0.122 0.092

Table 63: A-3 Coagulation Reagents Results Alternative Model

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4.5. B-1 Professional Video Tape Sample Results

In the professional tape sample, the results show a different impact of the Marketing Mix on

firm performance. The results are shown in Table 64. The overall fit of the structural model was

very good (TLI = 0.963; CFI = 0.972; RMSEA = 0.059). The product in this sample was video tapes

sold to professional recording studios. Two of the four components of the Marketing Mix have a

significant impact on customer satisfaction, product (H1a) and place/logistics (H1d). The

place/logistics construct has the strongest impact on customer satisfaction. Customer

satisfaction does have a significant effect on share of business and as such hypothesis two is

supported. In addition, the product and place/logistics constructs have an indirect effect on

share of business through customer satisfaction. This result highlights the importance of

superior logistics performance as it directly affects customer satisfaction and indirectly affects

share of business. This means that suppliers who provide superior products and logistics

services to their customers are the most successful.

Availability of the desired tape is critical because of tight schedules, which makes logistics

performance very important. The best way for a supplier to succeed is to provide a high-quality

video tape with characteristics that professional users desire and make them easily available

when customers need them. Users of professional video tape placed a higher importance on

logistics attributes than product attributes as evidenced by the larger estimate. It may be

concluded that the video tape that is available is more appreciated than the one that has the

highest technical specifications.

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H1a: Product Satisfaction 7.369 2.201 3.348 <0.001 0.222



H1d: Place Satisfaction 9.297 2.166 4.292 <0.001 0.434


Table 64: B-1 Professional Tape Results




of the relationship between customer satisfaction and share of business increased in magnitude.

The difference is not statistically significant. Overall, the fit of the alternative model shown in

Table 65 was equal (TLI = 0.962; CFI = 0.972; RMSEA = 0.060). Preferred share of business

explains a slightly larger percentage of the variance (20.8 percent) in the model than current

share of business (19.1 percent). The explanatory power of customer satisfaction stays the

same at 43.7 percent.


H1a: Product Satisfaction 7.369 2.201 3.348 <0.001 0.222



H1d: Place Satisfaction 9.297 2.166 4.292 <0.001 0.434


Table 65: B-1 Professional Tape Results Alternative Model

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4.6. B-2 Consumer Video Tape Sample Results

In the second electronics sample, video tapes for consumers sold to retailers, slightly different

results were obtained. The results are shown in Table 66. Overall, the fit of the model was good

(TLI = 0.935; CFI = 0.954; RMSEA = 0.084). Two of the four components of the Marketing Mix

have a significant impact on customer satisfaction, promotion/personal selling (H1c) and

place/logistics (H1d). In contrast to the professional users, retailers seem to value good

salespeople. In this sample, the product performance did not have a significant impact.

Customer Satisfaction has a significant effect on share of business and hypothesis two is

supported. In addition indirect effects are observed as promotion/personal selling and

place/logistics impact share of business through customer satisfaction. This adds additional

evidence to the importance of logistics attributes regarding business performance.



H1b: Price Satisfaction -0.159 2.297 -0.069 0.945 -0.009




Table 66: B-2 Consumer Tape Results




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Table 67 was similar (TLI = 0.932; CFI = 0.951; RMSEA = 0.086). Preferred share of business

explains a slightly larger percentage of the variance (9 percent) in the model than current share

of business (6.8 percent). The explanatory power of customer satisfaction stays the same at

43.1 percent.



H1b: Price Satisfaction -0.159 2.297 -0.069 0.945 -0.009




Table 67: B-2 Consumer Tape Results Alternative Model

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4.7. C-1 Plastics Resin Sample Results

In the plastic resin sample, the respondents’ were buyers in a manufacturing environment, as

compared to the hospitals, movie studios, and retailers from previous samples. The results are

shown in Table 68. The overall model fit was good (TLI = 0.926; CFI = 0.943; RMSEA = 0.073).

One component of the Marketing Mix, price, has a significant impact on customer satisfaction

(H1b). The direct effects of product and promotion/personal selling factors are not significant.

Customer satisfaction has a significant effect on share of business, which supports hypothesis

two. In addition, the results provide evidence for a significant indirect effect of the price

construct through customer satisfaction on share of business.







Table 68: C-1 Plastic Resin Results






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share of business (1.2 percent). The explanatory power of customer satisfaction stays the same

at 25.4 percent.







Table 69: C-1 Plastic Resin Results Alternative Model

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4.8. D-1 Golf Balls Sample Results

The last three samples are from the golf industry and in each of them volume retailers and pro

shops were surveyed. The overall model fit was good (TLI = 0.950; CFI = 0.961; RMSEA = 0.069).

The results of the golf balls sample are shown in Table 70. None of the components of the

Marketing Mix have a significant impact on customer satisfaction and as such there is no

support for the hypothesis that the Marketing Mix has a significant impact on customer

satisfaction. An analysis of the performance scores shows that variability of performance scores

is lower for this sample than the other two sporting goods samples. Customer satisfaction has a

significant impact on share of business, so there is support for hypothesis two.





H1d: Place Satisfaction -1.763 4.143 -0.426 0.670 -0.078


Table 70: D-1 Golf Balls Results






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share of business (9.8 percent). The explanatory power of customer satisfaction stays the same

at one percent.







Table 71: D-1 Golf Balls Results Alternative Model

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4.9. D-2 Golf Clubs Sample Results

The data in the second sample in the golf industry is a survey of volume retailers and pro shops

regarding golf clubs. The overall model fit was good (TLI = 0.939; CFI = 0.955; RMSEA = 0.066).

The results are shown in Table 72. Two constructs have a significant impact on customer

satisfaction, product (H1a) and promotion/personal selling (H1c). These are the only significant

relationships in this sample. This is a disconnect because the relationship between customer

satisfaction and share of business is not significant. In other words, there is no statistical

relationship between customer satisfaction and share of business. In this sample no indirect

effects from the Marketing Mix, through customer satisfaction, on share of business can be

reported.







Table 72: D-2 Golf Clubs Results



significance and the magnitude of the variables generally remains the same, but the strength of

the relationship between customer satisfaction and share of business is higher. The difference

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is statistically significant. Overall, the fit of the alternative model shown in Table 73 was similar

(TLI = 0.942; CFI = 0.957; RMSEA = 0.068). Preferred share of business explains a much larger

percentage of the variance (62.9 percent) in the model than current share of business (1.2

percent). The explanatory power of customer satisfaction stays the same at 18.9 percent. The

indirect effects of product and promotion/personal selling, through customer satisfaction, on

preferred share of business are significant.







Table 73: D-2 Golf Clubs Results Alternative Model

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4.10. D-3 Golf Shoes Sample Results

The results of the golf shoes sample are shown in Table 74. The overall fit of them model is

good (TLI = 0.958; CFI = 0.971; RMSEA = 0.071). One component of the Marketing Mix,

promotion/personal selling had a significant impact on customer satisfaction (H1c). The

relationship between customer satisfaction and share of business is significant which offers

support for hypothesis two. Therefore, the indirect effect between promotion/personal selling

and share of business is significant.







Table 74: D-3 Golf Shoes Results







explains a much larger percentage of the variance (24.7 percent) in the model than current

150

share of business (18.8 percent). The explanatory power of customer satisfaction stays the

same at 46.9 percent.







Table 75: D-3 Golf Clubs Results Alternative Model

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4.11. Sample Comparison

After analyzing the results of each sample individually, an overall assessment of the results from

all samples is provided. The impact of the Marketing Mix components on customer satisfaction

is shown in Table 76. The table shows the sign and the significance of the coefficient

representing each hypothesis. A marginal significance represented by a p-value between 0.05

and 0.10 is denoted by “†”. One star (*) denotes a p-value between 0.01 and 0.05. Two stars

(**) denote a p-value between 0.001 and 0.01. Three stars (***) denote a p-value of less than

0.001. If a coefficient is not significant it is denoted with “n.s.”. In the last row of the table the

number of significant relationships is shown.

The same the Marketing Mix components are not significant for more than one sample. Even in

samples A-2 and A-3, which are based on the same product, the results are different. It seems

that promotion/personal selling has a more consistent impact on customer satisfaction than the

other components of the Marketing Mix. The other constructs are significant three out of nine

times. Based on the fact that differences between samples appear in the results, it must be

made clear that different types of industries, products and organizations were surveyed. Based

on the results in this study, customer service attributes are not equally important nor do they

have the same influence on customer satisfaction across all samples. It is not possible to use

previous data to generalize what components of the Marketing Mix have a significant impact on

customer satisfaction. If managers want to know what areas are key to the success of their

company, they must collect data and analyze it.

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Industry Sample Name Product Price Promotion/ Pers. Selling

Place/ Logistics

Health Services

A-1 (Blood Banking)

n.s. 0.14 * 0.42 *** 0.21 **

Health Services

A-2 (Coagulation)

0.56 ** n.s. 0.46 *** n.s.

Health Services

A-3 (Coagulation)

n.s. 0.49 *** n.s. n.s.

Electronics B-1

(Professional Tape) 0.22 *** n.s. n.s. 0.43 ***

Electronics B-2

(Consumer Tape) n.s. n.s. 0.54 ** 0.24 *

Plastics C-1

(Commodity Resin) n.s. 0.32 * n.s. n.s.

Sporting Goods

E-1 (Golf Balls)

n.s. n.s. n.s. n.s.

Sporting Goods

E-2 (Golf Clubs)

0.26 † n.s. 0.26 ** n.s.

Sporting Goods

E-3 (Golf Shoes)

n.s. n.s. 0.50 *** n.s.

Statistically Significant Relationships: 2/9 3/9 5/9 3/9

Table 76: Overall Impact of the Marketing Mix on Customer Satisfaction

The impact of the customer satisfaction on share of business is shown in Table 77. Overall, the

hypothesis was supported five out of nine times. In addition there were several indirect effects,

where there were significant relationships between a construct and customer satisfaction and

between customer satisfaction and share of business.

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Industry Sample Name Satisfaction Indirect Effects

Health Services A-1

(Blood Banking) 0.25 ***

Price, Promotion, and Place

Health Services A-2

(Coagulation) 0.08 † n/a

Health Services A-3

(Coagulation) n.s. n/a

Electronics B-1

(Professional Tape) 0.44 *** Product and Place

Electronics B-2

(Consumer Tape) 0.26 ** Promotion and Place

Plastics C-1

(Commodity Resin) 0.11 ** Price

Sporting Goods E-1

(Golf Balls) 0.31 *** Price

Sporting Goods E-2

(Golf Clubs) 0.11 † n/a

Sporting Goods E-3

(Golf Shoes) 0.43 *** Product

Statistically Significant Relationships: 6/9

Table 77: Overall Impact of Customer Satisfaction on Share of Business

The impact of customer satisfaction on preferred share of business was assessed as well and the

results are shown in Table 78. In sample A-2 current share of business is marginally significant,

but preferred share of business is highly significant. Another difference is in sample E-2, where

current share of business has a marginally significant impact on customer satisfaction, but

preferred share of business is highly significant.

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Health Services A-1



Health Services A-2

(Coagulation) 0.19 *** n/a

Health Services A-3


Electronics B-1


Electronics B-2


Plastics C-1

(Commodity Resin) 0.15 *** Price

Sporting Goods E-1


Sporting Goods E-2

(Golf Clubs) 0.79 *** Promotion

Sporting Goods E-3


Statistically Significant Relationships: 8/9

Table 78: Overall Impact of Customer Satisfaction on Preferred Share of Business

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4.12. Summary

In this chapter the research results were described. The nine samples were analyzed and the

results were compared. The promotion/personal selling construct had the most consistent

impact on customer satisfaction. The other constructs were significant in three samples each.

Customer satisfaction had a significant impact on share of business in five samples. In the next

chapter, the implications of the results for theory and practice are described. In addition, the

limitations of the study are described and extensions for future research are proposed.

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CHAPTER 5.

CONCLUSIONS

In this chapter, a summary and the conclusions of the research are presented. The results were

provided in Chapter 5, and this chapter contains an evaluation and interpretation of the results.

As specific hypotheses were tested and evaluated in the previous chapter, the overall impact of

the Marketing Mix on both outcome variables is described in this chapter. Based on the results,

it is not possible to argue that one component of the Marketing Mix has a stronger impact on

firm performance than the others. The primary research objective was to investigate the

relative effect of logistics attributes versus the other components of the Marketing Mix

customer satisfaction and share of business and replicate the model under different conditions.

First, a summary of the research purpose is provided. Second, the research objectives and

hypotheses are reviewed. Third, the findings are summarized. Fourth, the research limitations

are described. Fifth, opportunities for future research are illustrated. Sixth, implications for

theory are described. Seventh, implications for practice are described. The chapter ends with

overall conclusions.

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5.1. Summary of Research Purpose

Many of the previous studies on customer service focused only on a single industry and few

were replication studies. If a study is conducted in a particular industry, then the results may be

valid only for that industry. The goal for this dissertation was to address this shortcoming by

using a multi-industry approach with nine samples enabling replication of the research model

(Hubbard and Armstrong 1994, Hubbard and Vetter 1996). By using multiple samples, a model

can be developed on one sample and then validated with the others. This approach yields

stronger results because it minimizes the chance for misspecification of the model (Ehrenberg

2004).

The need for replication has been voiced several times in the past (Furchtgott 1984, Lubin 1957,

Sterling, Rosenbaum and Weinkam 1995). The need for replication is adressed with this

dissertation. The results of this research show that the Marketing Mix components that have a

significant impact in one industry or even in one sample may not have a significant impact in

others. Consequently, the findings of past research studies that were based on a single sample

may be questioned. This research reinforces the need for replication.

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5.2. Review of Research Objectives and Hypotheses

The variables that managers use to select and evaluate suppliers were summarized into one of

the four components of the Marketing Mix: product, price, promotion/personal selling and

place/logistics. The main gap that has been addressed is the differential effect of logistics

attributes versus the other components of the Marketing Mix. The use of both customer

satisfaction and share of business as outcome variables enabled a better understanding of how

the Marketing Mix may affect supplier financial performance. As established in Chapter 1, the

specific research questions of the dissertation were:

1. What are multi-item scales to assess the performance of customer service elements in

business-to-business relationships across multiple industries?

2. What is the relative importance of the components of the Marketing Mix in business-to-

business settings in several industries?

3. What is the influence of the components of the Marketing Mix on customer satisfaction

and share of business?

Each component of the Marketing Mix is measured as a latent variable with several attributes,

making up the variable. The attributes are summarized as importance-weighted averages of

different aspects of the construct. In order to clarify the research model, a short review of the

constructs is provided:

1. The product construct is made up of attributes describing the performance, quality, new

product development and support provided for the product.

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2. Price contains attributes related to level and competitiveness of pricing and the

satisfaction with billing procedures.

3. Promotion/personal selling attributes are related to the efforts of the salesperson.

4. Place/logistics attributes evaluated different aspects of logistics performance like

delivery reliability, delivery flexibility, lead time, and problem solving of delivery issues.

5. Customer satisfaction is the overall evaluation of satisfaction with a supplier.

6. Share of business denotes the percentage of business given to a supplier.

Hypotheses

The following formal hypotheses were tested in using structural equation modeling (SEM).

Each hypothesis relates to a relationship in the structural model. The components of the

Marketing Mix are believed to influence customer satisfaction. More specifically, the

relationships between the individual components of the Marketing Mix were analyzed.

H1a: Product has a significant impact on customer satisfaction.

H1b: Price has a significant impact on customer satisfaction.

H1c: Promotion/personal selling has a significant impact on customer satisfaction.

H1d: Place/logistics has a significant impact on customer satisfaction.

While customer satisfaction is an important construct in the literature, it does not directly

translate into profitability or market share. In order to understand the effect of customer

160

satisfaction on share of business, the direct link was tested. It is believed to be a generally

positive relationship (Rust, Zahorik and Keiningham 1996). In addition, indirect effects between

the Marketing Mix and share of business through customer satisfaction are assessed.

H2: Customer satisfaction has a significant impact on share of business.

The research model, shown in Figure 6, displays the tested hypotheses in a path diagram.

Figure 6: Research Model with Hypotheses

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5.3. Summary of Findings

The overall impact of the Marketing Mix on customer satisfaction is shown in Table 79. One star

(*) denotes a p-value between 0.01 and 0.05. Two stars (**) denote a p-value between 0.001

and 0.01. Three stars (***) denote a p-value of less than 0.001. A marginal significance is

denoted by “†”. If a coefficient is not significant it is denoted with “n.s.”.

Industry Sample Name Product Price Promotion/ Pers. Selling

Place/ Logistics

Health Services

A-1 (Blood Banking)

n.s. 0.14 * 0.42 *** 0.21 **

Health Services

A-2 (Coagulation)

0.56 ** n.s. 0.46 *** n.s.

Health Services

A-3 (Coagulation)

n.s. 0.49 *** n.s. n.s.

Electronics B-1

(Professional Tape) 0.22 *** n.s. n.s. 0.43 ***

Electronics B-2

(Consumer Tape) n.s. n.s. 0.54 ** 0.24 *

Plastics C-1

(Commodity Resin) n.s. 0.32 * n.s. n.s.

Sporting Goods

E-1 (Golf Balls)

n.s. n.s. n.s. n.s.

Sporting Goods

E-2 (Golf Clubs)

0.26 † n.s. 0.26 ** n.s.

Sporting Goods

E-3 (Golf Shoes)

n.s. n.s. 0.50 *** n.s.

Statistically Significant Relationships: 2/9 3/9 5/9 3/9

Table 79: Overall Impact of the Marketing Mix on Customer Satisfaction

The first result is that no consistent pattern emerged as to which components of the Marketing

Mix affect customer satisfaction. This shows that the customer service attributes that have the

162

most impact on customers can vary by circumstances in the market. The impact of customer

satisfaction on share of business is shown in Table 80.


Health Services A-1



Health Services A-2

(Coagulation) 0.08 † n/a

Health Services A-3


Electronics B-1


Electronics B-2


Plastics C-1

(Commodity Resin) 0.11 ** Price

Sporting Goods E-1


Sporting Goods E-2

(Golf Clubs) 0.11 † n/a

Sporting Goods E-3


Significant Relationships: 6/9

Table 80: Overall Impact of Customer Satisfaction on Share of Business

The impact of customer satisfaction on share of business is fairly consistent, with six significant

relationships, two that are marginally significant and one non significant relationship. This also

enabled several indirect relationships of the Marketing Mix on share of business, when the link

between a component of the Marketing Mix and customer satisfaction was significant and the

link between customer satisfaction and share of business.

163

5.4. Research Limitations

As with any research, there are limitations that must be considered. The first limitation is the

possible impact for multicollinearity on the results. The second limitation is the use of context-

specific attributes to estimate the components of the Marketing Mix. Each of these limitations

will be described briefly.

Multicollinearity

The latent variables are left to correlate freely in the structural model. The correlations are

always significant and removing those paths would have resulted in poor fit of the model. One

possible problem is that multicollinearity may persist in the SEM models (Grewal, Cote and

Baumgartner 2004). The most common problem with multicollinearity is that type II errors may

occur, meaning that a relationship, which in reality is significant, is shown as non-significant

(Jagpal 1982). Restricting the latent variables to be uncorrelated would not be an option,

because conceptually it would be unreasonable to expect Product, Price, Promotion, and Place

to not be correlated. This modeling strategy was used previously (Stank, Goldsby and Vickery

1999), and is a compromise that seems prudent in this research. The best safeguards against

multicollinearity issues are large sample size, discriminant validity, and high measure reliability

(Grewal, Cote and Baumgartner 2004). For all samples, adequate sample size, discriminant

validity, and sufficiently high AVE and CR exist, but the threat of multicollinearity cannot be

eliminated completely.

164

Context-Specific Attributes

It was not possible to use exactly the same questions across all the samples, because each had

to give adequate customization to specific industry nuances. Across the product construct there

are 56 different questions. For the price construct, there are 25 different questions. There are

16 questions used for the promotion/personal selling construct. And 25 different questions are

used for the place/logistics construct. As a result, the questions are not entirely consistent

across the samples. Completely standardized surveys like SERVQUAL (Parasurman, Zeithaml and

Berry 1988) have suffered because of their inability to be specific enough and take into account

heterogeneity of different industries (Parasurman, Zeithaml and Berry 1991). The questions for

the surveys were developed during interviews with customers of the sponsoring firms. The

surveys were developed to provide an accurate account of the attributes used to select and

evaluate suppliers. If the surveys were developed with the premise that differences between

industries would be examined, then some common and some industry-specific questions may

be used (Baumgartner and Steenkamp 1998).

165

5.5. Opportunities for Future Research

The premise of this research was to investigate the impact of place/logistics versus the other

components of the Marketing Mix on customer satisfaction and indirectly share of business.

While this research question was investigated, several other issues emerged that warrant

further investigation. Each sample represents the situation at the point in time when the data

were collected. While A-2 and A-3 are two surveys that dealt with the same product, three

years apart, they are analyzed as two samples. An opportunity for future research is to use

longitudinal data to investigate how the impact of the Marketing Mix changes over time for a

particular industry.

The current model evaluates primary, secondary and tertiary suppliers in an equal manner.

However, it could be determined how the effect of these attributes varies between primary

suppliers and other suppliers. Establishing empirical evidence of logistics management’s impact

on business outcomes improves logistics managers’ understanding of their contribution to

financial performance. Another source of variance that could be investigated is the difference

between small specialized stores and larger mass retailers in the sporting goods samples.

In this research structural modeling was used to determine the current impact of the

components of the Marketing Mix on customer satisfaction and indirectly on share of business.

It does not consider areas where all suppliers might be underperforming, where an

improvement in the performance of one supplier might lead to increases in customer

satisfaction and share of business.

166

5.6. Implications for Theory

Across the nine samples, it was determined which of the components of the Marketing Mix had

a significant impact on customer satisfaction and indirectly on share of business. One piece of

prior evidence that points to logistics having a stronger impact on share of business is the work

by Sterling and Lambert (1987). The place/logistics construct has a significant impact on

customer satisfaction in three samples (A-1, B-1, and B-2). Similar to previous results

(Daugherty, Stank and Ellinger 1998, Stank, et al. 2003), the current study does point to a

complex relationship between the place/logistics construct and customer satisfaction and firm

performance. Logistics attributes, together with the other components of the Marketing Mix

are an important driver of customer satisfaction and share of business and the impact varies by

industry.

The impact of promotion/personal selling on customer satisfaction is evident in five samples,

which is the most consistent relationship of any component of the Marketing Mix. This is not a

surprising result because the salesperson can influence the expectations of the customer. A

salesperson that can set realistic expectations has a stronger effect on customer satisfaction

than one who overpromises. The salesperson can be regarded as a promise-maker and product

and place/logistics are fulfilling that promise. Price has a different relationship because it is

subject to promise-making during the negotiation and serves as a background to evaluate

product and place/logistics.

167

5.7. Implications for Practice

Due to the general nature of this research, no specific recommendations to managers are made,

but general suggestions can be asserted. If specific recommendations for a company are

required, then an analysis that charts importance and performance of several attributes versus

competitors is preferable (Stock and Lambert 2001). This research is focused on a high-level

holistic perspective in each cross-sectional sample. This research was focused on what was

leading to customer satisfaction and share of business. Business people are more interested in

what could impact customer satisfaction and share of business if performance was improved.

The result that promotion/personal selling has a strong impact on customer satisfaction and

only a minimal direct impact on share of business has also implications for managers. It is

possible that salespeople, who customers regard as good, do not drive higher share of business

directly. It is important at this point to remember that each sample only provides a snapshot in

time. No longitudinal analysis is performed. Samples A-2 and A-3 are on the same product

three years apart, and it seems that in the later sample the effect of promotion/personal selling

is not significant as it was in the previous sample.

The construct that is controlled by the logistics function – place - has a strong impact on firm

performance in some samples. Managers must be aware that in those samples better logistics

performance can lead to better outcomes for the firm. This is especially important in those

samples where the place/logistics construct is significant. In those samples one or more of the

other components of the Marketing Mix are significant and it is important that the marketing

168

and logistics functions coordinate their efforts. Firm performance is influenced by activities that

take place in these two functions.

In four of the nine samples retailers are surveyed (B-2, D-1, D-2, and D-3). There is one

distinction between the retailers in the electronics industry and the sporting goods industry.

Video tapes are sold in larger electronics or general retailers, and some of the golf equipment is

often sold in smaller more specialized stores. It must not be assumed that differences in results

are only explained by differences in industries because the type of retailer may also be a reason

why different components of the Marketing Mix are significant.

For managers interested in understanding which factors drive customer satisfaction and share of

business in their companies, this research shows the importance of collecting data on their

actual customers and analyzing it. Relying on data collected in other industries or on other

products cannot provide management with the data necessary to make informed decisions that

are relevant in their specific situations. Even looking at data that is several years old, may prove

troublesome if the differences between A-2 and A-3 are considered.

169

5.8. Overall Conclusions

The analysis of the nine samples revealed the importance of individual components of the

Marketing Mix varied by situation. It was not possible to determine a generalizable pattern in

the components of the Marketing Mix that has a significant impact on customer satisfaction and

indirectly on share of business. Other efforts to create a general framework for customer

service did not succeed (Parasurman, Zeithaml and Berry 1991). This research provides further

evidence that caution should be used in generalizing findings based on one study. The results

that were significant in one situation may not be significant again, but without replicating the

research it would not be possible to know.

170

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Documents

The Impact of Product, Price, Promotion and Place Logistics on Customer Satisfaction and Share of Business