The impact of alignment between virtual enterprise and information technology on business performance in an agile manufacturing environment

The impact of alignment between virtual enterprise and

information technology on business performance

in an agile manufacturing environment

Qing Cao*, Shad Dowlatshahi1

Henry W. Bloch School of Business Administration, University of Missouri-Kansas City,

5110 Cherry Street, Kansas City, MO 64110, USA

Available online 28 November 2004

www.elsevier.com/locate/dsw

Journal of Operations Management 23 (2005) 531–550

Abstract

Manufacturing companies are facing rapid and unanticipated changes in their business environment. Agile manufacturing

(AM) is a manufacturing paradigm that focuses on smaller scale, modular production facilities, and agile operations capable of

dealing with turbulent and changing environments. From several enablers of AM, virtual enterprise (VE) and information

technology (IT) were chosen. This empirical study explored the impact of the alignment between VE and IT on business

performance in an AM setting. Extensive data collection strategy and several tests are used to establish the reliability and validity

of the data collected. Extensive analyses of the data using structural equation molding were performed for five hypotheses. The

results indicate that both VE and IT had positive influences on business performance. It was also established that the alignment

between VE and IT had a positive impact on business performance. Further, it was shown that the impact of the alignment

between VE and IT on business performance was more significant than the impact of VE and IT on business performance

individually. In conclusion, the assessment of the results along with future research directions is provided.

# 2004 Elsevier B.V. All rights reserved.

Keywords: Agile manufacturing; Virtual enterprise; Information technology; Business performance

1. Introduction

Global competition has brought about changes that

are characterized by product proliferation with shorter

and uncertain life cycles, innovative process technol-

* Corresponding author. Tel.: +1 816 235 6242;

fax: +1 816 235 6506.

E-mail addresses: [email protected] (Q. Cao),

[email protected] (S. Dowlatshahi).1 Tel: +1 816 235 2233; fax: +1 816 235 6506.

0272-6963/$ – see front matter # 2004 Elsevier B.V. All rights reserved

doi:10.1016/j.jom.2004.10.010

ogies, and customers who simultaneously demand

quick response, lower costs, and greater customiza-

tion. Companies must cope effectively with contin-

uous and unexpected changes in order to become

competitive. The ability to respond quickly and

effectively (time-based competition) and to satisfy

customer needs has become a defining characteristic

of competitiveness for many manufacturing compa-

nies.

Mass production, despite improvements made by

just-in-time and lean production strategies, is essen-

.

Q. Cao, S. Dowlatshahi / Journal of Operations Management 23 (2005) 531–550532

tially a system favoring large-scale and hierarchical

corporate structures. On the other hand, academic

research has established a link between agility/

flexibility and firm competitiveness (Giachetti et al.,

2003a,b; Yusuf et al., 2003; Vokurka et al., 2002;

Sharifi and Zhang, 2001; Upton, 1995, 1997; Jordan

and Graves, 1995; De Groote, 1994). Although some

of these works used the words agility and flexibility

interchangeability, Gunasekaran (1999) stated that

flexibility was the basis for agility. Agile manufactur-

ing (AM) is an emerging manufacturing paradigm,

which considers agility a key concept necessary to

survive against competitors under an unexpectedly

turbulent and changing environment. The AM

suggests that smaller scale, modular production

facilities, and cooperation between enterprises would

be the principal pattern of competitiveness for the next

generation (Goldman et al., 1995; Sahin, 2000).

Several enablers of AM were identified concep-

tually in the AM literature (Sharp et al., 1999;

Gunasekaran, 1999; Yusuf et al., 1999; Sharifi and

Zhang, 2000, 2001). According to Sharp et al. (1999),

AM enablers include core competencies, virtual

enterprise (VE), rapid prototyping, concurrent engi-

neering, multi-skilled and flexible people, continuous

improvement, team working, change and risk manage-

ment, information technology (IT), and empowering.

Based on an extensive literature review, Gunasekaran

(1999) proposed a research framework for the design

of agile manufacturing systems that included four

dimensions: strategies, technology, people, and sys-

tems. Gunasekaran (1999) argued that VE is one of the

key strategies needed to achieve agility in manufac-

turing. He further stated that ‘‘agile-enabling tech-

nologies such as Internet, multimedia, EDI, electronic

commerce . . . need to be suitably incorporated within

the scope of the VE in order to achieve agility in

manufacturing (Gunasekaran, 1999, pp. 100–101)’’.

Yusuf et al. (1999) attempted to identify the drivers of

AM. They claimed that the core drivers of AM include

VE, core competence management, capability for

reconfiguration, and knowledge-driven enterprise.

They further pinpointed the fact that IT (i.e. EDI)

plays a major role in VE. Technological capabilities,

especially IT, were also viewed by other AM

researchers as major agility drivers (Sharifi and

Zhang, 2000; Sharifi and Zhang, 2001). Other AM

literature also recognized the relationship between IT

and VE (Martinez et al., 2001; Khalil and Wang,

2002). According to Martinez et al. (2001), VE is

supported by extensive use of information and

communication technologies. Khalil and Wang

(2002) argued that advanced IT has made it possible

to manage the complexity of a VE environment more

efficiently and effectively. This study focuses on two

of the AM enablers, namely, VE and IT. The VE and

IT seem to represent the common thread and

consensus of many works as the most relevant

enablers of the AM. This study further explores the

relationship between VE and ITand their alignment on

a firm’s business performance in an agile manufactur-

ing environment.

2. Review of literature

Why do we know so little about the impact of VE

and IT on firms’ business performance within the

context of AM in the literature? To provide answer for

this question, the definitions, basic information, and

review of literature for AM, VE, and IT are presented

next.

2.1. Agile manufacturing

The concept of agility has received a great deal of

attention by AM researchers and practitioners alike.

Although a number of definitions for agility have been

given, a common thread focuses on being able to

function and compete within a state of dynamic and

continuous change. One such definition for agility was

proposed by (Sarkis, 2001) where agility was defined

as the ability to thrive in an environment of continuous

and often unanticipated change. DeVor et al. (1997)

defined AM as the ability of a producer of goods and

services to operate profitably in a competitive

environment of continuous and unpredictable change.

The AM encompasses smaller scale, modular produc-

tion facilities, and cooperation between enterprises. In

a more technical sense, Quinn et al. (1997) defined

AM as the ability to accomplish rapid changeover

from the assembly of one product to the assembly of a

different product. The authors stressed one important

aspect of agility: the minimum amount of change in

tooling and software required by changeover between

the manufacture of different assemblies. Goldman

Q. Cao, S. Dowlatshahi / Journal of Operations Management 23 (2005) 531–550 533

et al. (1995) viewed AM as having the following

strategic dimensions: enriching the customer, coop-

erating both internally and externally to enhance

competitiveness, organizing to both adapt to and thrive

on change and uncertainty, and leveraging the impact

of people and information by nurturing an entrepre-

neurial culture in the company.

Although some prior AM studies provided some

metrics and measures for agile manufacturing, a

consensus regarding agility factors has not yet

emerged (Giachetti et al., 2003a,b). Table 1 sum-

marizes the prior studies regarding AM measures and

metrics.

As Table 1 indicates, there are two types of AM

measures: operational measures for empirical research

and structural measures for mathematical mappings.

Our study focuses on the operational measures of AM.

Operational agility measures were first investigated by

the Agility.

Forum (1994) which proposed four strategic

dimensions of agile competition and was later

expanded by Goldman et al. (1995) as we mentioned

previously. Dove (1994) discussed the agility mea-

surement and argued that four indicators including

cost, time, robustness, and scope could be useful in

monitoring the capability of a process to respond to

unanticipated change. Metes et al. (1998) extended the

change proficiency domains introduced by Dove

(1994) to agile networking to be used as an AM

metric. However, the AM measures mentioned in

these four studies were very general in nature.

Table 1

Summary of prior studies for AM metrics

Reference AM measure AM foc

Agility Forum (1994) Strategic dimensions Operatio

Dove (1994) Strategic dimensions Operatio

Goldman et al. (1995) Strategic dimensions—extension

of agility forum (1994)

Operatio

Metes (1998) Agility scorecard—based

on Dove (1994)

Operatio

Kumar and

Motwani (1995)

Agility index Operatio

Martinez (2000) Agility index—based on

Kumar and Motwani (1995)

Operatio

Goranson (2000) Agility distance metric Structur

Agility time delay metric Structur

Although these AM measures served well in

conceptualizing AM research frameworks, more

detailed AM constructs were needed to be developed

in order to operationalize AM empirical research.

Kumar and Motwani (1995) developed another

operational AM measure, the agility index. This

index was used to determine the effectiveness of a firm

to compete based on the element of time. The agility

index provided the composite value of the strategic

agility position of a firm on a percentage scale. The

agility index was empirically tested by Martinez

(2000) who adapted the audit approach to conduct a

focused study of 80 medial instrument device

manufacturers. However, the agility index approach

was based on the notion that agility was a direct

indicator of an enterprise’s time-based competitive-

ness, which in reality is only one dimension of AM.

Moreover, the empirical survey conducted by Marti-

nez (2000) did not consider and explore the relation-

ships among various constructs.

This study not only provides operational AM

measures, but also proposes a research framework at

both holistic (construct) and dimensional (bivariate)

levels. Moreover, this study develops a more extended

set of constructs and further examines the relation-

ships among various constructs based on the AM

theories.

Further studies in AM included: Brown and

Bessant (2003), Chang et al. (2003), Giachetti et al.

(2003), Jin-Hai et al. (2003), Kathuria and Porth

(2003), Steenhuis and Boer (2003), Sharifi and Zhang

us Description

nal Four strategic dimensions of agile competition

nal Cost, time, robustness, and scope

nal Enriching the customer, cooperating both internally

and externally to enhance competitiveness, organizing

to both adapt to and thrive on change and uncertainty,

and leveraging the impact of people and information

nal Many individual enterprise attributes measured on a

five point scales

nal Yy are subjective measures of agility factory

during time period j

nal Empirical survey based on Kumar and Motwani (1995)

e Number of each node type raised to the power of its type

e Number of sub-conversations (loops)


(1999, 2000, 2001), Ramasesh et al. (2001), Sharp et

al. (1999), Sarkis (2001), and Gunasekaran (1999). In

summary, the majority of the literature regarding AM

was either conceptual or exploratory with limited

managerial implications. Most of the studies lacked

theoretical foundation, empirical evidence, and rig-

orous analysis. In particular, Ramasesh et al. (2001)

and Gunasekaran (1999) underscored the need for

further rigorous, academic, and empirical research in

the area of AM.

2.2. Virtual enterprise

VE is one of the enablers of AM that facilitates

customers to rapidly obtain the products that they want

(Cho et al., 1996; Sharp et al., 1999). VE can be

described as a network of organizations or firms from

which temporary alignments are formed. These

alignments combine the specific core capabilities of

its members in order to rapidly exploit manufacturing

opportunities associated with a specific product or

service. Afterward, the temporary alignment is

dissolved and the members become available for

another virtual and temporary project (Hoogeweegen

et al., 1999; Christie and Levary, 1998). Thus, VE

constitutes a natural outgrowth or evolution of both

tapered and non-integration strategies and network

organizational structures (Fitzpatrick and Burke,

2001). Companies gain competitive advantages by

providing customers with better and faster service. In

practice, many manufacturing companies have begun

to use the VE concept to gain and maintain a

competitive edge. For example, Dell Computer, by

leveraging its VE with electronic commerce has

enabled itself to compress its supply chain and become

closer to its customers (Maglitta, 1997). Dell

Computer, a virtual manufacturer, growing two to

three times faster than its rivals, has boasted its

earnings and unit shipments four times better than the

industry averages.

In a manufacturing setting, a VE is constructed by

partners from different companies, who collaborate

with each other to design and manufacture high

quality and customized products (Fitzpatrick and

Burke, 2001). A VE is product-oriented, team-

collaboration styled, and featured as fast and flexible

operations. Thus, a VE is distinctively different from a

traditional enterprise. The authors argued that one

efficient way to satisfy customer needs is to

collaborate with qualified partners with the necessary

physical resources and capabilities. This collaboration

is viewed as VE formation. The VE organization

structure should be fluid and organic and it should

generate the smooth flow of product, process, and

business-related information. They argued that VE

was synonymous with the emergence of organiza-

tional structures that relies upon the extensive use of

outsourcing, strategic alliances, and other forms of

partnering. The authors claimed that VE could yield

many situational or competitive advantages such as

sharing infrastructures, R&D, and resources; linking

complementary core competencies; reducing concept-

to-cash time through information sharing; expanding

production capabilities; gaining access to markets

and sharing markets or customer loyalty; migrating

from selling products to selling solutions. Although

the authors presented the importance of the VE

concept in manufacturing settings, their work fell

short of providing any linkages between VE and AM

practices.

Further studies in VE included: Browne and Zhang

(1999), Sharifi and Zhang (1999), and Sharp et al.

(1999). In summary, many VE studies pinpointed the

link between VE and AM, however these studies did

not include any empirical analysis to support their

claims. Sharp et al. (1999) called for empirical

analysis of an AM model with a link to VE. Further,

these linkages were not related to the business

performances of the firms.

2.3. Information technology

IT is regarded as a major enabler and facilitator of

the AM (Sharp et al., 1999; Coronado et al., 2002).

Frayret et al. (2001) presented a strategic framework

for designing and operating AM through a distributed

network (based on the Internet technology) of inter-

dependent and responsible manufacturing centers.

For example, IT applications of enterprise resource

planning (ERP) and data communications allow

AM to achieve time reductions and quality improve-

ment in product design and development. Sharp et al.

(1999) pointed out that IT helped to reduce

hierarchical management control and facilitate com-

munication among employees and thus enhance

agility.


There has been a large number of works that have

explored the relationships between IT and operations/

manufacturing in general. One special issue of the

Decision Sciences Journal was devoted to this topic.

Coronado et al. (2002), however, explored the

evolution of IT in manufacturing and the importance

of IT in supporting AM. The authors argued that

despite the growing importance of IT in manufactur-

ing operations, few examples in the literature had

investigated the requirements of IT to support agility.

The findings of their study provided the opportunity to

start building guidelines for the identification of IT

requirements in order to support agility. However,

these works were conceptual in nature and did not

include scientific presentation and validation of IT

requirements. According to Coronado et al. (2002), an

extensive empirical analysis was required to validate

the IT requirements in AM.

IT applications also support the communication

necessary to coordinate activities in VE environ-

ments. Agility is gained by reducing hierarchical

managerial control, setting up workers in teams, and

empowering them to make decisions. VE literature

suggested that IT was the essential foundation for the

formation and management of VEs (Strader and

Shaw, 1998; Burn and Barnett, 1999; Cohen and

Mankin, 1999; Kock, 2000; Khalil and Wang, 2002).

Strader and Shaw (1998) presented a framework of

information infrastructure for VE. The authors

considered electronic data interchange (EDI), inter-

net protocols (TCP/IP), local area network (LAN),

and data base management systems to be essential IT

requirements for a VE.

Further studies in IT included: Knudsen (2003),

Khalil and Wang (2002), Chen (2001), Kock (2000),

Palaniswamy and Frank (2000), Cohen and Mankin

(1999), Newing (1997), Kennedy (1997), Chellappa et

al. (1996), Miller (1996), and Holland et al. (1992).

These studies specified the IT requirements as EDI,

Groupware, Intranets, Extranets, and ERP. However

these studies did not indicate how or to what degree IT

impacted the VE. No further evaluation of the

effectiveness of the IT impact was presented. As the

literature review showed, there was a great need for

empirical research of IT requirements in AM.

Although there seemed to be a general consensus

regarding the identification and use of IT require-

ments, the discussion regarding the effectiveness and

performance evaluation of the IT requirements were

largely absent in the literature.

2.4. Research questions and organization of the

paper

This paper argues that the interaction between and

among IT, VE, and AM is essential for companies to

reduce product design and development cycle time, to

increase product life cycle, to reduce overall product

life cycle costs, and to provide better and more

effective customer services. In order to show a

tangible impact of this interaction, there must be a

connection between VE, IT, and AM and business

performance of the firms.

This paper, therefore, addressees two central

research questions of what and how. First, what are

the critical factors (VE and IT enablers) needed in

developing a systematic framework to explore AM

effectiveness? Second, how to empirically determine

the impact of the alignment between VE and IT on

firms’ business performance in AM?

Section 3 of the paper will focus on developing

research framework and hypotheses formulation. The

instrument development, research design, and data

collection strategy will be presented in Section 4. The

entire survey instrument is presented in Appendix A.

Extensive reliability and validity tests will be

performed and are presented in Section 5 and

Appendix B. The evaluation of research hypotheses

will be presented in Section 6. The conclusions and

assessments of the results and the future research

directions will be presented in Section 7.

3. Research framework and hypothesesformulation

This study extends the conceptual model of AM

proposed by (Sharp et al., 1999) by addressing the

issue of alignment between VE and IT and the impact

of this alignment on business performance. Fig. 1

presents the proposed conceptual model.

Dimensions of VE were adapted from (Fitzpatrick

and Burke, 2001). The dimensions were sharing

infrastructures, R&D, and resources, linking comple-

mentary core competencies, reducing concept-to-cash

time through information sharing, increasing produc-


Fig. 1. A conceptual model of VE, IT, and business performance

There are three basic constructs in the model VE, IT, and business

performance. The relationships among the constructs are shown by

five hypotheses formulated as follows: H1, the use of information

technology positively supports the achievement of virtual enterprise.

H2, the achievement of virtual enterprise positively influences a

firm’s business performance. H3, the use of information technology

positively influences a firm’s business performance. H4, the align-

ment between information technology and virtual organization

positively influences a firm’s business performance. A corollary

for H4 could be stated in terms of a new hypothesis as follows: H5,

the alignment between VE and IT influences business performance

more significantly than does either the VE or IT individually.

Fig. 2. A bivariate model of alignment between VE and IT.

tion capabilities, gaining access to markets and

sharing markets or customer loyalty, and migrating

from selling products to selling solutions.

Five of the most commonly stated information

technologies supporting VE and AM include EDI,

Groupware, Intranets, Extranets, and ERP (Strader

and Shaw, 1998; Khalil and Wang, 2002). These five

are selected as dimensions of IT construct in this study.

The business performance construct is a complex

and multi-faceted concept (Chan et al., 1997). In the

business strategy literature, it has often been suggested

that multiple measures should be used when trying to

assess business performance (Venkatraman and

Ramanujam, 1986; Chan et al., 1997, 1998; Sabherwal

and Chan, 2001). However, most of the operations

strategy research only employed one dimensional

measure—profitability—in assessing business perfor-

mance (Badri et al., 2000; Ward and Duray, 2000).

In attempting to overcome the pitfalls of the

business performance measure commonly utilized by

the operations strategy research, this study employed

multiple measures of the refined business performance

instrument to assess the business performance (Chan

et al., 1997; Venkatraman and Ramanujam, 1986). The

business performance instrument used in this research

included four dimensions: market growth, profit-

ability, product-service innovation, and company

reputation. These measures were used in other infor-

mation systems strategy research and were found reli-

able (Chan et al., 1998; Sabherwal and Chan, 2001).

In this study, the business performance measures were

adapted from Chan et al. (1997) with a small

modification. Although the subjective nature of the

data gathered is a limitation of the current study,

subjective data are frequently used in this type of

research and their use is considered to be acceptable

(Chan et al., 1997; Sabherwal and Chan, 2001).

While Fig. 1 presents a conceptual model of the

relationships among the three constructs, Fig. 2

presents a dimension-specific (bivariate) view of these

constructs (Drazin and Van de Ven, 1985). The

conceptual model suggests that relationships between

constructs are meaningful, whereas the bivariate view

suggests that these constructs can be disaggregated

into several dimensions and that the relationships

among these dimensions can be meaningfully tested

(Chan et al., 1997). The conceptual model can also be

used to verify the structural contingency theory—the

overall fit of the model, using structural equation

modeling (Kline, 1998). In this study, both models will

be tested.

Path analysis allows researchers to specify and test

structural models that reflect a priori assumptions

about spurious associations, and the direct or indirect

causal effects among observed variables (Kline,

1998). Path analysis is a viable methodology for

capturing relationships among variables because it is

concerned with estimating the magnitude of the links

between variables. Path analysis uses these estimates

to provide information about an underlying causal

process (Asher, 1983). In this study, covariance

structure models were employed to estimate path

coefficients by simultaneously solving the system of

equations and accounting for covariance among

variables within the model.

The legends in Fig. 2 correspond to the items in the

survey instrument as shown in Table 2. The actual

survey instrument is reproduced in Appendix A.


Table 2

The items of the survey instrument

Construct: VE

Item [VE1] Resource sharing

Item [VE2] Core competencies

Item [VE3] Time reduction

Item [VE4] Capacity increase

Item [VE5] Market share enhancement

Item [VE6] Quality improvement

Construct: IT

Item [IT1] EDI

Item [IT2] Groupware

Item [IT3] Intranets

Item [IT4] Extranets

Item [IT5] ERP

Construct: business performance

Dimensions of [MG] market growth

Item [MG1] Market share gains

Item [MG2] Sales growth

Item [MG3] Revenue growth

Dimensions of [FP] financial performance

Item [FP1] Return on investment

Item [FP2] Return on sales

Item [FP3] Liquidity

Item [FP4] Cash flow

Item [FP5] Profitability

Dimensions of [PI] product innovation

Item [PI1] Developments in business operations

Item [PI2] Development in products and services

Dimensions of [CR] company reputation

Item [CR] reputation among major customer segments

4. Research methodology and design

This study uses a two-part research design in order to

increase the reliability and validity of the data collected.

Part one involved constructing a questionnaire. This

process included reviewing and analyzing previous

and current literature, developing the framework in

Section 3, and conducting interviews with key employ-

ees of several AM companies. Based on all of these, a

series of questions addressing the key variables of the

study were developed. A pilot study was conducted

by distributing the preliminary questionnaire to the

managers of several AM manufacturing companies in

the Midwest region of the United States. Managers were

asked to examine the degree to which the preliminary

questionnaire captured the measured constructs and

how easy or difficult the preliminary questionnaire was

to complete. In part two, the revised questionnaire

based on the pilot study was used to gather responses

from respondents of 500 manufacturing companies.

The responses were then collected and analyzed. The

analyses included descriptive statistics, factor analysis,

extensive reliability and validity analysis, and structural

equation modeling using LISREL.

By using Standard Industrial Classification (SIC)

codes from Dun and Bradstreet directories, 500

manufacturing companies were selected. Detailed

company information was gathered using the Refer-

enceUSA database. The surveys were mailed to five

manufacturing industries (e.g., SIC 356: General

Industrial Machinery and Equipment and Industry;

SIC 357: Computer and Office Equipment; SIC 353:

Construction, Mining, and Materials Handling; SIC

358: Refrigeration and Service Industry Machinery;

SIC 359: Miscellaneous Industrial and Commercial).

The survey was targeted to managers whose titles

included Operations Manager, Director of Operations,

and Manufacturing Manager. The unit of analysis for

this study was at the individual respondent level at a

manufacturing company where the survey was mailed.

4.1. The profile of participating organizations and

respondents

A total of 500 questionnaires were distributed in a

single mailing. From that mailing, 114 responses

constituted a 22.8% response rate. Out of 114

responses, 102 were usable resulting in an actual

response rate of 20.4%. Among the 12 unusable

responses, nine did not meet the AM criteria (see next

paragraph) and the other three did not contain

sufficient data for further analysis. This response rate

is not unusual when the unit of analysis is the firm

level and the questionnaire involves an extensive

organizational level questions (Griffin, 1997).

To ensure that the respondents represent AM

companies, the survey questionnaire contained the

agility need level determination scoring model

proposed by Sharifi and Zhang (1999). In this scoring

model, the higher the need scores, the higher agility

needs. The agility need level involved seven agility

factors which included: marketplace nature, compe-

titors’ circumstances, technology changing situation,


Table 3

The profile of participating companies

Type of industry/company profile Number of respondents Percentage of respondents (%)

Construction, mining, and materials handling 15 15

General industrial machinery and equipment and industry 18 18

Computer and office equipment 32 31

Refrigeration and service industry machinery 15 15

Miscellaneous industrial and commercial 22 21

Total 102 100

Number of employees

Less than 500 37 36

>500–100 53 48

More than 1000 16 16

Annual sales (in millions)

Less than 100 17 17

>100–500 41 40

>500–1 billion 33 32

More than 1 billion 11 11

criticality of relation with suppliers, customer

requirements change level and rate, social/cultural

changes, and products/processes complexity. Respon-

dents were asked to score each agility factor on the

scale of 1 to 10, where 10 was the highest agility.

Respondents had to score a minimum of 5 for each

agility factor or an overall score of 50 in order to meet

the standard of being an AM company (Sharifi and

Zhang, 1999).

Table 3 presents a breakdown of the number of each

type of industry participating in this study.

5. Assessment of measurement quality

Several tests were performed to establish reliability

and validity of the data collected. These tests proved

the reliability and validity of the instrument and the

data in this study. The details of these analyses, which

were performed by LISREL, are presented in three

sections in Appendix B.

Table 4

Path analysis at the construct levels

Path between Path coefficient t-value

IT and VE (H1) 0.25 3.56*

VE and business performance (H2) 0.19 2.78*

IT and business performance (H3) 0.17 2.25*

Alignment of VE and IT and business

performance (H4)

0.23 3.14*

* Significant path at 0.05 level.

6. Evaluation of research hypotheses

Based on the framework presented in Figs. 1 and 2,

two sets of analyses (based on LISREL) at the

construct and bivariate level were performed for all

hypotheses. The results of hypothesis testings at the

construct level are summarized in Table 4.

All t-values were significant at 0.05 level. This

indicated that all hypotheses were verified at the

construct level.

To gain additional insights, hypotheses testings at

the bivariate level were performed. To determine

whether two constructs at the bivariate level are

related, we need to demonstrate that at least one path

between dimensions of the two constructs has a path

coefficient which is significant (Cao and Schnieder-

jans, 2004; Badri et al., 2000; Ward and Duray, 2000;

Ward et al., 1994). We now evaluate all hypotheses

from the bivariate standpoint.

H1. The use of information technology positively

supports the achievement of virtual enterprise.

Hypothesis 1 empirically tests whether there is a

direct and positive relationship between IT and VE. If

there is more than one significant path between an IT

dimension and a VE dimension, the hypothesis is

supported. This rule also applies to H2–H4.


Table 5

Path analysis of Hypothesis 1


EDI to

Resource sharing 0.24 2.69*

Time reduction 0.34 3.72*

Groupware to


Time reduction 0.26 2.94*

Quality improvement 0.21 2.27*

ERP to


Market share enhancement 0.25 2.72*


Intranet to



Extranet to

Core competencies 0.20 2.10*




Table 6

Path analysis of Hypothesis 2


Resource sharing to

Market growth 0.20 2.13*

Financial performance 0.41 3.99*

Core competencies to



Product innovation 0.20 2.21*

Company reputation 0.18 2.01*

Time reduction to


Capacity increase to


Market share enhancement to



Quality improvement to




Using the specifications for the bivariate model

(Fig. 2) where there are five dimensions (variables)

representing the IT and six dimensions (variables)

representing VE, path coefficients were determined

for the entire sample by employing the path analysis

method. Thirteen paths between IT and VE were

found to be significant for the entire sample (see

Table 5). Note that for brevity, non-significant

paths were eliminated from Table 5 and the

subsequent tables. Because there were 13 significant

paths for H1 between IT and VE, hypothesis 1 was

supported.

H2. The achievement of virtual enterprise positively

influences a firm’s business performance.

This hypothesis tests the impact of VE on firms’

business performance. Based on the specifications in

the bivariate model (Fig. 2), there were six

dimensions (variables) representing the VE and

three dimensions (variables) representing business

performance. The path coefficients were determined

for all respondents. Table 6 shows the 12 significant

paths between VE and business performance.

Hypothesis 2 was supported.

H3. The use of information technology positively

influences a firm’s business performance.

Hypothesis 3 examines the link between IT and

firms’ business performance. Using the specifications

in the bivariate model (Fig. 2) there were five

dimensions (variables) representing the IT and three

dimensions (variables) representing business perfor-

mance. Path coefficients were determined for all

respondents. The nine significant paths between VE

and business performance were shown in Table 7.


H4. The alignment between information technology

and virtual organization positively influences a firm’s

business performance.

This hypothesis tests whether the alignment

between IT and VE has a positive influence on firms’

business performance. According to Venkatraman

(1989), the concept of alignment has served as an

important building block for theory development in

the strategic management research arena. Euclidean

distance method was employed to compute the

alignment score between VE and IT. The relationship


Table 7

Path analysis of the relationship between IT and business perfor-

mance


EDI to


Groupware to



Intranet to



Extranet to




ERP to



between VE and IT is then measured using path

coefficient analysis.

The computation of the alignment score in this

study involves the following two steps. First, the

misalignment between six dimensions of VE and five

dimensions of IT was calculated using the Euclidean

distance method (Joshi et al., 2003; Sabherwal and

Chan, 2001; Venkatraman, 1989).

Euclidian distance ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX

ðVEi � ITjÞq

where VEi is the normalized score for ith VE dimen-

sion. ITj is the normalized score of the jth IT dimen-

sion. The S is the summation of the various values of i

ranging from 1 to 6 for VE construct and values of j

ranging from 1 to 5 for IT construct.

For example, the scores for VE dimensions were as

follows: resource sharing = 4.2, core competen-

cies = 3.2, time reduction = 3.5, capacity increase =

2.3, market share enhancement = 2.8, and quality

improvement = 3.8. The scores for IT dimensions

were as follows: EDI = 3.1, Groupware = 4.0, Intra-

nets = 2.5, Extranets = 4.1, and ERP = 2.9.

Based on the above scores, misalignment or

Euclidean distance is calculated to be:

Euclidian distance ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX

ðVEi � ITjÞq

¼ffiffiffiffiffiffiffiffiffiffiffi23:62

p

¼ 4:9

Second, compute the alignment score by subtract-

ing the respective misalignment score from the

maximum misalignment score of the whole sample.

For example, if the maximum misalignment score of

the sample is 7.5, the misalignment score is then

converted to an alignment score as follows:

Alignment Score for a given respondent = (max

misalignment score from the entire sample � mis-

alignment score of the respondent) = (7.5 � 4.9) = 2.5.

Normalization is used to seek control for cross-

industry differences. In this research, we used the

approach proposed by Dess et al. (1990) to correct for

across-industry differences. Prior to the data analysis,

the sample was split based on industry. This produced

a total of five sub-samples. Normalized (standardized)

scores of all the research variables were computed for

each sub-sample. The sub-samples were recombined

and then the normalized scores were used for the data

analysis.

Based on the alignment scores and Table 4 (fourth

row), the path coefficient between alignment and

business performance was 0.23 with a t-value of 3.14

indicating that the alignment between information

technology and virtual organization positively influ-

enced firms’ business performance. As a result,


H5. The alignment between VE and IT influences

business performance more significantly than does

either the VE or IT individually.

Based on the results of H2, H3, and H4, H5 tests

whether the alignment between VE and IT influences

business performance more strongly than does either

of the VE or IT individually.

Table 4 compared the path coefficients between

business performance and VE, IT, and the alignment

between IT and VE separately. Higher path coeffi-

cient between the alignment of VE and IT and

business performance (0.23) as compared to

individual path coefficients of 0.19, 0.17 for VE

and IT on business performance suggested that

the alignment between VE and IT had a stronger

impact on the business performance than did either

VE or IT individually. As a result, Hypothesis 5 was

also supported. This is a significant point as it

shows that the alignment of VE and IT was more

crucial in enhancing firms’ business performance


Table 8

Trangularized half matrix of significant paths among VE, IT, and business performance

Dim VE1 VE2 VE3 VE4 VE5 VE6 IT1 IT2 IT3 IT4 IT5 MG FP PI CR

VE1 X X X X X X

VE2 X X X X X

VE3 X X X

VE4 X

VE5 X X X X X

VE6 X X X X X

IT1 X

IT2 X X

IT3 X X

IT4 X X X

IT5 X

MG

FP

PI

CR

than did VE or IT acting alone on firms’ business

performance.

7. Conclusions, assessment, managerial

implications, and future research directions

This paper has explored the link between ITand VE

and the impact of this link on business performance

within the context of AM research. Most of the studies

in AM were conceptual in nature and lacked empirical

evidence to support their proposed models and

conclusions. This research not only provides a

conceptual framework to systematically explore the

relationship between AM and business performance,

but also provides empirical evidence and detailed

statistical analysis for the relationships between

various constructs involved.

More specifically the following relationships were

tested using the path analysis approach: (1) the

relationship between the VE and IT; (2) the relationship

between the VE and business performance; (3) the

relationship between the IT and business performance;

and (4) the relationship between the alignment of ITand

VE on business performance. The results were all

statistically significant. All five hypotheses in this study

were empirically supported. The detailed results were

outlined in Section 6 of the paper.

The most interesting and powerful aspect of these

results indicated that the alignment between VE and

IT had a stronger impact on the business performance

than did either VE or IT individually. This shows that

synergy and interaction effect among the enablers of

AM could be more of a determining factor for success

of AM than are the individual enablers. This fact also

indicates that achieving AM is a multi-disciplinary

endeavor.

Table 8 presents the significant dimensions among

the paths of VE, IT, and business performance at the

bivariate level. In Table 8, X indicates a statistically

significant relationship between any two dimensions

or paths. This table represents the summary of results

obtained in Tables 5–7.

From Table 8, the following additional conclusions

and insights could be obtained:

1. R
esource sharing (VE1) is a dimension of VE that
is most affected by the use of IT (four of five paths

are significant). This indicates that IT assists and

facilitates the use of resource sharing in VE

environment. This is important, as resource sharing

is a crucial feature of VE. This means that EDI,

Groupware, Intranets, and Extranets are enablers of

resource sharing.

2. C
ore competencies (VE2) is a dimension of VE
that is least affected by the use of IT (one of five

paths is significant). A firm’s core competencies are

usually developed internally and reflect a firm’s

internal strength. The only IT dimension to have a

significant effect on core competency was the ERP

dimension. ERP usually contributes to the devel-

opment of core competencies.


3. M
arket share enhancement and quality improve-
ment (VE5 and VE6) are two dimensions of

VE that are moderately affected by the use of IT

(three of five paths are significant). In the case of

quality improvement, IT could improve the quality

system of a firm. In the case of market share

enhancement, IT could be used to promote and

develop marketing strategies of a firm with respect

to its competitors.

4. M
arket growth (MG) dimension of business
performance construct is most affected by VE

(five of six paths are significant). VE, by nature,

facilitates collaboration among various organiza-

tions and enterprises in order to develop new

markets and new products that may not have

existed previously. There appears to be a direct

relationship between the use of VE and developing

markets in order to enhance a firm’s business

performance.

5. P
roduct innovation (PI) and company reputation
(CR) dimensions of the firms surveyed are least

affected by VE (two of six path are significant).

Company reputation of the firms might be some-

what independent of the use of VE. It is, however,

surprising that the ability to innovate products and

processes of the firms is not significantly affected

by the use of VE.

6. F
inancial performance (FP) dimension is moder-
ately affected by VE (three of six paths are

significant). This simply indicates that the use of

VE could eventually have some positive impact on

the firm’s financial performance.

7. F
inancial performance dimension of the firm is
most affected by the use of IT (four of five paths are

significant). This implies that the use of IT could

significantly contribute to the efficiency and

effectiveness of business operations of the firms,

thereby reducing the overall costs and improving

the overall financial performance of the firms.

8. P
roduct innovation and company reputation
dimensions of the firms surveyed are least affected

by the use of IT (one of five paths is significant).

The use of IT should typically have some positive

impact on product innovation while it might have a

negligible impact upon the company reputation at

least in the short run.

9. M
arket growth of the firms is moderately affected
by the use of IT (three of five paths are significant).

The use of IT assists in developing products and

markets that would have been otherwise infeasible

to develop.

There are several managerial implications of this

study. First, different types of IT have different

impacts on various VE dimensions. Managers need

to focus on and implement specific IT dimensions in

order to enhance or achieve certain dimensions of

VE. Table 8 provides a guideline for identifying

specific significant relationships between IT and VE

dimensions. As such, companies need to make wise

use of IT in order for it to be in congruence with the

specific requirements of VE. Second, the impact of

VE and IT is essential for improving business per-

formance in an AM setting. There are several sig-

nificant paths between IT and VE; and business

performance proves this point. Third, The overall

effectiveness of the alignment between VE and IT

can be measured by relating it to the overall business

performance of the firm. This provides opportunities

for managers to convince corporate executives of the

monetary usefulness and economic justification of

using IT and engaging in VE endeavors. Fourth, the

overall success of AM depends on how well the

companies engaged in this endeavor consider the

importance and the use of synergy among the agility

enablers. The integration and totality of the agility

enablers provides a larger impact and benefits for

AM success than does the sum of each individual

agility enabler alone.

Future research directions should include the

isolation of a single manufacturing industry in order

to eliminate the possible impact of some of the

external factors. This could also result in obtaining

industry-specific information and conclusions. Per-

ceptual instruments were employed in this study to

measure constructs. In the future, archival measures of

the business environment, which do not rely on

managers’ perceptions and are more objective, can be

employed in lieu of perceptual measures. For business

performance dimension, future studies need to find a

way to use meaningful and comparable performance

measures.


Appendix A.


Appendix A (Continued ).


Appendix A (Continued ).

Appendix B.

B.1. Data preparation

The objective of data preparation is to test

normality of each dimension of the three constructs

in the proposed model (Fig. 1). Kline (1998) stated

that the estimation procedures widely used in

structural equation modeling typically assume normal

distributions for continuous variables. Skewness and

kurtosis are two ways that a distribution can be non-

normal. West et al. (1995) suggested an approach to

significance tests of normality by interpreting the

absolute values of the skewness and kurtosis indices.

They considered scores to be moderately non-normal

if they demonstrated skewness index values ranging

from 2.0 to 3.0 and kurtosis values ranging from 7.0 to

21.0. Extreme non-normality is defined by skewness

index values greater than 3.0 and kurtosis values great

than 21.0. Kline (1998) noted that scale item scores

with absolute values of the skewness index greater

than 3.0 are described as extremely non-normal.

Absolute values of kurtosis greater than 10.0 may

indicate a problem and values great than 20.0 may

suggest an even more serious problem.

The resulting skewness values for all 47-scale items

collected from the 102 respondents were all below

1.50; and thus they meet the rule for the normality test

of skewness. As for the kurtosis test, none of the scale

item had an absolute value of kurtosis greater than 2.2,

which meets the rule for the normality test for kurtosis.

As a result, it is assumed that all 47-scale items are

normally distributed, and hence are acceptable for

further analysis using structural equation modeling

techniques as suggested by Kline (1998).

B.2. Reliability tests

Cronbach’s alpha coefficient is based on the

correlations among the indicators that comprise a


Table B.1

Cronbach’s coefficient alpha for constructs and dimensions

Construct Dimension Item Cronbach’s alpha

VE = 0.759 VE1 0.783

VE2 0.614

VE3 0.634

VE4 0.623

VE5 0.701

VE6 0.732

IT = 0.812 IT1 0.683

IT2 0.821

IT3 0.715

IT4 0.823

IT5 0.742

Business performance = 0.891 Market growth = 0.720 MG1 0.594

MG2 0.663

MG3 0.617

Financial performance = 0.826 FP1 0.813

FP2 0.828

FP3 0.824

FP4 0.833

FP5 0.793

Product innovation = 0.784 PI1 0.759

PI2 0.750

Company reputation = 0.772 CR 0.772

measure, with higher correlations among the indica-

tors associated with high alpha coefficients (Pedhazur

and Schmelkin, 1991). Davis (1995) noted that

Cronbach’s alpha is the most widely used method

of reliability assessment in operations management

research. Cronbach’s alphas were calculated for all

constructs and dimensions in the conceptual model as

suggested by Vickery et al. (1993); and Flynn et al.

(1990). These values are presented in Table B.1.

All Cronbach’s alphas in Table B.1 exceed the

alpha value of 0.70, which is generally considered as

adequate for assessing reliability in empirical research

(Kline, 1998; Nunnally, 1978). Therefore, the scale

items used in this research are considered reliable.

B.3. Construct validity

Construct validity attempts to identify the under-

lying construct(s) being measured and determines how

well the test represents them (Cooper and Schindler,

1998). There are three ways in which construct

validity is assessed: (1) a test of unidimensionality, (2)

a test of convergence validity, and (3) a test of

discriminant validity.

B.3.1. The unidimensionality test

The unidimensionality test provides evidence of a

single latent construct (Flynn et al., 1990). There are

two common methods for assessing the unidimen-

sionality of a measure: exploratory factor analysis

(EFA) and confirmatory factor analysis (CFA) (Kline,

1998). The major difference between EFA and CFA is

that under EFA, the association between the scale

items and latent variables are not pre-specified, while

in CFA the associations are specified (Kim and

Mueller, 1978). This study employs CFA to test the

unidimensionality of the constructs because CFA is a

better technique for assessing unidimensionality than

EFA (O’Leary-Kelly and Vokurka, 1998). In this

paper, the CFA is employed to assess the unidimen-

sionality.

The results of the individual scale item CFA

measures for the VE and IT constructs are presented in

Table B.2, which include standardized factor loadings,

t-values, and R2 values. The overall CFA fit indices

were in the acceptable range, and thus indicated that

the model had a good fit. All scale items loaded on

their intended dimensions. Standardized loadings for

scale items ranged from 0.51 to 0.91, which represents


Table B.2

CFA Measures of VE and IT constructs

Item Loading t-value R2

VE1 0.70 9.21 0.49

VE2 0.51 6.43 0.26

VE3 0.83 11.26 0.71

VE4 0.89 13.79 0.84

VE5 0.91 11.01 0.68

VE6 0.72 9.31 0.52

IT1 0.67 9.45 0.48

IT2 0.49 7.28 0.31

IT3 0.80 10.75 0.73

IT4 0.91 12.56 0.79

IT5 0.84 11.39 0.75

Table B.4

Summary of CFA fit indices for the conceptual model

Fit indices Values

SRMR 0.063

CFI 0.91

RMSEA 0.057

x2/d.f. 2.802

Critical N 51

GFI 0.902

AGFI 0.843

moderate-to-high values. Moreover, t-values for scale

items ranged from 6.43 to 13.79 and thus exceeded the

2.0 suggested rule. Therefore, the conceptual model

was a good fit, and all scale items were unidimen-

sional.

Similar CFA tests were performed for the business

performance construct in Table B.3. In each case

similar results, to those demonstrated for the VE and

IT constructs, were achieved.

B.3.2. Convergence validity test

Convergent validity relates to the degree to which

multiple methods of measuring a variable provide the

same results (Spector, 1992; Churchill, 1979). CFA fit

indices are based on the maximum likelihood fitting

function, which performs much better than those

indices derived from the generalized least squares

Table B.3

CFA measures of the business performance construct

Dimension Loading t-value R2

Market growth 0.91 9.42 0.83

Financial performance 0.92 10.18 0.89

Product innovation 0.81 7.75 0.72

Company reputation 0.77 10.82 0.63

approach (Hu and Bentler, 1998). Stand alone indices

include standardized root-mean-square residual

(SRMR), root-mean-square-error of approximation

(RMSEA), goodness-of-fit index (GFI), adjusted GFI

(AGFI), competitive fit index (CFI), x2/d.f., and

Critical N (Marsh et al., 1988).

Hu and Bentler (1998) recommended a maximum

value close to 0.08 for SRMR; and a maximum cut-off

value close to 0.06 for RMSEA. Bollen (1989)

suggested a minimum cut-off value close to 0.9 for

CFI. Joreskog and Sorbom (1993) recommended the

minimum cut-off value close to 0.9 for GFI and AGFI.

Kline (1998) suggested a maximum cut-off x2/d.f.

ratio of 3.0. Critical N allows researchers to assess the

fit of a model relative to identical hypothetical models

estimated with different sample sizes (Hoelter, 1983).

Critical N is computed based on 2% and its degrees of

freedom. Thus, a Critical N that is lower than the

actual sample size in CFA shows that CFA has

sufficient power to detect some trivial problems

causing a poor fit (Joreskog and Sorbom, 1993).

Item Loading t-value R2

MG1 0.61 7.00 0.40

MG2 0.63 7.53 0.45

MG3 0.68 7.40 0.50

FP1 0.73 9.30 0.54

FP2 0.64 8.48 0.44

FP3 0.67 8.71 0.47

FP4 0.67 8.72 0.48

FP5 0.84 11.14 0.75

PI1 0.74 10.27 0.61

PI2 0.76 10.64 0.62

CR 0.77 10.82 0.63


Table B.5

Results of discriminant validity x2 difference test

Pairwise construct comparison x2 values

Unconstrained Constrained Difference

Virtual Enterprise vs. business performance 40.34 55.68 15.34*

Information technology vs. virtual enterprise 73.42 87.53 14.11*

Business performance 51.47 61.29 9.82*

* Significant at p = 0.01 level.

Table B.4 shows the summary of the CFA measures

of the conceptual model. The SRMR (0.063), RMSEA

(0.057), and x2/d.f. (2.802) measures met the

requirements of a good fit. CFI (0.91) also exceeded

the minimum cut-off value of 0.90. Critical N (51) was

lower than the sample size of 102 in the study. The

threshold of GFI is 0.90 and the cut-off value for AGFI

is 0 843. Both GFI and AGFI and all other indices

show good CFA fit for the conceptual model.

B.3.3. Discriminant validity test

Discriminant validity is the degree to which

measures of different latent variables are unique

(Hensley, 1999). That is, in order for a measure to be

valid, the variance in the measure should reflect only

the variance attributable to its intended latent variable

and not to other latent variables. If a construct has

discriminant validity, scale items measuring different

constructs should have low correlations (Spector,

1992). CFA was employed to assess the discriminant

validity (x2 difference test using a significance of

p = 0.01 level).

Table B.5 presents results of discriminant validity

using the x2 difference test. For each of these three

pairwise comparisons, the x2 difference between the

unconstrained model and the constrained model was

significant at the p = 0.01 level. As a result, all three

constructs were related but conceptually they pre-

sented distinct traits. In summary, all scale items used

in this research met the requirements of normality,

scale reliability, and instrument validity tests.

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Documents

The impact of alignment between virtual enterprise and information technology on business performance in an agile manufacturing environment