Inter-organizational communication as a relational competency:

Antecedents and performance outcomes in collaborative

buyer–supplier relationships

Antony Paulraj a,*, Augustine A. Lado b,1, Injazz J. Chen c,2

a Department of Management, Coggin College of Business, University of North Florida, Jacksonville, FL 32224, United Statesb Clarkson University, School of Business, 8 Clarkson Street, PO Box 5790, Potsdam, NY 13699, United States

c Department of Operations Management and Business Statistics, College of Business Administration,

Cleveland State University, Cleveland, OH 44115, United States

Received 14 October 2005; received in revised form 26 November 2006; accepted 2 April 2007

Available online 12 April 2007

www.elsevier.com/locate/jom

Journal of Operations Management 26 (2008) 45–64

Abstract

Inter-organizational communication has been documented as a critical factor in promoting strategic collaboration among firms.

In this paper, we seek to extend the stream of research in supply chain management by systematically investigating the antecedents

and performance outcomes of inter-organizational communication. Specifically, inter-organizational communication is proposed as

a relational competency that may yield strategic advantages for supply chain partners. Using structural equation modeling, we

empirically test a number of hypothesized relationships based on a sample of over 200 United States firms. Our results provide

strong support for the notion of inter-organizational communication as a relational competency that enhances buyers’ and suppliers’

performance. Implications for future research and practice are offered.

# 2007 Elsevier B.V. All rights reserved.

Keywords: Inter-organizational communication; Relational competency; Buyer–supplier relationships and performance; Knowledge sharing;

Information exchange

1. Introduction

That communication is the essence of organizational

life has been well documented by communication and

management scholars and practitioners (e.g., Fulk and

Boyd, 1991; Reinsch, 2001; Yates and Orlikowski,

1992). Similarly, literature in relationship marketing

has recognized how collaborative communication is

* Corresponding author. Tel.: +1 904 620 1166;

fax: +1 904 620 2782.

E-mail addresses: apaulraj@unf.edu (A. Paulraj),

alado@clarkson.edu (A.A. Lado), i.chen@csuohio.edu (I.J. Chen).1 Tel.: +1 315 268 6608; fax: +1 315 268 3810.2 Tel.: +1 216 687 4776; fax: +1 216 687 9343.

0272-6963/$ – see front matter # 2007 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2007.04.001

critical to fostering and maintaining value-enhancing

inter-organizational relationships (e.g., Anderson et al.,

1994; Mohr and Nevin, 1990; Mohr et al., 1996; Schultz

and Evans, 2002). Reflecting its centrality to business

performance, one business executive asserted, ‘‘com-

munication is as fundamental to business as carbon is to

physical life’’ (Reinsch, 2001, p. 20).

Operations management researchers have also

documented how inter-organizational communication

enhances buyer–supplier performance (e.g., Carr and

Pearson, 1999; Carter and Miller, 1989; Claycomb and

Frankwick, 2004; Newman and Rhee, 1990; Prahinksi

and Benton, 2004; Cousins and Menguc, 2006).

However, such work has remained disjointed, largely

anecdotal, and without a strong theoretical underpinning.

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6446

In empirical studies, researchers have typically con-

sidered communication as a facet of a broader construct,

such as supply management (e.g., Chen et al., 2004), or

examined the extent to which the use of select

communication strategies by buyer firms enhances

supplier firm operational performance (e.g., Prahinksi

and Benton, 2004). What has not been systematically

investigated is the extent to which communication

between buyer and supplier firms mediates the links

among key antecedents and outcome variables within a

coherent theoretical framework. Such an investigation is

needed in order to advance theory building and empirical

testing in supply chain management.

With this goal in mind, we develop a conceptual

model linking key antecedents and outcomes of inter-

organizational communication within the context of

collaborative buyer–supplier relationships. Drawing on

the relational view of strategic management (Dyer and

Singh, 1998), we conceptualize inter-organizational

communication as a relational competency, which

mediates the links between several antecedent and

outcome variables for buyer and supplier firms. Using

structural equation modeling, we provide a holistic test

of the hypothesized relationships (Bagozzi et al., 1991),

and document the extent to which the empirical

evidence concerning the strategic role of inter-

organizational communication is cumulative.

We believe the relational view of strategic manage-

ment provides the relevant theoretical framework for

the present investigation for at least three reasons. First,

the relational view takes the inter-organizational level

of analysis and addresses the extent to which relational

capabilities form the basis of durable strategic

advantages (Dyer and Singh, 1998; Kanter, 1994; Lado

et al., 1997; Madhok and Tallman, 1998). This

theoretical perspective extends the ‘‘resource-based

view’’ (RBV), a firm-level theory of sustained

competitive advantage (Barney, 1991; Wernerfelt,

1984). Taken together these perspectives provide a

robust theoretical framework for explaining how

strategic advantage is gained or lost based largely on

endogenous strategic factors. Second, users of the

relational view examine how relational competencies,

enable firms to gain and sustain collaborative advan-

tage (Kanter, 1994). In contrast, users of the RBV focus

on explaining how firm-specific resources and cap-

abilities characterized by value, rareness, imperfect

imitability, and non-substitutability form the basis of

sustained competitive advantage (e.g., Barney, 1991).

Finally, the relational view places a premium on

behavioral phenomena, such as inter-organizational

communication as the drivers of firm performance.

Thus, by viewing inter-organizational communication

as a relational competency and empirically investigat-

ing its mediating role in the links between key

antecedents and outcomes, we seek to gain a better

understanding of the strategic importance of this

construct within the context of buyer–supplier relation-

ships.

In the following section, we briefly review literature

in the relational view to provide a theoretical backdrop

to our proposed model of the antecedents and outcomes

of inter-organizational communication. Then, we

develop the theoretical rationale of our proposed model,

drawing on related research in strategic management,

operations management, and marketing, among others.

In Section 3, we explain our research methodology,

including data collection procedure, construct oper-

ationalization and measurement, hypothesis testing and

results. Section 4 presents discussion and implications

of the study findings. In Section 5, we highlight some

limitations of the study and offer suggestions for future

research.

2. Theory and hypotheses

2.1. Relational competencies and supply chain

management

Given the increasing importance of strategic

collaboration among supply chain partners (Contractor

and Lorange, 1988; Kanter, 1994), the issue of how

relational competencies generate sustainable strategic

advantage has attracted a great deal of scholarly

attention (Dyer and Singh, 1998; Kale et al., 2000;

Lorenzoni and Lipparini, 1999). The development of

relational competencies requires that firms adopt a

collaborative managerial mindset for building strategic

advantage (Ohmae, 1989). Leaders in such firms

articulate a ‘‘strategic intent’’ by creating an imbalance

between the firm’s strategic goals and their current

stocks of resources and capabilities (Hamel and

Prahalad, 1994). Such a strategic intent then drives

firms to acquire, access, or develop additional resources

through cooperation. Additionally, firms may form

strategic partnerships to access or acquire unique and

valuable resources that they lack, or leverage ‘‘social’’

resources, such as reputation, status, and legitimacy

(Eisenhardt and Schoonhoven, 1996). Thus, firms that

emphasize cooperation among supply chain partners

may achieve greater economic benefits compared to

those that espouse traditional, zero-sum-based notion of

competition. For example, Toyota’s cooperation with its

suppliers enhances its competitive position as well as

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 47

that of its suppliers in the global automobile industry

(Dyer and Nobeoka, 2000). With a market capitalization

greater than that of General Motors, Ford, and Chrysler

combined, Toyota is also by far the world’s most

profitable automaker (Liker, 2004).

2.2. Communication as a relational competence

As a relational competency, communication among

supply chain members may foster inter-organizational

learning that is crucially important to competitive

success (Powell et al., 1996). Organizations often learn

by collaborating with other organizations and especially

by sharing tacit, critical information and knowledge

(Grant, 1996; Kogut and Zander, 1992). Open, frequent

communication is essential to the maintenance of these

value-enhancing relationships (Christopher, 1992).

Such communication can facilitate knowledge devel-

opment (Kotabe et al., 2003; Takeishi, 2001) and foster

greater understanding of complex competitive issues

related to supply chain success (Grant, 1996; Kogut and

Zander, 1992). Specifically, the frequent exchange of

information on strategic and operational matters may

foster greater confidence, build cooperation and trust,

reduce dysfunctional conflict and, thus, generate

relational rents (Anderson and Narus, 1990; Anderson

and Weitz, 1992). Such inter-organizational commu-

nication also may lead to increased behavioral

transparency and reduced information asymmetry

(Heide and Miner, 1992), thereby lowering transaction

costs and enhancing transaction value (Dyer, 1997;

Zajac and Olsen, 1993). Therefore, as a relational

competency, inter-organizational communication may

generate ‘‘relational rents,’’ referring to benefits that

Fig. 1. Proposed model of inter-organiz

accrue through relationship-specific assets (Dyer and

Singh, 1998; Madhok and Tallman, 1998).

2.3. Model and hypotheses

Fig. 1 provides the conceptual model linking three

key antecedents (long-term relationship orientation,

network governance, and information technology) and

outcomes of inter-organizational communication within

the context of collaborative buyer–supplier relation-

ships. The model is grounded in the logic of the

relational view of strategic management (Dyer and

Singh, 1998), which also builds on Macneil’s (1980)

relational exchange theory. Accordingly, having a long-

term orientation is a key factor in forming, developing,

and maintaining value-enhancing relational exchanges

(e.g., Mohr et al., 1996; Ganesan, 1994). Exchange

partners with this orientation are likely to rely on norms

of fair dealing, such as solidarity, flexibility, mutuality

and information exchange that have been documented

to yield positive-sum benefits (Macneil, 1980; Kauf-

mann and Stern, 1988; Heide and John, 1992).

Additionally, researchers have documented the

extent to which network governance is critical to

managing inter-organizational exchange relationships

(e.g., Borys and Jemison, 1989; Powell, 1990). Dyer

and Singh (1998) specifically identified network

governance as a key source of relational rents. Likewise,

communication theorists have documented how net-

work governance contributes to communication effec-

tiveness within and between organizations (Fulk and

Boyd, 1991). Thus, the use of network governance

might foster collaborative communication among

supply chain partners (Mohr et al., 1996), and facilitate

ational communication (Model 1).

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6448

the exchange of knowledge and ideas for mutual gains

(Dyer and Singh, 1998; Kale et al., 2000).

Finally, in contemporary supply chains, information

technology plays a key role in reducing both internal

and external coordination costs (Kim and Mahoney,

2006) and facilitating the development and exchange of

strategically relevant information and knowledge

among supply chain partners (Dyer and Singh, 1998;

Fulk and Boyd, 1991). Additionally, information

technology may be the source of sustainable compe-

titive advantage insofar as it is embedded in organiza-

tional routines and processes for developing and

deploying relational capabilities (Bharadwaj, 2000;

Mata et al., 1995; Powell and Dent-Micallef, 1997).

Thus, based on the logic of the relational view,

information technology might promote greater com-

munication and serve as a critical embedding mechan-

ism, which may generate durable strategic advantages

for the supply chain partners.

Furthermore, we propose that inter-organizational

communication plays a mediating role in the links

between the three antecedent variables and buyer and

supplier firms’ performance. That is, the effects of these

antecedent factors on supply chain performance are

transmitted through inter-organizational communica-

tion (e.g., Mohr et al., 1996). We elaborate on the links

among the antecedents and outcomes of inter-organiza-

tional communication next.

2.3.1. Long-term relationship orientation

A long-term relationship orientation may promote

collaborative communication and enable supply chain

partners to build stronger relational bonds (De Toni and

Nassimbeni, 1999; Kotabe et al., 2003; Mohr et al.,

1996; Powell et al., 1996). With such an orientation,

supply chain partners are able to focus on knowledge

development and exchange and increase investment in

relational competencies (Madhok and Tallman, 1998).

Insofar as these relational competencies are ‘‘socially

created,’’ resulting from ongoing collaborative com-

munication among exchange partners (Mohr et al.,

1996), and not easily tradable in strategic factor markets

(Dierickx and Cool, 1989), they may confer durable

strategic advantages for the supply chain partners (Kale

et al., 2000; Dyer and Singh, 1998). Thus, a long-term

relationship orientation on the part of buyer and supplier

firms provides the strategic context necessary for

fostering collaborative communication. Such an orien-

tation also enables the exchange parties to cultivate

relational norms that promote cooperation for mutual

gains (Morgan and Hunt, 1994; Heide and John, 1992;

Macneil, 1980). Put differently, with a long-term

orientation, ‘‘anticipated gains from mutual coopera-

tion’’ are possible because ‘‘the future casts a [long]

shadow back upon the present, affecting current

behavior patterns’’ (Parkhe, 1993, p. 799).

When supply chain partners adopt a long-term

orientation, they tend to rely on ‘‘understandings and

conventions involving fair play and good faith’’ (Okun,

1980, p. 8), such that any agreements between them are

enforceable largely through internal processes rather

than through external arbitration or the courts (Dyer and

Singh, 1998). Thus, such an orientation enables the

communication and exchange of information and

knowledge, lowers transaction costs and enhances

transaction value through strategic collaboration. In

contrast, a short-term oriented, adversarial buyer–

supplier relationship focused on transaction cost

economizing can inhibit the development of relational

competencies, frustrate collaborative communication,

and heighten opportunism, which ultimately dissipates

relational rents (Ghoshal and Moran, 1996). This

reasoning leads to the following hypothesis:

Hypothesis 1. Long-term relationship orientation is

positively related to effective communication between

the buyer firms and their suppliers.

Based on the above rationale that long-term

relationship orientation can lead to effective commu-

nication and our discussion in Section 2.3.4 that

communication is positively related to buyer and

supplier performance, communication appears to

mediate long-term relationship orientation and buyer

and supplier performance. Since some studies (e.g.,

Jones et al., 1997; Vickery et al., 2003) have suggested

that long-term relationship can result in improved firm

performance, this study will also test for possible direct

effect in the competing models in Section 3.8.

2.3.2. Network governance

Network governance refers to inter-firm coordina-

tion that is characterized by informal social systems in

contrast to the use of hierarchical authority (Jones et al.,

1997). These social systems, or ‘‘relational norms’’

include solidarity, mutuality, flexibility, information

exchange, and role integrity (e.g., Heide and John,

1992; Kaufmann and Stern, 1988; Macneil, 1980).

Communication, though conceptually distinct from

relational norms, plays an important role in activating

and translating these relational norms into value-

enhancing relational assets (Mohr et al., 1996). Thus,

network governance enables the exchange of informa-

tion among supply chain partners, and facilitates the

development and maintenance of value-enhancing

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 49

relational exchanges among supply chain partners

(Dyer and Chu, 2003; Poppo and Zenger, 2002; Powell,

1990). Further, network governance facilitates inter-

firm communication, which, in turn, may contribute to

collaborative advantage by fostering knowledge devel-

opment and exchange, and promoting the use of

‘‘richer’’ communication media, such as face-to-face

interactions among supply chain partners (Jones et al.,

1997; Powell, 1990; Zaheer and Bell, 2005). In the

context of buyer–supplier relationships, little empirical

work exists, documenting network governance as an

antecedent of inter-organizational communication.

Thus, we forward the following hypothesis for

empirical testing:

Hypothesis 2. Network governance is positively

related to effective communication between the buyer

firms and their suppliers.

2.3.3. Information technology

Increasingly, information technology (IT) has

become a vital element in contemporary supply chain

systems, transforming the way exchange-related activ-

ities are performed (Palmer and Griffith, 1998).

However, resource-based theorists have argued that

supply chain partners that rely on ‘‘off-the-shelf’’

information technology are unlikely to achieve compe-

titive advantage because such IT does not fulfill the

criteria of value, rareness, imperfect imitability, and

non-substitutability associated with rent-yielding

resources and capabilities (Barney, 1991; Mata et al.,

1995). Additionally, those who subscribe to the

‘‘strategic necessity’’ hypothesis believe that IT, at

best, may be a source of competitive parity; that is, it is a

necessary investment to avoid competitive disadvantage

but, by itself, it does not provide a sufficient basis for

generating sustained competitive advantage (Clemens

and Row, 1991; Mata et al., 1995). Brynjolfson’s (1993)

notion of IT productivity paradox – the fact that

increases in IT investments at the firm level do not seem

to produce commensurate productivity (i.e., efficiency)

gains, even though such gains are evident at the

macroeconomic level – is often invoked to support this

view. Epitomizing this position, Carr (2003) asserted:

‘‘IT does not matter.’’

In contrast, users of the relational view have

documented how IT can generate sustainable compe-

titive advantage by facilitating collaborative commu-

nication and fostering relational capabilities (e.g.,

Grover and Malhotra, 1997; Walton and Marucheck,

1997). Accordingly, sustainability of advantage is

possible when IT resources facilitate collaborative

communication, leading to the development of com-

plementary capabilities (e.g., Powell and Dent-Mical-

lef, 1997). Firms such as Dell and Wal-Mart have made

significant IT investments in their supply chain

management systems and derived substantial benefits

from these investments (Subramani, 2004). Further-

more, investments in IT may be associated with a

flattening of organizational structure, which increases

the flow of information across organizational functions

or units (Kim and Mahoney, 2006). We should

emphasize, however, that it is not the mere investment

in IT, per se, which is the source of competitive

advantage. Rather, the sustainability of strategic

advantage depends on how information technology is

used to foster collaborative communication between

supply chain partners, and facilitate the development

and use of complementary capabilities (Dyer and Singh,

1998; Kale et al., 2000). Therefore, we hypothesize that:

Hypothesis 3. Information technology is positively

related to effective communication between the buyer

firms and their suppliers.

2.3.4. Inter-organizational communication and

buyer–supplier performance

Effective and efficient communication between

supply chain partners reduces product and perfor-

mance-related errors, thereby enhancing quality, time,

and customer responsiveness (Chen and Paulraj, 2004;

Dyer, 1996). When buyers and suppliers share

important information relating to materials procure-

ment and product design issues, they are more likely to

(1) improve the quality of their products, (2) reduce

customer response time, (3) reduce the costs of

protecting against opportunistic behavior, and (4)

increase cost savings through greater product design

and operational efficiencies (Carr and Pearson, 1999;

Turnbull et al., 1992). Dyer and Singh (1998) conclude

that relational rents are possible when alliance partners

combine or exchange idiosyncratic assets, knowledge,

and resources/capabilities, and employ effective gov-

ernance mechanisms that lower transaction costs or

permit the realization of rents through the synergistic

combination of knowledge and capabilities. Further-

more, empirical research shows that strategic alliances

in which partners exchange timely, accurate, and

relevant information, and share critical and ‘‘sensitive’’

information are more successful than alliances that do

not exhibit those communication behaviors (Carter and

Miller, 1989; Chen and Paulraj, 2004; Mohr and

Spekman, 1994; Newman and Rhee, 1990). Thus,

communication contributes to competitive advantage

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6450

by enhancing operational efficiency, quality, flexibility,

and customer responsiveness. Although studies have

documented the benefits of information sharing for

buyer firms (e.g., Clark and Fujimoto, 1991; Lamming,

1993), few studies have specifically focused on supplier

firm performance (Carter and Miller, 1989; Kotabe

et al., 2003). Thus, we investigate the effects of

interorganizational communication on both buyer

firms’ and supplier firms’ performance. We should

note, however, that although communication may

increase the created value within the partnership,

individual firm’s performance ultimately depends on

how much of the additional value is captured by each

partner (e.g., Bowman and Ambrosini, 2000; Ghosh and

John, 1999). With this caveat in mind, we submit the

following hypotheses:

Hypothesis 4. Inter-organizational communication is

positively related to buyer firms’ performance.

Hypothesis 5. Inter-organizational communication is

positively related to supplier firms’ performance.

As stated previously, communication is proposed as

a mediator of the links between the antecedents and

outcomes of the buyer–supplier relationships. The

relational view of strategic management supplies the

rationale for this claim. Within this view, Dyer and

Singh (1998) identified several relational variables,

including inter-firm knowledge-sharing routines and

complementary resources and capabilities as critical to

fostering collaborative advantages. Insofar as commu-

nication enables the sharing of information and

knowledge between exchange partners (e.g., Fulk and

Boyd, 1991; Mohr et al., 1996), it may function as a

critical mediating construct by transmitting the effects

of the three antecedent variables (long-term relationship

orientation, network governance, and information

technology) on buyer and supplier performance.

Additionally, open and frequent communication facil-

itates the development of relational capabilities (Kale

et al., 2000), which may yield mutual benefits for the

exchange partners. To the extent that communication is

the ‘‘essence of organizational life’’ (e.g., Fulk and

Boyd, 1991; Weick, 1987), and provides a medium that

fosters a wide-range of value-enhancing organizational

processes (e.g., Reinsch, 2001), investigating the

mediating role of inter-organizational communication

in the current study is warranted. Thus, we hypothesize

that

Hypothesis 6a. Inter-organizational communication

mediates the relationships between the antecedent

variables (long-term relationship orientation, network

governance, and information technology) and buyer

performance.

Hypothesis 6b. Inter-organizational communication

mediates the relationships between the antecedent vari-

ables (long-term relationship orientation, network gov-

ernance, and information technology) and supplier

performance.

3. Methodology

3.1. Unit of analysis

To be sure, most of the constructs used in this study,

such as communication are individual-level constructs.

Organizations do not communicate; people do. How-

ever, in order to translate these behavioral constructs at

the dyadic, inter-organizational level, which is the unit

of analysis for this study, we presume that ‘‘individual

views on [supply chain management] issues will be a

function of their organizational roles’’ (Ring and Van de

Ven, 1994, p. 95). And, individuals who occupy

strategic positions in their organizations would be

more knowledgeable about the strategic aspects of inter-

organizational exchange relationships. Thus, we tapped

responses to the questionnaire from ‘‘key informants,’’

including vice presidents of purchasing, supply chain

management, and materials management of US

manufacturing firms. The use of key informants as

sources of data is standard practice in strategic

management research (e.g., Venkatraman and Rama-

nujam, 1986). In this study, we relied on key informants

from the buyer side of the inter-organizational dyad to

provide responses to the survey items. The approach of

surveying the buying firms’ executives to study the

buyer–supplier relationship has been widely adopted in

the field of operations management (e.g., Carr and

Pearson, 1999; Shin et al., 2000).

3.2. Data collection

A cross-sectional mail survey was utilized for data

collection. The target sample frame consisted of

members of the Institute for Supply Management

(ISM) drawn from U.S. firms covered under the two-

digit SIC codes between 34 and 39. A 7-point Likert

scale with end points of ‘‘strongly disagree’’ and

‘‘strongly agree’’ was used to measure the items. The

buyer and supplier performance were measured using 7-

point Likert scale with end points of ‘‘decreased

significantly’’ and ‘‘increased significantly’’. In an

effort to increase the response rate, a modified version

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 51

of Dillman’s total design method was followed (Dill-

man, 1978). All mailings, including a cover letter, the

survey, and a postage-paid return envelope were sent via

first-class mail. Two weeks after the initial mailing,

reminder postcards were sent to all potential respon-

dents. For those who did not respond, a second wave of

mailing of surveys, cover letters, and postage-paid

return envelopes were mailed approximately 28 days

after the initial mailing. Of the 1000 surveys mailed, 46

were returned due to address discrepancies. From the

resulting sample size of 954, 232 responses were

received, resulting in a response rate of 24.3%. A total

of 11 were discarded due to incomplete information,

resulting in an effective response rate of 23.2% (221/

954). The final sample included 35 presidents/vice

presidents (16%), 138 directors (62%), 33 purchasing

managers (15%), and 15 others (7%). The respondents

worked primarily for medium to large firms with nearly

36% working for firms employing more than 1000

employees. Nearly 60% of the firms had a gross income

of greater than $100 million. With respect to the annual

sales volume, the respondents were evenly distributed

among the different groups. The respondents were also

distributed evenly among the six SIC codes selected.

Non-response bias was tested in two ways. First, the

sample and the population means of demographic

variables: namely, number of employees, and sales

volume were compared to check for any significant

difference. The t-tests yielded no statistically significant

differences (at 99% confidence interval) between the

sample and population. Additionally, the responses of

early and late waves of returned surveys were compared

to provide additional support of non-response bias

(Armstrong and Overton, 1977; Lambert and Harring-

ton, 1990). Along with the 10 demographic variables,

30 randomly selected variables were included in this

analysis. The final sample was spilt into two, depending

on the dates the responses were received. The early-

wave group consisted of 123 responses while the late

wave group consisted of 98 responses. t-Tests

performed on these two groups yielded no statistically

significant differences (at 99% confidence interval).

These results suggest that non-response may not be a

problem.

3.3. Measures

The variable, ‘‘Long-term Relationship Orientation’’

is operationalized by items tapping the extent to which

the buying firm (a) expects its relationships with key

suppliers to last a long time, (b) works closely with key

suppliers to improve product quality, and (c) views the

suppliers as an extension of the company (Krause and

Ellram, 1997; Shin et al., 2000). ‘‘Network Govern-

ance’’ covers items that mirror (a) fewer management

levels, (b) informal social systems, (c) permeable as

well as flexible firm boundaries, and (d) non-power-

based relationships (Jones et al., 1997; Stock et al.,

2000). The indicators of ‘‘Information Technology’’

measure the adoption of (a) computer-to-computer

electronic links, (b) technology enabled transaction

processing, and (c) advanced systems for tracking and

managing the business process (Carr and Pearson,

1999). ‘‘Inter-organizational Communication’’ is oper-

ationalized in terms of the extent to which the firm and

its key suppliers (a) share critical, sensitive information

related to operational and strategic issues, (b) exchange

such information frequently, informally and/or in a

timely manner, (c) maintain frequent face-to-face

meetings, and (d) closely monitor and stay abreast of

events or changes that may affect both parties (Krause

and Ellram, 1997; Carr and Pearson, 1999; Carr and

Smeltzer, 1999). Finally ‘‘Supplier Performance’’ and

‘‘Buyer Performance’’ are measured by indicators

tapping the dimensions of (a) cost, (b) quality, (c)

volume and scheduling flexibility, (d) speed and

reliability of delivery, and (e) rapid responsiveness

(Jayaram et al., 1999; Medori and Steeple, 2000).

Since firm size could significantly affect the scope of

resource allocation (Ettlie, 1983), we have included

number of employees (Graves and Langowitz, 1993) and

annual sales volume (Cool and Schendel, 1987) to

control for any effects of firm size on supplier and buyer

performance. Because both of these variables were

measured using categorical scales, they were included

into the structural model as dummy variables. Number

of employees was included in the model as a dummy

variable with firms having less than or equal to 500

employees coded as 0 and firms having more than 500

employees coded as 1. Annual sales volume was

included in the model as a dummy variable with firms

having annual sales volume less than $100 million

coded as 0 and firms having annual sales volume greater

than or equal to $100 million coded as 1. Operation

management researchers have used these (admittedly

crude) measures to control for size differences between

small and large firms, and industry effects in structural

equation models (e.g., Cousins and Menguc, 2006;

Fynes and Voss, 2001).

3.4. Common method variance

Since we collected the information on the variables

of interest from a single respondent within a single firm,

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6452

common method bias may present a problem. The

potential for common method bias was assessed in two

ways (Podsakoff et al., 2003). First, the Harman’s

(1967) single factor approach was used to test this

potential problem. According to this test, if common

method bias exists, (1) a single factor will emerge from

a factor analysis of all survey items (Podsakoff and

Organ, 1986), or (2) one general factor accounting for

most of the common variance existing in the data will

emerge (Doty and Glick, 1998). An un-rotated factor

analysis using the eigen value-greater-than-one criter-

ion revealed eight distinct factors that accounted for

67% of the variance. The first factor captured only 26%

of the variance in the data. Since a single factor did not

emerge and the first factor did not account for most of

the variance, we concluded that common method

variance might not be an issue. To reinforce this

conclusion, we conducted a second test, following the

procedure recommended by Widaman (1985). In this

approach, two different latent variable models – a

measurement model including just the traits (multiple

factors), and a measurement model including a method

factor in addition to the traits – were tested (Williams

et al., 1989; Podsakoff et al., 2003; Ketokivi and

Schroeder, 2004). Although the results from these

analyses indicated that the method factor marginally

improved model fit (normed fit index [NFI] by 0.02,

non-normed fit index [NNFI] by 0.01, comparative fit

index [CFI] by 0.01), it accounted for only 10% of the

total variance, which is significantly less than the

amount of method variance (25%) observed by

Williams et al. (1989). Also, the path coefficients and

their significance were not much different between the

two models, suggesting that they were robust in spite of

the inclusion of a methods factor. Based on the results of

these analyses we could reasonably conclude that the

results were not inflated due to the existence of common

method variance in the data.

3.5. Instrument development

Prior to data collection, the content validity of the

instrument was established by grounding it in existing

literature. Pre-testing the measurement instrument

before the collection of data further validated it.

Researchers as well as purchasing executives affiliated

with the Institute for Supply Management (ISM) were

involved in this process. These experts were asked to

review the questionnaire for structure, readability,

ambiguity, and completeness (Dillman, 1978). The

final survey instrument incorporated minor changes to

remove a few ambiguities that were discovered during

this validation process. As indicated earlier, multi-item

scales were developed to measure the theoretical

constructs. Before conducting factor analysis, the scales

were tested for normality and outliers using the Kaiser–

Meyer–Olkin (KMO) measure of sampling adequacy

and the Bartlett test of sphericity. For the theoretical

constructs in this study, the KMO score was 0.85 and the

Bartlett test of sphericity was 4613.76 with a

significance level of p < 0.0001. These numbers

suggest the data could be reliably tested using factor

analysis. Given that supplier and buyer performance (as

shown in Appendix B) were operationalized using

formative indicators (Diamantopoulos and Winklhofer,

2001; Jarvis et al., 2003) for which conventional

techniques (such as LISREL) are not appropriate for

assessing their reliability and validity, we did not

include them in the instrument development process in

line with Heide’s (2003) suggestion.

Construct validity and unidimensionality were

established using exploratory factor analysis (EFA)

and confirmatory factor analysis (CFA). The results of

these analyses are provided in Appendix A. As

anticipated, most of the indicators loaded onto their

underlying constructs during EFA using principal

components method. The Eigen values for these factors

were above the 1.0 cut-off point (Hair et al., 1998) while

the percentage of variation was around 64%. The factor

loadings were also above the cut-off point of 0.30 (Hair

et al., 1998). CFA measurement model was used to

further establish unidimensionality and construct

validity. The values for the model fit indices (goodness

of fit [GFI] = 0.90, adjusted goodness of fit [AGFI] =

0.86, NNFI = 0.95, CFI = 0.96, root mean square

residual [RMSR] = 0.06, root mean square error of

approximation [RMSEA] = 0.05, and normed x2

[NC] = 1.62) show that the model fits the data well

and hence establish unidimensionality. The standar-

dized coefficients and t-values for the individual paths

show that all the indicators are significantly related to

their underlying theoretical constructs and, hence,

exhibit convergent validity.

Discriminant validity was established using CFA.

Measurement models were constructed for all possible

pairs of the theoretical constructs. These models were

tested on each selected pair by allowing for correlation

between the two constructs, and fixing the correlation

between the constructs at 1.0. A significant difference in

x2 values for the fixed and free solutions indicates the

distinctiveness of the two constructs (Bagozzi et al.,

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

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 53

Table 1

Assessment of discriminant validity

Factors LR NG IT CO

Long-term relationship orientation (LR) –

Network governance (NG) 208.39 –

0.53–0.73

Information technology (IT) 466.21 366.83 –

0.12–0.40 0.05–0.37

Inter-organizational communication (CO) 220.45 246.36 530.34 –

0.58–0.78 0.41–0.65 0.32–0.56

First row: x2 differences between the fixed and free solution (significant at the 0.001 level [for 1 d.f.]). Second row: confidence interval f � se (none

of them include 1.00).

(Marcoulides, 1998). If this confidence interval does not

include the value of 1, the constructs exhibit dis-

criminant validity. As can be seen in Table 1, all the

differences between the fixed and free solutions (in x2)

are significant. Furthermore, the table shows that none

of the confidence intervals include the value of 1.

As an alternative test, we compared the squared

correlation between two latent constructs to their

average variance extracted (AVE) estimates (Fornell

and Larcker, 1981). According to this test, discriminant

validity exists if the items share more common variance

with their respective construct than any variance the

construct shares with the other constructs. Therefore,

the squared correlation between each pair of constructs

should be less than the AVE for each individual

construct. Comparing the correlation coefficients given

in Table 2 with the AVE values given in Appendix A, we

can conclude that none of the squared correlations is

higher than the AVE for each individual construct. In

fact, the highest squared correlation of 0.38 between

inter-organizational communication and long-term

relationship orientation (with a correlation of 0.62)

was much lower than the AVE for the two constructs

(0.66). These results collectively provide strong

evidence of discriminant validity among the theoretical

constructs.

Reliability was assessed using internal consistency

method via Cronbach’s alpha (Cronbach, 1951;

Table 2

Correlation between theoretical constructs

Factors Mean S.D.

Long-term relationship orientation (LR) 5.690 0.934

Network governance (NG) 5.061 1.078

Information technology (IT) 4.368 1.443

Inter-organizational communication (CO) 5.100 1.039

Supplier performance (SP) 4.904 0.754

Buyer performance (BP) 4.715 0.707

Nunnaly, 1978). Typically, reliability coefficients of

0.70 or higher are considered adequate (Cronbach,

1951; Nunnaly, 1978). All constructs had a Cronbach’s

alpha greater than 0.70 (see Appendix A). This result

establishes the reliability of all the theoretical con-

structs. Alternatively, following Bagozzi and Yi (1988),

we computed composite reliability (CR) scores to

assess construct reliability. According to these authors,

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. As reported

in Appendix A, all factors have CRs greater than 0.70.

In addition, the AVE values for all constructs exceed

0.50. Taken together, the results from the instrument

development process show that the theoretical con-

structs exhibit good psychometric properties.

3.6. Hypothesis testing

The hypothesized structural equations model (Fig. 1)

was tested using LISREL (Joreskog and Sorbom, 1999),

with variance–covariance matrices for the latent

variables and residuals used as input. Given the

satisfactory measurement results, we used summated

scores to measure the model’s latent constructs. The use

of summated scores reduces the model’s complexity,

identification problems, and the variable-to-sample

ratio (Calantone et al., 1996). In the hypothesized

LR NG IT CO SP BP

1.00

0.53 1.00

0.23 0.18 1.00

0.62 0.45 0.40 1.00

0.26 0.14 0.20 0.28 1.00

0.22 0.21 0.21 0.28 0.58 1.00

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6454

Table 3

Indirect and total effects

Exogenous variables Endogenous variables

Inter-organizational communication Supplier performance Buyer performance

Indirect Total Indirect Total Indirect Total

Long-term relationships

orientation (LR)

0.00 0.53** (0.48) 0.10** (0.13) 0.10** (0.13) 0.10** (0.14) 0.10** (0.14)

Network governance (NG) 0.00 0.14** (0.15) 0.03* (0.04) 0.03* (0.04) 0.03* (0.04) 0.03* (0.04)

Information technology (IT) 0.00 0.19** (0.26) 0.04** (0.07) 0.04** (0.07) 0.04** (0.08) 0.04** (0.08)

Note: **t-Values significant at p � 0.01; *t-Values significant at p � 0.05.

structural model, the measurement coefficients were

constrained to one and the corresponding error

coefficients were constrained to zero. The model

parameters were estimated using the method of

maximum likelihood (Joreskog and Sorbom, 1999).

The values for the model fit indices (GFI = 0.98,

AGFI = 0.90, NFI = 0.97, NNFI = 0.93, CFI = 0.98,

RMSR = 0.05, RMSEA = 0.08, and NC = 2.45) show

that the model fits the data very well. t-Tests show that

all five hypothesized relationships between the ante-

cedents and communication and between communica-

tion and buyer performance and supplier performance

(Hypotheses 1–5) were found to be significant at the

0.01 level. All the indirect and total effects were

statistically significant at or above the 95% confidence

level (see Table 3).

The hypotheses linking the antecedents to commu-

nication were all statistically significant and in the

expected directions. Specifically, the paths leading to

communication from: (1) long-term relationship orien-

tation (b = .48; t = 8.10; p < .01); (2) network govern-

ance (b = .15; t = 2.58; p < .01); and (3) information

technology (b = .26; t = 5.22; p < .01) were statistically

significant. Further, significant parameter estimates

were found for indirect effects of long-term relationship

orientation on (a) supplier performance (b = .13;

t = 3.68; p < .01), and buyer performance (b = .14;

t = 3.93; p < .01); network governance on (a) supplier

performance (b = .04; t = 2.19; p < .05), and buyer

performance (b = .04; t = 2.24; p < .05); and informa-

tion technology on (a) supplier performance (b = .07;

t = 3.24; p < .01), and buyer performance (b = .08;

t = 3.41; p < .01). Thus, in addition to having direct

effects, the antecedents also have indirect effects on the

performance measures through communication. The

second set of hypotheses (Hypotheses 4 and 5)

positively links communication with buyer and supplier

performance. The parameter estimate for the paths

between communication and buyer performance

(b = .29; t = 4.49; p < .01); and communication and

supplier performance (b = .27; t = 4.12; p < .01) were

significant and in the expected direction. Among the

four paths linking the control variables and the

performance constructs, only the path between number

of employees and supplier performance (b = .21;

t = 2.13; p < .05) was found to be significant.

3.7. Mediation analysis

To test for the mediating effect of inter-organiza-

tional communication in our model (Hypotheses 6a and

6b), we used structural equation modeling (SEM)

approach, following the suggestion of James et al.

(2006). This approach is preferable to the commonly

used Baron and Kenny (1986) method in that it: (a)

allows for the testing of full as well as partial mediation

(James et al., 2006); (b) reduces the chances of type I

error (Schneider et al., 2005), and (c) exhibits greater

statistical power (MacKinnon et al., 2002). Further-

more, as a confirmatory approach, SEM permits the

simultaneous and holistic testing of the hypothesized

relationships while accounting for all other effects of

the variables.

As hypothesized, the paths from the antecedents to

inter-organizational communication were positive and

statistically significant, as were the paths from inter-

organizational communication to supplier performance

and buyer performance. This result empirically docu-

ments that inter-organizational communication may

function as a mediator in the links between the

antecedent variables and buyer and supplier perfor-

mance. Further, we compared the proposed model

(Model 1) with a partial mediation model (Model 3

which is explained in Section 3.8) to closely examine

the extent to which inter-organizational communication

partially or fully mediates these hypothesized relation-

ships (Donavan et al., 2004; Schneider et al., 2005). The

result for the partial mediation model is presented in

Table 4. Though the overall fit of the partial mediation

model (Model 3) was not satisfactory, as indicated by

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 55

Table 4

Results of structural equation modeling of competing models

Proposed model

(full mediation)

Direct

model

Partial mediation

model

Rival model

Model 1 Model 2 Model 3 Model 4

Structural paths

Long-term relationship orientation! inter-organizational communication 0.48** 0.48**

Network governance! inter-organizational communication 0.15** 0.15**

Information technology! inter-organizational communication 0.26** 0.26**

Long-term relationship orientation! supplier performance 0.14 0.14 0.22**

Long-term relationship orientation! buyer performance 0.03 0.03 0.12

Network governance! supplier performance �0.03 �0.03 0.00

Network governance! buyer performance 0.08 0.08 0.11

Information technology! supplier performance 0.10 0.10 0.13

Information technology! buyer performance 0.14 0.14 0.18*

Inter-organizational communication! supplier performance 0.27** 0.15 0.15

Inter-organizational communication! buyer performance 0.29** 0.18* 0.18*

Inter-organizational communication! long-term relationship orientation 0.62**

Inter-organizational communication! network governance 0.45**

Inter-organizational communication! information technology 0.40**

Model fit statistics

x2 19.59 82.81 10.70 67.86

d.f. 8 1 2 11

CFI 0.98 0.86 0.98 0.90

NNFI 0.93 0.86 0.79 0.75

RMSEA 0.08 0.56 0.14 0.16

PNFI 0.28 0.03 0.07 0.35

AIC 75.35 139.01 78.45 120.18

CAIC 198.50 292.94 227.98 230.13

Variance explained (R2)

Supplier performance 0.10 0.12 0.12 0.10

Buyer performance 0.09 0.10 0.11 0.08

Note: **t-Values significant at p � 0.01; *t-Values significant at p � 0.05.

the NNFI and RMSEA values, which are not within the

recommended ranges, we compared the two models

based on the significance of the paths between the

antecedents and the performance variables. As shown in

Table 4, although the paths between inter-organiza-

tional communication and performance outcomes

reduced in significance from Model 1 to Model 3, they

were still found to be significant at the p < 0.10 level.

The direct link between long-term relationship

orientation and supplier performance was found to be

marginally significant (b = .14; t = 1.67; p < .10),

indicating that the effect of long-term relationship

orientation on supplier performance is partially

mediated by inter-organizational communication. In

contrast, the effect of long-term relationship orientation

on buyer performance was found to be non-significant

(b = .03; t = 0.40; n.s.), indicating that this relationship

is fully mediated by inter-organizational communica-

tion. The direct link between network governance and

(1) supplier performance (b = �.03; t = �0.43; n.s.),

and (2) buyer performance (b = .08; t = 1.00; n.s.) were

both found to be non-significant. This result shows that

inter-organizational communication fully mediates the

relationships between network governance and buyer

and supplier performance. Finally, the path linking

information technology and supplier performance was

found to be non-significant (b = .10; t = 1.37; n.s.),

while the path leading to buyer performance was found

to be marginally significant (b = .14; t = 1.88; p < .10).

This result shows that while inter-organizational

communication fully mediates the relationship between

information technology and supplier performance, it

only partially mediates the relationship between

information technology and buyer performance.

3.8. Competing models

Following recommended practice in structural

equations methodology (Bollen and Long, 1992;

Kelloway, 1998), we compared our proposed model

with alternative or rival models in order to ascertain

which model fits the data the best. As discussed

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6456

previously, in our proposed model, inter-organizational

communication is posited to fully mediate the links

among the three antecedent variables and buyer–

supplier performance. Alternatively, a ‘‘direct effects’’

only model might be posited in which communication

along with the three antecedent variables are directly

linked to the performance outcomes (Model 2). The

logic for this model is based on the view that

communication and the other three antecedents con-

stitute a set of supply management capabilities, which

are directly associated with buyer–supplier perfor-

mance (e.g., Chen et al., 2004). Furthermore, commu-

nication might serve as a ‘‘partial’’ rather than ‘‘full’’

mediator of the relationships between the antecedent

and outcome variables. As discussed previously, both

the RBV and relational view of strategic management

suggest that the three antecedents may themselves be

the sources of ‘‘relational rents;’’ that is, they might

have ‘‘direct’’ effects (in addition to any ‘‘indirect’’

effects) on the outcome variables of interest (e.g., Dyer

and Singh, 1998; Kale et al., 2000). Accordingly, in

addition to the paths in the proposed model, this partial

mediation model also adds direct paths from each

antecedent to the performance constructs (Model 3).

Finally, it could be argued that inter-organizational

communication might function as the antecedent of the

three variables above, which are then linked to the

outcome variables. Thus, long-term relationship orien-

tation, network governance, and information technol-

ogy are treated as intervening variables coming between

inter-organizational communication and buyer and

supplier performance (Model 4). The logic for this

model is analogous to that in Chen et al. (2004) who

posited strategic purchasing to drive several supply

management capabilities (including long-term orienta-

tion and communication), leading to performance

outcomes.

Similar to the approach taken by Morgan and Hunt

(1994), the proposed model is compared to three

competing models along the following criteria: (1)

overall model fit as measured by NNFI, CFI, and

RMSEA; (2) percentage of models’ hypothesized

parameters that are statistically significant; (3) the

explained variance in the outcome variables, as

measured by their squared multiple correlations

(SMCs); (4) parsimony, as measured by the parsimo-

nious normed fit index (PNFI) and RMSEA. Addition-

ally, the proposed and alternative models were further

compared using the Akaike’s Information Criterion

(AIC) and the Consistent Akaike’s Information Criter-

ion (CAIC). Though there are no specific cutoffs for

AIC and CAIC, a smaller value for each of these indices

represents a better fit (Akaike, 1987; Bozdogan, 1987).

Moreover, the use of information criteria such as AIC

and CAIC is valuable when comparing structural

equation models that differ with respect to restrictive-

ness (Rust et al., 1995; Tabachnick and Fidell, 2001).

As shown in Table 4, the model fit statistics generally

suggest that the proposed model fits the data relatively

well. Specifically, the fit indices including CFI, NNFI,

and RMSEA are well within acceptable ranges. In

addition to these model fit indices, AIC and CAIC

clearly indicate that the proposed model is better than

the other three competing models. Moreover, the

explanatory power of the outcome variables (supplier

performance and buyer performance) in the proposed

model was comparable to the other models. All five

hypothesized relationships were significant at the

p < 0.01 level, providing support for the efficacy of

the proposed model.

The direct model (Model 2) performed significantly

worse than the proposed model. As evident from Table 4,

all fit indices (CFI, NNFI, and RMSEA) for this model

are significantly lower than the proposed model. Only

one out of eight paths are supported at the p < 0.05 level.

Moreover, the explanatory power of the outcome

variables is not significantly different from the proposed

model. The PNFI of the proposed model (0.28) exceeds

that of the Model 2 (0.03). Although there are no

guidelines for determining a significant difference in

PNFI values, the PNFI for the proposed model increased

significantly. So, in addition to the model fit indices, the

proposed model also accomplished a great deal of

improvement in parsimony. Additionally, the AIC

(139.01) and CAIC (292.94) for the non-nested direct

model are much higher than the proposed model,

indicating that the proposed model is significantly better

than the direct model. These results collectively provide

strong evidence for the superiority of the proposed model

over the direct model.

Model 3 treats inter-organizational communication

as a partial mediator of the relationships between the

antecedents and the performance outcomes. Our

proposed model is actually a restricted version of

Model 3. Though this model fared much better than the

direct model (Model 2), it was found to be inferior to the

proposed model. As shown in Table 4, all fit indices

(CFI, NNFI, and RMSEA) are lower than the proposed

model. Except for CFI, the other fit indices in fact are

not within the recommended ranges. Among the 11

paths in this partial mediation model, only four paths are

supported at the p < 0.05 level. Moreover, the PNFI of

the proposed model (0.28) is much higher than that of

Model 3 (0.07). In addition, the AIC (78.45) and CAIC

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 57

(227.98) for Model 3 are higher than those for the

proposed model, indicating that the proposed model is

better than the partial mediation model. Though the

explained variance in the outcome variables is margin-

ally higher, the results collectively provide strong

evidence for the superiority of the proposed model.

Furthermore, since the proposed model (Model 1) is

nested within the partial mediation (Model 3) model, we

also compared the x2 values of the two models (Rust

et al., 1995). The x2 difference test requires a

comparison of the x2 values and associated degrees

of freedom of two models. Finding a significant

difference in the x2 values would be interpreted as

support for the less restricted of the two models (Model

3), while a non-significant difference would suggest that

the proposed model fits the data statistically as well as

the partial mediation model. The x2 difference obtained

in comparing the partial and full mediation model was

not statistically significant (Dx2 = 8.89, d.f. = 6,

p > 0.10), confirming that the proposed model fits

the data at least as well as the less restricted partial

mediation model. These results collectively support the

notion that inter-organizational communication is a full

mediator of most of the relationships between the

antecedents and the performance outcomes.

Finally, Model 4, which posits inter-organizational

communication to be an antecedent rather than a

mediator variable, was compared with the proposed

model. The fit indices for this rival model are much

lower than the proposed model. Except for CFI, the

other fit indices are not within their satisfactory ranges.

Of the nine paths in this rival model, only five paths are

supported at the p < 0.05 level. Although the rival

model has achieved an improvement in parsimony when

compared to the proposed model, the AIC (120.18) and

CAIC (230.13) values are much higher than the

proposed model, indicating that the proposed model

is better than the rival model. Therefore, comparison of

the model fit indices and information criteria like AIC

and CAIC collectively provide strong support for the

proposed model, thereby supporting our claim that

inter-organizational communication functions as a

mediator rather than an antecedent within the nomo-

logical structure of the proposed model. These analyses

also clearly document that the proposed model is the

best-fitting model compared to the three competing

models.

4. Discussion and implications

We have documented how inter-organizational

communication constitutes a relational competency

that generates sustainable strategic advantage for supply

chain partners. As a relational competency, commu-

nication takes on the quality of a ‘‘quasi-public good’’

in that it tends to increase in value when used and shared

and, thus, fosters ‘‘positive-sum’’ benefits for the supply

chain partners. By empirically documenting the

antecedents and outcomes of inter-organizational

communication within the context of dyadic, buyer–

supplier relationships, this study has sought to increase

the explanatory power and predictive scope of emerging

models of supply chain management. Our findings in

support of the hypotheses linking the antecedent

variables with communication (Hypotheses 1–3) pro-

vide compelling evidence concerning the strategic

importance of long-term relationship orientation, net-

work governance, and information technology, respec-

tively, in fostering collaborative communication within

buyer–supplier relationships.

Operations management researchers have docu-

mented how a long-term relationship orientation

results in improved coordination of tasks or activities

between buyer and supplier firms (De Toni and

Nassimbeni, 1999). Such an orientation enables

exchange partners to develop greater confidence in

one another, display cooperative and trusting beha-

viors, and increase investments in relationship-specific

assets in order to accomplish mutual goals. In line with

this work, the finding in support of hypothesis

Hypothesis 1 suggests that having a long-term

relationship orientation can increase collaborative

communication between supply chain partners which

is necessary for disseminating and sharing strategi-

cally important information and knowledge for mutual

gains. By facilitating such relational exchanges,

supply chain partners who adopt a long-term orienta-

tion are able to synergistically combine their resources

and capabilities in order to develop a stronger basis for

strategic advantage (e.g., Shan et al., 1994; Madhok,

2002). It is important to emphasize, however, that a

long-term orientation does not refer to calendar time. It

is possible for a buyer firm to engage in transaction-

based, spot-market contracting with a supplier for a

long period of time, without realizing the benefits of

collaborative communication (Mohr et al., 1996).

Rather, long-term orientation reflects the strategic

mindset needed for a supply chain partner to cultivate

the norms of solidarity, mutuality, flexibility and

information sharing, which are the hallmarks of

relational contracting (e.g., Macneil, 1980; Heide

and John, 1992).

This study also empirically documents the role of

network governance in fostering inter-organizational

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6458

communication, leading to sustainable competitive

advantage for supply chain partners. Strategy research-

ers have investigated the direct effects of network

governance on inter-firm performance (e.g., Poppo and

Zenger, 2002; Uzzi, 1997; Zaheer and Venkatraman,

1995). Network governance may also contribute to firm

performance through fostering relational behaviors and

capabilities, such as inter-organizational communica-

tion. Our finding in support of Hypothesis 2 suggests

that network governance may foster the collaborative

communication necessary for the development and

exchange of rent-yielding relational assets among

supply chain partners (e.g., Lorenzoni and Lipparini,

1999; Dyer and Singh, 1998). This finding is also in line

with research on Japanese Keiretsu (i.e., clusters of

interdependent buyer–supplier firms), which documents

how such networks generate greater benefits for

member firms (relative to non-members) by, among

other things, facilitating communication, fostering trust

and reciprocity, and enhancing overall productivity

(e.g., Gerlach, 1992; Lincoln et al., 1992; Dyer, 2000).

The result of our empirical investigation also

supports the claim about the strategic importance of IT

in fostering and facilitating rent-yielding relational

capabilities (Mata et al., 1995; Powell and Dent-

Micallef, 1997; Zhang and Lado, 2001). The finding

of a significant link between IT and inter-organiza-

tional communication (Hypothesis 3) indicates that IT

can contribute to collaborative advantage through

leveraging relational competencies. Additionally, the

significant effect of IT on inter-organizational com-

munication suggests that, contrary to Carr (2003), IT

does matter insofar as it facilitates the use of

collaborative communication between supply chain

partners. More than just ‘‘cables and wires,’’

contemporary ITs, which can integrate data, voice

and image, are capable of simulating the attributes of

the ‘‘rich media’’ (Daft and Lengel, 1986) necessary

for the development, exchange, and use of strategi-

cally valuable (tacit) knowledge between supply chain

partners.

The finding of significant, positive linkage between

inter-organizational communication and performance

for both buyer and supplier firms (Hypotheses 6a and

6b) is contrary to a recent study by Prahinksi and

Benton (2004). These researchers did not find support

for their proposition that the use of collaborative

communication by buyer firms enhanced the perceived

performance of supplier firms. It is possible that this

lack of empirical support was more an artifact of their

survey sample (consisting of firms in the North

American automobile industry) than an empirical

disconfirmation of the substantive linkage between

inter-organizational communication and supplier per-

formance, per se. This industry has been characterized

by a long history of adversarial relationships between

management and labor unions, as well as the use of

spot market contracts between automobile companies

(buyers) and supplier firms (Mudambi and Helper,

1998). Thus, supplier firms might view any efforts on

the part of buyer firms (i.e., auto manufacturers) to

employ collaborative forms of communication merely

as manipulative tactics to extract short-term benefits

from the suppliers, rather than as genuine attempts to

cultivate long-term, win–win relationships. In con-

trast, our empirical finding is more in line with

research by Mohr and Nevin (1990), Mohr and

Spekman (1994), and Mohr et al. (1996), which

documents that the use of collaborative communica-

tion strategies may deepen cooperation and trust, and

engender greater performance benefits for the

exchange partners.

Finally, this study documents that inter-organiza-

tional communication may function as a mediator of the

links between the key antecedents – long-term relation-

ship orientation, network governance, and information

technology – and outcome variables within the context

of dyadic buyer–supplier relationships. The proposed

full mediation model was found to be superior to other

competing models. Furthermore, the documented

mediation effects might support the claim of commu-

nication theorists and practitioners that ‘‘communica-

tion is as fundamental to business as carbon is to

physical life’’ (Reinsch, 2001, p. 20) insofar as it

enables buyer and supplier firms to accrue relational

rents.

The mediating role of communication, however,

seems to vary for buyer and supplier firms, and for the

different antecedent variables. For buyer firms, com-

munication appears to function as a partial mediator of

the relationship between information technology and

performance. This suggests that in addition to having

significant direct effect, investments in information

technology might contribute to buying firm’s perfor-

mance through fostering effective communication

between buyer and supplier firms. The significant

indirect effect of IT on performance suggests that by

facilitating communication, IT may engender compe-

titive advantage by easing the flow and exchange of

knowledge and other idiosyncratic, relationship-spe-

cific assets (Madhok and Tallman, 1998). Commu-

nication was also found to partially mediate the

relationship between long-term relationship orienta-

tion and supplier performance. On the other hand,

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 59

communication seems to fully mediate the relation-

ships between (a) long-term relationship orientation

and buyer performance, (b) network governance and

supplier performance, (c) network governance and

buyer performance, and (d) information technology

and supplier performance. In conclusion, this investi-

gation documents that inter-organizational commu-

nication plays a complex mediating role in the links

between the antecedent and outcome variables, and its

effect varies for buyer and supplier firms. Thus,

managers and researchers need to appreciate the

nuances involved in building strategic advantage

through collaborative communication.

5. Conclusion and directions for future research

In this study, we have sought to advance theory and

research in supply chain management by developing a

parsimonious model of antecedents and outcomes of

inter-organizational communication. The empirical

findings in support of the hypothesized relationships

corroborate our main theoretical assertion that inter-

organizational communication can be viewed as a

relational competency that yields strategic advantage

for the collaborating firms. From a practical viewpoint,

this study shows that building collaborative commu-

nication skills or competencies can have direct, positive

effects on the bottom lines of the supply chain partners.

Additionally, interorganizational communication

functions as an important mediating construct that

has different effects on outcomes for supplier and buyer

firms. For supplier firms, this finding suggests that

merely adopting a long-term relationship orientation is

necessary but not sufficient for achieving strategic

advantage. Furthermore, managers of supplier firms

would need to hone skills for effective communication

in order to fully reap the benefits of long-term

relationships with buyer firms. For the buyer firms,

establishing a network form of governance may not be

sufficient for achieving strategic advantage; such a

governance form may only engender strategic advan-

tage through providing an inter-organizational context

that is conducive to collaborative communication. Thus,

a nuanced understanding of the roles of these factors in

shaping an inter-organizational exchange context that is

conducive to collaborative communication is key to

effectively managing buyer–supplier relationships for

mutual benefits.

At this juncture, we should acknowledge some

limitations of our study that would provide opportu-

nities for further research. Although our model of

communication has the advantage of parsimony, it

might also sacrifice breath and richness of explanation.

Thus, in the future, researchers should include other

factors such as geographic dispersion (Jones et al.,

1997), and cultural compatibility (Teece et al., 1994)

that could influence communication among supply

chain partners. Also, the role of trust and commitment in

directly facilitating inter-organizational communication

and enhancing transaction value should be explicitly

assessed. Furthermore, in testing the hypothesized

relationships using covariance-based SEM we assumed,

in line with prior research in operations management

(e.g., Chen et al., 2004; Prahinksi and Benton, 2004),

that all the measures used to tap the selected variables

were ‘‘reflective’’ rather than ‘‘formative.’’ In the future,

researchers might consider testing the hypothesized

relationships using other SEM approaches, such as

partial least squares (PLS) that are better suited for

investigating formative constructs (Diamantopoulos

and Winklhofer, 2001).

Another limitation of this research relates to the

sample population. Having drawn from a list of ISM

members, the results of this research are generalizable

only to the firms included in the ISM database.

Although this study sample covered a wide range of

firms in the ISM database in terms of industry

membership and demographic variables, we suggest

that future research include a mixed population of

respondents from multiple sources, including service

firms, as well as domestic and international compa-

nies in order to increase the scope of generalizability

of the results. Finally, this study focused on the

dyadic relationship as the unit of analysis, and

assumed the buying firm’s perspective. Since the

supplier also plays a significant role in affecting

the quality of the dyad, there is a need to examine the

exchange relationship from the supplier’s perspective

as well. Therefore, we urge future researchers to

adopt the ‘‘strategic network’’ (Gulati et al., 2000) as

a unit of analysis and investigate the antecedents and

outcomes of communication from multiple view-

points. Despite these limitations, we believe that this

study makes a compelling case for viewing inter-

organizational communication as a critically impor-

tant relational competency that can be leveraged for

mutual gains within collaborative buyer–supplier

relationships.

Acknowledgement

We would like to thank the Institute for Supply

Management (ISM) for its administrative and financial

support of this research.

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–6460

Appendix A

Indicator (Cronbach’s alpha, eigen value,

composite reliability, average variance extracted)

Principal component

factor loading

Measurement model

Std. coefficient t-Valuez

Long-term relationship orientation (a = 0.85; eigen value = 2.65; CR = 0.88; AVE = 0.66)

We expect our relationship with key suppliers to last a long time 0.85 0.67 –

We work with key suppliers to improve their quality in the long run 0.62 0.73 9.26

The suppliers see our relationship as a long-term alliance 0.79 0.81 12.01

We view our suppliers as an extension of our company 0.69 0.85 10.19

We give a fair profit share to key suppliers*

The relationship we have with key suppliers is essentially evergreen**

Network governance (a = 0.81; eigen value = 2.84; CR = 0.88; AVE = 0.65)

We have a permeable organizational boundary that facilitates better communication

and/or relationship with our key suppliers

0.72 0.74 –

Our relation with the suppliers is based on interdependence rather than power 0.81 0.73 9.32

Our organizational structure can be characterized as a flexible value-adding network 0.70 0.73 9.48

Our organizational/supply structure does not involve power-based relationships 0.83 0.67 8.50

The decision making process in our organization is decentralized*

We have few management levels in our relationship with suppliers**

Information technology (a = 0.84; eigen value = 3.45; CR = 0.95; AVE = 0.75)

There are direct computer-to-computer links with key suppliers 0.77 0.69 –

Inter-organizational coordination is achieved using electronic links 0.75 0.75 9.48

We use information technology enabled transaction processing 0.80 0.80 10.00

We have electronic mailing capabilities with our key suppliers 0.60 0.55 7.27

We use electronic transfer of purchase orders, invoices and/or funds 0.67 0.50 7.41

We use advanced information systems to track and/or expedite shipments 0.77 0.70 9.04

Inter-organizational communication (a = 0.86; eigen value = 3.90; CR = 0.92; AVE = 0.66)

We share sensitive information (financial, production, design, research, and/or competition) 0.70 0.57 –

Suppliers are provided with any information that might help them 0.67 0.66 8.67

Exchange of information takes place frequently, informally and/or in a timely manner 0.80 0.85 8.99

We keep each other informed about events or changes that may affect the other party 0.77 0.88 9.14

We have frequent face-to-face planning/communication 0.77 0.74 8.26

We exchange performance feedback 0.62 0.61 7.30

Model fit indices: normed x2 = 1.62 (�2.0); goodness of fit index = 0.90 (�0.90); adjusted goodness of fit index = 0.86 (�0.80); non-normed fit

index = 0.95 (�0.90); comparative fit index = 0.96 (�0.90); root mean square residual = 0.06 (�0.10); root mean square error of approxima-

tion = 0.05 (�0.10). Note: *Items dropped after EFA; **Items dropped after CFA; zAll t-values are significant at p < 0.05 level.

Appendix B

Performance measure

Supplier performance

Quality

Cost

Volume flexibility

Scheduling flexibility

On-time delivery

Delivery reliability/consistency

Prompt response

Buyer performance

Product conformance to specifications

Production costs

Volume flexibility

Delivery speed

Delivery reliability/dependability

Rapid confirmation of customer orders

Rapid handling of customer complaints

A. Paulraj et al. / Journal of Operations Management 26 (2008) 45–64 61

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