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International Journal of Information Management 32 (2012) 35–49

Contents lists available at ScienceDirect

International Journal of Information Management

journal homepage: www.elsevier .com/ locate / i j infomgt

Knowledge complementarity and knowledge exchange in supply channel

relationships

Kyung Kyu Kim a,∗, Narayan S. Umanath b,1, Joo Young Kim a, Fred Ahrens c, Beomsoo Kim a,2

a Yonsei University, Seoul, SouthKoreab University of Cincinnati, 328 Lindner Hall, Cincinnati, OH 45221-0211, United Statesc Department of Information Systems, University of Cincinnati, Cincinnati, OH 45221-0211, UnitedStates

a r t i c l e i n f o

Article history:

Keywords:

Knowledge exchange

Knowledge complementarity

Supply chain management

a b s t r a c t

Existing literature on knowledge exchange in inter-organizational relationships (e.g., a supply channel)

reveals two opposing forces at work: (1) collaborative behavior and (2) opportunistic behavior. A con-

current assessment of the opposing perspectives and the contingencies under which each is relevant for

supply channel performance can add valuable insights about the dynamics of knowledge exchange. We

juxtapose the two behavior patterns using social capital theory and transaction cost economics (TCE)

respectively as the explicators and employ knowledge complementarity as the contingency to recon-

cile the opposing behavior patterns. The choice of knowledge complementarity in this role stems from

ample theoretical and empirical support in prior literature about the criticality of this factor in inter-firm

knowledge exchange.

We propose a research model, and use data from a field study of 82 firms in the Electronics Manufactur-

ing Services (EMS) industry to test our model. Our findings indicate that overall inter-organizationaltrust

(a surrogate for social capital) and knowledge complementarity promote knowledge exchange behavior

in a supply channel. The retarding effect of risk of opportunism (a TCE dimension) manifests only when

knowledge complementarity is low. However, when knowledge complementarity is high, contrary to

expectations, inter-organizational trust appears to impede knowledge exchange. Our post hoc analysis of

this intriguing, counterintuitive result leads us to knowledge interdependence and dependence asym-metry as potentially critical antecedents to knowledge complementarity. Implication of our findings to

academic research and supply chain scenario is also articulated.

© 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Although knowledge exchange (KE) in inter-organizational

relationships has become increasingly important, few companies

have fully exploited the knowledge resources of their partners.

Organizations in a supply chain require access to partner firms’

knowledge (e.g., about markets, products, and raw materials, etc.),

which they consider essential or useful to their operations, pro-

viding mutual benefits (Barratt & Oke, 2007; White, Daniel, &

Mohdzain, 2005). Knowledge from customer and/or supplier orga-nizations may help improve overall supply chain performance and

their own internal decision making and operating performance

(Mabert & Venkataramanan, 1998). However, academic scholars

(e.g., Bowersox, Closs, & Cooper, 2010) predict that it may be a

∗ Corresponding author. Tel.: +82 2 21234525.

E-mail addresses: [email protected] (K.K. Kim), [email protected] (N.S.

Umanath), [email protected] (J.Y. Kim), [email protected] (F. Ahrens),

[email protected](B. Kim).1 Tel.: +1 513 5567195.2 Tel.: +822 2123 4527.

long time before firms cooperate to form a fully collaborative end-

to-end supply chain. Why can only a few firms benefit from the

knowledge of their partners? Scholars in both operations manage-

ment (OM) and strategic management have researched inter-firm

knowledge exchange, but differences in focus between the various

approaches have left us with an incomplete understanding of what

causes knowledge exchange to occur and how it benefits the firms

throughout a supply chain. In one line of research, scholars have

focused on organizational social capital perspectives, asserting that

networks of relationships are valuable resources (i.e., capital) foran organization and conduits for knowledge exchange. The social

capital view asserts thatrelational rentsare possible when partners

combine or exchange knowledge and/or when they employ effec-

tive governance mechanisms that permit the realization of rents

through a synergistic combination of assets, knowledge, or capa-

bilities (Dyer & Singh, 1998). On this view, supply chain partners

are considered the means with which to acquire the complemen-

taryresourcesand capabilities thatfirms otherwise lack.Thus, from

the socialcapital view, an effective strategy requires a firmto share

valuable knowledge systematically with its partners in return for

access to their knowledge bases.

0268-4012/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijinfomgt.2011.05.002

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36 K.K. Kimet al./ International Journal of InformationManagement 32 (2012) 35–49

In another line of research, in contrast, transaction cost eco-

nomics (TCE) has focused on firms’ concerns with transaction risks

when they release their knowledge resources outside the firm

(e.g., Clemons & Hitt, 2004; Madhok & Tallman, 1998). The inter-

organizational knowledge exchange dilemma is that “(1) being a

good partner invites exploitation by partners attempting to max-

imize their individual appropriation of the joint learning, and

(2) such opportunistic learning strategies undercut the collective

knowledge development in a collaborative relationship” (Larsson,

Bengtsson, Henriksson, & Sparks, 1998). An organization’s oppor-

tunistic behavior may result in gaining more knowledge relative

to its partners in the short-term, but this exploitation is likely to

jeopardize supply-chain relationships and/or cause other partners

to behave opportunistically as well (Park & Ungson, 2001). The

probable result is that neither organization will contribute to the

knowledge exchange process. This relative withholding of knowl-

edge reduces the overall effectiveness of knowledge exchange in

a supply chain, potentially defeating the purpose of knowledge

exchange.

Inter-firmrelationshipsseem necessary for inter-organizational

knowledge exchange to occur, but knowledge exchange may be

hindered by concerns with transaction risk. In order to enhance

our understanding of knowledge exchange among supply chain

partners, a concurrent assessment of opposing theories based on

the social capital perspective and transaction risk perspective and

the contingencies under which each theory is relevant to supply-

chain performance is crucial. By examining the literature (e.g.,

Stieglitz & Heine, 2007) and the related theories (Milgrom, Qian,

& Roberts, 1991), we have identified an important contingency,

namely, knowledge complementarity (KC), which may help us

understand behavior patterns in knowledge exchange. This study

seeks to explore the contingency effect of knowledge comple-

mentarity on knowledge exchange activity among supply chain

partners.

In this paper, we formulate a research model driven by compet-

ing theories of social capital and transaction risk and develop and

test appropriate hypotheses, using data collected from purchas-

ing managers at 82 firms in the Electronics Manufacturing Services(EMS)industry.Specifically, the sample firmsare intermediate pro-

ducers in the EMS industry that rely on first-tier suppliers for their

parts and components. These intermediate producers assemble

low-level parts fromfirst-tiersubcontractors into stable intermedi-

ate components.The finaloutputs fromthe intermediate producers

are sold to manufacturers who finally proceed to mount them on

specific models to be sold to final customers. Intermediate pro-

ducers do not have access to the final market for electronics. The

flow of intermediate products in complex industrial activities pro-

vides a good context within which to study knowledge exchange

in procurement and supply relationships.

The next section reviews the relevant literature proposing the-

ories about inter-firm knowledge exchange. The third section

discusses the direct effects of social capital and transaction riskperspectives on inter-organizational knowledge exchange. It also

describes the moderating effects of knowledge complementarity

on the relationship between forces from the transaction risk/social

capital perspectives and knowledge exchange. The fourth sec-

tion describes our research method, and the fifth section presents

empirical results. We conclude with a discussion of our results and

their implications.

2. Inter-firmknowledge exchange

Pervasive electronic networks enable companies to exchange

knowledge far more efficiently and effectively than ever before.

This improvement in the economics of knowledge exchange and

transaction costs has caused entire industries to reorganize rad-

ically and dramatically, unbundling the traditional value chain

(Hagel & Singer, 1999). Firms are relying more on outside part-

ners to facilitate cost-effective delivery of value to customers,

since the relevant knowledge is often located outside the firm’s

particular field (Ye & Agarwal, 2003). Indeed, the proliferation

of inter-organizational collaborative relationships is considered

to be driven by the challenge of growing knowledge intensity

(Adler & Kwon, 2002), which has made knowledge exchange in

inter-organizational relationships ever more important in today’s

globalized business environment.

However, many of the challenges stemming from knowledge

exchange arise from barriers between source and recipient firms.

Hsiao, Tsai, and Lee (2003) identify typical barriers to knowledge

exchange, including unwillingness to share important knowledge

(e.g., Nahapiet & Ghoshal, 1998), lack of the recipient’s absorptive

and assimilation capacity (e.g., Lane & Lubatkin, 1998), obstacles to

effective knowledge search (e.g., Rivikin, 2001), unproven knowl-

edge content (e.g.,Szulanski,1996), technological incompatibilities

(e.g., Weill & Vitale, 2002), knowledge-conversion difficulties (e.g.,

Nonaka, 1994), and socio-political issues (e.g., Hayes & Walsham,

2001).

A review of prior studies reveals that these barriers can be

explained according to one of two opposing theories – namely,

social capital theory and transaction cost economics – which sug-

gest that the forces behind the knowledge exchange phenomenon

may be simultaneously in harmony and at conflict. This study fur-

ther explains the two opposing theoretical perspectives.

Transaction risk, a component of transaction costs, is the

possibility that a trading partner will behave opportunistically

(Williamson, 1985), leading to uncertainty about the level and

division of benefits accruing from increased knowledge exchange.

Among the sources of transaction risk frequently cited in the lit-

erature (e.g., transaction specific capital, loss of resource control,

and information asymmetries), loss of resource control is particu-

larly relevant to inter-firm knowledge exchange. Loss of resource

control occurs if resources, transferred as part of the relationship,

cannot be returned or controlled during the relationship (Clemons& Hitt, 2004). Information or know-how is an example of important

resources subject to loss of control. Once knowledge is transferred

and assimilated into the recipient’s knowledgebase, it is difficult,if

not impossible, for the source firm to control access and the subse-

quent use of that knowledge. The recipient firm may deliberately

use knowledge for its own economic benefits at the expense of

the source firm and the entire supply chain perhaps by sharing it

across competing supply channels. In order to manage this trans-

action risk, supply chain participants may not be willing to share

important knowledge with each other. For instance, the buyer may

consider it necessary to maintain a certain level of information

asymmetrywith the supplier,especiallyregarding the downstream

supply chain data lest the supplier should use the downstream

data opportunistically at the expense of the supply chain perfor-mance (i.e., fear of disintermediation) (Bowersox et al., 2010). The

transaction risk perspective can also explain other challenges to

knowledge exchange arising from barriers such as lack of motiva-

tion and unwillingness to share important knowledge (Malhotra,

Gosain, & Omar, 2005).

In contrast, social capital theory asserts that networks of rela-

tionships constitute a valuable resource for the conduct of social

interactions. Social capital is defined as “the sum of the actual

and potential resources embedded within, available through, and

derived from the network of relationships possessed by an indi-

vidual or social unit” (Nahapiet & Ghoshal, 1998, p. 243). The

social capital perspective offers a rationale for cooperative behav-

ior such as knowledge exchange (e.g., Hult, Ketchen, Cavusgil, &

Calantone, 2006). The primary motivation behind the formation

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K.K. Kimet al./ International Journal of Information Management 32 (2012) 35–49 37

of inter-organizational relationships is to gain access to valu-

able partner-held resources. Mutual need makes alliances for the

exchange of resources even more likely. For example, sharing

knowledgeabout ultimatemarket demand among cooperative sup-

ply chain participants is known to be effective for dealing with

the “bullwhip” effect,3 consequently improving the performance

of the entire supply chain (up/down stream). The “bullwhip” effect

is said to be a core problem in supply-chain management, because

it distorts demand information transmitted upstream in the sup-

ply chain(e.g., Lee,1997). This supposedlyhappens when a supplier

forecastsdemand patterns on the basis of theorder history from its

immediate downstream partner (buyer), instead of on the basis of

salesinformation fromultimate customers. This lineof research has

identified barriers to knowledge exchange between the source and

the recipient as the recipient’s lack of relative absorptive capacity,

obstacles to effective knowledge search, and unproven knowledge

content.

Table 1 summarizes the barriers to knowledge exchange predi-

cated on social capital theory or transaction risk theory.

In summary, the existing literature on knowledge exchange

among supply chain partners suggests two counteracting forces

underlying knowledge exchange: (1) firms are willing to share

knowledge with supply-chain partners and trust, a social capital,

facilitates such an exchange; and (2) firms want to maintain some

degree of knowledge/information asymmetry to guard against

channel partners’ opportunistic behaviors.

3. Theoretical frameworkand researchmodel

3.1. Theoretical framework

Efficient and effective transfer of data and information can

occur via market transactions or through hierarchical governance

structures (Williamson, 1985). However, inter-organizational phe-

nomenon such as knowledge sharing in a supply channel often

requires reciprocal patterns of communication and exchange,

and may be better served by relational/network forms of orga-

nizations less guided by a formal structure of authority andmore driven by reciprocity, collaboration, etc. The mold of a

relational/network structure facilitating long-term interactions,

complementarity, reciprocity, collaboration, trust, etc. is used here

to portray exchange behavior in inter-organizational networks.

Social exchange theory (SET) serves as the theoretical umbrella

to model the phenomenon. SET here serves as the meta-theory

providing a robust theoretical base for binding the kernel theories

of social capital, TCE, and the economic theory of complementar-

ity where the kernel theories enunciate the three constructs of

specific interest to the supply channel context, viz., social capital,

opportunism, and network complementarity. The domain of the

theoretical framework is ‘Knowledge’ (Fig. 1).

Social exchange theory (Kelley & Thibaut, 1978) posits that

social behavior is the result of an exchange process. The purpose of this exchange is to maximize benefits and minimize costs. Accord-

ing to SET, actors in a network evaluate the payoff of a relationship

against the available alternatives; if there are better alternatives or

the costs outweigh the rewards, one or more members of the net-

work will prefer to terminate or abandon that relationship (Blau,

1964). Cost–benefit analysis and comparison of alternative are the

building blocks of SET (Kelley & Thibaut, 1978).

While there are many reasons for firms to enter into relation-

ships (e.g., joint production, product promotion, etc.), we restrict

3 Th e bullwhip ef fect is w hen “ the var iance o f or der s may be lar ge r th an

that of sales and the distortion tends to increase as one moves upstream” (Lee,

Padmanabhan, & Whang, 1997, p. 546).

our attention to the intellectual property domain – knowledge

sharing in a supply channel. Thus, our research framework (Fig. 1)

models exchange behavior in the knowledge domain transpiring in

the context of inter-organizational networks informed by SET.

Pursuant to the SET conceptualization, payoff in an inter-

organizational relationship is essentially value generated less the

cost incurred in the relationship. At the outset, we posit that the

social capital of the inter-organizational relationship creates the

value to promote exchange behavior, while opportunism includ-

ing the consideration of alternatives constitutes the cost capable of

retarding the exchange behavior.

Risk of opportunistic behavior is an inherent threatin anyinter-

organizational relationship – e.g., supply channel. To the extent a

dyadic relationship entails knowledge exchange between partici-

pating partners thevery exchange process is vulnerable to this risk.

This is the case in point motivated by self-interest in any dyadic

exchange scenario – the self-interest case. TCE (Williamson, 1985,

1991) tackles the issue along the lines of managing opportunism

using governance structures – a cost component. Alternatively, the

very need to work together can spontaneously induce mutual trust

in the dyad in order to reduce the risk of opportunism simply

because trust can make knowledge exchange less costly (Zaheer,

McEvily, & Perrone, 1998) – the risk reduction case.

While data and information exchange may flourish through

routine market transactions (Kim & Umanath, 2005), knowl-

edge complementarity is almost always a necessary condition for

knowledge exchange (Hamel, Doz, & Prahalad, 1989). Knowledge

complementarity refers to the characteristic of the knowledge that

may determine its value as a tradable commodity. It is the effect

resulting from combining two distinct bodies of knowledge whose

agglomeration is super-additive – i.e., the combined knowledge set

has more embedded knowledge than the simple additive values of

the parts. This is ratified by the economic theory of complemen-

tarity which shows that the mathematical relationship between

a system of complementary variables, and the returns and costs

are super-modular and sub-modular respectively (Milgrom et al.,

1991). Here, super-modularity means super-additive value syn-

ergies, while sub-modularity means sub-additive value synergies(Tanriverdi & Venkatraman, 2005). Knowledge complementarity

often serves as the compelling reason for the dyad in a supply

channel to work together and exchange knowledge despite the

inherent threat of opportunistic behavior by either partner, simply

because the benefit accrued by the supply channel partners collec-

tively and/or individually outweighs the expected costdue to riskof

opportunism – therisktolerancecase. In essence, where is theincen-

tive for two parties to enter into a relationship at all to exchange

knowledge unless they possess complementing knowledge?

In short, our theoretical framework juxtaposes social capital

and opportunism as the principal opposing forces – value and cost

respectively in the payoff structure enunciated in the SET – in the

exchange scenario. Network complementarity is introduced as the

common denominator that promotes exchange behavior by accru-ing benefits (contribution to the payoff) that makes it worthwhile

to tolerate the risk of opportunism – a compensatory mechanism

even at low levels of social capital. As a consequence, we would

expecta collective effectof socialcapital andnetwork complemen-

tarity enhancing exchange behavior, since they mitigate threat of

opportunism in different, possibly cumulative ways.

3.2. Research model

Our research model (Fig. 2) is predicated on the broader theo-

retical framework presented in Fig. 1. Here, we juxtapose impact

of organizational trust and risk of opportunism on knowledge

exchange in terms of the opposing viewpoints of social capital

theory and TCE respectively. Social capital theory views inter-

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Table 1

Barriers to knowledge exchange.

Social capital related barriers Transaction risk related barriers

1. Lack of the recipient’s absorptive and assimilation capacity (e.g., Lane &

Lubatkin, 1998)

1. Unwillingness to share important knowledge (e.g., Nahapiet & Ghoshal,

1998)

2. Knowledge-conversion difficulties (e.g., Nonaka, 1994) 2. Concerns about “Knowledge Poaching” (e.g., Clemons & Hitt, 2004)

3. Obstacles to effective knowledge search (e.g., Rivikin, 2001) 3. The source firm’s lack of motivation (e.g., Gupta & Govindarajan, 2000)

4. Unproven knowledge content (e.g., Szulanski, 1996), Technological

incompatibilities (e.g., Weill & Vitale, 2002)

4. The source firm’s unfavorableaccess to knowledge(e.g., Hsiao et al., 2003)

5. Socio-political issues (e.g., Hayes & Walsham, 2001). 6. Loss of resource control (e.g., Williamson, 1985)

Fig. 1. Research framework based on social exchange theory.

organizationaltrust as a source of competitive advantagefor supply

chains, whereas, from the TCE perspective, the intimacy neces-

sary to realize the potential of the relationship exposes firms to

risk of opportunism (Madhok & Tallman, 1998). Likewise, resource

complementarity among the supply channel partners facilitates

Control Variable

IT Infrastructure

Knowledge

Exchange

(KE)

Knowledge

Complementarities

(KC)

Risk of

Opportunism

(RO)

Inter-Organizational

Trust

(T) H1

H2

H3

H4

H5

Fig. 2. Research model.

cooperation for obtaining mutual benefits; and to the extent

knowledge is an organizational resource, knowledge complemen-

tarity promotesthe cooperative knowledge exchange behavior.The

simultaneous impact of inter-organizational trust and knowledge

complementarity on knowledge exchange is hypothesized as an

interaction effect. As a contrast, we also examine the collective

effect of risk of opportunism and knowledge complementarity on

knowledge exchange.

IT infrastructure as a research variable may be relevant for data

and information exchange (e.g., EDI). In a knowledge exchange

context, IT infrastructure often remains a part of the context (e.g.,

supply channel). The inclusion of IT infrastructure in our research

model is more for partialling out any residual effect due to its

presence (e.g., inadvertent presence of explicit knowledge in the

exchange). Therefore, our research model portrays IT infrastruc-

ture as a control variable rather than a hypothesized effect. The

individual hypothesis development follows.

3.2.1. Direct effects of social capital and the risk of opportunistic

behavior

(1)Organizational trust as an enabler of knowledge exchange

Social capital theorists have focused much attention on the

structural properties of relationships (Adler & Kwon, 2002), such

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as tie strength at the dyadic level. While strong ties are impor-

tant conditions of knowledge exchange, Levin and Cross (2004)

assert that the relational dimension of social capital, i.e., organi-

zational trust, mediates the relationship between tie strength and

knowledge exchange. Tsai and Ghoshal (1998) also find that at the

department level the structural dimension of social capital stim-

ulates trust and perceived trustworthiness, which, in turn, allow

departments to exchange more resources (including knowledge).

As described above, the previous literature suggests that organi-

zational trust mediates between knowledge exchange and other

dimensionsof social capital (e.g.,relationship structure). Therefore,

we have chosen to focus on the organizational trust dimension of

social capital.

Moreover, the trust literature (see Mayer, Davis, & Schoorman,

1995 for reviews) provides considerable evidence that trusting

relationships lead to greater knowledge exchange. Morgan and

Hunt(1994) define organizationaltrust as convictionabout the cer-

tainty and honesty of a trading partner, while Zaheer et al. (1998)

define trust as the collective trust that every member of an orga-

nization puts into another trading partner. If supply chain partners

trust each other, they are more likely to share certain types of

knowledge (Modi & Mabert, 2007). For example, tacit and abstract

knowledge are susceptible to exploitation by partners (Dyer &

Nobeoka, 2000). Consequently, an organization shares tacit knowl-

edge only when it really trusts that its partners will use it for the

benefit of the partnership.

For the receiving organization, trust makes knowledge trans-

fer less costly (Zaheer et al., 1998) by reducing conflicts and the

need to verify information. As a consequence, knowledge-seeking

organizations in a trusting relationship are readily willing to absorb

partners’ knowledge (Mayer et al., 1995). Furthermore, knowledge

seekers are vulnerable to the benevolence of the knowledge source

(Lee, 1997) and mutual trust tends to facilitate such benevolence.

Knowledge seekers who trust a source’s competence to make sug-

gestions and influence their thinking are more likely to heed and

take action on that knowledge. Trusting a knowledge source to be

benevolent and competent should increase the likelihood that the

knowledge receiver will benefit from the interaction.In summary, in high-trust relationships, organizations are apt

to be more open to the potential for value creation through the

exchange and combination of resources. Social capital in the form

of high trust enables supply-chain participants to engage in more

socialexchangeand to take actionthatwouldusuallybe considered

risky in such exchanges(Putnam, 1993). From these facts we derive

the following hypothesis:

Hypothesis 1. Inter-organizational trust positively influences

knowledge exchange behavior among supply-channel partners.

(2) Risk of opportunistic behavior as an inhibitor of knowledge

exchange

Inter-organizational relationships are inherently temporal,

unstable, and disfavored (Williamson, 1991). The stability of inter-organizational relationships is affected by such factors as

opportunism, complexity in monitoring behaviors, and difficulty

in coordination among partners (Park & Ungson, 2001). These

characteristics are relevant to knowledge exchange between

supply-chain partners and affect the success of the cooperative

relationship. Depending on the participants’ private incentives,

inter-organizational relationships may generate either coopera-

tive or competitive behaviors between partners (Gulati, 1995).

Cooperative inter-organizational relationships may fail due to

opportunistic hazards that arise as each firm pursues its own

individual interests rather than collective interests. Opportunistic

behavior may allow for immediate gratification of short-termgoals

of a partner without the need to face the uncertainty of long-term

returns. The vulnerability due to a partner’s self-interested behav-

ior is exacerbated in situations in which the relevant resources and

behavior are not readily transparent (Park & Ungson, 2001).

In TCE, opportunism or opportunistic behavior means self-

interest seeking with guile, involving somekind of deliberatedeceit

and the absence of moral restraint (Williamson, 1985). It could

involve deliberately withholding or distorting information, per-

formance shirking, or failing to fulfill promises and obligations.

It occurs in business transactions especially where performance

measures are ambiguous, and where goals of trading partners

are incongruent (Ouchi, 1980). Because the risk of opportunistic

behavior is ever present, firms must have recourse to safeguards.

Mechanisms such as optimal contract structures (e.g., Williamson,

1991), the alignment of incentives (e.g., Dyer & Singh, 1998), and

governance structures (e.g., Williamson, 1985) may decrease the

risk of opportunism. However, a relationship dominated by protec-

tion against opportunism makes firms reluctant to form unilateral

and voluntary commitments outside the terms of the contract

and therefore tend to perceive a greater need to take costly and

elaborate safeguards (Madhok & Tallman, 1998). This tendency

diminishesthe level of value created andrealized through the rela-

tionship.

In the automobile industry, for example, an automaker’s strate-

gic knowledge in specific domains may be diffused to competitors

through shared suppliers. According to Takeishi (2002), some sup-

pliers intentionally transfer technological knowledge learned from

one automaker to another, and some automakers try to learn new

technology and effective practices from others through common

suppliers. As a result, the strategic knowledge of an automaker

becomes public knowledge because of its supplier’s opportunistic

behavior. Concerns about this kind of transaction risk may inhibit

supply-chain participants from exchanging knowledge. Hence, the

following hypothesis:

Hypothesis 2. The risk of opportunistic behavior does not facili-

tateknowledgeexchangebehavioramong supply channel partners.

3.2.2. Contingency effect of knowledge complementarity

When competing theories are available, any behavior patterncan alwaysbe substantiated oneway or theother. Does this require

acceptance of onetheory andrefutationof theother? Only repeated

empirical trials with regular and reliable results can support such

an inference. However, it is likely that two different patterns pre-

dicted by two competing theories may both be detected under

different circumstances. In other words, no theory is ever proven

or rejected, and repeated failure to falsify a theory strengthens a

theory. Evidence of falsification should be carefully evaluated to

seek out violations of any assumption(s) behind the theory in the

study and/or contingencies that may explain the effect. This spirit

of enquiry guides the following contingency analysis.

Although a trusting relationship has the potential to positively

influence knowledge exchange, as predicted by Hypothesis 1, the

mere presence of a conducive relationship need not necessarilypromote knowledge exchange. Likewise, even when firms discern

transaction risk in knowledge exchange, they may be willing to

exchange knowledge if the expected benefits from the exchange

significantly exceed the expected costs of transaction risk. The

effect of one or the other of the two conflicting forces may govern

knowledge exchange contingent on other factors. We posit that

knowledge complementarity is such a contingent factor. Supply

chains are often characterized by knowledge complementarity and

our theoretical framework (Fig. 1) proposes that knowledge com-

plementarity in a dyad promotes knowledge exchange behavior.

When knowledgeis exchanged between supply chain partners,the

knowledge must be useful and effectively integrated into the sup-

ply chain. We posit that effective integration requires that at least

some knowledge possessed by the two firms be complementary.

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According to the strategic management literature, complemen-

taritiesrepresentan enhancementof resource valueand arisewhen

a resource produces greater returns in the presence of another

resource than by itself (e.g., Chung, Singh, & Lee, 2000; Milgrom

et al., 1991). Applying the definition of complementarities to

knowledge complementarity, it refers to knowledge resources that

collectively generate greater rents than the sum of those obtained

from the individual knowledge of each partner (Dyer & Singh,

1998). Hamel et al. (1989) suggest that mutual gains are possible

if partners’ individual strengths can complement each other since

each partner in an alliance should be able to access the comple-

mentary capabilitiesof itspartner.For instance,in thecontext of an

inter-organizational relationship in the EMS industry, intermediate

producers have expert manufacturing knowledge and architec-

tural knowledge (how to coordinate various components for a

product). Meanwhile, suppliers have component-specific knowl-

edge, that is, knowledge about particular components, including

technology, raw materials, and manufacturing process (Takeishi,

2002). Firms participating in a supply chain collectively develop

situation-specific knowledge by creating new combinations of

complementary knowledge. For example, intermediate producers

may devise new ways of manufacturing components by consid-

ering the strengths and weaknesses of raw materials used by

suppliers. Productivity gains in the supply chain (e.g., time to

market, responsiveness to changing environments, overall product

quality) are possible when participating firmsadjust theiractivities

according to partnership-specific knowledge.

Since supply chain partners can create more synergistic knowl-

edge by combining theircomplementaryknowledge, the likelihood

of voluntary knowledge exchange will increase. Malhotra et al.

(2005) find that the value of a partner’s knowledge affects knowl-

edge exchange in supply chain relationships. Other studies (e.g.,

Gupta & Govindarajan, 2000) likewise assert that the perceived

value of a partner’s knowledge is an important antecedent to

inter-firm knowledge exchange. Hence, we propose the following

hypothesis:

Hypothesis3. Knowledge complementarity positively influencesknowledge exchange behavior among supply channel partners.

Knowledge complementarity refers to knowledge stocks that

have the capacity to collectively generate synergistic value result-

ing from the interactions among complementary knowledge

components (Kim, Shin, & Lee, 2010). The synergistic value is fea-

sible only when both the partners’ complementary resources are

present and accessible. Hamel et al.(1989) assert that mutualgains

are possible if partners can complement (i.e., compensate for) each

other’s weaknesses since each partner in an alliance can access the

complementary capabilities of its partner. Thus, the potential gains

from knowledgecomplementarity will be realized when the acces-

sibility of partner’s knowledge is allowed through the cooperative

relationship engendered by mutual trust. Since high knowledge

complementarity between supply-chain partners offers the poten-tial for significant mutual gains through knowledge exchange,

mutual trust which fosters exchange presents a win–win situa-

tion when knowledge complementarity is high. On the other hand,

opportunistic behavior by either party, when knowledge comple-

mentarity is high, is detrimental to both parties since the partners

run the risk of jeopardizing a difficult-to-replace relationship (a

loose–loose situation) which often outweighs possible short-term

gains of opportunism. Thus, under high knowledge complemen-

tarity partners’ self-interests guide them toward mutual trust

enjoined by the social capital perspective rather than opportunistic

behavior suggested by the transaction risk perspective. In addi-

tion,opportunities existfor high-trusting relationships to integrate

complementaryknowledgecreatively so as to increase valuefor the

supply chain partners. On the contrary, when the knowledge being

exchanged is not complementary and thus not expected to cre-

ate synergistic effects, supply chain partners do not see any value

of knowledge exchange, even in relationships with a high level of

trust. Therefore, we hypothesize:

Hypothesis 4. The interaction between knowledge comple-

mentarity and inter-organizational trust influences knowledge

exchange behavior in the supply channel.

Hypothesis 4a. When knowledge complementarity in the sup-ply channel is high, increase in inter-organizational trust positively

influences knowledge exchange behavior among supply channel

partners.

Hypothesis 4b. When knowledge complementarity in the sup-

ply channel is low, increase in inter-organizational trust does not

influence knowledge exchange behavior among supply channel

partners.

When the knowledge possessed by supply chain partners is not

quite complementary, the collective good of knowledge exchange

becomes insignificant, no matter the level of trust. The only possi-

ble motivation for knowledge exchange in this case is to selectively

aggrandize the partner’s knowledge for one’s own good rather

than the collective good of the supply chain. Consequently, whencomplementarity in the supply channel is low and therefore not

expected to create synergistic effects, supply chain partners do not

see any value in knowledge exchange, and concerns about transac-

tion risk dominate over the social capital perspective. The effect of

concerns about transaction risk on knowledge exchange intensifies,

as the knowledge to be exchanged becomes less complementary.

On the contrary, when knowledge complementarity is high,

opportunistic behavior by either party is detrimental to bothparties

since the partners run the risk of jeopardizing a difficult-to-replace

relationship (a loose–loose situation), which often outweighs

the possible short-term gains of opportunism. Basis resource-

based view (RBV) and TCE, Conner and Prahalad (1996) assert

that strategic alliances provide the “more valuable, opportunism-

independent knowledge” (p. 489). The opportunism-independent

knowledge encompasses both knowledge substitution and flexibil-

ity effects. When knowledge complementarity is high, the benefit

of opportunism-independent knowledge that an alliance provides

through knowledge exchange ought to outweigh short-term gains

of opportunism. In other words, gains from opportunistic behavior

may be too trivial to outweigh the loss of valuable, opportunism-

independent knowledge. This supports the following hypothesis:

Hypothesis 5. The interaction between knowledge complemen-

tarity and the risk of opportunism influences knowledge exchange

behavior in the supply channel.

Hypothesis 5a. When knowledge complementarity among sup-

ply channel partners is low, increase in the risk of opportunistic

behavior negatively influences knowledge exchange behavior

among supply channel partners.

Hypothesis 5b. When knowledge complementarity among sup-

ply chain partners is high, increase in the risk of opportunistic

behavior does not influence knowledge exchange behavior among

supply channel partners.

3.2.3. Control variable: inter-organizational IT infrastructure

When organizations are connected through electronic net-

works, inter-organizational IT infrastructure can facilitate knowl-

edge exchange. Inter-organizational IT infrastructure encompasses

the underlying inter-organizational system resources that can be

harnessed to exploit resources held by supply chain partners. In

particular, it refers to IT resources such as database, software, and

networks for an inter-organizational relationship (Weill & Vitale,

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2002). Compatible infrastructure channels help reduce the costs

of knowledge exchange and leverage appropriate resources during

the knowledge exchange process (Colombo & Mosconi, 1995), lead-

ing to more confidence and receptive attitudes toward knowledge

exchange.

Meanwhile, a shortage of system resources has been found to

inhibit the assimilation of incoming knowledge. Zhu (2004) asserts

thatfirms with poor IT infrastructure (e.g.,stand-alone, proprietary

systems) often have difficulties in connecting to their customers,

suppliers, and business partners, causing delays in collecting and

exchanging market information among supply chain participants.

Electronic connectivity creates the potential for developing a

response-basedbusinessmodel (in contrast to an anticipatory busi-

ness model),4 which seeks to reduceforecast reliance through joint

planning and the rapid exchange of knowledge between supply

chain participants. When all members of the supply chain syn-

chronize their operations, they can reduce overall inventory and

eliminatecostly redundancies. Thus,the presence of an appropriate

inter-organizational IT infrastructure influences supply chain per-

formance primarily by facilitating knowledge exchange between

supply chain partners.

The research model is presented in Fig. 2.

4. Method

4.1. Sample

While this study involves the dyadic exchange relationships of

Electronic Manufacturing Services (EMS) companies and their sup-

pliers, we examine the phenomenon from the buyer’s perspective.

The data required for this study were collected from managers and

buyers responsible for supplier relationships in intermediate pro-

ducers in the EMS industry. The sample frame consisted of the 550

companies that participated in a national electronics show. Among

these firms, we asked 400 EMS manufacturers if they were inter-

ested in participating in ourresearch. If thefirms agreed, they were

asked to provide contact information about the firm’s purchasing

managers so thatwe couldcollectdata about the research variables.

The purchasing managers were asked to respond to the instrument

in the context of an ongoing relationship through which an impor-

tantcomponent for theirproduction process was beingsourced.For

the questions about which the purchasing managers did not have

enough knowledge, they were encouraged to contact appropriate

experts inside the firm such as manufacturing, R&D, and informa-

tion technology. Two follow-up emails were sent five and ten days

after the initial contact. Participants emailed completed question-

naires to the researchers. Eighty-five responses (21.3% response

rate) were received within 14 days. Among those, three were elim-

inated because they were incomplete, resulting in a final sample

of 82 EMS firms. With the exception of a few large corporations,

most firms were small to medium. The annual sales of the sample

firmsfor the year 2007wereas follows: 76.8% made less than U$10million, 19.5% made between U$10 million and U$100 million, and

3.7% made over U$100 million.

To evaluate any systematic differences for non-responses,

ANOVAs were performed on all independent variables between

early responses and late responses, i.e., after the follow-up emails.

No statistically significant differences occurred between the two

groups at the 0.05 level of significance.

4 In the response-based business model, the sequence of events is initiated by

a sale followed by material purchase, custom manufacturing, and direct customer

delivery. By contrast, the typical stages of the anticipatory business model include

forecasting, purchasing materials, manufacturing, filling warehouses, selling, and

then delivering goods.

4.2. Measures

We adapted most of the survey items (see Table 2) from preex-

isting scales in the literature. Main adaptations were made in the

wording of the measurement items in order to reflect the supply

chain research context. Though instruments to measure the key

constructs in our hypotheses are available, a few measures require

either modification or development.

Knowledge exchange is the process through which one supply

chain member is affected by the experience of another (Argote &

Ingram, 2000). For this construct, we measure the extent to which

the supply chain partners exchange knowledge in engineering,

production, raw materials, and new product development. These

measures were constructed by modifying instruments developed

by Kotabe, Martin, and Domoto (2003) and Pham (2006).

Inter-organizational trust was measured on the basis of two

dimensions, proposed by Bensaou and Venkatraman (1995): (1)the

degree of mutual trust between the two firms; and (2) the degree

of comfort in sharing sensitive information with the supplier. For

opportunistic behavior, respondents were asked to describe the

potentialof theirpartner’s behaving opportunistically.Opportunis-

tic behavior was defined to include distorting information, failing

to fulfill promises or obligations, appropriation of the partner firm’s

technology, and delivering substandard products (Parkhe, 1993).

Knowledge complementarity refers to the extent to which the

knowledge stocks of supply chain partners collectively generate

greater rents than the sum of those obtained from the individ-

ual knowledge stock of each partner. In an upstream supply chain,

buyer–supplier relationships involve ongoing mutual adjustment

between the buyer’s and the supplier’sdesign and production oper-

ations (Kotabe et al., 2003). Takeishi (2002) asserts that buyers in

general have expert knowledge in manufacturing such as a higher

level of architectural knowledge (how to coordinate various com-

ponents for a product), while suppliers have component-specific

knowledge. Specifically, the buyer’s knowledge comprises three

different areas – production, product design, and component rela-

tionships – in its products. The supplier’s knowledge also covers

three domains, namely, raw material characteristics, the technicalstrengths of its products, and technical constraints of its products.

The authors developed an instrument of nine items to measure

knowledge complementarity between the three dimensions of

buyer’s manufacturing knowledgeand thethree dimensionsof sup-

plier’s component knowledge.

InterorganizationalIT infrastructurerefers to shared technology

and technologyservicesacross supply chainpartners. Followingthe

work of Weill and Vitale (2002), we adapted six items: hardware

compatibility, data consistency, common application interface,

network connectivity, datasecurity, andthe interpretability of elec-

tronically transferred data.

4.3. Survey administration

Preliminary testing of the research instrument involved struc-

tured interviews. Pretests of theinstrument were performedin two

companies, and three focus group interviews were conducted with

respondents to ensure that the target informants understood the

researchers’ wording. Multiple-structured interviews were con-

ducted with the managers of purchasing divisions to test the face

validity of the instrument. The respondents were asked to indicate

their responses to questions on a seven-point scale with bipolar

adjectives of the form, Not at all – Very much so. On the basis of

responses to these interviews, a few questions were rephrased to

better reflect industry-specific situations and to improve clarity.

The twocompanies that participatedin the pretests of the research

instruments were not a part of the final sample.

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Table 2

Operationalization of the constructs.

Construct Assessment References

Knowledge exchange The extent to which an organization exchanges knowledge

with its supply chain partners

Pham (2006) and Kotabe et al. (2003)

Inter-organizational trust Degree of mutual trust between the two firms and degree of

comfort in sharing sensitive information with the supplier

Bensaou and Venkatraman (1995)

Risk of o pportunistic b ehavior The p otential f or t he p artner’s b ehaving o pportunistically Parkhe (1993)

Knowledge complementarity The extent to which knowledge stocks of supply chain

partners collectively generate greater rents than the sum of those obtained from theindividual knowledgeof each partner

Developed by the authors

Inter-organizational ITInfrastructure Shared technology and technologyservices across the supply

chain partners

Weill & Vitale(2002)

5. Results

5.1. Measurement properties of constructs

Since we made minor wording changes to theoriginalquestions

to customize the instrument to a supply chain setting, principal

component analysis was performed on the data to ascertain the

integrity of each dimensionality. Table 3 provides standardized

parameter estimates (factor loadings) of the items for the underly-

ing dimensions. We used a conservative cut-off value of .60 for the

factor loadings, in contrast to the often cited rule of thumb for the

cut off value for factorloadings, which is .50 (Nunnally & Bernstein,

1994).

Five factors with eigen-values greater than 1, which accounted

for 70.63% of the variance in the data set, were extracted. All items

were loaded together on the intended factors. One item (KE1) was

discarded from subsequent analyses since it was loaded on two

factors and its loading was lower than the cut-off value of 0.6.

The pattern of observed loadings indicates that the multi-item

scales measure independent constructs, thereby further support-

ing the unidimensionality and discriminant validity of the scales.

Each multi-item measure was obtained by computing the average

score provided by the respondent across the relevant items. We

assessed multicollinearity among the variables by using variance

inflation factor (VIF) values from the SPSS regression module. The

results show that the VIF scores for constructs were well below the

threshold of 10, indicating that multicollinearity was not a prob-

lem in this study. Descriptive statistics (e.g., the number of scale

items in each measure, the mean, the standard deviation, the range

of values, and reliability coefficients) and the simple correlations

among the research variables appear in Table 4.

5.2. Hypotheses testing

A multivariate general linear model using SPSS 15.0 was con-

ducted to assess the hypotheses. If the theoretical interpretation

suggests that knowledgeexchange is most likely when twofactors,

such as trust and high knowledge complementarity, are present(or, knowledgeexchangeis least likelywhen both therisk of oppor-

tunism and low knowledge complementarityare present), then the

functional form of the interaction is multiplicative (Blalock, 1965).

Since a product term is a legitimate way to express an interaction

in the multiple regression procedure, we viewed our problem as a

multiple regression model, as presented in Table 5, using Type 3

analysis.5

The research hypotheses were tested by examining the size and

significance of the model coefficients. Table 5 shows the unstan-

dardized coefficients and model statistics for the analyses. While

5 Type 3 analysiscomputes thesum ofsquaresfor each effectafter partiallingout

the effects dueto allother variables in theregression model.

standardized coefficients (beta coefficients) provide an estimate

of the effect of a variable relative to others in the model, such an

estimate is not accurate for interaction terms and is therefore not

interpretable (Frazier, Tix, & Barron, 2004). Consequently, we have

used unstandardized coefficients to interpret the results.

The results show that inter-organizational trust significantly

influences knowledge exchange between supply chain partners,

thus supporting H1 (t = 1.961, p=0.054). Knowledge comple-

mentarity also has a significant direct effect on knowledge

exchange (H3, t = 2.647, p= 0.010). Further, the interaction between

knowledge complementarity and inter-organizational trust (H4,

t =−2.914, p= 0 .031) turns out to be significant. Also, the risk

of opportunism does not facilitate knowledge exchange behav-

ior between supply chain partners (H2, t = 0.519, p= 0.605). The

interaction between the risk of opportunism and knowledge com-

plementarity (H5, t =−0.461, p=0.646) is not significant either.

Inter-organizationalIT infrastructure does not show any significant

associations with knowledge exchange.

In order to gain deeper insight into the contingency hypothe-

ses (interaction effects), H4 and H5, sub-sample analyses were

performed.6 The sub-hypotheses of H4 and H5 essentially indicate

that when knowledge complementarity is high, the social capi-

tal perspective will prevail over transaction risk concerns. When

knowledge complementarityis low, we expectthe opposite.To test

these drill-down contingency hypotheses, we divided our sample

into two as follows. Before the sample split, in order to check the

normality assumption of KC, the Shapiro–Wilk test was performed

to find that the data came from a normally distributed population.

Then, sample firms within one standard deviation (1.026) around

the mean value (4.757) of knowledge complementarity (KC) were

dropped because they are neutral in terms of KC. Firms with KC

values higher than one half standard deviation (0.513) from the

mean were classified as the high KC group (24 firms, mean= 5.92,

SD = 0.603), whereas firms with KC values lower than onehalf stan-

dard deviation from the mean were classified as the low KC group

(25 firms, mean= 3.53, SD=0.528). One way ANOVA test reveals

that these two groups were significantly different in terms of KC

(t = 12.947, p= 0.000).Table6 presentstheresultsofthesplitsample

analysis.The results show that for the group with low knowledge

complementarity, the risk of opportunism shows a significant neg-

ative relationship with knowledge exchange, thus supporting H5a

(t =−2.529, p=0.020), whereas the impact of trust on knowledge

exchange (H4b, t = 0.410 p= 0.686) is, as expected, not significant.

6 Given that the hypothesized interaction (H5), (knowledge complementarity x

risk of opportunism), is notstatistically significant in themain model (see Table5),

one may question the appropriateness of sub-sample analysis in evaluating this

interaction. However, since the two sub-sample analyses are necessary to eval-

uate the other significant interaction between knowledge complementarity and

inter-organizational trust, we carried out sub-sample analyses. Nevertheless, inter-

pretations of H5a and H5b in thesub-sampleanalyses aretentative.

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Table 3

Factor analysis results.

Variables Factors

1 2 3 4 5

Opportunism (Oppor)

Oppor1 .174 −.139 .123 .788 .043

Oppor2 −.260 .188 .000 .700 .100

Oppor3 −.070 .111 −.109 .828 .043

Oppor4 −.085 −.292 −.070 .748 −.129

Inter-organizational trust

Trust1 −.030 −.045 −.117 .067 .838

Trust2 .062 −.086 .005 −.013 .849

IT infrastructure (Infra)

Infra1 .111 .067 .838 −.129 −.029

Infra2 .178 .029 .853 −.078 −.090

Infra3 −.019 .048 .837 .005 −.133

Infra4 .101 −.046 .677 .105 .003

Infra5 .226 .122 .709 −.059 .227

Infra6 .132 .067 .730 .165 −.111

Knowledge complementarity (KC)

KC1 .822 .045 .104 −.058 −.246

KC2 .869 .180 .124 −.069 −.160

KC3 .872 .087 .080 .037 −.175

KC4 .883 .186 .058 .044 .160

KC5 .863 .303 .099 .079 .102

KC6 .851 .270 .100 −.022 −.024

KC7 .827 .250 .124 −.174 .212KC8 .871 .167 .135 −.141 .150

KC9 .882 .141 .105 −.086 .096

Knowledge exchange (KE)

KE1 .421 .528 .051 .016 −.034

KE2 .309 .773 −.050 −.045 .039

KE3 .155 .805 −.027 −.159 −.061

KE4 .186 .837 .013 −.045 −.099

KE5 .009 .671 .241 −.054 −.079

KE6 .115 .731 .330 .041 −.095

KE7 .301 .651 −.063 .180 −.002

KE8 .288 .665 .072 .019 .131

Variance explained (%) 32.846 12.191 10.707 8.758 6.129

Extraction method: principal components analysis.

Rotation method: Varimax with Kaiser normalization.

Table 4Descriptive statistics of the research variables.

No. of items Mean (SD) Min Max Cronbach’s Alpha Oppor Infra Trust KC

Opportunism (Oppor) 4 3.234 (1.136) 1.00 6.25 0.775 1

IT Infrastructure (Infra) 6 4.130 (1.256) 1.00 6.83 0.881 −0.013 1

Inter-organizational trust (Trust) 2 4.955 (1.228) 1.50 7.00 0.707 0.052 −0.086 1

Knowledge Complementarity (KC) 9 4.757 (1.026) 2.33 7.00 0.966 −0.036 0.271** 0.015 1

Knowledge Exchange (KE) 8 4.514 (0.949) 2.13 6.50 0.892 −0.085 0.178 −0.095 0.485***

N =82.** p

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Table 6

Results of regression analyses (the dependent variable is knowledge exchange).

Model 1: high complementary group+ Model 2: low complementary group++

Variables Standardized coefficient t P Standardized coefficient T p

Opportunism (Oppor) 0.020 0.103 0.919 −0.489 −2.529 0.020

Trust −0.401 −2.702 0.014 0.079 0.410 0.686

IT Infrastructure (Infra) 0.164 1.072 0.296 −0.101 −0.538 0.596

R2 0.368 0.289

Adj. R2 0.274 0.188

F 3.890( p= 0.024) 2.850 ( p= 0.062)

+ N = 24++ N =25.

summary of the findings for the hypothesized effects appears in

Table 7.

Finally, we examined the potential for our results to be

explained by common method variance. Two types of statistical

analyses were conducted to assess the threat of common meth-

ods bias: (a) Harman’s one-factor test (Podsakoff, MacKenzie, Lee,

& Podsakoff, 2003), and (b) Lindell and Whitney’s (2001) marker

variable test. First, in Harman’s one factor test, the emergence of

a single factor that accounts for a large proportion of the vari-

ance in factoranalysissuggests a commonmethods bias (Podsakoff

et al., 2003). No such single factor emerged and the five factorsin Table 3 accounted for 32.8%, 12.2%, 10.7%, 8.7%, and 6.1% of

the variance, respectively for the total 70.6% variance. Second, the

Lindell–Whitney (2001) marker variable test uses a theoretically

unrelated (marker ) variable to adjust the correlations among the

model’s principal constructs. Because a marker variable does not

have a theoretically expected relationship with the study’s princi-

palconstructs,a high correlation would indicate common methods

bias (Malhotra et al., 2005). For robustness, the test was performed

with one otherwise unused variable for which there exists little

theoretical basis for a relationship (supplier asset specificity). The

average correlation of the study’s principalconstructs withsupplier

asset specificity (r = 0.101, t =0.397) was low and not significant,

providing no evidence of common methods bias.

6. Discussion and conclusions

6.1. Discussion

Our study focuses on the crucial yet long overlooked question

of why partners sometimes hesitate to share knowledge despite the

apparentbenefits fromknowledgesharing . This ledus to a concurrent

assessment of opposing viewpoints – one based on the social cap-

ital theory and the other informed by transaction risk perspective.

Since no theory is ever proven or refuted, exploration of contingen-

cies under which each theory is relevant is a valuable theoretical

contribution. Our examination of the literature and related theo-

ries (e.g., Chung et al., 2000; Hamel et al., 1989; Kim et al., 2010;

Milgrom et al., 1991; Tanriverdi & Venkatraman, 2005) suggests

that knowledge complementarity is an important factor by which

to understand the challenges of knowledge exchange in an inter-

organizational relationship.

Ratification of H1 (trust promotes knowledge exchange behav-

ior) and H2 (opportunism impedes knowledge exchange behavior)

in a supply channel context essentially contributes to the rein-forcement of both the social capital theory and the TCE perspective

respectively. That is, trusting partners in a supply chain are pre-

disposed to greater openness to the potential for value creation

through the exchange and combination of knowledge resources,

while, in the presence of inter-organizational trust, risk of oppor-

tunism is irrelevant to knowledge exchange behavior.

An implication of greater significance though is the ratification

or refutation of any contingency that clarifies the conditions under

which the opposing theories become relevant. Since the role of

knowledge complementarityin an inter-organizationalcontexthas

been theorized (Milgrom et al., 1991; Tanriverdi & Venkatraman,

2005) and empirically tested (Chung et al., 2000; Hamel et al., 1989;

Kim et al., 2010), examination of knowledge complementarity as a

possible contingency capable of explaining the opposing concep-tualizations of social capital theory and TCE perspective is in itself

a contribution to academic research. Ratification of H3 (knowledge

complementarity promotes knowledge exchange behavior) bol-

stersour thesisthat mutual gainsare possible if partners’individual

strengths can complement each other. Further, it reinforces prior

research findings in yet another context – i.e., a supply channel.

Next, our contingency analysis reveals two findings hitherto

not reported by prior research. First, notwithstanding our broad

finding that opportunism impedes knowledge exchange behavior

Table 7

Results of hypotheses testing.

Hypothesis Support p-ValueYes No

H1 Inter-organizational trust→Knowledge exchange * 0.054

H2 Risk of opportunism does not facilitate knowledge

exchange

* 0.605

H3 Knowledge complementarity→knowledge exchange * 0.010

H4 Interaction between knowledge complementarity and

inter-organizational trust→knowledge exchange

* 0.031

H4a Under high KC, inter-organizational trust→Knowledge

exchange

* 0.014 (opposite effect)

H4b Under low KC, inter-organizational trust has no influence

on knowledge exchange

* 0.686

H5 Interaction between knowledge complementarity and risk

of opportunism→knowledge exchange

* 0.646

H5a Under lowKC, risk of opportunism→knowledge exchange * 0.020

H5b Under high KC, risk o f o pp or tu nism has no influe nce o n

knowledge exchange

* 0.919

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(H1), when knowledge complementarity is low, a stronger effect

of opportunism on knowledge exchange behavior manifests – a

statistically significant negative coefficient, implying reduction in

knowledge exchange behavior as opposed to the irrelevance of

opportunism to knowledge exchange behavior when knowledge

complementarity is high (statistically insignificant positive coeffi-

cient).

The contingencyeffect of knowledge complementarityon inter-

organizational trust (social capital) is thought provoking. When

knowledge complementarity among supply chain partners is high,

our result shows that the social capital perspective does indeed

best explain firms’ behavior. However, this effect is contrary to

the predicted positive direction (H4a). In other words, in situa-

tions of high knowledge complementarity, our data indicate that

inter-organizational trust retards knowledge exchange. This coun-

terintuitive finding challenges the conventional wisdom that a

trusting relationship among supply chain partners is a precondi-

tion for knowledge exchange and opens the doors for investigating

second order contingencies under which, in a situation of high

knowledge complementarity, inter-organizational trust may be

irrelevant to knowledge exchange behavior or even retard knowl-

edge exchange behavior. The first step, however, in the post hoc

analysis of this unexpected finding is to examine carefully the

assumptions and controls underlying this study. Such an exami-

nation follows below.

Our first suspicion was that while the measure (instrument)

for knowledge complementarity strictly follows the definition

based on the resource-based view (Dyer & Singh, 1998), extrane-

ous factors may have influenced the measurement (e.g., subjects’

responses). For instance, Kumar, Scheer, and Steenkamp (1995)

propose that interdependence or mutual dependence in a dyadic

relationship can enhance trust and thereby performance, and

dependence asymmetry or the imbalance between the partners’

dependence as a dysfunctional force that can destabilize trusting

relationships by creating conflicts.Our study hasnot explicitly con-

trolled for knowledge interdependence/dependence asymmetry,

and it is conceivable that our subjects’ response to the mea-

sure of knowledge complementarity may have been influencedby thoughts about knowledge interdependence/dependenceasym-

metry. If knowledge interdependenceamong supply chainpartners

is high, the stakes are high, and inter-organizational trust alone

may be insufficientto promoteknowledgeexchange.Perhapswhen

trust is bolstered by behaviors like mutual commitment among

supplychain partners it may promote knowledge exchange. On the

contrary, excessive trust with insufficient commitment may also

trigger the limiting of knowledge exchange as a precaution. This

condition may be exacerbated if dependence asymmetry among

supply chain partners plagues knowledge complementarity, that

is, if one party is more dependent on another party in the sup-

ply chain for complementary knowledge. Accordingly, given that

knowledge complementarity promotes knowledge exchange (H3)

and inter-organizationaltrust promotes knowledge exchange (H1),the only way the interaction among two partners can negatively

impact knowledge exchange is when such a negative dimension

(perhaps the knowledge interdependence/dependence asymme-

try component) exists and manifests in the interaction term. In

the Type 3 analysis reported in Tables 5 and 6, the interaction

term represents only the effect not captured by the independent

effects of the two variables. In other words, in hindsight, we are

inclined to conclude that after the positive effects of knowledge

complementarity and inter-organizational trust are partialled out

through the main effects (H1 and H3), the negative effect triggered

by perhaps knowledge interdependence/dependence asymmetry

and trust will manifest in the interaction effect (H4a). To verify

this inference, we examined a restricted regression model with-

out the main effect terms of knowledge complementarity and

inter-organizational trust [Full model: KE= f (Trust, KC, Trust * KC);

Restricted model: KE= f (Trust*KC)] and, as expected, the inter-

action term of these two variables yielded a significant positive

effect (t =1.75, p= .043)–that is, the relatively mild negative com-

ponent of the interaction is obscured by the dominant positive

effect resulting from the additive independent effects of knowl-

edge complementarity and inter-organizational trust. Clearly,

these post hoc explanations are speculative and can be confirmed

or refuted only by empirical testing –a springboard for future

research.

An alternative explanation of this counterintuitive finding is

predicated on challenging the conventional wisdom that inter-

organizational trust is always conducive to knowledge exchange-

even in an environment of high knowledge complementarity. For

instance, the moderating effect captured by the interaction term

(knowledge complementarity× inter-organizational trust) is sus-

ceptible to misperception as a ‘complementing’ effect (H4a) since

that is thepopularstancein priorwork (Siggelkow,2002). Two vari-

ablesare saidto interact as complements when themarginalbenefit

of each variable increases in the level of the other variable. How-

ever, there is another type of moderation, i.e., substitution. When

two variables interact as substitutes, the marginal benefit of each

variable decreases in the level of the other variable (Siggelkow,

2002). Given a positive baseline effect of organizational trust as

well as knowledge complementarity on knowledge exchange (H1

and H3), a substitution effect where the marginal benefit of trust

decreases when knowledgecomplementarity is highought to entail

an insignificantpositive ora significant negative interaction (T * KC)

effect as seen in H4a. In other words, our empirical finding is

amenable to an interpretation as a substitution effect since the

main effects of both inter-organizational trust (H1) and knowl-

edgecomplementarity (H3) are significantly positive,whereas their

interaction is significantly negative. The theoretical rationale for

this position again relies on the role of knowledge interdepen-

dence and dependence asymmetry in this mix (Palmatier, Dant,

& Grewal, 2007). First, knowledge complementarity is possible

without any knowledge interdependence. However, knowledge

interdependence is impossible without the existence of comple-menting knowledge between the supply channel partners. In other

words, knowledge interdependence is essentially a sub-construct

of the multi-dimensional construct of knowledge complementarity

– i.e., knowledge complementarity subsumes knowledge interde-

pendence (Palmatieret al.,2007). In a supplychain relationship, the

presence of interdependence in design, production and technical

knowledge may preclude the need for inter-organizational trust;

in other words, the partners may have no choice but to exchange

knowledge because of knowledge interdependency. By not sharing

knowledge they risk failure, a risk, perhaps higher than trusting an

untrustworthy or potentially opportunistic partner in the supply

chain. They may be able to curb opportunistic behavior by institut-

ing governance mechanisms (Williamson, 1991), though they may

not succeed without access to complementary knowledge. Orga-nizational trust therefore need not always be a precondition for

knowledge exchange, and, under certain conditions (e.g., substi-

tution by knowledge complementarity), may even be irrelevant

to knowledge exchange (H4). For instance, simply the absence of

mistrust may be sufficient when there are other exogenous fac-

tors motivating knowledge exchange (Bakker, Leenders, Gabbay,

Kratzer, & Engelen, 2006) – e.g., knowledge complementarity or

its subconstructs, knowledge interdependence and/or dependence

asymmetry. In fact, compelling reason to work together can occur

even in mistrusting relationships (Ahlstrom, Lamond, & Ding,

2009).

Another avenue to explore for clarifying the counterintuitive

finding of H4a is based on the process and content dimensions

of knowledge (Hass & Hansen, 2007). Process refers to the efforts

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involved in adapting knowledge obtained for a task; while, con-

tent refers to the quality of knowledge – i.e., the substantiveness

dimension. It is conceivable that even in situations with high trust

and high knowledge complementarity, the supply channel partners

may not share“substantive”knowledgeif, for instance, high depen-

dence asymmetry punctuates the relationship; a power imbalance

implicit in dependency asymmetry may be responsible for this

behavior pattern in the dyad. In other words, one or both part-

ners may simply go through the motions of knowledge exchange

by sharing knowledge without much value.7

Interorganizational IT infrastructure exhibits no significant

impact on knowledge exchange behavior. A plausible explana-

tion for this result would be that the nature of knowledge to

be exchanged in this research context is either inappropriate for

exchange through the IT infrastructure or actually impossible to

be exchanged through this medium. Knowledge can be classi-

fied into two types: (1) explicit knowledge (i.e., know-what) and

(2) tacit knowledge (i.e., know-how) (Hildreth & Kimble, 2002).

Explicit knowledge refers to easily codifiable knowledge that can

be transmitted “without loss of integrity once the syntactical rules

requiredfor deciphering itare known”(Kogut& Zander, 1992: 386).

Meanwhile, know-how refers to knowledge that is tacit, situation

specific, and difficult to codify (Dyer & Singh, 1998). Since know-

how is tacit and difficult to codify, it is difficult to transfer through

inter-organizational IT infrastructure. Complementary knowledge

that is exchanged between supply chain partners is often tacit, sit-

uation specific, and difficult to codify. For example, they exchange

professional and expert knowledge about raw materials or manu-

facturing processes, ideas about new product development, and a

trial experimentation of new knowledge when they develop a new

market or new product. These types of knowledge often include

non-codifiable, tacit components and thus, may not be appropriate

to exchange through interorganizational IT infrastructure.

Theunexpected findings in this studynot only open up opportu-

nities for future research, butalso serveas a precautionaryguidance

for other researchers trying to study knowledge complementarity.

From a managerial perspective, one approach to partnership

management (e.g., McCarter & Northcraft, 2007) holds that man-aging the inherent tension between cooperation and competition

in inter-firm relationships is essentially a social dilemma. To real-

ize the potential for joint value creation, partners must exchange

knowledge and make investments that are specific to the rela-

tionship (Zeng & Chen, 2003). However, fulfilling these basic

requirements of alliances renders the valuable investments and

proprietary knowledge of partners vulnerable to opportunistic

behavior because each partner may be tempted to use this knowl-

edge to pursue itsown interests. Recognizing this inherent tension,

alliance researchers have disagreed as to whether partners should

pursue “stormy open marriages” (Roehl & Truitt, 1987), or “cooper-

ativespecialization” (Zeng & Hennart, 2002). Ourfocusin this paper

has been the exploration of the contingencies under which behav-

ior patterns triggered by either confident knowledge exchange(the social capital perspective) or cautious knowledge exchange

(transaction risk perspective) governs knowledge exchange activ-

ity among supply chain partners. In this regard, the managerial

prescriptions that emerge from our findings lean toward coop-

erative specialization – i.e., confident knowledge exchange in a

trusting relationship because opportunism is subdued to insignif-

icant levels in the presence of inter-organizational trust (social

capital). The need for cautious knowledge exchange behavior is

called for when the knowledge complementarity among partners

in the supply channel is rather low since this condition tends

7

We thank the anonymous reviewer for this specific suggestion.

to trigger opportunistic behavior patterns. An interesting finding

of significant value to supply chain managers is that even when

knowledge complementarity is high one cannot take for granted

that trusting relationships is always a necessary condition for

promoting knowledge exchange behavior. Compelling reasons to

work together can occur even in mistrusting relationships. Like-

wise, sometimes, simply the absence of mistrust may be sufficient

when thereare exogenousfactors likeknowledgecomplementarity

motivating knowledge exchange behavior. Finally, even in a high-

trust relationship knowledge exchange behavior may sometimes

be dampened or even retarded subject to the influence of other

exogenouscontingencies – one oughtto watchfor knowledgeinter-

dependenceand/or dependenceasymmetriesin the supply channel

relationships.

6.2. Future research

The principal impetus for our future research is the set of unex-

pected findings reported in this paper. An essential condition for

knowledgeexchange is the presence of complementaryknowledge,

which cannot be developed internally in either a timely or a cost-

effective manner (Park & Ungson, 2001). The more complementary

knowledgesupplychainpartnershave,the less effortis required for

the source firm to develop relevant knowledge, as a consequence

of which more synergistic effects can be realized. For this reason, it

is important to think about the proportion of the partner’s knowl-

edge that is synergy-sensitive to the focal firm’s knowledge. An

important question for the focal firm then is how to evaluate the

degreeof complementarityof the partner firm’s specializedknowl-

edge. Our scrutiny of the understudied construct of knowledge

complementarity sheds light on its richness. Our post hoc search

through the literature led us to a dimensionality of knowledge

complementarity emerging from the dual constructs of knowledge

interdependence and dependence asymmetry. Interestingly, most

research to date accepts as a premise that interdependence, in

general, positively affects exchange performance in a supply chan-

nel context because dependence increases both partners’ desire to

maintain the relationship and the level of adaptation they willfullyundertake (Palmatier et al., 2007). This premise in the knowledge

exchange scenario, however, has not yet been validated. Likewise,

dependence asymmetry – that is, the imbalance between partners’

dependence – has been found to negatively impact exchange per-

formance. Dependence is the need to maintain a relationship to

achieve planned goals, and both interdependence and dependence

asymmetry are crucial to understanding its impact on exchange in

a dyadic relationship ( Jap & Ganesan, 2000).

Knowledge complementarity can be of two kinds. The first

occurs when the bringing together of two knowledge sources

creates “new” knowledge that neither one of the contributing

knowledge sources could create or sustain by itself – the idea of

the whole is greater than the sum of the parts; the second occurs

when the knowledge sources provide unidirectional or mutualcat-alyticeffects in enhancing thecapability/potency of each other. It is

this lattertype of knowledge complementaritythat maybe vulner-

able to the forces of knowledge interdependence and dependence

asymmetry. At another level, the properties of “knowledge” should

be examined to determine to what extent they are conducive to

complementation. For instance, unlike explicit knowledge, tacit

knowledge may not be quite amenable to sharing via organized

mechanisms such as information technology. Tacitknowledge may

add significant value to supply chain performance and therefore

cannot simply be ignored because it cannot be easily captured by

technology.

Interestingly, both dimensions of dependence, components of

or antecedents to knowledge complementation, are shown to

influence trust. For instance, interdependence, by reducing rela-

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tionship problems and the convergence of interests, seems to

have a positive effect on trust (Palmatier et al., 2007). Con-

versely, trusting relationships tend to increase interdependence

in a dyad because duplicating relational bonds with a different

entity entails additional investments of time, effort and other

tangible and intangible resources (El-Ansary, 1975). Dependence

asymmetry, on the other hand, can undermine trust as part-

ners’ interests diverge, resulting in less willingness to compromise

(Kumar et al., 1995). This behavior pattern, when explored further,

may shed light on the interactive effect of knowledge complemen-

tarity and interorganizational trust on knowledge exchange in our

study.

In addition to the richer dimensionality of knowledge com-

plementarity, this ex post literature reminds us of the interactive

forces binding knowledge complementarity and interorganiza-

tional trust. It also appears that trust and commitment are

mutually integrative constructs and that, in the context of

knowledge exchange, interorganizational trust with and with-

out commitment may play different roles (Morgan & Hunt,

1994) and require further theoretical and empirical investiga-

tion.

6.3. Limitations

The limitations of our study deserve some mention. The data

for this study were collected from 82 firms, all located in the same

country. The interpretation of this study’s results is therefore con-

strained by the particular cultural characteristics of one country.

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