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Journal of Operations Management 32 (2014) 88–98

Contents lists available at ScienceDirect

Journal of Operations Management

jo ur nal ho me pa ge: www.elsev ier .com/ locate / jom

re relational ties always good for knowledge acquisition?uyer–supplier exchanges in China

evin Zheng Zhoua, Qiyuan Zhanga, Shibin Shengb, En Xiec,∗, Yeqing Baod

Faculty of Business and Economics, University of Hong Kong, Pokfulam, Hong KongCollat School of Business, University of Alabama at Birmingham, Birmingham, AL 35205, USASchool of Management, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, ChinaCollege of Business Administration, University of Alabama in Huntsville, Huntsville, AL 35899, USA

r t i c l e i n f o

rticle history:eceived 1 July 2012eceived in revised form8 November 2013ccepted 3 January 2014vailable online 13 January 2014

a b s t r a c t

Relational ties between manufacturers and their suppliers serve as an important strategic resource forvalue creation and realization. However, conflicting evidence exists regarding their role in the acquisitionof specific knowledge. This study proposes that relational ties have a nonlinear effect on specific knowl-edge acquisition and that this nonlinear relationship is conditional on contract specificity and competitiveintensity. Results from a sample of 385 manufacturer–supplier exchanges in China demonstrate that abuyer’s relational ties with its major supplier have an inverted U-shaped effect on specific knowledgeacquisition from this supplier; this inverted U-shaped relationship is stronger (steeper) when contract

eywords:elational viewransaction cost economicselational tiesontract specificityompetitive intensity

specificity is high and competition is more intense. These findings suggest that managers should under-stand the benefits and downsides of relational ties in acquiring specific knowledge and avoid buildinghighly embedded ties when they draft detailed contracts or competition is highly intensive.

© 2014 Elsevier B.V. All rights reserved.

nowledge acquisition

. Introduction

The acquisition, assimilation, and exploitation of heteroge-eous, valuable, knowledge-based resources contribute criticallyo a firm’s competitive advantage and superior performance (Huntnd Davis, 2012; Nonaka, 1994; Tsang, 2002). Research in supplyhain and strategic management indicates that abnormal returnserive from not only resources within a firm but also those out-ide of the firm’s boundaries (Cheung et al., 2010; Cousins andenguc, 2006). Attaining external resources often involves acquir-

ng knowledge from external ties (Capaldo, 2007; Carey et al., 2011).or example, firms embedded in cohesive ties could gain access toomplex, noncodified information (Li et al., 2010a; Perry-Smith andhalley, 2003), whereas loosely connected firms can obtain novelnd nonredundant information from exchange parties (Capaldo,007; Hansen, 1999).

In supply chain management studies, researchers highlight theositive role of relational ties in fostering performance and knowl-dge acquisition (Carey et al., 2011). As Cousins et al. (2006) show,

∗ Corresponding author. Tel.: +86 29 82668536; fax: +86 29 82668700.E-mail addresses: kevinzhou@business.hku.hk (K.Z. Zhou), bessieqy@hku.hk

Q. Zhang), ssheng@uab.edu (S. Sheng), xieen@mail.xjtu.edu.cn (E. Xie),aoy@uah.edu (Y. Bao).

272-6963/$ – see front matter © 2014 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.jom.2014.01.001

increased socialization between the buyer and supplier contributesto the creation of relational capital that leads to deeper interfirmcommunication. Li et al. (2010a) find that manufacturers can gainaccess to tacit, hard-to-imitate knowledge through interactionswith their major suppliers. Carey et al. (2011) further argue thatsocial ties act as conduits for information flows. Through frequent,in-depth interactions with channel members, firms acquire bothobservable and, perhaps more important, tacit components ofknowledge (Yli-Renko et al., 2001). However, recent supply chainmanagement research cautions about the potential dark side ofhighly embedded ties (Lechner et al., 2010; Villena et al., 2011).Lechner et al. (2010) posit that highly connected ties create alock-in trap and harm the performance of strategic initiative unitsby creating pressures to reciprocate with existing partners. Villenaet al. (2011) argue that strong ties also may become a source ofblindness by restricting information flows and increasing the riskof opportunistic exploitation. Thus it remains unclear whetherrelational ties facilitate or inhibit knowledge flows betweenembedded parties.

Moreover, though relational ties offer a critical informal gov-ernance mechanism, extant studies rarely consider how relational

ties, formal mechanisms (e.g., contracts) jointly affect knowledgeacquisition (Carey et al., 2011; Li et al., 2010a). Early studies arguedthat relational ties would offer effective, self-enforcing safeguards,provide access to privileged, difficult-to-copy know-how, such that

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ontracts would become unnecessary (e.g., Dyer and Singh, 1998;zzi, 1997). More recent developments instead posit that by spec-

fying roles, rules, procedures, contracts provide an adaptationramework in which trusted parties can coordinate knowledgeransactions (Li et al., 2010a; Zhou and Xu, 2012). Because firmseploy both formal, informal mechanisms to govern exchanges,ore assessments are needed to understand the joint effect of

elational, formal governance mechanisms in affecting knowledgecquisition.

In addition, the value of relational ties is likely conditionaln the industrial context that surrounds the exchange (Acquaah,007; Ketokivi and Schroeder, 2004). At the industry level, businessecisions and outcomes depend critically on the degree of com-etitive intensity (Porter, 1985). When competition is high, manyivals fight for limited resources, which lead to resource instabil-ty and sparseness (Ang, 2008). Facing a high level of competition,rms also may find it difficult to attract quality partners (Li et al.,008), so they use collaborative ties as buffers against competi-ive forces and pathways to much needed technologies and skillsWu and Pangarkar, 2010). In this case, competitive intensity mayomplicate the relationship between relational ties and knowledgecquisition.

To address these research gaps, we build on a relational viewDyer and Singh, 1998) and transaction cost economics (TCE)Williamson, 1985) to examine how a manufacturer’s relationshipith its major supplier affects its acquisition of specific, complex

nowledge. Our study contributes to supply chain and knowledgeanagement literature in several ways. First, previous studies limit

heir attention to the linear effects of relational ties; we investi-ate the nonlinear impact of relational ties on specific knowledgecquisition by considering both the benefits and risks of relationalies. Whereas prior research emphasizes how strong ties bene-t the acquisition of specific, complex knowledge, we argue thatery strong ties may be detrimental for such knowledge acquisi-ion. Second, extending extant literature on formal and informalovernance, we assess the interaction effect between relationalies and formal contracts on knowledge acquisition. Third, becauserm strategies are bound by the surrounding context, we considerhe contingent role of competitive intensity on the relationshipetween relational ties and knowledge acquisition. With thesefforts, we aim to uncover the nonlinear and contingent relation-hips between relational ties and interfirm knowledge acquisition.

. Knowledge acquisition through buyer–supplier ties

Knowledge acquisition refers to the extent to which a firm obtainsnformation resources from its exchange partners (Tsang, 2002).1

everal types of knowledge can be acquired from external ties,amely product, process, and management knowledge (Capon andlazer, 1987). Accumulated management knowledge influences

he organizational design of a firm; product and process knowl-dge determine the firm’s fulfillment of production tasks andperational performance in a supply chain (Germain et al., 2001).

roduct and process knowledge, manifested as the set of skillsnd technologies involved in product manufacturing, is charac-erized by complex, product-specific features (Modi and Marbert,007). Such knowledge is most likely acquired through interfirm

1 We refer to the activity by which a focal manufacturer acquires knowledgefrom” its major supplier, which is different from “acquiring knowledge with”nother party. This latter activity is more evident in the context of strategic alliances,uch that allied companies communicate and develop shared knowledge stocks forroduct co-development (Koka and Prescott, 2002). In our buyer–supplier contexts,he focal manufacturer acquires knowledge from its major supplier to develop itswn products, which makes “acquired from” activity more relevant.

Management 32 (2014) 88–98 89

ties and connections and almost impossible to acquire throughmarket exchanges (Nonaka, 1994). Accordingly, we focus on howrelational ties affect the acquisition of specific, complex productand process knowledge, instead of novel knowledge.

By integrating external know-how into their own knowledgestructure, firms improve their capability to develop and man-ufacture their own products (Hunt and Davis, 2012; Yli-Renkoet al., 2001). For example, apparel manufacturers connected withdifferent fabric suppliers can better design and produce garments ifthey grasp the material contents of each fabric and acquire the tacitskills to work with these fabrics (Uzzi, 1997). In the IT industry,hardware companies interact frequently with their software sup-pliers to understand complex software codes and design matchinghardware products.2 In this regard, acquiring external product-related knowledge and technologies from supply chain partnersreinforces the focal firm’s core competencies for manufacturingits current products and also stimulates the formation of specificskills for developing future competencies (Paiva et al., 2008).

Yet the amount of knowledge a firm can acquire from strate-gic partners depends on their willingness to share informationand know-how (Yli-Renko et al., 2001). Because firms possessunique resources to support their own competitive advantage, theyremain always sensitive and reserved in their knowledge shar-ing with external partners (Kale et al., 2000). Therefore, firmsundertake facilitating initiatives, such as building relational ties, toenhance partners’ cooperative incentives and create opportunitiesfor knowledge acquisition (Villena et al., 2011).

2.1. A relational view of knowledge acquisition

Individual or organizational embeddedness is important to theacquisition of information and knowledge (Burt, 1992). Gulati(1998) proposes two aspects of embeddedness, structural and rela-tional, such that the former focuses on structural properties ofnetworks (e.g., structural holes, network centrality; Burt, 1992),while the latter addresses the strength of ties at the dyadic level(Granovetter, 1973). In line with a relational embeddedness view,we theorize that relational ties reflect the dyad between a manufac-turer and its major supplier, characterized by varying interaction,trust, mutual commitment, and reciprocity (Poppo and Zenger,2002).

According to the relational view, a firm may dedicate spe-cific investments to improving its exchange relationships, createcomplementarities with its partner’s external resources, or deviseinformation sharing routines to facilitate knowledge transfer (Dyerand Singh, 1998; Lavie, 2006). Dyer and Singh (1998) furtherposit that self-enforcing structures such as cohesive relational tieshave the greatest influence on the promotion of knowledge flows.Functioning as reliable information conduits, relational ties facili-tate flows of high-quality information and fine-grained knowledge(Rowley et al., 2000). As a social governance mechanism, relationalties also secure and enhance knowledge flows among exchangeparties through accumulated social capital and intensified collec-tive norms (Dhanaraj et al., 2004).

The efficacy of relational ties for facilitating knowledge flowsdepends on their strength. Tie strength is a function of inter-action frequency, emotional intensity, intimacy, and reciprocitybetween exchange parties (Granovetter, 1973). Accordingly, rela-tional ties represent a continuum, with weak ties at one end

and strong ties at the other. Weak ties imply infrequent, distantrelationships between loosely connected parties, whereas strongties feature high levels of closeness, reciprocity, and indebtedness

2 This information came from in-depth interviews with senior purchasing man-agers.

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Capaldo, 2007; Hansen, 1999). Compared with weak ties, strongies are more effective in fostering the flow of complex and specificnow-how (Hansen, 1999; Perry-Smith and Shalley, 2003). Consis-ent with this line of research (Capaldo, 2007; Granovetter, 1973;ansen, 1999), we treat relational ties as a continuous variable andxamine how varying levels of tie strength affect the acquisition ofpecific product and process knowledge.

.2. A TCE view of knowledge acquisition

As a prevalent framework for understanding how firmshoose governance arrangements to manage risky and uncer-ain exchanges (Williamson, 1996), TCE emphasizes the use oformal mechanisms, such as specific contracts, to facilitate inter-rm exchanges (Eccles, 1981; Williamson, 1985). In supply chainanagement, when exchange parties make idiosyncratic invest-ents for the relationship, managers craft detailed contracts to

rotect their investments from opportunistic expropriation (Lit al., 2010b). Written contracts explicitly codify supply chainartners’ rights, duties, and responsibilities, so they help sup-ress opportunism, create mutual gains, and facilitate cooperationWilliamson, 1996).

Most TCE studies do not treat knowledge acquisition as an out-ome, but recent advances show that detailed contracts can fosterhe acquisition of explicit knowledge (Li et al., 2010a). By codifyingnformational requirements ex ante, contracts establish a bind-ng framework for fulfilling informational obligations (Williamson,996). Thus the written agreement aligns partners’ interests andeduces the agency problems inherent to business transactions,hich is conducive to ex post knowledge acquisition from exchangearties (Li et al., 2010a).

In addition, TCE indicates that business transactions arembedded in a larger transactional context, so the performancemplications of transactional activities rest on firms’ capabilities toespond effectively to various exchange uncertainties (Williamson,996). At the market level, organizations face uncertainty arisingrom product and capital market competition, because competi-ion constitutes a source of natural selection pressures (Williamson,985). The existence of many, strong competitors influences thenvironmental munificence that a firm confronts and the responsest offers (Ang, 2008). In this regard, competitive intensity is a fun-amental characteristic, at the core of the market structure, thatay shape interfirm knowledge acquisition.The relational view and TCE together provide constructive, dis-

inct insights into how to facilitate knowledge flows across firmoundaries. We seek to incorporate these two views to examine

nterfirm knowledge acquisition in two ways. First, though previoustudies emphasize the positive effect of relational ties on specific,omplex knowledge acquisition, we posit that a curvilinear rela-ionship may exist, such that very strong ties can be detrimental.econd, the value of relational ties may be bounded by exchangeharacteristics and transactional contexts. Accordingly, we assesshe contingent value of ties in relation to contractual design andndustrial competition.

. Hypotheses development

.1. Direct effects of relational ties

Relational ties contribute to acquisition of specific knowledgeor two main reasons. First, they provide access to high-quality,imely information, which enhances the quality of knowledge

cquisition. Uzzi (1997) postulates that knowledge flows embed-ed in cohesive relations contain not only codified, fine-grained

nformation but also privileged, difficult-to-imitate know-how.hereas explicit knowledge can be transferred through formal,

Management 32 (2014) 88–98

systematic procedures, tacit know-how is more proprietary and canbe reached only through frequent interactions and active involve-ment by the partners (Dhanaraj et al., 2004; Li et al., 2010a).Cohesive relational ties are thus necessary to acquire tacit andquality information.

Second, relational ties create norms of reciprocity and solidar-ity between exchange parties (Rindfleisch and Moorman, 2003),which increases the efficiency of knowledge acquisition. Normsof reciprocity entail the exchange of favors of equivalent value(Granovetter, 1985). They motivate knowledge sources to coop-erate (Ireland and Webb, 2007) and bear the potential risk ofknowledge leakage (Inkpen and Tsang, 2005), which facilitates thetransfer of knowledge across organizational boundaries. Norms ofsolidarity signify a bilateral expectation that both parties value therelationship (Heide and John, 1992). When exchange parties sharethe same value, the recipient is more likely to be open and recep-tive to the knowledge offered by the source. As Hansen (1999)observes, when relational ties are present, manufacturers have bet-ter opportunities to seek instructions from intimate suppliers, andsuppliers are willing to spend more time articulating their know-how. Lechner et al. (2010) also show that relational ties makeinformation sharing more efficient, through greater mutual under-standing.

However, these positive effects may decline and even becomenegative if relational ties are too strong. First, firms that are over-embedded in idiosyncratic relational ties suffer from unnecessaryobligations that require continuous investments of their time andresources (Lechner et al., 2010; Villena et al., 2011). When the man-ufacturer takes on additional responsibilities and dedicates moreresources to help the supplier, out of a sense of social obligation,the resulting lock-in situation inhibits learning at both the firm andindividual levels (Lechner et al., 2010; Uzzi, 1997). At the firm level,over-embeddedness creates pressures for a buyer to yield to itssupplier to avoid adverse social consequences, which distracts thebuyer from its goal accomplishment and reduces its efforts to learnadditional information and knowledge (Lechner et al., 2010). At theindividual level, heavy social obligations inhibit managers’ learningefforts by constraining their cognitive capabilities to process infor-mation and lowering their motivation in knowledge acquisition(Villena et al., 2011).

Second, cohesive ties imply collectivism, but too much cohesionmay lead to collective blindness, which severely limits firms’ open-ness to information and undermines their acquisition efficiency(Nahapiet and Ghoshal, 1998). The source may become overly opti-mistic regarding its understanding of the transaction and shareonly information that it considers important. The recipient may getcomplacent with its existing partner and become blindness with-out qualifying shared knowledge or expanding its search horizons(Koka and Prescott, 2002; Lechner et al., 2010). Such complacencyundermines the motivation for continuous learning and inhibitsknowledge transfer between the two closely connected parties(Villena et al., 2011). As Levinthal and March (1993: 100) explain,“success will decrease search,” because an organization respondsto successful knowledge acquisition by lowering its search inten-sity and aspirations. Such collective blindness reduces the buyer’ssearch motivation and effort and jeopardizes its knowledge acqui-sition from the supplier.

Third, opportunistic behavior and malfeasance may arise as rela-tional ties reach high levels (Granovetter, 1985). With a high levelof trust in the supplier, the buyer tends to reduce its monitoring(Villena et al., 2011). When the supplier senses this change in thebuyer’s monitoring efforts, it may reduce its sharing of product- or

process-related information. However, strong relational ties likelyprevent the buyer from switching suppliers, regardless of the per-formance of this dyadic relationship (Kim et al., 2006). Then thesupplier may take advantage of the buyer’s trust and engage less in

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imely and accurate feedback, slowing interfirm knowledge flowsVillena et al., 2011).

Overall, relational ties increase access to quality knowledge andmprove information acquisition efficiency, unless they become tootrong, in which case they burden acquisition activity with heavybligations, collective blindness, and potential supplier oppor-unism. Collectively, we predict,

1. Relational ties have an inverted U-shaped relationship withnowledge acquisition.

.2. Contingent role of contract specificity

Contract specificity refers to formal specifications that stipulatehe rules of behavior for each party and major objectives to bechieved (Williamson, 1985). Detailed contracts specify the poli-ies and procedures for task fulfillment, information disclosure, andispute resolution (Cannon and Perreault, 1999). Unlike relationalies that facilitate the acquisition of specific knowledge throughnformal norms and interactions, contractual agreements estab-ish formal and legal stipulations that specify knowledge flows innterfirm exchanges (Dahlstrom et al., 1996).

We posit that contract specificity strengthens the positive effectf relational ties when ties are at relatively low or moderate lev-ls. Because relational norms have not been fully established whenie strength is low to moderate, exchange parties still might havembiguous or incomplete understanding of each other’s informa-ion needs and resource obligations (Cannon et al., 2000). Specifiedontracts decrease this ambiguity and enhance mutual understand-ng by offering a formal documentation of the exchange parties’nformation-related duties (Williamson, 1996), thus improving theuality of shared information.

Detailed contractual provisions also specify procedures fornowledge exchange (Heide and John, 1992), which build up a con-uit for efficient knowledge flows through relational ties. Whenies are at relatively low to moderate levels, relational protectionased on norms may still be unreliable as a means to safeguard

nterfirm exchanges (Li et al., 2010b). Detailed contracts ensurenowledge exchanges through third-party surveillance and pun-shment (Liu et al., 2009). This formal safeguard fosters exchangeartners’ confidence in knowledge sharing, thus improving the effi-iency of knowledge acquisition through relational ties.

Detailed contracts coupled with high levels of relational tiesay inhibit knowledge acquisition though. First, strong ties cre-

te collective blindness that limits partners’ openness to externalnformation. Specific contracts stipulate task and contingency pro-isions ex ante and render limited autonomy to exchange partieso adapt ex post (Jap and Ganesan, 2000). Such a constraint makesollective blindness more severe, because learning is an adaptiverocess; partners likely suffer the myopia of focusing on codified

nformation without searching for or assimilating knowledge out-ide these standard contractual procedures and policies (Levinthalnd March, 1993).

Second, for exchange partners with strong ties, detailed con-racts can signal distrust and hurt the feeling of both sides,ndermining their motivation to share information cooperativelyGhoshal and Moran, 1996). Whereas strong ties depend on norma-ive conventions to coordinate exchanges, specific contracts rely onegulatory authorities or courts to impose sanctions (McFadyen andannella, 2004; Perry-Smith, 2006). The third-party enforcementf contracts conflicts with the self-enforcing obligation of strong

ies (Antia and Frazier, 2001), which undercuts the relational com-

itments between buyers and suppliers. Exchange partners thenay act more opportunistically and exhibit less willingness to share

heir knowledge.

Management 32 (2014) 88–98 91

In summary, whereas high (vs. low) contract specificitystrengthens the positive effects of ties by further improving thequality and efficiency of knowledge acquisition, it also enhancesthe negative impacts of ties by aggravating collective blindness andsupplier opportunism issues. Therefore, we predict that contractspecificity strengthens the inverted U-shaped relationship betweenrelational ties and knowledge acquisition.

H2. The inverted U-shaped effect of relational ties on knowledgeacquisition is stronger (steeper) when contract specificity is high,and vice versa.

3.3. Contingent role of competitive intensity

Competitive intensity refers to the extent of competition in a par-ticular industry (Porter, 1985). Competition has long been viewedas the most salient environmental factor affecting firms’ resourceallocation, operational capabilities, and use of collaborative ties(Porter, 1985). Heavy competition generates unpredictable changesin the demand–supply equilibrium and puts firms in vulnerablepositions (Ang, 2008). They must act proactively and rapidly in theface of intensified competition; otherwise, they will be driven outof the market (Li et al., 2008).

We predict that when competition is greater, a moderate level ofrelational ties is beneficial for knowledge acquisition. First, as com-petition intensifies, firms need high quality information to achieveeffective competitive responses (Luo, 2003). Obtaining qualityinformation from suppliers without relational ties is unlikely;rather, buyers turn to trusted suppliers to secure access to updated,valid information (Li et al., 2008). As Kim et al. (2008) docu-ment, when competition is high, manufacturers proactively searchfor and acquire knowledge from existing suppliers. Meanwhile,suppliers have a strong motivation to share their idiosyncraticknowledge with manufacturers to achieve bilateral adaptation(Ang, 2008).

Second, intensified competition compels firms to act quickly toadapt to the changing environment. However, coordinating withnew market players is difficult and time consuming, because theylack a common ground and mutual trust (Ang, 2008). By estab-lishing norms of information sharing and solidarity, preexistingrelational ties provide a reliable means for firms to achieve jointaction, knowledge transfer, and efficient adaptation (Lee, 2007).Mahapatra et al. (2012) observe that firms operating in highly com-petitive markets have greater propensity to collaborate to accessimportant resources that are unavailable to their competitors.

We further argue that when competition is high, strongrelational ties may be counterproductive to knowledge acquisi-tion. Competition makes concerns about unnecessary obligationsinduced through strong ties more salient. Facing competitivepressures, firms engage in more frequent adaptation and compro-mise, to consolidate their existing partners (Ang, 2008; Wu andPangarkar, 2010). When exchange partners invest significant timeand resources to help each other, they devote fewer resources toattaining optimal knowledge acquisition (Lechner et al., 2010).

To counteract competition, exchange parties also must trans-fer and assimilate knowledge in a timely manner (Ang, 2008). Ina highly competitive environment where firms need to engage invarious price and promotion rivalries, market conditions changerapidly, so any firm’s market knowledge becomes obsolete quickly(Porter, 1985). However, excessive relational ties generate collec-tive blindness, such that the buyer and supplier become overly

complacent in their thinking and knowledge reservoir (Villenaet al., 2011). Collective blindness thus becomes a more critical con-cern when competition is high, and strong relational ties likelyinhibit efficient knowledge acquisition.

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Heavy competition also forces firms to spend more resourcesnd energy to deal with the changing needs of the market (Pelhamnd Wilson, 1996), which divert its attention from interfirmonitoring. Without strict monitoring, the partner can opportunis-

ically exploit the knowledge recipient’s vulnerability, by providingncomplete or distorted information disclosures (Granovetter,985). As Peng (2003) explains, when the transactional environ-ent becomes more competitive, informal information processing

hrough network ties is less reliable, because deviations are hardero punish. Supplier opportunism then represents a greater hurdleo the buyer’s knowledge acquisition.

Taken together, high (vs. low) competition strengthens the pos-tive effects of ties by improving knowledge quality and acquisitionfficiency, but it also enhances the negative effects of ties byggravating unnecessary obligations, collective blindness, and sup-lier opportunism. Therefore, we predict that competitive intensitytrengthens the inverted U-shaped relationship between relationalies and knowledge acquisition.

3. The inverted U-shaped effect of relational ties on knowledgecquisition is stronger (steeper) when competitive intensity is high,nd vice versa.

. Method

.1. Sampling and data collection

Our empirical setting involves the buyer–supplier relationshipsf manufacturing firms in China. Chinese firms have a long traditionf using relational ties (guanxi) to conduct business, though the usef contracts has become more prevalent with economic reformsZhou and Poppo, 2010). In addition, the industrial environmentaries dramatically across different regions in China (Sheng et al.,011). As a result, it provides a rich context to test the interplaymong ties, contracts, and industry competition.

We developed a survey instrument by following the proceduresecommended by Gerbing and Anderson (1988). First, we con-ucted 10 in-depth interviews with senior managers to understandusiness practices, especially networking activities by executivesnd knowledge and information flows across organizational part-ers. These interviews revealed pervasive relational ties amongusiness partners. Second, on the basis of the exploratory inter-iews and an extensive review of previous literature, we developedn English version of the questionnaire, translated it into Chinese,nd then commissioned a back-translation by two independentranslators to ensure conceptual equivalence. Third, to ensure theontent and face validity of the constructs, we conducted five in-epth interviews with senior managers of target manufacturingrms and asked them to verify the relevance and completeness ofhe measures. On the basis of these in-depth interviews, we revisedome questionnaire items to enhance their clarity. Fourth, we con-ucted a pilot test with 40 purchasing managers, who answeredll the survey questions and provided feedback about the ques-ionnaire design. We finalized the instrument on the basis of theesults of this pilot study.

For the final survey, we randomly selected a sample of 1000rms from a list of manufacturing firms in Beijing and Shang-ai, provided by a marketing research firm. These sample firmsepresented the four-digit Chinese Industrial Classification codes311–4290. The sample covered a broad spectrum of industries,

ncluding mechanical, materials, chemicals, electronics, and tex-iles. A senior procurement manager from each firm served as the

ey informant; our in-depth interviews revealed that these man-gers were highly familiar with their suppliers and would be theost knowledgeable about the focal firm’s ties with and acquisition

f specific knowledge from suppliers.

Management 32 (2014) 88–98

We collaborated with local researchers and hired and trainedinterviewers to run the survey onsite. The interviewers met themanagers in their offices, presented them with the survey, col-lected the completed survey, and answered their questions. Thismethod is critical for obtaining reliable and valid data in emerg-ing economies (Zhou et al., 2006). The interviewers first contactedthe purchasing managers by telephone to solicit their participation.In total, 476 managers agreed to participate, and 403 eventuallycompleted the onsite interviews. The informants identified one oftheir firm’s top five suppliers and answered the survey questionsregarding their ties and interactions with that specific supplier.After dropping samples with excessive missing values, we obtained385 usable samples, for a response rate of 38.5% (385 of 1000 firms).

The average age of these firms was 10.3 years, and their averagesize, in terms of the number of employees, was 344, with averageannual sales revenue of US$23.7 million. In addition, 57.7% weredomestic firms, whereas 42.3% were foreign-owned firms or jointventures. On average, the focal manufacturer–supplier relationshiphad existed for five years. As a quality control follow-up, we called40 respondents at random to confirm that they had participated inthe interviews and found no signs of cheating. A comparison of theresponding and nonresponding firms indicated no significant dif-ferences with regard to key firm characteristics, such as ownershiptype, firm size, or annual sales revenues, so nonresponse bias wasnot a serious concern for our study.

4.2. Measures

In Appendix A, we report the measurement items and theirvalidity assessments. Following Rowley et al. (2000), Uzzi (1999),and Wegener (1991), we developed a measure of relational ties toassess the interaction, closeness, and reciprocity of the relationshipbetween the manufacturer and supplier. The measure of contractspecificity was adapted from Cannon and Perreault (1999), to exam-ine the degree to which the contract clearly specifies and details theobligations and responsibilities of each party and documents anyagreements between them. For competitive intensity, we used scalesadapted from Jaworski and Kohli’s (1993) work, which assessed thedegree of competition a firm faces in its industry. On the basis ofRindfleisch and Moorman’s (2001) study, we developed a measureof knowledge acquisition to assess the extent to which manufactur-ers acquired specific product and process skills from suppliers.

We controlled for firm, relationship, and industry variables thatmight influence information and knowledge exchanges within thesupply chain. Firm size may affect knowledge acquisition, becauselarger firms tend to have more resources; we used the logarithmof the number of employees as an indicator. We also used twodummies to control for the impact of firm type, namely, foreignwholly owned and international joint venture (IJV) buyers, becauseforeign firms have greater learning capabilities in China’s market(Li et al., 2010a).

For relationship characteristics, we considered three variables:relationship tenure, buyer concentration, and supplier concentra-tion. Relationship tenure should relate positively to knowledge andinformation sharing (Kotabe et al., 2003), so we measured thenumber of years the manufacturer had been doing business withthe supplier. To control for a firm’s impact and importance in therelationship, derived from its share of the partner’s business, weincluded buyer concentration and supplier concentration. The formerequals the percentage (0–100%) of the buyer’s total annual demandfor focal products obtained from the supplier, and the latter is the

percentage of the supplier’s total annual sales of focal products soldto that buyer (Rokkan et al., 2003).

Finally, we used five dummy variables to control forindustry heterogeneity across mechanical, materials, chemicals,

ations Management 32 (2014) 88–98 93

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lectronics, and textiles. Other industries represented the baselineroup.

.3. Construct validity

To assess the reliability and validity of the measures, we appliedn overall confirmatory measurement model. Each measurementtem loaded only on its latent construct, and all latent constructs

ere set correlated. The key fit indexes (�2(112) = 352.48, p < .001;

oodness-of-fit index = .90, comparative fit index = .93, normed fitndex = .90, root mean square error of approximation = .07) andactor loadings (all statistically significant at p < .001) suggestedcceptable model fit. The composite reliability and average vari-nces extracted (AVE) of each construct exceeded the .70 and .50hresholds (see Appendix A).

We used two standards to assess discriminant validity. First,e ran chi-square difference tests for all the constructs in pairs, toetermine whether the constrained model (correlation fixed at 1)as significantly worse than the unconstrained model (correlation

stimated freely). All the chi-square differences were highly signif-cant (e.g., knowledge acquisition vs. relational ties, ��2 = 47.70,

< .01), in support of discriminant validity (Gerbing and Anderson,988). Second, the AVE of each construct was much higher than itshared variances with other constructs, which again supported dis-riminant validity. Overall, the results indicated that the measuresossessed adequate reliability and construct validity.

.4. Common method bias

Because we used only one informant in the interviews, com-on method bias could be a potential issue. Therefore, we applied aethod variance (MV) marker to assess potential common method

ias, as suggested by Lindell and Whitney (2001). We used a scaleheoretically unrelated to at least one variable in the analysis as the

V marker, which provided a proxy for common method variance.or our test, the marker was a four-item variable to assess gov-rnment support for the focal firm (Li and Atuahene-Gima, 2001;ronbach’s = .92). The lowest positive correlation between gov-rnment support and other latent variables (r = .02) served to adjusthe correlations among the variables. As we show in Table 1, only

of 33 significant correlations became non-significant after theV adjustment, and no significant correlations related to the latent

onstructs became insignificant. Therefore, common method biasas unlikely to be a serious concern in the study.

. Analyses and results

To test our hypotheses, we employed a stepwise regressionpproach that revealed the explanatory power of each set ofariables (Cohen et al., 2003). To deal with the potential multi-ollinearity between the quadratic and interaction terms, we meanentered each scale that constituted an interaction or quadraticerm and created the interaction terms by multiplying the rele-ant mean-centered scales (Cohen et al., 2003). In these models,he largest variance inflation factor (a multicollinearity indica-or) emerged from the interaction between contract specificitynd squared relational ties, with a value of 2.72, substantially lesshan the critical threshold of 10. Therefore, multicollinearity didot appear to be a significant issue. In Table 2, we present theesults of the standardized regression estimates, allowing for a

irect comparison between coefficients with respect to the relativexplanatory power of the independent variables. Model 1 includedhe control variables. Model 2 added the independent variable andts quadratic term to test the main effects, whereas Models 3 and 4

Fig. 1. Direct and contingent effects of buyer–supplier ties.

added the interactions individually. Model 5 included all predictorsin the regression.

As Table 2 shows, the control variables accounted for 9.0% ofthe total variance in knowledge acquisition (F = 3.13, p < .01). Firmsin mechanical manufacturing and material industries seemed toachieve greater knowledge acquisition than firms in other indus-tries. In Model 2, adding the focal independent variables and thequadratic term of relational ties increased the R-square value by .16(p < .01). Adding the interaction terms in Models 3–5 also increasedthe R-square value significantly compared with Model 2 (�R2 = .03,.02, and .05, respectively; all p < .01), which supports that contractspecificity and competitive intensity have significant moderatingeffects.

In H1 we predicted an inverted U-shaped relationship betweenrelational ties and knowledge acquisition. According to Table 2,Model 2, relational ties related positively to knowledge acquisition( = .31, p < .01), but the quadratic term exhibited a negative relation( = −.14, p < .01). Therefore, relational ties displayed a curvilinearrelationship with knowledge acquisition. To depict this curvilinearrelationship, we used Cohen et al.’s (2003) approach and plottedthe relationship in Fig. 1a: Relational ties have an inverted U-shapedeffect on knowledge acquisition. We calculated the simple slopes ofthe curvilinear relationship at low and high levels (i.e., one standarddeviation below/above mean) for the relational ties. The resultsshowed that relational ties exerted a positive effect on knowledgeacquisition when they were low ( = .94, p < .01) but a negativeeffect if those relational ties were high ( = −.32, p < .05). A furthersimple slope calculation indicated that the turning point (i.e., loca-tion of the dotted line in Fig. 1a, where the first [partial] derivativeof knowledge acquisition with respect to relational ties equaled 0)occurred for relational ties = 5.98. Overall, these results support H1.

With H2 we predicted that contract specificity strengthensthe inverted U-shaped relationship between relational ties andknowledge acquisition. In Table 2, Model 5, the first-order inter-action between relational ties and contract specificity was positive

( = .13, p < .05), whereas the second-order interaction was negative( = −.18, p < .05). For greater clarity, we plotted the relationshipbetween relational ties and knowledge acquisition for the low andhigh levels of the moderators in Fig. 1b and c, respectively (Cohen

94 K.Z. Zhou et al. / Journal of Operations Management 32 (2014) 88–98

Table 1Descriptive statistics and correlations.

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Knowledge acquisition .35** .07 .18** −.01 .01 .04 .08 .17** .14** .12* −.07 −.04 .06 −.12*

2 Relational ties .36** .24** −.04 −.01 .05 .16** .15** .31** .27** −.05 .05 .03 −.03 −.023 Contract specificity .09 .26** .02 .00 .08 .06 .00 −.06 −.07 −.15** .08 .01 −.04 −.084 Competitive intensity .20** −.02 .04 −.08 −.05 .02 −.19** −.14** .02 −.03 −.11* −.06 −.11* .005 Firm size .01 .01 .02 −.06 .08 .03 .13** .12* .05 −.01 −.07 .13** −.14** .006 Foreign wholly owned .03 .07 .10 −.03 .10* −.30** −.07 .07 .04 −.04 .03 .09 −.09 −.027 IJV .06 .18** .08 .04 .05 −.27** .03 .08 .09 .04 −.10* .09 −.04 .038 Relationship tenure .10* .17** .02 −.17** .15** −.05 .05 .29** .22** .06 −.03 −.07 −.06 −.059 Buyer concentration .19** .32** −.04 −.12* .14** .09 .10* .30** .56** −.01 .06 −.01 −.03 −.08

10 Supplier concentration .16** .28** −.05 .04 .07 .06 .11* .24** .57** −.06 −.03 −.08 −.03 −.0811 Mechanical .14** −.03 −.13* −.01 .01 −.02 .06 .08 .01 −.04 −.34** −.23** −.22** −.21**

12 Chemical −.05 .07 .10 −.09 −.05 .05 −.08 −.01 .08 −.01 −.31** −.12* −.12* −.12*

13 Electronics −.02 .05 .03 −.04 .15** .11* .11* −.05 .01 −.06 −.21** −.10* −.09 −.0914 Materials .08 −.01 −.02 −.09 −.12 −.07 −.02 −.04 −.01 −.01 −.20** −.10* −.07 −.0815 Textile −.10* .00 −.06 .02 .02 −.00 .05 −.03 −.06 −.06 −.19** −.10 −.07 −.0616 MV marker .36** .26** .02 .09 .00 −.01 .15** .04 .20** .16** .01 −.09 .02 .00 .01

Mean 3.96 4.86 5.31 4.56 2.26 0.23 0.19 4.98 33.55 48.59 0.38 0.14 0.06 0.06 0.06Std. deviation 1.14 1.12 1.31 1.05 0.43 0.42 0.39 4.22 25.07 28.01 0.49 0.34 0.25 0.24 0.23

Notes: N = 385. Zero-order correlations are below the diagonal; adjusted correlations for potential common method variance (Lindell and Whitney, 2001) are above thed

ecskchf(pt

i

TS

N

iagonal.* p < .05.

** p < .01.

t al., 2003). Fig. 1b reveals the differential moderating effects ofontract specificity: When relational ties were relatively low (leftide of the dotted line), they exerted a stronger positive effect onnowledge acquisition (steeper slope) at high (vs. low) levels ofontract specificity. However, when relational ties were relativelyigh (right side of the dotted line), knowledge acquisition declined

aster as relational ties increased when contract specificity was highvs. low). A further simple slope calculation indicated a turning

oint (i.e., location of the dotted line in Fig. 1b) when relationalies = 5.73.

We also employed Cohen et al.’s (2003) approach to quantify thempact of relational ties on knowledge acquisition at low and high

able 2tandardized regression results.

Model 1 Model 2

t-Value t-Value

Control variablesFirm size −.02 −.42 .01 .26

Foreign wholly owned .05 .98 .01 .25

IJV .04 .82 −.04 −.72

Relationship tenure .04 .82 .06 1.16

Buyer concentration .11 1.76 .08 1.31

Supplier concentration .09 1.50 .01 .22

Mechanical .17** 2.81 .19** 3.40

Chemical .00 .06 .02 .31

Electronics .02 .31 .02 .32

Material .12* 2.20 .15** 3.05

Textile −.05 −.97 −.05 −1.12

Direct effectsContract specificity .00 .07

Competitive intensity .25** 5.19

H1: relational ties (RT) .31** 6.09

RT2 −.14** −2.85

Moderating effectsH2: RT × contract specificity

RT2 × contract specificity

H3: RT × competitive intensity

RT2 × competitive intensity

R2 .09 .25

�R2 .16**

ote: N = 385.* p < .05 (two-tailed).

** p < .01 (two-tailed).

values of the moderators. As Table 3, Panel A, shows, the coefficientof RT2 was more negative as contract specificity increased (from−.07 to −.13 to −.19), consonant with a steeper curvilinear effectwhen contract specificity was high in Fig. 1b. These results providesupport for H2.

We examined the moderating effect of competitive intensityto test H3. According to Table 2, Model 5, the first-order interac-tion between relational ties and competitive intensity was positive

and significant ( = .10, p < .05), and the interaction between thequadratic term of relational ties and competitive intensity wasnegative and significant ( = −.17, p < .01). As Fig. 1c shows, whenrelational ties were relatively low (left side of the dotted line),

Model 3 Model 4 Model 5

t-Value t-Value t-Value

.02 .42 .01 .26 .02 .43

.00 .02 .02 .30 .00 .09−.05 −.98 −.03 −.69 −.05 −.96

.05 1.08 .06 1.20 .05 1.13

.08 1.40 .08 1.28 .08 1.37

.05 .86 .00 .08 .04 .71

.19** 3.52 .19** 3.43 .19** 3.57

.02 .32 .02 .31 .02 .35

.03 .54 .01 .28 .02 .50

.15** 3.20 .15** 3.17 .16** 3.33−.04 −.75 −.05 −1.08 −.04 −.72

.07 1.21 .00 −.04 .08 1.35

.24** 5.10 .32** 5.37 .32** 5.46

.36** 6.76 .29** 5.60 .34** 6.39−.27** −4.55 −.06 −1.23 −.21* −3.25

.14* 2.38 .13* 2.18−.16* −2.14 −.18* −2.55

.10* 1.99 .10* 2.02−.16* −2.51 −.17** −2.78

.28 .27 .30

.03** .02** .05**

K.Z. Zhou et al. / Journal of Operations Management 32 (2014) 88–98 95

Table 3Centered regression equation with curvilinear by linear interaction.

Regression equation

Centered regression equation Knowledgeacquisition = −.13 × RT2 + (−.05) × CS × RT2 + (−.09) × CI × RT2 + .34 × RT + .08× CS × RT + .10 × CI × RT + .07 × CS + .34 × CI + 3.51

Panel ARegression equation with respect to contract specificity Knowledge

acquisition = [−.13 + (−.05) × CS] × RT2 + (.34 + .08 × CS) × RT + .07 × CS + 3.51Level of contract specificity

Low = −1.31 Knowledge acquisition = −.07 × RT2 + .24 × RT + 3.42Mean = 0.00 Knowledge acquisition = −.13 × RT2 + .34 × RT + 3.51High = 1.31 Knowledge acquisition = −.19 × RT2 + .44 × RT + 3.60

Panel BRegression equation with respect to competitive intensity Knowledge acquisition = [−.13 + (−.09) × CI] × RT2 + (.34 + .10 × CI) × RT + .34 × CI + 3.51Level of competitive intensity

Low = −1.05 Knowledge acquisition = −.04 × RT2 + .24 × RT + 3.15Mean = 0.00 Knowledge acquisition = −.13 × RT2 + .34 × RT + 3.51High = 1.05 Knowledge acquisition = −.23 × RT2 + .44 × RT + 3.87

N tion. Rl ective

kttraptiit

6

6

oSotseneieotioko

sEisMoiketkf

otes: Coefficients are unstandardized; the control variables are omitted for illustraevels of the moderators are one standard deviation below or above the mean, resp

nowledge acquisition increased faster with increasing relationalies when competition was high (vs. low). However, when rela-ional ties were relatively high (right side of the dotted line),elational ties had a more negative effect on knowledge acquisitiont high (vs. low) levels of competition. The curves reached a turningoint when relational ties = 5.38. Table 3, Panel B, also shows thathe coefficient of RT2 grew more negative as competitive intensityncreased (from −.04 to −.13 to −.23), consonant with the steepernverted U-shaped effect at high competition in Fig. 1c. Overall,hese results support H3.

. Discussion

.1. Theoretical contributions

Relational ties in a supply chain represent an important learningpportunity for firms to achieve competitive advantages (Dyer andingh, 1998; Hult et al., 2006). This study adds to our understandingf why firms should maintain but be cautious about their relationalies with supply chain members. By revealing a curvilinear relation-hip between relational ties and knowledge acquisition, this studychoes prior operations management studies that call for exami-ations of both the bright and dark sides of relational ties (Cousinst al., 2006; Krause et al., 2007; Villena et al., 2011). By demonstrat-ng the differential moderating effects of contract specificity, wextend operations management research and offer new evidencef how informal and formal mechanisms jointly influence businessransactions. By examining the industrial competition surroundingnterfirm exchanges, this study also addresses the critical questionf when relational ties are helpful for the acquisition of specificnowledge. In turn, our study makes three major contributions toperations and supply chain management literature.

First, we highlight the dual role of relational ties in acquiringpecific and complex knowledge from supply chain partners.xtant studies acknowledge that weak ties are good for assimilat-ng novel information, whereas strong ties facilitate complex andpecific information flows (Capaldo, 2007; Molina-Morales andartínez-Fernández, 2009). Instead of examining the acquisition

f novel knowledge, our study focuses on how relational tiesnfluence the acquisition of specific, complex product and processnowledge from exchange parties. We find an inverted U-shaped

ffect of relational ties, which indicates that a moderate level ofies is optimal, but very strong ties are detrimental to specificnowledge acquisition. As our results suggest, relational tiesoster the acquisition of specific knowledge by providing exchange

T = relational ties; CS = contract specificity; CI = competitive intensity. Low and highly.

partners with access to high-quality, timely information, as wellas by enhancing the efficiency with which firms can assimilateacquired knowledge. However, when relational ties become toostrong, they inhibit, rather than enhance, the acquisition of specificknowledge, due to the increased risk of heavy obligations, collec-tive blindness, and supplier opportunism. In response to Villenaet al.’s (2011) call, our study offers new insights to help explainthe theoretical inconsistency between the relative merits and darksides of relational ties in buyer–supplier relationships.

Second, this study enriches extant supply chain governance lit-erature pertaining to the collective use of formal and informalmechanisms. Operations management scholars have offered con-flicting views on how the joint use of formal contracts and informalrelations influences firm behaviors and performance in a supplychain (Li et al., 2010b; Liu et al., 2009; Lumineau and Henderson,2012; Stouthuysen et al., 2012). Our findings reveal that detailedcontracts strengthen the inverted U-shaped relationship betweenrelational ties and the acquisition of specific knowledge. When rela-tional ties are at low to moderate levels, contracts provide a formalframework to support the ties’ effects on the acquisition of specificknow-how. However, when ties grow too strong, the deploymentof detailed contracts signals distrust, and the legal enforcement ofthose formal contracts conflicts with the self-enforcing nature ofhighly cohesive ties, which impairs interfirm knowledge acquisi-tion. Thus, when contract specificity is high, a moderate level ofrelational ties is associated with optimal knowledge acquisition.In contrast, both low and high levels of ties are associated with alower degree of knowledge acquisition (see Fig. 1b). These findingsoffer new insights into the ongoing controversy regarding the col-lective use of formal and informal governance mechanisms (Li et al.,2010b).

Third, operations management research has called for additionalassessments of the ways in which supply chain practices dependon external environments (Patel, 2011; Sousa and Voss, 2008). Ourstudy enriches this contingency view by examining the curvilinearmoderating effects of competitive intensity. When competition ishigh, a moderate level of relational ties can improve the qualityof information and efficiency of cooperation, whereas a high levelwould suffer more from unnecessary obligation, collective blind-ness, and supplier opportunism. As our findings show (Fig. 1c), amoderate level of ties is best for knowledge acquisition when com-

petition is high; both low and high levels of relational ties insteadrelate to lower degrees of knowledge acquisition. By incorporatinga contingency view into the analysis of the nonlinear effect of ties,we develop a more nuanced understanding of how various levels

9 ations

oe

6

Fapmiwrabobt

isctwuf

wcomwsWtbcdrsso

6

avsmprd

fafgtgtoiohs

CR = .87AVE = .70

that detail the obligations of bothparties.

6 K.Z. Zhou et al. / Journal of Oper

f relational ties differ in their roles with respect to the industrynvironment.

.2. Managerial implications

Our study offers several implications to supply chain managers.irst, managers should be aware of the benefits and downsidesssociated with relational ties. Although ties with supply chainartners provide access to valuable information, a strong relationay create a lock-in risk, such that it prevents the firm from obtain-

ng know-how from its partners. For example, Man Group, theorld’s largest publicly traded alternative investment provider,

ecently ended an eight-year relationship with BlueCrest, in anttempt to reduce its overly strong presence in the managed futuresusiness (Cauchi, 2011). Similarly, Toyota and JCI decided to phaseut a long-term partnership in Trim Masters, their IJV, seeminglyecause they sensed the risks associated with their long-term rela-ionship (Villena et al., 2011).

Second, managers should understand how to employ formal andnformal mechanisms to manage knowledge acquisition from theirupply chain partners. When managers intend to draft detailedontracts, it is beneficial to develop a moderate level of relationalies to achieve a high level of knowledge acquisition. However,hen tie strength is already very strong, managers should avoidsing detailed contracts, because contracts become dysfunctionalor embedded ties.

Third, managers should recognize the industrial conditions inhich relational ties may benefit knowledge acquisition. When

ompetition is high, firms can leverage their preexisting ties tobtain valuable knowledge resources. For example, to fight Google’sarket leadership, Microsoft deepened its existing partnershipith Facebook, in an attempt to enhance the performance of its Bing

earch engine by tapping people’s social connections (Fowler andingfield, 2010). However, competition also makes very strong

ies detrimental to knowledge acquisition, so firms should avoidecoming too strongly embedded when procuring information inompetitive environments. Overall, our findings warn about theark side of very strong ties and suggest that companies shouldeduce their reliance on highly embedded suppliers; instead, firmshould develop a supplier network with moderate levels of tietrength to keep them open to broader informational resources andpportunities.

.3. Limitations and further research

Our study has several limitations that additional research canddress. We limit our investigation to buyer–supplier relationships,iewed from the buyer perspective, which may differ from theupplier perspective. For example, with regard to tie strength, aanufacturer may adopt a sense of cohesion toward its focal sup-

lier, even if the supplier regards the relationship as distant. Furtheresearch based on matched dyadic data could reveal whether theifferent sides perceive and behave similarly or not.

The cross-sectional design of this study also prevents usrom testing causal relationships in our theoretical model. Anlternative specification might suggest that knowledge trans-er influences the choice and evolution of relational or formalovernance (Hoetker and Mellewigt, 2009). Longitudinal inves-igations would be required to examine the causality of theovernance mechanism–knowledge acquisition linkage. A longi-udinal study also could enable consideration of the life cyclef collaborative ties (Autry and Golicic, 2010). Researchers could

nvestigate how a buyer–supplier relation emerges and evolvesver time, whether it reaches the dark side of relational ties, andow formal governance interacts with relational ties at varioustages.

Management 32 (2014) 88–98

Extant research indicates that different types of ties may carrydifferent characteristics and perform different functions (Lazzariniet al., 2008; Sheng et al., 2011). For example, strong and weakties have differential implications for different types of knowledgeacquisition. Our study only measures and examines the acquisitionof specific product/process know-how; it does not distinguish theacquisition of existing knowledge from that of novel knowledge.Further studies involving different types of ties and knowledgewould offer a richer understanding.

We also assume that relational norms underlie the effects of tieson knowledge acquisition. Levin and Cross (2004) note that rela-tional ties promote knowledge flows through competence trust andbenevolence trust, whereas Hansen (1999) suggests that strong tiescan lead to knowledge redundancy. Further research could explorethe mediating logic (e.g., trust, norms, knowledge redundancy)through which relational ties affect performance outcomes.

Finally, the universality of relational ties in both developed anddeveloping economies has been well documented (Burt, 1992; Liet al., 2008). Our findings are specific to a Chinese context, whichmay differ from other economies in terms of tie utilization andtransactional traditions. Further research should corroborate ourfindings in other advanced and emerging economies, to fully graspthe role of relational ties in a supply chain.

Acknowledgement

The authors thank the three anonymous reviewers, the Asso-ciate Editor, and the Editor Thomas Choi for their helpful comments.This study was supported by the General Research Fund from theResearch Grants Council, Hong Kong SAR Government (Project no.HKU 757810H) and the grant from the National Natural ScienceFoundation of China (NSFC, 71172185).

Appendix A. Constructs and items

Construct and source Description Factorloadings

Knowledge acquisition(Rindfleisch andMoorman, 2001)CR = .90AVE = .75

The extent to which your company haslearned from this partnership(1 = “very low” to 7 = “very high”):

• Specific skills and competencies(e.g., technology) of production.

1.00

• Specific skills for improvingproduct quality.

.95

• Specific R&D know-how. .92

Relational ties (Rowleyet al., 2000; Uzzi, 1999;Wegener, 1991)CR = .88AVE = .55

• Our company has a close socialrelationship with this supplier.

.87

• There is a sense of “being in thesame boat” between our company andthis supplier.

1.00

• Our company and this supplieroften visit each other.

1.00

• Our company and this supplieroften have activities that are purelysocial, such as after-workget-togethers.

.82

• Our company and this supplieroften offer favors to each other.

.92

• The relationship between ourcompany and the supplier is in goodshape.

.81

Contract specificity(Cannon and Perreault,1999)

• We have specific, well-detailedagreements with this supplier.

1.00

• We have customized agreements .97

• We have detailed contractualagreements specifically designed withthis supplier.

.97

ations

Nae

R

A

A

A

A

B

C

C

C

C

C

C

C

C

C

C

D

D

D

E

F

G

G

G

G

G

K.Z. Zhou et al. / Journal of Oper

Construct and source Description Factorloadings

Competitive intensity(Jaworski and Kohli,1993)CR = .83AVE = .50

• Competition in our industry iscut-throat.

.84

• There are many “promotion wars”in our industry.

.99

• Any product that a company canoffer, others can easily match.

.82

• Price competition is a hallmark ofour industry.

1.00

• There are many competitors in ourindustry.

.93

otes: All items, except otherwise specified, used Likert scales (1 = “strongly dis-gree,” and 7 = “strongly agree”). CR = composite reliability; AVE = average variancextracted.

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