the social capital of the top marketing team, inter-firm

The Social Capital of the Top Marketing Team, Inter-firm Market Learning Capability, and Business

Performance: A Test of a Mediating ModelKwaku Atuahene-Gima

M A R K E T I N G S C I E N C E I N S T I T U T E

W O R K I N G P A P E R S E R I E S

W O R K I N G P A P E R • R E P O R T N O . 0 2 - 1 1 8 • 2 0 0 2

This study was supported by financial assistance from the City University of Hong Kong under strategic research grant number 7000918.

MSI was established in 1961 as a not-for-profit institute with the goal of bringing together business leaders and academics to create knowledgethat will improve business performance. The primary mission was to provide intellectual leadership in marketing and its allied fields. Over theyears, MSI’s global network of scholars from leading graduate schools of management and thought leaders from sponsoring corporations hasexpanded to encompass multiple business functions and disciplines. Issues of key importance to business performance are identified by theBoard of Trustees, which represents MSI corporations and the academic community. MSI supports studies by academics on these issues anddisseminates the results through conferences and workshops, as well as through its publications series.

This report, prepared with the support of MSI, is being sent to you for your information and review. It is not to be reproduced or published,in any form or by any means, electronic or mechanical, without written permission from the Institute and the author.

The views expressed in this report are not necessarily those of the Marketing Science Institute.

Copyright © 2002 Kwaku Atuahene-Gima

M A R K E T I N G S C I E N C E I N S T I T U T E • R e p o r t S u m m a r y # 0 2 - 1 1 8

1000 Massachusetts Avenue • Cambridge, MA 02138 USA • 617.491.2060 • www.msi.org

The Social Capital of the Top Market-ing Team, Inter-firm Market LearningCapability, and Business Performance:A Test of a Mediating ModelKwaku Atuahene-Gima

To compete in an environment of rapid innovation, technological change, andmarket complexity, firms must invest in inter-firm relationships as a means of mar-ket learning and capability building. Yet current research views market learning asan internal firm-specific capability, and has neglected the role of marketing person-nel in the process.

This report explores how the top marketing team’s relationships with internal func-tions, and with managerial contacts in other firms, influence the organization’s“inter-firm market learning capability” (defined as organizational processes for theacquisition, integration, and utilization of market information and knowledge thatreside in other companies with which the firm has formal relationships). It alsoexamines the effect of this capability on the organization’s performance.

Study and Findings

In a survey of Australian manufacturing firms, the author finds:

❒ Inter-firm market learning capability is related positively to business performance.

❒ The top marketing team’s “internal social capital” (that is, the degree oftrust, reciprocity, and respect that exists between the team and other depart-ments in the firm, and the team’s willingness to seek advice and ideas fromother people) is related positively to inter-firm market learning capability.

❒ “External social capital” in the form of relational intensity (that is, the fre-quency of contact and closeness of interaction between the team and theircontacts in other firms) is related positively to inter-firm market learningcapability.

❒ Whereas inter-firm market learning capability fully mediates the positiveeffect of the top marketing team’s network comfort on business performance,it partially mediates the positive effect of relational intensity. This suggeststhat inter-firm market learning capability is a critical conduit through whichsocial capital of the top management team influences performance.

❒ Relationship quality has a direct, U-shaped effect on business performance.Low levels of trust, integrity, and concern between firms hurt performancebut high relationship quality levels enhance business performance.

Managerial Implications

These findings suggest that building inter-firm market learning capability is funda-mentally a social interaction process and that managers need to take actions thatfacilitate effective internal and external social interactions on the part of the firm’stop marketing team.

The associability and network comfort of the top marketing team may be facili-tated by joint departmental activities, common goals, location proximity, andreward systems that ensure reciprocity and collective effort between marketing andother departments. The results also suggest that top managers should encouragethe top marketing team to develop a deeper involvement in the formation andoperation of the firm’s inter-firm relationships. They should seek to employ mar-keting personnel who cognitively value external market knowledge, and appreciatethe value of inter-firm relationships as market intelligence sources.

Management should also provide marketing personnel with clearer guidelinesregarding the role that inter-firm market knowledge plays in the value-creatingactivities of the organization. Accomplishing all these goals requires increasedresources for the marketing function to develop and maintain external relation-ships and to improve its expertise in internal and external relationship building.

Kwaku Atuahene-Gima is Professor of Innovation Management and Marketing,Department of Management, City University of Hong Kong.

Contents

Introduction ...........................................................................................................3

Theory Development .............................................................................................7

Inter-firm Market Learning Capability ..............................................................7

TMT’s Internal Social Capital and Inter-firm Market Learning Capability .......9

TMT’s External Social Capital and Inter-firm Market Learning Capability.....10

Inter-firm Market Learning Capability and Business Performance...................12

TMT’s Social Capital, Inter-firm Market Learning Capability, and Business Performance .................................................................................13

Research Method..................................................................................................15

Sample and Data Collection............................................................................15

Measures..........................................................................................................16

Scale Purification.............................................................................................19

Analysis and Results .............................................................................................23

Discussion ............................................................................................................27

Theoretical Implications..................................................................................28

Managerial Implications ..................................................................................30

Limitations and Future Research Directions....................................................30

Conclusion ...........................................................................................................33

Appendix. Confirmatory Factor Analysis Results ..................................................35

Note .....................................................................................................................37

References.............................................................................................................39

Tables

Table 1. Correlation Matrix and Descriptive Statistics of Measures .................21

Table 2. Hierarchical Regression Analysis of the Antecedents and Effects of Inter-firm Market Learning Capability .......................................23

Figure

Figure 1. Conceptual Framework of Top Marketing Teams’ Social CapitalAntecedents and Outcomes of Inter-firm Market Learning Capability .........8

Marketing Science Institute 3

IntroductionToday, to compete in an environment of rapid innovation, technological change,and market complexity, firms need a breadth and depth of knowledge that theymay not be able to develop internally (Grant 1996). They must invest in inter-firmrelationships as a means of market learning and capability building. For example,to enhance its capability to develop and market small cars, GM instituted formalprocesses for accessing and internalizing market and product knowledge via theexpertise of its partners (Business Week 2001). Many other firms are pursuing com-petitive advantages through market knowledge acquisition and exploitation (Achrol1997; Srivastava, Shervani, and Fahey 1998). Through inter-firm relationships, afirm can obtain access to market knowledge, which is not available for purchase incapital markets, and which requires time and expense to build up single-handedly,if at all (Anderson, Håkansson, and Johanson 1994; Achrol and Kotler 1999; Mor-gan and Hunt 1999).

In spite of the topic’s theoretical and practical importance, few studies have investi-gated market learning in inter-firm relationships. Indeed, current research appearsto view market learning as an internal firm-specific capability. However, as Huntand Lambe (2000, p. 28) argue, firms often create superior value for customersand enhance their performance by acquiring and exploiting market knowledgefrom other firms, and they point to a lack of research on the antecedents and out-comes of such capability. An exception is a recent study by Rindfleisch and Moor-man (2001) which found that inter-firm relationship “social capital” (in the formof frequency of contact between alliance partners) and structure (as reflected byknowledge redundancy) influenced product and process information acquisitionand utilization in product development alliances.

We build on and enrich this research in four important ways. First, we build onthe concept of inter-firm market learning capability posited in the Rindfleisch andMoorman (2001) study. While they focused somewhat narrowly on the amount ofinformation acquired in product development alliances, we focus on inter-firmmarket learning capability as the organizational processes for the acquisition, integra-tion, and utilization of market information and knowledge that reside in other compa-nies with which the firm has formal relationships. These inter-firm relationships maybe partnerships, alliances, and other forms of vertical collaborations involving firmsat different stages of the value chain, or horizontal collaborations between andamong competitors or potential competitors. Based on resource-based theory (Bar-ney 1991; Teece, Pisano, and Sheun 1997), inter-firm market learning is adynamic capability that influences business performance (see Hunt and Lambe2000).

Second, we focus on the role of marketing personnel in inter-firm market learning,a topic that has received little research attention. To a large extent, marketing per-sonnel are the embodiments of the organization’s marketing skills and knowledge.The top marketing team (TMT) plays a key role in fostering an environment that

4 Marketing Science Institute

is conducive to market knowledge acquisition, integration, and exploitation. Thus,a focus on the TMT’s internal and external relationships will enhance understand-ing of the firm’s inter-firm market learning capability. For example, Achrol andKotler (1999) theorized that because networked organizations (those with formal-ized, long-term contractual relationships with other organizations) focus primarilyon processing information and creating knowledge, more and more marketingactivities are characterized by the managing of inter-firm relationships (p. 161).Marketing integrates and coordinates inter-firm information and resource flowsnot only through the effective management of external relationships (p. 153) butalso through effective internal relations that ensure dense lateral connections, lowdepartmental walls, and openness to the environment (p. 151).

Third, we provide a more complete test of social capital theory by examining howthe TMT’s internal and external relationships affect market learning capability.Rindfleisch and Moorman (2001) posit that external relationships (such as fre-quency of contact between the firm and its alliance partners) are antecedents ofknowledge acquisition and exploitation in new product alliances. However, socialcapital theory clearly suggests that resources accrue to individuals or groupsthrough durable internal and external relationships (Adler and Kwon 2002; Leanaand Van Buren 1999). Our test allows us to discern whether these internal andexternal dimensions provide different contributions to inter-firm market learningcapability and business performance. Practically, knowledge of the differentialeffects of internal and external social capital will reveal new interventions for build-ing inter-firm market learning capability and for facilitating marketing’s role in theperformance of the increasingly networked organization.

Fourth, we examine the mediating role of inter-firm market learning capabilitybetween the TMT’s social capital and business performance, a question that hasbeen largely ignored by scholars. That is, does inter-firm market learning capabilityfully or partially mediate the effects of the social capital antecedents on businessperformance? Empirical support for a full mediation would suggest the need forresearch focused on inter-firm market learning processes as an important depen-dent variable. It would also imply that managers should emphasize intermediateinter-firm market learning processes in evaluating the firm’s value creation activitiesand marketing performance, and in allocating scarce resources. Support for a par-tial mediation would indicate that both the TMT’s social capital and inter-firmmarket learning capability are more powerful than previously envisaged. Thiswould suggest that researchers and managers pay equal attention to both con-structs as antecedents of business performance.

In summary, in this study, we integrate resource-based and social capital theories toshed light on the question of how the internal and external relationships of theTMT affect inter-firm market learning capability and, in turn, enhance businessperformance. Our contributions are summarized into the following three researchquestions:

❒ Does inter-firm market learning capability, per se, influence business performance?


❒ If so, do the TMT antecedents of inter-firm market learning capability, as identified in social capital theory, also exert an influence on business performance?

❒ Can social capital, as embedded in the firm’s TMT, be leveraged for busi-ness performance through inter-firm market learning capability?

In addition to the theoretical and practical contributions advanced above, thisstudy contributes to the literature methodologically. We empirically demonstratethe construct validity of inter-firm market learning capability and of several otherconstructs derived from social capital theory. Our measures can serve as usefultools for future efforts to understand and harness the value of marketing’s interac-tions for leveraging inter-firm market knowledge. In the next section of the paperwe develop the hypotheses. We then present the study methods followed by empir-ical tests of the hypotheses. We conclude with a discussion of the study’s results.


Theory Development

Inter-firm Market Learning Capability

Day (1994, p. 38) describes the capability of market-driven firms as involvingcomplex bundles of skills and collective learning that are engendered through orga-nizational processes (see also Li and Calantone 1998). Previous research suggeststhat such knowledge acquisition and exploitation processes are key capabilitiesbecause they involve socially complex learning processes and relational skills thatare unique to the firm. They are embedded in the cognitive activities of the firmand cannot be easily observed, duplicated by competitors, or purchased (Hunt andLambe 2000; Hunt and Morgan 1995).

Resource-based theory (Barney 1991) suggests that organizational processes thatresult from such socially complex phenomena unique to the firm are more likely tolead to competitive advantages that are hard for competitors to duplicate. Thus,knowledge acquisition and exploitation processes are isolating mechanisms that arekey drivers of sustainable competitive advantage (e.g., Day 1994; Day and Wensley1988; Hunt and Lambe 2000; Hunt and Morgan 1995). This resource-based per-spective is consonant with the view that firms are repositories and integrators ofknowledge resources (Grant 1996). Hence, the firm’s ability to create marketableproducts is determined not primarily by its physical or financial assets, but is gen-erated from its intangible, knowledge-based resources. However, to create value, itis not enough for the firm to acquire knowledge; knowledge must be developed,nurtured, and integrated with the existing knowledge base throughout the organi-zation. This perspective underlies our definition of inter-firm market learningcapability, presented earlier.

Our conceptualization of inter-firm market learning capability differs from Rind-fleisch and Moorman’s (2001) “amount of product and process informationacquired from new product alliances.” Inter-firm market learning capability reflectsa set of organizational routines or processes for acquiring, integrating, and exploitinginter-firm market knowledge. It implies a form of organizational absorptive capac-ity (Cohen and Levinthal 1990; Zahra and George 2002), describing the ability toacquire, assimilate, and exploit external market knowledge. Our definition suggeststhat inter-firm market learning is a dynamic capability embedded in routines andprocesses that enable the organization to adapt to changing market conditions(Cohen and Levinthal 1990; Zahra and George 2002). These processes ensure notjust a greater amount of high quality inter-firm information, but its acquisition,integration, and exploitation.

External knowledge acquisition and exploitation is a process engendered by thesocial capital accruing from managerial relationships (Cohen and Levinthal 1990;Rindfleisch and Moorman 2001; Yli-Renko, Autio, and Sapienza 2001; Zahra andGeorge 2002). Social capital refers to the information, influence, and solidaritythat accrue to individuals or groups by virtue of possessing durable relationships(Adler and Kwon 20002; Nahapiet and Ghoshal 1998). Adler and Kwon (2002)


argued that social capital resources contribute to firms’ innovativeness and effec-tiveness by reducing transaction costs within and between firms, notably informa-tion search and decision-making costs. The authors made a distinction betweeninternal and external social capital. Internal social capital pertains to the resourcesthat accrue to individuals and units as a result of the extent and quality of theirrelationships within a firm. Such relationships, in particular those features that givethem cohesiveness such as trust and respect, facilitate the sharing of information.External social capital pertains to the resources that reside in the relationshipsbetween individuals or groups from different firms. Social capital theory contendsthat firms create competitive advantage through the combination of new and exist-ing knowledge by harnessing the value of these internal and external relationships.

An integration of the resource-based perspective with social capital theory suggeststhat a theory of inter-firm market learning capability must recognize the role ofboth internal and external managerial relationships. Accepting that marketing per-sonnel are the embodiments of the firm’s market knowledge suggests that theamount and quality of the TMT’s relationships with other departments within thefirm and with boundary personnel in the inter-firm relationships are criticalantecedents of inter-firm market learning capability. This insight frames our con-ceptual model presented in Figure 1.

Figure 1. Conceptual Framework of Top Marketing Teams’ Social Capital Antecedents and Outcomes of Inter-firm Market Learning Capability

Control Variables

• Technological uncertainty• Market uncertainty • Principal industry

TMT Internal Social Capital

• Associability• Network comfort

Inter-firm Market Learning Capability Business Performance

TMT External Social Capital

• Relational intensity• Relationship quality

Control Variables

• TMT size• Age of the firm• Firm size• Marketing capacity• Knowledge redundancy• # of firms in relationship• Type of inter-firm relationship


TMT’s Internal Social Capital and Inter-firm Market Learning Capability

To obtain inter-firm market knowledge, the focal firm must be able to manage itsinternal relationships to increase the willingness of all units to recognize externalfirms as potential sources of market knowledge. Such willingness will determinethe investment in organizational processes that can capture inter-firm marketknowledge. Social capital researchers argue that knowledge acquisition andexploitation depend not only on the extent and quality of relationships amonginternal units of the firm but also the degree to which they are comfortable withnetworking (i.e., interacting and cooperating with each other) (Nahapiet andGhoshal 1998; Tsai and Ghoshal 1998). For example, Leana and Van Buren(1999) proposed that internal social capital results from associability (i.e., trust,reciprocity, and respect between units) and the willingness of managers to interact.Such relational resources ensure communication, collective goals, and effort amongunits towards the generation and recombination of new knowledge.

Marketing scholars also have highlighted that the interaction between marketingand other functions facilitates positive outcomes such as understanding and profi-ciency in organizational activities (Atuahene-Gima and Evangelista 2000; Fisher,Maltz, and Jaworski 1997; Maltz and Kohli 1996). The degree of trust and inter-departmental connectedness between different units of the firm also influences thecollection and use of market information (e.g., Jaworski and Kohli 1993; Maltzand Kohli 1996). Consequently, we examine the TMT’s internal relationships inthe form of associability and network comfort as antecedents of inter-firm marketlearning capability.

TMT Associability. TMT’s associability refers to the degree of mutual trust, reci-procity, and respect that exists between the TMT and other departments in thefirm (Leana and Van Buren 1999). It reflects the ability of interacting units withinfirms to cooperate and mutually adjust with each other—which is manifested astrust, communication, and close interaction (Sivadas and Dwyer 2000). TMTassociability enhances inter-firm market learning capability for three reasons. First,the generation and integration of market knowledge in inter-firm relationshipsrequire knowledge of the sources of information in partner firms (Leana and VanBuren 1999). Associability facilitates this task because it ensures greater communi-cation and understanding among internal units concerning the role that differenttypes of market information residing in inter-firm relationships have in the firm’sactivities. Second, it ensures different departments’ agreement on the firm’s goals,and on the importance of inter-firm market knowledge in achieving them. Lack oftrust and respect generate tension between marketing and other units, thereby sti-fling any consensus regarding where and how to source and use market informa-tion (Jaworski and Kohli 1993; Maltz and Kohli 1996). Finally, trust and respectbetween the TMT and other departments promotes greater confidence in the com-petence and reliability of marketing. This ensures greater cooperation and con-certed effort between marketing and other units, thus ensuring precision inlocating and using inter-firm market knowledge. In sum, TMT associability leadsto greater value being placed on inter-firm market knowledge and on the processesto leverage such knowledge by all units in the firm.


TMT’s Network Comfort. Social psychologists describe network comfort as thedegree to which an individual or group is comfortable with seeking advice andinformation from external entities such as family, friends, and acquaintances (Azrinand Besalel 1982). Some individuals and groups avoid networking because theyfeel embarrassed asking others for advice and information. Those with greater net-work comfort are more effective in social interactions and have greater success inobtaining external opportunities than those with less network comfort (Granovet-ter 1995). Following these descriptions, we define the TMT’s network comfort asits willingness to interact to seek advice and ideas from other people. The con-struct implies an ability and willingness to recognize the TMT’s limitations and toengage outsiders as sources of information.

A TMT uncomfortable with the idea of seeking advice and information from otherpeople in its day-to-day work is inward looking, less confident, and less inquisitive.Such a team is unlikely to propose or support the building of organizationalprocesses to leverage the knowledge that resides with the firm’s relationship part-ners. Since the acquisition and use of inter-firm market knowledge is psychologi-cally inconsistent with such a team, it is likely to develop a “not-invented-here”syndrome, may have little, if any, social interactions, and may not recognize accessopportunities or the value of inter-firm market knowledge (see Inkpen 1998).

The preceding discussion leads to the following two hypotheses:

H1: The TMT’s associability is positively related to inter-firm market learningcapability.

H2: The TMT’s network comfort is positively related to inter-firm market learn-ing capability.

TMT’s External Social Capital and Inter-firm Market Learning Capability

In addition to internal relationships, social capital theory (Adler and Kwon 2002)suggests that success in obtaining inter-firm knowledge also depends on the will-ingness of partners in the relationship to share information, which in turn,depends on their perception of the nature and quality of the inter-firm relation-ship. Intensive and high quality interpersonal relationships between the boundarypersonnel as well as the structure of the inter-firm relationship offer significantsocial capital benefits in the form of information, knowledge, and other resourceexchanges (Nahapiet and Ghoshal 1998). Thus, previous research in new technol-ogy ventures found that inter-firm information acquisition and exploitation areimpacted by relational intensity (frequency of interaction) and relationship quality(trust) (Yli-Renko, Autio, and Sapienza 2001). Research in marketing also stressesthe importance of the reciprocity, closeness, and bonding between boundary per-sonnel in inter-firm relationships that transcends economic exchange (Achrol1997; Anderson and Narus 1990; Hunt and Morgan 1995; Mohr, Fisher, andNevin 1996; Morgan and Hunt 1999). Following these studies, we focus on theTMT’s relational intensity and relationship quality as external social capitalantecedents of inter-firm market learning capability.1


Relational Intensity. Relational intensity refers to the frequency of contact andcloseness of interaction between the TMT and their managerial contacts in theinter-firm relationship. It reflects the strength of the relationship between theTMT and the contact personnel in the other firms (Rindfleisch and Moorman2001). Yli-Renko, Autio, and Sapienza (2001) make two arguments for linking fre-quent and close interactions with the knowledge acquisition and exploitationprocess in inter-firm relationships. First, frequent interactions permit the parties inthe relationship to become more comfortable with each other’s competence andreliability. Second, frequent and close interactions enhance each party’s knowledgeof the other’s systems and operations, thereby reducing their search costs. Thesequalities of relational intensity also foster an increased sense of interdependencewith boundary personnel in other firms.

Since market knowledge is tacit and can only be acquired and exploited throughan iterative process and intimate contact, relational intensity is particularly impor-tant in fostering its acquisition and exploitation (Day 1994; Kale, Singh, and Perl-mutter 2000). To acquire rich and fine-grained information, the focal firm needsto develop mechanisms that ensure close and frequent interactions to obtain accessopportunities (Kraatz 1998). As the example of GM indicated (Business Week2001), knowledge that resides in inter-firm relationships can be more effectivelyassessed via organizational routines that ensure multiple interactions with bound-ary personnel of partner firms (Inkpen 1998, p. 75).

In support of these arguments, Mohr, Fisher, and Nevin (1996) find that frequentand close communication in inter-firm relationships encourages the sharing ofinformation. Rindfleisch and Moorman (2001) find a positive relationshipbetween relational intensity and new product information acquisition from alliancepartners.

Relationship Quality. Relationship quality reflects trust, commitment, and theabsence of opportunistic tendencies between the parties in a relationship(Geyskens, Steenkamp, and Kumar 1998). Specifically, we define relationship qual-ity as the degree to which the relationship between the TMT and their managerialcontacts in inter-firm relationships is characterized by mutual trust, integrity, andconcern for the success of each other’s business operations. Relationship quality isan important dimension of external social capital (Leana and Van Buren 1999;Nahapiet and Ghoshal 1998) because it reflects confidence in the honesty andbenevolence of the parties in the relationship. It promotes a long-term perspectiveand the perception of mutual gain between parties so that they forgo short-terminterests and refrain from opportunism (see Doney and Cannon 1997; Ganesan1994; Sivadas and Dwyer 2000).

Previous research (e.g., Yli-Renko, Autio, and Sapienza 2001) has argued that rela-tionship quality encourages the development of mutual expectations and under-standing. This ensures that little time is wasted in bargaining and monitoringpartner activities, facilitating the speedy location and acquisition of information.Indeed, parties in a high quality relationship are thought to be more flexible andexperimental in information sharing. By ensuring openness and transparency,


relationship quality allows for greater information exchange because it increases thewillingness of the parties to share both proprietary company and industry informa-tion. Relationship quality also makes it easier for parties in inter-firm relationshipsto understand who controls critical information and where it is located (Kale,Singh, and Perlmutter 2000). These benefits increase the willingness of the focalfirm to invest in inter-firm market learning processes. The preceding discussionleads to the following two hypotheses:

H3: Relational intensity is positively related to inter-firm market learning capability.

H4: Relationship quality is positively related to inter-firm market learning capability.

Inter-firm Market Learning Capability and Business Performance

Resource-based theory suggests that an important source of differentiation in firmperformance is knowledge-based capability (Barney 1991; Teece, Pisano, andSheun 1997). Thus, business performance depends on the firm’s ability to createand combine new knowledge with existing knowledge (Tsai and Ghoshal 1998)and to integrate complementary knowledge that may not be available in-house(Cohen and Levinthal 1990; Grant 1996). A firm’s capability to acquire and utilizeexternal market knowledge residing in inter-firm relationships is one such capabil-ity (Hunt and Lambe 2000; Morgan and Hunt 1999). Inter-firm market learningcapability improves business performance by enhancing the breadth and depth ofknowledge that is available to the focal firm and by enhancing the speed and effi-ciency of value-creating activities such as innovation (Yli-Renko, Autio, andSapienza 2001). Inter-firm market learning capability helps the firm to develop anew perceptual schema about existing innovation and other organizationalprocesses for value creation and delivery in order to effectively respond to newenvironmental cues (Zahra and George 2002).

Inter-firm market knowledge is also helpful in upgrading existing products toincrease their value to customers (Morgan and Hunt 1999, p. 284; Sivadas andDwyer 2000). Sourcing and using market information from partners in inter-firmrelationships widens the firm’s market knowledge sources, thereby improvingexploration of new ideas and exploitation of existing ideas in new ways (see Kokaand Prescott 2002; Zahra and George 2002). Furthermore, by spotting trends intheir partners and internalizing the knowledge, the focal firm creates greateropportunities for cost saving and other economies throughout its operations (Huntand Lambe 2000; Zahra and George 2002). In brief, inter-firm market learningcapability facilitates the capture and use of unique, diverse, and rich market knowl-edge, thereby enhancing business performance. Stated formally,

H5: Inter-firm market learning capability is related positively to business performance.


TMT’s Social Capital, Inter-firm Market Learning Capability, and BusinessPerformance

Previous research has reported direct effects of social capital on both knowledgeacquisition and performance (see Rindfleisch and Moorman 2001). However, anydirect effects study tends to leave the more fundamental intervening processesbetween antecedents and outcomes virtually unexplored (Brown and Eisenhardt1995). This argument implies that the links between social capital, knowledgeacquisition, and performance may be more complex than has previously been theo-retically argued and empirically tested. For example, Rindfleisch and Moorman(2001) find a direct positive relationship between external social capital (frequencyof contact) and external knowledge acquisition from product development alliancepartners, as well as between external knowledge acquisition and performance out-comes such as new product creativity. This suggests that it may be the knowledgeacquisition process that is most proximal to performance and that social capitalresources influence performance through their effects on this variable.

We argue earlier that inter-firm market learning capability improves business per-formance. In addition, we predict that a TMT’s social capital is antecedent tointer-firm market learning capability. These two groups of hypotheses implicitlyassume that inter-firm market learning capability is the conduit through which aTMT’s social capital influences business performance. Thus, inter-firm marketlearning capability is a process variable that coverts the social capital of the TMT(input) into business performance (output). Hence,

H6: Inter-firm market learning capability will fully mediate the relationshipbetween the TMT’s internal and external social capital and business performance.


Research Method

Sample and Data Collection

The study’s sample consisted of 600 strategic business units from manufacturingfirms in two industry groups comprising pharmaceutical, chemical, biotech, andmedical equipment; information technology electronics; computer and softwaredevelopment; and telecommunications. The sample was selected randomly from acommercial list provided by Kompass Australia. After pre-notification, we mailed athree-page survey to the companies; three weeks later we followed up non-respon-dents with a telephone reminder and replacement questionnaire. We received 195usable questionnaires, a response rate of 32.5 percent. We compared the early andlate respondents and found no significant differences on characteristics such asfirm size, number of firms in the inter-firm relationship, and firm age. The averageTMT size of the sample was 5.32, with average annual sales of $478.10 million.

A number of procedures were adopted to ensure the quality of the data. First, likeprevious studies of inter-firm relationships (e.g., Morgan and Hunt 1994; Rind-fleisch and Moorman 2001), we used the key informant approach. The informantswere selected through initial telephone contact with participating companies. Weobtained our data from senior marketing executives such as vice presidents, direc-tors, or managers of marketing. Following recommended best practice in using thekey information approach in inter-organizational research (see Kumar, Stern, andAnderson 1993), we also asked the informants to indicate their degree of knowl-edge of the selected inter-firm relationship on a 7-point scale (1 = “Not at AllKnowledgeable” to 7 = “Extremely Knowledgeable”). The mean of 6.00 (s.d. =.85) is evidence of the informants’ knowledgeability.

Although we were assured of the quality of our key informants, as a further valida-tion we asked them to seek consensus with knowledgeable colleagues if this washelpful in completing the questionnaire. This allowed us to verify whether multi-ple informants’ consensus ratings differed from single key informant’s ratings ofthe questionnaire items (see Kumar, Stern, and Anderson 1993). We compared theresponses of the 22 percent of informants who consulted colleagues with those ofthe 78 percent who completed the questionnaire on their own, and found no sig-nificant differences. Following several past studies in inter-firm relationships (e.g.,Kale, Singh, and Perlmutter 2000; Rindfleisch and Moorman 2001; Yli-Renko,Autio, and Sapienza 2001), we asked the key informant to focus on the firm’smajor inter-firm relationship. As argued in previous research, this has two advan-tages. First, it reduces the burden on the informant, and second, it ensures validityby capturing the unique attributes of a specific relationship rather than all thefirm’s inter-firm relationships. The average number of firms in the major inter-firmrelationship in the sample was 3.27 (s.d. = 1.25).

We restricted recall time to three years by focusing the key informant on the busi-ness unit’s performance in the most recent three-year period. Finally, we motivated


the key informants to provide valid data by assuring confidentiality and by offeringa summary of the research results—information that would be meaningless in theabsence of accurate data.

Measures

The measures used in the study are presented in the appendix. The study designincorporated a variety of other procedures to minimize artificial response consis-tencies, whereby the key informants infer the expected relationships between vari-ables and then shade their responses so that they are consistent with the expectedrelationships. For example, we used a 7-point scale for the measures of the maindependent variable—business performance—and 5-point scales for the measures ofall other variables but the key informant knowledge measure discussed above (seeAvolio, Yamarino, and Bass 1991; Li and Calantone 1998).

Second, we followed the post-hoc method suggested by Podsakoff and Organ(1986). We conducted a second survey with the same respondents, two monthsafter the original data collection, to obtain data on the main dependent variables—inter-firm market learning capability and business performance. The idea behindthis practice is that the expected relationships between variables would not besalient to the informant at this later time, hence, less, if any, shading would occur.Therefore, significant correlations between the later measures and original mea-sures would confirm the validity of the original measures and an absence of com-mon method bias (Fichman and Kemerer 1997; Yli-Renko, Autio, and Sapienza2001).

From the second survey, we obtained data from 79 of the original 195 respon-dents. The measure of inter-firm market learning capability in the follow-up surveycorrelated significantly with the original measure (r = .85, p < .001). The measureof business performance in the follow-up survey correlated significantly with theoriginal measure (r = .41, p < .001). Although not perfect, we believe that thesecorrelations, coupled with the use of multiple item constructs, the consistency ofmultiples and single informant data, and the different scale formats, would tend toreduce the effects of common method bias, if any.

After completing this method, a statistical remedy of common method variancewas also employed. We conducted the Harman’s one-factor test where all the vari-able measures were entered into a single factor analysis as recommended by Pod-sakoff and Organ (1986) and adopted in several previous studies (e.g.,Atuahene-Gima and Li 2002). The results uncovered neither a single factor nor ageneral factor that could account for the majority of the covariance in the vari-ables. This provided additional evidence that common method variance was not aproblem in the sample.

Inter-firm market learning capability (α = .90) involves organizational processes thatallow the focal firm to identify and integrate the market information that resides inthe firms in the selected inter-firm relationship. It follows that an operationalizationof the construct should not only include the acquisition, but also the sharing andintegration of market knowledge to facilitate combination and recombination


(see Grant 1996). Accordingly, we measured the construct with the average score of10 items that reflect the collection, sharing and integration, and use of inter-firmmarket knowledge by the focal firm. Specifically, we reviewed the literature (e.g.,Achrol 1997; Achrol and Kotler 1999; Day 1994) and adapted statements describ-ing processes for market knowledge acquisition and exploitation to the inter-firmcontext. We then pretested these measures along with the entire instrument, with15 marketing executives. Based on the feedback, we made changes to improve theclarity of the questionnaire. A sample item is “we have processes for learning aboutmarketplace developments that our partners know about” (1 = “Strongly Disagree”to 5 = “Strongly Agree”).

We used the average of five items to measure business performance (α = .88). Theitems capture the extent to which the firm has performed better or worse than itsmajor competitor in the last three years following the formation of the major inter-firm relationship in terms of sales growth, market share, new product growth,growth in profit, and market share growth. The scale format was 1 = “MuchWorse” to 7 = “Much Better,” and was adapted from the work of Narver and Slater(1990).

To measure TMT associability (α = .89), we asked the informant to agree or dis-agree with four statements that describe the degree to which the TMT had close,personal relationships with members of other departments, and the extent towhich the relationships were characterized by mutual trust, reciprocity, and respect(1 = “Strongly Disagree” to 5 = “Strongly Agree”). The scale is an adaptation ofKale, Singh, and Perlmutter’s (2000) descriptions of relational capital to reflect theinteraction between marketing and other units of the firm. The four items wereaveraged for an overall score.

We measured the network comfort of the TMT by using the average score of threeitems. Social capital researchers typically discuss network comfort as reflecting thepropensity of the individual or group to seek external help. Such a description isconsonant with the importance that the individual or group attaches to social rela-tionships and the frequency of their interactions (see Azrin and Besalel 1982; Gra-novetter 1995). Drawing on this research, we asked informants to indicate on a5-point scale (1 = “Strongly Disagree” to 5 = “Strongly Agree”) whether the TMTmembers (1) contact people they know for advice regarding their work, (2) call orvisit someone to get information about a problem, and (3) ask for a referral tosomeone who might have helpful information about their work. This is a forma-tive scale because the different forms of interaction are not necessarily correlated.

We measured relational intensity (α = .75) with the average score of four state-ments that asked informants to indicate on a 5-point scale (1 = “Strongly Dis-agree” to 5 = “Strongly Agree”) the frequency of interaction, closeness, andreciprocity between the TMT and managerial contacts in the firms in the selectedrelationship. This measure is an adaptation of items used by Yli-Renko, Autio, andSapienza (2001) and Kale, Singh, and Perlmutter (2000) reflecting the frequencyof interaction of the parties in an inter-firm relationship.


In previous research relationship quality is typically measured by determining thedegree of trust (honesty and benevolence) that exist between the parties in a rela-tionship (e.g., Crosby, Evans, and Cowles 1990; Doney and Cannon 1997; Gane-san 1994). Using this related work, we measured relationship quality (α = .83)with five items. Specifically, we asked the key informants to indicate on a 5-pointscale (1 = “Strongly Disagree” to 5 = “Strongly Agree”) the degree to which theybelieved the relationship between the TMT and managerial contacts in the otherfirms in the relationship was characterized by mutual trust, integrity, commitmentto each other’s business interests, and concern for the success of each other’s busi-ness. We averaged the five items to form the construct’s measure.

Control Variables. To provide a stronger test of the theory developed in our model,we controlled for a number of firm, inter-firm relationship, and industry variablesthat might affect both inter-firm market learning capability and business perfor-mance. We controlled for TMT size (number of people who constituted the firm’sTMT) because large teams are believed to have more social connections than smallteams. Hence, large teams are more likely to have better opportunities to obtainricher, yet more diverse information from their inter-firm relationships than smallteams would. Firm size (the logarithmic transformation of number of employees)reflects the resource availability as well as the complexity and extensiveness of thefirm’s strategic processes. Thus, it may influence external information collectionand performance. Age of the firm was measured by the number of years the firmhas been in existence. Research suggests that older firms may be inert and lesslikely to generate new capabilities, implying a lower inclination to source externalknowledge (McEvily and Zaheer 1999).

We also controlled for the firm’s marketing capacity (α = .77), defined as the rela-tive strength of its marketing competencies compared with major competitors.This is a broad construct that encompasses the firm’s marketing assets such ascapabilities in understanding its customers, competition, and market conditions, aswell as in marketing strategy development and implementation (Day 1994; Dayand Wensley 1988; Srivastava, Tasadduq, and Fahey 1998). We measured market-ing capacity by four items that asked the key informants to rate on a 5-point scale(1 = “Much Worse” to 5 = “Much Better”) how their firms compared with themajor competitor in terms of investments in marketing assets (such as productdevelopment, market research, customer service, pricing), extensiveness of distribu-tion channels, investments in brand building and advertising, and reputation foreffective marketing and customer service.

We controlled for three characteristics of the inter-firm relationship. The first isknowledge redundancy—the extent to which the information and knowledge baseof the focal firm’s partners overlap with its own knowledge. Rindfleisch and Moor-man (2001) found that knowledge redundancy improves new product outcomesbut hinders knowledge acquisition from external partners. An indicator of knowl-edge redundancy is the degree of similarity between the capabilities of the focalfirm and its partners in the inter-firm relationship. For example, these authorsmeasured knowledge redundancy by the degree of similarity in the product devel-opment skills, knowledge, and resources of the focal firm and its alliance partner.


Following this example, and using a 5-point scale (1 = “Strongly Disagree” to 5 =“Strongly Agree”), we measured knowledge redundancy (α = .73) with the averageof four items that tap the degree of similarity between the focal firm and the firmsin the selected inter-firm relationship in terms of target customers, type of innova-tions developed, technological skills, and manufacturing processes.

The second factor is the number of firms in the inter-firm relationship. A largenumber of firms in a relationship means that there are better opportunities, andthus greater incentives for building routines for market knowledge acquisition andexploitation (Zaheer and Zaheer 1997). The third factor, type of inter-firm rela-tionship, was measured by asking informants to indicate whether or not theselected major inter-firm relationship was a vertical collaboration (that is, withfirms along the value chain) (coded as 0) or was a horizontal collaboration (that is,with competitors or potential competitors) (coded 1). Tapping information fromvertical and horizontal inter-firm relationships may differ and thus, necessitate dif-ferent investments and processes to capture and use market information, and alsoinfluence performance (Yli-Renko, Autio, and Sapienza 2001). Further, firmsalong the value chain may have greater propensity to share information than director potential competitors.

Finally, we controlled for three industry factors. These included market and tech-nological uncertainty, because they may engender organizational processes forlearning from other firms and influence also business performance. The four spe-cific measures for technological uncertainty (α = .89) were adapted from Jaworskiand Kohli (1993), reflecting the speed of change, uncertainty, and unpredictabilityof technological developments in the firm’s industry. We averaged the items tomeasure the construct. We measured market uncertainty (α = .76) with the averageof four new items that capture the unpredictability of changes in the market envi-ronment, speed of change in the competitive environment, uncertainty of competi-tor actions, difficulty of forecasting customer demand, and speed of change incustomer preferences. We controlled for the effects of the firm’s major industrysince industry conditions such as growth stage, complexity of underlying technolo-gies, and other such characteristics may have influence over the firm’s informationacquisition and its performance.

Scale Purification

We established the reliability and validity of the measures following the standardprocedures recommended by Anderson and Gerbing (1988). We performedexploratory factor analysis on each of the constructs to determine the unidimen-sionality of its measurement items and underlying factor structure. After confirm-ing the unidimensionality of the constructs, we used confirmatory factor analysis(CFA) to establish their validity. We followed the established procedure of estimat-ing submodels (e.g., Atuahene-Gima and Li 2002; Sivadas and Dwyer 2000; Rind-fleisch and Moorman 2001) for two reasons. First, because of sample sizeconstraints, all the 44 construct indicators could not be included in a single modelwithout violating the five-to-one sample size to parameter estimate ratio. Second,

grouping maximally similar constructs provides a more stringent test of discrimi-nant validity (Campbell and Fiske 1959; Rindfleisch and Moorman 2001).

We ran two separate measurement models to examine maximally similar constructs(excluding the formative scale items for network comfort). We grouped inter-firmmarket learning capability, business performance, market uncertainty, technologicaluncertainty, and marketing capacity into the first CFA analysis. The second CFAanalysis grouped measures for the TMT’s associability, relational intensity, relation-ship quality, and knowledge redundancy. Each measurement item was restricted toload on its hypothesized factor. All items had significant loadings on their expectedconstructs. The loadings and model fit indices showed that our models fit the dataquite well (see the appendix).

Next, we examined the discriminant validity of the measures in two ways. First, weconducted a chi-square difference test for all the pairs of constructs to see if theywere distinct from each other. The process involved collapsing each pair of con-structs into a single model and comparing its fit with that of a two-constructmodel (Anderson and Gerbing 1988; Rindfleisch and Moorman 2001). In eachcase, a two-factor model had a better fit than a single-factor model. We also reportthe average variance extracted, which assesses the amount of variance captured bythe construct’s measures relative to measurement error and the correlations (φ esti-mates) among the latent constructs in the model. Estimates of .50 or higher indi-cate validity for a construct’s measure. All but two of our constructs (relationalintensity and knowledge redundancy) achieved this criterion, a not uncommonoccurrence with marketing constructs.

Fornell and Larcker (1981) suggest that to demonstrate discriminant validity, theconstruct’s average variance should be greater than the shared variance between theconstruct and other constructs in the model (i.e., the squared correlation betweentwo constructs). This can be demonstrated in a correlation matrix which includesthe correlations between different constructs in the lower left off-diagonal elementsof the matrix, and the square roots of the average variance extracted values, calcu-lated for each of the constructs along the diagonal (Hulland 1999). An examina-tion of Table 1 reveals that the diagonal elements are significantly greater than theoff-diagonal elements, thereby satisfying this criterion of discriminant validity.

Finally, to establish the internal consistency of the measures we computed boththeir Cronbach alpha and composite reliabilities. As reported previously, the alphascore for each construct was above the widely accepted threshold of .70 for eachvariable. Further, the composite reliabilities reported in the appendix exceed thelevel of .70. Table 1 presents the correlation matrix and descriptive statistics of themeasures.


Tab

le 1

. Co

rrel

atio

n M

atri

x an

d D

escr

ipti

ve S

tati

stic

s o

f M

easu

res

Varia

bles

12

34

56

78

910

1112

1314

1516

1. In

ter-

firm

mar

ket l

earn

ing

capa

bilit

y.8

3a

2. B

usin

ess

perf

orm

ance

.28*

*.8

2

3. T

MT

asso

ciab

ility

.36*

*.1

8*.8

3

4. T

MT

netw

ork

com

fort

.49*

*.1

5*.0

7N/

A

5. T

MT

rela

tiona

l int

ensi

ty.4

3**

.25*

*.1

3.1

9*.6

7

6. T

MT

rela

tions

hip

qual

ity.3

2**

–.07

.21*

*.2

1**

.46*

*.7

2

7. T

MT

size

–.02

–.05

–.15

*.0

5–.

20*

–.17

*N\

A

8. A

ge o

f the

firm

–.19

*–.

02–.

04.0

4–.

02.0

1.0

9N\

A

9. F

irm s

ize (l

og)

–.23

**–.

07–.

11.0

4.0

6–.

11.3

8**

.41*

*N\

A

10. M

arke

ting

capa

city

.27*

*.5

6**

.22*

*.0

7.2

9**

.09

–.02

.07

–.03

.73

11. N

umbe

r of f

irms

in in

ter-

firm

rela

tions

hip

.11

.08

.06

.04

.10

.05

–.11

–.04

–.06

.03

N\A

12. T

ype

of in

ter-

firm

rela

tions

hip

.04

.12

.23*

*–.

04.1

4.1

3–.

16*

–.01

–.17

*.1

5–.

09N\

A

13. K

now

ledg

e re

dund

ancy

.16*

–.05

.06

.04

.23*

*.1

9*–.

11–.

07–.

05–.

08–.

02.1

0.6

2

14. T

echn

olog

ical

unc

erta

inty

.03

–.11

–.17

*.1

2.1

2.0

0.0

6–.

03.1

5*–.

12.1

3–.

04–.

00.7

2

15. M

arke

t unc

erta

inty

–.01

–.16

*–.

09–.

03.2

0*.1

0–.

09.0

2–.

04–.

13.0

3.1

0.0

1.2

3**

.82

16. P

rinci

pal i

ndus

try

.09

.16*

.07

–.13

.20*

*.1

4–.

05.0

2–.

08.1

2–.

07.2

8**

.10

–.19

*–.

10N\

A

Mea

n3.

194.

763.

683.

152.

693.

355.

3242

.53

5.49

3.21

3.27

3.20

3.43

3.33

Stan

dard

dev

iatio

n.6

4.9

5.7

4.7

4.6

9.6

52.

8230

.68

1.96

.68

1.25

.76

1.00

.76

aDi

agon

al e

lem

ents

in b

old

are

squa

re ro

ots

of a

vera

ge v

aria

nce

extra

cted

.*

p<

.01;

**p

< .0

01


Analysis and ResultsTo test hypotheses 1–4, we regressed inter-firm market learning capability on thecontrol variables and the four independent variables. The results are presented inTable 2 (Model 2). We examined the variance inflation factors (VIF) and found thatthe highest VIF in any model was less than 3, well below the accepted cut-off of 10,indicating that multicollinearity is not a serious problem. All regressions met themajor model assumptions; that is, no violations were found in the plots of standard-ized residuals as compared to the predicted values, in the normal probability plots ofthe standardized residuals, and with regard to the independence of error terms.


Table 2. Hierarchical Regression Analysis of the Antecedents and Effects of Inter-firm MarketLearning Capability a

Inter-firm Market Learning CapabilityBusiness Performance

Model 1 Model 2 Model 3 Model 4 Model 5

Control variablesTMT size .16* .17* .01 .00 .03Age of the firm .08 .01 –.05 –.04 –.04Firm size (log of number of employees) –.29*** –.22** –.02 –.02 –.02Marketing capacity .28*** .16* .63*** .57*** .53***Number of firms in inter-firm relationship .09 .04 .09 .07 .06Type of inter-firm relationship –.08 –.14* –.06 –.08 –.05Knowledge redundancy .18* .10 –.01 –.03 –.05Technological uncertainty .24** .18** –.02 –.06 –.10Market uncertainty .11 .03 –.09 –.14* –.14*Principal industry .26** .18* .15* .10 .07

Independent variablesTMT associability .28*** .02 .04TMT network comfort .32*** .16** .10TMT relational intensity .21*** .22** .17*TMT relationship quality .07 –.13* –.14*TMT relationship quality squared .10+ .12*

Mediating variableInter-firm market learning capability .20**

∆R2 .22 .06 .02∆F 13.16*** 2.97** 5.39**R2 .27 .48 .49 .54 .56Adjusted R2 .21 .43 .46 .49 .50F 4.70*** 8.38*** 11.75*** 9.45*** 9.52***N 139 139 134 134 134

a Standardized regression coefficients are reported. + p < .10* p < .05

** p < .01*** p < .001


As shown in Table 2 (Model 2), the internal and external social capital factorsexplained a significant amount of variance in inter-firm market learning capabilityabove the contribution of the control variables (∆R2 = 22, F = 13.16, p < .001).Regarding internal social capital, we found that TMT’s associability (β = .28, p < .001) and network comfort (β = .32, p < .001) are positively related to inter-firm market learning capability, as predicted in H1 and H2. Regarding externalsocial capital, we found that relational intensity (β = .21, p < .001) is positivelyrelated to inter-firm market learning capability, supporting H3. H4, pertaining torelationship quality, was not supported. Of the control variables, TMT size (β = .17, p < .001), marketing capacity (β = .16, p < .05), technological uncer-tainty (β = .18, p < .01), and industry sector (β = .18, p < .01) are each positivelyrelated to inter-firm market learning capability. In contrast, firm size (β = –.22, p < .001) and type of inter-firm relationship (β = –.14, p < .05) have a negativerelationship with inter-firm market learning capability.

To test whether inter-firm market learning capability fully mediates the relation-ship between a TMT’s social capital and business performance, we conducted athree-stage analysis to determine whether the three conditions for mediation asoutlined by Baron and Kenny (1986) were satisfied. The data in Model 2 (Table 2)test the condition that the independent variables should predict the proposedmediator (inter-firm market learning capability). As indicated above, this conditionis satisfied for three constructs: TMT’s associability, network comfort, and rela-tional intensity. Model 4 (Table 2) tests the second condition that the independentvariables significantly predict the dependent variable, business performance. Theresults indicate that the TMT’s network comfort (β = .16, p < .05) and relationalintensity (β = .22, p < .001) have positive and significant relationships with busi-ness performance. TMT’s associability is unrelated to business performance. Surprisingly, relationship quality (β = –.13, p < .05) is negatively related to busi-ness performance. As expected, the control variable marketing capacity (β = .57, p < .001) has very strong positive effect on business performance. Market uncer-tainty (β = –.14, p < .05) is negatively related to business performance.

Model 5 presents a test of the third condition for mediation, which requires theeffects of the independent variables on business performance to drop substantiallyfor partial mediation and to become insignificant for full mediation, when enteredinto the model together with the mediator. The addition of inter-firm marketlearning capability to the regression equation adds 2 percent (∆R 2 = .02, F = 5.39, p < .01) to the explained variance in business performance (Model 5). This resultsupports the positive relationship that was predicted in H5 (β = .20, p < .05).

However, unlike in Model 4, TMT network comfort is not significant in Model 5,suggesting that inter-firm market learning capability fully mediates its effect onbusiness performance. In contrast, the effect size of one external social capital construct (relational intensity) on business performance is substantially reduced (β = .17, p < .05), suggesting that it is partially mediated by inter-firm marketlearning capability. The effect of the second external social capital construct, relationship quality, remains unchanged (β = –.14, p < .05), suggesting a negative


relationship with business performance. We investigated this surprising effect fur-ther by examining whether the relationship may be curvilinear. To test this, weentered a squared term for relationship quality into the model. As indicated inModel 5, the squared term has a positive effect on business performance (β = .12,p < .05), suggesting that relationship quality has a U-shaped relationship withbusiness performance. These results suggest that H6 is partially supported.

Discussion Current research portrays an atomistic view of market knowledge acquisition andexploitation processes. It has also neglected the role of marketing personnel in theprocess. Recognizing that a key aim for firms in entering into inter-firm relation-ships is to acquire and integrate market knowledge, scholars have called for aninvestigation of the factors that influence inter-firm market learning capability andits effects on performance (Hunt and Lambe 2000). Few studies have responded tothis call. In this study, we do so by measuring inter-firm market learning capabilityand examining its antecedents in the form of the internal and external social capi-tal of the TMT, as well as its effects on business performance.

Our results suggest that the internal social capital of the firm’s TMT (networkcomfort) and its external social capital (relational intensity) have significant posi-tive effects on inter-firm market learning capability. We also found that inter-firmmarket learning capability has a positive relationship with business performance. Italso fully mediates the effect of one internal social capital variable, TMT’s networkcomfort, and partially mediates the effect of one external social capital variable ofthe TMT (relational intensity) on business performance. Relationship quality has aU-shaped relationship with business performance, unmediated by inter-firm mar-ket learning capability. These results show that the links between the TMT’s socialcapital, inter-firm market learning capability, and business performance are morecomplex than previously envisaged.

The findings pertaining to a TMT’s associability and network comfort suggestthat the marketing team’s internal relationships are important in helping the firmto appreciate the value of, and to be willing to mobilize, inter-firm market knowl-edge. TMTs characterized by high associability and network comfort have fewerinternal relationship barriers and cognitive inhibitions regarding the value of otherfirms as intelligence units. Our results therefore reinforce the critical importance ofmanagers’ internal social capital to the operations of the firm (Leana and VanBuren 1999; Sivadas and Dwyer 2000), and are consistent with the finding thatmarketing’s interaction with other functions is an important ingredient in a firm’smarket knowledge competence (Li and Calantone 1998). In addition, our resultsshow that the TMT’s associability has an indirect positive effect on business per-formance through inter-firm market learning capability. In contrast, the direct pos-itive effect of the TMT’s network comfort on business performance is fullymediated by inter-firm market learning capability. These findings support the the-ory that internal social capital may engender business performance through theacquisition of external knowledge (see Adler and Kwon 2002), a fact that has notbeen investigated, or has been obscured in previous studies.

Our prediction about the role of the TMT’s external social capital received partialsupport. We found that inter-firm market learning capability is facilitated by rela-tional intensity. Relational intensity also has a positive relationship with businessperformance, albeit partially mediated by inter-firm market learning capability.


These results corroborate recent findings by Rindfleisch and Moorman (2001) andare consistent with research that shows that learning in inter-firm relationships isfacilitated by frequent and close interactions (Yli-Renko, Autio, and Sapienza2001). However, our results are the first to shed light on the role of both internaland external relationships of the top marketing personnel in leveraging marketknowledge from inter-firm relationships.

Relationship quality has an insignificant effect on inter-firm market learning capa-bility but a significant negative effect on business performance at lower levels andpositive effect at higher levels. Previous research (e.g., Yli-Renko, Autio, andSapienza 2001) has reported a negative effect of relationship quality on knowledgeacquisition from the venture’s key partner. The logic for this result is that a firmcould become over-embedded in a relationship such that it develops too muchtrust in partners to provide information, thereby reducing its own motivation toacquire the necessary external information. Similarly, marketing scholars haveargued that a high relationship quality may serve as a filter for information andperspectives that degrade the quality of the knowledge that is acquired by a groupor firm (Maltz and Kohli 1996). Recent empirical research has also shown thatsupervisee trust may have negative consequences for sales performance (Atuahene-Gima and Li 2002). Relationship quality may also generate what Gargiulo andBenassi (2000, p. 186) refer to as cognitive lock-in, whereby the firm or groupbecomes stuck in a specific relationship, thereby isolating itself from novel infor-mation and ideas (Adler and Kwon 2002; Koka and Prescott 2002).

All these arguments have been made without the benefit of the knowledge of thepotential nonlinear effects of relationship quality on performance. Our results sug-gest that the downside of relationship quality appears to be manifested in businessperformance rather than in inter-firm market learning capability but only at lowerlevels. At higher levels relationship quality has a positive relationship with businessperformance, suggesting an overall U-shaped relationship. This finding is a majorcontribution because it contradicts the received wisdom discussed in the precedingparagraph. Our result cautions the uncritical acceptance of the negative view ofrelationship quality because its effect on business performance appears to be morecomplex.

Theoretical Implications

There are several theoretical implications from this study. First, this study takes thefirst step in empirically isolating several antecedents of inter-firm market learningcapability and in examining its effect on business performance. Examining thedirect effect of inter-firm market learning capability on business performance leadsto the conclusion that it is an important source of competitive advantage as envis-aged by Hunt and Lambe (2000) when calling for an empirical examination ofinter-firm market knowledge acquisition and exploitation.

Second, this study provides a step towards a better theoretical exposition and under-standing of the effects of the TMT’s internal and external social capital on marketknowledge acquisition and exploitation, and their contribution to business perfor-mance. Marketing scholars are increasingly adopting a social capital perspective



when examining the role of marketing in the firm (e.g., Achrol and Kotler 1999).However, to date, few empirical studies have employed social capital theory tounderstand inter-firm market knowledge acquisition and exploitation as an organiza-tional capability and the role played by marketing personnel. Barney (1991) hasargued that the manager is a unique resource, and that managerial talent is an isolat-ing mechanism that is a source of competitive advantage. Thus, by explicitly concep-tualizing the internal and external social capital of the TMT, this study sheds lighton how the relational behaviors of marketing personnel inside and outside the orga-nization uniquely benefit the firm’s performance through inter-firm market learningcapability. Overall, our results imply that the TMT’s social interactions are impor-tant firm resources that must be nurtured and sustained.

Third, this study provides empirical evidence for the potential intervening role ofinter-firm market learning capability between the TMT’s social capital and busi-ness performance. We show that inter-firm market learning capability propels theeffects of the TMT’s social capital (associability, network comfort, and relationalintensity) onto business performance. This observation reinforces our argumentthat the concept of inter-firm market learning capability should be construed andunderstood in terms of a social interaction process. Our results suggest that theTMT’s relational intensity has even greater power than the theory would suggest.It appears that this form of the TMT’s external social capital has carry-over effectsto business performance beyond its effects on inter-firm market learning capability.The U-shaped effect of relationship quality implies that lower levels of trust mayhurt business performance but higher levels enhance business performance, andthat this effect is unmediated by inter-firm market learning capability. Theseresults point to the complexity of the relationship between the TMT’s social capi-tal, inter-firm market learning capability, and business performance. They suggestthe need for researchers to tease out direct and indirect effects when investigatingthe potential value of inter-firm market learning capability and its antecedents.

Fourth, the lack of influence of knowledge redundancy (a control variable) onbusiness performance deserves discussion. Based on previous research we expectedit to hinder inter-firm market learning capability and engender higher businessperformance. Both expectations were not met. Contrary to the argument thatknowledge redundancy reduces the willingness for information sharing in inter-firm relationships (Rindfleisch and Moorman 2001), it appears that it has littleeffect on the firm’s external market knowledge acquisition. This result suggests theneed for future research to examine this construct in more detail, perhaps focusingon potential moderating factors between the inter-firm relationship structure andknowledge acquisition and exploitation processes.

This study shows that the integration of resource-based and social capital theoriesprovides an efficient explanatory platform to understand the links between theTMT’s social capital, inter-firm market learning capability, and business perfor-mance. The convergence provides a creative illustration of marketing’s internal andexternal social interactions as catalysts for inter-firm knowledge acquisition andexploitation. Taking account of this theoretical dynamic might change our percep-tion and evaluation of the role of marketing in the firm, not only in theory butalso in practice.


As a final advance of the literature, this study takes the first step in measuring andestablishing the construct and predictive validity of inter-firm market learningcapability, thereby contributing to the emerging relational view of marketing capa-bilities (e.g., Achrol 1997; Achrol and Kotler 1999; Morgan and Hunt 1999). Thisprovides opportunities for researchers to explore the variety of core research ques-tions that relate to how such a capability is developed and how it influences firmperformance.

Managerial Implications

At a practical level our framework is a challenge to the atomistic view that the firmdevelops and exploits market knowledge single-handedly. Given the central role ofinter-firm market knowledge resources to the focal organization, such a view isclearly undesirable. This research provides the rationale and the means for top man-agement to build inter-firm market learning capability. The results provide severalavenues through which top management can accomplish this task effectively.

First, the results suggest that building inter-firm market learning capability is fun-damentally a social interaction process. Thus, there is a need for actions that facili-tate effective internal and external social interactions on the part of the firm’sTMT. Given that acquiring the market knowledge that resides in inter-firm rela-tionships entails the collective effort and contribution of other units, our findingssuggest that TMTs that lack network comfort, and the trust and respect of otherdepartments are unlikely to be effective in building inter-firm market learningcapability. The TMT’s associability and network comfort may be facilitated byjoint departmental activities, common goals, location proximity, and reward sys-tems that ensure reciprocity and collective effort (see Leana and Van Buren 1999;Maltz and Kohli 1996).

Second, our results pertaining to external social capital of TMTs suggest that topmanagers should encourage TMTs to develop a deeper involvement in the forma-tion and operation of the firm’s inter-firm relationships. The top management mayneed to employ marketing personnel who cognitively value external market knowl-edge that is owned and controlled by other firms. These people do not display a“not-invented-here” syndrome, and appreciate the value of inter-firm relationshipsas market intelligence sources, and hence would facilitate inter-firm market learningcapability. Management could provide marketing personnel with clearer guidelinesto accurately ground their perceptions of the role that inter-firm market knowledgeplays in the value-creating activities of the organization. This could be achieved by,for instance, organizing training sessions to expose top marketing personnel to thecapabilities, the other firms’ competitors, and the general strategies of firms in therelationship. Accomplishing these objectives also calls for increased resources for themarketing function to develop and maintain external relationships and to improveits expertise in both internal and external relationship building.

Limitations and Future Research Directions

Like all survey-based studies, generalizations beyond the sample must be viewedwith some caution. Further, the variance in inter-firm market learning capability


that is accounted for by the TMT’s social capital antecedents is moderate. Theselimitations suggest the need for future research to investigate inter-firm marketlearning capability with other samples and other potential antecedents. Althoughwe predicted and found a positive relationship between inter-firm market learningcapability and business performance, such a capability may not be functional in allcircumstances. Thus, there is a need for future research to explore the factors thatmay moderate the impact of inter-firm market learning capability on business per-formance. Such a research design will test a key tenet of resource-based theory thata capability will lead to sustainable competitive advantage when deployed judi-ciously and in combination with other complementary capabilities and in theappropriate environment (Barney 1991; Teece, Pisano, and Sheun1997; Zahra andGeorge 2002). Similarly, another limitation of the current study is that we did notexamine the factors that may moderate the relationship between the TMT’s inter-nal and external social capital and both inter-firm market learning capability andbusiness performance. Social capital may have both functional and dysfunctionaleffects (see Adler and Kwon 2002; Burt 1997; Koka and Prescott 2002). Thus,future research should examine the conditions under which TMTs’ social capitalmay be more or less effective.

Finally, research suggests that a particular inter-firm relationship may be embeddedin other interorganizational relationships (Achrol 1997; Achrol and Kotler 1999;Anderson, Håkansson, and Johanson 1994). This dimension, like knowledgeredundancy, highlights a structural aspect of inter-firm relationships, describing theconnections between a primary relationship and a broader interorganizational net-work. Highly embedded networks involve interactions with third parties and amulti-directional flow of information. Hence, to the extent that the primary inter-firm relationship provides a bridge to other firms in a broader network, it increasesthe access opportunities for novel and diverse external market knowledge (Burt1997). For example, McEvily and Zaheer (1999) found that such bridging ties area source of competitive advantage because they broaden and deepen the marketknowledge of the firm. In addition, Yli-Renko, Autio, and Sapienza (2001) foundthat where the primary inter-firm relationship provides links to a broader network,the firm is exposed to a broader set of learning opportunities that enhance bothmarket knowledge acquisition and innovation. This empirical evidence suggeststhat, in such an interorganizational configuration, the social capital of the market-ing function may link the focal organization to more distant, novel, and diversemarket knowledge. Since such information is a critical ingredient in radical inno-vation, penetration of new markets, and overall organizational renewal (Danneels2002, in press), future research should investigate more extensively the role of themarketing function’s social capital in these renewal processes.


ConclusionIncreasingly, marketing is being called upon to play a more decisive role in theactivities of the networked organization. This study deepens our understanding ofhow marketing’s social interactions can engender a critical marketing capability forsuch an organization. The results illuminate the value of inter-firm market learningcapability and provide insights into some of the interventions that managers cantake to encourage its development in the firm. We believe that the integration ofresource-based and social capital theories holds exciting opportunities in pursuingthe unfinished task of understanding marketing’s role in harnessing the benefits ofthe firm’s inter-firm relationships.


Appe

ndix

: Con

firm

ator

y Fa

ctor

Ana

lysi

s Re

sults

Con

stru

ctO

pera

tion

al M

easu

res

of C

onst

ruct

Mod

el 1

Mod

el F

it I

ndic

es: χχ

2=

354.

66, χχ

2 /df

= 1

.87,

GFI

= .8

6, I

FI =

.93,

CFI

= .9

0, N

NFI

= .9

3, R

MSE

A =

.06

SFL

*t-

valu

e

Plea

se in

dica

te y

our

agre

emen

t w

ith

each

of

the

follo

win

g st

atem

ents

reg

ardi

ng y

our

SBU

in it

s re

lati

onsh

ip

wit

h fir

m p

artn

ers

in y

our

maj

or in

ter-

firm

rel

atio

nshi

p.•

We

have

a f

orm

al p

roce

ss f

or f

requ

ently

col

lect

ing

cust

omer

and

mar

ket

info

rmat

ion

and

know

-how

.6

27.

22fr

om o

ur p

artn

ers.

• W

e ha

ve p

roce

sses

for

lear

ning

abo

ut m

arke

tpla

ce d

evel

opm

ents

tha

t ou

r pa

rtne

rs k

now

abo

ut.

.66

8.09

• W

e ha

ve a

pro

cess

for

acc

umul

atin

g co

mpe

tito

r in

form

atio

n av

aila

ble

wit

h ou

r pa

rtne

rs.

.64

7.61

• W

e re

gula

rly

stud

y th

e ne

w p

ract

ices

, pro

duct

s, m

arke

t, an

d te

chno

logy

dev

elop

men

ts o

f ou

r pa

rtne

rs.

.74

9.20

• W

e re

gula

rly

com

bine

mar

ket

info

rmat

ion

and

know

-how

fro

m p

artn

ers

wit

h ou

r ow

n re

sour

ces

for

inte

rnal

use

..7

39.

15•

We

oper

ate

an e

ffec

tive

pro

cess

for

tra

nsfe

rrin

g an

d sh

arin

g m

arke

t kn

owle

dge

from

par

tner

s ac

ross

all

func

tion

s..6

98.

53•

We

freq

uent

ly in

tegr

ate

mar

ket

know

ledg

e fr

om p

artn

ers

wit

h ou

r in

tern

al k

now

ledg

e in

per

form

ing

our

acti

viti

es.

.82

10.7

7•

We

resp

ond

quic

kly

to m

arke

t in

form

atio

n fr

om o

ur p

artn

ers

in o

ur v

alue

cre

atin

g ac

tivi

ties

..6

16.

99•

We

have

an

effe

ctiv

e pr

oces

s fo

r st

orin

g an

d re

trie

ving

mar

ket

info

rmat

ion

from

par

tner

s fo

r fu

ture

use

..7

79.

90•

We

brai

nsto

rm o

n ho

w t

o us

e m

arke

t in

form

atio

n an

d kn

ow-h

ow f

rom

our

par

tner

s in

our

act

ivit

ies.

.64

7.79

How

has

you

r bu

sine

ss u

nit

fare

d in

its

perf

orm

ance

ove

r th

e la

st t

hree

yea

rs fo

llow

ing

the

form

atio

n of

the

in

ter-

firm

rel

atio

nshi

p?•

Sale

s gr

owth

com

pare

d to

you

r m

ajor

com

peti

tor

.72

6.99

• M

arke

t sh

are

com

pare

d w

ith

your

maj

or c

ompe

tito

r.9

69.

87•

New

pro

duct

gro

wth

com

pare

d w

ith

your

maj

or c

ompe

tito

r.7

57.

36•

Gro

wth

in p

rofit

com

pare

d w

ith

your

maj

or c

ompe

tito

r.6

97.

00•

Mar

ket

shar

e gr

owth

com

pare

d w

ith

your

maj

or c

ompe

tito

r.8

69.

17

How

doe

s yo

ur s

trat

egic

bus

ines

s un

it c

ompa

re w

ith

its

maj

or c

ompe

tito

r ov

er t

he la

st t

hree

yea

rs in

the

fol

low

ing

area

s?•

Inve

stm

ents

in m

arke

ting

ass

ets

(e.g

., pr

oduc

t de

velo

pmen

t, m

arke

ting

res

earc

h, c

usto

mer

ser

vice

, pri

cing

).7

57.

78•

Ext

ensi

vene

ss o

f di

stri

buti

on c

hann

els

.66

6.76

• In

vest

men

ts in

bra

nd b

uild

ing

and

adve

rtis

ing

.67

6.87

• R

eput

atio

n fo

r ef

fect

ive

mar

keti

ng a

nd c

usto

mer

ser

vice

.71

6.99

How

doe

s ea

ch o

f th

e fo

llow

ing

stat

emen

ts d

escr

ibe

your

SB

U’s

tech

nolo

gy e

nvir

onm

ent

over

the

last

thr

ee y

ears

?•

The

tec

hnol

ogy

in o

ur in

dust

ry h

as b

een

chan

ging

rap

idly

..8

610

.64

• Te

chno

logi

cal c

hang

es a

re u

npre

dict

able

in o

ur in

dust

ry.

.63

7.26

• T

here

hav

e be

en m

ajor

cha

nges

in p

rodu

ct t

echn

olog

y id

eas

in o

ur in

dust

ry.

.67

7.82

• T

here

hav

e be

en m

ajor

tec

hnol

ogic

al d

evel

opm

ents

in o

ur in

dust

ry.

.68

7.85

Inte

r-fi

rm M

arke

t L

earn

-in

g C

apab

ility

(N

ew s

cale

influ

ence

d by

Day

199

4;D

ay a

nd W

ensl

ey 1

988)

CR

= .9

6, A

VE

= .6

9

Bus

ines

s Pe

rfor

man

ce(I

nflu

ence

d by

Nar

ver

and

Slat

er 1

990)

CR

= .9

1,AV

E =

.67

Mar

keti

ng C

apac

ity

(New

sca

le in

fluen

ced

bySr

ivas

tava

, She

rvan

i and

Fahe

y 19

98)

CR

= .7

5,

AVE

= .5

3

Tec

hnol

ogic

al U

ncer

tain

ty(J

awor

ski a

nd K

ohli

1993

)C

R =

.81,

AV

E =

.52


How

doe

s ea

ch o

f th

e fo

llow

ing

stat

emen

ts d

escr

ibe

your

bus

ines

s un

it’s

mar

ket

envi

ronm

ent

over

the

last

thr

ee y

ears

? •

The

com

peti

tive

mar

ket

envi

ronm

ent

chan

ges

very

rap

idly

..8

812

.19

• C

ompe

tito

r ac

tion

s ar

e hi

ghly

unc

erta

in.

.55

6.49

• C

usto

mer

dem

and

is v

ery

diff

icul

t to

for

ecas

t in

our

indu

stry

..9

313

.32

• C

usto

mer

pre

fere

nces

cha

nge

quit

e ra

pidl

y..9

012

.64

Mod

el 2

Mod

el F

it I

ndic

es: χχ

2=

158.

51, χχ

2 /df

= 1

.44,

GFI

= .8

8, I

FI =

.95,

CFI

= .9

4, N

NFI

= .9

3, R

MSE

A =

.05

Plea

se in

dica

te y

our

degr

ee o

f ag

reem

ent

wit

h th

ese

stat

emen

ts in

rel

atio

n to

the

top

mar

keti

ng t

eam

’s re

lati

onsh

ip

wit

h ot

her

depa

rtm

ents

in y

our

busi

ness

uni

t.•

The

re is

clo

se, p

erso

nal i

nter

acti

on w

ith

func

tion

al m

anag

ers

in t

he f

irm

..8

210

.94

• T

he r

elat

ions

hip

wit

h ot

her

depa

rtm

ents

is c

hara

cter

ized

by

mut

ual r

espe

ct.

.89

12.4

7•

The

rel

atio

nshi

p w

ith

othe

r de

part

men

ts is

cha

ract

eriz

ed b

y m

utua

l tru

st.

.90

12.6

1•

The

rel

atio

nshi

p w

ith

othe

r de

part

men

ts is

cha

ract

eriz

ed b

y hi

gh r

ecip

roci

ty.

.66

8.07

How

doe

s ea

ch o

f th

e fo

llow

ing

stat

emen

ts d

escr

ibe

your

top

mar

keti

ng t

eam

’s re

lati

onsh

ip w

ith

thei

r m

anag

eria

l co

ntac

ts in

the

par

tner

fir

ms

in y

our

maj

or in

ter-

firm

rel

atio

nshi

p?•

We

have

ver

y fr

eque

nt b

usin

ess

inte

ract

ions

wit

h th

em.

.76

8.89

• B

ecau

se o

f ou

r fr

eque

nt in

tera

ctio

ns, w

e ca

n be

des

crib

ed a

s a

tigh

tly k

nit

grou

p..5

96.

53•

We

have

ver

y fr

eque

nt s

ocia

l int

erac

tion

s w

ith

them

..6

06.

61•

Rec

ipro

city

and

fav

ors

are

impo

rtan

t in

our

rel

atio

nshi

ps.

.73

8.37

How

doe

s ea

ch o

f th

ese

stat

emen

ts d

escr

ibe

the

rela

tion

ship

bet

wee

n th

e to

p m

arke

ting

tea

m a

nd t

heir

man

ager

ial

cont

acts

in t

he p

artn

er f

irm

s in

you

r m

ajor

inte

r-fir

m r

elat

ions

hip?

• W

e ke

ep e

ach

othe

r’s in

tere

sts

in m

ind.

.82

9.87

• T

here

is v

ery

high

reg

ard

for

each

oth

er’s

inte

grit

y..6

06.

92•

We

cons

ider

eac

h ot

her

as t

rust

wor

thy.

.61

7.22

• W

e ar

e co

ncer

ned

that

eac

h ot

her’s

bus

ines

s su

ccee

ds.

.75

9.27

• W

e ca

n re

ly o

n ea

ch o

ther

to

fulfi

ll ou

r re

spon

sibi

litie

s to

the

oth

er.

.79

9.28

To w

hat

exte

nt is

eac

h of

the

fol

low

ing

stat

emen

ts t

rue

of y

our

SBU

and

the

par

tner

fir

ms

in t

his

maj

orin

ter-

firm

rel

atio

nshi

p?•

Our

bus

ines

ses

have

sim

ilar

type

s of

cus

tom

ers.

.69

7.35

• O

ur b

usin

esse

s de

velo

p si

mila

r in

nova

tion

s..6

56.

81•

Our

bus

ines

ses

have

sim

ilar

tech

nolo

gica

l ski

lls.

.62

6.63

• O

ur b

usin

esse

s us

e si

mila

r m

anuf

actu

ring

ski

lls a

nd t

echn

olog

ies.

.57

5.95

*Sta

ndar

dize

d fa

ctor

load

ing

Not

es:

CR

= C

ompo

site

rel

iabi

lity

AVE

= A

vera

ge v

aria

nce

extr

acte

d

Mar

ket

Unc

erta

inty

(New

sca

le)

CR

= .8

9,AV

E =

.68

TM

T A

ssoc

iabi

lity

(New

sca

le in

fluen

ced

byK

ale,

Sin

gh, a

nd P

erlm

ut-

ter

2000

) C

R =

.89,

AV

E =

.68

TM

T R

elat

iona

l Int

ensi

ty(K

ale,

Sin

gh, a

nd P

erlm

ut-

ter

2000

) C

R =

.77,

AV

E =

.45

TM

T R

elat

ions

hip

Qua

lity

(Ada

pted

fro

mC

rosb

y, E

vans

, and

Cow

les

1990

; Don

ey a

nd C

anno

n19

97)

CR

= .8

4,

AVE

= .5

2

Kno

wle

dge

Red

unda

ncy

(New

sca

le in

fluen

ced

byR

indf

leis

ch a

nd M

oorm

an20

01)

CR

= .7

6,

AVE

= .3

9

Note1. As mentioned later in the paper, we control statistically for knowledge redun-dancy because it is not an attribute of the individual or group, but rather the structure of the inter-firm relationship.



ReferencesAchrol, Ravi S. (1997), “Changes in the Theory of Interorganizational Relations in

Marketing: Toward a Network Paradigm.” Journal of Academy of MarketingScience 25 (1), 56–71.

——, and Philip Kotler (1999), “Marketing in the Network Economy.” Journal ofMarketing 63 (Special Issue), 146–63.

Adler, Paul S., and Seok-Woo Kwon (2002), “Social Capital: Prospects for a NewConcept.” Academy of Management Review 27 (1), 17–40.

Anderson, James C., and David W. Gerbing (1988), “Structural Equation Model-ing in Practice: A Review and Recommended Two-Step Approach.” Psycho-logical Bulletin 103 (3), 411–23.

——, Håkan Håkansson, and Jan Johanson (1994), “Dyadic Business Relation-ships Within a Business Network Context.” Journal of Marketing 58 (4),1–15.

——, and James A. Narus (1990), “A Model of Distributor and ManufacturerFirm Working Relationships.” Journal of Marketing 54 (1), 42–58.

Andrews, J., and Daniel C. Smith (1996), “In Search of Marketing Imagination:Factors Affecting the Creativity of Marketing Programs for Mature Prod-ucts.” Journal of Marketing Research 33 (May), 174–87.

Atuahene-Gima, Kwaku, and Felicitas Evangelista (2000), “Cross-functional Influ-ence in New Product Development: An Exploratory Study of Marketingand R&D Perspectives.” Management Science 46 (October), 1269–84.

——, and Haiyang Li (2002), “When Does Trust Matter? Antecedents and Con-tingent Effects of Supervisee Trust on Performance in Selling New Productsin China and the United States.” Journal of Marketing 66 (July), 61–81.

Avolio, Bruce F., J. Francis Yamarino, and Bernard M. Bass (1991), “IdentifyingCommon Method Variance with Data Collected from a Single Source: AnUnresolved Sticky Issue.” Journal of Management 17 (3) (September),521–87.

Azrin, N. H., and V. B. Besalel (1982), Finding a Job. Berkeley, Calif.: Ten SpeedPress.

Barney, Jay (1991), “Firm Resources and Sustained Competitive Advantage.” Jour-nal of Management 17 (1), 99–120.

Baron, Rueben M., and David A. Kenny (1986), “The Moderator-Mediator Vari-able Distinction in Social Psychological Research: Conceptual and Statisti-cal Considerations.” Journal of Personality and Social Psychology 51 (6),1173–82.


Brown, Shona, and Kathleen Eisenhardt (1995), “Product Development: PastResearch, Present Findings, and Future Research.” Academy of ManagementReview 20 (2), 343–78.

Burt, Ronald S. (1997), “The Contingent Value of Social Capital.” AdministrativeScience Quarterly 42 (June), 339–65.

Business Week (2001), “GM Tries to Show Who’s Boss: It Must Rally Its GlobalPartners and Tap Their Expertise.” (March 12),18–24.

Campbell, Donald T., and Donald W. Fiske (1959), “Convergent and Discrimi-nant Validity by the Multitrait-Multimethod Matrix.” Psychological Bulletin56 (March), 81–105.

Cohen, W. M., and Daniel A. Levinthal (1990), “Absorptive Capacity: A New Per-spective on Learning and Innovation.” Administrative Science Quarterly 35(March), 128–52.

Crosby, Lawrence A., Kenneth R. Evans, and Deborah Cowles (1990), “Relation-ship Quality in Services Selling: An Interpersonal Influence Perspective.”Journal of Marketing 54 (July), 68–81.

Danneels, Erwin (2002), “The Dynamics of Product Innovation and Firm Com-petencies.” Strategic Management Journal (in press).

Day, George S. (1994), “The Capabilities of Market-Driven Organizations.” Jour-nal of Marketing 58 (October), 37–52.

——, and Robin Wensley (1988), “Assessing Advantage: A Framework for Diag-nosing Competitive Superiority.” Journal of Marketing 52 (April), 1–20.

Doney, Patricia M., and Joseph P. Cannon (1997), “An Examination of the Natureof Trust in Buyer-Seller Relationships.” Journal of Marketing 61 (April),35–51.

Fichman, Robert G., and Chris F. Kemerer (1997), “The Assimilation of SoftwareProcess Innovations: An Organizational Learning Perspective.” ManagementScience 43 (10), 1345–63.

Fisher, Robert J., Elliot Maltz, and Bernard J. Jaworski (1997), “Enhancing Com-munication Between Marketing and Engineering: The Moderating Role ofRelative Functional Identification.” Journal of Marketing 61 (July), 54–70.

Fornell, Claes, and David F. Larcker (1981), “Evaluating Structural EquationModels with Unobservable Variables and Measurement Error.” Journal ofMarketing Research 28 (February), 39–50.

Ganesan, Shankar (1994), “Determinants of Long-Term Orientation in Buyer-Seller Relationships.” Journal of Marketing 58 (April), 1–19.

Gargiulo, Martin, and Mario Benassi (2000), “Trapped in Your Own Net? Net-work Cohesion, Structural Holes, and the Adaptation of Social Capital.”Organization Science 11(2), 183–96.

Geyskens, Inge, Jan-Bennedict E. M. Steenkamp, and Nirmalya Kumar (1998),“Generalizations about Trust in Marketing Channel Relationships UsingMeta-Analysis.” International Journal of Research in Marketing 15 (3),223–48.

Granovetter, Mark (1995), Getting a Job, 2nd ed. Chicago, Ill.: University ofChicago Press.

Grant, Robert M. (1996), “Prospering in Dynamic Competitive Environment:Organizational Capability as Knowledge Integration.” Organization Science7 (4), 375–87.

Hulland, John (1999), “Use of Partial Least Squares (PLS) in Strategic Manage-ment Research: A Review of Four Recent Studies.” Strategic ManagementJournal 20 (2), 195–202.

Hunt, Shelby, and C. J. Lambe (2000), “Marketing Contributions to BusinessStrategy: Market Orientation, Relationship Marketing and Resource-Advantage Theory.” International Journal of Management Reviews 2(March), 17–43.

——, and Robert M. Morgan (1995), “The Comparative Advantage Theory ofCompetition.” Journal of Marketing 59 (April), 1–15.

Inkpen, Andrew C. (1998), “Learning and Knowledge Acquisition Through Inter-national Strategic Alliances.” Academy of Management Executive 12 (4),69–80.

Jaworski, Bernard J., and Ajay K. Kohli (1993), “Market Orientation: Antecedentsand Consequences.” Journal of Marketing 57 (1), 53–70.

Kale, Prashant, Harbir Singh, and Howard Perlmutter (2000), “Learning and Pro-tection of Proprietary Assets in Strategic Alliances: Building RelationalCapital.” Strategic Management Journal 21 (3) (March), 217–37.

Koka, Balaji R., and John E. Prescott (2002), “Strategic Alliances as Social Capital:A Multidimensional View.” Strategic Management Journal 23 (September),795–816.

Kraatz, Matthew S. (1998), “Learning by Association? Interorganizational Net-works and Adaptation to Environmental Change.” Academy of ManagementJournal 41 (6), 621–43.

Kumar, Nirmalya, Loius W. Stern, and James C. Anderson (1993), “ConductingInterorganizational Research Using Key Informants.” Academy of Manage-ment Journal 36 (6), 1633–51.



Leana, Carrie R., and Harry J. Van Buren III (1999), “Organizational Social Capi-tal and Employment Practices.” Academy of Management Review 24 (3),538–55.

Li, Tiger, and Roger Calantone (1998), “The Impact of Market Knowledge Com-petence on New Product Advantage: Conceptualization and EmpiricalExamination.” Journal of Marketing 64 (4), 13–29.

Maltz, Elliot, and Ajay Kohli (1996), “Market Intelligence Dissemination acrossFunctional Boundaries.” Journal of Marketing Research 33 (February),47–61.

McEvily, Bill, and Akbar Zaheer (1999), “Bridging Ties: A Source of Firm Hetero-geneity in Competitive Advantage.” Strategic Management Journal 20 (12)(December), 1133–56.

Mohr, Jakki J., Robert J. Fisher, and John R. Nevin (1996), “Collaborative Com-munication in Interfirm Relationships: Moderating Effects of Integrationand Control.” Journal of Marketing 60 (July), 103–15.

Morgan, Robert M., and Shelby Hunt (1994), “The Commitment-Trust Theoryof Relationship Marketing.” Journal of Marketing 58 (3), 20–39.

——, and —— (1999), “Relationship-Based Competitive Advantage: The Role ofRelationship Marketing in Marketing Strategy.” Journal of Business Research46 (3) (November), 281–90.

Nahapiet, Janine, and Sumantra Ghoshal (1998), “Social Capital, Intellectual Cap-ital and Organizational Advantage.” Academy of Management Review 23(2), 242–66.

Narver, John C., and Stanley F. Slater (1990), “The Effect of a Market Orientationon Business Performance.” Journal of Marketing 54 (October), 20–35.

Podsakoff, Philip M., and Dennis W. Organ (1986), “Self-reports in Organiza-tional Research: Problems and Prospects.” Journal of Management 12 (Winter), 531–44.

Rindfleisch, Aric, and Christine Moorman (2001), “The Acquisition and Utiliza-tion of Information in New Product Alliances: A Strength-of-Ties Perspec-tive.” Journal of Marketing 65 (2), 1–18.

Sivadas, Eugene, and F. Robert Dwyer (2000), “An Examination of OrganizationalFactors Influencing New Product Success in Internal and Alliance-BasedProcesses.” Journal of Marketing 64 (January), 31–49.

Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), “Market-based Assets and Shareholder Value.” Journal of Marketing 62 (January),2–18.

Teece, David J., Gary Pisano, and Amy Sheun (1997), Dynamic Capabilities andStrategic Management.” Strategic Management Journal 18 (7) (August),509–33.

Tsai, Wenpin, and Sumantra Ghoshal (1998), “Social Capital and Value Creation:The Role of Interfirm Networks.” Academy of Management Journal 41 (4),464–76.

Wilson, Elizabeth M., and Gary Lilien (1992), “Using Single Informants to StudyGroup Choice: An Examination of Research Practice in OrganizationalBuying.” Marketing Letters 3 (July), 297–305.

Yli-Renko, Helena, Erkko Autio, and Harry J. Sapienza (2001), “Social Capital,Knowledge Acquisition, and Knowledge Exploitation in Young Technol-ogy-Based Firms.” Strategic Management Journal 22 (6/7) (June/July),587–613.

Zaheer, Akbar, and Srilata Zaheer (1997), “Catching the Wave: Alertness, Respon-siveness and Market Influence in Global Electronics Networks.” Manage-ment Science 43 (1), 1493–509.

Zahra, Sharker A., and Gerard George (2002), “Absorptive Capacity: A Review,Reconceptualization, and Extension.” Academy of Management Review 27(2), 185–203.


MARKETING SCIENCE INSTITUTE1000 Massachusetts AvenueCambridge, MA 02138 USA

Phone: 617.491.2060www.msi.org

the social capital of the top marketing team, inter-firm

Documents