An empirical investigation of the process of knowledge transfer in ...

Journal of International Business Studies (2004) 35, 407–427

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An empirical investigation of the process of knowledge transfer in international strategic alliances Bernard L Simonin The Fletcher School, Tufts University, Medford, MA, USA Correspondence: BL Simonin, The Fletcher School, Tufts University, Medford, MA 02155, USA. Tel: þ 1 617 627 5255; Fax: þ 1 617 627 3712; E-mail: [email protected]

Abstract This research proposes and tests a basic model of organizational learning that captures the process of knowledge transfer in international strategic alliances. Based on a cross-sectional sample of 147 multinationals and a structural equation methodology, this study empirically investigates the simultaneous effects of learning intent, learning capacity (LC), knowledge ambiguity, and its two key antecedents – tacitness and partner protectiveness – on technological knowledge transfer. In the interest of expanding our understanding of the organizational mechanisms that both hinder and facilitate learning, the concept of LC is refined into three distinct components: resource-, incentive-, and cognitive-based LC. Further, the strength of the relationships between these theoretical constructs and knowledge transfer is examined in light of the possible moderating effects of organizational culture, firm size, and the form and competitive regime of the alliance. Consistently, learning intent (as a driver) and knowledge ambiguity (as an impediment) emerge as the most significant determinants of knowledge transfer. Moreover, the effects of partner protectiveness and LC on the learning outcome are moderated by the firm’s own culture towards learning, the size of the firm, the structural form of the alliance, and the fact that partners may or may not be competitors. Journal of International Business Studies (2004), 35, 407–427. doi:10.1057/palgrave.jibs.8400091 Keywords: strategic alliances; knowledge transfer; learning capacity

Introduction Over the past 15 years there has been a growing interest in international strategic alliances and how organizations learn from their partners and develop new competencies through their collaborative efforts (Inkpen, 2002; Mowery et al., 2002). This distinct and multidisciplinary line of inquiry has generated a wealth of conceptual work and ‘theorizing’, but a limited amount of empirical work (Mjoen and Tallman, 1997; Simonin, 1999a). Not much has changed since the early warnings of Mowery et al. (1996, 78): Received: 26 February 2003 Revised: 12 February 2004 Accepted: 1 April 2004 Online publication date: 8 July 2004

Empirical research on the role of knowledge within the firm and alliances within firm strategy, has been hampered by the widespread reliance on anecdotes and assertion, rather than statistical evidence.

Process of knowledge transfer in strategic alliances

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To address these limitations, the current study will attempt to depart from speculative grounds in favor of empirically based research that relies on a large survey sample and on a structural equation methodology. Commenting on alliance learning research, Inkpen (2002, 277) confessed: Now that there is a solid base of antecedents research, the next step is theoretical and empirical work that integrates the diverse perspectives and establishes some causal links across the variables.

Organizational Motivation

Accordingly, it is the aim of this study to develop and test such an integrated model. Based on a crosssectional sample of 147 multinationals and a latent variable approach, this study will introduce and empirically investigate the process of knowledge transfer in international strategic alliances. The postulated model identifies and integrates various key organizational mechanisms that either enable

or hinder knowledge transfer. Specifically, it will account for the concurrent effects of learning intent, learning capacity (LC), knowledge ambiguity and tacitness, and partner protectiveness on technological knowledge transfer (see conceptual model in Figure 1). Although many studies have identified the importance of these variables separately, their simultaneous effects have yet to be examined and assessed empirically. In particular, the model will refine the concept of LC into three distinct components: resource-, incentive-, and cognitive-based LC. Further, the strength of the relationships between these theoretical constructs and knowledge transfer will be examined in light of the possible moderating effects of four key theoretical constructs: organizational culture, firm size, alliance form (equity vs non-equity), and the competitive regime of the alliance.

Theoretical model and background At the heart of the learning process in alliances are knowledge-specific, partner-specific (at the level of Learning Outcome

Organizational Mechanism LEARNING INTENT

H3a (+)

Learning Capacity (L.C.)

H3b (+)

H3c (+)

H1 (+) RESOURCE-BASED L.C.

H2a (+)

INCENTIVE-BASED L.C.

H2b (+) KNOWLEDGE TRANSFER

H2c (+)

Learning Hindrance

COGNITIVE-BASED L.C.

PARTNER PROTECTIVENESS

H4 (-) H6 (+) AMBIGUITY

H7 (+) TACITNESS

Moderating Effects Organizational Culture Firm Size Alliance Form Competitive Regime

Figure 1

Conceptual model.

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the knowledge seeker, knowledge provider, and their inter-relationship), and context-specific variables (Simonin, 1999a). This view is echoed by Inkpen (2002), who frames the antecedents of alliance learning into learning partner characteristics, teaching partner characteristics, knowledge characteristics, relationship factors, and alliance form. Building on this commonality, the proposed model represents a parsimonious, but yet complete, way of accounting for these various facets of the learning process: learning intent, learning capability, organizational culture, size (knowledge-seeker level); partner protectiveness (knowledge-holder level); tacitness and knowledge ambiguity (knowledge level); and alliance form and competitive regime (context level). This model also captures the fundamental steps between stimulus and response: motivation to learn, capacity to learn, and learning outcome. Finally, the model contrasts learning drivers and learning impediments by isolating organizational specificities and mechanisms that facilitate and hinder knowledge transfers in international alliances. In line with past research that has focused on the technology or technical-capability side (e.g., Hagedoorn and Schakenraad, 1994; Zander and Kogut, 1995; Appleyard, 1996), this study focuses on technology and process know-how. The various components of the proposed model are introduced, leading to the formulation of the specific hypotheses that govern their inter-relationships; in addition, the subsequent sections offer a close template to the operationalization of the variables in the model.

Learning drivers: learning intent and capacity Learning intent In the context of individual learning, motivation to learn is one of the major determinants of learning (Kelly, 1974; Filley et al., 1976). In an interorganizational setting such as a strategic alliance, learning intent (Hamel, 1991) describes the same self-determination, desire and will of an organization to learn from its partner or collaborative environment. It captures the degree of desire for internalizing a partner’s skills and competencies (Pucik, 1988; Hamel, 1990). It is clear that strong rationales for collaborating do not necessarily correspond to a strong learning intent. While the rationales for entering an alliance are numerous and well documented (Glaister and

Buckley, 1996), the focus on learning and knowledge transfer as motives has led to the emergence of a distinct stream of research that covers: (1) how knowledge is managed in strategic alliances (Tiemessen et al., 1997; Khanna et al., 1998; Inkpen, 2002; Martin and Salomon, 2002; Zeng and Hennart, 2002); (2) how knowledge is transferred across partners (Appleyard, 1996; Dodgson, 1996; Mowery et al., 1996, 2002; Baughn et al., 1997; Choi and Lee, 1997; Simonin, 1999a, b); (3) how knowledge is acquired from the parents by the joint venture itself (Lyles and Salk, 1996); (4) how knowledge about collaborating per se develops over time and impacts on collaborative outcomes (Doz, 1996; Powell et al., 1996; Dyer and Singh, 1998; Kale et al., 2002; Simonin, 2002); and (5) how knowledge impacts performance (Lane et al., 2001; Appleyard, 2002; Dussauge et al., 2002; Reuer et al., 2002; Tallman and Jenkins, 2002). A by-product of this research focus has been some degree of fascination with the notion of ‘learning race’ a` la Hamel (1990, 1991) or Reich and Mankin (1986). More recently, a strong research countermovement has surfaced pointing to the pitfalls and possible fallacy of this learning race metaphor. Zeng and Hennart (2002, 189), in particular, argue that racing is not as frequent as commonly believed, and that it should not be viewed as a goal for joint ventures, but rather as a consequence of poor joint venture design and management when parties are attempting to follow potentially more rewarding strategies of cooperative specialization. Mowery et al. (2002) acknowledge that few scholars have distinguished between these ‘learning’ and ‘co-specialization’ alliances. More importantly, these authors warn that the impact of absorptive capacity varies across these types of alliance and, therefore, that the literature’s main emphasis on learning alliances fails to capture the full understanding of the impact of absorptive capacity and alliance outcomes. The model proposed in this study addresses this criticism and reconciles the two views of alliances (learning race vs co-specialization) by formally accounting for an organization’s learning intent and postulating:



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Hypothesis 1: The higher the learning intent, the higher the level of knowledge transfer.

Learning capacity Under his discussion of awakening LC, Thompson (1995) acknowledges that the starting point for learning in the natural world is curiosity. That desire to learn plays the role of a stressor that helps enact the proper cognitive capacity and translates into the deployment of other complementary resources. Although motivation corresponds to a required level of stress favorable to stimulating learning, the actual level of learning is subject to cognitive and physical limitations as well as to internal constraints. LC at the organizational level can be thought of as the equivalent of bounded rationality at the individual level. Bounded rationality corresponds to the limited capacity of human beings to obtain, store, process, and share information accurately (Simon, 1978). Under Daft and Huber’s (1987) information-processing paradigm, organizational learning can be decomposed similarly into information acquisition, distribution, interpretation, and organizational memory. Under this approach, efficient communication systems are the key to alleviating the problem of bounded rationality. In turn, the efficiency of communication systems depends on the magnitude and appropriateness of resources allocated to that end. In a broader sense, LC, as the collection of all these dedicated resources, is both the engine and the bottleneck of the learning system. LC is related to, but at the same time distinct from, absorptive capacity. Originally, Cohen and Levinthal (1990, 128) defined absorptive capacity as the ‘ability to recognize the value of new external knowledge, assimilate it, and apply it to commercial ends.’ Whereas early investigations have focused on the issue of ‘prior related knowledge’, absorptive capacity remains a much more complex construct (if not concept). Unfortunately, as argued by Zahra and George (2002, 186), empirical studies do not always capture the rich theoretical arguments and the multidimensionality of the construct. In the context of learning alliances, research on absorptive capacity (Lane et al., 2001; Inkpen, 2002; Mowery et al., 2002) has evolved with a strong focus on the characteristics of the particular combination of partners. Dyer and Singh (1998) refer to it as ‘partner-specific absorptive capacity’. This conception of absorptive capacity emphasizes partner similarities (Lane and Lubatkin, 1998) and the breadth and depth of


overlap between partners (Mowery et al., 2002) rather than firm-level singularities. In comparison, LC is focused less on the specific combination of partners and the joint space (membrane) of the alliance. Rather, it is concerned with the firm-specific levers and resources that can be manipulated so that external knowledge can be recognized, assimilated, and applied beyond the joint space. In short, LC corresponds to the actionable side of absorptive capacity. In their reconceptualization of absorptive capacity, Zahra and George (2002) identify four complementary capabilities: knowledge acquisition, assimilation, transformation, and exploitation. For each one of these capabilities, LC represents the firm’s specific resources and assets that can be deployed operationally to drive the process and enhance its efficiency. LC is a fundamental determinant of absorptive capacity. Greater LC translates into greater absorptive capacity. Hamel (1990) names the capacity of organizations to learn from their partner receptivity. Among the factors determining receptivity, Hamel identifies the appropriateness of resource deployment, incentive systems, attitude towards learning, and the propensity to unlearn as crucial. Accordingly, in the context of this study (see Figure 1), we decompose LC into three separate components that correspond to three distinct classes of organizational routines and mechanisms that facilitate knowledge transfers in strategic alliances: (1) resource-based LC; (2) incentive-based LC; and (3) cognitive-based LC. Finally, the propensity to unlearn is also accounted for in the proposed model, not as a direct antecedent of learning, but rather as a manifestation of a particular organizational culture that may or may not be conducive to learning (moderating variable). Looking at the first component of LC – resourcebased LC – the appropriateness of resource deployment corresponds to the commitment of both human and tangible support assets. As recognized by Inkpen (2002), many studies have stressed the importance of managerial resources in the learning process, but few have shed light on how the process actually works. We contend that two factors have to be present. First, sufficient personnel should be involved in the alliance. Resource-poor staffing strategies


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(Pucik, 1988), driven by cost considerations rather than an investment outlook, certainly diminish the capacity to learn (usually by denying the slack resources necessary to learning). Limited staffing results in a constant struggle to solve immediate problems, leaving no leeway for learning. Second, the involvement of key personnel in the venture, at both the management and the operational level, is crucial for effective learning. Low quality of staff assigned to alliances (Pucik, 1988) can plague the learning process. Other damaging practices include insufficient lead time for staffing decisions and dependence on the partner for staffing as well as the deliberate lack of assignment of talented personnel outside headquarters or corporate labs or, worse, the dumping of sidetracked employees. Next to human resources, support assets in the form of information processing, logistic, financial, and communication capabilities are needed to help in the acquisition, processing, storage, and diffusion of relevant information and knowledge components. Does LC represented by the combination of these resources impact on the learning outcome? In their follow-up investigation of Hungarian international joint ventures, Lane et al. (2001) showed some partial support for the relationship between absorptive capacity and knowledge learned. Likewise, Mowery et al. (2002) have reported that, indeed, the outcome of learning alliances is influenced by partner-specific absorptive capacity. In the more inclusive context of this study, it is also expected that resource-based LC drives the learning outcome: Hypothesis 2a: The higher the resource-based LC, the higher the level of knowledge transfer. Whereas resource-based LC relates to the deployment of human resources and other physical assets, incentive-based learning capacity corresponds to explicit institutional routines, systems, rules and guidelines that clarify individual expectations and duties, steer learning activities in non-ambiguous terms, foster a learning orientation, and induce commitment to a learning objective. Building on Pucik’s (1988) framework of organizational learning obstacles in alliances, two specific manifestations of this type of LC are considered: (1) the existence of an actual reward system, and (2) the presence of a clear learning agenda. Successful knowledge-seeking firms reward alliance managers for what they learn (Baughn et al., 1997). A reward system for

learning can take many forms (e.g., a direct monetary incentive, a factor in promotion and advancement, or a source of formal recognition and acknowledgement in the organization). To be effective, a learning agenda must not only be clearly defined and codified in a language understood by all but it must also be communicated to the relevant parties (Pucik, 1988). As with resourcebased LC, incentive-based LC is expected to affect significantly the learning outcome: Hypothesis 2b: The higher the incentive-based LC, the higher the level of knowledge transfer. The third component of LC, cognitive-based LC, captures general attitudes and beliefs towards learning that prevail in the organization. In line with Walsh’s (1995, 294) observation that ‘by understanding organization-level cognition, we may be closer to appreciating the essence of organizing’, the parallel intent here is to move closer to appreciating the nature of learning capabilities. As with the study of organizational change, the existence of cognitive impediments to learning has been well established. For instance, discussing barriers to organizational learning, Mai (1996, 27) argues that blind spots – ‘skilled incompetence’ that prevents us from noticing inherent contradictions in our actions – are key impediments to learning that are a consequence of our own intellectual arrogance. As an antidote, he recommends humility with regard to the value and validity of other perspectives. In the context of strategic alliances, a common related cognitive limitation pertains to what Hamel et al. (1989) have coined the ‘arrogance of leadership syndrome’. Under this orientation, a firm tends to develop an aura of superiority vis-a`-vis its partner; it starts to assume the position of a teacher and to project one of a student onto its partner. In unequivocal terms, Hamel (1990) further argues that ‘humility is another prerequisite for learning.’ On the other hand, ostentatious displays of technological mastery or excessive zeal in showcasing superior competency may turn into a sure leakage of know-how to partners only happy to oblige by listening. Not only resource- or incentive-based organizational procedures, competencies, and assets, but also, more subtly, cognitive-based LC is expected to affect learning outcomes: Hypothesis 2c: The stronger the cognitive-based LC, the higher the level of knowledge transfer.



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Learning intent on LC Nonaka and Takeuchi (1995, 74) argue: The knowledge spiral is driven by organizational intention, which is defined as an organization’s aspiration to its goals. Efforts to achieve the intention usually take the form of strategy within a business setting. From the viewpoint of organizational knowledge creation, the essence of strategy lies in developing the organizational capability to acquire, create, accumulate, and exploit knowledge. That is, intent must translate into capability building and resource deployment. Low priority given to learning activities (Pucik, 1988) is compounded by the problem of not being able to easily price intangible assets that are the direct outcome of learning. Under these circumstances, the absence of a clear valuation tends to limit the allocation of funds and resources to the learning purpose. As a result, a conservative cost-driven rather than investment-driven outlook is likely to prevail, one that does not favor the build-up of the LC. In a way, the deployment of appropriate human and support capabilities essential to the transfer of knowledge is strictly contingent upon the actual commitment of top management to the learning agenda. Overall, learning intent is expected to affect LC. A strong motivation to learn from collaboration represents the first necessary step in designing an explicit plan for facilitating the learning process: Hypothesis 3a: The higher the learning intent, the higher the resource-based LC. Learning intent is not just expected to influence the deployment of appropriate human and physical resources. It is also expected to translate into the conception of explicit institutional routines and guidelines that will help shape individual expectations and steer learning activities. From the presence of an actual reward system to the existence of clear learning agenda, incentive-based LC is driven by the real motivation and commitment of the organization to learn, past corporate rhetoric and more abstract vision statements. In fact, Von Krogh et al. (2000) insist that a proper knowledge vision must specify what knowledge organizational members need to seek and create. That is, the overall intent needs to be articulated and codified in a language and a format that resonate with organizational members. In this spirit, Nonaka and Takeuchi (1995, 75) argue:


To create knowledge, business organizations should foster their employees’ commitment by formulating an organizational intention and proposing it to them. Thus, as with resource-based LC, we expect intent to influence positively the degree of incentivebased LC. Hypothesis 3b: The higher the learning intent, the higher the incentive-based LC. Finally, it is also postulated that the will to learn is conducive to the establishment of a stronger cognitive-based LC. Stereotypes and beliefs about oneself and others can be resilient for individuals and organizations. When it comes to learning from others, fear of failing, losing face, and punishment represent strong barriers to learning (Mai, 1996). Nevertheless, these inhibitors can be neutralized with proper intent and perspectives in place; cognitive changes can take place. For instance, in response to the ‘not invented here’ syndrome, ‘opening doors’ strategies, as coined by Mai (1996), can be rolled out. These are strategies for encouraging inclusion (i.e., corporate initiatives to invite other points of view, to create opportunities for joint problem-solving). Given the right impetus (e.g., motivation to learn), cognitive-based LC can turn more favorable. Hypothesis 3c: The higher the learning intent, the stronger the cognitive-based LC.

Learning impediments: partner protectiveness, ambiguity, and tacitness Partner protectiveness As the various components of LC support knowledge transfer, the degree of protectiveness of a partner inhibits such transfers. In their conceptualization of knowledge transfer capacity, Martin and Salomon (2002) distinguish between source transfer capacity and recipient transfer capacity. Whereas recipient transfer capacity relates to LC in this study, source transfer capacity encapsulates the ability of a firm to articulate its own knowledge, to assess the needs and capabilities of the recipient, and to transmit that knowledge effectively. Next to the intrinsic ability of the transferor lies its own willingness to engage in such a transfer. This duality between ability and willingness of the knowledge repository has been identified as an important challenge for researchers (Mowery et al., 2002).


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In strategic alliances, the protection of proprietary knowledge from partners is a vital issue to many firms (Pisano, 1988; Baughn et al., 1997; Simonin, 1999a; Inkpen, 2002). Transferring partners must have an incentive to palliate the cost typically associated with the transfer (Dyer and Singh, 1998). If not, partners can adopt explicit measures, deploy shielding mechanisms, and engage in defensive actions to protect the transparency of their competencies, particularly when the embodied knowledge is explicit and held by only a few experts (Hamel, 1991; Inkpen and Beamish, 1997). Hence protection of technological knowhow is likely to be prevalent and actively managed. Therefore, partner protectiveness is expected to lead to greater knowledge ambiguity and directly impede knowledge transfer. Hypothesis 4: The more protective the partner is of its knowledge, the lower the level of knowledge transfer.

Ambiguity Far from being readily or easily transferred from the originator to a user, knowledge faces barriers and is relatively immobile (Kogut and Zander, 1992; Tiemessen et al., 1997). Knowledge transfer depends on how easily that knowledge can be transported, interpreted, and absorbed (Hamel et al., 1989). In this process, Hedlund and Zander (1993) pointed to the need to consider the more subtle aspects of knowledge – in particular its ambiguity, its resistance to clear communication, its embeddedness in context, and its idiosyncrasy. Likewise, Crossan and Inkpen (1995) acknowledged that successful jointventure learning strategies call for firms to overcome the ‘ambiguity associated with their partner’s skills’. All these studies are indicative of the existence of an important underlying latent construct – knowledge ambiguity – that needs to be explicitly recognized and integrated in modeling efforts (Simonin, 1999a, b). When studying knowledge ambiguity, a fundamental starting point resides in Reed and DeFillippi’s (1990) observation that a strong barrier to imitation originates from the inability of competitors to comprehend the competencies that are sources of competitive advantage. These authors expanded on Lippman and Rumelt’s (1982) concept of causal ambiguity – that is, the basic ambiguity concerning the nature of the causal connections between actions and results. In this study, knowledge ambiguity is defined as a similar lack of

understanding of the logical linkages between actions and outcomes, inputs and outputs, and causes and effects that are related to technological or process know-how. It encapsulates the degree of transferability of information, know-how, competence, knowledge, or skills. If ambiguity in skill and resource deployment – which are sources of competitive advantage – creates barriers to imitation among competitors (Reed and DeFillippi, 1990) and within the firm (Szulanski, 1996), it also affects negatively the propensity to learn from a strategic alliance partner. It ultimately affects the transfer outcome. Hypothesis 5: The greater the degree of knowledge ambiguity, the lower the level of knowledge transfer. As argued above, partner protectiveness is expected to have a similar direct, negative effect on knowledge transfer. At the same time, protectiveness is also expected to be an antecedent of knowledge ambiguity. That is, the knowledge held by more protective partners is likely to be more causally ambiguous. Hypothesis 6: The more protective the partner is of its knowledge, the greater the degree of knowledge ambiguity.

Tacitness Numerous studies have acknowledged the criticality of tacitness (e.g., Kogut and Zander, 1993; Zander and Kogut, 1995; Choi and Lee, 1997), but few have examined empirically its exact importance in light of other key theoretical constructs related to knowledge transfer (cf. Simonin, 1999a, b). Tacitness is often associated with Polanyi’s (1967) observation that we can know more than we can tell. The dichotomy between tacit and explicit knowledge is based on whether knowledge can or cannot be codified and transmitted in a formal, systematic language or representation, and has been well documented (Kogut and Zander, 1993; Zander and Kogut, 1995; Choi and Lee, 1997). Theoretically, Reed and DeFillippi (1990) singled out tacitness as a key source of ambiguity that raises barriers to imitation. Empirically, in their study of the transfer of manufacturing capabilities, Zander and Kogut (1995) found that, indeed, the degree to which capabilities are codifiable and teachable (i.e., are non-tacit) significantly influences the speed of their transfer. Thus, in this study, next to partner protectiveness, which is also



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expected to exert a direct effect on the learning outcome, tacitness is posited as the main antecedent of ambiguity. Hypothesis 7: The more tacit the partner’s knowledge, the greater the degree of knowledge ambiguity.

Moderating effects: the role of organizational culture, firm size, alliance form, and competitive regime The previous relationships are likely to be moderated by four important variables: organizational culture, firm size, alliance form, and competitive regime. Both size and organizational culture represent pertinent firm-level variables that influence the context in which learning takes place. Some organizations are more driven and successful than others in their approach to develop an open context propitious to learning. Von Krogh et al. (2000, 25) argue that the most fundamental organizational barrier to knowledge creation is the company ingrained paradigms or worldview. These paradigms are useful in socializing new members and keeping the organization coherent through shared norms, values and goals, and they also have ‘the power to make or break knowledge creation’. In their discussion of the barriers to leading learning in organizations, Meisel and Fearon (1996, 205) echo this view, and claim that the greatest barrier to learning is often our inability to ‘unlearn’, to leave the comfort of worktested ideas of the way things are supposed to be done. The propensity to unlearn corresponds to a necessary safeguard against competency traps and the effect of superstitious learning. In this regard, Hedberg (1981) maintains that the process of understanding requires both the learning of new knowledge and the propensity to discard obsolete or misleading knowledge. Only through reviewing the principles underlying corporate dogma, challenging old premises, questioning prevalent organizational procedures and norms, can new ideas find a favorable terrain to grow. In fact, in the alliances context, Hamel (1990) argues that, to learn, a firm must be capable of challenging existing belief. For our study, organizational culture will then refer more specifically to this type of learning culture in an organization. It represents the degree to which employees are encouraged to rethink the logic of current behaviors, to question established routines and beliefs, and to challenge


established wisdom. Keeping in mind the influen¨ n in this domain, we tial work of Argyris and Scho will use the terms ‘single-loop’ and ‘doubleloop’ to distinguish between these two types of organizational culture and to contrast their effect empirically. Likewise, the moderating effect of two alliancelevel variables will be considered: (1) the role of alliance form (equity vs non-equity), and (2) the case of different competitive regimes. With respect to the former, it has been argued that the extent of learning and knowledge transfer among partners is likely to depend on the alliance structure (Osborn and Hagedoorn, 1997; Dyer and Singh, 1998; Anand and Khanna, 2000). Indeed, Mowery et al. (1996) found some evidence that equity joint ventures were more effective conduits than contract-based alliances for the transfer of complex capabilities. Therefore, a closer examination of the role of equity on the proposed model of knowledge transfer is warranted. Some alliances involve partners that are direct competitors, whereas others feature organizations that do not compete directly and are likely to remain this way. The former context may provide a better strategic window onto a competitor/partner’s technology or competence, but it is also likely to be surrounded by greater levels of protectiveness and mistrust. Some have argued that alliances between competitors tend to create contexts that particularly favor inter-partner learning (Dussauge et al., 2002). Others have wondered: Does competition among partner firms in endproduct markets interfere with knowledge sharing in alliances even when partner-specific absorptive capacity is high? (Mowery et al., 2002, 292). Thus, for a more complete understanding of the process of knowledge transfer it is pertinent to examine the extent to which the postulated model is affected by these conditions. Rather than explicitly formulating detailed hypotheses on the nature and direction of these moderating effects, an exploratory approach is used here.

Methods Sample The population for this study consists of large and medium US companies (sales greater than $50


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million and a workforce of more than 500 employees). Accordingly, a sample of 1000 public and privately owned US companies was randomly drawn from the Corptech directory. From the directory, key executives were identified as potential respondents, following a screening process similar to Parkhe (1993). The strategic nature of the survey’s content, with its focus on crosscorporate boundaries issues such as knowledge transfer, and the probing of organizational issues such as culture necessitated the participation of top executives whose understanding and field of action encompass the overall organization. These top executives were the most able to observe and to determine the impact of a specific alliance on the rest of the organization’s activities.

Instrument The questionnaire design and the implementation and conduct of the survey were based on the total design method approach (Dillman, 1978). The questionnaire itself prompted the respondents to focus on a current (at least 1-year old) or past but recent (terminated less than 3 years ago) international strategic alliance with which they were the most familiar. Respondents were invited to focus on the technological expertise of their partner and on the technological aspects of the alliance activities. In addition to general facts and descriptive information about the alliance under scrutiny, the questionnaire included specific questions related to the partner, the collaborative objectives of each party, the context of the alliance, and issues of knowledge transfer pertaining to technology and process know-how. Most of the items in the questionnaire followed seven-point Likert-type scales. Respondents From the 192 companies that participated in the study, 147 completed usable questionnaires were collected, yielding a response rate not atypical for this kind of research. The level of participation was even more gratifying when considering the profile of the respondents, the sensitive nature of many questions, and the detailed nature of the questionnaire. The majority of the respondents were top executives (i.e., presidents, CEOs, vicepresidents, directors, or general managers) in some of America’s largest corporations. On average, these respondents have been personally involved with the alliance under scrutiny for a period of 5 years, suggesting an appropriate level of aware-

ness and expert knowledge with the collaborative phenomenon. Over 50% of the companies included in the study had a sales volume greater than $350 million and a workforce larger than 2500 employees. The possibility of non-response bias was checked by comparing the characteristics of the respondents with those of the original population sample. The calculated t-statistics for the number of employees (t¼0.19, Po0.85), employee growth (t¼1.01, Po0.31), sales volume (t¼0.11, Po0.91), exports as a percentage of sales (t¼0.28, Po0.78), and age of the company (t¼1.63, Po0.10) are all statistically insignificant, suggesting that there are no significant differences between the respondent and nonrespondent groups. Furthermore, as all measures were collected in the same survey instrument, the possibility of common method bias was tested using Harman’s one-factor test (Scott and Bruce, 1994; Konrad and Linnehan, 1995). A principalcomponents factor analysis on the questionnaire measurement items yielded five factors with eigenvalues greater than 1.0 that accounted for 66% of the total variance. As several factors, as opposed to one single factor, were identified, and as the first factor did not account for the majority of the variance (only 24%), a substantial amount of common method variance does not appear to be present (Podsakoff and Organ, 1986).

Measures The latent variables in the model are measured by multiple indicators. All measures were assessed via a seven-point interval scale ranging from ‘strongly disagree’ to ‘strongly agree’. These scales were reverse-coded where appropriate. The wording of these measurement items in the questionnaire and their source in the literature are given in Appendix A. To proceed with the multi-group comparisons advocated for the investigation of the role of organizational culture, firm size, alliance form, and competitive regime, the sample was divided (median-split) along each of these variables. This split-half of the sample was performed one variable at a time, resulting in four sets of two groups in total. Firm size was measured by the number of employees as reported in Corptech’s directory, and alliance form was self-reported in the questionnaire. Model and analysis To assess the relationships posited by the theoretical model in Figure 1, the maximum-likelihood



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¨ reskog and So ¨ rbom, 1996) LISREL VIII program (Jo was used. This structural equation model approach is characterized by its flexible interplay between theory and data, bridging theoretical and empirical knowledge for a better understanding of the real world (Fornell, 1982). Such analysis allows for modeling based on both latent (unobservable) variables and manifest (observable) variables – a critical feature in the case of the hypothesized model as most of the constructs are abstractions of unobservable phenomena. Furthermore, structural equation modeling takes into account errors in measurement, variables with multiple indicators, and multiple-group comparisons. In a second stage, the structural equation modeling module of the STATISTICA 6.1 program (StatSoft, 2003) was used to assess the robustness of the initial results and the stability of the models estimated by LISREL. First, all the proposed models (main and multiple groups) were re-estimated; second, a series of Monte Carlo simulations were performed on each postulated model and groups using STATISTICA’s Monte Carlo Analysis program.

Results Main model In terms of the quality of the measurement model for the full sample, the constructs display satisfactory levels of reliability, as indicated by composite reliabilities ranging from 0.82 to 0.99 and shared variance coefficients ranging from 0.62 to 0.98 (computed from the LISREL loading estimates following Fornell and Larcker’s (1981) formula). Convergent validity can be judged by looking at both the significance of the factor loadings and the shared variance. The amount of variance shared or captured by a construct should be greater than the amount of measurement error (shared variance 40.50). All the multi-item constructs met this criterion, with each loading (l) being significantly related to its underlying factor (t-values greater than 4.17) in support of convergent validity. Likewise, a series of w2 difference tests on the factor correlations showed that discriminant validity was achieved among all constructs (Anderson and Gerbing, 1988). In particular, discriminant validity was established for the three latent variables encapsulating LC (resource based, incentive based, and cognitive based). This was done one pair of variables at a time by constraining the estimated correlation parameter between them (e.g., resource based and incentive based for instance) to 1.0 and


then performing a w2 difference test on the values obtained for the constrained (w2¼153.24, d.f.¼79) and unconstrained models (w2¼135.39, d.f.¼78) (Anderson and Gerbing, 1988). The resulting significant difference in w2 (Dw2¼17.85, Dd.f.¼1) indicates that the two constructs are not perfectly correlated and that discriminant validity is achieved (Bagozzi and Phillips, 1982). That is, from a measurement model point of view, the constructs resource- and incentive-based LC represent two distinct constructs, not one. Likewise, discriminant validity is also achieved between resource-and cognitive-based (Dw2¼58.22, Dd.f.¼1) and between incentive- and cognitive-based LC (Dw2¼58.22, Dd.f.¼1). Turning to the structural model itself, Table 1 reports the parameter estimates and goodness-of-fit indicators of the structural equation system. Although the overall w2 is significant (w2¼142.81; 86 d.f.; Po0.00), as might be expected with this statistic’s sensitivity to sample size (Bagozzi and Yi, 1988; Bentler, 1990), the ratio of w2 to degrees of freedom (1.66, less than 3) corresponds to a satisfactory fit (Carmines and McIver, 1981), and the other fit indices (NNFI¼0.91; NFI¼0.85; CFI¼0.93) and the low standardized root mean square residual (RMR¼0.07) are all within acceptable ranges and show that a substantial amount of variance is accounted for by the model (Bagozzi and Yi, 1988). Hence, the model is a reasonable representation of the data. Looking at the parameter estimates, a first, notable result consists of the significant positive effects of learning intent on knowledge transfer in support of Hypothesis 1 (g11¼0.59, t¼7.34). That is, stronger (weaker) learning intent corresponds to greater (smaller) knowledge transfer outcomes for an alliance partner. Likewise, both partner protectiveness and ambiguity display significant direct negative effects on knowledge transfer in support of Hypothesis 4 (g12¼0.15, t¼2.23) and Hypothesis 5 (b15¼0.50, t¼5.08), respectively. Finally, LC in the form of incentive-based programs has a significant positive effect on knowledge transfer in support of Hypothesis 2b (g11¼0.28, t¼2.24). Taken simultaneously, these initial results offer some strong empirical support to a fundamental model of learning that rests on the interplay between motivation, capability, task ambiguity, and learning outcome. Overall, a substantial amount of variance is explained in the endogenous variables knowledge transfer (R2¼0.73) and ambiguity (R2¼0.33) by the model.


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Table 1

Structural parameter estimates and goodness-of-fit indices (full sample)

Hypotheses

Paths

Estimate

t-value

H1 H2a H2b H2c H4 H5

Learning intent-knowledge transfer Resource-based LC-knowledge transfer Incentive-based LC-knowledge transfer Cognitive-based LC-knowledge transfer Partner protectiveness-knowledge transfer Ambiguity-knowledge transfer

g11 0.59 b12 0.14 0.28 b13 0.09 b14 g12 0.15 b15 0.50

7.34** 1.03 2.24** 1.45 2.23** 5.08**

H3a H3b H3c H6 H7

Learning intent-resource-based LC Learning intent-incentive-based LC Learning intent-cognitive-based LC Partner protectiveness-ambiguity Tacitness-ambiguity

g21 g31 g41 g52 g53

0.23 0.08 0.30 0.07 0.55

2.64** 0.85 3.13** 0.75 3.25**

NFI¼0.85; standardized RMR¼0.07; NNFI¼0.91. w2 (86 d.f.)¼142.81; CFI¼0.93; P-value¼0.00; n¼147. **Significant at the Po0.05 level.

Focusing on the individual effects, the significant relationship between ambiguity and knowledge transfer constitutes an empirical verification of Reed and DeFillippi’s (1990) theoretical postulate that causal ambiguity constitutes a key barrier to imitation. Out of the three proposed organizational mechanisms encapsulating LC (i.e., resource-, incentive-, and cognitive-based LC), only incentive-based LC shows a significant effect on knowledge transfer. Although these variables have been shown to represent three distinct constructs (discriminant validity), the presence of a significant correlation (0.54, Po0.01) between resource- and incentive-based LC is not unexpected. Turning to the exogenous variables in the model, Table 1 reveals that only tacitness (g53¼0.55, t¼3.25), not partner protectiveness (g52¼0.07, t¼0.75), is significantly related to ambiguity. That is, Hypothesis 6 is rejected but Hypothesis 7 is supported. This also means that partner protectiveness tends to be related to knowledge transfer in a direct way (Hypothesis 4), not through the mediation of knowledge ambiguity (failed Hypothesis 6). Likewise, it has been established that learning intent displays a significant direct effect on knowledge transfer (Hypothesis 1); as with the prior case, learning intent fails to affect knowledge transfer indirectly through the mediating effect of LC. Although learning intent exerts a significant positive effect on resource-based (g21¼0.23, t¼2.64) and cognitive-based LC (g41¼0.30, t¼3.13) in support of Hypotheses 3a and 3c respectively, it has been shown that, in turn, these last two constructs fail to relate to knowledge transfer (Hypotheses 2a and 2c rejected). Conversely, whereas incentive-based LC

has been shown to relate to knowledge transfer (Hypothesis 2b supported), learning intent does not seem to be one of its antecedents (g31¼0.08, t¼0.85; Hypothesis 3b rejected). That is, stronger (weaker) learning intent is associated with the presence of more (less) resource- and cognitive-based LC (not incentive-based LC), while, simultaneously, out of these three organizational capabilities, incentivebased LC is the only one directly affecting knowledge transfer. Although not formally hypothesized, a pertinent question concerns the relative magnitude of these identified significant effects. Are all these comparable effects equal, or does any one of them affect knowledge transfer, ambiguity, or LC to a greater extent? Three sets of comparable effects are examined, one set at a time: (1) positive direct effect of incentive-based LC vs learning intent on knowledge transfer; (2) negative direct effect of ambiguity vs protectiveness on knowledge transfer; and (3) positive effect of learning intent on resourcebased LC vs cognitive-based LC. To test such hypotheses under LISREL, a w2 difference test is performed between a model where the estimates are free and a model where these estimates are constrained to be equal. For the first pair of effects, the corresponding test reveals that, statistically, the positive effects of incentive-based LC and learning intent on knowledge transfer are of the same magnitude (Dw2¼3.66, Dd.f.¼1; NS). For the second comparison, a difference exists (Dw2¼7.10, Dd.f.¼1; Po0.01), revealing that the overall negative effect of ambiguity on knowledge transfer is



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greater in magnitude than the effect of partner protectiveness. This result is consistent with prior research pointing to the criticality and multifaceted nature of causal ambiguity as a barrier to imitation and knowledge transfer (Reed and DeFillippi, 1990; Simonin, 1999a, b). Lastly, learning intent is found to relate to resource- and cognitivebased LC the same way (Dw2¼0.32, Dd.f.¼1; NS).

Moderating effects and model robustness These first results shed some important light on the process of learning and on key organizational mechanisms that facilitate and hinder technological knowledge transfer between alliance partners. However, further refinement is desirable through the investigation of the possible moderating effects of organizational culture, firm size, alliance form, and competitive regime. Indeed, Table 2 shows that the previous results differ somewhat across groups characterized by a different organizational culture. Again, organizational culture here (measured by moderating variable M1 in Appendix A) refers to the learning culture of the organization (the ability to rethink the logic of current behaviors, to question established routines and beliefs, or to challenge established wisdom). On this basis, the two groups to be contrasted are very distinct (mean¼5.58 for the ‘double-loop’ group vs mean¼3.18 for the ‘single-loop’ group; means are significantly diffe-

Table 2

rent: t¼17.00, Po0.00). The results in the ‘singleloop’ group are almost identical to the general results – significant effect of learning intent, incentive-based LC, partner protectiveness, and ambiguity on knowledge transfer; learning intent on resourcebased LC; and tacitness on ambiguity – with the exception of the relationship between learning intent and cognitive-based capacity, which is now nonsignificant (Hypothesis 3c not supported). In comparison, the ‘double-loop’ group shows that learning intent has a significant effect not only on resource-based but also on cognitive-based LC (in support of Hypotheses 3a and 3c). As with the ‘single-loop’ group, learning intent and ambiguity are shown to have a significant effect on knowledge transfer (in support of Hypotheses 1 and 5), and the effect of tacitness on ambiguity is also found significant (in support of Hypothesis 7). What further varies with the ‘double-loop’ group is that none of the LC resources and mechanisms is found to be related to knowledge transfer (Hypotheses 2a, 2b, and 2c are rejected); also, unlike the ‘singleloop’ group, partner protectiveness does not relate to knowledge transfer directly. Rather, it relates to it indirectly through the significant mediating effect of ambiguity (both Hypotheses 5 and 6 are supported). This pattern of results across the two groups is consistent with a type of substitution effect depending on the type of organizational

Structural parameter estimates and goodness-of-fit indices for two-group comparison on organizational culture and firm size

Paths

Hypotheses

Organizational culture Double loop (n1¼91)

Single loop (n2¼56)

Large (n1¼74)

Small (n2¼73)

0.50** 0.30 0.59** 0.03 0.23** 0.34**

0.53** 0.08 0.42** 0.03 0.21** 0.38*

0.43** 0.66 0.67 0.10 0.05 0.79**

0.18* 0.04 0.08 0.16 0.66*

0.00 0.09 0.25** 0.33 1.49**

0.34** 0.33** 0.35** 0.14 0.44**

Learning intent-knowledge transfer Resource LC-knowledge transfer Incentive LC-knowledge transfer Cognitive LC-knowledge transfer Partner protectiveness-knowledge transfer Ambiguity-knowledge transfer


0.43** 0.07 0.10 0.10 0.09 0.66**

Learning intent-resource LC Learning intent-incentive LC Learning intent-cognitive LC Partner protectiveness-Ambiguity Tacitness-Ambiguity

H3a H3b H3c H6 H7

0.19** 0.02 0.26** 0.17* 0.38**

CFI¼0.89 Standardized RMR¼0.12 w2 (172 d.f.)¼265.63 **Significant at the Po0.05 level. *Significant at the Po0.10 level.


Firm size

CFI¼0.82 Standardized RMR¼0.12 w2 (172 d.f.)¼378.76


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learning culture: whereas the presence (absence) of an explicit incentive-based learning system may determine the level of knowledge transfer for ‘single-loop’-driven firms, it is not consequential for ‘double-loop’-driven organizations. Is it evidence of a higher order of learning at work? An important element in clarifying this point is to go beyond examining the moderating effect of organizational culture on the learning process and to assess the impact of organizational culture on the actual level of knowledge transferred. Based on the mean of the three indicators of knowledge transfer (Y1 to Y3 in Appendix A), the difference in the level of knowledge transfer under a ‘double-loop’ learning culture (mean¼3.83) and a ‘single-loop’ culture (mean¼ 3.29) is found to be statistically significant (t¼2.34, Po0.05). This last finding is important in that it demonstrates empirically the superiority of an organizational culture that fosters non-conformity. Table 2 also reports the results of the multiple group analysis for the second moderator: firm size (on average, about 1000 employees and sales of $250 million for the ‘small’ firm group vs over 20,000 employees and sales of $ 2.5 billion for the ‘large’ firm group). Consistent with the main results, the effects of learning intent and ambiguity on knowledge transfer, learning intent on cognitivebased LC, and tacitness on ambiguity are significant for both small and large firms (in support of Hypotheses 1, 5, 3c, and 7). In terms of differences between the two groups, a first noticeable result is that the direct effects of incentive-based LC and partner protectiveness on knowledge transfer are significant only for large firms (in support of Hypotheses 2b and 4). Conversely, learning intent significantly affects resource- and incentive-based LC simply in the case of small firms. That is, Hypotheses 3a and 3b are supported in the case of small firms, but not of large ones. Taken together, these distinct results show that, for small firms, the full range of resources, routines, and mechanisms constituting the firm’s LC relates directly to learning intent; at the same time, this LC (or lack thereof) fails to relate to knowledge transfer. Large firms, in comparison, do not display a significant effect between learning intent and LC (with the exception of cognitive-based LC); on the other hand, LC (in the form of incentive-based LC) does affect knowledge transfer. We now turn to the two alliance-level moderators: alliance form (equity vs non-equity) and competitive regime (measured by moderating variable M2 in Appendix A: the ‘competitive’ group

corresponds to the case of partners viewed as strong and very strong competitors; the ‘not competitive’ group corresponds to the case of partners judged to be a weak competitor or not a competitor). Table 3 reveals that, similar to the case of firm size, the two sets of group comparisons display similar patterns of effects for the effect of learning intent and ambiguity on knowledge transfer, learning intent on cognitive-based LC, and tacitness on ambiguity (in support of Hypotheses 1, 5, 3c, and 7). For alliance form, equity and non-equity-based alliances differ in that, for non-equity-based alliances, partner protectiveness and cognitive-based LC relate to knowledge transfer (Hypotheses 4 and 2c supported) whereas, for equity-based alliances, it is incentive-based LC that affects knowledge transfer (in support of Hypothesis 2b). In addition, for nonequity-based alliances, learning intent is found to be a significant antecedent of the three LC components (in support of Hypotheses 3a, 3b, and 3c), in contrast to equity-based alliances, where it is only an antecedent of cognitive-based LC (Hypothesis 3c). Thus, for non-equity-based alliances, there is both a significant direct effect of learning intent on knowledge transfer and an indirect effect through cognitive-based LC. Differences across competitive regimes exist as well. For alliances falling under a ‘competitive’ regime, partner protectiveness is associated with knowledge transfer in a direct way (Hypothesis 4 supported) whereas, in the ‘not competitive’ regime, it is related to knowledge transfer indirectly through the significant mediating role of ambiguity (both Hypotheses 5 and 6 are supported). More interestingly, the two groups differ significantly with respect to the role of LC. Under the ‘competitive’ regime, none of the LC resources and mechanisms is found to be an antecedent of knowledge transfer (Hypotheses 2a, 2b and 2c are rejected), reminiscent of the case of ‘double-loop’driven firms. In sharp contrast, under the ‘not competitive’ regime, the three constructs encapsulating LC show a significant effect on knowledge transfer. While the positive effects of incentive- and cognitive-based LC are in support of Hypotheses 2b and 2c, the significant negative effect of resourcebased LC on knowledge transfer is more surprising and counter to the originally formulated Hypothesis 2a. This last result translates into: under a ‘not competitive’ regime, the higher (lower) the resource-based LC of the firm, the lower (higher) the level of knowledge transfer. This finding may point to inefficiencies or ceiling effects in resource deployment, but it warrants further discussion.



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Table 3

Structural parameter estimates and goodness-of-fit indices for two-group comparison on competitive regime and alliance form

Paths

Hypotheses

Competitive regime

Alliance form

Competitive (n1¼60)

Not competitive (n2¼87)

Non-equity (n1¼83)

Equity (n2¼64)

Learning intent-knowledge transfer Resource LC-knowledge transfer Incentive LC-knowledge transfer Cognitive LC-knowledge transfer Partner protectiveness-knowledge transfer Ambiguity-knowledge transfer


0.57** 0.09 0.16 0.01 0.16* 0.41**

0.41** 0.42** 0.54** 0.19** 0.09 0.82**

0.43** 0.04 0.06 0.27** 0.41** 0.42**

0.41** 0.36 0.68** 0.02 0.04 0.70**

Learning intent-resource LC Learning intent-incentive LC Learning intent-cognitive LC Partner protectiveness-ambiguity Tacitness-ambiguity

H3a H3b H3c H6 H7

0.12 0.11 0.33** 0.01 0.53**

0.19** 0.10 0.15* 0.16* 0.39*

0.26** 0.16* 0.13** 0.01 0.29*

0.07 0.11 0.10** 0.23 0.82**



**Significant at the Po0.05 level. *Significant at the Po0.10 level.

Although not formally hypothesized, another issue of interest concerns the possible structural differences between these two competitive regimes when it comes to outcomes, motivation, and protectiveness. A series of independent t-tests reveals that, in fact, the two groups do not differ with respect to the level of partner protectiveness (mean¼4.23 for ‘competitive’ regime vs mean¼3.83 for ‘not competitive’ regime; t¼1.45, NS), learning intent (mean¼3.55 for ‘competitive’ regime vs mean¼3.38 for ‘not competitive’ regime; t¼0.50, NS), and knowledge transfer (mean¼3.54 for ‘competitive’ regime vs mean¼3.69 for ‘not competitive’ regime; t¼0.62, NS). Finally, given the relatively small size of the main sample and subsamples used in group analysis, all the models were re-estimated with a second technique to assess the robustness of the LISREL estimates and the stability of the results. First, the structural equation modeling module of the STATISTICA 6.1 program was used as a direct comparison. Second, a series of Monte Carlo simulations was performed using STATISTICA’s Monte Carlo Analysis program. For each model and group, a Monte Carlo analysis (using the Bootstrap data option, with 100 sampling iterations and a sample size reduction of 5–10% for re-sampling purposes) provided the basis for re-estimating the path coefficients and their significance. Overall, the initial LISREL results held with the repeat analyses, conferring some degree of reliability to our findings.


Discussion and future research Through a structural equation modeling approach, this study has focused on the process of technological knowledge transfer between international strategic alliance partners. We proposed and tested a comprehensive model that explicitly articulates the role of various key variables that in past research received only partial and independent attention. Rather than focusing on any one specific relation, it is the simultaneity of all the hypothesized relationships that confers integrity and relevance to the model. The following discussion of the results, shortcomings, and future research directions will: (1) recap the main findings by highlighting the consistently critical role of learning intent, ambiguity, and tacitness at the core of the learning process; (2) underscore the changing role of LC and partner protectiveness across various conditions; and (3) discuss the dynamic nature of the learning process on the basis of the presence of likely substitution effects.

Learning intent, ambiguity, and tacitness at the heart of knowledge transfer The overall results point to the fundamental roles played by learning intent, knowledge ambiguity, and tacitness. The significant effects of these three


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constructs on knowledge transfer are found consistently across the main analysis (full sample) and the various group analyses. They are therefore at the heart of the learning process. As isolated by Hamel (1990) and others, learning intent represents a key component of the learning process. The pervasive nature of this effect helps reconcile the emerging divide between the proponents and critics of the learning race. Indeed, some alliances have a learning undertone whereas others rest on co-specialization (Mowery et al., 2002; Zeng and Hennart, 2002). What separates the two, and paradoxically unifies them at the same time, is the degree of learning intent by the partners: that is, this single construct. It is pertinent to note that, regardless of the competitive regime of the alliance, learning intent was found to exert the same direct effect on knowledge transfer. Furthermore, in absolute terms, no difference in learning intent was observed for companies paired with strong competitors and those paired with non-competitors. Overall, these results tend to show that, if there is a learning race in strategic alliances, it is principally a race against oneself. These results also speak to the disconnect between learning motivation and degree of competition in alliances. For instance, a strong learning intent can exist in a non-competitive setting (e.g., a critical benchmarking program by Xerox on L.L. Bean, the catalog clothing company), whereas the converse is also possible: a weak or nonexisting learning intent in a competitive alliance setting (e.g., a straight-forward cross-distribution arrangement between two global competitors). Turning to learning inhibitors, the significant effect of ambiguity on knowledge transfer provides some empirical support both to Lippman and Rumelt’s (1982) observations that ambiguity acts as a powerful block on both imitation and factor mobility, and to Reed and DeFillippi’s (1990, 96) postulate that ‘barriers to imitation are dependent on the ambiguity in a firm’s competency-based advantage.’ These results underline the theoretical importance of ambiguity and – coinciding with a resurgence of interest (Szulanski, 1996; Mosakowski, 1997; Simonin, 1999a, b) – the need to account formally for this construct in future research. In terms of the antecedents of ambiguity, partner protectiveness is found to play a role only when either the alliance regime is non-competitive or the prevailing organizational culture of the knowledge seeker is double-loop oriented. On the other hand, the effect of tacitness on ambiguity is consistently significant across the various analyses.

This last result concurs with Zander and Kogut’s (1995, 85) findings that ‘the more codifiable and teachable a capability is, the higher the ‘risk’ of rapid transfer.’ As such, and as evidenced by recent research foci (e.g., Martin and Salomon, 2002), the seminal work of Polanyi (1967) remains timely and fundamental to understanding the flow of knowledge transfer between partners of strategic alliances. This result also underlines the desirability of implementing knowledge codification programs whenever active knowledge sharing is a requisite or agreed-upon objective of the alliance. In light of the rise of programs targeting the codification of corporate knowledge (see Davenport and Prusak, 1998), the task, far from being obvious, is nevertheless feasible. Positive outcomes are attainable. At the same time, much remains to be learned about the mechanics and limits of codifying knowledge, the value of data-basing corporate knowledge, and the challenges of designing and managing an efficient corporate knowledge system.

LC and partner protectiveness: different conditions, different roles The effects of learning intent, ambiguity, and tacitness have been identified at the heart of the knowledge transfer process in a consistent way, but the role of LC and partner protectiveness is more contrasted. The effect of these two variables on knowledge transfer depends on pre-existing organizational and contextual factors. The presence of these nuances in the results reveals that the actual learning process is dynamic, and more intricate than often assumed or represented in the literature. With respect to LC, a first striking result concerns the general lack of significance of the resource- and cognitive-based components. Only the incentivebased component of LC seems to have any recurring influence. Cognitive capacity (encapsulated here by attitudes and beliefs towards teaching vs learning from a partner, in the spirit of Hamel et al.’s (1989) ‘arrogance of leadership syndrome’) shows a positive direct effect on knowledge transfer only for non-competitive and non-equity-based alliances. Thus, in what could be considered a favorable learning environment (in that no competitive pressure or threats exists), cognitive capacity or limitations do play a significant role in the learning outcome. Going back to the example of a benchmarking collaboration between two organizations that operate in radically different sectors, one could easily see why such cognitive elements would affect the actual transfer of knowledge from



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one partner to the other. One partner may not be perceived as credible, and the other may feel superior. Thus, under non-competitive alliance regimes, the results seem to support Mai’s (1996) contention that humility with regard to the value and validity of other perspectives is not only an antidote to intellectual arrogance, but also a key enabler of learning. Of course, this study has focused on one facet of cognitive-based LC, and much conceptualizing and testing remain to be done to fully comprehend the role of this component. More surprising are the results pertaining to resource-based LC: only significant under noncompetitive alliance regimes, where it displays a negative effect on knowledge transfer. The lack of general effect across samples is in sharp contrast with what prior conceptual work has suggested when it comes to resource deployment in alliances (Pucik, 1988; Hamel, 1990, 1991; Inkpen, 2002). A possible explanation is the existence of ceiling effects and inefficiencies in resource deployment, an important subject for future research. At the same time, these results are not inconsistent with other empirical findings. For instance, Buchel and Killing (2002) found that initial staffing of the joint venture is negatively correlated with joint venture performance (measured by satisfaction). These authors explain that sending the best people actually leads to committing individuals with strong parental bonds that may backfire in a collaborative setting. Such rationale certainly applies to a learning context, where the presence of too many experts could lead to paralysis and additional frictions with the partner, impeding learning. The fact that, under a non-competitive regime, higher (lower) resource-based LC corresponds to lower (higher) levels of knowledge transfer is certainly consistent with this explanation. In non-competitive environments, it seems to be a case of diminishing returns, where ‘more is less’: increasing resource deployment adds complexity, inertia, administrative, and coordination costs at the expense of learning. The process derails. For greater clarity on this issue, some additional research is needed, particularly on the question of optimal levels of resource deployment. As noticed above, of the three LC components, the significant effect of incentive-based LC on knowledge transfer is the most widespread. This effect, detected in the main analysis, holds for large firms, single-loop-oriented firms, non-competitive alliance regimes, and equity alliances. Thus there is some partial support for the idea that learning


needs to be recognized and rewarded to build learning organizations (Marquardt, 1996; Davenport and Prusak, 1998). It also means that a learning agenda needs to be clearly articulated and communicated. As the results show, this is more of an issue for large organizations and organizations involved in more formally structured alliances. One possible explanation is that, in light of the more complex nature of such organizations, competing tasks and priorities exist at all times. Thus it is more important to clearly signal what the actual primary objectives are. Consistent with this view is the fact that the effect is present for singleloop- but not double-loop-oriented firms. When organizational culture does not encourage rethinking the logic of current behaviors, questioning established routines and beliefs, or challenging established wisdom, again, clear incentives and signals become critical. They are needed to foster employees’ commitment beyond any doubt (Nonaka and Takeuchi, 1995). Turning to organizational mechanisms that hinder learning, partner protectiveness is found to have a significant direct effect on knowledge transfer in the main analysis as well as in the case of large firms, single-loop-oriented firms, competitive alliance regimes, and non-equity alliances. In the case of non-competitive regimes and doubleloop-oriented firms, the effect of protectiveness on knowledge transfer is mediated through knowledge ambiguity. When interpreting these findings, one must be cautious of the fact that partner protectiveness may not always be detectable or observable. In its finest form, it may be totally transparent to the knowledge seeker. From a managerial point of view, it would be wise for managers to probe regularly their partner’s disposition by formulating specific requests that force the issue; only then could an unequivocal opinion be formed and corrective steps adopted if necessary (Simonin, 1999a). Non-equity as opposed to equity-based alliances may be more propitious for detecting the presence of partner protectiveness (clearer signals due to formal communication needs and articulated requests) as well as less favorable for mitigating its effects (more limited options to bypass the protective wall). As protectiveness is likely to evolve over time, a greater research focus on alliance life cycles is warranted.

Substitution effects: a dynamic learning process Turning to the relationship between learning intent and LC, the main analysis reveals a significant


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effect between intent and both resource- and cognitive-based LC. These effects hold for small firms, double-loop-oriented firms, non-competitive and non-equity-based alliances. Thus intent seems to translate into greater capacity and resource deployment in environments that are less bureaucratic, more flexible, open, and actionable. This finding echoes Swieringa and Wierdsma’s (1992) observation that bureaucracies are more susceptible to learning difficulties because of their lack of need, courage, will and ability to learn. Likewise, the fact that the cognitive side of LC can be affected by learning intent carries some important implications. The influence of decision makers and chief knowledge officers is not restricted simply to building a proper knowledge strategy, culture, and infrastructure (Davenport and Prusak, 1998). It can also succeed in more tactical ways by shaping attitudes, beliefs and expectations with regard to learning in specific initiatives. In contrast to resource- and cognitive-based LC, learning intent seems to have no effect on incentive-based LC, with the exception of small firms and non-equity alliances. The case of small firms is instructive in that, unlike large firms, small firms display a significant and complete association between learning intent and capacity, but not between capacity and learning outcomes. In a way, the build-up and deployment of LC from the manifestation of an original intent may be more actionable and observable in smaller organizations. What is not detected is the impact of this capacity. Possibly, LC may be superseded by other key intervening variables such as the source of asymmetric bargaining power between partners (Khanna et al., 1998). The pace of critical capacity building may be slower or, simply, its sheer level may be insufficient, as smaller firms tend to lack the types of resources mandated by alliances (Hagedoorn and Schakenraad, 1994). Beyond capacity, it is important to remember that, for both small and large firms, learning intent exerts a significant direct effect on knowledge transfer. In a type of compensatory effect, small organizations tend to rely more exclusively on their motivation to learn. Of course, with respect to the effect of firm size, our study has looked only at large and medium-sized firms; future research should also consider more extreme cases of small firms (e.g., start-ups). A similar type of substitution effect with regard to the role of LC is observed in the case of doubleloop-oriented firms. Whereas single-loop firms experience a significant effect of incentive-based

LC on knowledge transfer, double-loop firms lack any direct effect between capacity and learning outcomes. Is this the case of a proper organizational learning culture supplanting the need for formal organizational resources and mechanisms? Is this evidence of greater efficiency? What this study was able to show is that the learning process is dynamic; different critical paths are enacted under different conditions. More importantly, this study has also established statistically that greater learning outcomes are achieved under a ‘double-loop’ learning culture than under a ‘single-loop’ culture. That is, an organizational culture that fosters non-conformity and encourages critical thinking is indeed a better learning organization. Although many researchers have stressed the importance of developing the right organizational culture for encouraging learning (Marquardt, 1996; Davenport and Prusak, 1998), the question of quantifying these learning dividends had remained wide open. Given the fact that there may be more or less advantageous ways to achieve the same learning outcomes, the next pertinent question is one of optimal choices. In particular, the question of trade-offs between generic organizational culture and specific organizational mechanisms warrants further research. The dynamic nature of the learning process is well represented in the differences observed between a competitive and non-competitive alliance regime. Here too, the role of LC fluctuates from being significant in non-competitive regimes to non-significant in competitive regimes. In terms of the role of competition, Mowery et al. (2002) have reported that learning seems to be attenuated when alliance partners are direct competitors. In comparison, the current study shows that there is no difference in the level of knowledge transferred under competitive and non-competitive regimes. In a sense, this result is a middle path between the observations of Mowery et al. and the argument of Dussauge et al. (2002) that alliances between competitors tend to create contexts that particularly favor inter-partner learning. Maybe it is both. The context is favorable in that there is, by default, something there to be learned from a competitor; however, owing to self-interest and other variables, the path to get at it is more complex and inhibited. What our results show is that there is no difference in learning outcomes, but the path may be different. On the protectiveness front, Inkpen (2002) acknowledges that firms may be very reluctant to share knowledge when there is a high



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competitive overlap between partners. Our study uncovers no such difference in levels of perceived protectiveness between partners that are strong direct competitors and partners that are not competitors. Finally, no substantive difference in learning intent was detected between the two competitive regimes. Instead, this study recognizes the existence of a dynamic learning process whose core is meaningful to all contexts. The aim of this study was to advance our understanding of the process of knowledge transfer across alliance partners at both the conceptual and empirical levels. Conceptually, the model has replicated at the organizational level a fundamental model of individual learning (motivation–capacity– learning outcome). In particular, it has introduced LC as a key determinant of absorptive capacity in a way that refocuses attention on firm-level, not simply partner-specific-level, variables. It is important to understand the issue of similarities (Lane and Lubatkin, 1998) and overlap between partners (Mowery et al., 2002), but it is also fundamental to consider the role of firm-specific factors. LC represents the actionable side of absorptive capacity, its basic operating system. Further refinement of the construct, its underlying dimensions, and measurement are needed. Finally, alternative research designs are needed to capture the duality

of knowledge transfer in alliances as it pertains to both voluntary transfers and involuntary spillovers, and existing vs jointly created knowledge. These effects must be disentangled with greater precision. On the empirical side, when interpreting and building on the results of this study, one must still keep in mind that ‘correlation is not causation’. If the linear equations system isomorphic to the path diagram does fit the data well, it is encouraging, but hardly proof of the truth of the causal model (StatSoft, 2003). Although this study constitutes a significant attempt to operationalize and test complex organizational variables, its measurement model restrictions are a clear reminder that much empirical work remains to be done on construct development and validation. Our understanding of inter-organizational knowledge transfer, be it interfirm or intra-firm, as well as of its underlying rationales, processes, organizational drivers and impediments, and efficiencies, will benefit from similar empirical undertaking based on large samples and latent variable modeling.

Acknowledgements I am grateful to the two anonymous reviewers and the Editors, Nicolai Juul Foss and Torben Pedersen, for their helpful comments.

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Appendix A: Questionnaire items

Knowledge transfer

Source: Simonin (1999a)

Y1 Your company has learned a great deal about the technology/process know-how held by your partner. Y2 Your company has greatly reduced its initial technological reliance or dependence upon the partner since the beginning of the alliance. Y3 The technology/process know-how held by your partner has been assimilated by your company and has contributed to other projects developed by your company. Ambiguity


Y4 The technology/process know-how held by your partner is easily transferable back to your company. Y5 The association between causes and effects, inputs and outputs, and actions and outcomes related to the technology/process know-how held by your partner is clear.

Strongly Strongly disagree agree 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

Strongly Strongly disagree agree 1 2 3 4 5 6 7 1 2 3 4 5 6 7

Resource-based Adapted from Hamel (1990) learning capacity and Pucik (1988) Y6 Your company has committed a lot of personnel to this alliance. Y7 The staff assigned by your company to this alliance is composed of highly trained and talented personnel. Y8 Your company has committed a lot of physical, financial, organizational, and logistical resources to support the seeking, diffusion and sharing of information originating from this alliance.

Strongly Strongly disagree agree 1 2 3 4 5 6 7 1 2 3 4 5 6 7

Incentive-based Adapted from learning capacity Pucik (1988) Y9 There are clear incentives or a well-established reward system designed to encourage employees to seek and repatriate information from this alliance. Y10 For this alliance, a learning agenda has been clearly defined and communicated for all personnel involved.

Strongly Strongly disagree agree 1 2 3 4 5 6 7

Cognitive-based Adapted from Hamel (1990) learning capacity and Pucik (1988) Y11 In general, your staff involved with the alliance believe that they have less to learn from, than to teach to your partner.



1 2 3 4 5 6 7

1 2 3 4 5 6 7


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Partner protectiveness X1

X3


Your partner is very protective of its technology/process know-how.

Tacitness X2




Your partner’s technology/process know-how is easily codifiable (in blueprints, instructions, formulas, etc.). Your partner’s technology/process know-how is more explicit than tacit.

1 2 3 4 5 6 7

Learning intent

Adapted from Hamel (1990) and Pucik (1988) When deciding to enter into the alliance, your company had a strong desire, determination and will to learn about a particular technology/process owned by your partner. This alliance is viewed as a means to learn about a particular technology/process held by your partner, rather than as a way to simply use or rent this know-how.


Organizational culture Adapted from Hamel (1990) (moderator) M1 In your company, rethinking the logic of current behaviors, questioning established routines and beliefs, or challenging established wisdom, is encouraged.


X4 X5

1 2 3 4 5 6 7

Competitive regime Newly developed) (moderator) M2 To what extent do you consider your partner an actual or future competitor? (please check one) Very strong competitor ( ) Strong competitor ( ) Weak competitor ( ) Not competitor ( )

About the author Bernard L Simonin is an Associate Professor of Marketing and International Business at the Fletcher School of Law and Diplomacy (Tufts University). He holds a PhD in International Business from the University of Michigan (Ann Arbor). His research interest focuses on symbiotic marketing and sponsorship, market orientation, customer satisfaction, brand alliances, strategic alliances, global strategy, learning organizations, and knowledge manage-

ment. His work has been published in the Journal of Marketing Research, Journal of Advertising, Journal of International Marketing, Journal of International Business Studies, Journal of Business Research, International Executive, Global Focus, Academy of Management Journal, and Strategic Management Journal. He has taught at the University of Michigan, University of Washington, University of Illinois, Harvard University, and Kasetsart University in Thailand.

Accepted by Nicolai Juul Foss and Torben Pederson, Departmental Editors, 1 April 2004. This paper has been with the author for one revision.
