Absorptive Capacity in Strategic Alliances: INVESIGATING THE EFFECTs OF INDIVIDUALs’ Social and Human Capital on Inter-Firm Learning
Shawn M. Lofstrom
(301) 405-3522
Management and Organization Department
Robert H. Smith School of Business
3340 Van Munching Hall
College Park, MD 20742
Presented at the Organization Science Winter Conference, 2000 in Keystone, Colorado
Introduction
The last two decades of the 21st Century have witnessed a significant increase in the frequency and depth of inter-firm collaboration (Bleeke and Ernst, 1991; Contractor and Lorange, 1988; Fortune, 1992; Hagedoorn, 1995: Hladik, 1985). Increased globalization of markets and rapid technological change have made it increasingly difficult for firms to develop internally the skills necessary to compete in many product markets. The received wisdom suggests that alliances — defined as collaborative arrangements between two firms which involve the exchange and sharing of multiple resources for the co-development of technologies or products — are absolutely essential to a company’s strategy and competitive future (e.g. Ohmae 1989, Gulati, 1996; Houghton in Fortune, 1992). Consequently, the study of strategic alliances is a major area of interest that bears significance for contemporary organizations.
A prominent view of strategic alliances suggests that inter-firm collaboration is a mechanism by which a firm can leverage its skills, acquire new competencies, and learn (e.g. Kogut, 1989; Hamel, Doz, and Prahalad, 1989; Huber, 1991; Larsson, Bengtsson, Henriksson, and Sparks, 1998; Lyles, 1988; Powell and Brantley, 1992). For the partnering firms, alliances represent interfaces with its environment that provide access to valuable external information and knowledge (Arora and Gambardella, 1990; Powell, Koput and Smith-Doerr, 1996; Teece, 1992). As such, these arrangements can provide opportunities for firms to assimilate information, internalize skills, and develop new capabilities. Moreover, research has suggested that social networks, competencies, and the relative configuration of skills and organizational practices of the partnering firms can influence the level of learning through alliances (e.g. Hamel, 1991; Lane and Lubatkin, 1998; Mowery, Oxley and Silverman, 1996; Shan, Walker and Kogut, 1994).
However, although studies have recognized the importance of individuals for alliances and learning more generally, few studies have incorporated the role of individuals into explanations for firm learning in alliances. Research has found that the bonds between key individuals are central mechanisms that initiate alliance formation (e.g. Larson, 1992) and sustain inter-firm relationships (Seabright, Levinthal and Fichman 1992). Individuals also embody the knowledge-based resources that evoke problem solving and learning and contribute the most to a firm’s ability to utilize information (Allen, 1977; Simon, 1985). Moreover, the primary basis of the firm’s ability to capitalize on external information rests on the ability of individuals to access, assimilate and utilize information (Cohen and Levinthal, 1990; 131). Despite these insights, researchers of strategic alliances have placed much greater emphasis on environmental conditions, and organizational level resources, practices and tendencies than individuals’ social or human capital as explanations for learning in alliances.
This paper integrates the theory of absorptive capacity (Cohen and Levinthal, 1989; 1990; 1994) and network theory (Burt, 1992; Granovetter, 1973; 1985) to examine the context embedded within the alliance in order to augment our understanding of what leads to learning in alliances. The combination of these two perspectives accentuates issues of information access and assimilation. Network theory highlights the importance individuals’ social capital that resides in the relationships that connect key individuals to others as primary resources that effect access to information. The theory of absorptive capacity specifies the nature of human capital or knowledge-based resources that allows individuals, and firms through their key employees, to capitalize on external information and learn through alliances[1]. Consequently, the framework suggests that firm’s differ in their ability to value, assimilate and utilize external information and their ability to access information. Moreover, these differences influence firm learning in alliances.
This paper opens the ‘black box’ of alliances and systematically investigates the relationships between variations in individuals’ networks and knowledge and the firm’s ability to learn in alliances. I propose that the networks represented by the ties between key individuals and their information/advice contacts provide access to information. Moreover, differences in the strength of these relationships are also critical sources of variation that affect access to information and the potential for learning. In addition, individuals’ knowledge influences the extent to which individuals assimilate and utilize external information (Cohen and Levinthal 1990; 1994). I suggest that differences in the knowledge-based resources held by individuals influences learning to the extent that it tempers individuals’ ability to assimilate and utilize new information. My central proposition is that, in addition to environmental, industry and organizational factors, the networks and knowledge of individuals have distinct and important roles for learning through alliances.
Theory and Hypotheses
Social Capital: Individuals’ Networks
Bourdieu first defined social capital as “the aggregate of … resources which are linked to the possession of a durable network of institutionalized relationships” (1985; 248). Consistent with this definition, researchers have suggested that social capital is derived from both structural (Burt, 1992; 1997; 1998; Nahapiet and Ghoshal, 1998) and relational dimensions of individuals’ networks (Coleman 1988; Granovetter, 1973; 1985; Nahapiet and Ghoshal, 1998). Individuals’networks, however, vary along these dimensions and these differences influence the value or level of social capital in their networks. Thus, I suggest that differences in network structure and relational strength influence the extent to which individuals are able to access and discover new information, and ultimately learn in ways that can benefit their firm. Specifically, network theory indicates that networks rich in non-redundant ties provide greater information access benefits (Burt, 1992). Further, networks rich in strong trusting ties (Granovetter, 1985; Krackhardt, 1992) also facilitate access to valuable external information and influence the likelihood that learning occurs within alliances.
Network Structure: Non-redundant Ties
Networks that are rich in non-redundant ties can be defined as networks that link a focal actor to contacts that are not otherwise connected (Burt, 1992) and provide access to “valuable pieces of information” (1992: 13). While the value of social capital includes information and control benefits (Burt, 1992) in this paper I focus on information benefits, and variations in network configurations believed to influence information access. It follows that the networks of key individuals’ that represent different configurations and provide differential access to information. Moreover, given that new information is needed for learning (Anderson, 1993), the access benefits derived from these networks are a key source of variation that influences the extent to learning occurs in alliances.
Burt (1992) suggests that networks characterized by greater non-redundancy in ties provide access to a greater range of new, unique and different information than networks that lack non-redundant ties. This access, however, rests on the premise that non-redundant ties tap into fundamentally different domains or pools of information (Burt, 1992). Non-redundant networks are more suited to accessing a greater range of information because the disconnection between contacts implies that individuals are connected to unique, novel and non-overlapping sources of information. In contrast, networks with redundant ties link the individual to contacts that “run in the same social circle”, belong to a common pool or domain of information and circulate the same information. As a result, the networks characterized by redundant ties are likely to provide similar or overlapping information while non-redundant network ties provide new or novel information.
Since learning requires access to new information (Simon, 1991; Anderson, 1993), the networks through which each individual gains access to information influence the chances that the individual, and consequently the organization will learn. Redundant network structures, because they limit access to new information, limit the level of learning that can occur. In contrast, non-redundant network structures in the individual’s networks tend to involve access to and exchange of a greater diversity of ideas and information that can facilitate learning.
H1Network non-redundancy is positively related to learning.
Network Relational Dimensions: Trust
Granovetter (1985) suggested that a primary value of social networks stems from the strength of network ties. Strong ties can be defined in terms of the “combination of the amount of time, emotional intensity, mutual confiding, and reciprocal services” (Granovetter 1973; 1361) in a relationship between two actors. Moreover, as interaction develops between actors, “…economic relationships … become overlaid with social content that carries strong expectations of trust and abstention from opportunism,” (Granovetter, 1985: 113).
Although virtually every exchange has some degree of trust (Arrow, 1962), trust defined as the expectation that individuals will fulfill obligations in predictable, fair and reliable ways (Anderson and Weitz, 1989; Cummings and Bromiley, 1996), has important effects on alliances, and in particular information access. The benefits of trust have been variously characterized as lowering transaction costs (Dyer, 1996), reducing the need for formal contracts (Larsen, 1992), making it easier to adapt to changing circumstances (Doz and Hamel, 1998) and facilitating conflict resolution (Ring and Van de Ven, 1994). Most important for this discussion is the belief that trust provides access to information. Trust between individuals reduces or eliminates the risk that individuals withhold information and increases the willingness of individuals to take risk, thereby allowing open exchanges of information (Nahapiet, 1996; Ring and Van de Ven, 1992; Starbuck, 1992; Zaheer, McEvily and Perrone, 1998). Specifically, research suggests that trust stimulates collaboration, promotes cooperation and facilitates access to tacit (Axelrod, 1984; Doz and Hamel, 1998; Krackhardt, 1992; Nelson and Winter, 1982; Uzzi, 1996). The implication of trust is that individuals are more likely to exchange deeper information and experiment with new combinations (Nahapiet and Ghoshal, 1998). Such information is often embedded in firm specific routines or specialized knowledge that is difficult to articulate knowledge but can be critical for learning (Cohen and Levinthal, 1990; Polyani, 1967; VanLehn, 1990). In other words, trust encourages deeper access to information and because these exchanges facilitate learning, trust increases the level of learning.
In summary, trust brings individuals together and increases the chances that individuals access, exchange and share information. A lack of trust in advice or information contacts reduces the likelihood that individuals will access and exchange information that is often critical for learning to take place. In contrast, higher levels of trust in these contacts increases individuals’ access to information and increases the chances that individuals will learn.
H2Strong ties (trust in one’s contacts) is positively related to firm learning.
Social Capital: Individuals’ Knowledge
Learning, however, requires more than access to information. Building on research of problem solving and cognition at the individual level (e.g. Bower and Hilgard, 1981), Cohen and Levinthal (1989, 1990, 1994) have suggested that firms differ in their ability to value, assimilate and utilize external information. Specifically, this ability or absorptive capacity depends on the cumulative experience within the firm and the extent to which this knowledge is related to external information. This theory of absorptive capacity highlights two important criteria of knowledge – it’s cumulative nature points to issues of expertise or competence and the relevance of internal knowledge suggests that a firm’s knowledge must be complementary to the external information accessed.
Previous research in other contexts (e.g. Allen, 1977) suggest that a large portion of a firm’s knowledge or human capital is accumulated through experience, originates and is applied in the minds of individuals and becomes embedded in the capabilities and practices individuals use to accomplish tasks. Each individual’s knowledge constitutes what the individual knows as a result of his or her specific experiences that are likely to vary. Further, the knowledge of individuals influences their ability to utilize external information (Anderson, 1993; Newell and Simon, 1972; Weick, 1979). The implication is that differences in the human capital allocated and embodied in individuals are sources of variability that influence learning in the alliance.
Knowledge Complementarity
Studies have found that the ability to learn from others depends upon the similarity of the knowledge bases involved (Boisot, 1995; Campbell, 1969). Related knowledge facilitates the internalization of new information because the basis of relatedness provides common rules for communication and facilitates the exchange of ideas and information (Boland and Tenkasi, 1995; Dearborn and Simon, 1958; Cohen and Levinthal, 1990; Lane and Lubatkin, 1998). Related knowledge ensures that individual are able to recognize and assess the value of new information and eases the process of assimilating new information or learning. However, at the same time, diversity of knowledge is critical element that allows knowledge to advance and learning to occur (Nonaka and Takeuchi, 1995). Diverse knowledge ensures that new information, different opinions, and new insight is available. Further, when different areas of expertise interact the chances for making new combinations and learning increase (Cohen and Levinthal, 1990).
The ideal structure of knowledge in an individual’s network “should reflect only partially overlapping knowledge complemented by non-overlapping diverse knowledge (Cohen and Levinthal, 1990: 134). The presence of only related knowledge ensures that individuals will communicate, but limits the opportunities for learning. In contrast, knowledge diversity by itself makes new and different ideas available but limits opportunities for learning because communication is stifled. I use the term ‘knowledge complementarity’ to refer to the extent to the knowledge of individuals is related to and at the same time is different from the knowledge of contacts in their information/advice networks. The presence of knowledge complementarity provides for communication, the exchange of diverse information and increases the chances that learning will occur. Consequently, when individuals’ have higher levels of knowledge complementarity with information/advice contacts learning is more likely to occur than when knowledge complementarity is lacking.
H3Knowledge complementarity is positively related to firm learning.
Expertise
A second predictor of individuals’ ability to utilize new knowledge is their own level of human capital or expertise within a specific domain. Expertise can be defined, generally, as the extent to which an individual understands a particular domain of knowledge. High levels of expertise develop over a long period of intense involvement in or commitment to an area and yields a comprehensive understanding of why and how something works or is done (de Groot, 1978; Simon, 1990). This level of understanding produces an inventory of knowledge regarding solutions that ‘work’ for familiar problem types and skills for solving technical problems (Anderson, 1993). Put differently, individuals with high levels of expertise are more likely to understand with great familiarity the laws, logic and rationale underlying the function or process of a specific domain. This familiarity provides individuals with the ability to identify critical configurations or “complexes” that contain several pieces of information, including the relationship between different elements and information about the solution in a complex situation (Bohn, 1994; Camerer and Johnson, 1991: 23; de Groot, 1978). Thus, they develop deep knowledge structures that allow them to more fully comprehend and more quickly make connections between their knowledge and new, external information.
‘Experts’ or individuals with higher levels of competence are more suitably skilled for integrating knowledge and information than individuals with less experience and expertise. High levels of expertise enable individuals to articulate their knowledge and beliefs about the processes driving performance and to think creatively and critically about problems (Nonaka, 1994). As a result, individuals with higher levels of expertise find it easier to assimilate and apply new information to solve problems more quickly and effectively (e.g. Singley and Anderson, 1989). In contrast, individuals with lower levels of expertise lack the understanding that enables effective problem solving and learning. As a result, when the firm assigns individuals with higher levels of expertise to work in the alliance it is more likely that learning will occur then when these individuals have less expertise.
H4Individuals’ expertise is positively related to firm learning.
Methodology
Sample Selection
The sample for this study was drawn from the population of alliances that focus on the development or extension of medical device technologies and were formed between U.S. private and public companies from January 1989 and January 1998[2]. Ward’s Business Directory of U.S. Private and Public Companies and the Medical Device Register (a comprehensive business-to-business reference database on medical-related companies) were used to identify a complete list of companies in the biotechnology, medical device and pharmaceutical industries[3]. Industry sources including The Grey Sheet, Windhover’s Pharmaceutical Strategic Alliance database, Recombinant Capital’s database, as well as standard public sources including Lexus-Nexus and company web pages were then searched to identify and triangulate information on the alliances these companies formed during the past ten years. Alliances were included only if they involved the development of medical device technologies focused on human therapeutics and diagnostics. Following the practice of other researchers, alliances focused solely on marketing or distribution and ‘as-is’ license exchanges, and joint ventures which establish a separate legal entity were excluded to hold constant issues related to technology or industry (e.g. Lane and Lubatkin, 1998; Shan, Walker & Kogut, 1994). These search efforts identified 571 alliances formed among 533 companies during the period from January 1989 through January 1998. From this population, 212 alliances were selected such that each alliance involved two unique partners, thus ensuring independence among the firms involved.
Research Design and Data Collection
The primary data source used to collect the data supporting this study were questionnaires mailed to key individuals involved in these alliances. Key individuals include scientists, physicians, and engineers that are central to the alliance effort. These individuals were identified from the various public sources mentioned above and company directories and contacted by telephone to verify the appropriateness of the alliance, the individual’s role in the alliance, and to obtain their agreement to participate in the study. Confidentiality of responses was emphasized in these conversations, and each company received an explicit nondisclosure statement.