CONTINGENT EFFECTS OF SOCIAL CAPITAL IN THE UK BIOTECHNOLOGY ALLIANCE NETWORK

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1 CONTINGENT EFFECTS OF SOCIAL CAPITAL IN THE UK BIOTECHNOLOGY ALLIANCE NETWORK ANASTASIOS KARAMANOS Assistant Professor Department of Management Ecole Supérieure des Sciences Economiques et Commerciales (ESSEC) ESSEC Asian Centre 100 Victoria Street National Library #13-02 Singapore Tel: Fax: ABSTRACT This event-study found empirical evidence from 181 technical alliances in biotechnology that the market reaction to alliance announcements is negatively related to a focal firm s prominence in the alliance network. It also found no evidence to suggest that firms learn to create value as they accumulate experience in alliances. INTRODUCTION Given that firms can have benefits or costs from network ties (Hagedoorn & Duysters, 2002) and the inconclusive nature of the existing literature (Portes, 1998), this paper investigates contingent effects of a biotechnology firm s social capital from its position in the alliance network on the stock market reaction to the firm s alliance announcements. It also introduces the event study methodology to the subject of alliance networks, social capital and performance. For analytic clarity and focus, in this paper, I restrict my attention to technical alliances in the biotechnology industry. ALLIANCE NETWORKS AND SOCIAL CAPITAL Social Capital from Network Closure Prior research has shown that prominent firms share noncodifiable information with confidence, because the development of norms of reciprocity and sanctions for the violation of trust dampens opportunistic behavior and gears the collaborations towards value creation (Uzzi, 1997). Also, prominent firms with multiple direct ties develop interdependency with their partners (Rowley, Behrens, & Krackhardt, 2000). Third, alliances have been postulated to provide access to external legitimacy and status similar to that provided by legitimating institutions (Stuart, Hoang & Hybels, 1999). According to institutional theorists, firms face pressures to conform to expectations of appropriate behaviors (e.g. Zuckerman, 1999). Social capital would be a component of the focal firm s stock market identity that is, the beliefs by stock market investors and analysts about what kind of stock this is, and stock market reactions depend on whether a firm s action can create a stock market identity gap (Benner, 2007; Zuckerman, 1999). The social capital of a prominent firm would then give a stock market identity to the firm as one isolated from knowledge residing outside its direct ties (Gulati, Nohria, & Zaheer, 2000). For 1

2 instance, Maurer & Ebers (2006) observed that the desire to honor the norms of repeated alliances may be detrimental to economic performance particularly in environment of greater technological uncertainty. Thus, the social capital of a prominent firm would give a stock market identity to the firm as one constrained in its resources for exploring knowledge outside its direct ties. Accordingly, I submit that, other things being equal, Hypothesis 1: There is a negative relationship between a focal firm s prominent position in the alliance network and the stock market reaction to the announcement of a new technical alliance by that firm As the overall size of a network increases, it is possible that fully connected network subgroups called clusters emerge. Clustering can create strong social norms and beliefs that encourage compliance to local rules and customs leading to a reduced need to formal controls. Portes (1998) noted that individual freedom and business initiative is stifled when individuals are compelled to conform to the shared norms. Norms of adhering to established standards and conventions can potentially stifle experimentation and creativity (Uzzi & Spiro, 2005). The internal cohesion and solidarity of a cluster can cause much of the information and knowledge shared within a cluster to become homogeneous and redundant (Burt, 1992). This can be a problem when competitive conditions change and firms need to access new and different information and resources from outside their local network. Thus, the social capital of a focal firm with cluster overlap centrality would give a stock market identity to the firm as one exploring knowledge across network clusters. Accordingly, I submit that, other things being equal, Hypothesis 2: There is a negative relationship between a focal firm s cluster overlap centrality in the alliance network and the stock market reaction to the announcement of a new technical alliance by that firm Social Capital from Structural Holes Whereas certain researchers stress the importance of network closure, other researchers stressed that building networks with structural holes, which refers to the social gap between two non-redundant partners, and firms can obtain entrepreneurial benefits from serving as intermediaries between partners who are not directly connected. Bridging structural holes gives entrepreneurial opportunities for information access, timing, referrals, and control (Burt, 1992: 2). This type of network can serve as a screening device (Leonard- Barton, 1984) that allows for relevant developments in different technologies may be brought to the firm's attention through its ties, in which case the firm has an opportunity to put technologies into novel combinations that enable it to provide unique offerings to the market (Koka & Prescott, 2008). Alternately, faced with a specific problem, a firm can activate its network to identify the sources that are likely to be well informed about the specific issue at hand (Freeman, 1982). Finally, brokering across structural holes gives the opportunity to form consortia that orchestrate the actions of multiple firms with diverse capabilities in order to create new markets and products (Koka & Prescott, 2008). Accordingly, I submit that, other things being equal, Hypothesis 3: There is a positive relationship between a focal firm s structural holes position in the alliance network and the stock market reaction to the announcement of a new technical alliance by that firm 2

3 RESEARCH METHODOLOGY Research Setting and Alliance Network Data I tested the hypotheses using strategic alliance agreements by UK publicly-traded dedicated biotechnology firms (DBFs) in Measures Dependent variable. To the extent that information regarding particular alliances may leak out prior to the actual announcement of the agreement, I used Cumulative Abnormal Returns (CAR) for the event window (-1,0,+1) as the dependent variable. Network matrices. I created alliance networks based on three-year windows (i.e., , , ), resulting in six snapshots of network structure (Schilling & Phelps, 2007). Each network snapshot was constructed as an undirected binary adjacency matrix and allowed me to calculate network variables using UCINET 6 and test whether the theory holds across time. Network prominence variables. The simplest prominence measure is degree centrality (Degree_C), which simply counts the number of alliances a firm has at any time. Eigenvalue centrality (Eigen_C) accords each firm a centrality that depends both on the number and the status of its ties. Another measure for prominence is betweeness centrality (Between_C) and it is calculated as the fraction of shortest paths between other firms that pass through the focal firm. In order to cover cluster formation, I calculated a centrality measure called cluster overlap centrality (Cluster_C), which counts the number of network clusters that a focal firm has direct ties to. Structural holes variables. Structural holes were measured using Burt's (1992) constraint variable (Structural_Holes), which is essentially a measure of the extent to which a firm is invested in partners who are invested in other firm's partners (see also Koka & Prescott, 2008). Control variables. I introduced the control variables Firm_Size (total assets lagged by one year), Alliance_Experience (cumulative number of a firm s alliances up to each year of the analysis), Same_Nationality, (1 when an alliance comprises of domestic partners and 0 when it includes international collaboration), Repeat_Partner (number of repeat alliances with the same partner), Alliance_Type (differences of deal scope), Horizontal (1 when a data point involves a horizontal collaboration and 0 when alliance partners have dissimilar business scope). Finally, I included a dummy variable for each year in the analysis (called Time1 to Time6) in order to capture effects that may arise from temporal market trends. RESULTS I tested the Hypotheses using cross-sectional OLS regression models with CAR (-1,0,+1) as the dependent variable, as shown in Table 1. All regression models include the control variables Firm_Size, Alliance_Experience, Firm_Age, Same_Nationality, Repeat_Partner, Alliance_Type, and Horizontal. The VIF statistics for all models were low, indicating that there is no risk of multicollinearity in the analysis Insert Table 1 about here

4 DISCUSSION AND CONLUSIONS Network prominence measured by degree centrality was found to negatively affect abnormal returns from technical alliance announcements. This may be a market signal to the firm that excessive alliance formation may result in diminishing returns to accessing and leveraging complimentary resources, as well as stretch the managerial capability and strain a firm s innovation efforts (Deeds & Hill, 1996). Network prominence in terms of cluster overlap centrality was also found to be a negative significant predictor, but the interpretation of this result may be different. Embeddedness in many clusters may indicate that a biotechnology firm experiences selection forces to comply with dominant designs and that it cannot deviate from prevailing industry recipes (Spender, 1989). By contract, firms at the periphery may enjoy more freedom from the environment (Gilsing & Nooteboom, 2005) to experiment freely with partners and produce more radical innovations with potentially more technological and economic value. As far as network prominence in terms of network status and power is concerned (measured by eigenvector centrality), our research suggests that this aspect of network performance is not very relevant in the uncertain environment of UK biotechnology. Also, network prominence in terms of control of information flow through indirect ties (measured by betweeness centrality) does not seem to have any effect. The possible explanation here is that in this environment indirect ties have no value as knowledge from each alliance is specific and dedicated, or protected. Finally, an entrepreneurial position in the network measured by the constraint variable (Burt, 1992) does not seem to have any effect on the stock market abnormal returns to technical alliance announcements. This result may be explained by Rowley, Behrens, & Krackhardt (2000) who argued that, when the knowledge being transferred is more codified, firms that bridge structural holes from diverse parts of the network are ideally suited for exploration-related activities. In our case, though, the industry was going through a period of uncertainty characterized by competing technologies and patent races, which we can presume did not allow for much codified knowledge to flow in the network. In such a situation, an entrepreneurial position that bridges structural holes should not have any effect on performance. Finally, I found no evidence to suggest that firms learn to create value as they accumulate experience in alliances, which confirms Anand & Khanna (2000), and Sampson (2005). The possible explanation for this result is that knowledge or learning from experience deteriorates rapidly over time (Sampson, 2005). This study provides some important implication for managers. As network prominence can have a negative effect on performance, firms should balance their efforts to develop ties to exploit knowledge held in cohesive networks structures, but at the same time maintain the capability to identifies and explore knowledge held by capable partners outside established clusters. Perhaps firms should have an alliance management capability (an alliance function) to be able to execute an alliance strategy. Moreover as alliance experience appears not to assist performance, firms have an additional reason to develop an alliance function (Sampson, 2005: 1027). Kale, Dyer, & Singh (2002) found a positive relationship between a firm s abnormal stock gains following alliance announcement and its alliance function, and suggested that it may be a signal of the market s confidence in the firm s ability to manage alliances effectively. REFERENCES Ahuja, G., & Lampert, C. M Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal, 22:

5 Anand, B. N., & Khanna, T Do firms learn to create value: The case of alliances. Strategic Management Journal, 21: Benner, M. J The incumbent discount: Stock market categories and response to radical technological change. Academy of Management Review, 32: Burt, R. S Structural holes: the social structure of competition. Cambridge, MA: Harvard University Press. Deeds, D. L., & Hill, C. W. L Strategic alliances and the rate of new product development: An empirical study of entrepreneurial biotechnology. Journal of Business Venturing, 11: Foster, R Innovation: The Attacker's Advantage. New York: Summit Books. Freeman, C The Economics of Industrial Innovation. Cambridge, MA: MIT Press. Gilsing, V. A., & Nooteboom B Density and strength of ties in innovation networks: An analysis of multimedia and biotechnology. European Management Review, 2: Gulati, R., Nohria, N., & Zaheer A Strategic networks. Strategic Management Journal, 21: Hagedoorn, J., & Duysters, G Learning in dynamic inter-firm networks: The efficacy of multiple contacts. Organization Studies, 23: Kale, P., Dyer, J. H., & Singh, H Alliance capability, stock market response, and longterm alliance success: The role of the alliance function. Strategic Management Journal, 23: Koka, B. R., & Persott J. E Designing alliance networks: The influence of network position, environmental change, and strategy on firm performance. Strategic Management Journal, 29: Leonard-Barton, D Inter-personal communication patterns among Swedish and Boston-area entrepreneurs. Research Policy, 13: Maurer, I., & Ebers, M Dynamics of social capital and their performance implications: Lessons from biotechnology start-ups. Administrative Science Quarterly, 51: Nooteboom, B., & Vossen, R. W Firm size and efficiency in R&D spending. In A. van Witteloostuijn (Ed.), Competition and cooperation, Deventer: Kluwer. Portes, A Social capital: Its origins and applications in modern sociology, Annual Review of Sociology, 24: Rowley, T., Behrens, D., & Krackhardt, D Redundant governance structures: an analysis of structural and relational participation in the steel and semiconductor industries. Strategic Management Journal, 21: Sampson, R. C Experience effects and collaborative returns in R&D alliances. Strategic Management Journal, 26: Schilling, M. A. and Phelps, C. C. 2007: Interfirm collaboration networks: The impact of large-scale networks structure on firm innovation. Management Science, 53: Spender, J. C Industrial recipes. Oxford, UK: Blackwell Publishing. Stuart, T. E., Hoang, H., & Hybels, R. C Interorganizational endorsements and the performance of entrepreneurial ventures. Administrative Science Quarterly, 44: Uzzi, B. D Social structure and competition in interfirm networks: The paradox of participation. Administrative Science Quarterly, 42(1): Uzzi, B., J. & Spiro Collaboration and creativity: The small world problem. American Journal of Sociology, 111: Zuckerman E The categorical imperative: Securities analysts and the illegitimacy discount. American Journal of Sociology, 104:

6 Table 1. Regression results for CAR(-1,0,1) as the dependent variable Constant Model I Model II Model III Model IV Model V Coeffs VIF Coeffs VIF Coeffs VIF Coeffs VIF Coeffs VIF (0.096) (0.099) (0.265) (0.165) (0.157) Firm_Size Alliance_Experience Same_Nationality Repeat_Partner Alliance_Type Horizontal Time1 Time2 Time3 Time4 Time5 Time * (0.019) (0.873) (0.753) (0.191) 0.189* (0.014) (0.936) (0.071) (0.928) (0.096) (0.421) (0.129) (0.735) Degree_C - Eigen_C * (0.018) (0.494) (0.788) (0.481) 0.184* (0.017) (0.849) (0.085) (0.941) (0.076) (0.493) (0.101) (0.829) ** (0.010) Between_C - - Cluster_C - - Structural_Holes (0.919) * (0.023) (0.657) (0.872) (0.367) 0.181* (0.020) (0.935) (0.112) (0.988) (0.147) (0.640) (0.309) (0.964) (0.283) (0.603) * (0.019) (0.613) (0.695) (0.348) 0.182* (0.019) (0.999) (0.054) (0.848) (0.078) (0.462) (0.122) (0.821) (0.111) (0.815) ** (0.006) (0.809) (0.618) (0.325) 0.178* (0.020) (0.910) * (0.044) (0.971) (0.060) (0.447) (0.082) (0.490) * (0.014) (0.800) R-SQUARE F-VALUE * * Deg. of freedom ** - p <.01; * - p <.05; + - p <.10 6