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Regional Networks, Alliance Portfolio Configuration, and Innovation Performance

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Abstract

To maintain and enhance innovation performance, many firms nowadays look for resources from external sources such as strategic alliances and regional network embeddedness. While considering the important interdependencies among different alliances, research has established an alliance portfolio perspective. From an alliance portfolio perspective, firms can consciously configure the dimensions of their alliance portfolios such as partner characteristics, relational properties, or structural properties. However, within the context of alliance portfolio configuration , the role of regional networks has been largely overlooked. As most high-tech firms are regionally clustered, this is an important research gap. In addressing this gap, this study explores the link between regional network density, alliance portfolio configuration, and its contribution to firm innovation performance. We examine how regional network density and alliance partner diversity influences firm level innovation output. We also investigate the moderating effect of overall network partner status and partner diversity on the link between regional network density and innovation performance. Our empirical evidence is derived from a longitudinal quantitative study of 1,233 German biotechnology firms. We find that regional network density and alliance partner diversity has an inverted U-shape effect on firm level innovation performance. However, overall network status as well as alliance partner diversity negatively moderates the link between regional network density and innovation output. Thus, our study contributes to a better understanding of the link between regional networks, alliance portfolio configuration, and firm level innovation performance.

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... In order to preserve as well as to enhance innovation performance, organizations must consistently and continuously expand their knowledge base (Bort, Oehme, & Zock, 2013). This frequently develops through the establishment of strategic alliances (Ahuja, 2000; Bort et al., 2013; Phelps, 2010; Srivastava & Gnyawali, 2011) and immersion into 'regional cluster networks' (Bort et al., 2013) that facilitate the innovation. ...
... In order to preserve as well as to enhance innovation performance, organizations must consistently and continuously expand their knowledge base (Bort, Oehme, & Zock, 2013). This frequently develops through the establishment of strategic alliances (Ahuja, 2000; Bort et al., 2013; Phelps, 2010; Srivastava & Gnyawali, 2011) and immersion into 'regional cluster networks' (Bort et al., 2013) that facilitate the innovation. ...
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Using data on U.S. investment banking firms’ syndication in underwriting corporate stock offerings during the 1980s, this study explores the factors that drive alliance formation between two specific firms. We compare resource complementarity, status similarity, and social capital as a basis of alliance formation. The findings indicate that the likelihood of investment banks’ alliance formation is positively related to the complementarity of their capabilities, as well as their status similarity. Social capital arising from banks’ direct and indirect collaborative experiences also plays a very important role in alliance formation. The number of deals given by a lead bank to a potential partner over the past three years has an inverted U-shaped relationship to the probability that the lead bank will invite the potential partner to form an alliance. Our findings indicate that status similarity and social capital have a stronger effect on alliance formation in initial public offering deals than in secondary offering deals, as the former are more uncertain than the latter. Using these findings, we discuss the role of complementarity, status similarity, and social capital in alliance formation. Copyright © 2000 John Wiley & Sons, Ltd.
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2In an influential paper, Freeman (1979) identified three aspects of centrality: betweenness, nearness, and degree. Perhaps because they are designed to apply to networks in which relations are binary valued (they exist or they do not), these types of centrality have not been used in interlocking directorate research, which has almost exclusively used formula (2) below to compute centrality. Conceptually, this measure, of which c(ot, 3) is a generalization, is closest to being a nearness measure when 3 is positive. In any case, there is no discrepancy between the measures for the four networks whose analysis forms the heart of this paper. The rank orderings by the
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Examines the correlation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. Also discusses the difficulty in balancing resource management between gaining new information about alternatives to improve future returns (i.e., exploration) and using information currently available to improve present returns (i.e., exploitation). Two models which evaluate the formation and use of knowledge in organizations are developed. The first is a model of mutual learning in a closed system having fixed organizational membership and stability. The second is a model which considers the ways in which competitive advantage is affected by knowledge accumulation. The analysis indicates that the choice to rapidly develop exploitation over exploration might be effective in the short term, but is potentially detrimental to the firm in the long term. (SFL)
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Examines how the performance of young firms is influenced by their interorganizational exchange networks and whether the prominence of business partners affects the ability to acquire critical resources, particularly capital. The following four hypotheses are posited: (1) the greater the prominence of the strategic alliance partners of a young company, the better the performance of the new venture; (2) the greater the prominence of the organizations that have acquired ownership stakes in a young company, the better the performance of the new venture; (3) the greater the prominence of the investment bank of a young company, the better the performance of the new venture; and (4) the greater the uncertainty about the quality of the company, the larger the impact of the prominence of the firm's exchange partners on its performance. Data used to test these hypotheses were gathered from 301 young, venture-capital-backed biotechnology firms. Results from the empirical analysis provide strong evidence that the characteristics and prominence of organizations affiliated with young firms have a direct affect on performance. Firms launch IPOs faster and the IPOs earn greater market value with reputable partners. In addition, the advantage of having prominent affiliates is contingent on the level of uncertainty about the startup's quality. The greater the uncertainty, the more that outside evaluators depend upon the prominence of affiliates to draw inferences about the firm's quality. It is clearly demonstrated that sponsorship has the capacity to substitute for accomplishment and experience as a basis for young firms' success. However, experience and accomplishments take on added significance for firms that lack notable sponsors. (SFL)
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Few studies have moved beyond the dyadic level of an ongoing alliance and examined factors contributing to the success of entering a series of alliances. In this paper we expect biotechnology firms over time to learn from their alliance experience and to develop general alliance capabilities. Specifically, we expect the speed with which they enter into new research alliances, e.g. their alliance formation rate, to be affected by capabilities built up in prior alliances as well as by characteristics of their partners. We use longitudinal event history data for the complete population of US biotechnology firms for 1973-1999 to test four hypotheses about factors affecting the rate of new alliance formation. Our analysis suggests that the speed of entering research alliances is affected by prior experience of the focal firm, but not by partner characteristics. Our findings provide evidence that biotech firms learn how to learn more effectively from multiple research alliances; however, this effect is generalized and not tied to specific characteristics of the alliance partner.