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Network advantage’s effect on exit performance: examining venture capital’s inter-organizational networks

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Abstract

This study investigates how a venture capital’s network affects its exit performance. While most network research focuses on individual characteristics such as centrality and density, this study explores the overall advantage that results from holding a network position (i.e., a network advantage). This network advantage is examined at the syndicate level, which is a group of organizations. Within this context, the study contemplates the syndicate’s network advantage and hones in on the syndicate’s network advantage diversity, because the composition of members’ network advantage within the syndicate as well as the syndicate’s network advantage itself may affect the syndicate’s performance. To do this, first it is hypothesized that both a venture capital syndicate’s network advantage and its network advantage diversity are positively associated with its exit performance. Next, the study predicts that a syndicate’s network advantage diversity negatively moderates the positive relationship between the syndicate’s network advantage and its exit performance. Then a two stage least squares analysis of 1137 venture capital syndicate investments largely confirms the predicted effects. A syndicate’s network advantage is shown to contribute to its exit performance. A syndicate’s network advantage diversity clearly weakens the positive relationship between the syndicate’s network advantage and performance, while its direct effect on performance is not significantly supported. Overall, these findings show that a syndicate’s network advantage and its diversity are critical determinants of its exit performance in the venture capital industry.

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Notes

  1. A syndicate with members of high levels of network advantage was already predicted to obtain high performance in Hypothesis 1.

  2. When two or more than two VCFs invest in a startup together, the investment was regarded as a syndicate one. If a VCF invests in a startup independently, the investment was not included in the sample of this study.

  3. The author set all the main diagonal elements to 0, each element rij and rji in the matrix R to 1 if a tie is formed, and 0 otherwise. According to this measure, a focal firm’s status is a positive function of the number of ties and the status of other firms with whom the focal firm forms ties. Also, the author followed the example of previous research for designating β in this measure, and set it equal to three-quarters of the reciprocal of the largest eigenvalue (Podolny 1993).

  4. As an alternative to the initial measure of a syndicate’s network advantage (i.e., the sum of a VCFs’ network advantage), the author also tested the model with the average value of the VCFs’ network advantage. The author found no significant changes from the initial result. However, the sum of the VCFs’ network advantage represents the syndicate’s network advantage more appropriately than the average of the VCFs’ network advantage. For example, a syndicate of 10 VCFs with a network advantage level 0.5 will possess a higher level of network advantage than a syndicate of two VCFs with a network advantage level 0.5. Also, the number of VCFs within a syndicate is already included in the model as a control. So, this study adopted the sum of VCFs’ network advantage as the measure of a syndicate’s network advantage.

  5. Diversity can be measured in multiple ways, such as Blau’s heterogeneity index (Blau 1977), Shannon’s measure of entropy (Shannon 1949), and a coefficient of variation (Harrison et al. 1988). However, because the observations used in this study have a numeric value instead of a categorical value, the coefficient of variation can measure diversity value much more appropriately than other measures. For example, both Blau’s heterogeneity index and Shannon’s measure of entropy regard these two different syndicates as the same (i.e., a syndicate A of member 1 with network advantage of 0.1 and member 2 with a network advantage of 0.9, and a syndicate B of member 3 with network advantage of 0.4 and member 4 with a network advantage of 0.5).

  6. Other options may exist in identifying the lead firm of a VC syndicate. So, the author adopted only the first two criteria: the first investor; and the investor that participated in all the rounds (if more than two investors were in the first round) and conducted the analyses. Though the number of observations was lower than the initial observation number (i.e., n = 642), the analyses yielded similar results.

  7. This variable was included in the two stage least squares models as the instrument. The author categorized 56 nationalities of the startups into six groups: the U.S., Canada, the U.K., other European countries, South Korea, and other countries. This categorization was conducted on the basis of the number of investments each group had in the 6713 VC investments during the focal period.

  8. The author categorized 17 industry clusters into five groups: bio/medical industry, software industry, Internet-specific industry, communication industry, and other industries. Similar to the variable of a startup’s nationality, this categorization was based on the investment number of each group among the 6713 VC investments during the focal period. The author controlled for industry effects by including four dummy variables to represent the first four industry groups.

  9. The author thanks an anonymous reviewer for suggesting that network advantage diversity might make a nonlinear effect on performance. To verify the effect, the author conducted an additional test by including a square term of network advantage diversity to Model 3. However, the result indicated that the nonlinear effect was insignificant.

  10. While the interaction term is composed of two interrelated variables, its effect is considered as quasi-moderation (Sharma et al. 1981). In Model 4, network advantage is the independent variable and network advantage diversity works as the quasi-moderating variable.

References

  • Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45, 425–455.

    Article  Google Scholar 

  • Aiken, L. S. & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.

  • Anokhin, S., Örtqvist, D., Thorgren, S., & Wincent, J. (2011). Corporate venturing deal syndication and innovation: The information exchange paradox. Long Range Planning, 44(2), 134–151.

    Article  Google Scholar 

  • Baumgartner, R. J., & Rauter, R. (2017). Strategic perspectives of corporate sustainability management to develop a sustainable organization. Journal of Cleaner Production, 140, 81–92.

    Article  Google Scholar 

  • Becker, M. H. (1970). Sociometric location and innovativeness: Reformulation and extension of the diffusion model. American Sociological Review, 35, 267–282.

    Article  Google Scholar 

  • Bendersky, C., & Hays, N. A. (2012). Status conflict in groups. Organization Science, 23, 323–340.

    Article  Google Scholar 

  • Blau, P. M. (1977). Inequality and Heterogeneity: A Primitive Theory of Social Structure (Vol. 7). Free Press, NY.

  • Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170–1182.

    Article  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Analytic Technologies, MA.

  • Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review, 73, 43–53.

    Google Scholar 

  • Brander, J. A., Amit, R., & Antweiler, W. (2002). Venture capital syndication: Improved venture selection vs. the value-added hypothesis. Journal of Economics and Management Strategy, 11(3), 423–452.

    Article  Google Scholar 

  • Burkhardt, M. E., & Brass, D. J. (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Administrative Science Quarterly, 35, 104–127.

    Article  Google Scholar 

  • Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Burt, R. S. (2001). Structural holes versus network closure as social capital (pp. 31–56). Theory and Research: Social Capital.

    Google Scholar 

  • Burt, R. S. (2010). Neighbor networks: Competitive advantage local and personal. Oxford University Press.

  • Burt, R. S. (2012). Network-related personality and the agency question: Multirole evidence from a virtual world. American Journal of Sociology, 118, 543–591.

    Article  Google Scholar 

  • Bygrave, W. D. (1988). The structure of the investment networks of venture capital firms. Journal of Business Venturing, 3, 137–157.

    Article  Google Scholar 

  • Castellucci, F., & Ertug, G. (2010). What’s in it for them? Advantages of higher-status Partners in Exchange Relationships. Academy of Management Journal, 53, 149–166.

    Article  Google Scholar 

  • Cattani, G., & Ferriani, S. (2008). A core/periphery perspective on individual creative performance: Social networks and cinematic achievements in the Hollywood film industry. Organization Science, 10, 824–844.

    Article  Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • Coleman, J. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95–120.

    Article  Google Scholar 

  • Cross, R., & Cummings, J. N. (2004). Tie and network correlates of individual performance in knowledge-intensive work. Academy of Management Journal, 47, 928–937.

    Google Scholar 

  • Dimov, D., & Milanov, H. (2010). The interplay of need and opportunity in venture capital investment syndication. Journal of Business Venturing, 25, 331–348.

    Article  Google Scholar 

  • Dushnitsky, G., & Shapira, Z. (2010). Entrepreneurial finance meets organizational reality: Comparing investment practices and performance of corporate and independent venture capitalists. Strategic Management Journal, 31, 990–1017.

    Google Scholar 

  • Filatotchev, I., Chahine, S., Wright, M., & Arberk, M. (2005). Founders' characteristics, venture capital syndication and governance in entrepreneurial IPOs. International Entrepreneurship and Management Journal, 1(4), 419–439.

    Article  Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1, 215–239.

    Article  Google Scholar 

  • Gargiulo, M., & Benassi, M. (2000). Trapped in your own net? Network cohesion, structural holes, and the adaptation of social capital. Organization Science, 11, 183–196.

    Article  Google Scholar 

  • Goerzen, A., & Beamish, P. W. (2005). The effect of alliance network diversity on multinational enterprise performance. Strategic Management Journal, 26, 333–354.

    Article  Google Scholar 

  • Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214.

    Article  Google Scholar 

  • Gompers, P. A & Lerner, J. (2004). The venture capital cycle. The MIT press, Cambridge, MA.

  • Greve, H., Rowley, T. & Shipilov, A. (2013). Network advantage: How to Unlock Value from Your Alliances and Partnerships. John Wiley & Sons, Ltd., United Kingdom.

  • Gulati, R., & Gargiulo, M. (1999). Where do interorganizational networks come from? American Journal of Sociology, 104, 1439–1493.

    Article  Google Scholar 

  • Guler, I. (2007). Throwing good money after bad? Political and institutional influences on sequential decision making in the venture capital industry. Administrative Science Quarterly, 52, 248–285.

    Article  Google Scholar 

  • Haigh, N., & Hoffman, A. J. (2014). The new heretics: Hybrid organizations and the challenges they present to corporate sustainability. Organization & Environment, 27(3), 223–241.

    Article  Google Scholar 

  • Harrison, D. A., & Klein, K. J. (2007). What's the difference? Diversity constructs as separation, variety, or disparity in organizations. Academy of Management Review, 32, 1199–1228.

    Article  Google Scholar 

  • Harrison, D. A., Price, K. H., & Bell, M. P. (1988). Beyond relational demography: Time and the effects of surface-and deep-level diversity on work group cohesion. Academy of Management Journal, 41, 96–107.

    Google Scholar 

  • Higgins, M. C., & Gulati, R. (2006). Stacking the deck: The effects of top management backgrounds on investor decisions. Strategic Management Journal, 27, 1–25.

    Article  Google Scholar 

  • Hochberg, Y. V., Ljungqvist, A., & Lu, Y. (2007). Whom you know matters: Venture capital networks and investment performance. Journal of Finance, 62, 251–301.

    Article  Google Scholar 

  • Human, S. E., & Provan, K. G. (1997). An emerging theory of structure and outcomes in small-firm strategic manufacturing networks. Academy of Management Journal, 40(2), 368–403.

    Google Scholar 

  • Jääskeläinen, M. (2012). Venture capital syndication: Synthesis and future directions. International Journal of Management Reviews, 14, 444–463.

    Article  Google Scholar 

  • Kogut, B. (2000). The network as knowledge: Generative rules and the emergence of structure. Strategic Management Journal, 21, 405–425.

    Article  Google Scholar 

  • Lee, P. M., & Wahal, S. (2004). Grandstanding, certification and the underpricing of venture capital backed IPOs. Journal of Financial Economics, 73, 375–407.

    Article  Google Scholar 

  • Lerner, J. (1994). The syndication of venture capital investments. Financial Management, 23, 16–27.

    Article  Google Scholar 

  • Lockett, A., & Wright, M. (2001). The syndication of venture capital investments. Omega, 29(5), 375–390.

    Article  Google Scholar 

  • McEvily, B., & Zaheer, A. (1999). Bridging ties: A source of firm heterogeneity in competitive capabilities. Strategic Management Journal, 20, 1133–1156.

    Article  Google Scholar 

  • Mehra, A., Dixon, A. L., Brass, D. J., & Robertson, B. (2006). The social network ties of group leaders: Implications for group performance and leader reputation. Organization Science, 17, 64–79.

    Article  Google Scholar 

  • Meuleman, M., & Wright, M. (2011). Cross-border private equity syndication: Institutional context and learning. Journal of Business Venturing, 26, 35–48.

    Article  Google Scholar 

  • Mowery, D. C., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2), 77–91.

    Article  Google Scholar 

  • Nahata, R. (2008). Venture capital reputation and investment performance. Journal of Financial Economics, 90, 127–151.

    Article  Google Scholar 

  • Parida, V., Pemartín, M., & Frishammar, J. (2009). The impact of networking practices on small firm innovativeness and performance: A multivariate approach. International Journal of Technoentrepreneurship, 2(2), 115–133.

    Article  Google Scholar 

  • Parida, V., Patel, P. C., Wincent, J., & Kohtamäki, M. (2016). Network partner diversity, network capability, and sales growth in small firms. Journal of Business Research, 69(6), 2113–2117.

    Article  Google Scholar 

  • Parida, V., Pesämaa, O., Wincent, J., & Westerberg, M. (2017). Network capability, innovativeness, and performance: A multidimensional extension for entrepreneurship. Entrepreneurship and Regional Development, 29(1–2), 94–115.

    Article  Google Scholar 

  • Podolny, J. M. (1993). A status-based model of market competition. American Journal of Sociology, 98, 829–872.

    Article  Google Scholar 

  • Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41, 116–145.

    Article  Google Scholar 

  • Ritter, J. R., & Welch, I. (2002). A review of IPO activity, pricing, and allocations. Journal of Finance, 57, 1795–1828.

    Article  Google Scholar 

  • Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25, 541–562.

    Article  Google Scholar 

  • Sampson, R. C. (2007). R&D alliances and firm performance: The impact of technological diversity and alliance organization on innovation. Academy of Management Journal, 50(2), 364–386.

    Article  Google Scholar 

  • Scott, J. (1991). Social network analysis: A handbook. Sage, London.

  • Shannon, C. E. (1949). Communication theory of secrecy systems. Bell System Technical Journal, 28(4), 656-715.

  • Sharma, S., Durand, R. M., & Gur-Arie, O. (1981). Identification and analysis of moderator variables. Journal of Marketing Research, 18(3), 291–300.

    Article  Google Scholar 

  • Sørensen, M. (2007). How smart is smart money? A two-sided matching model of venture capital. Journal of Finance, 62, 2725–2762.

    Article  Google Scholar 

  • Sorenson, O., & Stuart, T. E. (2001). Syndication networks and the spatial distribution of venture capital investments. American Journal of Sociology, 106, 1546–1588.

    Article  Google Scholar 

  • Sorenson, O., & Stuart, T. E. (2008). Bringing the context back in: Settings and the search for syndicate partners in venture capital investment networks. Administrative Science Quarterly, 53, 266–294.

    Article  Google Scholar 

  • Steier, L., & Greenwood, R. (1995). Venture capitalist relationships in the deal structuring and post-investment stages of new firm creation. Journal of Management Studies, 32(3), 337–357.

    Article  Google Scholar 

  • Stuart, T. E., Hoang, H., & Hybels, R. C. (1999). Interorganizational endorsements and the performance of entrepreneurial ventures. Administrative Science Quarterly, 44(2), 315–349.

    Article  Google Scholar 

  • Tian, X. (2011). The causes and consequences of venture capital stage financing. Journal of Financial Economics, 101, 132–159.

    Article  Google Scholar 

  • Tian, X. (2012). The role of venture capital syndication in value creation for entrepreneurial firms. Review of Finance, 16, 245–283.

    Article  Google Scholar 

  • Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 35, 439–465.

    Article  Google Scholar 

  • Williams, K. Y., & O'Reilly, C. A. (1998). Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20, 77–140.

    Google Scholar 

  • Williamson, O. E. (1979). Transaction-cost economics: The governance of contractual relations. Journal of Law and Economics, 22(2), 233–261.

    Article  Google Scholar 

  • Wincent, J. (2008). An exchange approach on firm cooperative orientation and outcomes of strategic multilateral network participants. Group & Organization Management, 33(3), 303–329.

    Article  Google Scholar 

  • Wincent, J., Anokhin, S., Örtqvist, D., & Autio, E. (2010). Quality meets structure: Generalized reciprocity and firm-level advantage in strategic networks. Journal of Management Studies, 47(4), 597–624.

    Article  Google Scholar 

  • Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. The MIT Press, Chicago, IL.

  • Wright, M., & Lockett, A. (2003). The structure and management of alliances: Syndication in the venture capital industry. Journal of Management Studies, 40(8), 2073–2102.

    Article  Google Scholar 

  • Zaheer, A., & Bell, G. G. (2005). Benefiting from network position: Firm capabilities, structural holes, and performance. Strategic Management Journal, 26, 809–825.

    Article  Google Scholar 

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Acknowledgements

This research is partly based on the author’s dissertation at the KATZ Graduate School of Business, University of Pittsburgh. The author gives special thanks to the committee members, Ravi Madhavan, Sue Cohen, and John Prescott, for their invaluable advice and wonderful encouragement. The author also thanks the two guest editors of the special issue for their insightful comments.

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  1. Korea Energy Economics Institute, Jongga-Ro 405-11, Jung-Gu, Ulsan, 44543, South Korea

    Sang Yoon Shin

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Shin, S.Y. Network advantage’s effect on exit performance: examining venture capital’s inter-organizational networks. Int Entrep Manag J 15, 21–42 (2019). https://doi.org/10.1007/s11365-018-0545-0

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