Structural embeddedness and innovation diffusion: the moderating role of industrial technology grouping
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Industrial technology grouping is a common phenomenon that occurs as an industry develops and evolves. However, the research on innovation diffusion has given little attention to the role of industrial technology grouping. This paper extends the prior research to analyze the impact of industrial technology grouping on innovation diffusion within the framework of structural embeddedness. In our empirical study, we selected a sample of patents in the smart phone industry during the 2004–2014 period. We used both hierarchical regression analysis and patent citation analysis to explore the impact of industrial technology grouping on innovation diffusion in the two dimensions of clustering and bridging ties, which yielded several valuable results. First, industrial technology grouping is a common phenomenon in the development of industrial technology. Moreover, the dynamic changes of technology clusters are an important driving force shaping the trends and diversity of industrial technology. Second, industrial technology grouping does not have a significant effect on firm innovation diffusion, whereas structural embeddedness directly affects innovation diffusion. Third, industrial technology grouping positively moderates the impact of structural embeddedness on firm innovation diffusion in both dimensions of clustering and bridging ties.
KeywordsIndustrial technology grouping Innovation diffusion Patent analysis Smart phone industry
Mathematics Subject Classification62J05 62G10
JEL ClassificationO32 O33
This work was supported by National Natural Science Foundation of China under Grant Nos. 71572026 and 71632004.
- Armstrong, A. K., Mueller, J. J., & Syrett, T. (2014). The smartphone royalty stack: Surveying royalty demands for the components within modern smartphones. http://ssrn.com/abstract=2443848. Accessed May 29, 2014.
- Freeman, L. C. (1978/79). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.Google Scholar
- Han, K., Oh, W., Im, K. S., Chang, R. M., Oh, H., & Pinsonneault, A. (2012). Value cocreation and wealth spillover in open innovation alliances. MIS Quarterly, 36(1), 291–316.Google Scholar
- Iyengar, R., Bulte, C. V. D., & Valente, T. W. (2010). Opinion leadership and social contagion in new product diffusion. Management Science, 30(2), 195–212.Google Scholar
- Lee, W., & Choi, J.-I. (2013). Industry-academia cooperation in creative innovation clusters: A comparison of two clusters in Korea. Academy of Entrepreneurship Journal, 19(3), 79–95.Google Scholar
- Makri, M., Hitt, M. A., & Lane, P. J. (2009). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31(6), 602–628.Google Scholar
- Merwe, R. V. D., & Heerden, G. V. (2009). Finding and utilizing opinion leaders: Social networks and the power of relationships. South African Journal of Business Management, 40(3), 65–76.Google Scholar
- Rogers, E. M. (1995). Diffusion of innovations. New York: Free Press.Google Scholar
- Schilling, M. A., & Phelps, C. C. (2005). Network effects and personal influences: The diffusion of an online social network. Journal of Marketing Research, 48(3), 425–443.Google Scholar
- Wang, H., Wang, W., Yang, J., & Yu, P. S. (2002). Clustering by pattern similarity in large data sets. In Proceedings of the 2002 ACM SIGMOD international conference on Management of data, 394–405.Google Scholar