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An Anatomy of Bengaluru's ICT Cluster: A Community Detection Approach

Published online by Cambridge University Press:  18 October 2019

Ekaterina Turkina  and
Ari Van Assche
Ekaterina Turkina*
Affiliation:
HEC Montréal, Canada
Ari Van Assche
Affiliation:
HEC Montréal, Canada
*
Corresponding author: Ekaterina Turkina (ekaterina.turkina@hec.ca)
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Abstract

We use community detection analysis to investigate the structure of Bengaluru's ICT cluster's inter-organizational network during the period 2015–2017. Building on the knowledge sourcing literature, we conjecture that cluster firms primarily build knowledge-seeking horizontal linkages with technologically similar companies, and that this splits the network into multiple technological communities within which firms are tightly connected, but between which linkages are scarce. We further propose that community-spanning firms which build horizontal linkages that bridge technological communities are more likely to conduct radical innovation than their peers. We finally argue that no relation exists between technological proximity and community formation in the network of vertical buyer-supplier relations. Using a voltage-based algorithm for community discovery, we draw empirical support for these predictions. We discuss the implications of our findings for Bengaluru's upgrading potential.

摘要

我们利用社区检测分析，对2015-2017年间班加罗尔ICT集群组织间网络进行了研究。基于知识源化相关文献，我们推测，集群企业主要与技术相似的企业建立寻求知识的横向联系，这就将整个集群组织间网络分割为多个技术社区，企业通常与其所在技术社区内的企业联系紧密，但缺乏与社区间企业的联系。我们进一步提出，与同类企业相比，跨社区建立横向联系的企业更有可能进行突破性创新。最后，我们认为在垂直的买方——供应商关系网络中，技术邻近性与社区形成之间没有关系。基于电压的社区发现算法结果表明，我们的预测得到了支持。同时，我们进一步讨论了本研究结果对班加罗尔升级潜力的启示。

Аннотация

С помощью анализа сообщества, мы исследуем структуру межорганизационной сети в ИКТ кластере в Бангалоре за период 2015–2017 гг. На основании литературы по источникам знаний, мы предполагаем, что кластерные компании прежде всего налаживают горизонтальные связи для получения знаний с компаниями в сходной технологической сфере, и что это разделяет сеть на несколько технологических сообществ, внутри которых компании тесно связаны, но связи между отдельными сообществами отсутствуют. Мы также предполагаем, что компании, которые связывают свои сообщества, а также развивают горизонтальные связи между разными технологическими сообществами, с большей вероятностью будут осуществлять радикальные инновации, чем другие компании. Наконец, мы утверждаем, что не существует никакой связи между технологической схожестью и формированием сообщества в сети вертикальных отношений между покупателем и поставщиком. Используя алгоритм, основанный на разности потенциалов, для исследования сообщества, мы получаем эмпирические подтверждения для этих предположений. Мы обсуждаем практическое значение наших выводов для повышения потенциала Бангалора.

Resumen

Usamos análisis de detección comunitaria para investigar la estructura de la red inter-organizacional del clúster TIC en Bengaluru en el periodo 2015-2017. Con base en la literatura de abastecimiento de conocimiento, conjeturamos que las empresas del clúster construyen principalmente vínculos horizontales para la búsqueda de conocimiento con empresas tecnológicamente similares, y que esto divide la red en múltiples comunidades tecnológicas dentro de las cuales las empresas están estrechamente conectadas, pero entre las cuales los vínculos son escasos. Adicionalmente proponemos que las que abarca la comunidad las cuales construyen vínculos horizontales que tienden puentes en las comunidades tecnológicas son más propensas a realizar innovaciones radicales que sus pares. Finalmente discutimos que no existe relación entre la proximidad tecnológica y la formación de comunidad en las redes verticales de comprador-proveedor. Usando un algoritmo basado en voltaje para el descubrimiento comunitarios, obtenemos apoyo empírico para estas predicciones. Discutimos las implicaciones de nuestros hallazgos para el potencial de mejoramiento de Bengaluru.

Keywords

business groupsknowledge sharingorganizational theorypatent and citation analysissocial networks企业集团知识共享组织理论专利与引文分析社会网络деловые кругиобмен знаниямиорганизационная теорияанализ патентов и цитированиясоциальные связиgrupos empresarialesintercambio de conocimientosteoría organizacionalanálisis de patentes y citacionesredes sociales
Type
Special Issue: The Innovation and Entrepreneurship Ecosystem in India
Information
Management and Organization Review , Volume 15 , Special Issue 3: The Innovation and Entrepreneurship Ecosystem in India , September 2019 , pp. 533 - 561
Copyright
Copyright © The International Association for Chinese Management Research 2019 

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Footnotes

Accepted by: Guest Editors Suresh Bhagavatula and Ram Mudambi, and Deputy Editor Johann Peter Murmann

References

REFERENCES

Alcácer, J., & Oxley, J. 2014. Learning by supplying. Strategic Management Journal, 35(2): 204–223.Google Scholar
Arora, A., & Gambardella, A. 2005. The globalization of the software industry: Perspectives and opportunities for developed and developing countries. Innovation Policy and the Economy, 5(1): 1–32.Google Scholar
Arya, V., & Mitra, R. 2013. IEEE International Conference on Smart Grid Communications (SmartGridComm).Google Scholar
Awate, S., & Mudambi, R. 2018. On the geography of emerging industry technological networks: The breadth and depth of patented innovations. Journal of Economic Geography, 18(2): 391–419.Google Scholar
Basant, R. 2008. Bangalore cluster: Evolution, growth, and challenges. In Yusuf, S., Nabeshima, K., & Yamashita, S. (Eds.), Growing industrial clusters in Asia: Serendipity and science: 147–193. Washington, D.C: World Bank Publications.Google Scholar
Bathelt, H., Malmberg, A., & Maskell, P. 2004. Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1): 31–56.Google Scholar
Barnes, J., & Hut, P. 1986. A hierarchical o(n log n) force-calculation algorithm. Nature, 324(4): 446–449.Google Scholar
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008 (10): P10008.Google Scholar
Boschma, R. 2005. Proximity and innovation: A critical assessment. Regional Studies, 39(1): 61–74.Google Scholar
Boschma, R., & Iammarino, S. 2009. Related variety, trade linkages, and regional growth in Italy. Economic Geography, 85(3): 289–311.Google Scholar
Boschma, R., & Ter Wal, A. 2007. Knowledge networks and innovative performance in an industrial district: The case of a footwear district in the South of Italy. Industry and Innovation, 14(2): 177–199.Google Scholar
Boschma, R., Minondo, A., & Navarro, M. 2013. The emergence of new industries at the regional level in Spain: A proximity approach based on product relatedness. Economic Geography, 89(1): 29–51.Google Scholar
Bresnahan, T., & Gambardella, A. 2004. Building a high-tech cluster: Silicon Valley and beyond. Cambridge, UK: Cambridge University Press.Google Scholar
Burt, R. 1992. Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.Google Scholar
Burt, R., & Burzynska, K. 2017. Chinese entrepreneurs, social networks and guanxi. Management and Organization Review, 13(2): 221–260.Google Scholar
Burt, R., & Opper, S. 2017. Early network events in the later success of Chinese entrepreneurs. Management and Organization Review, 13(3): 497–537.Google Scholar
Cantwell, J. A., & Mudambi, R. 2011. Physical attraction and the geography of knowledge sourcing in multinational enterprises. Global Strategy Journal, 1(3–4): 206–232.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.Google Scholar
Lin, N., Cook, K., & Burt, R. 2001. Social capital: Theory and research. New York: Aldine de Gruyter.Google Scholar
Crespo, J., Suire, R., & Vicente, J. 2014. Lock-in or lock-out? How structural properties of knowledge networks affect regional resilience. Journal of Economic Geography, 14(1): 199–219.Google Scholar
Crespo, J., Suire, R., & Vicente, J. 2016. Network structural properties for cluster long-run dynamics: Evidence from collaborative R&D networks in the European mobile phone industry. Industrial and Corporate Change, 25(2): 261–82.Google Scholar
D'Costa, A. P. 2006. Exports, university-industry linkages, and innovation challenges in Bangalore, India (Vol. 3887). Washington, DC: World Bank Publications.Google Scholar
Delgado, M., Porter, M., & Stern, S. 2014. Clusters, convergence, and economic performance. Research Policy, 43(10): 1785–1799.Google Scholar
DiTomaso, N., & Bian, Y. 2018. The structure of labor markets in the US and China: Social capital and guanxi. Management and Organization Review, 14(1): 5–36.Google Scholar
Dyer, J., & Chu, W. 2000. The determinants of trust in supplier-automaker relationships in the US, Japan and Korea. Journal of International Business Studies, 31(2): 259–285.Google Scholar
Dyer, J., & Singh, H. 1998. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4): 660–679.Google Scholar
Feldman, M., & Audretsch, D. 1999. Innovation in cities: Science-based diversity, specialization, and localized competition. European Economic Review, 43(2): 409–429.Google Scholar
Fleming, L., King, C. III, & Juda, A. I. 2007. Small worlds and regional innovation. Organization Science, 18(6): 938–954.Google Scholar
Fortunato, S. 2010. Community detection in graphs. Physics Reports, 486(3–5): 75–174.Google Scholar
Frenken, K., & Boschma, R. 2007. A theoretical framework for evolutionary economic geography: Industrial dynamics and urban growth as a branching process. Journal of Economic Geography, 7(5): 635–649.Google Scholar
Frenken, K., Van Oort, F., & Verburg, T. 2007. Related variety, unrelated variety and regional economic growth. Regional Studies, 41(5): 685–697.Google Scholar
Galunic, D., & Rodan, S. 1998. Resource recombinations in the firm: Knowledge structures and the potential for Schumpeterian innovation. Strategic Management Journal, 19(12): 1193–1201.Google Scholar
Gilsing, V., & Nooteboom, B. 2006. Exploration and exploitation in innovation systems: The case of pharmaceutical biotechnology. Research Policy, 35(1): 1–23.Google Scholar
Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. 2008. Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10): 1717–1731.Google Scholar
Girvan, M., & Newman, M. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12): 7821–7826.Google Scholar
Giuliani, E. 2007. The selective nature of knowledge networks in clusters: Evidence from the wine industry. Journal of Economic Geography, 7(2): 139–168.Google Scholar
Giuliani, E. 2013. Network dynamics in regional clusters: Evidence from Chile. Research Policy, 42(8): 1406–1419.Google Scholar
Giuliani, E., & Bell, M. 2005. The micro-determinants of meso-level learning and innovation: Evidence from a Chilean wine cluster. Research Policy, 34(1): 47–68.Google Scholar
Giuliani, E., Balland, P. A., & Matta, A. 2018. Straining but not thriving: Understanding network dynamics in underperforming industrial clusters. Journal of Economic Geography, 19(1): 147–172.Google Scholar
Hagedoorn, J., & Duysters, G. 2002. External sources of innovative capabilities: The preferences for strategic alliances or mergers and acquisitions. Journal of Management Studies, 39(2): 167–188.Google Scholar
Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. 2007. The product space conditions the development of nations. Science, 317(5837): 482–487.Google Scholar
Huggins, R., & Thompson, P. 2013. A network-based view of regional growth. Journal of Economic Geography, 14(3): 511–545.Google Scholar
Inaba, T., & Squicciarini, M. 2017. ICT: A new taxonomy based on the international patent classification. OECD Science, Technology and Industry Working papers, 2017/01, OECD Publishing, Paris.Google Scholar
Jackson, M. 2008. Social and economic networks. Princeton, NJ: Princeton University Press.Google Scholar
Jaffe, A. 1986. Technological opportunity and spillovers of R&D: Evidence from firms' patents, profits and market value. American Economic Review, 76(5): 984–1001.Google Scholar
Jaffe, A. 1989. Characterizing the ‘technological position’ of firms, with application to quantifying technological opportunity and research spillovers. Research Policy, 18(2): 87–97.Google Scholar
Karna, A., Täube, F., & Sonderegger, P. 2013. Evolution of innovation networks across geographical and organizational boundaries: A study of R&D subsidiaries in the Bangalore IT cluster. European Management Review, 10(4): 211–226.Google Scholar
Kogut, B., & Walker, G. 2001. The small world of Germany and the durability of national networks. American Sociological Review, 66(3): 317–335.Google Scholar
Koza, M. P., & Lewin, A. Y. 1998. The co-evolution of strategic alliances. Organization Science, 9(3): 255–264.Google Scholar
Lane, P. J., & Lubatkin, M. 1998. Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19(5): 461–477.Google Scholar
Laursen, K., & Salter, A. 2006. Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management Journal, 27(2): 131–150.Google Scholar
Lema, R., & Hesbjerg, B. 2003. The virtual extension. Roskilde University, mimeo.Google Scholar
Li, P. F. 2014. Horizontal versus vertical learning: Divergence and diversification of lead firms in the Hangji toothbrush cluster, China. Regional Studies, 48(7): 1227–1241.Google Scholar
Lorenzen, M. 2018. Global innovation and nascent local ecosystems: Local vs. MNE entry and the emergence of the digital creative industries cluster in Bangalore. Copenhagen Business School, mimeo.Google Scholar
Lorenzen, M., & Mudambi, R. 2012. Clusters, connectivity and catch-up: Bollywood and Bangalore in the global economy. Journal of Economic Geography, 13(3): 501–534.Google Scholar
Malmberg, A., & Maskell, P. 2006. Localized learning revisited. Growth and Change, 37(1): 1–18.Google Scholar
Manimala, M. 2008. Evolution of the Bangalore ICT cluster: A stage theory based on the crystal growth model. In Carayannis, E. G. & Formica, P. (Eds.), Knowledge matters: 104–128. London, UK: Palgrave Macmillan.Google Scholar
Manning, S. 2013. New Silicon Valleys or a new species? Commoditization of knowledge work and the rise of knowledge services clusters. Research Policy, 42(2): 379–390.Google Scholar
Maskell, P., & Malmberg, A. 1999. Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23(2): 167–185.Google Scholar
McAllister, D. 1995. Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1): 24–59.Google Scholar
Mesquita, L. F., & Lazzarini, S. G. 2008. Horizontal and vertical relationships in developing economies: Implications for SMEs' access to global markets. Academy of Management Journal, 51(2): 359–380.Google Scholar
Morrison, A. 2008. Gatekeepers of knowledge within industrial districts: Who they are, how they interact. Regional Studies, 42(6): 817–835.Google Scholar
Morrison, A., & Rabellotti, R. 2009. Knowledge and information networks in an Italian wine cluster. European Planning Studies, 17(7): 983–1006.Google Scholar
Mowery, D., Oxley, J., & Silverman, B. 1996. Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2): 77–91.Google Scholar
Mudambi, R. 2008. Location, control and innovation in knowledge-intensive industries. Journal of economic Geography, 8(5): 699–725.Google Scholar
Narula, R. 2002. Innovation systems and ‘inertia’ in R&D location: Norwegian firms and the role of systemic lock-in. Research Policy, 31(5): 795–816.Google Scholar
Newman, M. E. 2001. Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality. Physica Review E, 64(1 Pt 2): 016132.Google Scholar
Newman, M. E., & Girvan, M. 2004. Finding and evaluating community structure in networks. Physical review E, 69(2): 026113.Google Scholar
Nooteboom, B. 1999. Inter-firm alliances: Analysis and design. London, UK: Routledge.Google Scholar
Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & Van den Oord, A. 2007. Optimal cognitive distance and absorptive capacity. Research Policy, 36(7): 1016–1034.Google Scholar
Owen-Smith, J., & Powell, W. 2004. Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1): 5–21.Google Scholar
Patibandla, M., & Petersen, B. 2002. Role of transnational corporations in the evolution of a high-tech industry: The case of India's software industry. World Economy, 30(9): 1561–1577.Google Scholar
Pietrobelli, C., & Rabellotti, R. 2011. Global value chains meet innovation systems: Are there learning opportunities for developing countries? World Development, 39(7): 1261–1269.Google Scholar
Porter, M. 1998. On competition. Boston, MA: Harvard Business School Press.Google Scholar
Porter, M. 2003. The economic performance of regions. Regional Studies, 37(6): 549–78.Google Scholar
Rigby, D. L. 2015. Technological relatedness and knowledge space: Entry and exit of US cities from patent classes. Regional Studies, 49(11): 1922–1937.Google Scholar
Saxenian, A. 2006. The new argonauts: Regional advantage in a global economy. Cambridge, MA: Harvard University Press.Google Scholar
Schilling, M. A., & Phelps, C. C. 2007. Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7): 1113–1126.Google Scholar
Singh, J. 2005. Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5): 756–770.Google Scholar
Sonderegger, P., & Täube, F. 2010. Cluster life cycle and diaspora effects: Evidence from the Indian IT cluster in Bangalore. Journal of International Management, 16(4): 383–397.Google Scholar
Storper, M., & Venables, A. 2004. Buzz: face-to-face contact and the urban economy. Journal of Economic Geography, 4(4): 351–370.Google Scholar
Täube, F., Karna, A., & Sonderegger, P. 2018. Economic geography and emerging market clusters: A co-evolutionary study of local and non-local networks in Bangalore. International Business Review, in press.Google Scholar
Ter Wal, A., & Boschma, R. 2009. Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science, 43(3): 739–756.Google Scholar
Tholons, 2010. The Bangalore story: Becoming the outsourcing capital of the World. Concept Paper. Available from URL: http://www.tholons.com/nl_pdf/The_Bangalore_Story.pdfGoogle Scholar
Turkina, E., & Van Assche, A. 2018. Global network embeddedness and local innovation in industrial clusters. Journal of International Business Studies, 49(3): 706–728.Google Scholar
Turkina, E., Van Assche, A., & Kali, R. 2016. Structure and evolution of global cluster networks: Evidence from the aerospace industry. Journal of Economic Geography, 16(6): 1211–1234.Google Scholar
Van Assche, A. 2008. Modularity and the organization of international production. Japan and the World Economy, 20(3): 353–368.Google Scholar
Vijayabaskar, M., & Krishnaswamy, G. 2004. Understanding growth dynamism and its constraints in high technology clusters in developing countries: A study of Bangalore, Southern India. In Mani, S. & Romijn, H. (Eds.), Innovation, learning, and technological dynamism of developing countries: 178–201. New York: United Nations Press.Google Scholar
Watts, D., & Strogatz, S. 1998. Collective dynamics of ‘small-world'networks. Nature, 393(6684): 440–442.Google Scholar
Wolfe, D. A., & Gertler, M. S. 2004. Clusters from the inside and out: local dynamics and global linkages. Urban Studies, 41(5–6): 1071–1093.Google Scholar
Wuyts, S., Colombo, M. G., Dutta, S., & Nooteboom, B. 2005. Empirical tests of optimal cognitive distance. Journal of Economic Behavior & Organization, 58(2): 277–302.Google Scholar
Xu, S., & Yan, J. 2008. An improved voltage-based algorithm for community discovery. 3rd ed, International Conference on Intelligent System and Knowledge Engineering.Google Scholar
Zhao, C., & Burt, R. 2018. A note on business survival and social network. Management and Organization Review, 14(2): 377–394.Google Scholar
Zucker, L. G. 1986. Production of trust: Institutional sources of economic structure, 1840–1920. Research in Organizational Behavior, 8: 53–111.Google Scholar