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The Detailed Structure of Local Entrepreneurial Networks: Experimental Economic Study

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)

Abstract

Economic agents’ behavior during the last 40 years had tremendously changed from perfect competition to cooperation between them, and coopetition phenomenon was revealed. This phenomenon is always based on the certain entrepreneurial network. The paper is focused on entrepreneurial networks which are geographically localized. Such networks are formed as a result of two different types of cooperation: production cluster cooperation and cooperation in a community. The main goal of the present study is to find differences between internal structures of these two types entrepreneurial networks. Data was collected using experimental economic techniques, it was represented in the form of transactions between network agents and was aggregated over the certain time period. Social Network Analysis (SNA) methods and instruments were used in this research. Detailed structure analysis was based on the set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. The entrepreneurial networks of two production clusters and three cooperative communities were under investigation. These networks were compared with each other and also with random Bernoulli graphs of the corresponding size and density. It was found that cooperative community networks are more random and dense than the production cluster ones and their other parameters also differ. Discovered variations of network structures are explained by the peculiarities of agents functioning in these two type networks.

Keywords

Economic network Entrepreneurial network Social network analysis Experimental economics Communications Coopetition Localization Local payment system 

Notes

Acknowledgements

Present study was carried out under financial support of the Russian Fund of Fundamental Research grant №. 15-06-04863 “Mathematical models of local payment system lifecycles”.

References

  1. 1.
    Porter, M.E.: The Competitive Advantage of Nations. Harvard Business Review, pp. 72–91 (1990)Google Scholar
  2. 2.
    Mc Millan, D.W., Chavis, D.M.: Sense of community: a definition and theory. Am. J. Commun. Psychol. 14(1), 6–23 (1986)CrossRefGoogle Scholar
  3. 3.
    Pitelis, C., Sugden, R.: The Nature of the Transnational Firm. Routledge, London (2000)Google Scholar
  4. 4.
    Birch, D., Liesch, P.W.: Moneyless business exchange: practitioners’ attitudes to business-to-business barter in Australia. Ind. Mark. Manag. 27, 329–340 (1998)CrossRefGoogle Scholar
  5. 5.
    Gnyawali, D.R., Park, B.J.R.: Coopetition and technological innovation in small and medium-sized enterprises: a multilevel conceptual model. J. Small Bus. Manag. 47(3), 308–330 (2009). doi: 10.1111/j.1540-627X.2009.00273.x CrossRefGoogle Scholar
  6. 6.
    Wikipedia. The Free Encyclopedia. https://en.wikipedia.org/wiki/Coopetition
  7. 7.
    Uzzi, B.: The sources and consequences of embeddedness for the economic performance of organizations: the network effect. Am. Sociol. Rev. 61(4), 674–698 (1996)CrossRefGoogle Scholar
  8. 8.
    Lietar, B.A.: The Future of Money: Creating New Wealth, Work and Wiser World. M. KRPA Olymp: AST: Astrel, Moscow (2007)Google Scholar
  9. 9.
    Davis, D.D., Holt, C.A.: Experimental Economics. Princeton University Press, Princeton (1993)Google Scholar
  10. 10.
    Gesell, S.: The Natural Economic Order (Translation by Philip Pye). Peter Owen Ltd., London (1958)Google Scholar
  11. 11.
    Popkov, V.V., Berg, D.B., Ulyanova, E.A., Selezneva, N.A.: Commodity and financial networks in regional economics r-economy, vol. 1, issue 2, pp. 305–314 (2015). doi: 10.17059/e-2015-2-13. http://r-economy.ru/?page_id=257
  12. 12.
    Berg, D.B., Zvereva, O.M.: Identification of autopoietic communication patterns in social and economic networks. In: Khachay, M.Y., Konstantinova, N., Panchenko, A., Ignatov, D.I., Labunets, V.G. (eds.) AIST 2015. CCIS, vol. 542, pp. 286–294. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26123-2_28 CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 168–256 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Costa, L.D.F., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: a survey of measurements. Adv. Phys. 56(1), 167–242 (2007)CrossRefGoogle Scholar
  15. 15.
    Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. http://faculty.ucr.edu/~hanneman/nettext/
  16. 16.
    Phan, B., Engo-Monsen, K., Fjeldstad, O.D.: Considering clustering measures: third ties, means, and triplets. Soc. Netw. 35(3), 300–308 (2013)CrossRefGoogle Scholar
  17. 17.
    Faust, K.: Comparing social networks: size, density and local structure. Metodološki Zvezki 3(2), 185–216 (2006)Google Scholar
  18. 18.
    Marsden, P.V.: Network data and measurement. Ann. Rev. Sociol. 16, 435–446 (1990)CrossRefGoogle Scholar
  19. 19.
    Borgatti, S.P., Everett, M.G., Freeman, L.C.: Ucinet for Windows: Software for Social Network Analysis. Analytic Technologies, Harvard (2002)Google Scholar
  20. 20.
    Berg, D., Zvereva, O., Shelomentsev, A., Taubayev, A.: Autopoietic structures in local economic systems. In: 15th International Conference Proceedings on Multidisciplinary Scientific Geoconference SGEM 2015, Ecology, Economics, Education and Legislation, Ecology and Environmental Protection, vol. I, pp. 109–117, 18–24 June, 2015, Albena, Bulgaria. Published by STEF92 Technology Ltd. (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ural Federal UniversityEkaterinburgRussia

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