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Empirics of Dynamic Network in Actor-Oriented Model:Panel Estimation of the Parameters of Co-evolution of Network and Behavior Effects in Business Relations


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1 Department of Econometrics, University of Madras, Chennai-600 005, India
     

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Personal characteristics of individuals in business networks significantly influence the trading relations in supply chain management, especially when business environment is largely characterized by group based or family centric enterprises. The dynamics of actor linking as well as delinking in the network over time evolves a specific structure for the network that depends crucially on personal attributes of the traders. In this study, the topology of dynamic evolution of network and behavior effects in a business network among dealers in construction industry in a small town in India have been analyzed by using the actor-based dynamic network modeling approach. Using panel data, the SIENA estimates of the co-evolution of network parameters reveal that the dynamic network structure evolves over time, the individual attributes contribute to the changing dynamics of the evolving network, and the evolving network structure itself influences the establishment or severing of ties by traders in the network. The emerging empirical network is more of a rewired structure, with nodes linking and delinking at random but with specific estimated probabilities.

Keywords

Actor-oriented Model, Dealers Network, Dynamic Co-evolution, Individual Attributes, Network and Behavior Effects, Parameter Estimation.
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  • Albert, R., & Barabasi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47-97.
  • Barabasi, A-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512.
  • Bramoulle, Y., Galeotti, A., & Rogers, B. (Eds.) (2016). The Oxford Handbook on the Economics of Networks. Oxford: Oxford University Press.
  • Carley, K. M. (2003). Dynamic network analysis. In R. Breiger, K. M. Carley., & P. Pattison (Eds.), Dynamic social network modeling and analysis: Workshop summary and papers (133-145). Washington, DC: National Academy Press.
  • Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and methods on social network analysis. New York: Cambridge University Press.
  • Chandrasekhar, A. G. (2016). Econometrics of network formation. In Y. Bramoulle, A. Galeotti and B. Rogers (Eds.), The oxford handbook on the economics of networks (303-357). Oxford: Oxford University Press.
  • Colugnati, F. A. B. (2008). Dynamic social network modeling and perspectives in OPAALS frameworks. Second OPAALS (Open Philosophies for Associative Autopoietic Digital Ecosystems) Conference, Tampere, Finland.
  • de Marti, J., & Zenou, Y. (2011). Social networks. In I. Jarvie and J. ZamoraBonilla (Eds.), Handbook of philosophy of science (339-361). London: Sage Publications.
  • Doreian, P., & Stokman, F. N. (Eds.) (1997). Evolution of social networks. Amsterdam: Gordon and Breach Publishers.
  • Erdos, P., & Renyi, A. (1959). On random graphs. Publicationes Mathematicae, 6, 290-297.
  • Erdos, P., & Renyi, A. (1960). On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17-61.
  • Frank, O. (1991). Statistical analysis of change in networks. Statistica Neerlandica, 45, 283-293.
  • Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842.
  • Friedkin, N. E. (1998). A structural theory of social influence. Cambridge: Cambridge University Press.
  • Graham, B. S. (2015). Methods of identification in social networks. Annual Review of Economics, 7, 465-485.
  • Goyal, S. (2009). Connections: An introduction to the economics of networks. Princeton University Press.
  • Holland, P. W., & Leinhardt, S. (1976). Local structure in social networks. Sociological Methodology, 7, 1-45.
  • Holland, P. W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks, 5, 109-137.
  • Homans, G. C. (1974). Social behavior: Its elementary forms. New York: Harcourt Brace Jovanovich.
  • Jackson, M. O. (2008). Social and economic networks. Princeton: Princeton University Press.
  • Jackson, M. O. (2009). An overview of social networks and economic applications. In J. Benhabib, A. Bisin & M. O. Jackson (Eds.), Handbook of social economics, 1, 511-585. Amsterdam: North-Holland Elsevier Press.
  • Jackson, M. O., & Rogers, B. W. (2007). Meeting strangers and friends of friends: How random are social networks? American Economic Review, 97, 890-915.
  • Jackson, M. O., & Wolinsky, A. (1996). A strategic model of social and economic networks. Journal of Economic Theory, 71, 44-74.
  • Kadushin, C. (2011). Understanding social networks: An introduction to social network concepts, theories and findings. Oxford: Oxford University Press.
  • Lakshmanasamy, T., & Anil, C. (2012). Link loyalty in social networks: An econometric analysis of marketing channel networks in India. International Journal of Economics and Business Research, 4, 501-513.
  • Lakshmanasamy, T., & Anil, C. (2015). The effect of attributes of distribution channel members on supply chain management: An empirical analysis of social networks in business. International Journal of Logistics Systems and Management, 21, 160-180.
  • Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as a social process: A substantive and methodological analysis. In M. Berger, T. Abel and C. Page (Eds.), Freedom and Control in Modern Society (18-66). New York: Van Nostrand.
  • Mani, D., & Moody, J. (2014). Moving beyond stylized economic network models: The hybrid world of the Indian firm ownership network. American Journal of Sociology, 119, 1629-1669.
  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-444.
  • Myerson, R. (1977). Graphs and cooperation in games. Mathematics of Operations Research, 2, 225-229.
  • Myerson, R. (1991). Game theory: Analysis of conflict. Cambridge: Harvard University Press.
  • Naudet, J., & Dubost, C.-L. (2017). The Indian exception: The densification of the network of corporate interlocks and the specificities of the Indian business system (2000-2012). Socio-Economic Review, 15, 405-434.
  • Newman M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 26-113.
  • Snijders, T. A. B. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 21, 149-172.
  • Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. In M. E. Sobel., & M. P. Becker. (Eds.), Sociological methodology, 31, 361-395. Boston and London: Basil Blackwell.
  • Snijders, T. A. B. (2005). Models for longitudinal network data. In P. Carrington, J. Scott., & S. Wasserman. (Eds.), Models and methods in social network analysis (215-247). New York: Cambridge University Press.
  • Snijders, T. A. B. (2011a). Network dynamics. In J. Scott., & P. J. Carrington. (Eds.), Handbook of social network analysis (501-513). London: Sage.
  • Snijders, T. A. B. (2011b). Statistical models for social networks. Annual Review of Sociology, 37, 129-151.
  • Snijders, T. A. B. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and Its Applications, 4, 343-363.
  • Snijders, T. A. B., Steglich, C. E. G., & Schweinberger, M. (2007). Modeling the co-evolution of networks and behavior. In K. van Montfort, H. Oud & A. Satorra (Eds.), Longitudinal models in the behavioral and related sciences (41-71). Mahwah, NJ: Lawrence Erlbaum.
  • Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60.
  • Snijders, T. A. B., Steglich, C. E. G., Schweinberger, M., & Huisman, M. (2008). Manual for SIENA, Version 3.2, Groningen, The Netherlands: Interuniversity Centre for Social Science Theory and Methodology, Department of Sociology, University of Groningen and Department of Statistics, University of Oxford (the Siena webpage https://www.stats.ox.ac.uk/~snijders/siena/siena.html).
  • Steglich, C. E. G., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329-392.
  • Steglich, C. E. G., Snijders, T. A. B., & West, P. (2006). Applying SIENA: An illustrative analysis of the co-evolution of adolescents’ friendship networks, taste in music, and alcohol consumption. Methodology: European Journal of Research Methods for the Behavioural and Social Sciences, 2, 48-56.
  • van de Bunt, G. G., van Duijn, M. A. J., & Snijders, T. A. B. (1999). Friendship networks through time: An actor-oriented statistical network model. Computational and Mathematical Organization Theory, 5, 167-192.
  • Vega-Redondo, F. (2007). Complex social networks. Cambridge: Cambridge University Press.
  • Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of American Statistical Association, 75, 280-294.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
  • Wasserman, S., & Iacobucci, D. (1988). Sequential social network data. Psychometrika, 53, 261-282.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393 (6684), 440-442.

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  • Empirics of Dynamic Network in Actor-Oriented Model:Panel Estimation of the Parameters of Co-evolution of Network and Behavior Effects in Business Relations

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Authors

T. Lakshmanasamy
Department of Econometrics, University of Madras, Chennai-600 005, India

Abstract


Personal characteristics of individuals in business networks significantly influence the trading relations in supply chain management, especially when business environment is largely characterized by group based or family centric enterprises. The dynamics of actor linking as well as delinking in the network over time evolves a specific structure for the network that depends crucially on personal attributes of the traders. In this study, the topology of dynamic evolution of network and behavior effects in a business network among dealers in construction industry in a small town in India have been analyzed by using the actor-based dynamic network modeling approach. Using panel data, the SIENA estimates of the co-evolution of network parameters reveal that the dynamic network structure evolves over time, the individual attributes contribute to the changing dynamics of the evolving network, and the evolving network structure itself influences the establishment or severing of ties by traders in the network. The emerging empirical network is more of a rewired structure, with nodes linking and delinking at random but with specific estimated probabilities.

Keywords


Actor-oriented Model, Dealers Network, Dynamic Co-evolution, Individual Attributes, Network and Behavior Effects, Parameter Estimation.

References