Abstract
The main aim of this chapter is to define and explore the effects of the information spread and the actors who introduce fundamental changes in the information stream direction across the converging media channels. The actors in social networks and influence thresholds are important determinants that affect behaviour of the rest of the nodes in online social networks. Contrary to the popular notion it was proved during the study that the expression of opinion and high activity do not create influence. The most vocal nodes in the graphs were not the most influential or even next to the most influential.
The research presents evidence how technological convergence produces social divergence which is based on newly introduced “segregation” rules in the online social networks. The models of technology adoption and network structure are important and determine the effects of the information diffusion and the rate of contagion that affect the process of convergence, namely the shifts in cultural and social paradigms. While the quantity of information rises, the ability to process and the competence to operate with is bounded, this leads to the superiority of the most technologically and/or communicationally advanced individuals and organizations—the info traders. We define three categories (dimensions) of info traders and explain their role in the networks through the presented case studies.
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References
Altucher, J. (2011a). 10 unusual things you didn’t know about Steve Jobs. Available from www.huffingtonpost.com
Altucher, J. (2011b). 10 unusual things you didn’t know about Steve Jobs. Available from http://www.dailyfinance.com
Arthur, W. B. (1997). Competing technologies, increasing returns, and lock-in by historical small events. In B. Arthur (Ed.), Increasing returns and path dependence in the economy (pp. 13–32). Michigan: Michigan University Press.
Barabasi, L., & Reka, A. (1999, October 15). Emergence of scaling in random networks. Science, 286, 509–512.
Bass, F. (1969). A new product growth model for consumer durables. Management Science, 15, 215–228.
BBC News. (2008, January 7). Warner backs Sony blu ray format. Available from http://news.bbc.co.uk/2/hi/technology/7174591.stm
BG-mama.com (2008). Discussion forum. Available: http://www.bg-mamma.com/?topic=285851.0#
Bijker, W. E. (1995). Of bicycles, bakelites and bulbs: Towards a theory of sociotechnical change. Cambridge, MA: MIT Press.
Bijker, W. E., & Pinch, J. T. (1984). The social construction of facts and artifacts: Or how the sociology of science and the sociology of technology might benefit of each other. In W. E. Bijker, P. T. Hughes, & J. T. Pinch (Eds.), The social construction of technological systems (pp. 17–50). Cambridge, MA: MIT Press.
Bloch, F. (1995). Endogenous structures of association of oligopolies. The RAND Journal of Еconomics, 26(3), 537–556.
Bonacich, P. (1979). The common structure semigroup: А replacement for the Boorman and White joint reduction. American Journal of Sociology, 86, 159–166.
Callon, M. (1986). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. In J. Law (Ed.), Power, action and belief: A new sociology of knowledge. London: Routledge & Kegan Paul.
Chan, K., & Misra, S. (1990). Characteristics of the opinion leader: A new dimension. Journal of Advertising, 19, 53–60.
Christ, J., & Slowak, A. (2009). Why Blu-Ray vs. HD-DVD is not VHS vs. Betamax: The co-evolution of standard-setting consortia. FZID Discussion Papers, University of Hohenheim.
Daidj, N., Grazia, C., & Hammoudi, A. (2010). Introduction to the non-cooperative approach to coalition formation: The case of the Blu-Ray/HD-DVD standards’ war. Journal of Media Economics, 23(4), 192–215.
Еasly, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.
Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83, 1420–1443.
Hart, S., & Kurz, M. (1983). Endogenous formation of coalitions. Econometrica, 51, 1047–1064.
Judd, S., & Kearns, M. (2008). Behavioral experiments in networked trade. In T. Sandholm, J. Riedl, & L. Fortnow (Eds.), Proceedings of the 2008 ACM Conference on Electronic Commerce (pp. 150–159). New York: Association for Computing Machinery.
Katz, E., & Lazersfeld, P. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: Free Press.
Kearns, M., Suri, S., & Montfort, N. (2006). An experimental study of the coloring problem on human subject networks. Science, 313, 824–827.
Latour, B. (2005). Reassembling the Social: An introduction to actor-network-theory. New York: Oxford University Press.
Liebowitz, S. J., & Margolis, S. E. (1995). Path dependence, lock-in, and history. Journal of Law, Economics and Organization, 11(1), 205–226.
May, C., & Finch, T. (2009). Implementation, embedding, and integration: An outline of normalization process theory. Sociology, 43(3), 535–554.
Merton, R. (1968). Patterns of influence: Local and cosmopolitan influentials. In R. Merton (Ed.), Social theory and social structure (pp. 441–474). New York: FreePress.
Miller, J., & Page, S. (2004). Standing ovation problem. Available from http://www2.econ.iastate.edu
Mizruchi, M. S., & Bunting, D. (1981). Influence in corporate networks: An examination of four measures. Administrative Science Quarterly, 26(3), 475–489.
North, D. (1981). Structure and change in economic history. New York: Norton.
Nowotny, H., Scott, P., & Gibbons, M. (2001). Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge, UK: Polity Press.
Rogers, E. (2003). Diffusion of innovations. New York: Free Press.
Schelling, T. (1978). Micromotives and macrobehavior. New York: Norton.
The Economist. (2011). Steve Jobs: Beauty justifies wealth. Available from www.economist.com
Tidd, J. (2006). A review of innovation models. Imperial College London, 16
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Watts, D., & Dodd, P. (2007). Influentials, networks and public opinion formation. Journal of Consumer Research, 34, 441–458.
Wigand, R. T., & Frankwick, G. L. (1989). Inter–organizational communication and technology transfer: Industry–government–university linkages. International Journal of Technology Management, 4(1), 63–76.
Willamson, O. (1985). The economic institutions of capitalism (pp. 55–60). New York: The Free Press.
Zhang, Y., Zhou, S., Zhang, Z., Guan, J., Zhou, S. (2013). Rumor evolution in social networks. Physical Review E87. Available from http://arxiv.org/pdf/1102.1487.pdf
Zlatanov, B., & Koleva, M. (2014). Networks of collective power: (Non)movements and semantic networks. Conference paper, ECREA Conference, Lisbon.
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Zlatanov, B., Koleva, M. (2016). Info Traders, Innovation and Information Spread. In: Lugmayr, A., Dal Zotto, C. (eds) Media Convergence Handbook - Vol. 1. Media Business and Innovation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54484-2_18
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DOI: https://doi.org/10.1007/978-3-642-54484-2_18
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