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Emergent Relational Structures at a “Sharing Economy” Festival

  • Paola Tubaro
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

How do participants to an event engage with others? This paper examines the emergent relational structure at a “sharing economy” festival, the 2016 OuiShare Fest. A multi-level network analysis design explores the linkages between participation patterns of the “elite” (speakers) and other participants, to unveil the social processes through which status hierarchies emerge and actors manage ensuing tensions. Newly developed specifications for exponential random graph models reveal a tension between cooperation (among actors with shared thematic interests) and competition for audience, whereby conformism and differential use of reciprocity in attendance relationships generate an informal pecking order.

Keywords

Social networks Status ERGM 

References

  1. 1.
    Amati, V., Lomi, A., Mira, A.: Social network modeling. Ann. Rev. Stat. Appl. 5(1), 343–369 (2018)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Anand, N., Watson, M.R.: Tournament rituals in the evolution of fields: the case of the Grammy Awards. Acad. Manag. J. 47(1), 59–80 (2004)Google Scholar
  3. 3.
    Aspers, P., Darr, A.: Trade shows and the creation of market and industry. Sociol. Rev. 59(4), 758–778 (2011)Google Scholar
  4. 4.
    Bathelt, H., Schuldt, N.: Between luminaries and meat grinders: international trade fairs as temporary clusters. Reg. Stud. 42(6), 853–868 (2008)Google Scholar
  5. 5.
    Bathelt, H., Golfetto, F., Rinallo, D.: Trade Shows in the Globalizing Knowledge Economy. Oxford University Press, Oxford (2014)Google Scholar
  6. 6.
    Block, P.: Reciprocity, transitivity, and the mysterious three-cycle. Soc. Netw. 40, 163–173 (2015)Google Scholar
  7. 7.
    Borgatti, S.P.: Two-mode concepts in social network analysis. In: Myers, R.A. (ed.) Computational Complexity: Theory, Techniques, and Applications, pp. 2912–2924. Springer, New York (2012)Google Scholar
  8. 8.
    Brailly, J., Favre, G., Chatellet, J., Lazega, E.: Embeddedness as a multilevel problem: a case study in economic sociology. Soc. Netw. 44, 319–333 (2016)Google Scholar
  9. 9.
    Breiger, R.L.: Scaling down. Big Data Soc. 2(2), 2053951715602497 (2015)Google Scholar
  10. 10.
    Burt, R.S.: Social contagion and innovation: cohesion versus structural equivalence. Am. J. Sociol. 92, 1287–1335 (1987)Google Scholar
  11. 11.
    Centola, D., Gonzlez-Avella, J.C., Eguluz, V.M., San Miguel, M.: Homophily, cultural drift and the co-evolution of cultural groups. J. Confl. Resolut. 51, 905–929 (2007)Google Scholar
  12. 12.
    Duxbury, S.W.: Diagnosing multicollinearity in exponential random graph models. Sociol. Methods Res. (2018) online firstGoogle Scholar
  13. 13.
    Favre, G., Brailly, J.: Salons et définition de normes marchandes: le cas de la distribution de programmes de télévision en Afrique sub-saharienne. L’Année sociologique 65(2), 425–456 (2015)Google Scholar
  14. 14.
    Gould, R.V.: The origins of status hierarchies: a formal theory and empirical test. Am. J. Sociol. 107(5), 143–178 (2002)Google Scholar
  15. 15.
    Handcock, M., Hunter, D., Butts, C., Goodreau, S., Krivitsky, P., Morris, M.: ergm: fit, simulate and diagnose exponential-family models for networks. The Statnet Project (http://www.statnet.org), R package v. 3.8.0 (2017). https://CRAN.R-project.org/package=ergm
  16. 16.
    Hunter, D., Handcock, M., Butts, C., Goodreau, S., Morris, M.: ergm: a package to fit, simulate and diagnose exponential-family models for networks. J. Stat. Softw. 24(3), 1–29 (2008)Google Scholar
  17. 17.
    Lazega, E., Snijders, T.A.B. (eds.): Multilevel Network Analysis for the Social Sciences: Theory, Methods and Applications. Springer, New York (2016)Google Scholar
  18. 18.
    Lorraine, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1, 49–80 (1971)Google Scholar
  19. 19.
    Lusher, D., Koskinen, J., Robins, G.: Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press, Cambridge (2013)Google Scholar
  20. 20.
    Manzo, G., Baldassarri, D.: Heuristics, interactions, and status hierarchies: an agent-based model of deference exchange. Sociol. Methods Res. 44(2), 329–387 (2015)Google Scholar
  21. 21.
    Pallotti, F., Lomi, A., Mascia, D.: From network ties to network structures: exponential random graph models of interorganizational relations. Qual. Quant. 47(3), 1665–1685 (2013)Google Scholar
  22. 22.
    Phillips, D.J., Zuckermann, E.W.: Middlestatus conformity: theoretical restatement and empirical demonstration in two markets. Am. J. Sociol. 107(2), 379–429 (2001)Google Scholar
  23. 23.
    Podolny, J.M., Phillips, D.J.: The dynamics of organizational status. Ind. Corp. Chang. 5(2), 453–471 (1996)Google Scholar
  24. 24.
    Robins, G., Pattison, P.E., Wang, P.: Closure, connectivity and degree distributions: exponential random graph (p*) models for directed social networks. Soc. Netw. 31, 105–117 (2009)CrossRefGoogle Scholar
  25. 25.
    Sauder, M., Lynn, F., Podolny, J.M.: Status: insights from organizational sociology. Ann. Rev. Sociol. 38, 267–283 (2012)CrossRefGoogle Scholar
  26. 26.
    Snijders, T.A.B., Pattison, P.E., Robins, G., Handcock, M.: New specifications for exponential random graph models. Sociol. Methodol. 36(1), 99–153 (2006)CrossRefGoogle Scholar
  27. 27.
    Urry, J.: Social networks, travel and talk. Br. J. Sociol. 54, 155–175 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratoire de Recherche en Informatique (CNRS-LRI)Centre National de la Recherche ScientifiqueParisFrance

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