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The Dynamics of Cultural Influence Networks

Author

Listed:
  • J. Richard Harrison

    (University of Texas at Dallas)

  • Glenn R. Carroll

    (Stanford University)

Abstract

This article investigates the behavior of cultural influence networks over time, using a computer simulation based on a formal model of cultural transmission in organizations. In the formal model, every organizational member exerts some cultural influence on, and is influenced by, every other member; these influence paths constitute a dense social network and the weights of paths (ties) vary throughout the network. Over time, each organizational member's enculturation level changes in response to influence from other members, and the influence weight of each path changes in relationship to the cultural similarity of the individuals connected by the path. Virtual experiments explore the configuration and evolution of the cultural influence network under varying demographic conditions and influence principles. Demographic effects are studied by varying organizational size, hiring selectivity and turnover rates. Two principles for determining initial influence path weights are examined, cohort-based influence and random influence. The simulations show that the cultural influence network evolves over time to a robust configuration, fluctuating around a stable dynamic equilibrium as individuals enter and leave the organization. As turnover rates rise, cohort-based influence strengthens the influence network and reduces network inequality. In this model, cohort-based influence processes promote cultural stability in organizations.

Suggested Citation

  • J. Richard Harrison & Glenn R. Carroll, 2002. "The Dynamics of Cultural Influence Networks," Computational and Mathematical Organization Theory, Springer, vol. 8(1), pages 5-30, May.
    • Handle: RePEc:spr:comaot:v:8:y:2002:i:1:d:10.1023_a:1015142219808
    DOI: 10.1023/A:1015142219808
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    References listed on IDEAS

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    1. Harrington, Joseph E, Jr, 1998. "The Social Selection of Flexible and Rigid Agents," American Economic Review, American Economic Association, vol. 88(1), pages 63-82, March.
    2. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    3. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
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    Cited by:

    1. Juliette Rouchier & Paola Tubaro & Cécile Emery, 2014. "Opinion transmission in organizations: an agent-based modeling approach," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 252-277, September.
    2. James A. Kitts & Paul T. Trowbridge, 2007. "Shape up or ship out: social networks, turnover, and organizational culture," Computational and Mathematical Organization Theory, Springer, vol. 13(4), pages 333-353, December.
    3. Abhijit Ramalingam & Michael T. Rauh, 2010. "The Firm as a Socialization Device," Management Science, INFORMS, vol. 56(12), pages 2191-2206, December.
    4. Luis Almeida Costa & João Amaro Matos, 2014. "Attitude change in arbitrarily large organizations," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 219-251, September.
    5. Henchion, Maeve M. & Sorenson, Douglas, 2012. "Formal Food-related Networks in Ireland: A Case Study Analysis," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 2(4), pages 1-16, March.
    6. Thomas Feliciani & Andreas Flache & Michael Mäs, 2021. "Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines," Computational and Mathematical Organization Theory, Springer, vol. 27(1), pages 61-92, March.

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