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An algorithm for incorporating company reputation into business simulations: Variations on the Gold standard

Author

Listed:
  • Hugh M. Cannon

    (Wayne State University, hugh.cannon@wayne.edu)

  • Manfred Schwaiger

    (Munich School of Management, schwaiger@bwl.uni-muenchen.de)

Abstract

Gold recently presented a system-dynamic-based approach to the design of business simulations. In it, he argued that the focus of simulation design efforts have mostly been carried out at the subsystem level, developing independent algorithms that follow inconsistent logic. As a result, they do not lend themselves to integration into a single, dynamically interactive model. To address this, he drew on the economic theory of the firm to develop and test a system of interacting algorithms that gives equal emphasis to both demand and supply factors. Most important, Gold’s approach provides a common, theoretically anchored platform for integrating potentially conflicting functional algorithms. This article tests the robustness of this approach by using Gold’s model as a vehicle for capturing the effects of company reputation, a phenomenon that has emerged from a totally different (management and marketing) research tradition.

Suggested Citation

  • Hugh M. Cannon & Manfred Schwaiger, 2005. "An algorithm for incorporating company reputation into business simulations: Variations on the Gold standard," Simulation & Gaming, , vol. 36(2), pages 219-237, June.
  • Handle: RePEc:sae:simgam:v:36:y:2005:i:2:p:219-237
    DOI: 10.1177/1046878104272809
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    References listed on IDEAS

    as
    1. Beatty, Randolph P. & Ritter, Jay R., 1986. "Investment banking, reputation, and the underpricing of initial public offerings," Journal of Financial Economics, Elsevier, vol. 15(1-2), pages 213-232.
    2. Steven C. Gold & Thomas F. Pray, 2001. "Historical Review of Algorithm Development for Computerized Business Simulations," Simulation & Gaming, , vol. 32(1), pages 66-84, March.
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    5. Steven Gold, 2005. "System-dynamics-based modeling of business simulation algorithms," Simulation & Gaming, , vol. 36(2), pages 203-218, June.
    6. Lafferty, Barbara A. & Goldsmith, Ronald E., 1999. "Corporate Credibility's Role in Consumers' Attitudes and Purchase Intentions When a High versus a Low Credibility Endorser Is Used in the Ad," Journal of Business Research, Elsevier, vol. 44(2), pages 109-116, February.
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    Citations

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    Cited by:

    1. James N. Cannon & Hugh M. Cannon & James T. Low, 2013. "Modeling Tactical Product-Mix Decisions," Simulation & Gaming, , vol. 44(5), pages 624-644, October.
    2. Elizabeth J. Tipton Murff & Richard D. Teach & Robert G. Schwartz, 2007. "Beyond the Gold and Pray equation: Introducing interrelationships in industry-level unit demand equations for business games," Simulation & Gaming, , vol. 38(2), pages 168-179, June.
    3. John R. Dickinson, 2014. "A Mathematical Law for Assessing Outcome Values of Games," Simulation & Gaming, , vol. 45(3), pages 318-331, June.
    4. Precha Thavikulwat, 2017. "Recipes for Structural Fairness in Games," Simulation & Gaming, , vol. 48(5), pages 670-694, October.
    5. James N. Cannon & Hugh M. Cannon & Manfred Schwaiger, 2012. "Modeling the “Profitable-Product Death Spiralâ€," Simulation & Gaming, , vol. 43(6), pages 761-777, December.

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