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Efficient promotion strategies in hierarchical organizations

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  • Pluchino, Alessandro
  • Rapisarda, Andrea
  • Garofalo, Cesare

Abstract

The Peter principle has recently been investigated by means of an agent-based simulation, and its validity has been numerically corroborated. It has been confirmed that, within certain conditions, it can really influence in a negative way the efficiency of a pyramidal organization adopting meritocratic promotions. It was also found that, in order to bypass these effects, alternative promotion strategies should be adopted, as for example a random selection choice. In this paper, within the same line of research, we study promotion strategies in a more realistic hierarchical and modular organization, and we show the robustness of our previous results, extending their validity to a more general context. We also discuss why the adoption of these strategies could be useful for real organizations.

Suggested Citation

  • Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2011. "Efficient promotion strategies in hierarchical organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3496-3511.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:20:p:3496-3511
    DOI: 10.1016/j.physa.2011.05.021
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    References listed on IDEAS

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    1. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    2. Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2010. "The Peter principle revisited: A computational study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 467-472.
    3. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    4. 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, April.
    5. Steven E. Phelan & Zhiang Lin, 2001. "Promotion Systems and Organizational Performance: A Contingency Model," Computational and Mathematical Organization Theory, Springer, vol. 7(3), pages 207-232, October.
    6. Daan Frenkel, 2006. "Seeds of phase change," Nature, Nature, vol. 443(7112), pages 641-641, October.
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    Cited by:

    1. L. S. Di Mauro & A. Pluchino & A. E. Biondo, 2018. "A Game of Tax Evasion: evidences from an agent-based model," Papers 1809.08146, arXiv.org.
    2. Biondo, A.E. & Pluchino, A. & Rapisarda, A., 2018. "Modeling surveys effects in political competitions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 714-726.
    3. Alessandro Pluchino & Alessio Emanuele Biondo & Andrea Rapisarda, 2018. "Talent Versus Luck: The Role Of Randomness In Success And Failure," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-31, May.
    4. Goodall, Amanda H. & Osterloh, Margit, 2015. "Women Have to Enter the Leadership Race to Win: Using Random Selection to Increase the Supply of Women into Senior Positions," IZA Discussion Papers 9331, Institute of Labor Economics (IZA).
    5. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    6. Cheng, Yuan & Chang, Meng & Xue, Yanbo, 2020. "A computational study of promotion dynamics and organizational efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    7. Javarone, Marco Alberto, 2014. "Social influences in opinion dynamics: The role of conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 19-30.
    8. Sobkowicz, Pawel, 2016. "Agent based model of effects of task allocation strategies in flat organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 17-30.
    9. Caserta, Maurizio & Pluchino, Alessandro & Rapisarda, Andrea & Spagano, Salvatore, 2021. "Why lot? How sortition could help representative democracy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    10. Fetta, A.G. & Harper, P.R. & Knight, V.A. & Vieira, I.T. & Williams, J.E., 2012. "On the Peter Principle: An agent based investigation into the consequential effects of social networks and behavioural factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(9), pages 2898-2910.
    11. Udhayanan, Prateksha & Mishra, Swasti S. & Rao, Shrisha, 2021. "Firm dynamics and employee performance management in duopoly markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    12. George APOSTOLAKIS & Gert VAN DIJK, 2018. "Cooperative organizations and members’ role: A new perspective," CIRIEC Working Papers 1804, CIRIEC - Université de Liège.
    13. Alessio Emanuele Biondo & Alfio Giarlotta & Alessandro Pluchino & Andrea Rapisarda, 2016. "Perfect Information vs Random Investigation: Safety Guidelines for a Consumer in the Jungle of Product Differentiation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-26, January.
    14. Farias, B. & Rapôso, O. & Penna, T.J.P. & Girardi, D., 2021. "The Peter Principle and learning: A safer way to promote workers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).

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