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Fast-and-frugal trees as noncompensatory models of performance-based personnel decisions

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  • Luan, Shenghua
  • Reb, Jochen

Abstract

Employees’ performance provides the basis for many personnel decisions, and to make these decisions, managers often need to integrate information from different performance-related cues. We asked college students and experienced managers to make a series of performance-based personnel decisions and tested how well weighting-and-adding, compensatory logistic regression and lexicographic, noncompensatory fast-and-frugal trees (FFTs) could describe participants’ decision processes regarding both choices and reaction times. Results show that a significant proportion of the participants (i.e., nearly half of the college students and more than two-thirds of the experienced managers) applied FFTs to make such decisions, and that the majority of them adopted key features of FFTs adaptively in response to a manipulation of the required distributions of positive (bonus) or negative (termination) decisions. Overall, the process-oriented approach applied in our study provides insights on not only what cues managers use for performance-based personnel decisions, but also how they use these cues.

Suggested Citation

  • Luan, Shenghua & Reb, Jochen, 2017. "Fast-and-frugal trees as noncompensatory models of performance-based personnel decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 29-42.
  • Handle: RePEc:eee:jobhdp:v:141:y:2017:i:c:p:29-42
    DOI: 10.1016/j.obhdp.2017.05.003
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    Cited by:

    1. Phillips, Nathaniel D. & Neth, Hansjörg & Woike, Jan K. & Gaissmaier, Wolfgang, 2017. "FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(4), pages 344-368.
    2. Lutz Bornmann & Julian N. Marewski, 2019. "Heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 419-459, August.
    3. Bornmann, Lutz & Ganser, Christian & Tekles, Alexander, 2022. "Simulation of the h index use at university departments within the bibliometrics-based heuristics framework: Can the indicator be used to compare individual researchers?," Journal of Informetrics, Elsevier, vol. 16(1).
    4. Pollack, Jeffrey M. & Cardon, Melissa S. & Rutherford, Matthew W. & Ruggs, Enrica N. & Balachandra, Lakshmi & Baron, Robert A., 2023. "Rationality in the entrepreneurship process: Is being rational actually rational? Introduction to the special issue," Journal of Business Venturing, Elsevier, vol. 38(3).
    5. Koller, Sonia & Stephan, Ute & Ahmetoglu, Gorkan, 2022. "Ecological rationality and entrepreneurship: How entrepreneurs fit decision logics to decision content and structure," Journal of Business Venturing, Elsevier, vol. 37(4).
    6. Guercini, Simone & Milanesi, Matilde, 2020. "Heuristics in international business: A systematic literature review and directions for future research," Journal of International Management, Elsevier, vol. 26(4).
    7. repec:cup:judgdm:v:12:y:2017:i:4:p:344-368 is not listed on IDEAS
    8. Nathaniel D. Phillips & Hansjörg Neth & Jan K. Woike & Wolfgang Gaissmaier, 2017. "FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(4), pages 344-368, July.

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