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Are people better employees than machines? Dehumanizing language and employee performance appraisals

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  • Luke Fowler
  • Stephen Utych

Abstract

Objective Although performance appraisals are based on objective procedures, cognitive biases from appraisers may create avenues for errors in judgment of employee performance. Dehumanizing language, or metaphors that characterize humans in nonhuman terms (e.g., cogs in a machine), is one important way cognitive biases can occur Method We conduct a survey experiment to determine if dehumanizing language affects perceptions of employee value or competency within the context of performance appraisals. Result Findings show that when employees are referred to in mechanistic terms, respondents perceive that employee to be deserve hire compensation, and be more competent, as compared to referring to employees in human or animalistic terms. Conclusion Conclusions suggest dehumanizing language is an important type of cognitive bias that affects individuals in administrative environments, and the managerial and ethical implications of its use require further examination.

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  • Luke Fowler & Stephen Utych, 2021. "Are people better employees than machines? Dehumanizing language and employee performance appraisals," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 2006-2019, July.
  • Handle: RePEc:bla:socsci:v:102:y:2021:i:4:p:2006-2019
    DOI: 10.1111/ssqu.13057
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    References listed on IDEAS

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    1. David Johnson & John Barry Ryan, 2020. "Amazon Mechanical Turk workers can provide consistent and economically meaningful data," Southern Economic Journal, John Wiley & Sons, vol. 87(1), pages 369-385, July.
    2. T.K. Das & Bing‐Sheng Teng, 1999. "Cognitive Biases and Strategic Decision Processes: An Integrative Perspective," Journal of Management Studies, Wiley Blackwell, vol. 36(6), pages 757-778, November.
    3. Kevin Arceneaux, 2012. "Cognitive Biases and the Strength of Political Arguments," American Journal of Political Science, John Wiley & Sons, vol. 56(2), pages 271-285, April.
    4. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
    5. Mutz, Diana C. & Pemantle, Robin, 2015. "Standards for Experimental Research: Encouraging a Better Understanding of Experimental Methods," Journal of Experimental Political Science, Cambridge University Press, vol. 2(2), pages 192-215, January.
    6. Tuure Väyrynen & Sari Laari-Salmela, 2018. "Men, Mammals, or Machines? Dehumanization Embedded in Organizational Practices," Journal of Business Ethics, Springer, vol. 147(1), pages 95-113, January.
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