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Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence

Author

Listed:
  • Maria Figueroa-Armijos

    (EDHEC Business School)

  • Brent B. Clark

    (University of Nebraska Omaha)

  • Serge P. da Motta Veiga

    (EDHEC Business School)

Abstract

The use of artificial intelligence (AI) in hiring entails vast ethical challenges. As such, using an ethical lens to study this phenomenon is to better understand whether and how AI matters in hiring. In this paper, we examine whether ethical perceptions of using AI in the hiring process influence individuals’ trust in the organizations that use it. Building on the organizational trust model and the unified theory of acceptance and use of technology, we explore whether ethical perceptions are shaped by individual differences in performance expectancy and social influence and how they, in turn, impact organizational trust. We collected primary data from over 300 individuals who were either active job seekers or who had recent hiring experience to capture perceptions across the full range of hiring methods. Our findings indicate that performance expectancy, but not social influence, impacts the ethical perceptions of AI in hiring, which in turn influence organizational trust. Additional analyses indicate that these findings vary depending on the type of hiring methods AI is used for, as well as on whether participants are job seekers or individuals with hiring experience. Our study offers theoretical and practical implications for ethics in HRM and informs policy implementation about when and how to use AI in hiring methods, especially as it pertains to acting ethically and trustworthily.

Suggested Citation

  • Maria Figueroa-Armijos & Brent B. Clark & Serge P. da Motta Veiga, 2023. "Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence," Journal of Business Ethics, Springer, vol. 186(1), pages 179-197, August.
  • Handle: RePEc:kap:jbuset:v:186:y:2023:i:1:d:10.1007_s10551-022-05166-2
    DOI: 10.1007/s10551-022-05166-2
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