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New Talent Signals: Shiny New Objects or a Brave New World?

Author

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  • Chamorro-Premuzic, Tomas
  • Winsborough, Dave
  • Sherman, Ryne A.
  • Hogan, Robert

Abstract

Almost 20 years after McKinsey introduced the idea of a war for talent, technology is disrupting the talent identification industry. From smartphone profiling apps to workplace big data, the digital revolution has produced a wide range of new tools for making quick and cheap inferences about human potential and predicting future work performance. However, academic industrial–organizational (I-O) psychologists appear to be mostly spectators. Indeed, there is little scientific research on innovative assessment methods, leaving human resources (HR) practitioners with no credible evidence to evaluate the utility of such tools. To this end, this article provides an overview of new talent identification tools, using traditional workplace assessment methods as the organizing framework for classifying and evaluating new tools, which are largely technologically enhanced versions of traditional methods. We highlight some opportunities and challenges for I-O psychology practitioners interested in exploring and improving these innovations.

Suggested Citation

  • Chamorro-Premuzic, Tomas & Winsborough, Dave & Sherman, Ryne A. & Hogan, Robert, 2016. "New Talent Signals: Shiny New Objects or a Brave New World?," Industrial and Organizational Psychology, Cambridge University Press, vol. 9(3), pages 621-640, September.
    • Handle: RePEc:cup:inorps:v:9:y:2016:i:03:p:621-640_00
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    Cited by:

    1. Anna Lena Hunkenschroer & Christoph Luetge, 2022. "Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda," Journal of Business Ethics, Springer, vol. 178(4), pages 977-1007, July.
    2. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    3. Alina Köchling & Shirin Riazy & Marius Claus Wehner & Katharina Simbeck, 2021. "Highly Accurate, But Still Discriminatory," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 39-54, February.
    4. Neelam Kaushal & Rahul Pratap Singh Kaurav & Brijesh Sivathanu & Neeraj Kaushik, 2023. "Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis," Management Review Quarterly, Springer, vol. 73(2), pages 455-493, June.
    5. Alexander Simons & Isabell Wohlgenannt & Markus Weinmann & Stefan Fleischer, 2021. "Good gamers, good managers? A proof-of-concept study with Sid Meier’s Civilization," Review of Managerial Science, Springer, vol. 15(4), pages 957-990, May.

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