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A Call for Conceptual Models of Technology in I-O Psychology: An Example From Technology-Based Talent Assessment

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  • Morelli, Neil
  • Potosky, Denise
  • Arthur, Winfred
  • Tippins, Nancy

Abstract

The rate of technological change is quickly outpacing today's methods for understanding how new advancements are applied within industrial-organizational (I-O) psychology. To further complicate matters, specific attempts to explain observed differences or measurement equivalence across devices are often atheoretical or fail to explain why a technology should (or should not) affect the measured construct. As a typical example, understanding how technology influences construct measurement in personnel testing and assessment is critical for explaining or predicting other practical issues such as accessibility, security, and scoring. Therefore, theory development is needed to guide research hypotheses, manage expectations, and address these issues at this intersection of technology and I-O psychology. This article is an extension of a Society for Industrial and Organizational Psychology (SIOP) 2016 panel session, which (re)introduces conceptual frameworks that can help explain how and why measurement equivalence or nonequivalence is observed in the context of selection and assessment. We outline three potential conceptual frameworks as candidates for further research, evaluation, and application, and argue for a similar conceptual approach for explaining how technology may influence other psychological phenomena.

Suggested Citation

  • Morelli, Neil & Potosky, Denise & Arthur, Winfred & Tippins, Nancy, 2017. "A Call for Conceptual Models of Technology in I-O Psychology: An Example From Technology-Based Talent Assessment," Industrial and Organizational Psychology, Cambridge University Press, vol. 10(4), pages 634-653, December.
  • Handle: RePEc:cup:inorps:v:10:y:2017:i:04:p:634-653_00
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    Cited by:

    1. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.
    2. Arthur, Winfred & Keiser, Nathanael L. & Hagen, Ellen & Traylor, Zach, 2018. "Unproctored internet-based device-type effects on test scores: The role of working memory," Intelligence, Elsevier, vol. 67(C), pages 67-75.
    3. Makowski, Piotr Tomasz & Kajikawa, Yuya, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    4. 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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