Reframing talent identification as a status-organizing process: Examining talent hierarchies in teams through data mining

Sanne Nijs, Nicky Dries , Veronique Van Vlasselaer, Luc Sels

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We examine how peers form talent appraisals of team members, reframing talent identification as a status-organizing social process. Using decision trees, we modeled configurations of characteristics and behaviors that predicted dominant vs. parallel routes to achieving the status of most talented team member. Across 44 multidisciplinary teams, talent status was most often granted to peers perceived as having both leadership and analytic talent; a STEM degree served a dominant signaling function. Where previous studies assumed that degree operates as a specific status characteristic, we show that a STEM degree operates as a diffuse status characteristic, which predicts status in general. We thus discovered that status hierarchies in teams are also based on the type of talent—and not just the level of talent—members are perceived to possess. In so doing, we offer a proof of concept of what we call ‘talent hierarchies’ in teams, for future research to build on.
Original languageEnglish
JournalHuman Resource Management Journal
Publication statusAccepted/In press - 2021

Keywords

  • talent management; talent identification; talent hierarchies; status; decision trees, data-mining.

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