Talent Identification and Status: Using Data Mining to Understand Talent Hierarchies in Teams

Abstract

By reframing talent identification as a status-organizing process, the current study draws on status characteristics theory to examine how peers form spontaneous talent appraisals of their team members. Tree-based data mining techniques were used to model how different status characteristics and behaviors interact in predicting different, equifinal routes to achieving the status of most talented team member. Although multiple types of talent were valued by peers in our sample of 44 multidisciplinary teams (N=238), the status of most talented team member was most often granted to peers perceived as having both leadership and analytic talent, with a degree in science, technology, engineering, and mathematics (STEM) serving a dominant signaling function. Talent management practitioners could capitalize on knowledge of informal peer hierarchies to increase the legitimacy, transparency, and acceptance of the sensitive practice of formally singling out a small number of employees as ‘talents’ and awarding them additional resources.