Operationalizing emotional intelligence for team performance

Gretchen A. Macht, David A. Nembhard, Robert M. Leicht

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This study explores the gaps in research on the link between emotional intelligence (EI) and team performance with respect to operationalizations (i.e., mean, standard deviation, maximum, minimum, and proportion). The concept of EI is not new, nor revolutionary for predicting job performance. Studies use disparate psychometric measurements and environments that obscure relationships between metrics and team performance. Student engineers (n = 294) were combined into 185 teams in a reoccurring course and analyzed using an exhaustive best subset regression technique that yields equations pertaining to the EI scales and their operationalizations to team performance. Operationalizations of EI matter towards an effective understanding of its relationship to team performance. All aggregation methods related to team performance for one or more EI main scales, excluding Interpersonal Skills and General Mood. Differences were noted between operationalizations and direction of significant EI main scales, suggesting that further exploration is required. Future researchers should consider (1) expanding operationalizations of EI to include standard deviations, maximums, minimums, and proportions to relate to team performance; (2) separating and exploring EI analyses based on task types; and (3) that the potential for EI predicting team performance exists but with limitations in an engineering context. Relevance to Industry: This research can aid managers on the relative importance of emotional intelligence in team performance. Although relationships are complex from various EI scales to predicting team performance, such information can assist the managerial and human systems communities by clarifying previously undefined nuances present in other psychometric literature.

Original languageEnglish (US)
Pages (from-to)57-63
Number of pages7
JournalInternational Journal of Industrial Ergonomics
Volume71
DOIs
StatePublished - May 2019

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Public Health, Environmental and Occupational Health

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