Why Does Self-Reported Emotional Intelligence Predict Job Performance? A Meta-Analytic Investigation Of Mixed Ei

Keywords

Emotional intelligence; Heterogeneous domain sampling; Job performance; Personality; Self-efficacy

Abstract

Recent empirical reviews have claimed a surprisingly strong relationship between job performance and self-reported emotional intelligence (also commonly called trait EI or mixed EI), suggesting selfreported/mixed EI is one of the best known predictors of job performance (e.g., ρ =.47; Joseph & Newman, 2010b). Results further suggest mixed EI can robustly predict job performance beyond cognitive ability and Big Five personality traits (Joseph & Newman, 2010b; O'Boyle, Humphrey, Pollack, Hawver, & Story, 2011). These criterion-related validity results are problematic, given the paucity of evidence and the questionable construct validity of mixed EI measures themselves. In the current research, we update and reevaluate existing evidence for mixed EI, in light of prior work regarding the content of mixed EI measures. Results of the current meta-analysis demonstrate that (a) the content of mixed EI measures strongly overlaps with a set of well-known psychological constructs (i.e., ability EI, self-efficacy, and self-rated performance, in addition to Conscientiousness, Emotional Stability, Extraversion, and general mental ability; multiple R =.79), (b) an updated estimate of the meta-analytic correlation between mixed EI and supervisor-rated job performance is ρ =.29, and (c) the mixed EI-job performance relationship becomes nil (β = -.02) after controlling for the set of covariates listed above. Findings help to establish the construct validity of mixed EI measures and further support an intuitive theoretical explanation for the uncommonly high association between mixed EI and job performance-mixed EI instruments assess a combination of ability EI and self-perceptions, in addition to personality and cognitive ability.

Publication Date

1-1-2015

Publication Title

Journal of Applied Psychology

Volume

100

Issue

2

Number of Pages

298-342

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1037/a0037681

Socpus ID

84929783000 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84929783000

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