The Big Five and dark triad personality traits as predictors of emotional labour

As a result of examining the Big Five and dark triad traits in predicting emotional labour styles, the fact that agreeableness predicted deep acting and naturally felt emotions suggests that agreeableness should be given closer attention in recruitment and promotion decisions, especially in the service sector.

Authors

Institutions

Publication Details

Journal
Australian Journal of Psychology
Published
2024-08-12
DOI
https://doi.org/10.1080/00049530.2024.2389842
Citations
2
Primary Topic
Emotional Labor in Professions
Type
article
Field-Weighted Citation Impact
2.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Big Five and dark triad personality traits as predictors of emotional labour

Mark Damian Griffiths, Emrah Özsoy, Zeynep Ak Şahinoğlu
2 citations
Australian Journal of Psychology
Emotional Labor in Professions
2.26
article

The Big Five and dark triad personality traits as predictors of emotional labour

Mark Damian Griffiths, Emrah Özsoy, Zeynep Ak Şahinoğlu
article en
2 citations

Abstract

As a result of examining the Big Five and dark triad traits in predicting emotional labour styles, the fact that agreeableness predicted deep acting and naturally felt emotions suggests that agreeableness should be given closer attention in recruitment and promotion decisions, especially in the service sector.

Australian Journal of PsychologyVol. 76(1)
Sakarya University (TR), Nottingham Trent University (GB)
Decent work and economic growth
Openalex Percentile: Top 10%
Emotional Labor in Professions
2.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.