Linking emotional intelligence to turnover intention: LMX and affective organizational commitment as serial mediators
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The present study attempts to examine the mediating effect of leader member exchange (LMX) and affective organizational commitment on the relationship between followers' emotional intelligence (EI) and their turnover intention. Design/methodology/approach Using a cross-sectional design, survey data were obtained from 182 employees in Tunisia. Survey responses were analyzed using Model 6 in PROCESS (Hayes, 2017). Findings As predicted, LMX and affective organizational commitment were found to sequentially and totally mediate the causal relationship between EI and turnover intention. Research limitations/implications The limitations include using a cross-sectional design, convenience sampling and self-report measures for EI, LMX, affective commitment and turnover intention. Practical implications Organizations need to encourage more emotionally intelligent responses in employees which improve the quality of their leader–follower relationships. The quality of LMXs enhances the affective commitment that drives lower turnover intention. Originality/value While the relationship between EI and turnover intention has been theorized, this study is one of the first to enable us to explore the mechanisms underlying this relationship. Specifically, a sequential mediation model linking EI with turnover intention through LMX and affective commitment was proposed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it