Job demand and employee well-being
Bibliographic record
Abstract
Purpose The extant research on emotional labor (EL) has focused on positive and negative outcomes observed in the workplace; however, many fundamental questions remain unanswered. The research has yet to consider what factors buffer the negative outcomes of EL. The purpose of this paper is to investigate the relationship between workload job demand and employee well-being with mediating effects of surface acting (SA) and moderating effects of emotional intelligence (EI) in service organizations. Design/methodology/approach The authors used two wave data from a sample of 207 emergency medical technicians to test the hypotheses. Findings By integrating SA, EI and employee well-being with the conservation of resource theory, the authors found evidence of an indirect effect of workload job demand on emotional exhaustion and job satisfaction via SA. The results of moderated mediation show that the negative relationship between SA and job satisfaction was low when EI was high and the positive relationship between SA and emotional exhaustion was low when EI was high. Research limitations/implications A major limitation of the present study is that all the participants were male and drawn from a single profession within the same organization. Another limitation is that the data were collected through self-reports. Practical implications This research has important theoretical and practical implications for service organizations wishing to buffer the harmful effects of SA on employees. This study presents key theoretical implications for the EL and well-being literatures. An important practical implication is that EI is a good resource for managing SA’s negative outcomes. Originality/value The current study contributes to the extant research by showing that workload job demands have negative effects on employee well-being via SA resulting in reduced job satisfaction and increased emotional exhaustion. Further, the negative outcomes of SA on employee well-being can be buffered through EI by taking EI as an emotional resource. High level of EI helps employees to mitigate the harmful effects of SA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".