The influence of fair supervision on employees’ emotional exhaustion and turnover intentions
Bibliographic record
Abstract
Purpose Despite the importance of interactional fairness, it has been assessed less frequently in literature than has procedural and distributive justice. The effects of interactional fairness are at times stronger than the effects for procedural and distributive fairness, given that supervisors are prominent in any workplace environment and the chief source for interpreting information related to matters such as suitable business practices and goals needed by organizations. This study aims to examine the mediating mechanisms through which interactional justice influences emotional exhaustion and turnover intentions. Specifically, the hypothesis proposes that perceived organizational support and a sense of belongingness simultaneously mediates the relationship between interactional justice and emotional exhaustion, which in turn affects withdrawal cognitions. Design/methodology/approach The author draws on the literature and studies on the link between organizational justice, stress and turnover to develop the hypotheses, collecting data from 141 employees of different organizations and occupations. Findings Results of partial least squares structural equation modeling and Preacher and Hayes’ (2004) bootstrapping approach reveal that interactional justice is significantly positively associated with perceptions of organizational support and belongingness, which in turn is negatively associated with emotional exhaustion. Research limitations/implications Interactionally fair treatment engenders perceptions of organizational support and heightens a sense of belongingness, subsequently reducing the burden of physical and emotional fatigue on individuals and thereby freeing employees from engaging in turnover cognitions. Practical implications The study underscores the importance of fair supervisors. Results suggest that fair supervisors help employees estimate the extent to which their organization is supportive. In addition, fair supervisors reassure subordinates that they are valued, which in turn lessens the experience of emotional exhaustion, giving organizations a competitive advantage due to the more favorable behavioral intentions held by employees. Originality/value Interactional justice has been assessed less frequently in literature than has procedural and distributive justice. Research has overlooked the underlying process of how interactional justice reactions might motivate emotional exhaustion and turnover intentions responses. Thus, this study identifies an expanded group of mediators that link interactional justice to emotional exhaustion and turnover intentions.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".