Perceived Supervisor Remorse and Turnover Intentions: The Role of Organization Based Self-Esteem and Affective Commitment
Bibliographic record
Abstract
Leadership behavior is essential for retaining employees. Using positive actions, leaders engage and motivate followers to allow little or no provision for turnover intentions. Even in case of adverse conduct, supervisors may correct their wrongful behavior by apologizing for their misdeeds, which helps to retain followers. Utilizing self-consistency theory, we explore how organization-based self-esteem (OBSE) is a pivotal mechanism that explains the relationship between employees’ perception of supervisor remorse and their turnover intentions, alongside the moderating role of affective commitment. Our analysis of three-wave data collected from employees from Pakistani organizations revealed that perceptions of supervisor remorse decrease turnover intention through strengthening OBSE. Employees’ psychological bonding accentuates the mediating role of OBSE with their organization. In general, our research demonstrates a crucial mechanism, employees’ self-confidence about their organizational position, through which the effect of perceived supervisor remorse on turnover intention is explained. Also, the findings show how employees’ affective commitment acts as a boundary condition invigorating this indirect effect.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".