Individual and organizational determinants of turnover intent
Bibliographic record
Abstract
Purpose The purpose of this paper is to apply social exchange theory to predict the effects of procedural and interpersonal justice on turnover intentions. Specifically, it is predicted that organizational commitment mediates the effects of procedural justice on turnover intentions and that supervisory commitment mediates the effect of interpersonal justice on turnover intentions. Design/methodology/approach Surveys were administered to 212 call center employees to measure the effects of procedural justice, interpersonal justice, organizational commitment, supervisory commitment and turnover intentions. Mediation effects were tested using Baron and Kenny's methodology. Findings Support was found for a partial mediation effect of organizational commitment on the effect of procedural justice on turnover intentions; and for a full mediation effect of supervisory commitment on the effect of interpersonal justice on turnover intentions. Practical implications Reduction of turnover is a major problem for the call center industry, as considerable resources are spent training new employees. This research suggests that turnover intentions can be reduced by addressing problems with organizational procedures and with the treatment of employees by supervisors. Originality/value The findings of this study replicate the mediation effects of organizational commitment on the effect of procedural justice on turnover intentions in call centers. In addition, this is the first study of its kind to show the mediation effects of supervisory commitment on the effect of interpersonal justice on 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".