Continuance commitment and turnover: Examining the moderating role of negative affectivity and risk aversion
Bibliographic record
Abstract
This paper examines the moderating role of negative affectivity and risk aversion in the relationships of two bases of continuance organizational commitment (continuance–sacrifices and continuance–alternatives) to turnover, within a stress–coping perspective. More specifically, we propose that (a) the perspective of leaving is a source of stress for those who stay due to the fear of losing valued advantages (i.e. high continuance–sacrifices commitment) and (b) staying is perceived to be stressful by individuals who remain based on a lack of employment alternatives (i.e. high continuance–alternatives commitment). We argue that these perceptions are magnified by negative affectivity and risk aversion, resulting in individuals who present these traits to use avoidance–withdrawal strategies in coping with these situations. Accordingly, based on a sample of 509 human resource management professionals, we found (a) negative affectivity and risk aversion to strengthen the negative relationship of continuance–sacrifices commitment to turnover and (b) continuance–alternatives commitment to relate positively to turnover among individuals with high negative affectivity. We discuss the implications of these findings for our understanding of how commitment mindsets and personality traits affect turnover decisions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".