Opportunistic silence: ignited by psychological contract breach, instigated by hostile attribution bias
Bibliographic record
Abstract
Purpose Drawing on social exchange literature, this study explores the mediating role of affective commitment between employees' assessments of contract breaches and opportunistic silence, along with the invigorating effect of hostile attribution bias. Design/methodology/approach We tested the hypotheses using multi-wave data collected from employees working in higher education institutions in Pakistan. Findings Perceived contract breaches elicit intentional, selfish and retaliatory motives of silence, largely because employees lack emotional attachments to their organization. This mechanism is more prominent among employees who tend to blame others and perceive them as antagonistic even when they are not. Practical implications For human resource managers, this investigation highlights a crucial feature – affective commitment – by which employees' perceptions of psychological contract breaches facilitate opportunistic silence. Our results suggest that this process is more likely to intensify when employees have distorted thinking, motivating them to attribute the worst motives to their employer's actions. Social implications Perceived contract breaches within universities can have far-reaching societal consequences, affecting trust, reputation, economic stability, and the overall quality and accessibility of education and research. Addressing and preventing such breaches is essential to maintaining the positive societal role of universities. Originality/value This study provides novel insights into the process that underlies the connection between perceived contract breach and opportunistic silence by revealing the hitherto overlooked role of employees' hostile attribution bias, which renders them more susceptible to experiencing unfavorable forms of social exchange.
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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 | 0.005 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".