The Effects of Organizational Justice Perceptions Associated with the use of Electronic Monitoring on Employees' Organizational Citizenship and Withdrawal Behaviours: A Social Exchange Perspective
Bibliographic record
Abstract
The number of organizations choosing to electronically monitor their employees is increasing. Many of these organizations choose to implement these systems without fully understanding what effect they will have on their employees' attitudes and behaviours. The current study explored how fairness perceptions associated with the use of electronic monitoring impacts the extent to which employees are willing to engage in two types of discretionary behaviours--organizational citizenship and withdrawal behaviours. A social exchange approach was adopted. Data were obtained from 208 employees working for a Municipal government, a Police department and a call centre. Results confirmed that perceptions of justice associated with the use of electronic monitoring affect employees' willingness to engage in both organizational citizenship and withdrawal behaviours. It was also found that the relationship between perceptions of fairness associated with the use of electronic monitoring and citizenship and withdrawal behaviours was mediated by perceived organizational support, organizational trust, and affective commitment. Overall, the findings of the current study contribute to our understanding of the factors influencing employees' willingness to engage in loyal boosterism and withdrawal behaviours when organizations electronically monitor their employees. Practical and theoretical implications are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".