Collective Incentive Plans, Organizational Justice and Commitment
Bibliographic record
Abstract
This study examined the relationships among characteristics of short-term collective incentive plans (i.e., incentive plans that offer performance-based bonuses to employees), organizational justice, and organizational commitment. Based on a literature review, four characteristics of collective incentive plans were identified: 1) perceived instrumentality (perception of a link between performance and pay); 2) bonus paid (the amount paid to employees in the context of the plan); 3) the intensity of organizational communication (sources of information provided to employees); and 4) communication by the supervisor concerning the incentive plan. Our central hypothesis was that these characteristics would relate to employees’affective and continuance commitment through four dimensions of organizational justice, namely distributive, procedural, informational and interpersonal justice. Based on a sample of 313 members of three professional associations, structural equations model analyses revealed that the intensity of organizational communication about the incentive plan was indirectly related to continuance commitment through procedural justice and to affective commitment through informational justice. Similarly, communication by the supervisor about the incentive plan was indirectly related to affective commitment through informational justice and to continuance commitment through procedural justice. Finally, communication by the supervisor was also indirectly related to continuance commitment through interpersonal justice. This study shows that organizational and supervisory communication about the rules and workings of incentive plans plays a critical role in shaping employees’ perceptions of justice (namely procedural, informational and interpersonal justice), which indirectly influences affective and continuance commitment.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".