Disability status, individual variable pay, and pay satisfaction: Does relational and institutional trust make a difference?
Bibliographic record
Abstract
Although prior research suggests that disabled employees have different needs in the context of some HRM practices, we know little about their reactions to reward systems. We address this gap in the literature by testing a model using the 2011 British Workplace Employee Relations Survey (disabled employees, n = 1,251; nondisabled employees, n = 9,959; workplaces, n = 1,806) and find that disabled employees report lower levels of pay satisfaction than nondisabled employees, and when compensated based on individual performance, the difference in pay satisfaction is larger. We suggest that relational (derived from trust in management) and institutional (derived from firm‐wide policies and HRM practices, both intended to provide equitable treatment to disabled employees) forms of trust play important roles. The results of multilevel analyses show that when trust in management is high, the difference in pay satisfaction under variable pay is reduced. We find just the opposite for employees who work in organizations with a formal disability policy but without supportive HRM practices; the gap in pay satisfaction is exacerbated. However, the combination of the presence of a firm‐wide policy and HRM practices reduced the difference in pay satisfaction. Implications of the findings for theory, future research, and management practice 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 | 0.002 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".