Exploring perceived motivation-enhancing HR practices and employee–supervisor relationship quality on affective commitment in public hospitals: a multi-level perspective
Bibliographic record
Abstract
Purpose This study aims to examine the impact of supervisors’ perceptions of motivation-enhancing HR practices and the employee–supervisor relationship quality, on employees’ affective commitment through the mediating role of employees’ perceptions of motivation-enhancing HR practices. Moreover, the study investigates whether the employee–supervisor relationship quality moderates the relationship between supervisors’ and employees’ perceptions of motivation-enhancing HR practices. Design/methodology/approach The study draws on survey data from 542 employees and 38 supervisors in two large teaching tertiary public hospitals in Pakistan. Multilevel analysis in Mplus was carried out to test the hypothesized model. Findings Employee perceptions of motivation-enhancing HR practices mediate the relationship between supervisor perceptions of these practices and affective commitment and also between the employee-supervisor relationship quality and affective commitment. The moderating role of the employee–supervisor relationship quality was not supported. Practical implications This study suggests that to enhance employee affective commitment, the organization has two key priority areas of relevance: the organization can (1) invest in motivation-enhancing HR practices such as appraisal, promotion and compensation and (2) foster a positive employee-supervisor relationship. Both of these strategies influence the outcome of affective commitment through their role in stimulating positive employees’ perceptions of these motivation-enhancing HR practices. Originality/value The present study enriches our understanding of the antecedents (and their boundary conditions) of employee perceptions of HR practices.
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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 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.000 | 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".