Do HRM Practices Affect Employees’ Psychological Contract Profiles? An Empirical Investigation
Bibliographic record
Abstract
The purpose of the current study is to explore the relationship between human resource management (HRM) practices and employees’ psychological contract profiles and the effect of psychological contract profiles on employees’ job performance and work engagement. In doing so, we examined four aspects of HRM practices (job participation and involvement, staffing, training, and rewards) in determining psychological contract profile membership. Using a sample of 302 working adults collected for an online survey in the U.S., we identified three distinct psychological contract profiles, namely, transaction dominant, relation dominant, and a hybrid contract profile among survey participants. Results from latent profile analysis showed that reward-oriented HRM practices increased the likelihood of being classified into a relation dominant psychological contract profile. We also found significant interaction effect between staffing and reward-oriented HRM practices and organizational trust in predicting classification into a relation dominant psychological contract profile. Our analysis indicated that employees in hybrid profiles underperformed those having a dominant contract type, either transaction or relation dominant. We discussed the implications and limitations of our study and suggested future research avenues.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".