HRM systems and employee affective commitment: the role of employee gender
Bibliographic record
Abstract
Purpose Despite decades of studies on high-involvement human resource management (HRM) systems, questions remain of whether high-involvement HRM systems can increase the commitment of women. This study aims to contribute to the growing body of research on the cross-level effect of HRM systems and practices on employee affective commitment by considering the moderating role of gender. Design/methodology/approach Integrating social exchange theory with gender role theory, this paper proposes that gender responses to HRM practices can be different. The hypotheses were tested using data from 104 small- and medium-sized retail enterprises and 6,320 employees from Spain. Findings The findings generally support the study’s hypotheses, with women’s affective commitment responding more strongly and positively to employees’ aggregated perceptions of a shop-level high-involvement HRM system. The findings imply that a high-involvement HRM system can promote the affective commitment of women. Originality/value This study investigates the impact of both an overall HRM system and function-specific HRM sub-systems (e.g. training, information, participation and autonomy). By showing that women can be more positively affected by high-involvement HRM systems, this paper suggests that high-involvement HRM systems can be used to encourage the involvement and participation of women.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".