High involvement management practices as leadership enhancers
Bibliographic record
Abstract
Purpose – Based on the high-involvement management model and the Substitutes for Leadership theory, the purpose of this paper is to evaluate the moderating role of high-involvement management practices on the relation between managers’ transformational leadership and employees’ affective organizational commitment. Design/methodology/approach – Data were collected from employees of a large Canadian financial firm. Questionnaires were sent out and 219 received, representing a response rate of 63.3 percent. The hypotheses were tested using multiple regressions analysis with moderation effects. Findings – The results show three statistically significant interactions between transformational leadership and high-involvement management practices. More specifically, information sharing and power sharing practices acted as leadership enhancers, while skill development practices served as a leadership substitute. Practical implications – The results of this research could help immediate supervisors adjust their leadership strategies to their organizations’ HRM practices, and also guide top managers in choosing practices that can support these supervisors. Originality/value – This study contributes to the literature on leadership by considering how contextual factors may affect the influence of transformational leadership and by integrating HRM practices within the substitutes for leadership framework.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".