Impact of high-performance work systems on job satisfaction, organizational commitment, and intention to quit in Canadian organizations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to increase the understanding of the influence of high-performance work systems (HPWS) upon job satisfaction (JS), organizational commitment (OC) and intention to quit (QI). Design/methodology/approach – The data come from a questionnaire administered to 730 employees in different organizations. All questionnaires were administered “live,” in the presence of one or more members of the research team, with the ability to answer any of the respondents’ clarification questions. Findings – The results of this study allow the authors to better understand how the effects of HPWS are exerted on the intention to quit by highlighting the mediating role played by JS and OC. In addition, the results demonstrate a synergistic effect of HPWS, meaning that the combined effects of three sets of HR practices (skill-enhancing, motivation-enhancing and opportunity-enhancing practices) is greater than the sum of each set taken individually. Research limitations/implications – The cross-sectional nature of the study prevents the authors from inferring true causality between human resource management (HRM) practices and the attitudes and behaviors of employees. Only a longitudinal study measuring levels of JS, OC and quit intention before and after implementation of such practices would establish such causality. Practical implications – For leaders and managers of organizations seeking to reduce the rate of employee turnover, the results are eloquent: increased investment in a HPWS can significantly improve JS, helping to increase OC and reduce intention to quit. In the prevailing context of “talent war,” organizations that are the most proactive in the implementation of HRM systems, that is, systems designed to improve the skills of employees, to motivate them to use these skills and to empower them in their decision making at work, will be the employers that are more likely to retain skilled employees. Originality/value – This paper focusses on the complementary rather than aggregate effects of three sets of HRM practices, thus contributing to the discussion on the notion of complementarity among HRM practices, a notion that has been called into question in certain studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".