Impact of High-Performance Work Practices on Employee Outcomes in Employee-Owned Companies
Bibliographic record
Abstract
A central claim of scholarship on employee ownership (EO) is that it has the strongest impacts on employee outcomes when implemented in combination with other HR practices that are typically associated with high-performance work systems. However, our explanations for why these complementary practices are important remain underdeveloped. In this paper, we draw on theories about the psychology of ownership and the abilities-motivation-opportunities (AMO) framework from the industrial relations and HR literatures to theorize the effects of three types of what we call “ownership HRM” (OHRM) practices on employee attitudes and behaviors in a dataset of 881 employees in nine companies with employee stock ownership plans (ESOPs). We find that the extent to which employees perceive that they have influence, are engaged as owners through information sharing and business literacy training, and receive high quality communications about the ESOP have direct effects on organizational commitment, turnover intention, voice behavior, and helping behavior, and that these effects vary across these four outcomes. In addition, we find that psychological ownership moderates the impact of OHRM practices on employee attitudes and behaviors. Our findings reveal that there are multiple, complex pathways through which OHRM practices impact employee outcomes in EO companies, and a broader range of theoretical explanations for these impacts than has been captured in the literature.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".