Feeling like owners: the impact of high-performance work practices and psychological ownership on employee outcomes in employee-owned companies
Bibliographic record
Abstract
A central finding of scholarship on employee ownership (EO) is that EO has the strongest impacts on employee outcomes when implemented with other management practices that are typically associated with high-performance work systems (HPWS). However, our explanations for why these complementary practices matter remain underdeveloped. In this paper, we examine whether the extent to which employees feel like owners, using the construct of psychological ownership (PO), mediates the impact of three HPWS practices on four employee outcomes in a dataset of 881 employees in nine companies with employee stock ownership plans (ESOPs) in the United States. Our findings support our core argument that these practices make it more likely that employees will feel like owners, which in turn leads to positive impacts on employee outcomes. More specifically, 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 EO are positively associated with PO, and that PO is positively associated with organizational commitment, turnover intention, voice behavior, and helping behavior. We discuss the theoretical, management, and policy implications of our analysis and findings.
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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 | 0.001 |
| 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".