It's my business! The influence of psychological ownership on entrepreneurial intentions and work performance
Bibliographic record
Abstract
Summary Extant scholarship on psychological ownership has primarily focused on the organizational benefits that come from fostering employees' feelings of ownership without having to relinquish ties to actual ownership. It is unclear, however, if feeling like an owner is sufficient to satisfy employees' aspirational ownership intentions. By applying self‐verification theory to psychological ownership theory, we investigate how employees' psychological ownership influences their views about being a competent business owner, and the potential double‐edged implications for organizations as a result of these self‐views. Utilizing two separate studies, we find that psychological ownership is positively associated with entrepreneurial self‐efficacy, which, in turn, is positively associated with both entrepreneurial intentions and work performance. Furthermore, results show that employees' past work performance strengthens the positive relationship between psychological ownership and entrepreneurial self‐efficacy and the positive indirect relationship between psychological ownership and entrepreneurial intentions through entrepreneurial self‐efficacy. We discuss the theoretical and practical implications of fostering psychological ownership with current employees to glean the benefits and negate any potential drawbacks, such as high performers leaving the organization to start their own business.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| 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".