Founder dynamic psychological ownership: Impacts on self and others at work
Bibliographic record
Abstract
Abstract As ventures grow, founders must decide between hanging on to control over venture decision‐making or delegating authority to professional managers. This decision is challenging since founders are typically driven by strong feelings of ownership toward their ventures. Adopting a qualitative research design with a grounded theory approach, we investigate the psychological ownership impacts on self and others within the venture when founders delegate decision rights to professional managers. Our analysis draws on in‐depth interviews with 30 founders and 14 professional managers hired by the founders. We develop the first process model of founders' dynamic venture‐targeted psychological ownership and demonstrate how recalibrating psychological ownership is key to the successful delegation of authority to professional managers. Our conceptual model also outlines a novel relationship between recalibrated psychological ownership and founder identity work. We outline our theoretical contributions to psychological ownership and identity control theory and offer practical advice to founders and their professional managers to help with the successful recalibration of founders' venture‐targeted psychological ownership in support of effective delegation and venture growth.
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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 | 0.003 | 0.010 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".