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Record W4387261352 · doi:10.1111/apps.12505

Founder dynamic psychological ownership: Impacts on self and others at work

2023· article· en· W4387261352 on OpenAlexaff
Helena Zhu, Claudia Smith, Graham Brown

Bibliographic record

VenueApplied Psychology · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDelegateDelegationIdentity (music)New VenturesPublic relationsWork (physics)Psychological contractAutonomyControl (management)Principal–agent problemFeelingBusinessEntrepreneurshipSociologySocial psychologyManagementPsychologyEconomicsCorporate governancePolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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.

Opus teacher head0.035
GPT teacher head0.307
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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