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Record W4404473932 · doi:10.1037/apl0001250

Self-promotion in entrepreneurship: A driver for proactive adaptation.

2024· article· en· W4404473932 on OpenAlexafffund
Jean‐François Harvey

Bibliographic record

VenueJournal of Applied Psychology · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEntrepreneurshipPsychologyAdaptation (eye)Promotion (chess)ProactivityApplied psychologySocial psychologyBusiness

Abstract

fetched live from OpenAlex

Research in impression management has primarily examined how self-promotion affects one's image, neglecting the potential benefits of feedback on the underlying image that is being impression managed. This study bridges this gap by integrating impression management with social-cognitive theory to explore how self-promotion can enhance feedback from targets, thereby stimulating initiative-taking and proactive adaptation in the actor. Analyzing five-wave monthly survey data from 574 entrepreneurs, I find a positive relationship between self-promotion and experimentation, which positively associates with business-model adaptation. This indirect effect is observed exclusively among entrepreneurs confident in their capabilities, highlighting the critical role of self-efficacy. Furthermore, results from three scenario-based experiments demonstrate that higher levels of self-promotion elicit greater engagement from targets, with responses containing more constructive elements, such as ideas or concerns, thereby supporting my theory. My findings underscore the richer feedback generated from self-promotion, suggesting it plays a critical role in facilitating agentic behavior. This contributes to a more nuanced understanding of self-promotion's impact, proposing new avenues for future studies in impression management and entrepreneurship. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.034
GPT teacher head0.293
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2024
Admission routes2
Has abstractyes

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