Self-promotion in entrepreneurship: A driver for proactive adaptation.
Bibliographic record
Abstract
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).
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".