Experimentation and Business-model Transformation: The Self-Regulatory Benefits of Hype
Bibliographic record
Abstract
Research on hype in entrepreneurship has largely focused on image-related gains for entrepreneurs. As a result, it has overlooked the reciprocal nature of social interactions, which entails information exchanges that can help entrepreneurs self-regulate over time. To better assess the self-regulatory benefits of hype, we investigate the behaviors of entrepreneurs who must both build legitimacy and learn and adapt in order to succeed: pivoting entrepreneurs. Latent change score (LCS) modeling of five-wave monthly longitudinal data from 574 entrepreneurs during COVID-19 shows that hype leads to intraindividual increases in experimentation, which are associated with intraindividual increases in business-model transformation. We also find that entrepreneurs’ sense of efficacy acts as a boundary condition, as only those who are confident in their entrepreneurial capabilities leverage hype in such a way. In other words, provided they are not beset by self-doubt, pivoting entrepreneurs who promote their venture the most engage in more experimentation and implement more strategic changes over time. Our study offers key implications and opens avenues for future research on hype in entrepreneurship and contributes more broadly to our understanding of entrepreneurial expertise.
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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 | 0.006 | 0.048 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".