What Does Not Kill You Makes You Search: The Effects of Failure Threat and Self-Evaluation on Entrepreneurs’ Ego Networks
Bibliographic record
Abstract
Social network theory suggests that social networks, particularly diverse ones, are crucial for entrepreneurial resource acquisition and success. However, previous research has found that entrepreneurs do not necessarily develop diverse networks but tend to associate with similar others and develop closed networks. Building on problemistic search theory and perceptual control theory, we propose that as developing diverse networks consumes cognitive and time resources, entrepreneurs are more likely to do so when they face failure threats and do not evaluate themselves as able to address the threats. An experiment with 155 entrepreneurs in China found that failure threat increases entrepreneurs’ network diversity and that this effect is attenuated by self-affirmation. A longitudinal survey of 153 entrepreneurs in China showed that entrepreneurs whose self-worth is contingent upon business success develop social networks rich in structural holes in the short term and dense networks in the long term, and these effects are attenuated by entrepreneurial self-efficacy. These findings highlight the motivational and cognitive factors driving entrepreneurs’ social networks and contribute to social network theory, problemistic search theory, and perceptual control theory.
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 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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".