Are entrepreneurs influenced by risk attitude, regulatory focus or both? An experiment on entrepreneurs' time allocation
Bibliographic record
Abstract
Hybrid entrepreneurs¿ ¿ those who maintain a wage job while starting a new enterprise ¿ outnumber pure entrepreneurs in many countries. Yet, how hybrid entrepreneurs allocate their working hours between these two activities is not well understood. To better understand the relationship between hybrid entrepreneurs' division of time between their wage jobs and new enterprises we develop a model that captures hybrid entrepreneurs' decisions on the tradeoffs between financial risk and return as it relates to time allocation. We test two hypotheses based on utility theory, and challenge them with two hypotheses based on regulatory focus theory in a controlled experiment with 25 early stage entrepreneurs and 29 undergraduate students. In the computer-based experiment, entrepreneurs' and students' time allocation decisions (tied to monetary incentives) are used to test what would motivate them to work more or less hours in their entrepreneurial startups. We find that the actual time allocation decisions of the student group are somewhat in tune with utility theory, but that the entrepreneurs' time allocation decisions are better explained by regulatory focus theory. --------------------------------------------------------------------------------
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".