Where Barriers Meet Breakthroughs: Perceived Constraints, New Venture Ideation, and Regulatory Focus
Bibliographic record
Abstract
Previous research on the role of constraints in entrepreneurial behavior has yielded conflicting findings, with some studies highlighting the positive effect and others documenting the adverse impact. This paper seeks to reconcile this paradox by introducing a previously overlooked phenomenon: actors’ subjective evaluation of constraints, referred to as “perceived constraint.” Specifically, it investigates the role of perceived constraint across multiple stages of the ideation process. I hypothesize that one’s perceived constraint negatively impacts both the number of ideas one develops (idea quantity) and the quality of the idea one selects to pursue (idea quality). Additionally, I propose that idea quantity mediates the relationship between perceived constraint and idea quality. Considering individual-level factors, I further hypothesize that the promotion focus motive mitigates the negative relationship between perceived constraint and idea quantity, while the prevention focus motive strengthens the positive relationship between idea quantity and idea quality. An online survey of 590 participants recruited via MTurk provides empirical support for all hypotheses except for the mediation hypothesis.
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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 | 0.005 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".