The Role of Entrepreneurs’ Emancipatory Motive in Reward-Based Crowdfunding
Bibliographic record
Abstract
While scholars have started investigating entrepreneurial actions aiming at overcoming constraints (although primarily social), a key area remains under-studied: how these emancipatory entrepreneurs get funding for their ventures and whether there are critical differences in entrepreneurial pitches when founders wish to overcome social vs. personal constraints. Drawing on framing literature and regulatory focus theory, in this paper, I propose effective framing strategies for overcoming each type of constraints (social vs. personal) in reward-based crowdfunding platforms. I argue that when an entrepreneur founds a new venture in order to overcome any personal constraints, an entrepreneurial pitch framed with the prevention focus motive will receive a higher pledge amount from a backer than if it highlights the promotion focus motive. Alternatively, when a founder aims to overcome social constraints, a backer will commit more funds if the pitch is framed to evoke a promotion-oriented rather than a prevention-oriented motive. An online controlled experiment with 475 MTurk panel members supports these hypotheses.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".