Broken promises in crowdfunded projects: reasons and mitigating governance mechanisms
Bibliographic record
Abstract
Reward-based crowdfunding is a popular method of obtaining financing for new ventures, allowing to attract funding for projects from numerous individual backers, who pledge small amounts of money in return for future products/services, discounts, acknowledgement, or branded merchandise. Yet, more than half of all crowdfunded projects deliver rewards to backers late (i.e., after the promised date), and around 5% do not deliver at all. Relying on the premises of bounded reliability and bounded rationality of project creators, we pose the following research questions: 1) What are the reasons for failed commitments (i.e., delayed or cancelled delivery) in crowdfunded projects? 2) Which governance mechanisms prevent these failed commitments? We address these questions using a qualitative investigation of reward-based crowdfunded projects, comparing cases of successful and failed implementation.
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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 | 0.037 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".