Are Agriculture, Food, and Technological Projects, Including Social Innovation, Associated with Crowdfunding Success?
Bibliographic record
Abstract
Social innovation addresses social challenges, improving quality, productivity, and economic benefits. It fosters sustainable growth, job creation, and competitiveness by tackling social and environmental issues. However, securing funding for social innovation initiatives remains difficult due to risk perceptions. Crowdfunding has emerged as an effective financing alternative, by distributing risk among numerous individuals through digital platforms. With the lens of the signalling theory, this study investigates the impact of social innovation and other factors on the crowdfunding success of technological, agricultural and food projects on the La Ruche platform, which backs community projects in Quebec, Canada. Analyzing 203 projects that either achieved or missed their funding targets, we employ probit and logit models to provide comparative perspectives on marginal effects and predicted probabilities, thereby strengthening our analytical rigor. Our results indicate that higher funding levels and more contributors drive the success of crowdfunding campaigns, but this is significant only for agriculture and food projects. For technological projects, being classified as social innovation tends to send a negative signal to contributors, who do not seem to perceive the social or environmental benefits positively. Conversely, the social dimension of agriculture and food projects conveys a positive signal, facilitating funding acquisition.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".