Does the crowd mean business? An analysis of rewards-based crowdfunding as a source of finance for start-ups and small businesses
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the extent to which rewards-based crowdfunding really does provide financial support for start-ups and small businesses relative to other types of activity such as creative and cultural projects. Design/methodology/approach The paper reports findings from a series of multiple regression on a unique data set covering around 205,000 rewards-based crowdfunding projects across a number of leading platforms in the USA, the UK and Canada. Findings The authors report two main findings. First, rewards-based crowdfunding is highly inequitably distributed and that success is concentrated within a relatively small number of platforms and campaigns. Second, crowdfunding campaigns explicitly related to business perform relatively poorly compared with those in other categories; particularly those in creative areas such as music and dance. Originality/value These findings call into question the extent to which rewards-based crowdfunding really is a means by which significant numbers of start-ups can bridge gaps in the provision of finance.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".