Predictions of Crowdfunding Campaign Success: The Influence of First Impressions on Accuracy and Positivity
Bibliographic record
Abstract
Crowdfunding has quickly gained popularity in recent years, providing an additional way for entrepreneurial individuals and organizations (creators) to attract funds for their projects. Scholars have been interested in predicting the success of crowdfunding campaigns, by relating campaign characteristics to the actual success of these campaigns. We take one step back by studying the cognitive processes of the crowd. This paper uses an experimental approach to establish whether participants’ predictions on the success of crowdfunding campaigns based on first impressions are as positive and as accurate as those derived from more thorough analyses. We employ a two-study replication design, in which individuals estimate the success of crowdfunding campaigns in two conditions: with limited time and with unlimited time. The results show that prediction accuracy in both conditions is equal, yet shorter time availability results in assessments that are more negative. We discuss implications for creators and for funders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".