Bibliographic record
Abstract
Today’s digital era facilitates the rise of crowdfunding markets by allowing entrepreneurs to seek funding directly from crowds. Crowdfunding, as IT-enabled disintermediation, lowers entry barriers for crowds to invest in business projects and entrepreneurs to obtain funding, yet may exacerbate information asymmetry and absorb investor attention to process information about the potential projects. Therefore, understanding how distractions that divert investor attention influence investor behavior and crowdfunding performance is very important for entrepreneurs as well as crowdfunding platforms, especially in today’s fast-paced information era. To that end, we develop a model wherein investors with limited attention aggregate personalized information about (reward-based) crowdfunding projects and conduct comparative analyses on how rises in investors’ unit attention cost (associated with greater distractions) affect investor attention, investment decisions, and crowdfunding performance. We then exploit a novel measure of distraction—news pressure—to test the effects of distraction on investor engagement and crowdfunding performance empirically, and the results support our model predictions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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".