Optimal reward-based crowdfunding with co-creation
Bibliographic record
Abstract
While prior research has highlighted the advantages of reward-based crowdfunding, these studies do not guide firms and backers in innovative projects where backers are engaged in co-creational activities for product development before crowdfunding. To fill this gap, we develop a four-stage model where both firm and homogeneous backers devote efforts to co-creation for product design. The firm then chooses the funding mechanism, funding target, and pledging price in crowdfunding, followed by investment in production and selling in the retail market. We incorporate an essential aspect of crowdfunding into our model, allowing the firm to learn about the backers’ valuation of the product from the crowdfunding outcomes. Our proposed model is actually an extensive-form game with incomplete information, and we find the perfect Bayesian equilibrium by backward induction. Our result indicates that both fixed and flexible funding mechanisms have distinct advantages under specific conditions. Moreover, the firm should employ a markdown pricing strategy during the crowdfunding and retail stages under the fixed funding mechanism and a markup pricing strategy under the flexible funding mechanism. Furthermore, we identify conditions under which the firm could benefit from co-creation. Finally, we consider the model with two heterogeneous backers and reveal the equilibrium solutions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".