Tournaments to Crowdsource Innovation: The Role of Moderator Feedback and Participation Intensity
Bibliographic record
Abstract
Firms increasingly use innovation tournaments to crowdsource innovation ideas from customers. This article uncovers antecedents and consequences of customers’ participation intensity over the course of a tournament. More specifically, the authors theorize on the effects that the type and timing of moderating feedback have on tournament participants’ participation intensity, as well as the effect of the latter on idea quality. Through two longitudinal experiments using a commercial innovation tournament platform, the authors show that moderating feedback stimulates ideators’ participation intensity. They find that negative feedback increases participation intensity, as compared to no feedback and positive feedback. Moreover, negative feedback, either provided in isolation or together with positive feedback, is more effective during the early stages than in the later stages of a tournament. Using a large-scale managerial survey, the authors show that higher participation intensity leads to higher idea quality and better business performance. The effect of participation intensity on idea quality is stronger than the effect of number of ideas and as strong as the effect of number of participants on idea quality.
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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 | 0.019 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".