Incentivizing Mass Creativity: An Empirical Study of the Online Publishing Market
Bibliographic record
Abstract
This study examines the effects of incentive plans on the quantity and quality of creative production. We examine a serial publishing platform that switched from a uniform commission (UC) plan to a quantity-based commission (QBC) plan offering a bonus commission rate when writers’ production meets a preset threshold. Our analysis reveals that, for a given book, chapters published in the months when writers reached the preset threshold quantity, and thus earned bonus commission rates, exhibited higher quality, as measured by the chapter-to-chapter customer retention rate. Such a positive correlation was nonsignificant for books published under the UC plan. We interpret that the implementation of a QBC plan enhanced the complementarity between quantity and quality in the writers’ payoff function. Further empirical analysis shows that this effect persisted over time. Moreover, the degree of enhanced complementarity was lower for writers who earned commissions from multiple books. Our key result remains robust when measuring quality by reader comments sentiment. Overall, the findings underscore the critical role of well-designed incentives in enhancing the platform’s effectiveness in managing mass creativity. This paper was accepted by Raphael Thomadsen, marketing. Funding: This work was supported by the London School of Economics and Political Science [Internal Research Fund, Department of Management], the Hong Kong University of Science and Technology [Yuk-Shee Chan Professorship Fund], the Rotman School of Management, University of Toronto [China Research Initiative Grant], and McGill University [Desautels Faculty of Management Research Support F]. X. Li thanks LSE Department of Management for internal research fund support. M. Shi thanks HKUST Yuk-Shee Chan Professorship Fund and China Research Initiative Grant by Rotman School of Management, University of Toronto. C. Zhao thanks Desautels Faculty of Management Research Support Fund, McGill University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01566 .
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".