Managing Quality on Two-Sided Platforms in the Presence of Provider Competition
Bibliographic record
Abstract
Problem definition: We study two-sided markets in which competing platforms enforce service standards to control access of providers with heterogeneous service quality and employ pricing strategies to balance supply and demand. We further investigate the effectiveness of launching regulations, aimed to maximize social welfare, in enhancing quality, and we examine multihoming to yield additional insights. Methodology/results: We build a game-theoretic model wherein two platforms enforce service standards and prices, based on which heterogeneous providers and consumers decide whether and which platform to enroll. The transaction revenue from service matches is shared between the platform and the providers according to a pricing scheme, which comprises a service fee and a commission rate. Our results reveal that platforms’ strategies for balancing supply and demand depend on the consumer-to-provider ratio (termed as consumer size) and the value of high-quality service relative to that of low-quality service (termed as service value). Managerial implications: The standards enforced by platforms are not always monotone with respect to consumer size or service value. A large influx of consumers prompts platforms to enforce a high standard when service value is sufficiently high. Platforms can enforce different service standards, albeit only when they compete to balance providers and consumers. Platform competition can be a substitute for regulation in upholding and enhancing quality, especially when the commission rate is high. Regulation is more effective in enhancing quality on a monopolistic platform than on competing platforms. Relative to single homing, multihoming has inconsequential effects on the pattern for the enforcement of service standards, whereas it may lead platforms to raise prices. We alert regulators to consumer size, service value, and pricing scheme in addressing quality concerns in two-sided markets. Fostering competition can be more effective than launching regulations to enhance quality on platforms. Funding: The research of L. Jiang is supported in part by the National Natural Science Foundation of China [Grant 72171204] and the Research Grants Council of the Hong Kong General Research Fund [Grant PolyU15500922]. The research of X. Zhao is supported in part by the Natural Sciences and Engineering Research Council of Canada [Discovery Grant 06690], the Einwechter Faculty Research Grant, and the Lazaridis Institute Seed Fund. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0326 .
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 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".