Bibliographic record
Abstract
Problem definition: We consider a two-sided market competition problem where two platforms, such as Uber and Lyft, compete on both supply and demand sides and study the impact of precommitments in a variety of practically motivated instruments on the equilibrium outcomes. Academic/practical relevance: We extend a set of classic oligopoly pricing results to account for two-sided competition under demand uncertainty. Methodology: We investigate multi-stage competition games. Results: We start with a sufficiently low demand uncertainty. First, we show that a precommitment made on the less competitive (demand or supply) side (on price or wage) has a less intense outcome than no commitment (i.e., spot-market price and wage competition). Then we show that, somewhat surprisingly, if the competition intensities of both sides are sufficiently close, the commission precommitment, where the platforms first compete in setting their commission rates and then their prices, is less profitable than no precommitment at all, and vice versa. Furthermore, we show that the capacity precommitment, in which the platforms first commit to a matching capacity and then set price and wage simultaneously subject to the precommitted capacity, leads to the most profitable outcome of all competition modes and extends the celebrated Kreps-Scheinkman equivalency to the two-sided market (without demand uncertainty). Then we extend the comparisons of various competition modes to account for a relatively high demand uncertainty. We show that the comparison between the spot-market price and wage competition and the commission precommitment stays the same as that with a sufficiently low demand uncertainty. In addition, the more flexible competition modes, such as no commitment and commission precommitment, benefit from higher demand uncertainty (with a fixed mean demand) because of their operational flexibility in response to the market changes. Further, a relatively high demand uncertainty may undermine or enhance the value of the wage precommitment, as opposed to no commitment. Finally, we also account for platforms with asymmetric parameters and matching friction and find that our main insights tend to be robust. Managerial implications: Our results caution platforms that a precommitment to the wrong instrument can be worse than no commitment at all. Moreover, the regulation of classifying gig workers as employees, despite many of its benefits to workers, may lead to a less competitive market outcome and, surprisingly, hurt gig workers by paying them lower wages. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2021-04295]. Y. Liu was supported by the Hong Kong Research Grants Council, Direct Allocation Grant [Project ID P0036818]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1173 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".