Disintermediation Governance and Complementor Innovation: An Empirical Look at Amazon.com
Bibliographic record
Abstract
This study investigates how the platform’s disintermediation governance policy (i.e., the policy that disciplines complementors and consumers circumventing the platform to transact directly) affects complementors’ product innovation and launching strategies. We leverage a change in the Communication Guideline on Amazon.com (i.e., the focal platform), which prohibits complementors (i.e., sellers) from sending external website links, telephone numbers, and email addresses to buyers in direct messaging, as a policy shock to Amazon complementors. By conducting a difference-in-differences analysis, we find that the governance policy significantly reduces product innovation on the focal platform among complementors with direct selling channels as measured by the number of new products and the innovativeness of new products. These main effects are mitigated by complementor reputation on Amazon and complementor multihoming. Mechanism analyses show that complementors with direct selling channels increase the number of new products launched through those channels after the policy change (i.e., the switching effect). Moreover, complementors tend to strategically switch innovative and high-value products away from the focal platform while leaving less innovative but price-competitive products on it to attract Amazon consumers before diverting to their direct selling channels (i.e., the bait effect). Our results have implications for platform governance policies regarding disintermediation and its impact on complementor product innovation and launch strategies both on and off the focal platform. This paper was accepted by D. J. Wu, information systems. Funding: G. Gu gratefully acknowledges research support from the USC Marshall School of Business. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04439 .
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".