Value Appropriation between the Platform Provider and App Developers in Mobile Platform Mediated Networks
Bibliographic record
Abstract
The mobile ecosystem has recently experienced a transition in platform leadership from network operators to mobile operating system providers. In each system the platform provider exerts effort in order to attract other firms for generativity and profitability. In this paper, we identify and analyze the working mechanism of one business practice that significantly influences the ecosystem's generativity and platform provider's profitability via value appropriation. Revenue sharing has become a common practice in the mobile ecosystem following NTT DoCoMo's radical revenue-sharing model contributing toward mobile service success in Japan. Studies further argue that offering a wide portfolio of services through an attractive or innovative revenue-sharing model is one of key success factors in the mobile ecosystem. However, app developers have continuously claimed that they do not receive their fair share and the press reports a substantial number of disputes concerning revenue sharing between the platform provider and app developers. We propose a new bargaining model, the modified apex game, that investigates how value is likely to be appropriated between the platform provider and app developers within a given mobile platform mediated network. We support our theoretical predictions using data collected from the early mobile ecosystem by a network operator as well as the iOS and Android mediated networks.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".