Monetization on Mobile Platforms: Balancing in‐App Advertising and User Base Growth
Why this work is in the frame
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Bibliographic record
Abstract
Monetizing the growth of mobile platforms is increasingly important as more and more users adopt mobile platforms such as the Google’s Android OS and the Apple’s iOS. In this study, we use a differential game theoretical model to study the problem of joint advertising investment and in‐app advertising adoption decisions by platform owners and app developers on a mobile platform. A key finding is that a platform owner may delay or even not offer an in‐app advertising program if the revenue from such a program is low, which could explain the termination of Apple’s iAd in‐app advertising program. One unexpected result is that when determining advertising effort or the timing of an in‐app advertising program, a platform owner does not need to consider the app developer’s advertising effectiveness. Another interesting result is that an app developer acts strategically with an increase in ease of app searching: he either follows the platform owner to increase advertising or decreases it to take a free ride, depending on the effectiveness of his advertising effort. Finally, our analysis shows that in order to coordinate the mobile platform system, a central planner should adopt a mixed transfer payment scheme that includes both revenue sharing and advertising cost sharing, regardless of whether competition exists.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it