Platform Strategy: Managing Ecosystem Value Through Selective Promotion of Complements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Platform sponsors typically have both incentive and opportunity to manage the overall value of their ecosystems. Through selective promotion, a platform sponsor can reward successful complements, bring attention to underappreciated complements, and influence the consumer’s perception of the ecosystem’s depth and breadth. It can use promotion to induce and reward loyalty of powerful complement producers, and it can time such promotion to both boost sales during slow periods and reduce competitive interactions between complements. We develop arguments about whether and when a platform sponsor will selectively promote individual complements and test these arguments on data from the console video game industry in the United Kingdom. We find that platform sponsors do not simply promote “best in class” complements; they strategically invest in complements in ways that address complex trade-offs in ecosystem value. Our arguments and results build significant new theory that helps us understand how a platform sponsor orchestrates value creation in the overall ecosystem.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it