Going Beyond “Winner-take-all”: A Closer Look at Digital Platform Strategies
Bibliographic record
Abstract
Although digital platforms are often associated with the Big Five and winner-takes-all-or-most outcomes, more recent literature on network effects, ecosystems and complementors have raised doubts on the ubiquity of this scenario. In light of this need for more nuance as it relates to platform strategy, this paper proposes a new integrative typology of digital platform strategic stances that is a function of firm boundaries and various network characteristics, which can in turn be used to classify a greater variety of platform scenarios that can allow platform firms to scale, as well as the associated risks and opportunities. In addition to proposing a certain equifinality for scaling, this typology makes several theoretical contributions, including stances in which platform firms can replace indirect network effects with data network effects, the trade-offs between ecosystem control, platform specialization and platform generativity, and a stronger theoretical integration of the platform, innovation ecosystem and network literatures.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".