Evolution Mechanisms for Digital Platforms: A Review and Analysis across Platform Types
Bibliographic record
Abstract
<p>Digital platforms have emerged as a major organizational form in various industries including hospitality (e.g., Airbnb), innovation (e.g., Designhill), and software development (e.g., Apple iOS). Some platforms evolve successfully to build stable and growing ecosystems, while many fail to do so. The high rate of failure amongst platforms can be linked to platforms emulating mechanisms from successful platforms that are less applicable to their context. Therefore, understanding the evolution of platforms requires attention to variations in their evolution mechanisms across platform types. In this paper, we analyze the mechanisms of platform evolution across multiple platform types by reviewing the growing literature on digital platforms within major Information Systems (IS) journals, IS conferences and management journals. We identify three categories of evolution mechanisms, namely platform design, platform operations and capabilities, and platform ecosystem and governance. Our analysis points to variation in the applicability of each category across different types of platforms.</p>
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.014 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".