A Discriminating Alignment Theory of Innovation Ecosystem Architectures
Bibliographic record
Abstract
The architecture of innovation ecosystems—the distribution of productive activities and the structure of exchanges that integrate outputs—varies widely, and it has major implications for how ecosystems create value and which participants capture value. Some ecosystems are built around central platforms or standards, while others are not, and among platform-based ecosystems, the extent to which platform governance is centralized versus decentralized varies extensively. Existing strategy research lacks an account of which ecosystem architectures fit which contextual conditions, tending instead to attribute architecture to firms’ strategies and capabilities. In this paper, I analyze key dimensions of variety in ecosystem architectures to generate a typology of three ideal-type ecosystem architectures: firm-controlled platforms, shared-governance platforms, and symmetric-populations ecosystems. Building on this typology, I propose a discriminating alignment theory that explains why certain architectures fit specific configurations of contextual conditions. The discriminating alignment framework draws on foundations from transaction cost economics, modularity, and the game-theoretic approach to technical coordination. The framework maps the three ecosystem architectures, as well as a vertically integrated baseline architecture, to contextual conditions of value proposition complexity, demand heterogeneity, and environmental dynamism. The paper contributes to strategy research by helping us understand the antecedents of ecosystem architecture and by situating platforms and ecosystems within the markets-and-hierarchies framework of institutional economics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".