Overcoming the Early‐stage Conundrum of Digital Platform Ecosystem Emergence: A Problem‐Solving Perspective
Bibliographic record
Abstract
Abstract Platform sponsors and complementors co‐create value in digital platform ecosystems. But how does a digital platform ecosystem emerge in the incipient stage, especially in a situation where value co‐creation involves attracting complementors to platform sponsors who are unknown to one another? We posit that a platform sponsor’s choice of scope signals value co‐creation opportunities and thereby attracts complementors and consumers. We draw upon the problem‐solving perspective, rooted in the knowledge‐based view of the firm, to shift the emphasis away from the actor (‘who’) to the problem at hand (‘what’) and demonstrate how incipient platform sponsors can align their scope with the problem to stimulate ecosystem emergence. Using fuzzy‐set qualitative comparative analysis on a dataset of crowdfunding campaigns, we identify multiple pathways and associated propositions for successful emergence of digital platform ecosystems, notably for innovation, open‐source, and information ecosystems. The framework we conceptualize highlights novel considerations to overcome the early‐stage challenge of attracting participation to an ecosystem that is yet to emerge.
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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".