Collaborative Innovation Performance Within Platform-Based Innovation Ecosystems: Identifying Relational Strategies With fsQCA
Bibliographic record
Abstract
Collaborative innovation within platform-based innovation ecosystems (PIEs) relies upon the creation of effective partnerships between platform owners and complementors. Despite this, limited research examines the mechanisms that drive collaborative innovation performance within them. To address this important gap, this study performs a fuzzy set qualitative comparative analysis (fsQCA) on 203 Chinese technological firms with the aim of uncovering the distinct configurations of relational elements that drive collaborative innovation within PIEs. The findings reveal three strategies that are equally effective at delivering collaborative innovation: super-modular complementarity in relational operation dependence, unique complementarity in relational operation dependence, and coherence in relational norms dependence. Theoretically, the study contributes to the literature on interorganizational relationships in PIEs and collaborative innovation, by delineating essential relational structures and linking these relational elements to collaborative innovation performance. From a practical standpoint, both platform owners and complementors can use these findings to strengthen their collaborative innovation performance within PIEs.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".