Collaborative Innovation Performance Within Platform-Based Innovation Ecosystems: Identifying Relational Strategies With fsQCA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it