Navigating Interdependencies In Collaborative Innovation: A Data-Driven Dematel Framework
Bibliographic record
Abstract
Collaborative innovation is vital for organisational competitiveness, yet the literature still offers an incomplete picture of how its numerous drivers interact. This study advances that understanding by consolidating 34 factors from a content-centric review of recent research and distilling them to eight core variables: market dynamics, knowledge creation and acquisition, technological learning, trust, innovation culture, organisational learning, innovation capabilities and governance. We engage a ten-member panel of academics and industry experts and employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method - an innovative multi-criteria decision-making approach - to quantify the causal structure among these factors. The resulting network relationship map shows that trust, innovation culture and organisational learning form the principal engine of collaborative innovation, exerting the strongest net positive influence on the system. Knowledge creation and technological learning surface mainly as outcomes of this relational engine, while market dynamics and governance assume balanced, context-sensitive positions. Innovation capability emerges as a hinge factor, receiving almost as much influence as it delivers, thereby converting relational gains into competitive advantage. By integrating DEMATEL with network visualisation, the study provides one of the first data-driven blueprints for managing the dynamics of collaborative and open innovation. The reference model guides managers in prioritising actions—cultivating trust, fostering an experimentation-friendly culture, institutionalising learning routines and aligning governance with environmental turbulence - across both firm and network levels. Future research should examine the temporal evolution of these interactions and explore how emerging technologies such as AI, digital twins and blockchain further reshape collaborative innovation ecosystems.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".