Predicting Open Innovation Success from Trust Networks, Communication Density, and Collaborative Behaviors Using Graph Neural Networks
Keywords:
Open innovation, trust networks, communication density, collaborative behaviors, graph neural networks, organizational networksAbstract
Objective: The objective of this study was to develop and validate a graph neural network model for predicting open innovation success based on trust networks, communication density, and collaborative behaviors in Nigerian organizations.
Methods and Materials: This study employed a quantitative, cross-sectional research design involving 487 professionals from innovation-intensive organizations in Nigeria across the technology, manufacturing, finance, telecommunications, and energy sectors. Data were collected using validated instruments measuring inter-organizational trust, communication density, and collaborative behaviors, combined with objective network data extracted from organizational collaboration platforms. Multilayer networks were constructed in which nodes represented individuals and edges represented trust relations, communication ties, and collaborative interactions. A graph neural network architecture integrating graph convolutional and attention mechanisms was trained to predict open innovation success. Model performance was evaluated using root mean squared error, mean absolute error, and explained variance, and results were compared against baseline machine learning models.
Findings: The graph neural network demonstrated strong predictive performance (R² = 0.82, RMSE = 0.31, MAE = 0.24), significantly outperforming gradient boosting (R² = 0.64), random forest (R² = 0.61), and support vector regression (R² = 0.58). Explainability analysis revealed that trust networks exerted the strongest influence on innovation success (importance weight = 0.41), followed by collaborative behaviors (0.30) and communication density (0.29). Multilevel network analysis showed that team-level structures had the largest standardized effect on innovation success (β = 0.44, p < 0.001), followed by individual-level (β = 0.36, p < 0.001) and organizational-level networks (β = 0.28, p < 0.001).
Conclusion: The findings demonstrate that open innovation success is primarily driven by the structure and quality of relational networks, and that graph neural networks provide a powerful and superior framework for modeling innovation outcomes within complex organizational ecosystems.
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