A Hybrid Cooperative Game and Shapley Value Approach for Knowledge Sharing and Profit Allocation in Technology Supply Chain Strategic Alliance
Mostafa Jafari et al.
Abstract
Equitable profit allocation in strategic alliances within technology supply chains remains a formidable challenge, exacerbated by the inability of conventional game-theoretic models such as Nash bargaining and Stackelberg to effectively analyze dynamic, knowledge-driven contributions. These models, constrained by their bilateral and static design, fail to capture the intricacies of multi-agent interactions. This study introduces a hybrid game-theoretic framework utilizing the Shapley value's axiomatic fairness to allocate coalition profits by marginal contributions. The Shapley value surpasses equilibrium-based or power-centric approaches, offering superior suitability for complex multi-agent scenarios. Leveraging meticulously validated data including knowledge investment, absorptive capacity, and coordination costs—this framework employs Monte Carlo simulations to deliver statistically reliable contribution estimates, thereby overcoming the shortcomings of prior methodologies. Applied to an automotive supply chain, the model demonstrates substantial profit gains attributable to optimized knowledge-sharing processes. Absorptive capacity reflects the efficiency of organizational learning, coordination costs indicate potential frictions in collaborative processes, and knowledge investment captures the level of innovation intensity. The proposed model offers a robust, data-driven foundation for developing equitable profit-sharing mechanisms tailored to engineering management needs. At the policy level, it provides a scalable framework for strengthening supply chain resilience. This integrated approach contributes meaningfully to both the advancement of theoretical perspectives and the enhancement of practical strategies in supply chain management.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.