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<p>Manufacturing small and medium-sized enterprises (SMEs) often struggle with innovation because of resource and technology constraints. To address this challenge, in this study, a dynamic biform game model is constructed to analyse the formation and stabilization mechanisms of innovation alliances. A synergy hub that employs a dual-cost allocation strategy and greedy matching algorithm is a key model component. In each cycle, firms first determine their technological investments noncooperatively on the basis of the expected returns. The synergy hub then prioritizes matching the most efficient resources within the alliance to the technologically weakest firms, while costs are allocated according to the principle of group incentive compatibility. Simulation results demonstrate that this mechanism significantly stimulates innovation among heterogeneous firms. Specifically, by accessing synergistically empowered resources, technology- catch up firms increase their technology capacity more than 40% faster than through independent innovation. Resource-dependent firms, provided that they activate the pathway from resource empowerment to technological advancement, may achieve up to a 30% rebound in utility. However, synergistic effects exhibit notable industry heterogeneity. The empirical analysis indicates that more than 76% of the innovation performance improvement within the alliance is attributable to increased resource efficiency. This effect is most pronounced in the pharmaceutical industry, whereas in process-driven sectors such as those involving chemicals, diminishing returns may lead to suboptimal outcomes. This study confirms that an innovation alliance embedded with an incentive-compatible allocation mechanism not only improves innovation performance through resource pooling but also promotes dynamic stability by reshaping innovation pressure. The findings provide a clear theoretical foundation and quantitative reference for SMEs in terms of selecting innovation pathways and for policymakers in terms of designing industrial collaboration platforms.</p>

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Technology-resource interaction innovation decisions in small and medium-sized manufacturing enterprise alliances: A dynamic biform game model

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Xiaowei Ma,
  • Weimin Ma

摘要

Manufacturing small and medium-sized enterprises (SMEs) often struggle with innovation because of resource and technology constraints. To address this challenge, in this study, a dynamic biform game model is constructed to analyse the formation and stabilization mechanisms of innovation alliances. A synergy hub that employs a dual-cost allocation strategy and greedy matching algorithm is a key model component. In each cycle, firms first determine their technological investments noncooperatively on the basis of the expected returns. The synergy hub then prioritizes matching the most efficient resources within the alliance to the technologically weakest firms, while costs are allocated according to the principle of group incentive compatibility. Simulation results demonstrate that this mechanism significantly stimulates innovation among heterogeneous firms. Specifically, by accessing synergistically empowered resources, technology- catch up firms increase their technology capacity more than 40% faster than through independent innovation. Resource-dependent firms, provided that they activate the pathway from resource empowerment to technological advancement, may achieve up to a 30% rebound in utility. However, synergistic effects exhibit notable industry heterogeneity. The empirical analysis indicates that more than 76% of the innovation performance improvement within the alliance is attributable to increased resource efficiency. This effect is most pronounced in the pharmaceutical industry, whereas in process-driven sectors such as those involving chemicals, diminishing returns may lead to suboptimal outcomes. This study confirms that an innovation alliance embedded with an incentive-compatible allocation mechanism not only improves innovation performance through resource pooling but also promotes dynamic stability by reshaping innovation pressure. The findings provide a clear theoretical foundation and quantitative reference for SMEs in terms of selecting innovation pathways and for policymakers in terms of designing industrial collaboration platforms.