While achieving fruitful patents in innovation, enterprises can face bottlenecks in industrial transformation. The fundamental cause of such difficulties is the lack of pilot equipment. To this end, the science and technology (S&T) innovation platform introduces equipment sharing to solve the problems in transforming enterprise patents. Based on the premise that service demand is endogenous to platform service effort and user relationship resilience, this paper introduces revenue-sharing and cost-sharing contract mechanisms. It constructs a Stackelberg game model between S&T innovation platforms and enterprise users. Further, we explore the decision-making optimization involving platform service pricing, service effort, and a user's relationship resilience. Our main findings are: (1) service pricing and relationship resilience show supermodularity to the platform revenue while showing submodularity to the user revenue. (2) The optimal user relationship resilience always indicates a decreasing trend in the pricing of platform services. (3) The platform and users have their preferences for contract types. When the platform dominates the game, they tend to adopt a revenue-sharing contract. When the users dominate, they are more willing to implement a cost-sharing contract. (4) As the S&T innovation platform strengthens the connection between the platform and users through the revenue- and cost-sharing contracts, it further enhances the supply chain collaboration among equipment suppliers, technology innovation platforms, and users, thereby achieving the purpose of improving supply chain sustainability and resilience. Technological innovation is an essential means to improve supply chain sustainability and resilience.

Based on the provincial data of China’s high-tech industries from 2009 to 2019, this paper constructs a stepwise regression to analyze the effect of innovation inputs, independent and secondary innovation, and innovation value, while being mediated and moderated by innovation ability and innovation network, respectively. We found that in general, innovation inputs had a significant positive direct effect on innovation valuation: a one unit increase of independent innovation increased 0.60 units of innovation valuation, and a one unit increase of secondary innovation input increased 0.78 units of innovation valuation. Innovation ability was found to be a partial mediator for independent innovation (0.74), and a complete mediator for secondary innovation (0.90). Finally, the innovation network showed significant moderating effects in both innovation input methods. Empirical research indicates that China is entering an era shifting from secondary innovation to independent innovation, and Chinese high-tech companies should focus on independent innovation.