In an innovation ecosystem, the digital transformation decisions and game mechanisms of entities are paramount issues to be studied. Consequently, this study constructs a digital transformation SD evolutionary game model based on expectancy theory and Lyapunov’s first law to address the above issues. The results demonstrate the following: (1) Digital technology empowerment benefits, spillover effects, and supervision benefits are positively correlated with the willingness of the three players to engage in digital transformation; (2) Regardless of how the initial will of the players changes, the decision of the evolutionary game system is ultimately stable in (empower, transform, supervise). Compared with governments, platform centers, and nodal enterprises have a stronger will for digital transformation. However, the governments’ will is the key to the convergence speed of the game system to the equilibrium point. (3) If the static/dynamic spillover effect can cover the transformation loss, even if the transformation profits of nodal enterprises are negative, nodal enterprises will still choose the game strategy of "transformation". When the government subsidies are less than the initial value of 2, the game system has two possible strategy choices: (empower, nontransform, nonsupervise) and (empower, transform, supervise). As such, this study can fill the research gaps and address the barriers to digital transformation among stakeholders.

Based on the theory of differential games and guided by the realization of value co-creation, this paper discusses the value co-creation of a technology innovation platform, scientific innovation layer, and support layer in the digital innovation ecosystem. Given the dynamic change characteristics of digital technology innovation and resource integration, this paper constructs a differential game decision model. The conclusions are as follows: (1) The Stackelberg master–slave game and collaborative model have incentive effects. The returns in both methods increase over time and finally reach a stable value. In the collaborative game model, the effort level of participants is the highest and realizes the Pareto optimality. (2) The digital technology innovation capacity coefficient, digital technology assimilation capacity and absorption capacity coefficient, and resource integration cost coefficient are the key factors affecting the optimal return of the innovation ecosystem. (3) The two-way cost-sharing path can balance the innovation ecosystem, in which the technology innovation platform shares the cost and provides incentives to the scientific innovation layer and the support layer. The sharing ratio and incentive degree are positively correlated with the benefit of value co-creation. However, if the income distribution coefficient is not appropriately set, the participants’ income will decrease.

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