Digital orientation has attracted significant attention from policymakers and researchers due to its potential to drive innovation success. While prior research has largely examined the impact of digital orientation on specific types of innovation from a static lens, a holistic and dynamic perspective on its relationship with firms’ overall innovation performance remains limited. Drawing on dynamic capabilities theory, this study aims to bridge this gap by developing a moderated mediation model that incorporates the mediating role of digital capabilities and the moderating effect of environmental dynamism in the relationship between digital orientation and firms’ overall innovation performance. Using survey data from 368 Chinese firms, this study employs hierarchical regression analysis and the bootstrap method to test the hypotheses. The results indicate that digital orientation promotes digital capabilities (encompassing digital sensing, digital integrative, and digital transforming capabilities), which in turn facilitates firms’ overall innovation performance. Also, the effect of digital capabilities on innovation performance is moderated by environmental dynamism. This study contributes to the existing literature by offering a holistic and dynamic lens to elucidate the link between digital orientation and innovation performance, while also providing managerial insights for firms seeking to adopt digital orientation to boost innovation success in the evolving digital environments.

The rise of Industry 4.0 has led more manufacturers to embrace the concept of intelligent transformation. Understanding this relationship improves both transformation efficiency and the quality of innovation. This study, based on resource orchestration theory, empirically tests a sample of Chinese A-share listed manufacturing companies from 2007 to 2022. The key findings are as follows: (1) intelligent transformation significantly enhances the innovation quality of manufacturing firms, particularly in the eastern region, non-state-owned enterprises, and non-high-tech enterprises; (2) intelligent transformation, as a vital means of resource orchestration, optimizes resource allocation, thereby strengthening corporate financing, organizational resilience, and risk-taking capabilities, ultimately empowering the improvement in innovation quality; (3) the three sub-dimensions of intelligent transformation—intelligent sensing technology, intelligent production and manufacturing, and intelligent strategic layout—all significantly contribute to the enhancement of innovation quality in manufacturing firms. These conclusions provide new insights for manufacturing enterprises to utilize intelligent transformation for resource orchestration and offer valuable implications for the improvement in their innovation quality.

Under the conditions of economic integration and globalization, the importance of national innovation competitiveness is rapidly increasing. In order to study what combination of conditions can generate higher national innovation competitiveness, this study proposes an improved integrated framework for national innovation competitiveness and it examines the allocation conditions that affect the innovation competitiveness of countries with different income levels using data from the Global Competitiveness Report 2019. This research finds that, first, the means of achieving high innovation competitiveness output are more diversified for high-income and upper-middle-income countries, with countries at these two economic levels achieving high innovation competitiveness output in three scenarios. Second, lower-middle-income countries have a more homogeneous configuration for achieving high innovation competitiveness outputs, with only one scenario, which still holds after a series of robustness tests. Third, for high-income countries, commercialization is a key element affecting their innovation competitiveness enhancement. The study not only bridges the gap between existing theories and research methods but also provides a useful reference for countries at different levels of economic development to improve their innovation competitiveness.

Digital urban–rural sharing of social public resources (SPRs) is important for improving resource allocation efficiency and narrowing urban–rural disparities. This study applies a tripartite evolutionary game framework to analyze the strategic interactions among the government sector, the sharing supply side, and the sharing demand side in the digital urban–rural SPR sharing process. A system dynamics (SD) model is further constructed to simulate the dynamic evolution of the system under different initial conditions and parameter settings. The results show that the system generally evolves along a path of government initiation, demand-side response, and supply-side follow-up. Higher collaborative benefits, lower resource transfer costs, stronger government credibility, and appropriately designed subsidies promote active sharing and accelerate convergence toward a high-sharing stable outcome. In contrast, high transfer costs, weak collaborative incentives, and insufficient regulatory credibility inhibit sharing behavior or delay convergence. In addition, different initial cooperation levels mainly affect the convergence speed and fluctuation pattern of the evolutionary process. This study extends the application of the tripartite evolutionary game framework to the digital urban–rural SPR sharing context and combines it with SD simulation to reveal the system’s dynamic evolution mechanism. The findings provide practical implications for promoting digital urban–rural SPR sharing through moderate subsidies, reduced transfer costs, enhanced regulatory credibility, and strengthened collaborative mechanisms.

Over the past decade, artificial intelligence (AI) has been increasingly used in firm innovation. While AI has contributed to innovation improvement, direct evidence of its effectiveness in radical innovation is limited. This study fills this gap by empirically investigating the impact of AI on radical innovation and how this relationship is shaped by digital foundation and government subsidy from the perspectives of technological synergy and the external institutional environment. Using panel data from Chinese A-share listed firms from 2007 to 2023, this study empirically tests hypotheses through regression analyses. The findings reveal that AI adoption significantly promotes radical innovation, and this relationship is moderated by the characteristics of a firm’s digital foundation (i.e., degree and rate) as well as government subsidy. Specifically, a high degree of digital foundation hinders AI-driven radical innovation, while a fast rate enhances it. In addition, government subsidy strengthens the positive impact of AI adoption on radical innovation. A heterogeneity analysis further shows that both the timing (early vs. late) and pace (fast vs. slow) of AI adoption exert nuanced impacts: firms that adopt AI later and at a slower pace tend to achieve greater gains in radical innovation. This study advances research on radical innovation in the era of intelligence and provides managerial implications regarding the interplay of AI with internal digital foundation and external government subsidy.

In this paper, a dynamic model is presented to study retailer’s procurement strategy when supply stability is endogenously determined. The optimal supply stability as well as the optimal purchasing strategy are characterized with a quadratic cost function. Based on these models, the following findings are brought about. Firstly, when the difficulty level of building supply stability exceeds a certain threshold, it would be more profitable for the retailer to choose a less reliable supplier. Secondly, given that the suppliers can get positive profit, the retailer would choose the one who has the strongest ability to be reliable. Thirdly, the equilibrium supply to the retailer would always meet the demand on the retailing market. Finally, emergency procurement is shown to be an effective way to reduce the risk of supply chain disruptions. To better fit the real situations, an extended model which considers the impact of the stability on costs is further discussed.