In the context of economic turbulence, pursuing new development opportunities through digital transformation has become an inevitable choice for Chinese firms, with top executives serving as core decision-makers. Drawing on resource orchestration theory and the too-much-of-a-good-thing effect, this study examines the relationship between executive psychological resilience and enterprise digital transformation, as well as its boundary conditions, using panel data from Chinese A-share listed firms from 2011 to 2024. The results provide evidence of an inverted U-shaped relationship between executive psychological resilience and enterprise digital transformation, suggesting that an optimal level of psychological resilience exists for digital transformation. Beyond this level, manifestations of bounded rationality, including an underestimation of transformation risks and attentional biases, lead to inefficient resource orchestration. Industry capital intensity weakens this nonlinear relationship, while slack resources appear to shift the turning point to an earlier stage. By questioning the cognitive inertia that treats resilience as a universally beneficial personality trait, this study shifts the perspective on the relationship between executive psychological resilience and enterprise digital transformation from a linear to a nonlinear framework. It further extends the application of the resource orchestration theory in the digital transformation context and contributes to research on the micro-level drivers of digital transformation—specifically executive psychological traits.
Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development.