University of International Business and Economics

ABSTRACT This paper examines whether and how executives with financial backgrounds respond strategically to the digital transformation trend using the data of Chinese-listed firms from 2013 to 2023. Based on imprinting theory, our findings show that financial-background executives can significantly drive firms to conduct strategic digital transformation by only increasing digital-related information disclosure without making actual investments. Meeting legitimacy needs and gaining competitive advantages are identified as key motivations that lead financial-background executives to conduct strategic digital transformation. Heterogeneous analyses suggest that this effect is mainly driven by executives with security backgrounds, and would be more pronounced for non-state-owned and high-tech firms. Overall, we attribute strategic digital transformation to financial-background executives and provide policy implications for regulators in emerging economies to protect the interests of investors in the digital age.

ABSTRACT Africa faces an institutional trilemma in its pursuit of sustainable development: rapid digital expansion, rising entrepreneurial activity and persistent social–environmental fragilities coexist without delivering commensurate improvements in inclusive and green outcomes. This study investigates how digitalization and entrepreneurship jointly shape sustainable development in Africa and whether institutional quality reconciles this trilemma by enabling these forces to generate broad‐based socioeconomic and environmental gains. Using an unbalanced panel of 45 African countries over 2000–2023, the analysis integrates mediation, moderation, distributional heterogeneity, nonlinear threshold dynamics, temporal shifts and institutional‐regime subgroup robustness through advanced second‐generation estimators, including cross‐sectionally augmented autoregressive distributed lag, common correlated effects mean group, method of moments quantile regression and panel threshold regression. Three central findings emerge. First, digitalization significantly enhances inclusive growth and environmental efficiency, with entrepreneurship mediating a substantial share of these effects. Second, linear institutional moderation is weak; however, nonlinear enhancement effects and regime‐based subsample evidence demonstrate that digital and entrepreneurial gains materialize primarily after countries surpass a critical governance threshold. Third, distributional results show that digitalization delivers stronger benefits for lower‐performing economies, while institutional quality becomes increasingly decisive at higher performance levels and in more advanced digital phases. Overall, sustainable development in Africa depends not only on expanding digital access or promoting entrepreneurship but on strengthening governance capacity that aligns these forces toward inclusive and green transformation. The findings provide policy‐relevant insights for advancing SDGs 8, 9 and 13.

This study analyzes the impact of China’s smart city pilot policy (CSCP) on green total factor energy efficiency (GTFEE) using panel data from 245 cities (2006–2021). Applying the multi-period difference-in-differences (DID) method, results show that CSCP significantly promotes GTFEE. CSCP improves urban GTFEE by about 6.18%. Meanwhile, CSCP contributes to the enhancement of GTFEE by stimulating green technological innovation and improving the efficiency of resource allocation. These impacts are particularly evident in cities characterized by higher levels of digitalization, peripherality, and being non-resource-based. The findings provide a framework for advancing urban green transitions and integrating smart and green development strategies, highlighting the importance of technology innovation in global energy governance.

Spatial transcriptomics (ST) enables joint profiling of gene expression and spatial positions, thereby revealing spatially resolved biological functions. However, many existing ST analysis methods often fail to explicitly quantify the belief and uncertainty in decisions caused by noisy ST data, making it difficult to handle spots of varying quality in a fine-grained manner. In addition, domain identification is a fundamental and critical task in ST, but commonly used models that separate expression learning and clustering often struggle to learn cluster-friendly latent representations effectively. To address these issues, we propose PREST, a prototype-based evidence-aware integration framework for ST data. PREST performs multi-scale representation learning with fine-grained attention fusion and introduces learnable class prototypes to quantify belief and uncertainty in model decisions. We aim to align overall belief scores with latent semantic information to enhance uncertainty quantification and prototype learning, thereby promoting the learning of clustering-friendly representations. PREST further integrates an uncertainty-aware reconstruction module and spatial regularization to reduce overfitting to unreliable spots and promote denoised, discriminative representations. Extensive experiments on several benchmark datasets validate the effectiveness and superiority of our proposed PREST across various downstream tasks.