Grounded in regulatory focus theory, this study investigates the double-edged sword effect of generative AI adoption on sustainable entrepreneurial intentions and its underlying mechanisms. A questionnaire-based survey was conducted among 357 business students from public universities in China. The results reveal that generative AI adoption exerts a double-edged effect: it enhances sustainable entrepreneurial intentions by strengthening sustainable entrepreneurial self-efficacy through a promotion-focused pathway, while simultaneously undermining such intentions by heightening sustainable entrepreneurial fear of failure via a prevention-focused pathway. Moreover, artificial intelligence literacy moderates these relationships, amplifying the positive influence of generative AI adoption on entrepreneurial self-efficacy and attenuating its negative effect on fear of failure. This study enhances understanding of sustainable entrepreneurship amid the rise in generative AI, extends regulatory focus theory, and informs the development of AI-integrated sustainability education in academic institutions.

To advance breakthroughs in core technologies and foster the growth of technology-based enterprises, China has introduced the Sci-Tech Finance Pilot Policy with the aim of promoting sci-tech enterprise development through optimized financial resource allocation. Based on a sample of technology-based firms listed on China’s SME Board and ChiNext Board from 2009 to 2023, this study empirically examines the relationships between the Sci-Tech Finance Pilot Policy, scale expansion, and technological innovation using a multi-period Difference-in-Differences (DID) model. The key findings reveal that, first, the Sci-Tech Finance Pilot Policy simultaneously promotes corporate scale expansion and technological innovation, generating both scale and innovation effects; second, it generates scale and innovation effects by optimizing financial resource allocation, while scale expansion further induces additional innovation effects. Third, heterogeneity analysis reveals that the innovation effect of the Sci-Tech Finance Pilot Policy is stronger, and the scale effect is weaker when the technology-based enterprise is privately owned, possesses a solid R&D foundation, or operates in a favorable external innovation environment. The findings of this study demonstrate that technology finance policy promotes high-quality development through the synergy between scale and innovation, providing policy implications for developing countries in implementing the United Nations Sustainable Development Goals.

In the contemporary entrepreneurial environment increasingly shaped by artificial intelligence, the Artificial intelligence (AI) literacy of entrepreneurs plays an essential role in enhancing entrepreneurial resilience. However, the underlying mechanisms that explain this relationship remain inadequately explored. Grounded in the Conservation of Resources (COR) theory, this study examines the effect of entrepreneurs’ AI literacy on entrepreneurial resilience, specifically assessing the mediating effect of AI anxiety and the moderating effect of social support. Data were obtained from 330 Chinese entrepreneurs using a two-wave survey design. The analysis revealed a significant positive association between AI literacy and entrepreneurial resilience. Furthermore, AI anxiety mediates this relationship, suggesting that AI literacy enhances entrepreneurial resilience by alleviating AI anxiety. Moreover, all three forms of social support were found to buffer the negative effect of AI anxiety on resilience. Notably, the moderating effects of subjective support and support utilization are significantly stronger than those of objective support, highlighting the distinct roles of different support types. These findings deepen theoretical insight into the psychological pathways linking AI literacy to resilience and offer practical guidance for entrepreneurs in the AI era.

Amid the expansive evolution of the digital economy and the emergence of enhanced productivity paradigms, exploring the ways in which digital technology affordance propels corporate digital innovation via multifaceted cooperative routes is essential for reconfiguring industrial ecosystems, securing digital market advantages, and promoting superior advancement. This investigation employs the TOE model, merging fuzzy-set qualitative comparative analysis (fsQCA) with regression analysis. Using data from 2206 listed manufacturing companies from the A-share exchanges (2010–2023), it identifies multiple antecedent configuration pathways of digital technology affordance and examines their differential impacts on enterprise digital innovation. Key findings include the following: (1) no solitary factor serves as an obligatory prerequisite for high-quality digital technology affordance. (2) Four configuration pathways were identified: technology-organization-environment tripartite-propelled, technology-organization collaborative-propelled, technology-environment collaborative-propelled, and organization-environment collaborative-propelled variants. (3) The influence of digital technology affordance on digital innovation shows conditional dependence. Under the ternary-driven “technology-organization-environment” or synergy-driven “technology-organization” configurations, and absent conflicting enterprise goals, digital technology affordance promotes digital product innovation. Supported by collaborative configurations of technological investment, digital infrastructure, highly educated talent, institutional measures, and public service efficiency, it fosters digital process innovation. However, isolated technological investment, employees’ educational attainment, and institutional measures inhibit business model innovation. Other configurations lack significant impacts on digital business model innovation. This study elucidates the generation mechanism of digital technology affordance using configuration theory, offering empirical insights for managers to enhance digital innovation and drive high-quality economic development. The study enhances the theoretical depth by exploring technological foundations of digital technologies and addressing generalizability through framework adaptations for global contexts.

Against the backdrop of deep digital transformation and the increasing integration of AI into new product development (NPD), understanding the associations between different forms of human–AI interaction and team innovation has become an important organizational challenge. Grounded in the knowledge-based view and the Stimulus–Organism–Response (SOR) framework, this study investigates the associations between different forms of human–AI interaction and team innovative behavior, and the mediating role of knowledge domain coupling. Drawing on survey data collected from 268 NPD team managers serving as key informants for their respective teams in Chinese manufacturing firms, we examine two distinct forms of human–AI interaction: enhanced human–AI interaction (EHI) and impeded human–AI interaction (IHI). The results show that EHI is positively and significantly associated with both knowledge domain coupling and team innovative behavior, whereas IHI exhibits an inverted U-shaped relationship with knowledge domain coupling and team innovative behavior. Furthermore, complementary knowledge coupling and iterative knowledge coupling jointly serve as the knowledge-processing mechanisms linking human–AI interaction and team innovative behavior. This study contributes to the human–AI interaction and team innovation literature by explaining how different forms of human–AI interaction are associated with team innovation through distinct knowledge-processing mechanisms. The findings also offer practical guidance for managers seeking to dynamically balance different forms of human–AI interaction to foster innovation in AI-enabled NPD teams.

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning.