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As digital technologies reshape competitive dynamics, firms face increasing pressure to keep pace with the innovation activities of their peers. While digital transformation is often viewed as a driver of sustainable development, less attention has been paid to how peer firms’ digital innovation relates to corporate environmental, social, and governance (ESG) performance within the broader innovation system. Using panel data from Chinese A-share listed firms from 2012 to 2023, this study constructs an industry-level measure of peer digital technology innovation based on the digital patenting activities of other firms in the same narrowly defined industry. The results show that peer digital technology innovation is significantly associated with a decline in ESG performance, revealing trade-offs between digital competitive adaptation and sustainability outcomes. Mechanism analysis further provides suggestive evidence consistent with two potential channels: heightened digital risk perception and stronger managerial short-term orientation. Heterogeneity analysis shows that this negative association is pronounced in small and medium-sized enterprises, tech-intensive industries, and highly competitive markets. In addition, external governance conditions shape the magnitude of this relationship: stronger intellectual property protection amplifies the negative association, whereas greater investor attention attenuates it. Supplemental analysis of ESG rankings further indicates that digital competition may allow specific firms to enhance their relative standing. These findings provide new evidence on the unintended consequences of digital competition and contribute to the literature on corporate digitalization and sustainability. Full article

This study presents a systematic review of the literature on business localness, a concept which, despite increasing interest, remains theoretically fragmented. This fragmentation limits a comprehensive understanding of how firms are embedded in local economies and how localness can be systematically measured. Using the PRISMA methodology, 54 articles were analyzed from an initial set of 500 publications in the Scopus database. Combining bibliometric clustering and qualitative synthesis, the study identifies seven major research clusters and organizes them into broader research streams. The findings suggest that localness is primarily examined in terms of its relational, spatial and economic dimensions. Mechanisms such as knowledge diffusion, trust and access to resources are emphasized, as are outcomes relating to innovation, business performance, sustainability and regional development. However, the economic dimension remains fragmented and is rarely conceptualized as a distinct and measurable component of business localness. Combining findings from previously fragmented research streams, this study develops an integrative framework for business localness that incorporates spatial, relational, and economic dimensions of embeddedness and links them to the mechanisms and outcomes through which firms contribute to local economies. The study is limited by its reliance on English-language journal articles indexed in Scopus and by the conceptual nature of the proposed framework, which requires further empirical validation across different contexts and industries. By explicitly introducing economic embeddedness as a distinct analytical dimension, the framework extends existing embeddedness theory and provides a foundation for future empirical research on how firms contribute to local economic development and sustainability. Full article

Whether corporate digital spending can produce sustainability-related outcomes is still not well understood. Much of the existing discussion treats digital transformation as a broad strategic process, but firms make sustainability decisions through more concrete resource allocations, including investment in information systems, data infrastructure, and IT-based management tools. This study therefore focuses on IT investment and examines its relationship with corporate environmental, social, and governance (ESG) performance. Based on panel data for Chinese A-share listed firms from 2009 to 2024, we estimate fixed-effects models and further test the roles of technological spillovers, market competition, and ownership structure. The evidence indicates that firms with higher IT investment tend to report better ESG performance. This association remains significant when lagged IT investment is used, suggesting that the result is not merely driven by same-period correlation. Technological spillovers explain part of the relationship: IT investment appears to support ESG performance partly by improving technological learning, innovation diffusion, and knowledge accumulation. By contrast, market competition does not significantly change the IT investment–ESG relationship. Additional analysis shows that the positive effect is stronger in state-owned enterprises than in non-state-owned enterprises. The findings imply that IT investment can be viewed not only as a source of operational efficiency, but also as a digital resource that may strengthen firms’ capacity for sustainable governance. Full article

ESG (Environmental, Social, and Governance) performance is increasingly viewed as a strategic factor shaping firms’ innovation activities. However, existing studies have largely examined green innovation as a whole, with limited attention to low-carbon technological innovation as a distinct domain and insufficient understanding of its driving mechanisms and conditional heterogeneity. Using panel data on Chinese A-share listed companies from 2009 to 2024, this study employs a two-way fixed-effects framework to examine the effect of ESG performance on low-carbon technological innovation, and further investigates the underlying transmission mechanisms and heterogeneous effects. The results show that ESG significantly promotes low-carbon technological innovation, with a notably stronger effect on substantive innovation than on strategic innovation, indicating that ESG drives genuine technological advancement rather than superficial patent accumulation. Mechanism tests reveal that ESG facilitates innovation by easing financing constraints and enhancing government support. At the dimensional level, the environmental and social pillars exert significant positive effects, whereas the governance pillar does not. Heterogeneity analyses demonstrate that the promotional effect is more pronounced in state-owned enterprises, large firms, heavily polluting industries, and non-technology-intensive firms, revealing structural variation across firm characteristics. By isolating low-carbon innovation from the broader green innovation concept and identifying dual transmission channels, this study extends the literature on the economic consequences of ESG and provides evidence for designing differentiated green governance policies. Full article

In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI self-efficacy and digital human resource management (HRM) practices in translating AI adoption into employee performance outcomes. Survey data were collected from firms located in major Chinese cities (Beijing, Shenzhen, Xi’an, and Zhengzhou), resulting in 750 valid responses for analysis. The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes. Theoretically, this study advances knowledge management studies by highlighting the complementary roles of individual cognitive beliefs and HR systems in enabling AI-driven learning and capability development. Practically, the findings suggest that organizations should embed AI technologies in HR systems that foster learning, knowledge utilization, and continuous innovation. Full article

This study synthesises evidence on whether, why, and under what conditions digital technologies improve resource efficiency across multi-tier global value chains (GVCs) and examines the theoretical adequacy of dominant explanatory lenses. Following the PRISMA 2020 protocol, we searched Web of Science, Scopus, IEEE Xplore, and ProQuest, retaining 150 articles for qualitative synthesis and 137 for bibliometric science-mapping; themes were developed via multi-cycle coding and triangulated with co-citation and keyword co-occurrence networks. Reported efficiency gains are strongest when firms deploy integrated digital stacks combining IoT sensing, AI analytics, blockchain traceability, and digital twins that jointly enable visibility, verification, and simulation-based optimisation, a pattern based predominantly on observational and cross-sectional evidence. Outcomes are contingent on cross-firm capability complementarities, data-governance arrangements, regulatory congruence, and cyber-risk maturity. A key structural finding is the digital-regionalisation paradox: stringent data-compliance demands can re-anchor sourcing within regulatory blocs, concentrating rather than extending GVC geography. Building on these findings, we propose three theoretical extensions, namely ecosystemic capability bundling, digital-sustainability spillovers, and distributed eco-innovation, that advance Transaction Cost Economics, the Resource-Based View, Dynamic Capabilities, and GVC governance theories to better account for the sustainability and platform dimensions of contemporary digitalised value chains. Full article

A sustainable low-carbon transition requires policy that curbs emissions while accelerating green technological innovation. The EU Carbon Border Adjustment Mechanism (CBAM) imposes carbon costs on high-emission exports; yet, how it shapes exporters’ green innovation remains poorly understood. We develop an open-economy Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model embedding three CBAM transmission channels: cost-driven (higher carbon-intensive production costs), awareness-driven (firms’ forward-looking expectations), and information-enhancement (lower green R&D financing costs). The model decomposes CBAM’s green-innovation effects by jointly endogenizing forward-looking green R&D investment and carbon disclosure quality in general equilibrium. Calibrated to Chinese data and solved in Dynare 7.0, the model is simulated over forty quarters. Under the baseline calibration, simulations suggest a CBAM shock raises green R&D investment by approximately 6.5% at its peak and the green technology level by approximately 12.5% by quarter 40, while brown emission intensity falls by approximately 10%. Within this window the policy carries a net welfare cost of approximately 0.34% of steady-state consumption, concentrated in transition-period labor disutility, with most gains accruing later. Combining CBAM with R&D subsidies modestly reduces the within-window welfare cost and raises long-run green technology. Realizing this sustainability potential requires policy credibility, carbon-information infrastructure, and coordinated innovation support. Full article

Artificial intelligence (AI) is increasingly viewed as a key driver of sustainable development, yet evidence on its role in corporate sustainable green innovation remains limited. Drawing on Resource Orchestration Theory and Signaling Theory, this study examines the impact of AI capability on sustainable green innovation using panel data from Chinese A-share listed companies during 2014–2023. The results show that stronger AI capability significantly promotes sustainable green innovation. AI investment serves as a mediating mechanism. However, the estimated indirect effect is negative, suggesting that the process of AI implementation may involve resource reallocation and organizational adjustment costs before innovation benefits can be fully realized. Environmental investment strengthens the positive impact of AI capability, whereas digital transformation weakens it. Robustness tests confirm the reliability of the findings. Further analyses indicate that the positive effect of AI capability is more pronounced in non-state-owned enterprises, low-technology firms, and firms in the growth stage. By revealing the mechanisms and boundary conditions through which AI capability influences sustainable green innovation, this study enriches the literature on AI-enabled sustainability and offers practical insights for firms pursuing long-term sustainable development. Full article

This study examines the influence of green market orientation (GMO) and green entrepreneurial orientation (GEO) on business competitiveness in increasingly demanding environmental contexts. Drawing on the literature on strategic management and sustainability, this research analyzes how environmentally oriented market and entrepreneurial capabilities contribute to strengthening firms’ competitive positioning. Using a quantitative approach, data were collected from 200 companies in the agri-food and manufacturing sectors, and the data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that both GMO (β = 0.534; p < 0.001) and GEO (β = 0.395; p < 0.001) have a significant influence on competitiveness, with GMO showing a stronger impact. These findings suggest that firms integrating environmental market demands and fostering innovation, proactiveness, and risk-taking with an ecological focus achieve enhanced competitive performance. This study concludes that green-oriented strategic capabilities constitute key drivers of sustainable competitive advantages and represent a relevant pathway for organizational strengthening in dynamic and environmentally regulated markets. Full article

The application of artificial intelligence (AI) and the improvement of environmental, social, and governance (ESG) performance have become important concerns for contemporary firms. Understanding whether AI can be effectively integrated into corporate ESG practices has significant implications for sustainable development. Using panel data from Chinese A-share listed firms from 2009 to 2025, this study empirically examines the effect of AI application on corporate ESG performance through a fixed-effects model. The results show that AI application significantly improves corporate ESG performance, indicating that firms with higher levels of AI adoption tend to achieve better ESG outcomes. The heterogeneity analysis further reveals that this effect varies according to firms’ technological intensity, industry pollution characteristics, and the strength of regional environmental regulation. Specifically, the positive effect of AI application is more pronounced among firms with lower technological intensity, firms operating in non-heavily polluting industries, and firms located in regions with stricter environmental regulation. The mediation analysis shows that AI application enhances ESG performance by promoting human capital upgrading and green technological innovation, thereby strengthening firms’ internal capabilities and technological foundations for sustainable development. This study contributes to the literature by integrating AI application and ESG-oriented sustainable development within a unified analytical framework. Full article

This study examines how client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. Full article

As a key engine of corporate innovation, digital transformation permeates business management. Can digital transformation improve corporate financial flexibility by leveraging the external supply chain? Our sample comprises Chinese listed companies over the period from 2015 to 2024, employing Python 3.10 crawling to measure the degree of digital transformation and utilizing the entropy weight method to construct supply chain resilience. Using a moderated mediation model, this analysis examines how corporate innovation mediates the relationship between digital transformation and financial flexibility, and how supply chain resilience exerts a phased moderating effect along this pathway. The findings reveal the following: (1) Digital transformation has a positive effect on financial flexibility, where corporate innovation plays a mediating role. (2) The promoting effect of digital transformation on financial flexibility exhibits significant heterogeneity, varying with firm-specific micro-level characteristics and internal control quality. (3) Supply chain resilience plays a significant moderating role throughout the entire mediation path. It positively moderates the chain of “digital transformation → corporate innovation → financial flexibility”. This study provides empirical evidence on the mechanisms of digital transformation’s impact on corporate financial flexibility and offers a theoretical view for evaluating the outcomes of digital transformation from a financial perspective. Full article

Frequent introductions and revisions of climate policy instruments constitute a salient exogenous shock to firms’ strategic decisions. From a socio-technical systems perspective, climate policy uncertainty (CPU) represents an external institutional disturbance that reshapes the interaction between firms’ technological upgrading and organizational governance. Using panel data on 1783 Chinese listed firms from 2011–2024, we examined how CPU affects firms’ industrial intelligence. We employed fixed-effects models, mediation, and moderation analyses, supplemented by robustness tests. We found that, first, CPU significantly promotes firms’ industrial intelligence transformation. Second, board governance plays a key moderating role: higher board educational attainment, stronger innovation orientation, and an environmental or risk committee significantly strengthen CPU’s positive effect on industrial intelligence. Third, CPU promotes industrial intelligence mainly through two channels: an opportunity effect via increased R&D investment, and a pressure effect via reduced total factor productivity, pushing firms to adopt intelligent transformation to address productivity pressure. Moreover, this effect is stronger for firms in high-pollution industries, larger firms, and long-established firms. These findings suggest that corporate industrial intelligence is not merely a technological response, but a socio-technical adaptation process shaped by climate policy uncertainty, board governance, and resource reconfiguration. This study provides evidence on firms’ digital, intelligent, and green transformation under climate policy uncertainty and offers implications for board governance and sustainable adaptation. Full article

In the fuzzy front end of innovation, firms often lack sufficient citation, market, and performance data, which limits the usefulness of outcome-based approaches to screening early-stage product innovation opportunities. To address this problem, this study develops a text co-occurrence network-based measurement system for assessing early-stage product innovation opportunities in new product development. We first preprocess idea texts through concept extraction and semantic cleaning, and then construct an integrated semantic network by combining market-related texts with ideation data. The Leiden algorithm is applied to detect latent knowledge communities in the network. Building on this structure, we assess early-stage product innovation opportunities along two complementary dimensions: cross-domain knowledge recombination, capturing the extent to which an idea draws on concept communities that are otherwise weakly connected, and network structural perturbation, capturing the degree to which an idea reconfigures existing semantic boundaries and connection patterns. Based on community entropy and modularity change, we construct a composite indicator for the ex ante assessment of early-stage ideas with stronger product innovation potential. Compared with traditional approaches relying on patent citations, market outcomes, or expert judgments, the proposed method enables earlier screening of ideas that deviate from dominant semantic trajectories and may warrant further development attention. The framework is explicitly positioned as an ex ante screening and attention-allocation tool for early-stage product innovation opportunities, not as a deterministic predictor of later market success. Full article

Effectively harnessing the productive potential of local government debt while mitigating financial risks is critical for urban sustainable development. Focusing on the standardized development phase of local government debt in China, this study examines how local government debt affects green technological innovation using Chinese A-share listed firms from 2015 to 2021. We find that regulated explicit debt significantly promotes firms’ green innovation through three channels: green bond issuance, digital infrastructure investment, and improved firm profitability. In contrast, implicit debt exerts a nonlinear inhibitory effect, with significant negative impacts only after a threshold is exceeded. Heterogeneity analysis shows stronger effects in developed regions, areas with more optimized industrial structures, and state-owned, large, and high-ESG firms. Our findings suggest that optimizing debt structure and directing funds to productive green and digital sectors can effectively drive urban green innovation. Full article