Purpose This study investigates the configurational pathways underpinning digital-green synergistic transformation (DGT) and examines the enabling role of artificial intelligence (AI), thereby advancing understanding of how manufacturing firms navigate the dual imperatives of green development and digital upgrading. Design/methodology/approach Using 9,336 firm-year observations from 2,407 Chinese manufacturing firms over 2013–2023, this study draws on the Technology-Organization-Environment (TOE) framework and employs dynamic QCA to identify pathways and panel regressions to examine AI’s enabling effects and heterogeneity. Findings Results indicate that no single factor constitutes a necessary condition for DGT. We identify three distinct configurational pathways to high DGT: “endogenous dual-innovation driven” (SC1), “policy-driven and green-led” (SC2) and “policy-driven and digital-led” (SC3). AI significantly promotes DGT by enabling these pathways, with the largest estimated enabling effect observed for SC1, followed by SC3 and SC2. Heterogeneity analysis reveals that the enabling effect of AI is more pronounced in state-owned enterprises, firms with high market attention and those with high industrial chain integration. Originality/value This study contributes to the literature at the intersection of digitalization and greening by elucidating how multiple technological, organizational and environmental factors form configurational pathways to DGT. It further demonstrates AI’s enabling role in these pathways, providing insights into how firms can embed AI to achieve their strategic goals. By revealing how AI’s effects vary across firm, market and industry contexts, this study advances a contextualized understanding of AI-enabled transformation.
Purpose This study investigates digital service innovation as a socio-technical driver of ecosystem value co-creation in manufacturing. Guided by Service-Dominant Logic, it uncovers how digital service innovation – comprising smart interactive services and smart working technologies – enables resource liquefaction and usage-phase co-creation, while examining how market orientation (business-to-consumer versus business-to-business) conditions this relationship. Design/methodology/approach A quantitative analysis was conducted on a sample of 303 manufacturers. Hypotheses were tested using partial least squares structural equation modeling applied to a higher-order construct that captures smart working technologies and smart interactive services through reflective and formative structures. Findings Results confirm that digital service innovation serves as a foundational engine for ecosystem value co-creation. However, business-to-consumer environments significantly amplify this relationship compared to business-to-business settings, where structural inertia and competency barriers create interaction friction. Furthermore, smart working technologies and smart interactive services demonstrate an essential socio-technical synergy. Originality/value This research advances Service-Dominant logic by unpacking digital service innovation from a conceptual black box into a multidimensional socio-technical assemblage. By contrasting business-to-consumer and business-to-business realities, it explicitly establishes the contextual boundaries of ecosystem value co-creation, revealing how market structures dictate the velocity and efficacy of digitally enabled resource integration.
Purpose Organizations in emerging countries face increasing pressures and uncertainties associated with technological and market turbulence, challenges intensified by Industry 5.0 (I5.0). I5.0 proposes three dimensions: human-centric, sustainability and resilience. Resilience remains largely unexplored, especially in emerging economies such as Brazil. This study examines how manufacturing firms in emerging economies can develop digital adaptability when facing environmental turbulence. Design/methodology/approach We conducted a survey with 305 leaders from manufacturing firms across all regions of Brazil. We conceptualized digital adaptability as the combination of adaptation capability with base and front-end technologies, enabling manufacturing firms to achieve innovation and delivery performance associated with resilience in I5.0. Structural equation modeling was employed to test the hypothesized relationships. Findings The results indicate that technological turbulence encourages organizations to invest in digital technologies while simultaneously developing adaptation capabilities. In turn, digital adaptability positively influences innovation and delivery performance, thereby contributing to resilience in manufacturing firms. Originality/value This study advances the I5.0 resilience literature by conceptualizing digital adaptability as the interaction between adaptation capability and digital technologies. The findings distinguish the effects of base and front-end technologies on resilience-related outcomes and provide empirical evidence from manufacturing firms in an emerging economy context.
Purpose This study examines AI adoption in manufacturing SMEs and its impact on sustainability. It focuses on internal mechanisms, such as resource allocation efficiency and environmental management practices as mediators and highlights top management support as a moderator. The research shows AI fosters sustainability by improving operational efficiency and aligning sustainability goals. Design/methodology/approach Data were collected from 481 senior managers and employees in manufacturing SMEs in northern China. A structured questionnaire gathered responses, and PLS-SEM was used to analyze relationships among AI adoption, resource allocation efficiency, environmental management practices and sustainability. This approach thoroughly examines the complex dynamics between AI and manufacturing, enabling a comprehensive study of AI, manufacturing, and sustainability outcomes. Findings The results indicate that AI adoption is positively associated with sustainable performance, resource allocation efficiency and environmental management practices, which are key mediators. Top management support is crucial for maximizing AI's operational and environmental benefits. The study shows that AI supports efficiency and sustainability by enhancing resource management and environmental practices in SMEs. Originality/value This paper contributes to the literature on AI adoption and sustainable performance in manufacturing SMEs by proposing an integrated framework that links AI adoption to operational efficiency, environmental practices, and managerial support. By focusing on the internal mechanisms that enable AI to drive sustainability, the study fills a gap in understanding how AI can facilitate AI-driven transformation in SMEs. These findings provide valuable managerial insights for SMEs looking to leverage AI as a strategic tool to enhance sustainable performance.
Purpose This study examines why digital transformation has a non-monotonic effect on inventory leanness and how product diversification and supply-chain concentration condition this relationship. Design/methodology/approach The empirical analysis uses an unbalanced panel of 2,533 Chinese listed manufacturing firms and 19,299 firm-year observations from 2010 to 2023. Firm and year fixed effects are absorbed and conventional standard errors are reported in the primary models. Following Haans et al. (2016), the quadratic specification is treated as a parsimonious empirical approximation to a theory-derived marginal-effect pattern; the analysis tests endpoint slopes, the Lind–Mehlum joint U test, and Fieller confidence intervals; and evaluates conditional curvature, turning-point movement and shape-flip in the moderated models. Findings The findings reveal a U-shaped relationship between digital transformation and inventory leanness. Product diversification strengthens the curvature of this relationship, and this curvature-enhancing effect is further amplified by supplier concentration but weakened by customer concentration. Conditional analyses indicate that the complete U-shaped pattern depends on specific configurations, with limited shape reversals occurring only at the distributional tails. Practical implications Managers should distinguish exploration-related learning friction from the exploitation of retained digital routines. Product diversification raises the information-processing burden during early transformation but can create stronger inventory benefits after digital routines mature. Deep supplier coordination can reinforce these benefits through relation-specific digital assets, whereas dependence on a small number of powerful customers can limit portfolio-wide inventory optimization. Originality/value The study contributes by identifying two countervailing latent mechanisms – exploration-related learning friction and the exploitation of retained digital routines – that combine additively to generate a U-shaped relationship. It further shows how product complexity alters the curvature of this relationship and how supplier and customer concentration provide asymmetric boundary conditions. The analysis responds to methodological concerns in nonlinear research by separating curvature change, turning-point movement and shape-flip.
