Purpose: This study aims to (1) validate the effectiveness of customer integration in enhancing firms’ green innovation and (2) explore the respective impacts of regulatory pressure and market changes on the effectiveness of integration mechanisms, thereby identifying the optimal integration outcomes for firms across different contexts.Design/methodology/approach: Utilizing an integrated theoretical framework based on the extended resource-based view (ERBV) and contingency theory, this study examines the complex interactions among customer integration, regulatory pressure, market changes and green innovation. The analysis is based on a dataset of 293 responses from manufacturing firms in mainland China. Hierarchical regression analysis was employed to rigorously test the proposed hypotheses.Findings: The key outcomes of this research are summarized in two major findings: (1) integrating customers into a firm’s processes meaningfully boosts green innovation initiatives and (2) market dynamics have been found to amplify the positive impact of customer integration on green innovation, whereas regulatory pressure significantly and negatively moderates this relationship.Originality/value: This study uniquely integrates multiple theoretical perspectives, providing a richer understanding of how various contextual factors influence the relationship between customer integration and green innovation. These findings contribute original insights to the literature, highlighting how different contextual factors interact with customer integration and offering practical insights for firms to make strategic decisions in diverse contexts.

Purpose: The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach: This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings: This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value: From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.

Purpose: The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.Design/methodology/approach: To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.Findings: This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.Originality/value: This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

Purpose: This study aimed to (1) examine AI capability’s effect on business model design, focusing on novelty and efficiency, (2) explore how customer integration moderates the relationship between AI capability and novelty and (3) assess how supplier integration moderates the relationship between AI capability and efficiency. Design/methodology/approach: Grounded in dynamic capabilities theory and complementarity theory, this research investigated causal links among AI capability, customer integration, supplier integration, novelty and efficiency. Data from 490 valid questionnaires, collected via a survey of Chinese manufacturing firms, were analyzed using hierarchical regression to explore these complex relationships. Findings: The study revealed that (1) AI capability positively affected both novelty and efficiency in business model design, (2) customer integration amplified its positive effect on novelty and (3) supplier integration strengthened its positive effect on efficiency. Originality/value: By integrating theoretical perspectives, this research clarifies the distinct moderating roles of customer and supplier integration in a cooperative supply chain context. It underscores AI capability’s role in shaping business model design, contributing novel insights into AI and supply chain literature.

As the green market becomes increasingly saturated, companies must allocate limited resources to more precise and efficient projects. This study aims to explore and compare the impact of green supplier selection and green supplier integration on environmental performance, with a particular focus on the moderating role of government support. The research was conducted through a survey of 391 Chinese manufacturing firms by a specialized research institution, employing hierarchical regression analysis. The results indicate that both green supplier selection and integration have a positive impact on environmental performance, with green supplier integration having a more significant effect. Moreover, active government support weakens the relationship between green supplier selection and environmental performance but strengthens the relationship between green supplier integration and environmental performance. Given the study’s context, scope, and sample size, certain limitations exist. This research highlights more strategically significant supplier management practices and emphasizes the moderating role of government support in different contexts, while also providing valuable and practical recommendations for relevant practitioners.

Since the beginning of the 21st century, environmental protection and digital supply chains have increasingly garnered attention. As firms transition to green practices, how to achieve competitive advantage by reducing environmental costs has become one of the key concerns for many business managers. This study aims to examine how supplier and customer integration affect a firm’s environmental cost performance while considering the moderating effect of digital transformation. Following a survey conducted by a professional research agency on 800 Chinese manufacturing firms specializing in green products, the research model was tested using structural equation modeling and hierarchical regression analysis. The results indicate a significant positive relationship between both supplier integration and customer integration with a firm’s environmental cost performance. Additionally, positive digital transformation reinforces the relationship between supplier integration and customer integration. However, there are limitations due to the study’s background, scope, and sample size. This study underscores the impact of supplier integration and customer integration on a firm’s environmental cost performance, as well as the crucial moderating role of digital transformation. It contributes to the growing literature on sustainable supply chain management and provides valuable insights for relevant practitioners.