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.

This paper investigates how firms’ social sustainability practices can influence their social performance and, ultimately, financial performance. We include two corporate social sustainability practices: employee-oriented (employee well-being and equity) and socially driven (corporate social involvement) practices. Three leading social theories (social identity theory, social exchange theory, and resource-based view) are applied in explaining how firms’ social practices influence intermediate and bottom-line performance outcomes. Empirical results of 212 US manufacturing firms reveal that (1) the social orientation of the firm promotes firms’ social performances (employee-oriented and community-oriented outcomes) directly; (2) social orientation also indirectly promotes employee-oriented outcomes via employee well-being and equity practices, and so does community-oriented outcome via corporate social involvement practices; and (3) the firms’ social performances can enhance financial performance. The theoretical and managerial implications derived from these empirical results are discussed as well.

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.