Purpose This study examines how manufacturing SMEs convert sustainability-oriented digital capability into sustainable performance. Drawing on the Dynamic Capabilities View, it develops and tests a model in which green artificial intelligence (AI) capability, resource and coordination flexibility, and inbound and outbound open innovation jointly shape circular economy practices and, ultimately, sustainable performance. Design/methodology/approach Guided by a positivist philosophy and a quantitative survey design, data from 211 Vietnamese manufacturing SMEs were analyzed using PLS-SEM and polynomial regression with response surface methodology. Findings The results indicate that green AI capability enhances both resource and coordination flexibility. Resource flexibility supports inbound open innovation, whereas coordination flexibility drives outbound open innovation. Both forms of open innovation enhance circular economy practices, with balanced configurations yielding superior results. In turn, circular economy practices significantly contribute to sustainable performance. Practical implications The study provides actionable insights for managers and policymakers on leveraging green AI and innovation ecosystems to enhance resource efficiency, reduce waste, and meet sustainability regulations, particularly in emerging economies. Originality/value This study conceptualizes green AI as a sustainability-oriented dynamic capability and highlights the interplay between flexibility and open innovation in advancing circular and sustainable performance among manufacturing SMEs.

Purpose This study investigates how leanness, innovativeness, and knowledge management practices influence innovation quality in manufacturing firms and examines how innovation quality subsequently affects Triple Bottom Line performance. It further explores the moderating role of knowledge flow in strengthening the knowledge management–innovation quality relationship and assesses whether competitive intensity alters the impact of innovation quality on sustainability outcomes. Design/methodology/approach A quantitative, survey-based research design was employed, targeting managers responsible for lean operations, manufacturing operations, innovation, and sustainability. Data were collected from Malaysian manufacturing firms using purposive sampling. The proposed relationships were tested using structural equation modeling with the Partial Least Squares approach via SmartPLS 4. Findings The findings indicate that leanness does not have a significant direct effect on innovation quality in manufacturing settings. In contrast, innovativeness and effective knowledge management practices significantly enhance innovation quality. Knowledge flow further strengthens the positive influence of knowledge management on innovation quality. Innovation quality has a significant positive impact on Triple Bottom Line performance. However, competitive intensity negatively moderates the relationship between innovation quality and social and environmental performance. Originality/value This study contributes to manufacturing technology management literature by empirically examining innovation quality as a key mechanism linking lean practices, innovativeness, and knowledge management to sustainability performance. By incorporating the moderating roles of knowledge flow and competitive intensity, the study offers novel insights into how manufacturing firms can better align innovation and operational strategies with sustainability objectives. The findings provide actionable implications for managers in manufacturing environments, particularly in developing economies, seeking to improve innovation outcomes and sustainable performance.

Purpose This paper aims to examine how manufacturing enterprises manage the organizational changes triggered by artificial intelligence (AI) adoption and theorizes the staged evolution of human–AI collaboration through shifts in routines and dominant logic. Design/methodology/approach Drawing on dominant logic and routine dynamics theory, we apply the Gioia methodology to analyze in-depth interview data from 30 manufacturing managers. Findings The analysis uncovers a three-stage evolution of dominant logics – shifting from human-centric to alignment optimization and ultimately to value co-creation. Each stage drives changes in organizational routine, deepening the collaboration between humans and AI. Originality/value This paper proposes a theoretical model that captures the progression of human–AI collaboration. This model advances our understanding of AI-driven organizational change and provides actionable insights for manufacturers seeking intelligent transformation.

Purpose This study investigates the determinants of firm competitiveness and financial performance in the Asian manufacturing sector by integrating environmental, social and governance (ESG) practices and artificial intelligence (AI) adoption into behavioral competition models. Design/methodology/approach Drawing on panel data from 4,028 manufacturing firms across Asia between 2014 and 2024, we apply both the Boone indicator and return on equity (ROE) as core measures. Methodologically, we employ static multi-way fixed effects panel with Driscoll–Kraay standard errors (MWFE-DR) and dynamic two-step system GMM estimators to ensure robust results. Findings The findings reveal that liquidity, efficiency, firm size, AI-related imports and environmentally related tax revenues significantly enhance competitiveness and performance, while ESG variables strengthen explanatory power and improve model robustness. Originality/value Theoretically, the study develops and empirically validates a digital sustainability competitiveness framework (DSCF), extending the New Empirical Industrial Organization (NEIO) and resource-based view (RBV) traditions by demonstrating that digitalization and sustainability are central behavioral drivers of competition. Practically, the results provide actionable insights for policymakers, investors and industry leaders seeking to foster sustainable competitiveness and long-term financial resilience in emerging economies.

Purpose This paper examines the impacts of product innovation, service innovation and synergistic innovation on firm performance and explores the moderating effect of digitalization. Design/methodology/approach This paper employs an unbalanced panel of 2,888 listed manufacturers consisting of 19,099 firm-year observations from 2014 to 2023. Findings The empirical results show that service innovation has an inverted U-shaped effect on firm performance, while product innovation has a U-shaped effect. Synergistic innovation is positively related to firm performance. Digitalization exerts a positive moderating role on all three main effects. Originality/value This paper contributes to related literature by providing a more detailed understanding of the complementarity of service innovation and product innovation on value creation. Also, it offers both theoretical and practical insights by examining the moderating role of digitalization.

Purpose The article focuses on the limited cost awareness as a fundamental yet insufficiently understood barrier to advanced manufacturing technology (AMT) adoption and, consequently, enhanced business performance. Design/methodology/approach The article utilizes a longitudinal, interventionist case study (2015–2021) of an AMT-based supplier and its manufacturer customer in Finland. Findings The findings of this article show that decisions not to leverage the advantages offered by AMT may be based on inaccurate financial numbers, indicating limited cost awareness. Thus, considerable cost information asymmetry between an AMT supplier and their customer is problematic and hampers legitimizing AMT adoption decisions. Practical implications Realizing the efficiencies offered by AMTs and their associated services requires a business model transition, which is often justified on economic grounds, particularly through cost savings. However, if cost awareness is inadequate, the business model transition may not be possible and the potential benefits remain unrealized. As a response, the article proposes a framework to analyze and develop the cost awareness of the stakeholders in a customer-supplier dyad. Originality/value As a unique contribution to the AMT literature, the article argues the importance of utilizing truthful and up-to-date cost information to support the adoption of the AMTs and the benefits they potentially entail. The article brings together literature on value creation, AMT and digital servitization in a novel synthesis, paving the way for critical studies on costs in manufacturing technology adoption.

Purpose For manufacturing firms, Industry 4.0 technologies comprise the two layers of base technologies (foundational blocks) and front-end technologies (advanced manufacturing technologies). Yet, most extant technology adoption research overlooks the differences between these two technology layers. This study investigates the effects of firm-level strategic orientations (customer, competitor and learning) on Industry 4.0 technology adoption, focusing on the base and front-end technologies and the mediating role of technology orientation. Design/methodology/approach Drawing on survey data collected from Australian manufacturing firms and analysed using the PLS-SEM method. Findings This study finds that the relationships between customer, competitor and learning orientations on Industry 4.0 base technology adoption are fully mediated by technology orientation; however, the hypothesised relationships between those strategic orientations and front-end technologies are not supported. Research limitations/implications The findings highlight the importance of strategic orientations and the significant role of technology orientation on Industry 4.0 base technology adoption. However, the study suggests that other factors beyond strategic orientations influence the adoption of front-end technologies. Practical implications The findings highlight the importance of strategic orientations and the significant effects of technology orientation on Industry 4.0 base technology adoption. They also suggest that other factors beyond strategic orientations influence the adoption of advanced front-end technologies. They call for a differentiated policy response to support manufacturing firms in upgrading to different technology levels, e.g. base technologies and advanced front-end manufacturing technologies. Originality/value The study contributes to the strategic technology management literature by examining the adoption of base and front-end technologies as distinct Industry 4.0 technology layers and by providing empirical evidence of differential antecedents across these two technology layers. It calls for a differentiated policy response to support manufacturing firms in their Industry 4.0 technology upgrading to different technology levels. It also resonates with the Technology-Organisation-Environment (TOE) literature that recognises interdependencies among TOE elements in enabling technology adoption.

Purpose The need for smart maintenance (SM) is increasing as the manufacturing industry digitalizes. To facilitate the transformation of maintenance in digitalized manufacturing, scholars have developed a strategy development process (SDP) for implementing SM. However, the SDP must be tested and evaluated, as manufacturing companies and industries need explicit guidance and empirical evidence on how to use it. Design/methodology/approach This study employed action research to facilitate collaboration between researchers and maintenance professionals in a large Swedish manufacturing company, testing and evaluating the SDP for SM implementation. The study was conducted in multiple phases over two years, focusing on the real-world implementation of key activities in an industrial setting. Findings Implementing SM in the manufacturing industry resulted in a refined SDP. The study revealed synergies between the implementation steps, from concept to practice. This refined process advances benchmarking (Activities 1.1–1.4), streamlines goal setting, prioritization and planning of key activities (Activities 2.1–4.1) and ensures authorized elevation and cross-functional communication (Activities 5.1–5.2) for digitalization of maintenance. Practical implications The theoretical implications refine the SDP and confirm the value of creating, acquiring and transferring knowledge within maintenance organizations, thereby facilitating SM implementation with empirical evidence. The practical implications offer recommendations for factory and maintenance management, providing explicit guidance to manufacturing companies in developing maintenance for digitalized manufacturing. Originality/value The refined SDP is an evolutionary process that requires continuous learning. This reinforces the focus on organizational development rather than solely technological transformation, i.e. becoming a learning organization when implementing SM.

Purpose This study reveals novel patterns in manufacturing capability development for environmental innovation among manufacturing small and medium-sized enterprises (SMEs). The study examined how green supply chain management (GSCM) practices relate to Green product innovation (GPI) through leadership mediation and cultural moderation. Design/methodology/approach The study adopts a cross-sectional survey design. Data were collected from 517 manufacturing SMEs using structured questionnaires. The study adopts Partial Least Squares Structural Equation modelling and Artificial Neural Networks to examine and validate the findings. Findings GSCM practices relate positively to GPI both directly and through green leadership support mediation. A novel substitution pattern emerged between green organisational cultures and supply chain practices in their associations with leadership support. This challenged conventional complementarity assumptions. Green organisational cultures demonstrated the strongest association with innovation, while leadership effectiveness remained consistent across cultural contexts. Originality/value The study identifies substitution rather than complementarity between environmental mechanisms, advancing theory for resource-constrained contexts and providing selective capability development strategies for manufacturing managers.

Purpose This study introduces CLIMB2-OLIMP, a dual-maturity model designed to facilitate the structured integration of Generative Artificial intelligence (AI) (GenAI) into New Product Development (NPD) processes. The model aims to provide a comprehensive tool for organizations to integrate GenAI into their NPD processes, ensuring a strong operational foundation. Design/methodology/approach Developed using the Design Science Research approach, CLIMB2-OLIMP first evaluates an organization’s NPD maturity (CLIMB2) before assessing its readiness for GenAI implementation (OLIMP). The OLIMP component uniquely incorporates a prescriptive element, utilizing large language models (LLMs) to generate tailored improvement pathways with a clear cost-benefit perspective. A systematic literature review was conducted, and the model development involved iterative stages and expert validation. Findings Case studies in manufacturing organizations demonstrated the model’s effectiveness, revealing moderate NPD maturity but limited GenAI adoption. The research emphasizes structured AI integration, including workforce upskilling, strategic alignment, and ethical considerations. Practical implications CLIMB2-OLIMP provides diagnostic insights and actionable recommendations, serving as a comprehensive tool for organizations seeking to integrate GenAI into their NPD processes. It guides organizations in advancing their AI maturity with a clear cost-benefit perspective and supports a bold, entrepreneurial strategy for AI adoption. Originality/value CLIMB2-OLIMP is original in its dual-maturity approach, conditioning GenAI maturity assessment on NPD maturity, and its prescriptive component driven by LLMs, which addresses a significant gap in existing descriptive AI maturity models that lack actionable guidance and cost-benefit analysis.