Purpose Digital twin technology, a central component of Industry 4.0, has attracted growing attention for its potential to improve operational efficiency and sustainability in manufacturing. However, empirical evidence explaining how Digital Twin Adoption (DT) contributes to Circular Economy Practices (CE) through Lean Manufacturing Practices (LM) in small and medium-sized enterprises (SMEs) remains limited. This study examines the direct effect of DT on CE and the mediating role of LM in manufacturing SMEs. Design/methodology/approach Data were collected through a structured questionnaire distributed to managers of manufacturing SMEs. A total of 243 valid responses were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the relationships among DT, LM, and CE. Findings The results show that DT is positively associated with CE practices. DT also positively influences LM, which in turn significantly enhances CE. The mediation analysis further indicates that LM serves as an important operational mechanism through which DT contributes to CE outcomes, including resource efficiency, waste minimization, and closed-loop practices in SMEs. Practical implications Managers may improve both operational efficiency and CE by integrating DT initiatives with LM. Originality/value This study provides empirical evidence by integrating DT, LM, and CE within a single framework in the SME manufacturing context. The findings clarify the mediating role of LM in translating DT into CE, thereby contributing to research on Industry 4.0 and sustainable manufacturing.
PurposeThis article examines how five process-management paradigms - Reengineering, Total Quality Management (TQM), Lean Manufacturing (LM), Six Sigma (SS) and Lean Six Sigma (LSS) - interact with and enable Industry 4.0 (I4.0), reconceptualising process management (PM) as a strategic lever by mapping technological and organisational variables for its adoption. Design/methodology/approachFollowing PRISMA guidelines for a systematic literature review (SLR), we used Scopus and Web of Science (WoS) and conducted backward snowballing, analysing 120 peer-reviewed journal articles published between 2015 and 2025. FindingsThe results reveal an I4.0 implementation gap due to limited integration between process-management paradigms and socio-technical variables. We identify and systematise these variables in a structured multidimensional input-process-output (IPO) framework. Research limitations/implicationsThe inclusion-exclusion criteria excluded grey literature and non-English publications, and framework applicability may differ by sector and firm size, requiring further empirical validation. The IPO framework clarifies the components and interactions between social and technical variables, and provides a roadmap for managing the socio-technical complexity of I4.0 toward holistic integration. Originality/valueThis review integrates PM paradigms within a unified socio-technical IPO framework, which goes beyond existing classifications by revealing paradigm-specific contributions, misalignments and complementarities across organisational and technological dimensions of I4.0 adoption.
Purpose Additive manufacturing (AM) adoption remains limited not only by technical and firm-level barriers, but also by systemic AM adoption barriers that arise across organizational boundaries. This study investigates how inter-organizational management mechanisms can be systematically designed to overcome these barriers by shifting the focus from intra-firm measures to actor constellations embedded in the AM ecosystem. Design/methodology/approach The research employs a qualitative-exploratory design based on 19 expert interviews within the German AM ecosystem. The GABEK-WinRelan analysis reconstructs barrier constellations and their associative linkages; an ordonomic governance framework interprets these constellations as inter-organizational dilemma structures. Findings The study identifies four barrier constellations and governance responses: intellectual property right insecurity requires traceable data-handling commitments; quality ambivalence requires credible quality signaling through certified material systems; standardization fragmentation requires sector-specific collective alignment and skill shortages require intermediary-supported investment in qualification pathways. Practical implications The findings provide manufacturing managers with a diagnostic heuristic based on two questions: whether a systemic AM adoption barrier reflects a unilateral or collective coordination problem, and whether addressing it requires self-binding or an enabling commitment service. The study offers actionable management strategies to foster trust, interoperability, quality assurance and skill development within manufacturing networks embedded in the AM ecosystem. Originality/value This paper advances AM adoption research by conceptualizing persistent systemic AM adoption barriers as inter-organizational dilemma structures and by linking them to distinct governance logics. It thereby moves beyond technical or firm-centric explanations and provides an incentive-based vocabulary for explaining how credible commitments and coordinated investments can support AM adoption in manufacturing networks.
Purpose How to enhance green innovation efficiency (GIE) leveraging collaboration network structure (CNS) has become critical to the success of manufacturing firms. Therefore, this study aims to explore the relationship between network structural factors (accessibility, interconnectedness and independence) and GIE, as well as their underlying mechanism and boundary conditions. Design/methodology/approach Using panel data from Chinese listed manufacturing firms between 2005 and 2021, this study examines the impacts of three dimensions of CNS – network accessibility, interconnectedness and independence – on GIE. It also tests the mediating effects of technological niche overlap (TNO), technological niche breadth (TNB) and the moderating effect of environmental regulation (ER). Findings This study proves that network accessibility and independence boost GIE, which are partly mediated by TNO and TNB. Furthermore, ER amplifies positive effects of network accessibility and independence on TNO, TNB and GIE. Originality/value This study broadens the existing literature on green innovation and collaboration by clarifying the roles of multidimensional network structures in promoting GIE. The authors open the black box between CNS and GIE by proving the mediating roles of TNO and TNB, plus the moderating role of ER. The findings impart valuable insights for decision-makers on how to harness network effects and orchestrate resources in technological niches to achieve optimal green innovation outcomes.
Purpose The transition to circular manufacturing challenges product lifecycle management (PLM) systems, which remain optimized for linear workflows and lack integration of reuse logic. This study investigates how returned components can be systematically reintegrated into product configuration to support circularity. Design/methodology/approach Using a design science research approach, a framework and product variant master (PVM) model were developed through inductive coding of interviews and observations in an industrial pump manufacturing case. The study provides structured guides to support quality governance and system-level integration of reuse logic, enabling firms to translate circularity principles into operational configuration systems. Findings Findings reveal that reuse requires more granular component treatment than current PLM systems support. Key barriers include fragmented disassembly knowledge and incomplete component histories. Originality/value The PVM-based reuse logic enables configuration tools to incorporate reused components alongside new ones, improving traceability and operationalizing reuse, with digital product passports (DPPs) serving as a key facilitator of tacit knowledge management (KM).
Purpose This study undertakes a systematic literature review (SLR) exploring how the paradigms of lean, sustainable and smart (LSS) product design intersect within the evolving landscape of Industry 4.0. The review aims to trace the development of lean principles, sustainability-oriented strategies and digital technologies, identify where integration among them remains limited and highlight possible pathways for building a unified design framework. To address the temporal mismatch between lean's historical roots and Industry 4.0's digital design logic, the review distinguishes foundational antecedents from Industry 4.0-era integration evidence. Design/methodology/approach The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure. Searches were conducted across five leading databases – Scopus, Web of Science, IEEE Xplore, Emerald Insight and ScienceDirect – for English-language studies published from 1981 to 2024. Boolean keyword combinations were applied to capture literature on lean product development (LPD), sustainable design and smart or digital design. After duplicate removal and multi-stage screening, 214 peer-reviewed papers were selected. Each study was coded thematically within its respective domain, and a cross-domain synthesis was undertaken to assess the level of integration maturity. Thematic findings were derived through a hybrid content analysis (deductive pillar coding complemented by inductive theme refinement), and integrative claims related to smart/Industry 4.0 were interpreted primarily from post-2011/2015 studies where digital design technologies are substantively present. Findings The review shows that while LSS design approaches have each matured substantially, their systemic integration remains underdeveloped. Lean methods enhance efficiency and value flow but seldom extend to full life cycle or environmental considerations. Sustainable design embeds ecological and social responsibility but is often introduced reactively rather than proactively. Smart design relies on digital enablers such as artificial intelligence (AI), the Internet of Things (IoT) and digital twins to achieve adaptive intelligence yet faces challenges of interoperability and workforce capability. Only around 14% of studies combined more than one paradigm, underscoring limited cross-pollination. Major integration barriers include disjointed measurement systems, methodological silos and a lack of sector-specific frameworks. The analysis identifies five potential pathways that can align lean efficiency, sustainable lifecycle thinking and digital feedback mechanisms within an integrated design approach. These pathways are grounded in recurring patterns observed in the coded literature and are positioned as an evidence-informed research agenda rather than empirically validated prescriptions. Research limitations/implications This review focuses on peer-reviewed sources written in English and excludes patents or grey literature. Future studies should test the proposed pathways empirically, employ longitudinal data and develop quantitative cross-pillar metrics linking lean performance, sustainability impact and digital maturity. Practical implications For practitioners, the synthesis provides a consolidated evidence base demonstrating how LSS methods can be combined in product development. It highlights actionable tool bundles such as the integration of life cycle assessment, design for manufacturability and assembly and digital twin validation along with hybrid design-gate metrics that can support decision-making in industrial contexts. Practical insights are drawn primarily from Industry 4.0-era studies that explicitly incorporate digital feedback and data-enabled governance. Originality/value This review is the first PRISMA-compliant synthesis to systematically unite LSS product design perspectives. By clarifying where integration succeeds or fails, it bridges conceptual scholarship and industrial practice and establishes a structured research agenda for data-driven, sustainable product innovation in the era of Industry 4.0.
Purpose This study explores the role of digital technologies and artificial intelligence (AI) in enhancing demand forecasting in pharmaceutical supply chain and aim to identify the forecasting model that best supports accurate demand prediction and efficient manufacturing planning based on a Moroccan case study. Design/methodology/approach A bibliometric and systematic literature review was conducted alongside a case study using analytical hierarchy process (AHP)and technique for order of preference by similarity to ideal solution (TOPSIS). The forecasting models examined include traditional approaches, machine learning (ML) methods and hybrid models that integrate both statistical and ML-based techniques. Findings The study shows that AI-driven methods outperform traditional statistical approaches in capturing complex, nonlinear and seasonal demand patterns through ML forecasting models, by improving forecast accuracy, managing uncertainty and supporting better manufacturing planning across pharmaceutical supply chains. Research limitations/implications This systematic review is limited to published literature and case studies, which may introduce a bias toward successful ML applications and exclude proprietary industry data. Also, the conducted case study is related to one multinational pharmaceutical company for a dedicated market (Morocco). Future research should expand the analysis to multiple companies and markets and investigate the integration of demand forecasting models with specific manufacturing planning practices. Practical implications This study advances forecasting and operations management literature by integrating demand forecasting with manufacturing planning through an AHP–TOPSIS framework. It provides evidence from the literature and a practical case study within an emerging market pharmaceutical context. Originality/value This research provides a consolidated view of the forecasting models applied to the pharmaceutical supply chain and offers valuable insights on adopting ML approaches. It also bridges the gap between theory and practice by operationalizing the relationship between forecasting model selection and manufacturing scheduling outcomes.
Purpose Labor market shortages are making it increasingly difficult for organizations to find skilled workers. As a result, there is growing demand for technologies that either automate tasks or reduce the skill level required to perform them. This randomized controlled experiment examines whether assistive technology – specifically, projection-based work instructions – can enable individuals with lower educational levels to successfully perform tasks that typically require higher educational levels. Design/methodology/approach We conducted a field experiment involving 80 students enrolled in technical vocational programs. Participants were asked to assemble a car battery and were randomly assigned within their educational level (lower vs higher) to receive either paper-based or digital work instructions. Task performance was evaluated based on completion time, number of errors and help requests. Findings Participants using digital work instructions completed the task significantly faster than those using paper-based work instructions, regardless of their educational level. They also made fewer errors and requested less help. While participants with higher education levels generally performed the task quicker, lower-educated participants using digital work instructions matched the performance of higher-educated participants using paper-based work instructions. Originality/value This randomized controlled experiment demonstrates that assistive technology can enhance task performance to the extent that individuals with lower education can achieve results comparable to peers with higher education. These findings suggest that such technology not only improves worker productivity but also expands the available talent pool for organizations struggling with labor shortages. Future research should explore how assistive technology can support long-term skill development and employee learning.
Purpose-In resource-constrained and volatile environments typical of emerging economies, manufacturing firms face mounting pressure to enhance sustainability. While prior research has explored lean manufacturing and technological resilience separately, their integrated impact on business sustainability, particularly in disruption-prone environments, remains underexplored. Addressing this gap, the study investigates the mediating role of technological resilience in the lean-sustainability nexus, offering context-specific insights from small-scale manufacturers in Nigeria. Design/methodology/approach-Quantitative survey research design was employed, using data from 406 Nigerian small-scale food and beverage manufacturers. Partial least squares structural equation modelling was applied to test the hypothesized relationships and to assess the mediating role of technological resilience. Findings-The results reveal a partial mediation effect of technological resilience on the relationship between lean manufacturing and business sustainability. Lean manufacturing significantly enhances sustainability outcomes, but its impact is amplified when firms exhibit higher levels of technological resilience. Although lean manufacturing demonstrates a strong direct effect on sustainability, firms with higher technological resilience may experience additional gains, particularly in dynamic or uncertain environments. Thus, technological resilience acts as a contextual amplifier rather than a prerequisite. Originality/value-The study concentrates on small-scale manufacturers in an emerging economy, which expands the existing knowledge base to include less-developed countries' contexts and also presents empirical evidence on the alignment of lean practices with technological resilience as a means of improving sustainable performance. The results offer practical pointers for companies that are prone to fluctuating environments and support the global ongoing efforts towards achieving Sustainable Development Goal 9.
Purpose The purpose of this paper is to examine how top management teams’ (TMTs) functional background heterogeneity shapes the development of strategic agility in consumer goods manufacturers. The critical linkage between TMT heterogeneity and the two dimensions of agility—market and operational—has been explored to clarify how managerial characteristics influence firms’ capacity to realign resources and sustain competitiveness in volatile environments. Design/methodology/approach A text-based Latent Dirichlet Allocation (LDA) approach is used to identify different forms of agility from the annual reports of 546 consumer goods manufacturers (4,228 firm-year observations). A two-way fixed effects model is then employed to test the hypotheses. Findings Results show that TMT functional background heterogeneity fosters market agility but hinders operational agility. Moreover, both liquidity buffers and market competition attenuate these effects. Originality/value This study advances understanding of the micro-foundations of strategic agility by integrating TMT heterogeneity with organizational agility outcomes. It offers theoretical insights into how managerial characteristics shape agility and provides practical guidance for consumer goods manufacturers in responding effectively to rapidly changing environments.
Purpose This study investigates how institutional pressures (mimetic, coercive and normative isomorphism) influence blockchain technology adoption (BCTA) and its effects on business value creation, competitive advantage and sustainable business performance. It also examines business value creation and competitive advantage as mediators, and managerial risk-taking tendency as a moderator, providing a comprehensive view of blockchain-driven transformation in emerging-market manufacturing firms.Design/methodology/approach A time-lagged, survey-based quantitative design was used, with data collected in two waves from top management of manufacturing firms. A total of 374 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. Importance-performance map analysis (IPMA) was also conducted to identify key areas requiring managerial attention.Findings The findings show that all three types of institutional pressures positively influenced BCTA, with normative isomorphism being the strongest. The BCTA has a positive impact on business value creation, competitive advantage and sustainable business performance. Meanwhile, the findings reveal that business value creation and competitive advantage partially mediate the relationship between BCTA and sustainable business performance. Furthermore, risk-taking tendency plays a significant moderating role on all paths, highlighting the role of managerial disposition in performance improvements. IPMA identified BCTA and normative isomorphism as high-importance constructs with moderate performance, emphasizing key strategic areas.Originality/value This study enhances institutional theory by combining it with behavioral and technological adoption perspectives over time. It offers insights into how institutional forces and risk-focused leadership affect the performance of emerging technologies, such as blockchain, in developing economies. The results provide actionable insights for business leaders, technology managers and policymakers to implement and scale blockchain technologies effectively.
Purpose Drawing on the resource-based view and resource orchestration theory, this study investigates how AI capability, defined as a firm’s ability to deploy AI-based resources to enhance data processing and decision-making, interacts with downstream customer and upstream supplier integration to improve supply chain responsiveness and efficiency. This study further clarifies the distinct and differentiated pathways through which upstream and downstream integration shape the effectiveness of AI. Design/methodology/approach To test our conceptual model, we utilized survey data from 426 AI-adopting Chinese manufacturing firms. The hypothesized relationships were examined using hierarchical regression analysis to isolate the moderating effects of supply chain integration. Findings AI capability significantly enhances both supply chain responsiveness and supply chain efficiency. Customer integration strengthens the effect of AI capability on responsiveness, whereas supplier integration strengthens the effect on efficiency, indicating two distinct integration pathways through which AI creates supply chain value. Originality/value By the resource-based view and resource orchestration theory, this study clarifies how AI capability interacts with upstream and downstream integration to shape performance outcomes. The results offer actionable insights for managers seeking to align AI investments with appropriate integration strategies to build agile, efficient, and resilient supply chains.
Purpose Additive manufacturing (AM) is among the most disruptive technologies of Industry 4.0 due to its unique capabilities. Yet, empirical studies rarely analyze how AM influences firm performance (FP) through supply chain practices (SCP), particularly from a mediating perspective, and capture both linear and non-linear correlations. This study aims to examine the impact of AM on SCP, which in turn influences the FP in manufacturing firms undergoing digital transformation. Design/methodology/approach The model, grounded in the practice-based view (PBV) and dynamic capabilities theory, was tested using survey data from 193 Indian manufacturing firms. Partial least squares structural equation modeling (PLS-SEM) assessed hypothesized relationships, and artificial neural networks (ANN) identified non-linear effects. Findings The results show that AM implementation positively influences FP, with SCP serving as a key mediator. ANN analysis highlights process improvement, supply chain coordination, AM processes and customer service management as the most important predictors. These are specifically relevant to industrial firms seeking enhanced performance through technological innovation and flexible operational methods. Practical implications The study offers actionable insights for manufacturing managers by emphasizing not only the “what” (adopting AM) but also the “how” (embedding AM into supply chain routines) to achieve competitiveness. Originality/value This study extends PBV and dynamic capabilities theory by demonstrating how AM adoption, when aligned with SCP, enhances FP. It provides a novel framework explaining how firms can translate AM into operational routines and coordination practices, advancing the literature on digital transformation in manufacturing.
Purpose In the contemporary digital era, innovation represents a fundamental source of competitive advantage and sustainability within the manufacturing industry. Drawing on this premise, this study empirically examines how lean manufacturing (LM), Industry 4.0 Technologies (I4.0T), knowledge management (KM) and digital infrastructure (DI) influence manufacturing industry innovation performance (MIIP). It investigates the mediating roles of I4.0T and KM, and the moderating effect of DI, in elucidating the relationships among these constructs. Design/methodology/approach This study employs a mixed-methods, quantitative approach based on the resource-based view (RBV) and dynamic capabilities theory (DCT). Survey data from 263 respondents in Ethiopia's manufacturing sector were analyzed using partial least squares structural equation modeling (PLS-SEM) to test relationships, while fuzzy-set qualitative comparative analysis (fsQCA) identified configurations leading to high MIIP. Findings PLS-SEM reveals that LM has a significant influence on I4.0T and KM, which in turn enhance MIIP. While LM's direct link to MIIP is positive, it remains statistically insignificant. KM and I4.0T mediate between LM and MIIP. DI strengthens the LM to I4.0T relationship but does not significantly moderate LM to MIIP. FsQCA identifies KM as necessary for high MIIP and reveals several equifinal combinations of LM, I4.0T, KM and DI driving innovation performance. Originality/value This study advances RBV and DC Theory by developing and testing an integrated framework explaining how LM, I4.0T, KM and DI shape MIIP in emerging economy contexts. By combining PLS-SEM and fsQCA, the study captures net effects and configurational pathways through which operational, technological and knowledge-based capabilities drive innovation outcomes. The findings show that DI acts as a boundary condition shaping how lean and technological capabilities translate into innovation performance, revealing multiple equifinal pathways for achieving innovation success.
Purpose Although supply chain risk mitigation (SCRM) and supply chain efficacy (SCE) hold significant promise in influencing competitive performance (CP), their effectiveness depends on various factors. However, given the significant role of successive disruptions in the supply chain (SC) network, empirical research is lacking in identifying the critical success factors that maximize SCRM and SCE. This study aims to apply the resource-based view (RBV) perspective to examine how supply chain integration (SCI), supply chain absorptive capacity (SCAC) and ambidexterity (SCA) can facilitate SCE and CP. The mediating role of SCRM is also investigated. Design/methodology/approach The study population encompasses a range of key positions, such as operations managers and SC managers, across various manufacturing firms (MFs) in the Middle East. The data collected from 260 participants were rigorously analyzed using partial least square structural equation modeling (PLS-SEM). Findings The findings suggest that SCI, SCAC and SCA enhance SCRM positively and directly. In addition, SCRM significantly influenced SCE and CP was influenced by SCE. Moreover, SCRM significantly mediated the relationships between SCI, SCA, SCAC and SCE. Practical implications The findings suggest that MFs can enhance SCE and SCRM by achieving full SCI with their partners. This integration not only strengthens operational processes but also expands the SCA and SCAC. Consequently, these improvements enhance the CP of MFs, underscoring the critical role of collaboration and integration in achieving sustainable success in a dynamic market environment. Originality/value This study offers novel insights into the specific enablers of SCRM that can be used to enhance SCE. It also deepens our understanding of how CP can be optimally harnessed within the RBV framework, thereby contributing to the existing literature.
Purpose Our study analyzes the relationship between ambidexterity and servitization, considering the role of contextual factors and distinguishing between product-supporting and client-supporting services. Specifically, we propose that two internal factors (empowerment climate and managerial networking) and two external factors (environmental dynamism and competitive intensity) may moderate the effects of ambidexterity on servitization.Design/methodology/approach Our analysis relies on a survey of 1,055 CEOs of French manufacturing small and medium-sized enterprises. We tested our research hypotheses using hierarchical linear regressions and the Johnson-Neyman technique, enabling a detailed examination of conditional effects across the observed data range.Findings Results confirm that ambidexterity drives servitization, whether services support the product or the client. They also show that informal networking and competitive intensity moderate the effect of ambidexterity on servitization, but only with regard to services supporting the client.Originality/value This study offers a detailed and nuanced analysis of the contingency effects shaping the ambidexterity-servitization relationship, revealing distinct moderating influences depending on whether services support the product or the client.
Purpose This study investigates the contingent, nonlinear relationship between inventory flexibility and capacity utilization in manufacturing. It challenges the prevailing assumption of flexibility's universal benefit by examining the moderating roles of environmental dynamism and supply chain digitalization investment. Design/methodology/approach We analyzed a large panel dataset of 2,062 Chinese listed manufacturing firms from 2016 to 2023. A moderated moderation model was tested using fixed-effects regressions with industry-year clustered standard errors. To ensure robust identification, we employed instrumental variable and system GMM estimators to address endogeneity, complemented by propensity score weighting and extensive robustness checks. Findings The results confirmed a significant inverted U-shaped relationship between inventory flexibility and capacity utilization, indicating an optimal flexibility level. Environmental dynamism negatively moderated this relationship, flattening the curve and shifting the optimum. Supply chain digitalization investment positively moderated this negative effect, acting as a digital buffer that preserves the operational benefits of flexibility even under volatile conditions. Practical implications Managers should calibrate inventory flexibility toward a quantified optimum, avoiding both rigidity and over-agility. In dynamic environments, prioritizing digital infrastructure investment is essential before pursuing aggressive flexibility strategies. Building integrated data-model-decision loops is key to mitigating the coordination costs that erode flexibility's returns. Originality/value This research shifts the discourse from inventory levels and financial outcomes to inventory adjustment speed and operational efficiency. It contributes a novel framework that integrates inverted U-shaped baseline relationship, negative contingency of environmental dynamism, and digital buffering effect, drawing upon resource-based, contingency, and dynamic capabilities views. By specifying the mechanisms through which environmental dynamism undermines flexibility and digitalization counteracts these effects, it demonstrates how digitalization reconfigures the viability of traditional operational strategies under volatility.
Purpose Industry 4.0 transformed the manufacturing industry through automation, robotics, IoT, big data analytics and digital systems, and with it changed the workforce requirements, creating a critical skills gap between available capabilities and emerging technological demands. Design/methodology/approach This study systematically analyzed 913 manufacturing job postings from LinkedIn using natural language processing (NLP) and unsupervised machine learning techniques (K-means clustering) to identify 269 distinct skills through thematic analysis, which clustered into four dimensions: technical and soft skills, domain knowledge, physical demands and workplace conditions. Findings Key findings indicate that employer-stated requirements in LinkedIn job postings reflect demand for hybrid workforce profiles where traditional manufacturing competencies coexist with emerging digital requirements rather than being replaced. Communication skills and maintenance expertise dominate requirements, while substantial physical demands and workplace conditions persist despite technological advancement. Practical implications For practitioners, the proposed competency framework offers actionable guidance on hiring practices, curriculum development, and workforce development policies that address both cognitive and physical aspects of Industry 4.0 manufacturing employment. By bridging theory and application, the findings indicate that manufacturing technology management can support workforce preparation by aligning traditional technical expertise with digital literacy, collaborative capabilities and physical competencies to enable technology adoption and operational competitiveness. Originality/value This research contributes a scalable, data-driven methodology for real-time workforce intelligence, providing empirical and practical insight for manufacturing managers, human resource strategists and educators, while challenging technology-focused assumptions about manufacturing task transformation.
Purpose This study investigates how digital technologies reshape relational governance in manufacturing supply networks. Specifically, it introduces the concept of digitally reconstructed psychological contracts and examines how AI-enabled collaborative environments and knowledge-sharing mechanisms influence trust formation and resilience in manufacturing ecosystems.Design/methodology/approach Using an abductive qualitative approach, thirteen semi-structured interviews were conducted with senior professionals across manufacturing, logistics, consulting, and digital technology firms operating within US-based industrial supply chains. Interview data were analyzed iteratively through abductive coding and theoretical reconciliation, drawing on social-ecological resilience and Panarchy perspectives to refine an initial conceptual model.Findings The findings indicate that psychological contracts in digitally evolving manufacturing ecosystems are reconstructed through three distinct trust mechanisms: performance-based trust, lateral (peer-to-peer) trust and technology-mediated trust. These trust structures give rise to differentiated resilience pathways, including proactive, preemptive, digital and labor-centric resilience. The analysis further demonstrates that contextual conditions including digital maturity, feedback infrastructure, intellectual property sensitivity, and workforce stability shape the extent to which AI-enabled collaboration technologies influence relational governance and resilience outcomes.Originality/value This study contributes to manufacturing technology and supply chain research by introducing the concept of Supply Chain Resilience Potentiality (SCRP), a framework explaining how resilience emerges from the interaction of ecosystem pressures, digital infrastructures, knowledge-sharing mechanisms and reconstructed psychological contracts. It demonstrates that resilience arises from aligning digital infrastructures with relational governance, enabling trust-based coordination and adaptive responses across manufacturing networks.
Purpose This study examines the effect of servitization distinctiveness on manufacturing firm performance, given the optimal distinctiveness tension wherein it reduces competition but erodes legitimacy. We further explore the moderating roles of financial leverage and environmental, social and governance (ESG). Design/methodology/approach Based on 589 Chinese listed manufacturing firms from 2018 to 2021, the study employs fixed-effects panel models to test the hypotheses. We address endogeneity concerns using an instrumental variable approach and conduct multiple tests to ensure robustness of the results. Findings Servitization distinctiveness, which denotes the degree to which a firm's servitization deviates from the industry average, exhibits a U-shaped relationship with a manufacturer's performance. Financial leverage intensifies the U-shaped relationship. Although the aggregate ESG does not significantly moderate this U-shaped relationship, the governance dimension of ESG flattens it. Originality/value By combining the optimal distinctiveness perspective with servitization, a U-shaped relationship between servitization distinctiveness and manufacturing firm performance is theorized through two key mechanisms: legitimacy loss and competitive pressure reduction. It further demonstrates the contingencies of firms' financial leverage and the governance dimension of ESG, highlighting how internal resource availability and external trustworthiness shape the performance impact of servitization distinctiveness. This study extends the application of the optimal distinctiveness perspective to servitization and advances understanding of how manufacturing firms strategically balance differentiation and legitimacy in pursuing servitization.
