Purpose This paper examines how Design Science Research (DSR) frameworks are adopted, adapted and implemented across six engineering disciplines comparing framework selection, artefact types, evaluation methods, success factors, implementation challenges and cross-disciplinary methodological innovations. Design/methodology/approach A systematic literature review covered 406 papers published between 2015 and 2025. Following PRISMA 2020 guidelines, searches across seven databases identified 30 papers representing Software/Computer, Manufacturing/Industrial, Construction/Civil, Biomedical/Healthcare, Environmental/Socio-Environmental, and Mechanical/Aerospace Engineering disciplines. Data extraction used a structured template with dual independent coding across framework adoption patterns, artefact types, evaluation methods, success factors, and implementation challenges. Findings Disciplinary variation reveals an inverse relationship between framework standardisation and domain complexity: software engineering demonstrates highest standardisation through Peffers et al. (2007) DSRM, whilst manufacturing, healthcare and environmental engineering develop custom frameworks addressing distinctive requirements. Five universal success factors are identified: stakeholder engagement, iterative design, mixed evaluation methods, expert validation and clear benchmarks. Boundary conditions for each factor are documented. Cross-disciplinary innovations include echeloned DSR, Design-Develop-Decide, FAIR principles and statistical consensus methods. Originality/value This study provides the first systematic cross-disciplinary comparison of DSR adoption across six engineering disciplines, providing empirical grounding for the inverse relationship between domain complexity and framework standardisation, alongside evidence-based guidance for framework selection, evaluation design and challenge mitigation.
Purpose This study examines how Moroccan organizations integrate Industry 4.0 technologies and Six Sigma practices to improve operational performance, sustainability, and traffic management. It also explores how organizational and human factors influence the effectiveness of this integration in resource-constrained and heterogeneous operational environments. Design/methodology/approach A qualitative research design was adopted using semi-structured interviews with 37 managers, engineers, analysts, and municipal coordinators in Morocco. Data were collected through purposive sampling and analyzed using thematic analysis to identify patterns related to digital transformation, Six Sigma implementation, organizational dynamics, and sustainability practices. Findings The findings show that Industry 4.0 enhances real-time monitoring and predictive capability, while Six Sigma transforms digital data into structured continuous improvement. Leadership, workforce skills, organizational culture and digital maturity significantly influence implementation success. The integration also improves sustainability performance and traffic management optimization. Research limitations/implications The study is limited by its qualitative design and focus on Moroccan organizations, which may reduce generalizability. The findings are based mainly on participant experiences rather than longitudinal operational data, and sectoral differences may have influenced the results. Practical implications The study provides guidance for managers and policymakers by showing that Industry 4.0 technologies are more effective when integrated with Six Sigma practices. It also highlights the importance of leadership, employee training, and digital infrastructure development for sustainable operational improvement and traffic optimization. Originality/value This study develops a socio-technical understanding of Industry 4.0 and Six Sigma integration in an emerging economy context. It extends prior research by linking digital transformation not only to operational performance but also to sustainability and urban traffic optimization, while highlighting the moderating role of organizational and human factors.
Purpose The aim of this paper is to examine how digitalization capabilities related to technological, individual, and managerial dimensions influence organizational ambidexterity, specifically the balance between exploration and exploitation activities. Design/methodology/approach An empirical survey was conducted among 370 manufacturing and service firms operating in Greece. Initially, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Finally, the structural relationships among the latent factors were determined through Structural Equation Modelling (SEM). Findings The findings indicate that digitalization capabilities are positively associated with both exploitation- and exploration-oriented activities, with a stronger effect on exploitation, thereby underscoring their critical role in fostering sustainable organizational performance. Originality/value Unlike prior studies that primarily focus on broader industrial contexts, this research provides a regionally specific yet widely applicable perspective by examining the Greek manufacturing and service sectors. By doing so, we offer novel insights into the mechanisms through which I4.0 capabilities drive organizational ambidexterity, thereby bridging theoretical gaps and offering practical implications for firms navigating digital transformation. Research limitations/implications This study is limited by its focus on Greek firms, which may restrict the generalizability of the findings to other economic, cultural, or industrial contexts. The use of self-reported survey data introduces potential response bias and limits causal inference. Future research should employ longitudinal or cross-country designs and include objective performance indicators to validate and extend the model across diverse organizational settings.
Purpose This study aims to analyze how IT capability, Industry 4.0 implementation capability, and external cooperation intensity interact to enhance supply chain resilience in manufacturing firms. The research addresses the gap in understanding the sequential capability-building mechanisms through which digital resources are transformed into resilient supply chain outcomes by integrating Dynamic Capabilities Theory and the Relational View. Design/methodology/approach Using a survey-based research design, 580 firms were invited to participate. The effective response rate reached 52.41%, yielding a final sample of 304 valid observations which were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings The results demonstrate that IT capability significantly strengthens Industry 4.0 implementation capability, which in turn intensifies external cooperation and enhances supply chain resilience. The findings further reveal that the effect of IT capability on resilience is largely indirect, operating through cumulative digital and relational capabilities. This sequential mechanism highlights the complementary roles of technological reconfiguration and inter-organizational collaboration in building adaptive capacity. Originality/value This study contributes to the supply chain and operations management literature by integrating Dynamic Capabilities Theory and the Relational View into a unified framework that explains resilience in manufacturing companies. It advances knowledge by empirically distinguishing digital resources from digital implementation capability and demonstrating how resilience emerges from the interplay between internal technological transformation and external cooperation intensity.
PurposeThis paper addresses the persistent intention-implementation gap in Industry 4.0 adoption by introducing the concept of Organisational Implementation Intentions (OIIs) and examining how OIIs and firms' technological capabilities jointly determine whether strategic intent converts into implementation, use, and outcomes.Design/methodology/approachUsing a Straussian grounded-theory approach, we conducted 13 semi-structured interviews with senior managers and consultants in the Moroccan manufacturing sector.FindingsOur analysis shows that intention alone is insufficient; organisations need comprehensive pre-actional planning that specifies responsibilities, timelines, scope, procedural steps and contingencies to translate intentions into reliable implementation. OIIs narrow the intention-implementation gap, but their effectiveness depends critically on firms' technological capabilities. When aligned, OIIs and technological capabilities produce operational, strategic, and financial benefits.Originality/valueBy applying the implementation-intention concept at the organisational level, the study offers a testable, processual framework explaining how organisations can bridge the intention-implementation gap in Industry 4.0 settings.
PurposeThe present work investigates Critical Success Factors (CSFs) for the successful execution of Green Lean Six Sigma (GLSS) in the Indian manufacturing sector in alignment with Industry 4.0 (I4.0).Design/methodology/approachA systematic literature review (SLR) was conducted to ensure a comprehensive and structured examination of existing studies. Relevant articles were sourced from well-established electronic databases, including Science Direct, Taylor & Francis, Elsevier, Emerald, Google Scholar and Scopus, to identify the CSFs for the integration of GLSS with I4.0 technologies. Grey relational analysis was subsequently applied to prioritize the identified CSFs. Additionally, the findings of the study were validated using the best-worst method.FindingsThis study aims to identify and model the CSFs of GLSS-I4.0 initiatives that are crucial for the successful implementation of GLSS in manufacturing environments aligned with I4.0 technologies. A comprehensive review of the existing literature was carried out to identify 27 relevant CSFs within the GLSS-I4.0 framework that effectively address the specific needs of the manufacturing sector. The study reveals that CSFs 21 (Focused on leveraging technological innovation and industry best practices), CSFs 13 (Managing organizational change within GLSSI4.0 initiatives), CSFs 18 (Built-in flexibility and responsiveness within the GLSSI4.0 model), CSFs 10 (An organized approach to embedding I4.0 within GLSS), CSFs 1 (Robust project management and supervision), CSFs 19 (A culture of adaptability and continuous improvement aligned with GLSSI4.0 implementation), CSFs 20 (Institutional readiness for GLSS implementation supported by I4.0 technologies) and CSFs 12 (Availability of standard operating procedures for GLSS-I4.0 integration) were recognized as highly significant CSFs in enabling the successful integration of GLSS with I4.0 technologies and were considered as most significant CSFs for integrating GLSS approach with I4.0 technologies.Research limitations/implicationsThe study provides a strategic roadmap for the practitioners to facilitate the effective execution of GLSS practices within the I4.0 context. The integration of GLSS with I4.0 technologies offers a significant competitive advantage in the contemporary industrial landscape by enhancing an organization’s ability to deliver customized products that meet dynamically evolving customer requirements.Originality/valueThis study represents the first comprehensive investigation of CSFs for integrating GLSS with I4.0 technologies in the context of the Indian manufacturing sector Keywords: Manufacturing sectors; Green Lean Six Sigma; Industry 4.0; Critical Success Factors; Gray Relational Analysis; Best Worst Method.
PurposeDigital Technologies of Industry 4.0 (DTI4.0) have revolutionized decision-making processes, while Lean 4.0 (L4.0) has evolved to support continuous improvement and enhance integration with Logistics 4.0 (LG4.0). Recent studies indicate that L4.0 represents a transformation of traditional lean practices, reconfigured to meet the demands of DTI4.0, enabling firms to address challenges related to speed, complexity, and operational efficiency. However, despite the growing interest in these paradigms, there remains a lack of comprehensive empirical research examining the interrelationships and combined effects of L4.0, DTI4.0, and LG4.0. This study investigates how L4.0 influences both DTI4.0 and LG4.0, with a particular focus on the automotive and aerospace industries.Design/methodology/approachA theoretical framework was developed and empirically tested using survey data from 378 Mexican manufacturing firms, analyzed through Partial Least Squares-Structural Equation Modeling (PLS-SEM).FindingsThe findings reveal that the simultaneous implementation of L4.0 and DTI4.0 significantly enhances LG4.0 activities. Furthermore, the results confirm that DTI4.0 plays a mediating role in the relationship between L4.0 and LG4.0, offering both theoretical insights and practical implications for firms navigating digital transformation in production and logistics.Originality/valueThe paper addresses an important gap by proposing a conceptual framework that enhances the comprehension of the intricate relationships between L4.0, DTI4.0, and LG4.0, with particular emphasis on their adoption and reciprocal effects. In addition, the paper provides strong empirical insights that contribute to resolving inconsistencies in the literature concerning the mediating function of Industry 4.0 in linking Lean 4.0 with Logistics 4.0.
This study aims to evaluate a set of criteria representing the core capabilities that garment firms need to sustain competitiveness, including supply chain reconfiguration (SCR), dynamic capabilities (DCs) and environmental, social and governance (ESG), along with 20 sub-criteria. Subsequently, the study assesses 20 Vietnam’s garment companies. A hybrid multi-criteria decision-making (MCDM) framework is employed, combining the Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) to determine criteria priorities and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank Vietnam’s garment firms accordingly. The findings reveal that DC is the most influential factor in enhancing adaptability and competitiveness, followed by SCR and ESG practices. Among the evaluated firms, A5, A1 and A9 achieved the highest rankings, demonstrating superior integration of sustainability and operational flexibility. This study develops an integrated framework that combines SCR, DC and ESG to address a research gap in sustainability and operations management. By applying the SF-AHP and TOPSIS models, the research advances methodological approaches while offering practical insights for managers and policymakers. The findings provide valuable guidance for Vietnam’s garment sector in redesigning supply chains, strengthening DCs and aligning strategies with sustainable governance.
The article aims to analyse the barriers to Industry 5.0 adoption in the Indian manufacturing sector for improvement in organisational sustainability. Further, the study also provides different mitigation actions for barriers. The analysis indicates that “Lack of connection between physical and virtual systems”, “Digitisation and automation of the process” and “Financial constraints” are the most critical barriers to the adoption of I5.0 in the Indian manufacturing sector. The study is of the first kind that analyses the barriers to the adoption of I5.0 to enhance sustainability in India's manufacturing sector. Further, the study's uniqueness also lies in providing different mitigation actions and associated benefits for stakeholders.
PurposeAlthough the circular economy is an emerging topic, the related concepts remain at the development stage. For example, 114 definitions of the circular economy exist. However, despite such a significant quantity of research, there is still a considerable gap between theory and practice. This study aims to investigate the level of consensus between practice and academia in perceiving the circular economy, with a specific focus on the perceived barriers to circularity in the fast fashion industry.Design/methodology/approachUsing both a systematic literature review and primary investigation, 241 barriers were identified, and the relevant similarities and dissimilarities between them were noted.FindingsThe results demonstrate a lack of clear certification and an overlap between sustainability and circularity. Nevertheless, there was some convergence between the two study groups.Research limitations/implicationsIt is crucial to acknowledge potential variations in findings among the studies included because of their unique features and the challenges of generalising them to other contexts.Practical implicationsThis research can help align academic insights with practical challenges, enabling fast fashion companies to more effectively address circular economy barriers and implement sustainable strategies.Originality/valueWe present a comparative analysis of practitioners’ and academics’ perceptions of circular economy barriers, grounded in the research–practice gap.
This study investigates the transition from Industry 4.0 to Industry 5.0, focusing on the integration of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) to support human-centric, sustainable and resilient production systems. It aims to identify key trends, challenges and opportunities within this evolving industrial paradigm. A scientometric approach was employed using bibliometric and co-word analysis to examine global scientific literature on Industry 5.0. The study maps the evolution of research and technological advancements across sectors such as manufacturing, education, supply chains, and disaster management. The analysis highlights the growing importance of predictive maintenance, collaborative robots and cyber-physical systems in advancing sustainable and inclusive industrial practices. It also reveals increasing academic focus on ethical concerns such as workforce inclusion and data privacy. Emerging technologies like augmented reality and blockchain are identified as key enablers of Industry 5.0. The findings support the development of inclusive, human-centered technologies that enhance societal well-being and promote ethical digital transformation in educational and industrial contexts. This study contributes to the field by offering a comprehensive scientometric overview of Industry 5.0 literature and its applications. It underscores the significance of interdisciplinary research and ethical frameworks in achieving balanced technological and societal progress. Moreover, this study bridges the gap between theory and practice by offering actionable insights for SMEs, healthcare and digital supply chains. It contributes a methodological framework applicable to other emergent interdisciplinary fields beyond Industry 5.0.
In agri-food supply chains (AFSCs), food waste can be minimized, and food security can be improved with the assistance of artificial intelligence (AI). But the implementation of AI in AFSC is difficult due to various barriers. Therefore, this paper aims to examine the barriers in the AFSC and explores how these challenges can be addressed using AI. This article draws on academic research, business best practices and legislative frameworks to provide suggestions from a conceptual and qualitative perspective. This critical assessment takes into account the viewpoints of many stakeholders and examines the difficulties of using AI technology in AFSC. Our findings reveal the various barriers, such as for producers (lack of expertise, initial cost, data privacy concerns), for food processors (regulatory compliance, legacy systems, quality control, regulations and standards), for distributors (logistical challenges, seasonal variability, sustainability concerns, regulatory compliance) and for consumers (limited access to information, quality and freshness, complexity of the supply chain and cost fluctuations). This study does an in-depth analysis focusing on the application of AI or the challenges faced by it from the perspective of all major stakeholders involved in AFSC. Our study not only identifies these challenges, but it also recommends what efforts are necessary to mitigate these challenges.
PurposeThe study aims to conduct a bibliometric analysis of literature concerning Industry 4.0 (I4.0) applications in the healthcare sector, with a focus on identifying prevalent research themes and gaps in the existing literature and providing future research directions.Design/methodology/approachA systematic review of the literature has been done by retrieving literature on I4.0 applications in the healthcare sector using the Scopus database. Bibliometric analysis has been conducted, which incorporates science mapping techniques, including co-occurrence analysis and bibliographic coupling, to analyse the intellectual structure within a field of research.FindingsThe analysis revealed major thematic clusters, prominent authors, countries, sources and influential documents in the field of I4.0 in healthcare. The study identified six clusters, including the adoption of I4.0 technologies, data management, the healthcare value chain, cloud and fog computing, managing the COVID-19 pandemic and 3D for personalized drug dosing. We provide a framework for the successful deployment of I4.0 in healthcare to ensure a smooth integration of technologies. It offers valuable insights for researchers, practitioners and policymakers by guiding future research in the field and providing suggestions to leverage I4.0 technologies for enhanced healthcare delivery and sustainable integration with healthcare facilities.Originality/valueThe study advances the literature by providing a comprehensive analysis of I4.0 applications in healthcare and suggesting ways for their successful adoption.
This study examines the mediating role of upstream supply chain integration (internal, supplier process, and supplier product integration) in the relationship between innovation orientation and innovation performance under different levels of environmental dynamism. This study utilized a survey questionnaire to collect data from 482 manufacturing firms in Vietnam. The proposed hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results advance the body of knowledge by demonstrating that supply chain integration is a crucial mechanism for translating innovation orientation into innovation performance. Environmental dynamism plays a crucial role in enhancing the indirect connection between supplier product integration and innovation performance. Managers should adopt a differentiated approach to supply chain integration, tailoring integration strategies to the degree of environmental dynamism. Internal integration should be prioritized for consistent innovation performance, whereas supplier product integration becomes increasingly valuable under highly dynamic environments. This study advances the supply chain literature by disentangling the distinct roles of upstream supply chain integration dimensions and empirically validating their effects in an emerging market context. This reveals how supplier product integration creates value under environmental dynamism, offering firms actionable strategies to enhance innovation through the supply chain.
The rapid expansion of the electric vehicle sector has highlighted the significance of lithium-ion batteries (LIBs) and the need for specialized packaging. However, there exists a research gap in managing specialized packaging for LIBs during prototyping processes. This research, in a case study, aims to fill that gap by examining the challenges associated with specialized packaging in the automotive prototype context. After selection of our case company, its data were gathered using three rounds of expert input. The analysis then divides the identified specialized packaging challenges into categories; then Multi-Criteria Decision-Making (MCDM) is used to identify best-match lean tools for dealing with the categories. Analysis of the interview data collected revealed 24 challenges related to specialized packaging, which were categorized into 4 distinct themes. Through analysis of the focus group discussion, 13 challenges were identified within the 4 themes, as applicable to LIB-specialized packaging and subsequently formed the foundation of the survey. Furthermore, the application of multi-criteria decision-making to the survey data enabled the identification of lean tools appropriate for addressing the four identified themes of challenges. This paper contributed to the establishment of a structured method to facilitate the implementation of lean tools in the management of specialized packaging within the case of an automotive prototyping process.
Purpose The rapid evolution of Industry 4.0 (I 4.0) technologies has transformed supply chain (SC) operations, creating a need to redefine key performance indicators (KPIs) in quality management (QM). Addressing the lack of data-driven frameworks for evaluating Supply Chain Quality Management 4.0 (SCQM 4.0), this study identifies and prioritizes the most influential KPIs through the integration of machine learning (ML) techniques and managerial insights. Design/methodology/approach A mixed-method approach was employed. First, a systematic literature review (SLR) and expert interviews were conducted to identify relevant indicators. Second, a structured survey of 331 professionals from diverse industries was analyzed using seven supervised ML algorithms (SVM, KNN, RF, LDA, DT, RUSBoost and SVM 1-vs-All). The Random Forest (RF) algorithm achieved the highest accuracy and was applied to determine the final prioritization of KPIs. Findings The results indicate that indicators of digital innovation, supplier responsiveness, customer and supplier involvement, supplier resilience and customer satisfaction are the most critical drivers of SCQM 4.0 performance. The RF algorithm demonstrated superior predictive capability in modeling the relationships among multi-level indicators across upstream, internal and downstream dimensions. Practical implications The findings provide managers with a structured, data-driven framework to enhance quality integration and performance within digitalized supply chains. Implementing ML-based analytics supports proactive KPI monitoring, evidence-based decision-making and continuous quality improvement under I 4.0 conditions. Originality/value This study offers one of the first empirical, ML-based frameworks for assessing SCQM 4.0. It bridges conceptual and operational perspectives by integrating data analytics with managerial expertise, thereby extending Quality 4.0 (Q 4.0) and SC 4.0 literature through a multi-level, performance-oriented lens.
This study examines the effect of lean manufacturing practices (LMPs), namely total productive maintenance (TPM) and continuous improvement (CI), on the environmental, social and economic sustainability performance of manufacturing firms. A cross-sectional method was employed in the current study. A total of 236 responses were collected to validate the developed conceptual framework. The proposed relationships were confirmed through the application of partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4.0.9.5 software. The study illustrates that LMPs, notably TPM and CI, positively affect economic, environmental and social sustainability performance (SOCSP). The findings emphasize the necessity of implementing LMPs as crucial resources for improving the performance of manufacturing firms. This study is one of the few that examines the effect of LMPs on the economic, environmental and SOCSP of manufacturing firms in Tanzania, as the literature is largely based in developed nations. The results demonstrate that proper use of LMPs improves firms’ sustainability performance. The findings offer managers and policymakers valuable insights into effectively using lean practices to enhance the sustainability performance of manufacturing firms in developing countries.
The adoption of the metaverse in supply chain management (SCM) presents transformative potential to address inefficiencies such as real-time visibility gaps, demand forecasting inaccuracies and stakeholder collaboration challenges. However, its adoption is hindered by multifaceted barriers that remain underexplored in the literature. This study systematically identifies, analyses and prioritizes 12 critical barriers to metaverse adoption in SCM. This study uses an integrated methodology that merges a qualitative literature review with quantitative techniques, including “Interpretive Structural Modelling” (ISM) and “Cross-Impact Matrix Multiplication Applied to Classification” (MICMAC) analysis. Key findings reveal that foundational barriers such as lack of standards and lack of infrastructure occupy the highest level in the ISM hierarchy, exerting significant influence over dependent barriers like real-time data integration and stakeholder collaboration. MICMAC analysis further classifies barriers into autonomous independent linkage and dependent categories, highlighting their dynamic interdependencies. The study underscores the need for holistic strategies emphasizing technological readiness and ecosystem alignment to facilitate metaverse adoption. By offering a hierarchical framework and actionable insights, this study contributes to both academic discourse and practical implementation, aiding organizations in analysing the difficulties of metaverse integration for resilient, efficient and sustainable SCM.
PurposeThis article aims to develop reliable and valid factors/antecedents and their interrelationship for structuring the supply chains to become more resilient and sustainable. Resilience and sustainability are both expected components of a modern supply chain. Prima facie, these appear contradictory to each other; therefore, a resilient sustainable supply chain (RSSC) needs in-depth exploration to find the factors supporting and contradicting the resilience and sustainability.Design/methodology/approachThis paper explores the Indian manufacturing sector, through an empirical study, to understand and develop the reliable and valid factors/antecedents for an RSSC. First, a conceptual model of RSSC is proposed based on the extant literature evidence, domain knowledge and expert opinion. Second, hypotheses were developed to validate the proposed model by using statistical analysis of exploratory factor analysis, confirmatory factor analysis and structural equation modelling.FindingsThe findings of the study suggest that supply chain visibility, flexibility, collaboration, control network design and digitalisation are the important factors/antecedents to build an RSSC. It was also found that digitalization control and flexibility have a full mediating effect on sustainability, whereas visibility collaboration and network design have a partial mediating effect on sustainability. Resilience has a direct effect on sustainability.Originality/valueBased on the findings, the authors in this study proposed a more precise and contextual RSSC definition as compared to few definitions available in the extant literature.
