Purpose Despite substantial improvements in workplace safety at managerial and organizational levels over recent decades, accident rates remain high, with considerable social and economic impacts. This study explores the links between safety culture and high reliability organization principles, providing a multi-level framework to better understand how organizations can enhance safety performance. Design/methodology/approach This study adopts a practice-oriented, qualitative multiple-case methodology, examining three medium-sized Italian companies operating in the urban hygiene sector of public utility services. Data collection involved documentary analysis and semi-structured interviews to explore health and safety performance, management of variables affecting prevention and response to unforeseen events and the connections between high reliability and safety culture. Findings The study shows that safety culture can drive high reliability, but only when its components are understood and applied across the organization in an integrated way. The research also provides urban hygiene companies with an assessment method that combines both formal and informal aspects of safety culture to support their transition toward high reliability. Originality/value This research fills the gap in the literature regarding the integration between safety culture dimensions in organizations and high reliability principles, through an integrated and multi-level approach to safety.
Purpose The study aims to propose and preliminarily assess a conceptual model that explores how organisational culture (OC) shapes organisational performance (OP), alongside the roles of soft and hard total quality management (TQM) practices within the third sector organisations (TSOs) context. Design/methodology/approach Grounded in contingency theory, stakeholder theory and the competing values framework (CVF), the study assesses the model's relevance through reflexive thematic analysis of 10 semi structured interviews with TSO representatives in Saudi Arabia. Findings The findings support the proposed model and indicate that sustained OP depends on OC and the effective integration of both soft and hard TQM dimensions. While clan and hierarchy cultures initially appeared dominant, the analysis revealed notable gaps between cultural ideals and actual practices. Market and adhocracy cultures, critical for financial sustainability, were largely absent. Developing a coherent and balanced OC that incorporates all CVF culture types is essential for effective soft and hard TQM implementation and for achieving sustained performance in TSOs. Research limitations/implications As the proposed conceptual model has only undergone initial assessment through qualitative interviews, further quantitative analysis is required to establish its validity. Moreover, because the current findings are derived from the experiences of TSOs in Saudi Arabia, additional evaluation of the model across diverse contexts is necessary before drawing broader conclusions, ensuring that all relevant contextual factors are adequately considered. Practical implications Recognising which cultural types are dominant or absent, and understanding how they influence OP and TQM in both their soft and hard forms, will enable TSOs to manage their OC more effectively, leading to improved TQM implementation and stronger OP. Originality/value This research is the first to integrate OC, OP and both soft and hard TQM practices into a single model tailored to TSOs. This is particularly significant because TSOs continue to face performance challenges that TQM alone has not resolved, and although OC has been linked to these issues, no prior work has combined these variables within one model. Additionally, the study underscores the value of reflexive thematic analysis as a rigorous and insightful approach for qualitative business research.
PurposeThis research develops an integrated technological system combining multicriteria decision-making (MCDM) methods and machine learning (ML) techniques to support industrial maintenance in Industry 4.0 and digital transformation. The system supports strategic asset maintenance decisions, and its application has been validated at the Brazilian Mint (Casa da Moeda do Brasil). Design/methodology/approachA structured case study was conducted using qualitative and quantitative data from interviews, focus groups, direct observations and document analysis. Guided by an Operations–Maintenance fit lens, MCDM methods (AHP-AIP, MOORA, MULTIMOORA and Borda Count) and forecasting techniques (ARIMA, ANN) were implemented in a Python-based decision support system (DSS) to prioritize assets for predictive maintenance and forecast corrective maintenance needs. FindingsThe integration of MCDM and ML improved decision-making in asset maintenance by addressing complex data challenges. The developed system has enhanced the accuracy of maintenance decisions and is currently operational at the Brazilian Mint. The fit analysis shows that the DSS delivers the highest value under contingencies, clarifying when and where the tool is transferable beyond this specific organization. Originality/valueThis study contributes to the Maintenance 4.0 literature by (1) extending Contingency Theory by formalizing how digital maintenance DSSs depend on the alignment among asset criticality, information-processing requirements and analytical capabilities; (2) introducing an operations–maintenance fit perspective that explains how machine learning forecasting and MCDM jointly support the reconciliation of production throughput objectives with reliability and availability goals and (3) presenting a transparent, transferable and benchmark-ready DSS architecture, validated within a real industrial environment, that integrates four MCDM methods and two forecasting techniques to assess key performance metrics, such as overall equipment effectiveness, for cross-firm comparison.
Purpose It aims to develop the HoQ methodology by integrating Online Product Reviews (OPR) and expert evaluations and automatically determining normalized Customer Requirement (CR) weights to improve the product development process. Design/methodology/approach The research uses NLP and text mining to analyze OPRs and expert reports. CRs, CR importance, and local/competitor CR scores are extracted by Latent Dirichlet Allocation, and point-of-sale importance values are determined from Product Evaluation Platforms (PEP). Findings The study shows that automating the HoQ process using OPRs and expert assessments is effective. Fourteen CRs are identified, and a data-driven comparative analysis of brands is conducted. Integrating PEP sources yields more accurate PSI values, thereby enhancing product development based on CRs. Research limitations/implications The reliability of OPRs and expert assessments may affect the results. The methodology requires computational resources and expertise. Its applicability across different industries and its cross-checking with conventional HoQ remain to be tested. Practical implications This method offers businesses, especially SMEs, a cost-effective and efficient approach to aligning product features with CRs. It reduces reliance on traditional surveys and provides rapid, real-time insights. Social implications This study promotes transparency by incorporating OPRs and PEPs, allowing companies to meet CRs better and leading to higher satisfaction. Originality/value For the first time, it offers an automated approach to HoQ that integrates layered OPRs to determine normalized CR weights and uses PEPs for PSI determination. This innovation improves product development efficiency by saving time and cost while minimizing dependency on experts.
Purpose Global supply chain disruptions have grown in recent years, showing that organisational preparedness and recovery mechanisms are weak. Though past research has examined resilience strategies, little focus has been given to total quality management (TQM) as an internal strength to promote organisational resilience in the presence of disruption. This article addresses this gap by looking at the impact of four dimensions of TQM, namely employee involvement and training, top management commitment (TMC), process management and standardisation, and supplier relationship and quality integration (SRQI) on organisational resilience in Saudi Arabian organisations. Design/methodology/approach A quantitative and cross-sectional survey of 342 supply chain professionals was analysed using SPSS, which included reliability tests, correlation analysis and multiple regression. Findings Results have shown that TQM has strong predictive capacity in relation to resilience (R2 = 0.616), with SRQI (β = 0.282) and TMC (β = 0.250) showing the most important predictors. The positive effects were significant in each of the four dimensions (p < 0.01). Research limitations/implications The cross-sectional aspect of the research at hand restricts its capacity to portray the dynamic and time-related components of organisational resilience. The sample itself was limited to Saudi Arabian organisations, which might hamper generalisation of the findings to situations that define other economies or cultures. Practical implications Organisations need to strengthen TQM practices in order to improve preparedness and recovery during disruptions. Hence, investing in employee training, standardised processes and supplier collaboration helps firms in maintaining operational continuity and supply chain stability. Social implications Organisations need to strengthen TQM practices in order to improve preparedness and recovery during disruptions. Hence, investing in employee training, standardised processes and supplier collaboration helps firms in maintaining operational continuity and supply chain stability. Originality/value The discussion concludes that TQM is a strategic competence that enhances flexibility, recuperation and robustness of operations. The future implications include the scale-up of the model, harnessing the digital transformation preparedness and longitudinal studies to gauge the dynamics of resilience over time.
Purpose Drawing on institutional theory, contingency theory, and the resource-based view (RBV), this study examines and explains differences in ISO 9001:2015 implementation across eight warehouses in a post-conflict environment (four public and four private). It identifies key implementation gaps and examines the organizational and contextual factors shaping quality management outcomes in fragile settings. Design/methodology/approach A mixed-methods multiple-case design was employed across eight warehouses in post-conflict Syria (four public and four private). Quantitatively, ISO 9001:2015 implementation was assessed using a weighted checklist covering 253 indicators, supported by descriptive statistics, Cronbach's alpha (a = 0.89), independent-samples t-tests, one-way ANOVA and effect size estimation. Qualitatively, data were collected through 64 semi-structured interviews, 600 h of field observation and document analysis, and were analyzed using thematic analysis. Triangulation of quantitative and qualitative evidence enhanced the robustness, validity, and explanatory depth of the findings. Findings The results indicate substantial differences in implementation levels between sectors. Private warehouses achieved an average conformity level of 25.8%, compared to 7.4% in public warehouses, with the difference statistically significant (t (6) = 5.24, p < 0.001, Cohen's d = 2.76). Implementation remains critically low across both sectors. The weakest performance is observed in leadership, contextual analysis and continuous improvement. Private warehouses demonstrate stronger performance in customer-focused and operational dimensions. Qualitative findings identify six key mechanisms through which post-conflict fragility and sectoral differences jointly constrain quality management system (QMS) adoption, including bureaucratic decoupling, structural role ambiguity, resource immobility, documentation gaps, absence of improvement routines and market-driven selective adaptation. Originality/value This study provides one of the first empirical multi-case analyses of ISO 9001:2015 implementation in a post-conflict context. It advances theoretical understanding by demonstrating how institutional fragility, contextual misalignment and resource asymmetry interact to produce differential QMS outcomes across organizational types. The study introduces the concept of pre-implementation fragility and proposes a phased, sector-differentiated roadmap for QMS development in conflict-affected environments.
Purpose This study examines how digital twins (DT) enhances supply chain quality management (SCQM) and manufacturing resilience. Specifically, the study investigates the quality management and resilience capabilities enabled through DT adoption and identifies the organizational, technological and process-related prerequisites necessary for effective DT-enabled SCQM. Design/methodology/approach A scoping review was conducted to systematically map existing literature on DT applications in SCQM. A total of 23 peer-reviewed articles published in supply chain quality management, digital twin and cyber-physical system domains were analyzed. The review focused on identifying patterns in DT adoption, quality and resilience outcomes and enabling organizational and technological factors. Drawing on information processing theory (IPT), the study examines how DT capabilities align with organizational information processing needs under uncertainty and supply chain quality variability. Findings The findings indicate that DTs enhance SCQM through real-time monitoring, predictive quality analytics, simulation-driven decision support and continuous process optimization. DT-enabled organizations demonstrate improved visibility, faster response to disruptions, enhanced quality control and increased supply chain resilience. Effective implementation depends on integrated IT infrastructure, standardized and interoperable data systems, cross-functional collaboration and process integration across supply chain partners. The study further identifies maturity progression from foundational monitoring and integration capabilities toward predictive, prescriptive and resilience-oriented SCQM systems. Originality/value This study advances the theoretical understanding of DT-enabled SCQM by integrating digital twin, resilience, and quality management perspectives through an IPT lens. It proposes a digital twin maturity framework and an IPT–DT alignment framework that explain how organizations align digital twin capabilities with information processing requirements. The study also provides actionable guidance for practitioners seeking to enhance supply chain quality performance and resilience through DT adoption.
Purpose This study explores how LS5.0 can transform credit decision processes in the banking sector, focusing on integrating advanced technologies and Lean principles to address critical operational challenges. The research aims to identify how LS5.0 contributes to improving efficiency, personalising services and promoting agility in a competitive environment and under strict government supervision. Design/methodology/approach A multiple case study was conducted with five large banks in Brazil, covering all regions of the country, including three multinational institutions, to understand organisational perspectives and identify variability in credit decision processes through the adoption of LS5.0. Data collection involved interviews with bank experts and managers from each institution, all of whom were engaged in credit decision-making, providing comprehensive insights into the implementation of LS5.0. Findings The results show that LS5.0 speeds up decision-making by automating data processing and eliminating redundant steps, improving analytical accuracy and reducing waste. However, respondents highlighted challenges such as resistance to change and misalignment between credit and technology teams. Continuous training and collaboration were identified as key strategies to mitigate these barriers. The findings also highlight the need for broader performance metrics beyond lead time, incorporating credit risk and operational efficiency. Originality/value This research advances academic understanding by demonstrating the strategic potential of LS5.0 to address operational challenges, create customer value and increase organisational competitiveness. The integration of Lean principles with Industry 5.0 technologies offers new perspectives for operational excellence in the banking sector.
Purpose Registering a complaint is not easy for an individual customer. Getting the complaint resolved is more difficult, and resolving it to the level of satisfaction is highly improbable. This study identifies the problems faced by individual consumers in managing their complaints. It also proposes remedies to overcome these problems. A quality management systems approach, which requires root cause identification of customer problems and prevention of their reoccurrence, is recommended for effective complaint management. Design/methodology/approach The problems of individual consumers in registering and resolving complaints are compiled using the expert panel approach and the Delphi rating system. The same approach is also used for identifying the solutions. The COCD box concept is used to analyse the problems and solutions together. Findings Sixteen major problems faced by the individual consumers in managing their complaints are listed and rated in this study according to their pain potential. Fourteen solutions to overcome these problems are proposed and rated from the implementation difficulty perspective. Research limitations/implications On the theoretical front, the findings have implications pertaining to five different theories, namely, the exit voice theory, theory of justice, attribution theory, cognitive dissonance theory and the consumer complaint behaviour theory. On a practical level, the findings have direct implications for the consumers, developers of various quality management standards, certification agencies of quality systems, the organisations trying to capture the digital voice of their customers and the organisations working in the interest of individual consumers, for example consumer forums. Originality/value This study is unique in three ways. First, it differentiates customer complaint management processes for individual consumers and the organisational consumers. Second, it analyses both problems and solutions related to complaint management from the Quality 4.0 framework perspective. Third, it presents not only the problems faced by individual consumers but also proposes solutions that organisations can implement to empower individual consumers.
PurposeThe integration of digital technologies into quality management systems has led to the emergence of Quality 4.0 (Q4.0), a transformative paradigm aligned with the principles of Industry 4.0. This study presents a systematic literature review of 102 peer-reviewed articles indexed in the Scopus database between 1993 and 2025, offering a comprehensive and structured overview of the field. Design/methodology/approachBy employing a systematic review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework and a cluster analysis, the research identifies four key thematic areas: (1) integration of total quality management and Quality 4.0; (2) digital transformation of Quality 4.0 processes; (3) practical and theoretical applications of Quality 4.0 and (4) implementation readiness, success factors and barriers to Quality 4.0 implementation. FindingsDespite Quality 4.0’s growing relevance, the findings reveal significant research gaps, including limited empirical validations, an overemphasis on technological tools at the expense of organizational and human factors, scarce sector-specific applications and an emergent shift toward Q5.0, highlighting a nascent move toward human-centric and proactive quality control. This paper contributes to both academia and industry by synthesizing fragmented knowledge, proposing a future research agenda and offering actionable insights for the successful implementation of Quality 4.0. Originality/valueThis review offers substantial contributions to both academia and industry. For researchers, it synthesizes fragmented knowledge and provides a clear roadmap for future research, urging a shift towards empirical studies and a more holistic view of Q4.0. For practitioners and policymakers, the paper presents actionable insights and a strategic guide to navigate the challenges of digital transformation, emphasizing the importance of human capital development, organizational culture and risk management in achieving successful Q4.0 implementation. This work bridges the gap between theoretical frameworks and practical application, serving as a critical resource for all stakeholders.
PurposeThe Fourth Industrial Revolution (Industry 4.0) is transforming industrial maintenance into a strategic, technology-driven function. Maintenance 4.0, particularly predictive maintenance (PdM), leverages IoT, AI and Big Data to enhance asset reliability, reduce downtime, lower costs and improve overall operational efficiency. While these innovations promise substantial competitiveness gains, challenges such as high investment costs and specialized expertise persist. This study conducts a systematic literature review to map current Industry 4.0 maintenance technologies, identify their benefits, and explore implementation barriers. By systematizing existing knowledge, it fills a critical gap, providing insights into Maintenance 4.0's potential to drive industrial modernization and sustainable performance. Design/methodology/approachThis study employed a structured methodological framework combining exploratory research, a systematic literature review (SLR), and descriptive and content analyses. The exploratory stage identified key terms and concepts, guiding search strings applied to Scopus and Web of Science databases. Following the PRISMA protocol, 949 documents were screened, duplicates removed and relevance assessed, resulting in a final sample of 29 studies. Descriptive analysis mapped publication trends, countries, and maintenance types, while content analysis examined Industry 4.0 technologies, applications, benefits and challenges. A summary table synthesized findings, providing a comprehensive, accessible overview of Maintenance 4.0 practices and insights into digital transformation in industrial maintenance. FindingsAnalysis of 29 studies highlights predictive maintenance as the dominant focus within Industry 4.0-driven industrial maintenance. Key technologies identified include IoT, AI, Machine Learning, Big Data, Digital Twins and Cyber-Physical Systems, enabling real-time monitoring, data-driven decision-making and predictive interventions. Benefits include reduced costs, minimized downtime, enhanced reliability, extended equipment lifespan, improved sustainability, and optimized resource utilization. However, adoption is constrained by high initial investment, data quality issues, integration challenges with legacy systems, personnel shortages and cybersecurity concerns. This study provides a comprehensive synthesis of Maintenance 4.0 practices, demonstrating how digital technologies transform maintenance strategies, improve operational efficiency and foster industrial competitiveness. Research limitations/implicationsThis study advances research on Industry 4.0 and industrial maintenance by offering an integrated framework that synthesizes technologies, benefits, and implementation challenges of Maintenance 4.0. It strengthens theoretical understanding by positioning predictive maintenance as a central value-generation mechanism and by clarifying how digital technologies – such as IoT, AI, ML and big data – translate into performance outcomes. Furthermore, the study highlights the interaction between technological adoption and organizational constraints, supporting the development of more realistic, context-sensitive models. By moving beyond fragmented and technology-centric approaches, the research contributes to theory building and provides a foundation for future empirical and conceptual investigations in digital maintenance transformation. Practical implicationsThis study provides actionable insights for organizations implementing Industry 4.0 in maintenance. It highlights the need to prioritize investments in data infrastructure and data quality as key enablers of predictive maintenance and advanced analytics. The findings emphasize adopting an integrated and strategic approach, combining technologies such as IoT, AI and cloud computing to maximize efficiency and reliability. Additionally, the study underscores the importance of workforce development, training, and fostering a data-driven culture. By identifying barriers – including high costs, integration complexity, and cybersecurity risks – it helps managers anticipate challenges and design more effective implementation strategies, supporting better decision-making and improved operational performance in maintenance environments. Social implicationsThis study offers relevant social implications by highlighting how the adoption of Industry 4.0 technologies in maintenance can reshape workforce dynamics and organizational practices. The emphasis on workforce development and new skill requirements underscores the need for continuous learning and professional reskilling. By promoting data-driven cultures, the study supports more transparent and informed decision-making processes. Additionally, improved maintenance efficiency and reliability can enhance operational safety and reduce environmental impacts through optimized resource use and reduced downtime. However, challenges such as technological complexity and skill gaps may create inequalities, reinforcing the importance of inclusive strategies to ensure that digital transformation benefits employees and society more broadly. Originality/valueThis study provides a novel, comprehensive synthesis of Maintenance 4.0, addressing a critical gap by systematically mapping the adoption, applications and impacts of Industry 4.0 technologies in industrial maintenance. It establishes predictive maintenance as the central framework and underscores the transformative potential of IoT, AI, Machine Learning, Big Data, Digital Twins and Cyber-Physical Systems in optimizing operational efficiency, reducing costs and extending asset lifespan. By integrating measurable benefits with implementation challenges, the research offers actionable insights and a strategic roadmap for practitioners and policymakers, advancing theory and practice in digital maintenance. It positions Maintenance 4.0 as a key driver of competitiveness, sustainability and industrial modernization.
PurposeThe growing competitiveness of markets and the increasing complexity of production systems pose new challenges to quality control, demanding more effective and adaptive tools. Statistical quality control (SQC), although established as a strategic resource for reducing variability and preventing defects, it shows limitations when dealing with non-normal data distributions or when faced with data with high dimensionality. In this context, machine learning (ML) techniques have emerged as complementary solutions to enhance the detection of out-of-control and abnormal patterns in control charts. Therefore, this study aims to map the current advances, emerging trends and critical knowledge gaps in the application of ML for SQC. Design/methodology/approachThis article presents a combined bibliometric and systematic review of the literature. The bibliometric analysis is conducted on an initial set of 574 articles collected from scientific databases, while the systematic literature review is based on 80 studies published between 2010 and 2024, selected through the PRISMA protocol. The analysis aims to map the main techniques employed, the predominant types of tasks and learning, hyperparameter optimization techniques and data types employed, as well as the objectives proposed by the models. FindingsThe bibliometric analysis indicates an increasing trend in scientific publications, with research primarily concentrated in the fields of industrial engineering and applied computer science. China stands out as the leading contributor in terms of publication volume and the development of more robust co-authorship networks. Keyword analysis highlights the predominant use of artificial neural networks (ANN), support vector machines (SVM) and dimensionality reduction techniques in statistical process control, as well as the recent emergence of topics aligned with Industry 5.0 and human-centric manufacturing. The systematic literature review reinforces the predominance of ANN and SVM applied to SQC, mainly addressing classification tasks for detecting in-control and out-of-control process states. Most studies rely on simulated data and supervised learning approaches, with scarce use of real-world industrial data and a limited adoption of unsupervised and adaptive learning strategies, highlighting important gaps for future research. Future research should prioritize validation with real industrial datasets, the development of unsupervised and adaptive learning approaches capable of handling dynamic and non-stationary processes, and the integration of explainability, real-time analytics and human-centric principles in ML-based SQC systems. Originality/valueThis study identifies research gaps and proposes directions for future work, emphasizing the development of more robust, interpretable models suitable for connected industrial systems.
Purpose This study develops and validates an innovative framework combining strengths, weaknesses, opportunities and threats (SWOT) analysis, fault tree analysis and failure mode and effects analysis (FMEA) to enhance strategic decision-making, risk management and organizational assessment across industries. The framework aims to improve the quality of project outcomes by systematically identifying vulnerabilities and critical risks. Design/methodology/approach A systematic literature review identified gaps in existing methodologies. The framework was empirically validated through expert analysis and a case study involving a marine construction company. This process assessed internal and external factors, identified failure modes and uncovered systemic vulnerabilities, demonstrating practical applicability. Findings At a general level, the integrated sequence improves traceability from strategic posture to risk control actions, strengthens causal coherence in defining failure modes and stabilizes prioritization; in the validation case, it identified the organization as in the stability stage with a recommended defensive strategy set and achieved a 65% reduction in aggregate risk priority number, enabling evidence-based reprioritization and targeted mitigations. Research limitations/implications Validation is limited to a single sector, multi-industry longitudinal studies are needed to test generalizability and performance sustainability. Incorporating advanced decision analysis techniques could further refine the framework. Practical implications Managers can apply the framework, through a seven step process to: (1) quantify internal and external factors with internal factor evaluation/external factor evaluation (EFE) scores and translate them into concrete strategy sets, (2) reveal root causes and interdependencies by applying FTA to prioritized weaknesses and threats and (3) reduce risk by identifying and mitigating high risk failure modes using FMEA. Originality/value This research introduces a novel, scientifically grounded integration of strategic and risk assessment tools, advancing decision-making beyond traditional, intuitive approaches to enhance quality in complex project environments.
Purpose While online food delivery services are expanding worldwide, most prior research has focused on single countries and the pre-purchase stage. This study addresses that gap by examining the drivers of customer-perceived value in food delivery app use, its effects on well-being and loyalty, and the moderating role of consumer pessimism. Drawing on Consumption Value Theory (CVT), the Information Systems Success (ISS) model, and the literature on consumer pessimism, the study offers a holistic, multi-country perspective on the factors that shape customer experiences and behaviours with food delivery apps. Design/methodology/approach Data were collected through an online survey of current food delivery app users in four Global South countries: Pakistan (n = 261), Morocco (n = 169), Saudi Arabia (n = 283), and Jordan (n = 166). Research hypotheses were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4. Findings Across the four Global South countries examined, there is evidence that information, service and system quality are key determinants of overall customer-perceived value, which in turn enhances customer well-being and loyalty. The negative effect of consumer pessimism on perceived value is significant in two out of the four countries. Customer well-being consistently predicts loyalty across all countries, underscoring its importance for long-term relationships. However, the moderating role of consumer pessimism on the links between perceived value, well-being, and loyalty is mixed, revealing variation across national contexts. Originality/value This study is the first to examine the key drivers and outcomes of customer-perceived value in the online food delivery sector using multi-country data, breaking new ground by combining the ISS model, CVT, and research on consumer pessimism and well-being to deepen understanding of how perceived value is formed and its effects. Altogether, the results illuminate the structural relationships that shape customers' post-purchase experiences and behaviours and offer managers actionable insights to design strategies that build stronger customer value and loyalty across different international markets.
Purpose The management of higher education institutions, like any other production system, must ensure efficiency and effectiveness in both the generation and application of knowledge. This paper investigates the negative effects of the four dimensions of knowledge waste – waste of explicit knowledge, retention of tacit knowledge, overspecialization, and underused talent – on the performance of research groups from higher education institutions. Design/methodology/approach The research model was evaluated using survey data collected from a sample of 211 research groups across public and private higher education institutions in an emerging country. Partial least squares structural equation modeling (PLS-SEM) was used to test the research model. Findings The findings indicate that the waste of explicit knowledge exerts a negative effect on the performance of research groups within higher education institutions. Retention of tacit knowledge and underutilization of talent show no significant influence on performance outcomes. Notably, overspecialization – contrary to theoretical expectations – contributes positively to performance. Originality/value This study advances the literature by shifting the focus from the measurement of knowledge waste to its organizational consequences for research group performance in higher education institutions. It theorizes knowledge waste as a critical mechanism through which value generated from knowledge creation may be dissipated. The findings challenge the prevailing assumption of uniformly negative effects by revealing heterogeneous impacts across its dimensions. Notably, overspecialization may yield context-dependent positive outcomes, calling for a reconceptualization of knowledge waste as a non-homogeneous construct within the broader literature on the dark side of knowledge dynamics.
Purpose This study extends the resource-based view (RBV) by theorizing and empirically validating the conditions under which total quality management (TQM) practices enhance sustainable supply chain performance (SSCP) in manufacturing small and medium-sized enterprises (SMEs). Despite substantial growth in TQM and sustainability literature, theoretical understanding of how specific TQM dimensions interact with organizational culture to drive sustainability outcomes remains limited, particularly in resource-constrained emerging economy contexts. We address this gap by examining not only the direct effects of multidimensional TQM practices on SSCP but also the theoretically grounded moderating mechanisms through which quality culture amplifies these relationships.Design/methodology/approach Drawing upon RBV and institutional theory, we developed and tested a theoretical model using structural equation modeling and moderated regression analysis on survey data from 400 senior and middle managers across manufacturing SMEs in Ghana.Findings Results reveal differential effects of TQM dimensions on SSCP. Strategic planning, benchmarking, supplier quality management and information and analysis significantly enhance SSCP, while quality assurance shows no significant direct effect. Quality culture exhibits both direct and moderating effects, significantly strengthening the SSCP impacts of strategic planning, supplier quality management and information and analysis. These findings reveal that quality culture serves as a critical boundary condition, with TQM practices demonstrating superior effectiveness in organizations possessing strong quality-oriented values and norms. The model explains 52% of variance in SSCP.Practical implications For manufacturing SMEs in emerging economies, findings suggest that sustainable competitive advantage requires more than TQM practice adoption; it demands deliberate cultivation of supporting quality culture. Managers should prioritize three specific TQM dimensions (strategic planning, supplier quality management and information analytics) that demonstrate the strongest synergy with quality culture. Resource-constrained SMEs should focus implementation efforts on these high-impact practices rather than comprehensive TQM adoption. Specifically, firms should: (1) develop formal sustainability-oriented strategic planning processes before scaling TQM initiatives; (2) invest in supplier development programs that align supplier quality standards with sustainability objectives; (3) implement data analytics capabilities to track sustainability metrics and (4) cultivate organization-wide quality culture through leadership commitment, employee training and reward systems that recognize quality excellence. These actions provide actionable roadmaps for SMEs seeking to leverage quality management for sustainability.Originality/value This study is among the first to theoretically integrate and empirically validate the complex interplay between multidimensional TQM practices, quality culture and SSCP in a Sub-Saharan African context. By demonstrating that quality culture acts as a critical contingency factor that determines when and how TQM practices translate into sustainability outcomes, we provide novel theoretical insights that advance both RBV and TQM-sustainability scholarship. The study's value lies in its rigorous theoretical grounding, methodological rigor, and contextual relevance to emerging economies where sustainability challenges are most acute yet organizational resources are most constrained.
Purpose When analytical work is assisted by AI, the professional discipline traditionally embedded within Plan-Do-Check-Act (PDCA) can no longer remain implicit. It must be made explicit. This paper examines whether PDCA remains structurally adequate in hybrid human-AI reasoning environments and introduces Context-Intent-Plan-Deliver-Assure (CIPDA) as an extension designed to externalise professional interpretive discipline. ROMER is presented as the evidential mechanism operated by an AI within the Assure stage.Design/methodology/approach A controlled within-subject comparative design was conducted across 25 synthetic analytical scenarios replicated over fifteen complete runs in two independently executed series (375 observations per condition). Three reasoning conditions were tested; Control (unstructured prompting), PDCA-based structuring, and CIPDA-based structuring incorporating ROMER-guided assurance. Sessions were reset between runs to evaluate behavioural consistency under identical inputs. Primary measures were constraint-aware disposition, run-to-run variance, and interpretive traceability.Findings In a replicated flight-booking simulation involving 25 identical scenarios repeated across fifteen runs, the CIPDA condition produced near-perfectly consistent behaviour, correctly identifying the constraint conflicts and refusing to book in 97% of cases (mean 24.2/25, SD +/- 0.68). The unstructured and PDCA conditions produced inconsistent outcomes under identical inputs - 74% and 75% respectively - with neither condition establishing a stable ordering relative to the other across runs. Traceability followed the same pattern; reasoning transparency increased stepwise from Control to PDCA to CIPDA, where only CIPDA consistently produced a complete and inspectable reasoning trail. The central finding is therefore not improved performance but improved reliability; increasing structural explicitness within the reasoning sequence reduces behavioural variance in AI-assisted analytical tasks.Research limitations/implications This investigation is domain-bounded and conducted using a single model configuration across synthetic scenarios. Further cross-domain and cross-model replication is required. However, the findings highlight an emerging governance risk. Many organisational and standards-based management frameworks, including AI management system standards such as ISO/IEC 42001, remain structurally organised around PDCA. While PDCA remains effective for human-to-human operational management, it may not provide sufficient structural discipline when AI systems participate in complex analytical reasoning.Practical implications CIPDA (for the professional) with ROMER (for the AI), provide a structured method for evidencing AI-assisted judgement aligned with quality management practice, enabling consistent and inspectable reasoning under identical task conditions.Social implications Organisations and standards bodies that rely on PDCA as the structural foundation for AI governance - including frameworks such as ISO/IEC 42001 - may be assuming a degree of behavioural reliability that the evidence does not support for AI. PDCA governs AI outputs from the outside; it does not govern the reasoning process from within. Where AI systems participate directly in analytical work, this outside-in posture leaves interpretive variance unaddressed at its source. The findings suggest that governance frameworks should look inside the reasoning architecture, not only at its outputs. Originality/value The paper reconceptualises improvement cycles as reasoning architectures that must be externalised when analytical work is distributed between human and AI actors. It demonstrates empirically that structured interpretive commitment improves behavioural consistency and traceability at measurable computational overhead.
PurposeThis research aims to examine the impact of dynamic capabilities on the competitiveness of small- and medium-sized enterprises (SMEs) and the mediation role of innovation capabilities and product quality on the relationship between dynamic capabilities and competitiveness. Design/methodology/approachA framework was developed to illustrate the relationship between dynamic capabilities and the sustainable competitiveness of SMEs, grounded in the theory of dynamic capabilities. Data were collected through a survey from 459 SMEs operating in the manufacturing sector. Structural equation modeling (SEM) was used to analyze the research hypothesis and the path relationships based on the model developed. FindingsThe study's results showed that a firm's innovation capabilities, product quality and competitiveness are significantly and positively influenced by its dynamic capabilities. Furthermore, the relationship between dynamic capabilities and competitiveness is partially mediated through innovation capabilities. However, product quality did not mediate the impact of dynamic capabilities on competitiveness. Research limitations/implicationsManagerial and policy implications: the research finding provides practical implications for policymakers to design strategies on internal capability development. Furthermore, draws managerial attention to investing in internal capability focusing on continuous learning and internal and external resource integration to drive customer satisfaction, and monitoring the innovation landscape to ensure the firms remain at the forefront for their sustainable competitiveness. Originality/valueThe significance of SMEs in Ethiopian economic development is notable. However, most new ventures experience very low growth and high failure rates, which raises questions for both researchers and policymakers. Therefore, to the best of the authors' knowledge, this research paper is the first to analyze the influence of dynamic capabilities, innovation capabilities and product quality on the competitiveness of Ethiopian SMEs in the manufacturing sector.
Purpose This study evaluates the empirical interrelationship among Lean 4.0, value co-creation among port stakeholders and sustainability in the port sector, focusing on Brazilian terminals. It aims to identify enablers guiding operational excellence and sustainable development in complex port environments.Design/methodology/approach A hybrid multi-criteria decision-making approach combining the best-worst method and additive decision algorithm was employed alongside partial least squares structural equation modelling analysis. Data were collected from 107 Brazilian ports and terminals to quantify relationships among Lean 4.0, value co-creation and sustainability.Findings The study highlights Internet of Things, artificial intelligence and automation technologies as key enablers of Lean 4.0 in ports. Results show that Lean 4.0 and value co-creation jointly explain 33.8% of sustainability variance. Collaboration among stakeholders enhances operational efficiency, waste reduction and environmental performance, supporting circular economy practices. Lean 4.0's full potential emerges when combined with co-creation strategies rather than applied in isolation.Research limitations/implications The study focuses on Brazilian port terminals, limiting generalisability. Future research should explore other regions and longitudinal impacts, as well as interactions with circular economy and supply chain resilience strategies.Originality/value This research extends resource orchestration theory by empirically showing how Lean 4.0 and value co-creation jointly enhance sustainability in the port sector. It provides a sector-specific contribution while offering practical insights for quality managers and policymakers seeking to promote more sustainable, resilient and competitive port operations.
Purpose This study presents a scoping review with integrated bibliometric analysis to develop a Lean Maintenance Management framework for achieving sustainable operational excellence in the Latin American petroleum industry. This investigation examines how current maintenance optimisation approaches can be reframed through Lean principles to systematically eliminate waste while contributing to the United Nations sustainable development goals (SDGs).Design/methodology/approach Following PRISMA-ScR guidelines, this study maps existing literature to identify Lean transformation opportunities in petroleum maintenance. A thorough four-stage screening process found 39 articles out of 823 records from Scopus, Web of Science and OnePetro. Bibliometric analysis investigated two timeframes (2016-2020 and 2021-2025) to identify the journey from algorithmic optimisation to integrated management systems. Framework development integrates identified best practices with Lean principles contextualised for Latin American operations.Findings The analysis reveals significant implementation challenges, with only 7.7% of studies achieving full real-world validation while 20.5% demonstrate practical validation, indicating substantial waste in the research-to-practice pipeline. Maintenance-production integration appears in 38.5% of studies, aligning with Lean continuous flow principles. Four main themes emerged: algorithmic approaches, resource management, sustainability and computational methodologies. Contributions from Latin America show a good balance between theoretical progress and practical application, which suggests that the region is ready for Lean transformation.Practical implications The proposed framework addresses eight wastes specific to petroleum maintenance operations. It helps organisations cut down on maintenance costs by a lot while also promoting SDG 9 and SDG 12 through a culture of continuous improvement and systematic waste elimination. The framework provides actionable guidelines adapted to Latin American operational contexts, including hierarchical organisational structures and resource constraints.Originality/value This review provides the first comprehensive mapping of maintenance optimisation research through a Lean lens for the Latin American petroleum sector. By answering three key research questions and synthesising findings into an integrated framework, the study bridges the gap between advanced optimisation techniques and practical Lean implementation, proposing a contextualised framework that balances operational excellence with sustainable development objectives.
