Concerns regarding deteriorating climate conditions have heightened pressure on companies to mitigate the environmental impact of their operations. As a result, firms have employed quality management practices such as lean in their sustainable operational framework. However, there are contradictory studies reporting on the positive impact of lean operations on environmental performance. This study aims to contribute to this debate by: 1) understanding the impact of lean operations on environmental performance; and 2) examining the environmental (boundary) conditions, such as munificence, dynamism, and complexity, that moderate the relationship between lean operations and environmental performance. We draw on contingency theory to theorize that the effectiveness of lean depends on its alignment with external environmental conditions rather than operating uniformly across contexts. Using secondary data from Compustat and the Carbon Disclosure Project (CDP), our hypotheses are tested using hierarchical linear regression models with random effects. The statistical results show that lean operations have a positive impact on environmental performance. Furthermore, our results show that munificence and dynamism substantially hinder firms' ability to achieve eco-effectiveness, revealing the paradox of munificence and underscoring the need for managers to consider environmental context when implementing lean systems.
Organizations need quality systems that convert external sustainability pressures into agile, innovative operations. Moving beyond prior work that views the Chief Sustainability Officer (CSO) largely as a symbolic signal, we reconceptualize the CSO as an enterprise Sustainability-Quality Management System (S-QMS) architect who integrates external knowledge into standardized processes, data governance, and continuous improvement routines. Analyzing 15,282 firm-years (2009-2019) and Sustainalytics ESG ratings, we find that firms with CSOs exhibit significantly higher total ESG scores and higher Environmental, Social, and Governance (ESG) pillars, with average total scores approximately 0.323 point (7.8%) higher than firms without CSOs; results hold in OLS, propensity-score matched, and entropy-balanced samples. We also document appointment-year improvements, especially on environmental metrics, consistent with near-term quality system effects. Theoretically, we integrate Resource Based View (RBV) and signaling theory with dynamic capabilities and Quality Management routines (PDCA/Kaizen) to explain how CSOs transform external knowledge into process control, disclosure quality, and performance gains. For practitioners, our findings suggest that to maximize business excellence, CSOs should be empowered not just as strategists, but as operational leaders with authority over quality governance and process optimization.
Quality management in manufacturing often relies on acceptance sampling to ensure product reliability while protecting both producers and consumers. In reliability acceptance sampling, lot decisions are made according to whether sampled items satisfy specified lifetime or reliability requirements. In practical situations, lifetime data are observed at discrete time intervals because continuous monitoring of failures may be impractical or costly. This study proposes a methodological and computational framework for constructing reliability acceptance sampling plans for discrete lifetime data under the discrete Lindley distribution. The framework is developed under Binomial, Poisson, and Bivariate Poisson settings, with the bivariate formulation restricted to the independent case. The proposed plans are used to determine the minimum sample size required for lot acceptance and the corresponding true mean lifetime of accepted lots while controlling producer's and consumer's risks. The performance of the proposed sampling plans is evaluated through minimum sample size requirements and operating characteristic values. A real industrial dataset is presented to demonstrate the practical applicability of the methodology. The findings show that the proposed framework provides an effective and useful tool for reliability-based lot sentencing when lifetime observations arise in discrete form, subject to the stated distributional and independence assumptions.
This study examines how quality assurance actors in Islamic higher education institutions interpret and enact global quality standards within their everyday managerial practices. Drawing on an interpretive qualitative approach, the study employs interpretative phenomenological analysis to explore the lived experiences of 14 quality assurance actors across four Islamic universities in East Java, Indonesia. The findings show that global standards such as ISO 9001 and ISO 21001 are not applied as neutral or self-executing instruments but are continuously interpreted, negotiated, and adapted in relation to institutional values, professional roles, and organizational constraints. Quality assurance practices are shaped by sensemaking processes through which actors align global quality frameworks with Islamic values, academic traditions, and local governance structures. The study highlights the central role of quality assurance actors as interpretive intermediaries who translate global standards into contextually meaningful and operationally sustainable practices. By foregrounding actors' interpretive work, this study contributes an actor-centered perspective to quality assurance scholarship and offers insights into the implementation of global quality standards in value-based and non-Western higher education contexts.
This study examines the quality of work life (QWL) of food delivery riders in China and its implications for job satisfaction and intention to stay from a quality management perspective. Drawing on ERG and self-determination theories, we view QWL as a human-centered internal quality component shaped by work system design. Five QWL dimensions, such as pay, autonomy, competence, flexibility, and relational challenge, are examined to understand how they influence riders' satisfaction and retention. Survey data were collected from 253 food delivery riders. Structural equation modeling (SEM) assesses the net effects of QWL dimensions on job satisfaction and retention, while fuzzy-set qualitative comparative analysis (fsQCA) identifies combinations of conditions associated with high and low job satisfaction. The results show that all five QWL dimensions are related to job satisfaction, but only autonomy and relational challenge influence intention to stay indirectly through satisfaction. The fsQCA reveals seven paths to high job satisfaction and three paths to low job satisfaction. Overall, the findings provide insights into how platforms can improve workforce sustainability by supporting diverse work conditions.
PurposeDue to the incessant focus on climate change, international, regulatory, and social pressures are compelling organizations to adopt green supply chain management practices (GSCPs). This study analyzes the impact of a holistic set of Green Supply Chain Management (GSCM) practices including Internal Environment Management (IEM), Green Purchasing (GP), Cooperation with Customers (CC), Eco-Design (ED), Investment Recovery (IR), Reverse Logistics (RL), Green Distribution (GD), and Green Warehousing (GW) on sustainability performance (SP) constituting environmental, economic, and social performance. This investigation draws on the Natural-Resource-Based-view, which presents GSCM as a strategic capability that leads to Competitive Advantage (CA), which in turn leads to SP. Therefore, it examines the mediation of CA between GSCM & SP. In addition, the moderating role of Total Quality Management (TQM) is also examined.Design/methodology/approach392 experts from the Indian manufacturing industry were surveyed, and the data were analyzed using PLS-SEM through SmartPLS.FindingsThe findings suggest that a holistic set of GSCM practices positively impacts EVP, ECP, & SCP with partial mediation of CA, and an absence of moderation of TQM.Originality/ValueThe study extends the previous literature by considering a broader spectrum of GSCPs. Moreover, this study examined the moderation of TQM between GSCM & SP, which has rarely been analyzed previously. Implications: This study strengthens NRBV theory and directs managers and policymakers about the positive impacts of GSCM.
Contemporary manufacturing environments have shifted from intuition-based decisions to data-driven strategies, particularly those related to quality control. Quality remains central to manufacturing competitiveness; therefore, early detection of quality issues is important for minimizing rework, delays, and costs. This article proposes a modular data-driven quality management system (DD-QMS) that integrates 3-D scanning, machine learning, and near real-time decision support to enable proactive quality intervention during manufacturing. The framework follows a modular workflow in which high-resolution 3-D scans are converted into structured deviation profiles and analyzed through neural network models to classify components into acceptable, reworkable, and reject categories with real-time decision support. The DD-QMS is demonstrated through a case study of crankshaft manufacturing, in which the system detects dynamic imbalances and dimensional deviations early in the process. This workflow enables early visibility into deviations and complements existing inspection practices. This pilot implementation demonstrated that the DD-QMS improved root cause traceability, reduced rejection costs, and enabled faster decision cycles. The framework supports a strategic transition from reactive inspection to real-time self-regulating quality systems, aligning with the goals of smart manufacturing and Quality 4.0. Its modular design allows scalable implementation, offering practical value for both advanced and digitally transitioning manufacturing setups.
Quality management in dairy production is particularly important given the specific challenges of the sector, such as the high perishability of products, environmental and resource usage concerns, and strict hygiene and safety standards. Moreover, increasing globalization has intensified consolidation through mergers and acquisitions in the dairy industry. This has created an uneven competitive environment for small firms and reinforced the need for quality management approaches that remain simple, yet robust and effective, to sustain competitiveness. This study proposes an integrated decision model that combines traditional quality management tools with a multi-criteria decision-making (MCDM) approach to support the prioritization of improvement actions in a dairy company. The complexity of the problem, which involves multiple and often conflicting organizational objectives, justifies the use of an MCDM method. FITradeoff was selected due to its simplicity and flexibility, which enables the development of a robust model without excessive effort. Traditional quality tools such as the Pareto diagram, the Ishikawa diagram, check sheets, and an adapted ABC curve were used to identify and quantify non-conformities and their root causes, given their ease of application and their recognized role in supporting operational analysis. As a result of this integrated approach, 43 actions were ranked, indicating a path of prioritization for the organization in the direction of total quality management. This is a managerial contribution of the article. The implementation of these actions resulted in reducing non-conformities by similar to 13% in the initial months. Thus, this offers a practical solution for the organization and serves as a reference for other contexts. In theoretical terms, the study contributes to the area of quality management, providing a new integrated approach with quality tools and the MCDM method within the peculiarities of the dairy sector.
This study investigates how key consumer perceptions influence purchase intention (PI) and word-of-mouth (WOM) in the apparel industry. Drawing on the Stimulus-Organism-Response framework, corporate social responsibility (CSR) and quality constructs are modeled as external stimuli that affect organism, particularly satisfaction, which in turn drive consumer behavior. Using survey data the study employs partial least squares structural equation modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA) to test the relationships and assess relative importance and performance. Results show that product and service quality remain the dominant predictors of behavioral outcomes. Satisfaction as a key mediator transmits marketing stimuli to behavioral responses. IPMA findings reveal distinct motivational mechanisms: WOM is more socially driven, responding strongly to CSR cues, whereas PI is more value-driven, shaped primarily by value acceptability and fairness. Notably, contrary to the common assumption that CSR directly strengthens both advocacy and PI to a similar extent, our findings suggest CSR's influence is comparatively stronger for WOM than for immediate PI in this context. This research is the first model integrating CSR and quality factors in apparel consumer behavior, advancing theoretical understanding in consumer behavior and strategic quality management. It offers actionable insights for sustainability and consumer-focused quality strategies to enhance PI and WOM.
This study aims to improve the efficacy and efficiency of a Corrective Action and Preventive Action (CAPA) process within a medical device organization by utilizing Design for Lean Six Sigma. The primary objectives are to mitigate compliance risks and reduce the time required to complete a CAPA. This project illustrates the application of Design for Lean Six Sigma (DFLSS) principles and the structured Define, Measure, Analyze, Design, and Verify (DMADV) methodology in the redesign of a CAPA process. The use of the DMADV methodology reduced the average duration of CAPAs by just under 60%. This improvement was attributed to the data-driven structured problem-solving approach employed. To sustain these improvements, new processes and training documentation were developed, and metrics were defined for monitoring and further improvements. All changes were achieved in compliance with 21CFR Part 820 and without any non compliance to regulations. The study was conducted within a single organization; however, the findings can be leveraged by other regulated organizations to ensure compliance. Future research should consider applying the DMADV approach to other quality system processes in diverse organizational settings to validate its broader benefit. This study represents the first application of the DMADV DFLSS methodology outside of a manufacturing process; and to redesign a CAPA process within the highly regulated MedTech sector and also if one of the few applications of DFLSS to a regulated QMS process. The findings have broad implications for benchmarking that extend beyond other elements of quality systems and into numerous sectors.
This study applies and empirically validates the Quality 4.0 Capability Roadmap to assess its effectiveness in evaluating organizational readiness and maturity while supporting strategic development. Quality 4.0 refers to the integration of traditional quality management principles with the digital technologies characteristic of Industry 4.0, enabling data-driven, connected, and continuous improvement practices. Using a qualitative approach, the research explores three case studies-a service organization, a textile manufacturer, and a tool manufacturer-to provide in-depth insights into the challenges and opportunities of Q4.0 transitions. Data collection involved semi-structured interviews, which were analyzed through deductive coding, complemented by direct observation as a triangulation method. The findings demonstrate that the roadmap effectively assesses Q4.0 maturity across different sectors, enabling the creation of tailored strategic guidelines to facilitate digital transformation. However, it was found to be more tailored to typical manufacturing industries and that further refinement is needed to enhance its contextual sensitivity. This study makes a novel contribution by empirically validating a capability roadmap designed for Q4.0 transitions. By offering empirical evidence and strategic insights, this article provides valuable guidance for organizations navigating the complexities of Q4.0 adoption.
This study integrates Value Stream Mapping (VSM) and the Overall Equipment Effectiveness (OEE) and covers a critical research gap by exploring the relationship between lean wastes and OEE losses. VSM allows a view of the stream and a clear identification of the lean wastes. The OEE allows an in-depth view into the inefficiencies in operating equipment and the associated losses. Through a focused analysis of lean wastes and OEE losses followed by the investigation of their interconnections, the study provides guidance for system improvements. A new frame is constructed combining the VSM view and the OEE view. Subsequently, waste analysis, bottleneck analysis, and losses analysis are located on a novel map depicting an approach for enabling improvements within a system view. Based on the established connections between the wastes and losses, foundations for handling several system improvement project types are provided. A system improvement digitalization structure is introduced, and the related network is drafted. Accordingly, the digitalization of the identified lean improvement projects is outlined. The outcomes of the study compose a framework for analyzing lean wastes and OEE losses enabling digitalization of production systems. This work lays a foundation for future research and guides practical implementations.
Enhancing organizational performance in small and medium-sized enterprises has received growing attention, yet evidence of how specific Quality Management Practices (QMP) influence organizational performance (OP) remains limited. Purpose: This study examines the direct and mediated relationships between seven QMP dimensions and OP, grounded in the Resource-Based View (RBV). Methodology: We analyzed survey data from 195 managers of SMEs affiliated with the Oman Investment Authority (OIA) using partial least squares structural equation modeling (PLS-SEM). Results indicate that QMP are positively associated with OP overall. Findings: People engagement is not a significant predictor. Green innovation shows no significant direct effect on OP, but mediates the effects of leadership & management commitment, customer focus, people engagement, and relationship management on OP, with no mediation for process approach, evidence-based decision-making, or improvement. Significant of the study: These findings refine RBV-based explanations of capability performance links and clarify when GI acts as a pathway rather than a standalone driver. Practically, the study guides SME leaders and policymakers to prioritize QMP dimensions with the strongest direct and GI-mediated links to OP in resource-constrained settings.
Given the scarcity of empirical evidence concerning the relationship between particular quality management practices and business performance in service-oriented organizations, this study aims to investigate the effects of process management (PRM), elimination of waste (EOW), and just-in-time (JIT) methodologies on operational performance (OP) and business performance (BP) within the context of developing economies. A quantitative research design was adopted, employing survey questionnaire to collected data from 463 banking professionals of Bangladesh, a representative of developing country. These data were further analyzed through partial least square structural equation modeling (PLS-SEM) to test the proposed relationship. The findings demonstrate that PRM exerts a substantial positive influence on EOW (beta = 0.36, p < 0.01), JIT practices (beta = 0.45, p < 0.01), and OP (beta = 0.17, p < 0.01). EOW, in turn, significantly impacts JIT practices (beta = 0.14, p < 0.01) and OP (beta = 0.27, p < 0.01). Moreover, JIT practices positively affect both OP (beta = 0.33, p < 0.01) and BP (beta = 0.15, p < 0.05). OP significantly predicts BP (beta = 0.52, p < 0.01) and fully mediates the relationships between PRM and BP, as well as between EOW and BP, while partially mediating the JIT practices to BP relationship. The discerned findings offer actionable insights to different stakeholders, including bank managers and policymakers, emphasizing the importance of integrated quality management practices to improve operational efficiency and BP in developing country. These practices serve as a catalyst for integrating PRM, EOW, JIT practices within a unified framework in a developing-country banking context to improve OP and BP, thereby extending total quality management and operational performance literature.
This study examines the effects of Lean techniques on the sustainability performance of manufacturing processes, focusing on an Italian company producing steel and concrete processing machines. Using Sustainable Value Stream Mapping (Sus-VSM), it assesses economic, environmental, and social sustainability before and after a Lean project on the straightener production line. The research is based on a single case study conducted from March to September 2023. Sus-VSM was applied to map the production flow and select key sustainability indicators across the three dimensions. Data were collected through direct observation, internal documentation, and questionnaires. Results show that major benefits include shorter lead time, more efficient inventory management, reduced CO2 emissions from suppliers, full employee training, and a shift to 100% local sourcing. The study also identifies useful metrics for evaluating the influence of Lean practices on each sustainability dimension. Although previous research has discussed connections between Lean and sustainability, few studies have empirically examined all three dimensions within a single operational context. Addressing this gap, the study supports an integrated understanding of how Lean contributes to sustainable performance. It also provides a practical and replicable framework, showing how Sus-VSM can help manufacturing firms monitor and enhance sustainability.
