Despite the recognized importance of strategic alignment for competitiveness and of quality management maturity for achieving operational excellence, a significant literature gap persists in systematically integrating these concepts. This study addresses how to systematically align strategic alignment dimensions with quality management maturity. Using a systematic literature review and thematic analysis, 843 documents were screened, resulting in 47 articles for data coding and analysis. The primary contribution is a cross-mapping table of strategic alignment and quality management maturity dimensions, which delineates interrelationships between six strategic alignment dimensions (skills, governance, value, communication, stakeholder participation, and scope) and seven quality management maturity dimensions (context, leadership, process management, resource management, performance analysis and evaluation, and improvement, learning and innovation). The research provides an integrated framework elucidating interaction mechanisms between these domains. Practically, it offers managers a diagnostic tool for assessing organizational status and developing simultaneous enhancement strategies for both quality maturity and strategic alignment. This framework fosters a paradigm shift in managerial approaches and facilitates leveraging existing platforms to strengthen the interaction between strategic and quality management systems, ultimately supporting sustained organizational excellence.IntroductionIn today's competitive landscape, industrial organizations require strategic alignment between objectives and operational activities for sustainable success. Strategic alignment creates coherence between organizational plans and daily operations, particularly when quality management systems directly support strategic goals, enhancing operational efficiency and customer satisfaction (Alam et al., 2024; Zavareh, 2021).Integrating Total Quality Management with strategic planning improves performance and builds organizational resilience (Okonkwo et al., 2025). Quality Management Maturity measures how organizations establish and improve quality systems (Dima et al., 2023), becoming crucial in Industry 4.0 environments for managing interconnected systems (Eliwa et al., 2024).However, achieving synergy faces challenges including misaligned priorities and inadequate measurement tools. Previous research has predominantly used case studies, revealing a significant gap in systematic reviews simultaneously addressing strategic alignment and quality management maturity. This study therefore conducts a systematic review to identify key dimensions in both domains that can mutually facilitate organizational strategic alignment.Literature ReviewStrategic alignment creates organizational coherence by integrating strategy with operational components (Kaplan & Norton, 2006). Recent studies confirm that strategic alignment and Quality Management (QM) maturity interact to enhance organizational performance and sustainable success (Younis et al., 2023), Alam et al., 2024). Contemporary strategic frameworks include the Balanced Scorecard (Qies & Adnan, 2024), McKinsey 7S focusing on leadership and culture (Kanyangale, 2024), and dynamic capability models (Maulana et al., 2023). Quality management has evolved through models like integrated QM systems (Akmal et al., 2024) and maturity frameworks (Masana, 2021). However, significant research gaps persist. Comprehensive frameworks assessing strategic alignment's impact on QM effectiveness remain underdeveloped, particularly regarding organizational culture's mediating role. Studies have predominantly examined these constructs separately, with limited research on their systematic integration (Kim et al., 2024), Siddiqui & Waiker, 2025). Furthermore, contemporary alignment models remain IT-focused, lacking application across diverse operational domains. This systematic review addresses these gaps by examining integrated strategic-quality frameworks.Strategic alignment frameworks include the Contingency Theory (McAdam, Miller, & McSorley, 2016), Strategic Fit Process (Beer et al., 2005), Balanced Scorecard (Kaplan & Norton, 2016), Strategic Alignment Maturity Model (Luftman, 2003), Quality Strategy (5P) Model (Pryor et al., 2007), Strategic Alignment Matrix (Bertolotti et al., 2019), Resource-Product-Market Model (Reed, 2023), McKinsey 7S (Boateng & Yamoah, 2023), (Kanyangale, 2024). Quality management models encompass Baldrige (Prabowo & Saptadi, 2020), (AL-Hashem & Abu Orabi, 2021), ISO 9004 (Silva & Matos, 2022), (Akmal et al., 2024), APQC (Silva & Matos, 2022), UBEM Vedic[1] (Koura & Talwar, 2008), Indonesian Performance Excellence[2] (Upadhyaya & Bhat, 2020), TQM (Kulenović & Veselinović, 2021), (Chen, 2024), Shingo[3] (Edgeman, 2018), EFQM (Dima, Vitzilaiou, & Glykas, 2022), Martusewicz, Wierzbic, & Łukaszewicz, 2024), and KAIIAE Model[4] (Upadhyaya & Bhat, 2020).MethodologyThis study employs a qualitative systematic review with an exploratory approach, designed to establish a theoretical foundation and address the research question (Gough, Oliver, & Thomas, 2017). The protocol adheres to the PRISMA[5] framework to ensure transparency, accuracy, and reproducibility (Page et al., 2021). The research process was structured into six distinct stages.First, the research question was formulated to identify the key dimensions in Quality Management Maturity (QMM) models and strategic alignment frameworks. Second, a comprehensive search strategy was developed using structured Boolean queries with three core keyword categories: "strategic alignment," "quality management maturity," and integrative concepts. The search was executed across major academic databases, including Scopus, Web of Science, for publications from 1983 to January 2025.Third, study characteristics were defined using strict inclusion and exclusion criteria. Included studies were original research, review articles, books, and conference proceedings in English or Persian that explicitly examined the interaction between QMM models and strategic alignment frameworks in industrial organizations. Studies from non-industrial sectors, publications in non-peer-reviewed sources, and works in other languages were excluded.Fourth, a rigorous quality assessment and screening process was implemented. After duplicate removal, articles underwent title/abstract screening followed by full-text evaluation. To ensure reliability and validity, standardized data extraction forms were used and continuously updated. Multiple strategies were employed to minimize bias, including publication, language, and methodological biases. A double-blind screening process was conducted using the Rayyan web-based tool, where authors independently assessed each article. Conflicts were resolved through consensus discussions among all authors (Page et al., 2021).Fifth, data synthesis involved thematic analysis following Braun and Clarke's (2006) approach. Key concepts such as leadership, strategy, processes, and stakeholder engagement were coded and categorized into broader themes. The analysis integrated dimensions from established strategic alignment models (Kaplan & Norton, 2006; Henderson & Venkatraman, 1990) and QMM frameworks (ISO 9004, EFQM). Finally, the synthesized themes were refined and defined to form the conceptual foundation of the study's integrated framework, ensuring comprehensive coverage of both domains.ResultsThe systematic review identified significant growth in publications, with Scopus data showing an increase from 64 articles in 2023 to 134 in 2024, indicating growing scholarly interest in strategy-quality integration. Geographically, the United States (176 articles) and China (82 articles) lead research production.As shown in Table 1, key findings revealed six strategic alignment dimensions: skills, governance, value, communication, interested parties’ participation, and scope which are compatible with previous researches such as (Miyamoto, 2018). Whereas seven quality management maturity dimensions emerged: context, leadership, process management, resource management, Performance analysis and evaluation, and Improvement, Learning and Innovation are in line with (Glogovac, Ruso & Maricic, 2022). The integration mechanism operates through four interconnected levels: foundational (leadership/governance), enabling (resources/competencies), operational (processes), and cultural (values/communications).Discussion and ConclusionThis study demonstrates that strategic alignment emerges not randomly, but as the outcome of an integrated, mature quality management system. The Proposed framework addresses critical gaps in understanding the causal mechanisms between these domains.Practically, the study offers organizations diagnostic method for assessing alignment gaps and developing integrated roadmaps. The mapping enables managers to identify specific QM maturity requirements for achieving strategic objectives, particularly in industrial settings where process-standardization interfaces are crucial (Kaplan & Norton, 2006). The findings affirm that strategic alignment and QM maturity represent complementary dimensions of organizational excellence rather than discrete constructs.The study's primary contribution lies in developing an integrated framework mapping systematic relationships between quality maturity and strategic alignment dimensions. Theoretically, this provides a novel contingency model explaining causal mechanisms between the domains. Practically, it offers organizations diagnostic tools for assessing alignment gaps and developing integrated roadmaps.For future research, empirical validation through large-scale quantitative studies and longitudinal case research is recommended. Developing industry-specific integrated maturity models and investigating moderating variables like organizational size and age would further enhance understanding. Additionally, exploring each maturity level requirements for these dimensions presents promising research avenues.
In the competitive international market space, a company's export performance has been recognized as a key indicator of its success, and identifying the factors influencing it is of particular importance. Therefore, this study, with the aim of examining the simultaneous impact of "production capabilities" and "competitive strategies" on "export performance" through the "marketing capabilities" pathway in the AmadehLaziz Company, was conducted. This was an applied, descriptive correlational study. The sample size was calculated at 320 individuals, and the sampling method was stratified random. Data collection was done through questionnaires, and the validity and reliability of these questionnaires were examined and confirmed. The data was analyzed using structural equation modeling. The results indicated that production capabilities have a direct impact on marketing capabilities. Additionally, competitive strategies play a determining role in strengthening marketing capabilities. On the other hand, marketing capabilities have both direct and indirect impacts on export performance.IntroductionThe ready-to-eat and semi-prepared food industry in Iran has experienced considerable growth and now meets both domestic and export demands. The success of firms in this sector is highly dependent on their production capabilities—namely, the ability to design, manufacture, and deliver products that effectively meet customer needs. At the same time, competitive strategies play a vital role in positioning companies within domestic and international markets, ultimately influencing their export performance. Nevertheless, export success cannot be solely attributed to production capabilities or competitive strategies; marketing capabilities are equally essential for translating internal strengths into actual market achievements. Therefore, this study investigates the combined impact of production capabilities and competitive strategies on export performance, emphasizing the mediating role of marketing capabilities. The aim is to provide a comprehensive understanding of how these organizational drivers interact to enhance export outcomes in companies operating within the ready and semi-prepared food industry.Literature ReviewProduction Capabilities: Production capability refers to the utilization of technology in the market. According to Makhmudov and Bustonov (2021), production capacity is the most critical phase of the feasibility factor. Serious opportunities can be gained to achieve an advantage in this area, but merely paying attention to these changes is not enough. Moreover, if you do not have the power to supply to the market or produce, the other capabilities will not hold meaning. Therefore, companies must always keep units such as equipment, human resources, and technical support ready in their operations for production power (Marx, 2020).Competitive Strategies: Competitive strategies, depending on the characteristics and goals of each organization, are known as strategic guidelines that help enhance competitive advantage and maintain market position. These strategies can include choices such as cost leadership with a focus on process optimization, concentrating on specific markets or particular customer segments (Oyewobi et al, 2016).Marketing Capabilities: Marketing capability is a set of skills, behaviors, tools, procedures, and knowledge that marketing specialists must possess to deliver a business strategy. Apasrawirote et al (2022) define marketing capability as the integrated process of utilizing the company's resources (tangible and intangible) to identify specific consumer needs, achieve competitive product differentiation, and deliver the brand's unique value.Export Performance: The success or failure of a company in exporting products or services to foreign markets through strategic planning and implementation is known as export performance (Chugan & Singh, 2015). Export performance is also recognized as the share of a company in the export sector. For instance, a company attempts to sell goods in foreign markets, modify the product, and set the product price in the international market (Kambey et al, 2018).MethodologyThis research is applied in purpose, as its outcomes can support real-world improvements in firms’ export performance. From a methodological standpoint, it is a descriptive-correlational study employing survey data. Structural equation modeling (SEM) was used due to its capacity to assess complex causal relationships among latent variables. The statistical population comprised experts, managers, and specialists in production, marketing, and export departments at AmadehLaziz Company—one of the leading firms in the Iranian ready and semi-prepared food sector. Based on the general guidelines for SEM sample size, 320 participants were selected through stratified random sampling. Reliability was assessed using Cronbach’s alpha, while construct validity was examined through exploratory factor analysis (in SPSS 26) and confirmatory factor analysis. Data analysis was conducted using LISREL 8.83.Results and DiscussionSEM results demonstrate that production capabilities significantly and directly enhance marketing capabilities. Competitive strategies also exert a strong positive effect on marketing capabilities, suggesting that companies equipped with robust strategic approaches and strong production foundations are better positioned to achieve superior marketing outcomes. Moreover, marketing capabilities have a direct and significant effect on export performance, indicating that firms with stronger marketing competencies achieve more favorable export results. The study further confirms the mediating role of marketing capabilities: production capabilities and competitive strategies influence export performance indirectly through marketing capabilities, while their direct effects on export performance are not significant.Discussion and ConclusionThe findings highlight the critical role of production capabilities, competitive strategies, and particularly marketing capabilities in shaping export success within the ready and semi-prepared food industry. Companies aiming to enhance their export performance should develop integrated strategies that strengthen all three dimensions. The study emphasizes that effective marketing functions as a strategic bridge connecting production strengths and competitive positioning to tangible export outcomes. Enhancing marketing capabilities can therefore serve as a powerful lever for improving competitive advantage and international market performance.
Maintenance constitutes major cost components in the oil and gas industries and play a vital role in maintaining production capacity and preventing accidents and unplanned damage costs. Despite the potential benefits of Industry 4.0 technologies, the development of this sector has been relatively slow, highlighting the need to identify and analyze the key success factors of Maintenance 4.0 in oil and gas industries to address existing limitations. This study was conducted in two parts: a qualitative phase (aimed at identifying key success factors) and a quantitative phase (aimed at modeling and scenario analysis). The analysis methods included thematic analysis in the qualitative section and the use of DEMATEL and Fuzzy Cognitive Mapping (FCM) in the quantitative section. The findings revealed 58 initial codes categorized into 15 key success factors. Backward scenario analysis indicated the importance of “system connectivity and integration” and “smart equipment condition monitoring,” while forward scenario analysis highlighted “system connectivity and integration,” “investment,” and “smart equipment condition monitoring” as the most influential factors. Therefore, the synergy among technical factors (data collection, transmission, and storage), organizational factors (transformational leadership, skilled workforce, and change management), and environmental factors (efficient supply chain) should be carefully considered to successfully implement Smart Maintenance in the oil and gas industry under the Industry 4.0 framework.IntroductionIncreasing global competition, growing production complexity, and the emergence of Industry 4.0 technologies have elevated maintenance management to a critical success factor for industrial organizations (Rojek et al., 2022; Kumar & Galar, 2018). In this context, Smart Maintenance, enabled by technologies such as the Internet of Things (IoT), artificial intelligence, and data analytics, facilitates continuous condition monitoring, early failure prediction, and informed decision-making, thereby enhancing operational efficiency, reliability, and safety (Cao et al., 2020; Jasiulewicz & Gola, 2019). The oil and gas industry, characterized by asset-intensive operations and high operational criticality, represents one of the most promising domains for the implementation of Smart Maintenance. However, challenges including high implementation costs, limited availability of reliable data, difficulties in integrating emerging technologies with legacy systems, and cybersecurity concerns continue to hinder its widespread adoption (Majstorović, 2022; Mojarad et al., 2018; Achouch et al., 2022). Accordingly, this study seeks to address the following research questions:What are the critical success factors for Smart Maintenance Management implementation in the oil and gas industry within the Industry 4.0 paradigm?What is the relative importance of these factors in terms of their influence and dependence?What causal relationships exist among the identified critical success factors? And which intervention scenarios can most effectively enhance these factors and facilitate the successful implementation of Smart Maintenance Management? Literature ReviewA review of the literature indicates that research on Smart Maintenance has primarily focused on two main streams: investigating the opportunities and challenges associated with the implementation of Industry 4.0 technologies and identifying the success factors and barriers to Smart Maintenance adoption across various industries. Previous studies have confirmed the critical role of factors such as technological infrastructure, data quality, digital skills, management support, systems integration, and cybersecurity in the successful implementation of Smart Maintenance. In the oil and gas industry, research has mainly concentrated on the development of technical architectures, failure prediction models, and data-driven approaches to enhance maintenance performance. Nevertheless, existing studies have largely examined technical, managerial, or organizational dimensions separately and have paid limited attention to a comprehensive analysis of Smart Maintenance success factors within the specific context of the oil and gas industry. Furthermore, only a few studies have addressed the prioritization of these factors, the investigation of their causal relationships, and the development of intervention scenarios for performance improvement. Therefore, a comprehensive framework for identifying, prioritizing, and analyzing the dynamics of critical success factors for Smart Maintenance in the oil and gas sector is still lacking.MethodologyThis study employed a mixed-methods (qualitative–quantitative) design with an integrated approach conducted in three phases (the first phase being qualitative and the second and third phases being quantitative). In the first phase, with the aim of identifying the critical success factors of Smart Maintenance management in the oil and gas industry, qualitative data were collected through semi-structured interviews with experts in maintenance engineering and the oil and gas sector. The data were analyzed using thematic analysis based on Braun and Clarke (2006). In the second phase, the factors identified in the previous phase were further examined through a structured questionnaire to determine their levels of influence and dependence. This analysis was conducted by integrating the DEMATEL method and Fuzzy Cognitive Mapping (FCM), where the total relation matrix (T) derived from DEMATEL was used as the input interaction matrix for the FCM model. The FCM analysis was implemented using FCMapper software, while the network visualization was performed in Pajek. In the third phase, based on the results of the second phase, forward and backward scenarios were developed to analyze the effects of interventions on the critical factors. The validity and reliability of the findings were ensured through the criteria proposed by Guba and Lincoln, along with the calculation of the Holsti reliability coefficient.ResultsThe findings indicate that the successful implementation of Smart Maintenance in the oil and gas industry depends on the identification and effective management of critical success factors. In the qualitative phase, a total of 58 critical success factors were identified and classified into 15 thematic groups. The results revealed that intelligent condition monitoring, system integration and connectivity, and investment are the most central factors, exhibiting the highest level of centrality and the strongest relationships with other identified factors. In the second and third phases, the study focused on ranking the identified factors, developing a fuzzy cognitive map model, and conducting scenario analyses. Based on the proposed FCM model, among the 15 groups of critical success factors, “system needs recognition,” “appropriate organizational culture,” and “efficient supply chain” were identified as driving factors, while “change management” was classified as a receiver factor, and the remaining factors were categorized as ordinary factors. The backward scenario analysis highlighted the importance of system integration and intelligent condition monitoring, whereas the forward scenario analysis emphasized the critical roles of system integration, investment, and intelligent condition monitoring in enhancing the effectiveness of Smart Maintenance implementation.DiscussionThe findings indicate that the critical success factors for Smart Maintenance implementation in the oil and gas industry can be categorized into three main groups: technical, organizational, and environmental, with their synergy being a fundamental requirement for success. Technical factors include digital infrastructure, data quality and integration, intelligent condition monitoring, cybersecurity, and process optimization, which collectively form the operational foundation of the system. Organizational factors play a decisive role in technology adoption and utilization, encompassing strategy, change management, organizational culture, skilled human resources, and top management support. Environmental factors are mainly related to the specific characteristics of the oil and gas industry, such as harsh operational conditions and supply chain constraints, which directly influence system effectiveness. The results show that weakness in any of these three dimensions can significantly undermine the overall system performance. Ultimately, the successful implementation of Smart Maintenance requires the simultaneous integration of technical, organizational, and environmental factors within a holistic systems-oriented approach.ConclusionThis study aimed to identify and rank the critical success factors of Smart Maintenance in the oil and gas industry using an integrated DEMATEL and Fuzzy Cognitive Mapping (FCM) approach. In the qualitative phase, 58 factors were identified and categorized into 15 groups, among which intelligent condition monitoring, system integration, and investment exhibited the highest centrality. The FCM results indicated that “system needs recognition,” “organizational culture,” and “supply chain efficiency” act as driving factors, while “change management” functions as a receiver factor. Scenario analyses further emphasized the importance of system integration, intelligent monitoring, and investment. Overall, the findings highlight that the success of Smart Maintenance relies on the synergy of technical, organizational, and environmental factors.
The increasing volume of industrial waste and the environmental pressures caused by non-biodegradable products have highlighted the necessity of redesigning supply chains within the framework of a circular economy. Polyester carpets, as a high-consumption product with a short lifecycle, play a significant role in this challenge. This study presents a mixed-integer linear programming model for the design and optimization of a closed-loop supply chain in the Iranian polyester carpet industry. In the proposed model, forward and reverse flows are integrated across multiple levels, including raw material supply, production, distribution, collection of used products, refurbishment, and recycling. The objectives of the model are to minimize the total network costs and enhance environmental performance through the use of eco-friendly materials and clean technologies. To address the multi-objective nature of the problem, fuzzy programming and goal programming approaches are employed. The model is validated through a case study in the Iranian carpet industry using real-world data. The results show that the fuzzy programming approach provides much better environmental performance than other methods. The environmental index value in this method is equal to 259,658, which shows a significant improvement compared to the value of 54437 of the goal programming method. However, this improvement is naturally accompanied by an increase in the total network costs. Overall, the proposed model creates a favorable trade off between the total network costs and environmental goals.IntroductionIn recent years, increasing environmental concerns and the rapid growth of industrial waste have drawn researchers’ attention toward the development of sustainable supply chains and circular economy approaches. In this context, the textile industry, particularly polyester carpet, has become one of the main sources of environmental pollution due to high consumption levels and the non-biodegradable nature of its materials. Despite advantages such as affordability and attractive appearance, these products create significant waste management challenges due to their short lifespan and multilayer structure. Conventional disposal methods such as landfilling and incineration are not only unsustainable but also result in severe environmental impacts, including the release of microplastics and toxic gases. In this regard, closed-loop supply chains have been introduced as an effective solution to integrate forward and reverse flows and reduce environmental impacts. However, there is still a lack of comprehensive models that simultaneously consider both economic and environmental objectives in the carpet industry. Therefore, this study proposes a multi-objective mixed-integer linear programming model for designing a sustainable supply chain in the Iranian polyester carpet industry.MethodologyIn this study, a multi-objective mixed-integer linear programming (MILP) model is developed for designing a closed-loop supply chain network. The network consists of multiple echelons, including suppliers, manufacturing plants, retailers, customer zones, collection centers, refurbishment facilities, recycling centers, and industrial companies. In the forward flow, raw materials are delivered to manufacturing plants, where polyester carpets are produced and distributed to customer zones through retailers. In the reverse flow, used products are collected and classified based on their condition; reusable products are sent to refurbishment centers, while non-reusable items are directed to recycling facilities. The recycling process includes shredding and melt-spinning stages, enabling the recovery of polyester fibers. The proposed model includes two objective functions: minimizing total network cost and maximizing environmental performance through the use of eco-friendly materials and clean technologies. To solve this multi-objective problem, three approaches fuzzy programming, weighted goal programming, and multi-choice goal programming are applied. The model is implemented and solved in GAMS based on a real case study from the Iranian carpet industry.Results and DiscussionThe results indicate that different solution approaches exhibit different behaviors in balancing economic and environmental objectives. The fuzzy programming approach achieves the best environmental performance and provides a satisfactory balance between conflicting objectives, although this comes at the cost of higher total system cost. In contrast, weighted goal programming and multi-choice goal programming approaches, which focus on minimizing deviations from aspiration levels, yield lower costs but weaker environmental performance. The findings also show that refurbishment plays an important role in reducing costs and improving sustainability, while recycling enhances resource efficiency by returning materials to the production cycle. Furthermore, sensitivity analysis indicates that parameters such as demand, return rate, and recycling capacity have a significant impact on system performance, highlighting the importance of their optimal configuration.ConclusionThis study presents a comprehensive multi-objective framework for designing a sustainable closed-loop supply chain in the polyester carpet industry, enabling simultaneous decision-making based on both economic and environmental criteria. The results show that the fuzzy programming approach achieves the best balance between cost and sustainability, while goal-based approaches are more suitable under strict financial constraints. From a managerial perspective, the development of collection infrastructure, expansion of refurbishment and recycling facilities, and adoption of clean technologies are key strategies for improving system sustainability. Moreover, collaboration among manufacturers, policymakers, and consumers plays a crucial role in the successful implementation of such systems. Ultimately, the findings demonstrate that integrating environmental considerations into supply chain design not only reduces negative environmental impacts but also enhances overall system efficiency and long-term sustainability.For future research, incorporating uncertainty in key parameters such as demand, return rate, and cost through fuzzy or stochastic approaches can improve model accuracy. In addition, the use of metaheuristic algorithms for solving large-scale problems, the assessment of advanced environmental indicators such as carbon footprint, and the inclusion of social sustainability dimensions such as job creation in collection and recycling centers can further enhance the model’s applicability and development under real-world conditions.
Sustainable manufacturing systems have emerged as a strategic approach in process industries, aiming to reduce environmental impacts, optimize resource utilization, and enhance economic and social performance. Despite their potential benefits, the adoption and implementation of such systems in the petrochemical industry are confronted with multiple challenges that may impede achieving sustainability objectives. Therefore, a systematic and context-specific identification of these challenges is essential. This study aims to identify and contextualize the challenges associated with the adoption and implementation of sustainable manufacturing systems in the Iranian petrochemical industry using a mixed-methods research design. In the qualitative phase, a meta-synthesis approach was employed to extract common challenges reported in previous studies. In the quantitative phase, second-order confirmatory factor analysis was applied to validate and structure the identified challenges within the Iranian petrochemical context. The study population comprised managers, policymakers, industry experts, and academic scholars with relevant expertise in the petrochemical sector, from whom 225 respondents were selected using a convenience sampling method. The results of the meta-synthesis revealed 45 challenges categorized into 12 major dimensions. The confirmatory factor analysis indicated that 41 of these challenges are significant and applicable to the Iranian petrochemical industry. Moreover, human resources, financial/economic, and organizational dimensions were identified as the most critical challenge areas, with path coefficients of 0.33, 0.316, and 0.247, respectively. The findings provide valuable insights for improving strategic decision-making, resource allocation, and sustainability-oriented policies, and offer a solid foundation for future research in similar complex and technology-intensive industries.IntroductionManufacturing activities are major contributors to greenhouse gas emissions and intensive natural resource consumption, operating under increasing pressure from stringent environmental regulations, rising consumer expectations, and the need to sustain productivity and competitiveness (Vijay Kumar & Shahin, 2025). Consequently, sustainable manufacturing has emerged as an essential strategic approach that integrates economic, environmental, and social objectives and represents a core pillar of future industrial development (Harikannan & Vinodh, 2025). Through energy-efficient technologies, eco-friendly materials, waste reduction, and circular economy principles, sustainable manufacturing systems can mitigate environmental impacts while enhancing efficiency and competitiveness. This transition is particularly critical in the energy-intensive petrochemical industry, which faces severe environmental risks, investment constraints, technological limitations, and resource imbalances (Shabur et al., 2025). Despite its potential benefits, effective implementation requires a context-specific understanding of challenges, a gap that remains insufficiently addressed in the Iranian petrochemical industry. Addressing this gap, the current study seeks to answer the following research question:What are the key challenges associated with the adoption and implementation of sustainable manufacturing systems in the Iranian petrochemical industry?Literature ReviewThe global literature on sustainable manufacturing systems has grown rapidly, emphasizing emerging technologies such as artificial intelligence, digital twins, blockchain, circular economy practices, and the paradigms of Industry 4.0 and Industry 5.0 as key enablers of economic, environmental, and social performance. These studies highlight the potential of digitalization and human-centric approaches to enhance productivity, resilience, and sustainability in manufacturing. However, challenges related to technology integration, institutional and policy constraints, ethical issues, and cybersecurity persist. Empirical research using multi-criteria decision-making methods has prioritized barriers linked to technological readiness, human resources, and training. At the national level, studies mainly adopt quantitative approaches and focus on environmental, technological, and financial factors. Despite this progress, systematic and context-specific analyses of sustainable manufacturing challenges in energy-intensive industries, particularly the Iranian petrochemical sector, remain limited. This motivates the present study's integrated meta-synthesis and second-order factor analysis approach.MethodologyThis study has an applied purpose and adopts a mixed-methods design. In the qualitative phase, a meta-synthesis approach following the framework of Sandelowski and Barroso (2007) was employed to systematically identify and organize the barriers to the adoption and implementation of sustainable manufacturing systems. In the quantitative phase, second-order confirmatory factor analysis (CFA) using a formative–reflective measurement model was applied in SmartPLS to screen, validate, and contextualize the identified challenges within Iran’s petrochemical industry. Data were collected from industry experts and academic specialists, and construct reliability and validity were assessed through internal consistency, convergent validity, and discriminant validity criteria.ResultsThe findings show that effective adoption of sustainable manufacturing systems in Iran’s petrochemical industry relies on systematically identifying and contextualizing key challenges. The qualitative meta-synthesis of 29 studies identified 12 dimensions and 45 challenges across organizational, human, economic, technological, environmental, and institutional domains. Quantitative analysis using second-order formative–reflective CFA confirmed the robustness of the hierarchical model, with acceptable reliability, validity, and no multicollinearity issues. Bootstrapping results indicated that 41 challenges significantly contributed to the higher-order construct of sustainable manufacturing challenges. Among the dimensions, human resources, economic/financial, and organizational challenges had the highest weights, highlighting their dominant role and priority for managerial attention and policy intervention.DiscussionThe findings indicate that the challenges associated with adopting and implementing sustainable manufacturing systems in Iran’s petrochemical industry are multidimensional, systemic, and highly context-dependent. The identification of 12 dimensions and 45 challenges, with 41 empirically validated as significant, underscores the necessity of contextualizing and localizing theoretical frameworks of sustainable manufacturing. Results from the second-order confirmatory factor analysis reveal that these challenges do not reflect a single underlying factor but emerge from the interaction of organizational, human, economic, technological, and institutional dimensions. The prominence of human resource, economic–financial, and organizational dimensions suggests that the transition toward sustainable manufacturing is primarily a managerial, structural, and human-centered challenge rather than a purely technological one.ConclusionBy proposing a formative–reflective hierarchical model, this study provides a comprehensive framework for analyzing sustainable manufacturing challenges in Iran’s petrochemical industry. The results demonstrate that successful implementation of sustainable manufacturing systems requires a systemic, cross-functional approach grounded in realistic prioritization of key challenges. Emphasis on human capital development, organizational restructuring, and the design of stable financial and supportive policies plays a decisive role in facilitating this transition. The proposed framework offers a robust foundation for managerial decision-making, industrial policy formulation, and future academic research, and it can be adapted to other process-based industries with similar structural and technological characteristics.
