Interpretive Structural Modeling Research Articles

Improving halalness and food safety management systems in the Indonesian broiler supply chain: an interpretive structural modeling and Bayesian network approach

Purpose By exploring the halalness and food safety risks from the perspective of technology and the relationship among them, this study aims to make quantitative predictions of such risks in the broiler supply chain to determine the critical control points (CCPs) in Hazard Analysis Critical Control Point (HACCP). Design/methodology/approach This study integrates Interpretive Structural Modeling (ISM) and Bayesian Network (BN) to achieve the objectives. Data were collected from focus group discussions (FGDs) with experts and direct observations at the broiler supply chain. Findings This paper identified 19 risks in the Indonesian broiler supply chain. The risk for halalness and food safety reached 30.92%, indicating that assuring halalness and food safety remains improbable or unlikely. The two CCPs of halalness and food safety are the knife’s sharpness and the vehicle’s storage temperature. Research limitations/implications This study quantifies the halalness and food safety risks in the Indonesian broiler supply chain, but it only involves one step forward and one step backward in the slaughterhouse’s chain. Practical implications The findings can provide insights for stakeholders, such as business owners, employees, management system auditors and consumers, regarding the critical control points of halalness and food safety in the broiler supply chain to improve the halalness and food safety management systems. Originality/value This study’s novelty lies in the examination of halalness and food safety risks using a risk prediction model to determine CCPs for the HACCP plan in the broiler supply chain in Indonesia.

Modelling of critical success factors to improve the supply chain resilience for sustainable construction sector

PurposeThe construction industry has an intricate and constantly evolving supply chain (SC) that includes multiple stakeholders and processes. Enhancing SC resilience (SCR) is essential for promoting industry expansion and competitiveness. The SCR has attracted considerable interest because of the increasing worldwide challenges and threats facing SCs. This study introduces a conceptual framework for recognizing and classifying resilient supply chains’ critical success factors (CSFs) in sustainable construction.Design/methodology/approachThis research employs a sequential mixed-methods approach, combining qualitative and quantitative analyses. Initially, an extensive literature review and expert consultations were conducted to identify potential CSFs. The fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was then applied to classify these CSFs into cause-and-effect groups. To further analyse the interrelationships among the CSFs, interpretive structural modelling (ISM) was utilised, establishing a hierarchical framework. This integrated methodology provides a comprehensive understanding of the CSFs’ relative importance and their driving and dependence power, facilitating targeted interventions for enhancing supply chain resilience in the construction sector.FindingsThis integrated methodology establishes a hierarchical ranking of CSFs across five levels by revealing their causal interrelationships. The study addresses crucial SCR issues and provides actionable insights. The 17 finalized CSFs have 12 causal and five effect factors. The “safety measures and guidelines” have the highest driving potential, whereas the “availability of standardized materials/equipment” and “skilled human resources” depend highly on causal CSFs. The findings will offer a detailed perspective to narrow the gap between unsustainability and sustainability by enhancing the resilience of CSC.Practical implicationsIt offers a pragmatic framework for allocating resources and efforts to enhance the resilience of construction supply chains (CSCs). Policymakers can also use this study to create a more resilient SC for sustainable construction.Originality/valueThis study uniquely identifies and evaluates CSFs for SCR in the construction industry, providing a novel analysis of their driving power, dependence power and ranking. No previous studies have demonstrated such a comprehensive evaluation.

Exploring the challenges and opportunities for sustainability reporting adoption among small and medium enterprises: A case in a developing country in Asia

Sustainability has turned into a critical focus for businesses, drawing considerable interest from the commercial sector and scholarly environments. While empirical investigations have been conducted regarding sustainability reporting within small and medium enterprises, only a limited number of companies are subjected to increased pressure to adopt sustainability reporting practices, thereby ensuring enhanced transparency and disclosure in their financial and sustainability disclosures. This research, framed by Institutional Theory, delves into how challenges in sustainability reporting obstruct organizations from properly evaluating and sharing their progress on sustainability aims. With an explanatory research framework in place, we circulated survey questionnaires to 400 participants, who were randomly drawn from a population of 28,927 registered SMEs in Metro Manila, Philippines. The application of Interpretative Structural Modelling and MICMAC Analysis revealed that the absence of regulatory frameworks, governmental assistance, and sustainability infrastructure constitutes the most critical obstacles impacting other determinants. In contrast, neither the deficiency in sustainability awareness nor the inadequacy of training and skills demonstrated a considerable impact on the other identified barriers. This study clarifies the complex interactions and interrelations among the obstacles to sustainability reporting, thus providing significant perspectives for organizations aiming to overcome these difficulties. The findings suggest that business leaders and stakeholders can formulate targeted strategies and interventions to facilitate the adoption of sustainability reporting practices within organizations. The application of the institutional theory framework highlights that pressures arise from a diverse array of institutional actors, including regulators, customers, and local communities, which collectively shape corporate behavior and reporting methodologies.

Enhancing Emergency Cognitive Ability in College Students Under Emergencies: A Study of Influencing Factors and Hierarchical Relationships

This study addresses the need to enhance college students’ emergency cognitive ability by identifying key influencing factors and analyzing their hierarchical structure. To fill the gap in understanding these relationships, we used grounded theory to identify 12 influencing factors through a literature review, case analysis, and interviews. The interpretive structural modeling (ISM) method categorized these factors into three levels: direct, key, and root factors. Root factors such as risk awareness, crisis perception, and responsibility are core to the cognitive ability framework and have a profound impact on students’ responses to emergencies. Further, the matrix of cross-impact multiplications applied to classification (MICMAC) analysis categorized the factors based on driving force and dependency, showing strong interrelationships. The integration of ISM-MICMAC methods offers a novel approach to understanding the hierarchical influence among factors, enabling educational institutions and policymakers to design targeted emergency training programs. By incorporating information technology into the educational process, this research provides practical guidance for enhancing students’ preparedness and resilience in emergencies. The findings support policy development and the design of effective educational interventions, offering valuable insights for administrators, policymakers, and emergency management professionals in creating safer, more resilient educational environments.

Unveiling the strategic impact of big data analytics capabilities in the Saudi Arabian banking sector: an explorative approach

Purpose The purpose of this study is to identify a critical pathway of the effect of big data analytics capabilities (BDACs) on strategic vigilance based on hierarchical process and a capability approach. Design/methodology/approach The researcher adopted a qualitative approach using interviews and a quantitative approach based on the interpretative structural modeling (ISM) fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach. A primary theoretical approach was also conducted to identify BDACs previously cited in the literature. Findings Four main subdivisions of BDACs were identified: management capabilities, infrastructure flexibility, talent capability and technology. Management capabilities followed by big data technical knowledge and associated with talent capabilities generate a flexible infrastructure to enhance SV. A dynamic capability perspective of knowledge and information is also required for SV. Research limitations/implications Despite the opportunity of this research and the originality of results, some limitations have to be mentioned and can constitute further directives for future researchers, such as the problem of result generalization. First, this research was based in Saudi Arabia, and a comparative approach to defining BDAC on an international level can be more beneficial in providing an exhaustive list of these capabilities. Second, reliability issues, in this research can be addressed due to the use of qualitative data collection which is considered by many researchers as unspecified and can lack scientific rigor. Future studies can improve the number of interviews during the data collection process and data process using an advanced methodological approach. Third, the effect of BDAC in SV according to the hierarchical final modal is not quantified, future work can use this research model to appreciate each effect using a quantitative approach such as correlation and structural equation modeling while considering respondents with different profiles to take into account different point of view in this concern. Practical implications This research enriches the BDAC and MICMAC literature and contributes to this aspect in three main levels. First, by providing an additional empirical asset in this field, this study offers by the way a new case to the big data literature on the banking sector. Based on the limited knowledge as well as results collected from different databases and rigorously analyzed, this subject was not treated previously and the author could not find similar studies with the same approach dealing with the key BDACs in Saudi Arabia. Social implications This research presents three main implications for policymakers and researchers interested in big data analytics (BDA) through a capability and strategic perspective. First, to attain SV, they should prioritize the development of interactive interfaces and open platforms as the primary step before collecting information and deconstructing it to guarantee the generation of knowledge and make decisions effectively. Second, policymakers must introduce organizational technologies in terms of technology management, technical knowledge and technology for decision-making. This requires simultaneous sharing and communication according to relational management. Third, the research conclusions have many critical managerial ramifications for banks in Saudi Arabia while considering the adoption of BDAC. The importance of BDACs (especially technical aspects) in shaping the decision-making to be strategically vigilant emphasizes policymakers’ orientation by paying close attention to these aspects and specific training programs to facilitate the use of such technologies and guarantee strong security measures. Moreover, findings support a balance between technical and functional BDAC. Originality/value The adoption of a knowledge-based dynamic capabilities (KBDCs) view to analyze the interaction between different BDACs in banks in Saudi Arabia to be strategically vigilant using a mixed approach.

A data-driven ISM-BN model for safety analysis of inland shipping in the Pearl River Basin

Inland shipping of the Pearl River plays an important role in the Chinese shipping system. To ensure navigation safety, we collect reports of maritime accidents from 2015 to 2022 in the Pearl River basin. This article extracts influencing factors by collecting the experience of inland waterway safety navigation and analyzing accident reports. Then, this paper uses the interpretative structural modeling method (ISM) to build a correlation model. Using a data-driven Bayesian network (BN), it analyzes the impact of various factors on the safety navigation in the Pearl River. The model validation is completed by compared with tree augmented naive Bayes classifiers (TAN) network using the same validation samples, through validation with the test set, the prediction accuracy has improved by 25%. The results indicate factors such as vessel type, accident month, accident day and time, etc. have a significant impact on the safety of navigation in the inland Pearl River waterway. The method used can identify important risk factors for accidents and the average predictive probability of validation samples reaches 87.03%. These research results could be extended to maritime management efforts in the Pearl River Basin.

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Analyzing the factors influencing sustainable supply chain management in the textile sector

Sustainability one of the burning issues in the global supply chain, and it is increasingly becoming a concern for textile industries. Moreover, developing countries have become the production hub of apparel and textiles, produced a high volume of waste and posed a threat to the environment and local community. By addressing sustainability challenges, Sustainable Supply Chain Management (SSCM) enhances long-term business viability and provides a competitive advantage. In this project, by extensive literature review, forty-five factors were identified which are driving the development process of SSCM. Responses were collected using the Likert Scale (1–5) from the focus group to narrow down the findings and ten have been selected as more appropriate in the perspective of Bangladesh. Later these ten factors were analyzed to find out the most influential factor using the Interpretive Structural Modeling (ISM) method by contextual relation-making. Community Groups and Environmental Organizations were found to be the most impactful among all the factors having the highest driving power.

Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines

To effectively mitigate unsafe events and accident symptoms stemming from flight dispatchers’ human factors, this paper proposes a novel risk evaluation model to accurately identify and evaluate potential human risks associated with flight dispatchers. First, the HFACS (Human Factors Analysis and Classification System, HFACS) model is employed to construct a human risk assessment indicator system for flight dispatchers. Second, the hesitant fuzzy set is introduced to represent the uncertainty during experts’ evaluation, and the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) method is applied within a hesitant fuzzy environment to obtain rankings of human factors. Third, the hesitant fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory, DEMATEL)-ISM (Interpretive Structural Modeling, ISM) approach is constructed to analyze the correlation among human factors, leading to the establishment of a multi-level hierarchical structure model. Finally, a case study of risk assessment for human factors of flight dispatchers in Sichuan Airlines is conducted to demonstrate the effectiveness of the proposed method. The results revealed the flight dispatchers’ human factors associated with higher risks and identified the key factors with a larger impact on other factors in Sichuan Airlines. Subsequently, a multi-level hierarchical structure model comprising five layers is developed to investigate the internal correlations among human factors, facilitating the formulation of targeted improvement suggestions for the higher risk indicators and key influencing factors.

Examining the challenges impeding the utilization of construction materials with lower embodied carbon in the construction sector

ABSTRACT This paper aimed at determining the barriers preventing the extensive utilization of innovative materials in the construction sector. As a way of providing clear understanding of the complex interrelationship between the barriers, the literature review was supplemented by an expert-based survey to determine the barriers, whereas the hierarchical structure between the barriers was analyzed using the interpretive structural modelling (ISM) approach. Based on the outcome of the level partitioning of the ISM approach, it was found out that “reluctance to take responsibility for embodied carbon reduction (B14) was partitioned at the 8th level indicating that it is the most highly prioritized barrier considered to be the most crucial in preventing the use of low-carbon building materials in the construction industry. The paper produces vital knowledge and evidence that could be applied to encourage more sustainable construction practices by the wide adoption of alternative construction materials with lower embodied carbon in the construction sector. The implications inferred based on the findings of this paper seek to suggest remedies, strategies, and actions that could help to overcome the undesirable impact of the barriers that affect the utilization of low-carbon building materials, particularly amongst prominent stakeholders of construction projects.

Circular economy: the future nexus for sustainable and resilient supply chains?

Circular Supply Chain Management (CSCM) reduces the environmental impacts of the linear economy by optimizing resources and minimizing waste and it potentially enhances resilience through localization, digitalization, and collaboration. Using Interpretive Structural Modeling (ISM) and MICMAC analysis, this study explores the impact of CSCM practices on sustainability and resilience. Key practices like PMS, product as service, and recycled raw materials should be prioritized. CSCM practices must be implemented holistically for a successful transition. Additionally, social sustainability in circular supply chains needs more attention, as it influences other sustainability and resilience measures.

Assessment of primary school in Changsha based on AISM method

To tackle the challenge of a one-size-fits-all and inconsistent approach to evaluating school development across districts, we have zeroed in on five key urban districts in Changsha City. Fourteen secondary indicators, spanning four critical categories — campus environment, teaching facilities, financial investment, and teacher resources — were selected to comprehensively assess the development of elementary schools in 2021. This assessment was conducted using the Advanced Interpretive Structural Model (AISM). Drawing on the principles of partial order set theory, AISM delves into the partial relationships among these indicators, considering the raw data matrix, optimistic positive and negative distances, and program fit degree. Subsequently, Haas map technology is employed to graphically represent the partial order relationship of elementary school development in each region, providing a clear picture of the hierarchical structure and merit ranking of the evaluation objects. The evaluation results reveal stark differences in the development of elementary schools in Changsha's districts, primary schools in Furong and Kaifu districts stand out for their superior development quality, while Yuhua and Yuelu districts lag behind in terms of elementary school development. Based on these findings, we propose policy recommendations aimed at achieving balanced primary education across districts and ensuring the adequacy of teacher resources.

Challenges of Applying Circular Economy in Agricultural Sustainable Development: A Case Study of Kurdistan Province, Iran

Current production and distribution models in agriculture primarily operate based on the 'linear economy' approach. This means that after the exploitation of natural resources and the production of the final product, a significant portion of these resources and products, now contaminated or turned into waste, exit the economic cycle. The circular economy approach in agriculture seeks to reuse waste products and depleted inputs, thereby reducing the intensity of resource exploitation and taking a step towards sustainable agriculture. However, applying circular economy concepts in agriculture across different regions, especially in developing countries like Iran, can encounter various obstacles and challenges. The present study aims to identify and analyze these obstacles and challenges in the agriculture sector of Kurdistan Province, one of Iran’s key agricultural regions. For this purpose, after reviewing the literature and gathering expert opinions from the province's agrarian elites, 16 challenging factors were identified as potential barriers to expanding the circular economy in Kurdistan Province. These were examined and analyzed using the Interpretive Structural Modeling (ISM) approach. The required data were collected via a questionnaire in the spring of 2024. The research findings indicate that among the 16 factors analyzed, the key variables are the second, first, and third factors—namely, the illiteracy and low literacy rates among farmers, traditional and subsistence farming, and the small size and fragmentation of agricultural lands in Kurdistan Province. According to the experts and specialists interviewed, addressing these variables can significantly influence others, reducing the barriers and challenges facing the circular agricultural economy and the sustainable development of agriculture in Kurdistan Province.

A system dynamics model based on ISM for risk perception in emergency by employees in chemical industrial parks

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

Modeling the critical success factors for continuous usage intention of foodservice mobile apps

ABSTRACT Foodservice apps are gaining popularity and have garnered significant scholarly and industry interest, yet there is a limited understanding of the critical factors driving their continuous usage. Insights about critical success factors can help organizations anticipate this industry’s future. To address this gap, the study has developed a hierarchical model to evaluate the relationships among critical success factors affecting the continuous usage intention of foodservice apps. A five-level hierarchical structure is established using modified Total Interpretive Structural Modeling and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis. The analysis identifies and categorizes eight critical success factors through an extensive literature review verified by consulting experts. The findings have shown varying levels of influence and dependence among the critical success factors. Information quality, food quality, app design, and perceived healthiness were the most significant factors driving the intention for continuous usage. The study’s practical implications are significant for foodservice managers and app developers, suggesting that prioritizing factors like information quality, app design, and perceived healthiness can enhance user engagement and long-term app usage.

Research on Dynamic Evaluation Method of Tourist Destination Accessibility Based on Interpretive Structure Model

Because of its core role in the process of tourism, tourism destination has always been the focus of discussion among the industry, but the comprehensive evaluation of tourism destination is rarely mentioned. This paper selects tourism destination as the focus of investigation, gives the evaluation method of tourism destination, adapts to the trend of tourism modernization, and discusses the quantifiable model of tourism destination evaluation. This paper first selects several tourism destinations as samples, collects and combs the information related to spatial equity of tourism destinations, and determines the main problem categories of spatial equity of tourism destinations. Then, through the summary of relevant data and case analysis, a framework of tourism target accessibility risk factors is formed. At the same time, considering the interaction between risk factors, we then give a method to determine the weight that can improve granular computing. First, we use this method to obtain the quotient space family with hierarchical characteristics through the similarity relationship between data, and then we can calculate the importance of each feature in the quotient space family, so as to obtain the corresponding weight between different influence indexes. Finally, the paper takes the tourism destination spatial equity problem as the main research goal, and through the dynamic evaluation of the tourism destination spatial equity risk impact structure using the interpretive structure model, and describes the schematic diagram of its impact structure, so as to find out the direct, source, process and other factors that cause the problem.

Barriers to Developing a Citizen-Centric Smart City in China: From the Perspective of Citizens’ Sense of Gain

ABSTRACT Identifying barriers aligned with the development objectives and their interrelationships in Citizen-Centric Smart City (CCSC) is pivotal for devising tailored developmental strategies. Hence, this article aims to conduct a comprehensive exploration of these elements, with a specific focus on citizens’ sense of gain (CSG), a crucial component of citizen-centricity. The study conceptualizes the CCSC development objectives and identifies 20 barriers that align with them by employing a literature review and the Delphi method. Besides, the barriers are analyzed by adopting the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Interpretative Structural Modeling (ISM)-Cross-impact matrix multiplication applied to classification (MICMAC) method. The study reveals that CCSC in China should focus on making CSG more comprehensive and sustainable in five dimensions decomposed from the objectives, “Lack of departments to interface with citizens’ feedback” is identified as the barrier with the highest causality and centrality, and “Lack of guiding framework and policy system for CCSC” is a fundamental barrier to the China’s CCSC development. By identifying the critical barriers and suggesting practical solutions, this study can help policymakers and practitioners worldwide to prioritize their efforts towards creating more sustainable and CCSCs.

Selection of Flour Suppliers as the Main Ingredient of Onion Crackers Using the Interpretive Structural Modeling Method and Simple Multi-Attribute Rating Technique

Raw materials are very important because raw materials are something that will determine whether the products produced are good and by the wishes of consumers or not, so it is necessary to pay attention to the provider of raw materials, namely the supplier of raw materials. The purpose of this research is to be able to find out the best suppliers by relying on the criteria that are the key criteria. In this study, the Interpretive Structural Modeling (ISM) and Simple Multi-Attribute Rating Technique (SMART) methods were used. The ISM method is useful for sorting out the criteria used so that it is obtained only in the form of key criteria while the SMART method is useful for determining the best supplier based on the existing key criteria. The results of this study are obtained from 9 key criteria out of 12 criteria used, namely quality criteria, delivery of goods, price of goods, communication system, control in operation, service improvement, attitude, packaging ability, and geographical location. The supplier that has the highest criteria based on the key criteria used is Sinar Terang supplier with a score of 0.5895 followed by Cece (0.555), Av (0.555), Aroma (0.5305), Harapan (0.5155), and finally Laris (0.4005).

Mapping the critical factors of emerging technologies adoption intention in foodservice industry: a modified total interpretive structural modeling approach

ABSTRACT The advancement in technology is transforming the competitive ecosystem of the foodservice industry, thus enabling firms to enhance their intention to adopt emerging technologies. This transformation enables foodservice industry-based firms to focus on innovation and emerging technologies adoption within a rapidly changing business landscape. This study explores the critical factors of emerging technologies adoption intention in the foodservice industry and employs “modified total interpretive structural modeling” and “matrices impacts cruises multiplication applique classement” analysis to create a hierarchical model that establishes the relationship among identified factors impacting emerging technologies adoption intention. A five-level hierarchical model is developed. Findings of this study indicate that organizational characteristics and competitive pressure experienced by the foodservice industry are the driving factors that impact emerging technologies adoption intention. This paper helps to understand the dynamics between identified factors of technology adoption in the context of the foodservice industry using frameworks (“unified theory of acceptance and use of technology” and “technology acceptance model”). The proposed hierarchical model provides a structural framework that helps practitioners and researchers to understand the interrelation among critical factors of emerging technologies adoption intention in the rapidly evolving dynamic ecosystem of the foodservice industry.

Enabling sustainable circular economy in Indian municipal solid waste management system: an ISM and fuzzy MICMAC approach

PurposeMismanagement of municipal solid waste (MSW) has led to resource depletion, ecosystem degradation and socio-economic risks, emphasizing the necessity to promote sustainable circular economy (SuCE) practices. This article identifies relevant enablers and unveils their interconnected roles in facilitating a municipal solid waste management (MSWM) system aligned with the principles of SuCE.Design/methodology/approachTo identify pertinent enablers for MSWM systems based on SuCE principles, the study conducted a literature review and engaged experts from waste management sectors and academia to consolidate the final list of enablers. Following this, the study investigated the inter-relationships among these enablers using both Interpretive Structural Modeling (ISM) integrated and fuzzy Matrix Impact Cross-Reference Multiplication Applied to a Classification (fuzzy MICMAC) with MATLAB.FindingsThe study identified a comprehensive set of 23 enablers and categorized them into four control measures instrumental for the effective integration of SuCE principles in the context of the Indian MSWM system. Notably, the analysis highlighted that “authoritative pressure” and “policy transformation” are the highly influential enablers.Practical implicationsThe study emphasizes actionable implications for stakeholders in the Indian MSWM sector. Policymakers and governmental bodies can enforce “authoritative pressure,” “policy transformation” and “budget allocation”. Meanwhile, managers can focus on “promotion of SuCE awareness”, “formalizing the informal workforce”, “infrastructure modernization”, “stakeholder engagement and alignment”, “segregation strategy”, “disruption preparedness”, and “local-based strategy”.Originality/valueThis study proposed a unique ISM diagraph and cluster of enablers to enhance the novelty of the study.