Interpretive Structural Modeling Research Articles

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.

Dimensions of Job Demands Among New-Generation Employees Based on Online Reviews by Employees

Based on employees’ online reviews, this article analyzes the dimensions of job demand for new-generation employees, using a combination model of Latent Dirichlet Allocation, decision-making trials and evaluation laboratory, interpretative structural modeling, and cross-impact matrix multiplication (LDA—DEMATEL—ISM—MICMAC). The results show that job demand is composed of 10 dimensions, and there is significant interdependence between the dimensions. Changing one dimension will quickly affect the other dimensions. The dimension with the greatest influence degree is leadership and team atmosphere (s1), while the dimension with the highest affected degree and centrality degree is welfare and promotion (s3). Leadership and team atmosphere (s1), company culture and industry (s4), overall environment and platform (s5), and platform and technology (s7) were identified as the key factors. They play a causal role in job demand and have a significant impact on other dimensions. Dimensions such as working relationship and intensity (s2), welfare and promotion (s3), opportunity and resources (s6), business and industry development (s8), corporate prospects and personal development (s9), and work stress and position (s10) are affected by other dimensions and require special attention. The underlying need is present for the dimension of leadership and team atmosphere (s1), which has a direct or indirect impact on other dimensions in different ways.

Smart Logistics Facing Industry 5.0: Research on Key Enablers and Strategic Roadmap

While Industry 4.0 has played a significant role in advancing smart logistics, it has yet to provide adequate solutions for widespread concerns such as human centricity, sustainability, and resilience. The emergence of Industry 5.0 addresses and complements these shortcomings of Industry 4.0. However, there is currently a notable gap in the research regarding how Industry 5.0 can drive the transformation of smart logistics. To address this gap, this study develops a strategic roadmap that offers a solution to this issue. The research is initiated by conducting a comprehensive literature review with a focus on content, identifying 13 key enablers crucial for realizing smart logistics in Industry 5.0. Subsequently, this study establishes the hierarchical relationship among these key enablers through the application of the Fuzzy Interpretative Structural Model (FISM). Following this, the study employs the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) to compute the driving force and dependence of each enabler. The results underscore the significant roles of “Active support from the government” and “Human-centric manufacturing and logistics” as the most critical enablers for Industry 5.0. The strategic roadmap, informed by expert opinions, provides valuable insights for policymakers and implementers while explaining the methods and strategies needed to drive Industry 5.0 transformation in smart logistics. Furthermore, it determines the impact relationship between enablers and the optimal development order, facilitating their synergistic alignment. Ultimately, this strategic roadmap serves as an actionable guide for the logistics industry, steering it toward achieving smart logistics and fortifying competitiveness in the industry 5.0 era.

Factors influencing the turnover intention for disease control and prevention workers in Northeast China: an empirical analysis based on logistic-ISM model.

This study aimed to determine the current turnover intention among workers at Centers for Disease Control and Prevention (CDCs) in Northeast China and to investigate the factors contributing to this phenomenon. The cross-sectional study was conducted in May 2023 across four northeastern provinces of China. The study included a total of 11,912 valid participants who were CDC workers selected using a stratified cluster sampling method. The study assessed demographics, turnover intention, work resources, work perceptions, and psychological support through online questionnaires. The binary logistic regression analysis identified the factors associated with turnover intention for CDC workers, while the Interpretative Structural Modelling (ISM) revealed the hierarchical relationship between the influencing factors. The study found that 28.8% of the respondents reported high turnover intention. The binary logistic regression suggested that the risk factors of turnover intention among employees included gender, age, education, and several work-related, organizational, and psychological factors. The work-related factors were daily working hours, job burnout, and role overload. The organizational factor was unit support for training, unit's infrastructure conditions, leadership style, remuneration package and performance appraisal and promotion system. The psychological support factors were family support, professional identity, and organizational commitment. The ISM analysis results showed that the primary factors influencing CDC workers' intention to leave were internally connected and grouped into four categories according to their level of impact: surface, transition, deep, and essential factors. Notably, the essential factors were professional identity and organizational commitment. Nearly one-third of the respondents expressed a strong desire to resign from their employment. Turnover intention among CDC workers was subject to diverse influences. Early identification, detection, and targeted multidisciplinary interventions should be introduced to address the array of factors that affect staff, with particular emphasis on boosting the professional identity and organizational commitment of CDC workers.

Complex system fault diagnosis based on interpretive structural model

To realize the fault diagnosis of complex systems, a fault diagnosis method for complex systems based on the explanatory structural model is proposed. According to the fault mechanism analysis, the fault association matrix of system elements is established. The explanatory structural model is applied to transform the fault association relationship of complex systems into an intuitive hierarchical structural model through matrix transformation, so as to realize the structuring and hierarchical propagation of system faults. The PageRank algorithm is introduced to evaluate the influence and impact of system element faults. According to the influence and impact of the same-layer components and the fault transmission logic, the main cause of system fault transmission is clarified, which provides a basis for fault diagnosis. Finally, a specific application is carried out with a certain device as an example to verify the effectiveness of the method.

An ISM-MICMAC approach for analyzing the relationship between blockchain adoption criteria in a pharmaceutical supply chain: Tunisian case

Purpose In the context of digitalizing the supply chain of different sectors, the blockchain technology (BT) is emerged and becomes one of the most interesting and debated research topics. A few years ago, this technology was implemented for the first time in the financial sector, which is currently extended to be used in many other areas, mainly the health-care sector. The success of this technology stems from its ability to enhance the performance, security, consistency of sharing medical data within the whole system and the analysis of medical records. Technically, the BT is defined as a decentralized digital ledger that records transactions between the stakeholders in a supply chain. Thus, they could gain better control and find severe mistakes and the unsafe ones in the medical field. The purpose of this paper is to address a preliminary study of the BT adoption in a real pharmaceutical supply chain (PSC) of the Tunisian case. Design/methodology/approach Indeed, the authors propose an interpretive structural modeling (ISM) combined with the cross-impact matrix multiplication applied to classification (MICMAC) analysis. This methodology is known as an effective one used to identify the criteria that influence the implementation of the BT, analyze the relationships between them and delighting the most impactful ones. Findings The readiness criteria for the adoption of the BT for the considered system are identified which are 10 ones and the structural relationship between them is uncovered through many interviews with experts. Hence, the found results define the most crucial criteria that should be valorized amongst the other criteria. Originality/value The originality of this study stems from its theoretical and practical relevance regarding the potential of the pharmaceutical system and the importance of the integration of new technologies as the BT. The ISM-MICMAC approach seems to be very performant for such preliminary study of the BT adoption in the Tunisian pharmaceutical system.

Prioritizing port resilience drivers and strategies: a case study of Chilean ports

ABSTRACT Disruptive events increasingly challenge global port operations and infrastructure, emphasizing the need for robust resilience plans to maintain operational continuity during crises. Designing effective resilience strategies in port operations involves prioritizing key drivers and strategies by establishing a hierarchical structure and analyzing their systemic interactions. This paper proposes a methodology to prioritize resilience drivers with its strategies by considering multiple key port stakeholders. This approach aims to create a roadmap for implementing these drivers, providing ports with a comprehensive tool for planning and executing resilience strategies. The methodology integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with Interpretative Structural Modeling (ISM). To illustrate its development, a case study focusing on two primary stakeholders, terminals and port authorities, in the Chilean port context is presented. The findings reveal that the main resilience strategies in Chilean ports include remote access to information by port authorities, alternative transport modes for port access and exit, rescheduling dispatches and cargo reception by port terminals, communication and call chains of the port authority, and remote access to information by port terminals. These results offer practical implications for port operations, providing clear guidance for implementing resilience strategies.

Identifying challenges in implementing digital transformation in UK higher education

Purpose The purpose of this study is to systematically dissect strategies against the challenges stalling digital transformation (DT) in the UK higher education (HE) sector. It addresses the challenges impeding DT’s incorporation and offers a blueprint for fostering innovation and efficiency within academic institutions. Design/methodology/approach Using a systematic literature review, the research integrates a nuanced literature review with interpretive structural modelling (ISM). Through meticulous ISM analysis, including sensitivity analysis and level partitioning, a robust framework is developed to pinpoint and interrelate DT challenges. Findings This investigation delineates a spectrum of impediments to DT in HE, most notably the need for more digital understanding among educators, intensified by inadequate support and resources. The findings reveal that the effective integration of DT is hindered by factors such as insufficient educator digital skills, resistance to technology and the continuous evolution of digital infrastructure. Practical implications The study’s findings and the developed level partitioning diagram offer invaluable insight into how DT must be integrated into the curriculum to enhance HE. Additionally, it could further lead to research within digital infrastructure and how learning needs to be facilitated for students in HE. Originality/value This study breaks new ground by systematically illuminating the centrality of the educator skill gap. By contributing insights into the educator skill gap, it proposes a unique analytical model that underscores actionable pathways for advancing DT initiatives in HE institutions.

One size does not fit all: deciphering the interdependence between barriers hindering SMEs’ involvement in public sector procurement

PurposeDespite their significant economic impact, small and medium-sized enterprises (SMEs) remain underrepresented in public procurement. While previous research has identified the barriers facing SMEs in public procurement markets, a knowledge gap still exists on how these barriers influence one another in a specific context.Design/methodology/approachThis study developed a hierarchical structural model that reveals the contextual interrelationships between 14 notable barriers to SME involvement in public procurement, based on evidence from the publicly funded healthcare sector in the UK. The data were analyzed using interpretive structural modeling (ISM) and matrix impact of cross-multiplication applied to categorization (MICMAC).FindingsThe results systematically classify barriers based on their hierarchical relationships, offering a robust theoretical foundation for understanding their nature and interconnections.Practical implicationsThe findings can help scholars, government administrators and politicians in government-funded healthcare systems determine the most influential barriers to SME involvement and their causes, enabling them to build viable remedies.Originality/valueThis is the first empirical study to examine the relationship between the barriers to SMEs' public health procurement, using evidence from the public health procurement context.

Modelling inter-relationships of barriers to smart construction implementation

Smart construction technology offers fresh avenues for advancing the field of civil engineering. It seamlessly integrates across the entire life cycle of civil engineering projects, encompassing planning, design, construction, and maintenance, thereby fundamentally reshaping the landscape of civil engineering development. Nonetheless, it is essential to recognize that, presently, smart construction’s developmental stage remains relatively nascent. Its progression is subject to a myriad of adoption barriers, and the complex dynamics of their interactions remain insufficiently understood. Therefore, this study aims to (1) explore the barriers to the adoption of smart construction; (2) analyze the impact level of each barrier; and the interaction mechanism between the barriers (3) propose effective strategies to promote the development of smart construction. This study commences by identifying 16 major impediments to the adoption of smart construction through a comprehensive synthesis of existing literature and expert interviews. Subsequently, Euclidean similarity analysis is employed to harmonize varying expert assessments. Following this, the Decision-Making Trial and Evaluation Laboratory model is utilized to ascertain the degree of influence associated with each barrier. Further, the Interpretive Structural Model is employed to establish a hierarchical framework that illuminates the interdependencies among these barriers. Additionally, the Matrice d’Impacts Croisés Multiplication Appliqués à un Classement method is invoked to elucidate the roles and statuses of each barrier. Finally, strategies are proposed based on the results of the analysis. This study offers practical strategies for overcoming barriers and driving the adoption of smart construction, filling a critical gap in understanding by identifying key barriers and providing actionable insights, thus significantly advancing the field and empowering stakeholders for successful implementation and dissemination.

A Benchmarking Model for HRM Elements of Large Mining Industry: ISM-Based Approach

While human resource management (HRM) elements concerning the expansive mining industry have been acknowledged, they still need to be explored. This paper presents a novel and strategic endeavour to construct a benchmark model linking HRM elements specific to the large mining industry. Drawing from an extensive literature review, crucial HRM elements pertinent to the mineral industry are systematically identified and thoroughly examined. The study utilises the interpretive structural modelling (ISM) technique within the context of major Indian mining industries to unveil latent relationships among these HRM-related elements. Through six-phased interactions, a foundational model is established, illuminating these relationships. The findings are poised to positively impact the mining industry, contributing to enhanced system sustainability. Furthermore, this paper represents a pioneering effort as the first research to elucidate the relationship model of HRM elements within the mining industry. It encourages HRM researchers to delve further into the mining industry to address its challenges. This research serves as a foundational study and provides a platform for further investigations by HRM experts, researchers and field personnel, enabling a multifaceted exploration of HRM perspectives.

SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way.

Investigating the determinants of user adoption behavior in the context of conflicting health information: a DEMATEL-ISM model approach

PurposeThe purpose of this paper is to investigate the factors influencing user adoption behavior in the context of conflicting health information, thereby contributing to the understanding of users' information decision-making and adoption processes within intricate information environments. Furthermore, this research endeavors to offer guidance for mitigating the adverse effects of contradictory health information on users' cognitive perceptions.Design/methodology/approachFirst, this study used literature coding to identify factors that influence user adoption behavior in the context of conflicting health information. Secondly, we utilized the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to unveil the key influencing factors based on centrality and causality. Lastly, utilizing the Interpretive Structural Model (ISM), we constructed the multilevel hierarchical structure model of influencing factors to delineate the relationships among factors across different levels.FindingsThe key causal factors influencing user adoption behavior in the context of conflicting health information include user basic characteristics, experience accumulation, and information type. The key result factors consist of psychological emotion, perceived trade-offs, and the source credibility. The significant elements include users' psychological emotions, perceived trade-offs, emotional support, source credibility, and informational support. Furthermore, user adoption behavior in the context of conflicting health information is a manifestation of the interplay among surface-level, mid-level, and deep-level factors.Research limitations/implicationsTheoretically, this paper enriches the existing relevant theoretical framework and offers a novel perspective for further investigation into user adoption behavior concerning conflicting health information. Practically, this study extracts factors that can influence user adoption behavior in the context of conflicting health information which is an essential reference value for guiding and optimizing user adoption behavior.Originality/valueThis paper expands the system of factors influencing user adoption behavior in the context of conflicting health information from individual, information and social perspectives. Utilizing DEMATEL quantitative analysis and ISM multilevel hierarchical models, this research examines and illustrates the significance of the influencing factors and their interrelationships.

Modelling the drivers of intention to use energy-efficient appliances through interpretive structural modelling technique

Modelling the drivers of intention to use energy-efficient appliances through interpretive structural modelling technique