Interpretive structural modelling (ISM) has been further interpreted in the form of total interpretive structural modelling (TISM). These are graphical models that represent the hierarchical relationships and help in better and precise conceptualization and theory building. ISM only interprets the nodes in a digraph, but TISM interprets both nodes and links. The errors observed in applications of ISM and TISM reported in past have acted as motivation for this paper to provide checks and guidelines for correctness of total interpretive structural models. The paper first gives an overview of past applications of TISM. The process of TISM is first outlined and then the guidelines and thumb rules are provided to check the correctness of TISM at each step. Some typical errors in TISM models and their modifications are discussed to help future modellers to translate their ill-structured mental models into sound theoretical models. A discussion on usefulness of TISM for big data analytics for theory building is provided and future directions of research are outlined.

How to check correctness of total interpretive structural models

Purpose




The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph.




Design/methodology/approach




This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence.




Findings




This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence.




Practical implications




This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals.




Originality/value




By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.

Total Interpretive Structural Modeling (TISM): approach and application

Economic growth of a nation depends upon its capability to ensure the security of safe and quality food to its citizens. Despite having the vital role of food security in the growth, emerging economies are facing a number of challenges in their food logistics system. One of the most attention-seeking challenges is the management of security and safety of food commodities in food logistics. The prime concern is to monitor food quality throughout its supply chain and track the physical movement till it reaches end-consumers. This requires implementation of an effective and efficient traceability system. The successful implementation of traceability system requires consideration of multiple stakeholders’ perspectives. On the basis of critical success factor (CSF) theory and multiple stakeholders’ view on ensuring the security of quality food, this study proposes a comprehensive framework for implementation of traceability-based food logistics system. It attempts to identify and classify various CSFs necessary for the implementation of traceability system using a questionnaire-based survey followed by exploratory factor analysis. Further, an analysis of inter-relationships among the statistically significant CSFs is performed using total interpretive structural modeling, which considers multiple stakeholders’ views. The study helps in developing a comprehensive understanding of directional inter-relationships among CSFs and provides significant insights related to ways to improve consumer satisfaction through safe and quality food in food logistics.

Modeling critical success factors of traceability for food logistics system

Traceability plays a vital role in the success of Halal Supply Chain (HSC) HSC revolve around the essential dimension of Halal Integrity (HI), whereas traceability is seemed to be medium to assure integrity Thus, a need is felt to identify the factors which are critical to the successful implementation of traceability in Halal Supply Chain Management (HSCM) Identified Twelve Critical Success Factors (CSFs) through an extensive review of literature and opinion of experts Further, a contextual relationship among the CSFs is developed using Total Interpretive Structure Modelling (TISM) approach and derived a model The structural model is analyzed using Fuzzy MICMAC (Matrice d’Impacts Croises-Multipication Applique and Classment-cross-impact matrix multiplication applied to classification) approach to identify the importance of CSFs by driving and dependence power The primary result indicates towards; that improving the HSCM with the higher level of Halal awareness Assuring HI will enhance the consumer satisfaction which leads to a competitive advantage for the organization Academic researchers, industrial practitioners and Supply Chain executives can understand the complex interrelationship of CSFs by visualizing the TISM It can help the management, lobbies and government to develop the policies regarding the implementation

Implementing traceability systems in specific supply chain management (SCM) through critical success factors (CSFs)

The purpose of this paper is to identify and analyze the interactions among different readiness factors for implementing agility in healthcare organization. Total interpretive structural modeling (TISM) based readiness framework for agility has been developed to understand the mutual interactions among the factors and to identify the driving and dependence power of these factors.,The identification of factors is done by TISM approach used for analyzing the mutual interactions between factors. Cross-impact matrix multiplication applied to classification analysis is utilized to find the driving and dependent factors of agile readiness in healthcare.,This paper identifies 12 factors of readiness for change in literature review, which is followed by an expert interview to understand the interconnection of factors and to study interrelationships of factors. The study suggests that factors like environmental scanning, resource availability, innovativeness, cost effectiveness, organizational leadership, training and development are important for implementing/improving the readiness of agility in healthcare organizations.,This research focuses mainly on readiness factors for agility in healthcare sector.,Top management must stress on readiness factors that have a strong driving power for efficient implementation of agility in healthcare. This study helps the managers to take quick decisions, and continuous monitoring of readiness factors would be more beneficial to improve the quality of service, which makes the organization more agile.,In this research, TISM-based readiness for agile framework structural model has been proposed for healthcare organizations, which is a new effort for implementation of agility in healthcare.

Modelling the readiness factors for agility in healthcare organization: a TISM approach

Purpose – The purpose of this paper is to identify and analyze various critical success factors (CSFs) that can facilitate smartphone manufacturing in India. This paper further aims to understand the mutual interactions among these CSFs through identification of the hierarchical relationships among them. Design/methodology/approach – A framework for a smartphone manufacturing system has been developed, wherein the hierarchical inter-relationships between identified CSFs have been presented and interpreted using total interpretive structural modeling (TISM). Cross-impact matrix multiplication applied to classification analysis has been further employed to identify the driving power as well as dependence power of these CSFs. Findings – In the present research, 15 CSFs have been identified through literature review and expert opinions. The hierarchical framework developed using TISM has revealed the inter-dependencies among these CSFs. This research further categorizes CSFs into three clusters. The first clu...

Fahima Sherwani

Papers

A TISM modeling of critical success factors of smartphone manufacturing ecosystem in India