Interpretive Structural Modeling: Background, Concepts, and Application

Interpretive Structural Modeling: Background, Concepts, and Application

Aastha Behl, Abhinav Pal
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-7998-2216-5.ch001
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The chapter explains the technique of ISM (Interpretive Structural Modeling) in Multi-criteria decision Analysis (MCDA). ISM is a method to identify inter relationship among various factors pertaining to the authors' research problem. This technique starts with identification of key factors, collected through an extensive literature review of the study. The factors are validated with the help of expert opinions. A pair wise comparative analysis helps in developing a structural self-interaction matrix (SSIM). This is followed by conversion of SSIM to Reachability Matrix, partitioned into different levels. This helps in developing a diagraph, with the help of which an ISM Model is developed. It is also accompanied by MICMAC Analysis which presents a graphical picture of the driving power and dependence power of each factor considered for the study. The chapter would help in understanding the steps involved in the application of ISM technique with the help of hypothetical cases.
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Steps Involved In Ism Approach

Step 1: Identify the Basic Factors

The first step of ISM technique is to develop a matrix known as SSIM. For developing this matrix, the first and foremost work is to develop an extensive literature review of our study, which shall consist of all the factors relating to our Research problem affecting our study. The entire list of factors is then consulted with the experts in understanding the relationship between various factors related to the study. Delphi technique is used for interviewing the experts from various industries or academic groups who have sound knowledge about the factors which are considered for the study. The consensus of experts’ opinion helps us in identifying the relationship among all the factors and eliminating those which do not possess a relationship among them. The selection of experts for the consultation could be on the basis of experience and knowledge of that study. For this various brainstorming sessions and questionnaire are done for shortlisting the pair wise contextual relationship between the factors. For getting a clear idea about this, please refer to the flow diagram (Figure 1) for constructing ISM model given below:

Figure 1.

Flow diagram for preparing ISM model

Adapted by: Wu and Niu (2017)

Key Terms in this Chapter

AMOS: It is a visual program for Structural equation modelling (SEM), where models can be graphically drawn using simple drawing tools. It also performs the computations for SEM and displays the results.

Driving and Dependence Power: The driving power of a particular enabler is the total number of enablers (including itself), which it may help to achieve and the dependence is the total number enablers which may help to achieve it.

Delphi Technique: It is a forecasting process framework based on the results of multiple rounds of questionnaires sent to a panel of experts. Several rounds of questionnaires are sent out to the group of experts, and the anonymous responses are aggregated and shared with the group after each round.

Structural Equation Modelling: It is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs.

Multi Criteria Decision Analysis (MCDA): It is a general framework for supporting complex decision-making situations with multiple and often conflicting objectives that stakeholders groups and/or decision-makers value differently.

Exploratory Factor Analysis and Confirmatory Factor Analysis (CFA): Confirmatory factor analysis (CFA) is a multivariate statistical procedure that is used to test how well the measured variables represent the number of constructs. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are similar techniques, but in exploratory factor analysis (EFA), data is simply explored and provides information about the numbers of factors required to represent the data. In exploratory factor analysis, all measured variables are related to every latent variable.

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