Interpretive structural modeling
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1) The document describes Interpretive Structural Modeling (ISM), which is used to identify and summarize relationships among variables that define a problem or issue. 2) The objective of ISM is to identify and rank variables, establish relationships among them, and discuss managerial implications. 3) The ISM methodology involves listing variables, developing a Structural Self-Interaction Matrix, a Reachability Matrix, partitioning the matrices into levels, and forming a hierarchy of variables.
Interpretive structural modeling
- 1. Interpretive Structural Modeling Dr. G. P. Sahu (Assistant Professor – Information Systems) School of Management Studies Motilal Nehru National Institute of Technology, Allahabad. July 25, 2008 1
- 2. Interpretive Structural Modeling Interpretive Structural Modeling is used for identifying and summarizing relationship among specific variables, which define a problem or an issues. It is an interactive learning process. 2
- 3. Objective of ISM • To identify and rank the variables. • To establish the interrelationship among the variables. • To discuss the managerial implication of the research. 3
- 4. Steps of ISM Methodology 1. Variables affecting the system under consideration are listed. 2. The Structural Self Interaction Matrix (SSIM) is developed for the variables. 3. Reachability Matrix is developed from the SSIM. 4. Reachability Matrix obtained in step 3 is partitioned into different level. 5. Finally the hierarchies of the variables are formed. 4
- 5. Example of Interpretive Structural Modeling 5
- 6. Variables affecting Information and Communication Technology adoption in SME. Sl. Variables No. Supporting Studies 1 Relative Advantage Lee and Runge (2001). Khazanchi (2005); Seyal and Rahman (2003). 2 Social Expectation Lee and Runge (2001). Khazanchi (2005); Seyal and Rahman (2003). 3 Firm’s Innovativeness Lee and Runge (2001); Winston and Dologite (1999); Khazanchi (2005); Seyal and Rahman (2003). 4 Management Attributes Seyal and Rahman, (2003); Jeon et.al.(2006); Chahal and Kohali (2006). 6
- 7. Variables affecting Information and Communication Technology adoption in SME. Sl. Variables No. Supporting Studies 5 Organisational Attributes Seyal and Rahman (2003); Levenburg and Klein (2006). 6 Adoption Attributes Seyal and Rahman (2003); Jeon et.al. (2006), 7 End User experience Winston and Dologite(1999). 8 Owner knowledge Winston and Dologite (1999); Ihlstrom and Nilsson (2003); Seyal and Rahman (2003); Wymer and Regan (2005). 7
- 8. Variables affecting Information and Communication Technology adoption in SME. Sl. Variables No. 9 Extra organizational situation Supporting Studies Winston and Dologite(1999); Khazanchi (2005). 10 Government Support Jeon et.al. (2006); Wymer and Regan (2005); Jeon et.al. (2006); Wymer and Regan (2005). 11 Financial Resource Levenburg and Klein (2006); Khazanchi (2005) 8
- 9. Relative Advantage Social Expectation Firm’s Innovativeness Management Attributes Organisational Attributes Usage of Information and Communication Technology Adoption Attributes End User experience Owner’s knowledge Extra organizational situation Government Support Financial Resource 9
- 10. Interpretive Structural Modeling • • • • Personal interview is conducted of the two experts, one is academician and the other entrepreneurship consultant. It is asked them to establish the relationship between the various factors as follows: A, If ‘i’ is predictor of ‘j’. B, If ‘j’ is predictor of ‘i’. C, If ‘i’ and ‘j’ predict each other. D, If no predict each other. 10
- 11. Structural Self Interaction Matrix (SSIM) ISM methodology suggest the use of expert opinions based on the various management technique in developing the contextual relationship among the variables. 11
- 12. Structural Self-Interaction Matrix (SSIM) Elements 11 10 9 8 7 6 5 4 3 2 A 1 Relative Advantage A A A D D B A A A 2 Social Expectation A A A A D A A A D 3 Firm’s Innovativeness D D D D D D A D 4 Management Attributes A B D A D A A 5 Organizational Attributes A D A A D A 6 Adoption Attributes B D D A D 7 End User experience B A A A 8 Owner knowledge A D D 9 Extra Org. situation B D 10 Government Support D 11 Financial Resource 12
- 13. Reachability Matrix • A, If ‘i’ is predictor of ‘j’, then (i,j) is 1 and (j,i) is 0 • B, If ‘j’ is predictor of ‘i’ then (j,i) is 1 and (i,j) is 0 • C, If ‘i’ and ‘j’ predict each other then (i,j) is 1 and (j,i) is 1 • D, If no predict each other then (i,j) is 0 and (j,i) is 0 13
- 14. Reachability Matrix Elements 1 2 3 4 5 6 7 8 9 10 11 1 Relative Advantage 1 1 1 1 1 0 0 0 1 1 1 2 Social Expectation 0 1 0 1 1 1 0 1 1 1 1 3 Firm’s Innovativeness 0 0 1 0 1 0 0 0 0 0 0 4 Management Attributes 0 0 0 1 1 1 0 1 0 0 1 5 Organizational Attributes 0 0 0 0 1 1 0 1 1 0 1 6 Adoption Attributes 1 0 0 0 0 1 0 1 0 0 0 7 End User experience 0 0 0 0 0 0 1 1 1 1 0 8 Owner knowledge 0 0 0 0 0 0 0 1 0 0 1 9 Extra Org. situation 0 0 0 0 0 0 0 0 1 0 0 10 Government Support 0 0 0 1 0 1 0 0 0 1 0 11 Financial Resource 0 0 0 0 0 0 1 0 1 0 1 14
- 17. Level of Variables Level of variables are determined on the basis of intersection of Reachability Set and Intersection Set 17
- 18. Level of Variables Variable Reachability Set Antecend Intersection Set Set Level 1 1,2,3,4,5,9,10,11 1,6 1 VII 2 2,4,5,6,8,9,10,11 1,2 2 VI 3 3,5 1,3 3 II 4 4,5,6,8,11 1,2,4,10 4 IV 5 5,6,8,9,11 1,2,3,4,5 5 III 6 1,6,8, 2,4,5,6,10 6 III 7 7,8,9,10 7,11 7 III 8 8,11 2,4,5,6,7,8 8 II 9 9 1,2,5,7,9,11 9 I 10 4,6,10 1,2,7,10 10 V 11 7,9,11 1,2,4,5,8,11 11 II 18
- 19. Variable Hierarchy Extra Organizational Situation (9) Firm’s Innovativeness (3) Owner knowledge (8) Financial Resource (11) Organizational Attributes (5) Adoption Attributes (6) End User experience (7) Management Attributes (4) Government Support (10) Social Expectation (2) Relative Advantage (1) 19