interpretive structural modeling

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Interpretive Structural Modeling

Dr. G. P. Sahu(Assistant Professor – Information Systems)

School of Management StudiesMotilal Nehru National Institute of Technology, Allahabad.

July 25, 2008

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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.

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Objective of ISM

• To identify and rank the variables.

• To establish the interrelationship among the variables.

• To discuss the managerial implication of the research.

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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.

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Example of Interpretive Structural Modeling

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Variables affecting Information and Communication Technology adoption in SME.

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Variables 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).

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Variables affecting Information and Communication Technology adoption in SME.

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Variables 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).

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Variables affecting Information and Communication Technology adoption in SME.

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Variables Supporting Studies

9 Extra organizational situation

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)

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Relative Advantage

Firm’s Innovativeness

Management Attributes

Organisational Attributes

Adoption Attributes

End User experience

Owner’s knowledge

Extra organizational situation

Government Support

Financial Resource

Social Expectation

Usage of Information and Communication

Technology

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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.

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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.

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Elements 11 10 9 8 7 6 5 4 3 2

1 Relative Advantage A A A D D B A 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

Structural Self-Interaction Matrix (SSIM)

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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

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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

Reachability Matrix

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Variable Reachability Set

1 1,2,3,4,5,9,10,11

2 2,4,5,6,8,9,10,11

3 3,5

4 4,5,6,8,11

5 5,6,8,9,11

6 1,6,8,

7 7,8,9,10

8 8,11

9 9

10 4,6,10

11 7,9,11

Reachability Set

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Variable Antecend Set

1 1,6

2 1,2

3 1,3

4 1,2,4,10

5 1,2,3,4,5

6 2,4,5,6,10

7 7,11

8 2,4,5,6,7,8

9 1,2,5,7,9,11

10 1,2,7,10

11 1,2,4,5,8,11

Antecend Set

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Level of Variables

Level of variables are determined on the basis of intersection of Reachability Set and Intersection Set

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Variable Reachability Set

Antecend Set

Intersection 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

Level of Variables

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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)