Interpretive Structural Modelling of NAAC Criteria

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1 Interpretive Structural Modelling of NAAC Criteria Nameesh Miglani PhD Research Scholar Sunrise University Alwar, India Rajeev Saha Dept. of Mechanical Engg. YMCA University of Science and Technology Faridabad, India R.S. Parihar Registrar Sunrise University Alwar, India ABSTRACT The quality of higher education in a University requires proper identification of factors and estimation of the quality in quantitative terms. National Assessment and Accreditation Council (NAAC) evolved a methodology of assessment in quantitative terms involving self-appraisal by concerned institution and an assessment of performance by an expert committee. NAAC criteria's for University Education Quality has been evaluated using ISM (Interpretive Structural Modelling) technique. The Interpretive Structural Modelling (ISM) methodology helps to understand the interrelationships between University Education Quality criteria's. The structural model helps to explain the logic behind the relationships between different criteria's, thereby identifying the crucial ones in an orderly manner. Keywords Education Quality, National Assessment and Accreditation Council (NAAC), Interpretive Structural Modelling (ISM) INTRODUCTION NAAC assesses the quality of higher education institutions (HEIs) using seven major criteria s. Criteria s are closely inter-related and may also affect each other. The modelling of criteria s will eventually provide a structure to understand the crucial and non-crucial criteria. Interpretive Structural Modelling (ISM) is one of such technique, which can be used to evaluate criteria s based on their interactions. ISM is an advanced Interactive Planning methodology that allows a group of people, working as a team, to develop a structure that defines the interrelationships among a set of elements. The structure is obtained by answering a set of simple questions. The elements to be structured such as objectives, barriers, activities, etc. are defined by the group at the beginning of the ISM planning session. The group also specifies a relational statement that defines the type of relationship desired such as "aggravates", "enhances", "contributes to", "precedes", etc. ISM is a very efficient structuring technique. If there are N elements in the set that needs to be structured, the group would have to answer N x (N - 1) questions in order to fully define the relationships. Using the mathematics of ISM the group can fully define all the interrelationships by answering a much smaller number of questions. ISM was developed by Prof. John N. Warfield, Director of the Institute for Advanced Study of George Mason University in Fairfax, Virginia, when he was at the University of Virginia and at Battelle Memorial Institute. ( 258 Nameesh Miglani, Rajeev Saha, R.S. Parihar

2 259 Nameesh Miglani, Rajeev Saha, R.S. Parihar International Journal of Engineering Technology Science and Research Table 1 List of some of the fields in which ISM has been employed S. No. Field Paper Author (Year) 1. Management education in India Analysis of challenges for management education in India using total interpretive structural modelling 2. Waste management Analysis of key factors for waste management in humanitarian response: An interpretive structural modelling approach 3. Green supply chains Multi-objective decision modelling using interpretive structural modelling for green supply chains 4. Solar power Analysis of barriers to implement solar power installations in India using interpretive structural modelling technique 5. World-class manufacturing 6. Technology transfer in thin-film transistor liquid-crystal display (TFT-LCD) industry 7. automobile manufacturer distributor partnership 8. BPR of government s purchasing and bidding 9. Battery Manufacturing Industry in India Analysis of critical success factors of world-class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process An evaluation framework for technology transfer of new equipment in high technology industry A systematic procedure to evaluate an automobile manufacturer distributor partnership The Application of ISM to Re-designing of Government's Purchasing Process A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider Mahajan, R., Agrawal, R., Sharma, V., & Nangia, V. (2016) Trivedi, A., Singh, A., & Chauhan, A. (2015) Mangla, S., Madaan, J., Sarma, P. R. S., & Gupta, M. P. (2014) Ansari, M. F., Kharb, R. K., Luthra, S., Shimmi, S. L., & Chatterji, S. (2013) Haleem, A., Sushil, Qadri, M. A., & Kumar, S. (2012) Lee, A.H.I. and Wang, W.M. and Lin, T.Y. (2010) Chen, S.P. and Wu, W.Y. (2010) Zhang, L. and Gu, D. and Fang, Y. and Zhang, X. and Xu, J. (2009) Kannan, G. and Pokharel, S. and Sasi Kumar, P. (2009) 10. E-learning Analysis of e-learning influencing factors based on ISM Wang, X. (2009) 11. Flexible Manufacturing System 12. Knowledge management 13. Corporate governance enablers 14. Engineering education system 15. University-Industry alliance partner selection 16. Supply chain management An ISM approach to analyse interaction between barriers of transition to Flexible Manufacturing System Knowledge management barriers: An interpretive structural modelling approach An interpretive structural model of corporate governance ISM-CMAP-Combine (ICMC) for hierarchical knowledge scenario in quality engineering education University-Industry Alliance Partner Selection Method Based on ISM and ANP Supply chain risk mitigation: modelling the enablers 17. Automobile industries Analysis of interactions among the barriers of reverse logistics Raj, T. and Shankar, R. and Suhaib, M. (2009) Singh, M.D., & Kant, R. (2008) Grover, D., Shankar, R. and Khurana, A. (2007) Upadhyay R.K., Gaur S.K., Agrawal V.P. and Arora K.C. (2007) Ning, M. and Xue-wei, L. (2006) Faisal, M.N., Banwet, D.K. and Shankar, R. (2006) Ravi, V. and Shankar, R. (2005)

3 Interpretive Structural Modelling (ISM) can be used for identifying and summarizing relationships among specific variables, which define a problem or an issue (Sage, 1977 and Warfield, 1974). ISM provides us means by which order can be imposed on the complexity of such variables (Mandal and Deshmukh, 1994). Table 1 enlists some of the fields in which ISM methodology has been employed. ISM Methodology and Model Development To understand and simplify the complexity in a subject under study requires a methodical, systematic, and logical approach to find interrelationships between various elements of the subject. Interpretive structural modelling (ISM) is a qualitative tool that was developed by Warfield with the objective of understanding the complex relationships among elements related to a subject. The process starts with the identification of elements in a system, their prioritization and categorization through an understanding of their primacy, precedence, and causality over and among each other through independent and dependent linkages that are represented through a multi-level structural model [Warfield (1976), Gorvett and Liu (2006)]. ISM methodology was used to investigate the issue of information technology (IT) adoption and implementation in Indian manufacturing small- and medium-scale enterprise (SMEs) towards enhancing the capabilities of their supply chain (Thakkar et al., 2008). ISM methodology was used to understand the mutual influences of the IT enablers of supply chain (Jharkharia and Shankar, 2004). ISM methodology was also used to understand the mutual influences of the barriers in IT-enablement of supply chains (Jharkharia and Shankar, 2005). The ISM methodology is interpretive from the fact that the judgment of the group decides whether and how the variables are related. It is structural too, as on the basis of relationship; an overall structure is extracted from the complex set of variables. It is a modelling technique in which the specific relationships of the variables and the overall structure of the system under consideration are portrayed in a digraph model. ISM is primarily intended as a group learning process, but it can also be used individually. The various steps involved in the ISM methodology are as follows: Fig. 1 Methodology for quantification of University education system quality 260 Nameesh Miglani, Rajeev Saha, R.S. Parihar

4 Step 1: List all the variables affecting the system under consideration. Variables can be Objectives, Actions, and Individuals etc. Step 2: Establish contextual relationship among variables identified in step 1 with respect to each other. Step 3: A Structural Self-Interaction Matrix (SSIM) is developed for variables, which indicates pair wise relationships among variables of the system under consideration. Step 4: Reachability matrix is developed from the SSIM and the matrix is checked for transitivity. The transitivity of the contextual relation is a basic assumption made in ISM. It states that if a variable A is related to B and B is related to C, then A is necessarily related to C. Step 5: The reachability matrix obtained in Step 4 is partitioned into different levels. Step 6: Based on the relationships given above in the reachability matrix, a directed graph is drawn and the transitive links are removed. Step 7: The resultant digraph is converted into an ISM, by replacing variable nodes with statements. Step 8: The ISM model developed in Step 7 is reviewed to check for conceptual inconsistency and necessary modifications are made. These steps of ISM modelling are illustrated in Figure 1. As per step 1, NAAC criteria s as considered for University Education System Quality (UESQ) need to be designated as shown in Table 2. Table 2 Designation of criteria's for UESQ Designation C1 C2 C3 C4 C5 C6 C7 Criteria I. Curricular Aspects II. Teaching-Learning and Evaluation III. Research, Consultancy and Extension IV. Infrastructure and Learning Resources V. Student Support and Progression VI. Governance, Leadership and Management VII. Innovations and Best Practices Structural Self-Interaction Matrix ISM methodology suggests the use of the expert opinions based on various management techniques such as brain storming, nominal technique, etc., in developing the contextual relationship among the variables. Thus, in this research for identifying the contextual relationship among the University education quality criteria, four experts, two each from the NAAC peer team members and the academia, were consulted. These experts from the NAAC peer team and academia were well conversant with current University education quality practices having an average experience of more than20 years. Keeping in mind the contextual relationship for each variable, the existence of a relation between any two criteria (i and j) and the associated direction of the relation is questioned. Four symbols are used to denote the direction of relationship between the guidelines (i and j): V: Criteriai will help in achieving factor j; A: Criteria j will help in achieving factor i; X: Criteriai and j will help in achieving each other; and O: Criteriai and j are unrelated. 261 Nameesh Miglani, Rajeev Saha, R.S. Parihar

5 Table 3 Structural Self-interaction Matrix (SSIM) C2 C3 C4 C5 C6 C7 C1 V V X V O V C2 V A V O X C3 A O O V C4 V O A C5 A O C6 Table 3 shows the use of symbols V, A, X, and O in formation of SSIM. X Reachability Matrix To convert the SSIM into the binary reachability matrix with the dependence and enabling power all V, A and X is replaced by a digit 1 and O by 0 (zero). The substitution of l s and 0s are according to the following rules. - If the (i,j) entry in the SSIM is V, the (i,j) entry in the reachability matrix becomes 1 and the (j,i) entry becomes 0. - If the (i,j) entry in the SSIM is A, the (i,j) entry in the reachability matrix becomes 0 and the (j,i) entry becomes 1. - If the (i,j) entry in the SSIM is X, the (i,j) entry in the reachability matrix becomes 1 and the (j,i) entry also becomes 1. - If the (i,j) entry in the SSIM is O, the (i,j) entry in the reachability matrix becomes 0 and the (j,i) entry also becomes 0. The initial reachability matrix developed on the basis of the above procedure is presented in Table 4. Table 4 Initial Reachability Matrix C1 C2 C3 C4 C5 C6 C7 C C C C C C C The final reachability matrix is obtained by incorporating the transitivity s as enumerated in Step 4 of the ISM methodology. This is shown in Table 5. In this table, the driving power and dependence of each criterion s are also shown. The driving power of a particular criterion is the total number of guidelines (including itself) which it may help achieve. The dependence is the total number of criterion s which may help achieving it. These driving power and dependencies will be used in the MICMAC analysis, where the criterion s will be classified into four groups of excluded, dependent, relay, and influential (driver) criterion s as detailed below. 262 Nameesh Miglani, Rajeev Saha, R.S. Parihar

6 Table 5 Final Reachability Matrix C1 C2 C3 C4 C5 C6 C7 Driver Power C C C C C C C Dependence Excluded Variables These variables are close to the origin of the matrix having low driving power & low dependency. Also called Independent variables or autonomous variables, they have a weaker link to the system and do not influence future of the system. Dependent Variables Also known as resultant variables, these variables have low driving power & high dependency and are influenced by both influential variables and relay variables. Relay Variables These variables have high influence and high dependency and are unstable. Also known as linkage variables, any actions towards these variables may relay back through other variables. Influential Variables These variables have high driving power & low dependency. Figure 2 Driving power and dependence diagram The various levels of this analysis involve the criterion reachability set, antecedent set and intersection set. The reachability set consists of the criterion itself and the other criterions, which it may help achieve. The antecedent set consists of the criterion itself and other criterions, which may help achieving it. Thereafter, 263 Nameesh Miglani, Rajeev Saha, R.S. Parihar

7 intersection of these two sets is derived for all criterions. One by one the criterions having the same reachability set and intersection set are eliminated in each iteration. The results of the iterations are reproduced in Tables 6 to 9. Table 6 Iteration 1 Criterion Reachability Set Antecedent Set Intersection Set Level C1 C1,C2,C3,C4,C5,C6,C7 C1,C4,C7 C1,C4,C7 C2 C2,C3,C4,C5,C6,C7 C1,C2,C3,C4,C6,C7 C2,C3,C4,C6,C7 C3 C2,C3,C4,C6,C7 C1,C2,C3,C4,C7 C2,C3,C4,C7 C4 C1,C2,C3,C4,C5,C6,C7 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 C5 C5 C1,C2,C4,C5,C6,C7 C5 I C6 C2,C4,C5,C6,C7 C1,C2,C3,C4,C6,C7 C2,C4,C6,C7 C7 C1,C2,C3,C4,C5,C6,C7 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 Table 7 Iteration 2 Criterion Reachability Set Antecedent Set Intersection Set Level C1 C1,C2,C3,C4,C6,C7 C1,C4,C7 C1,C4,C7 C2 C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 C2,C3,C4,C6,C7 C3 C2,C3,C4,C6,C7 C1,C2,C3,C4,C7 C2,C3,C4,C7 C4 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 II C6 C2,C4,C6,C7 C1,C2,C3,C4,C6,C7 C2,C4,C6,C7 C7 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 C1,C2,C3,C4,C6,C7 II Table 8 Iteration 3 Criterion Reachability Set Antecedent Set Intersection Set Level C1 C1,C2,C3,C6 C1 C1 C2 C2,C3,C6 C1,C2,C3,C6 C2,C3,C6 III C3 C2,C3,C6 C1,C2,C3 C2,C3 C6 C2,C6 C1,C2,C3,C6 C2,C6 III Table 9 Iteration 4 Criterion Reachability Set Antecedent Set Intersection Set Level C1 C1,C2 C1 C1 V C3 C3 C1,C3 C3 IV The levels of the criterions helps in formulating the ISM model wherein first level means topmost priority towards implementation of concerned NAAC criteria, second level means second priority towards implementation of concerned NAAC criteria and so on. 264 Nameesh Miglani, Rajeev Saha, R.S. Parihar

8 Having identified the levels of the criterions through a number of iterations, the relationship between the criterions is drawn indicating the direction of the relation with the help of an arrow. The digraph drawn thus is examined to eliminate transitivity of relationships. The final model arrived at is represented by Figure 3. Figure 3 ISM-based model Conclusion MICMAC (Matriced'ImpactsCroisés Multiplication Appliquée á un Classement) analysis of developed ISM provides insight into the impact of variables on the total system. MICMACalso popularly known as Cross-Impact Matrix Multiplication Applied to Classification was developed by Duperrin and Godet in MICMAC analysis contains the following three steps: (1) Identify relevant variables: usually through brain-storming or based on expert opinions, variables related to the research topic are identified. A complete variable list is crucial for future studies and analysis. (2) Build the causal relationship between variables: causal relationship between the variables is built in this stage. (3) Identify key variables: this step is mainly about identifying key variables and factors that are important to overall system changes. In recent years, scholars applied MICMAC wildly in various fields; ISM and MICMAC were integrated to identify and classify key factors in an environmental impact assessment (Arya and Abbasi, 2001).ISM and MICMAC was used to analyze obstacles for the reverse logistics and how they affect one another in the automobile industry (Ravi and Shankar, 2005).ISM and MICMAC was adopted to analyze the dynamic of variables and figure out the key factor that reduces risks in the supply chain (Faisal, Banwet, and Shankar, 2006).ISM and MICMAC was used in the build-to-order supply chain, to analyze the relationship between supplier choice and evaluation standards (Kannan and Haq, 2007).ISM and MICMAC was also used to build a relationship model for variables in logistics service outsourcing in order to make shipping suppliers more efficient and productive (Qureshi, Kumar and Kumar, 2007).ISM and MICMAC was used to identify and classify the key criteria and their role in the assessment of 3pl services providers (Qureshi, Kumar and Kumar, 2008).ISM and MICMAC was used to analyze risks business face when contracting international projects in India (Jha and Devaya, 2008). The interaction between barriers to total quality management implementation was discussed using ISM and MICMAC analysis (Talib, Rahman and Qureshi, 2011). Analysis of third party 265 Nameesh Miglani, Rajeev Saha, R.S. Parihar

9 reverse logistics provider was done using ISM and MICMAC technique (Govindan, Palaniappan, Zhuand Kannan, 2012). Modelling of Supply Chain Management enablers was done using ISM and MICMAC analysis (Gorane and Kant, 2013). The study on flexible manufacturing system dimensions and their interrelationship employed ISM, fuzzy MICMAC and TISM to develop a FMS framework and formulated strategy to implement in Indian scenario (Dubey and Ali, 2014). Dubey and Singh (2015) employed ISM and fuzzy MICMAC analysis to understand complex relationship among JIT, lean behaviour, TQM and their antecedents. The dimensions of image management were explored using ISM and MICMAC analysis (Uppaal and Singh, 2016). MICMAC analysis is thus employed to analyze the driver power and the dependence power of the attributes under study. Subsequently, the driver power dependence diagram is constructed using final reachability matrix (Table 5) as shown in Fig. 2. The criterions are classified into four clusters (Fig. 2). Cluster I: Excluded Variables No criterion traced in this cluster. Cluster II: Dependent Variables C5 criterion traced in this cluster. Cluster III: Relay Variables C2, C3, C4, C6, and C7criterion traced in this cluster. Cluster IV: Influential Variables C1criterion traced in this cluster. REFERENCES [1] Ansari, M. F., Kharb, R. K., Luthra, S., Shimmi, S. L., &Chatterji, S. (2013). Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique. Renewable and Sustainable Energy Reviews, 27, [2] Arya, D.S. and Abbasi, S.A., Identification and classification of key variables and their role in environmental impact assessment: Methodology and software package intra, Environmental monitoring and assessment, 72(3), , [3] Chen, S. P., & Wu, W. Y. (2010). A systematic procedure to evaluate an automobile manufacturer distributor partnership. European Journal of Operational Research, 205(3), [4] Dubey, R., & Ali, S. S. (2014). Identification of flexible manufacturi ng system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), [5] Dubey, R., & Singh, T. (2015). Understanding complex relationship among JIT, lean behaviour, TQM and their antecedents using interpretive structural modelling and fuzzy MICMAC analysis. The TQM Journal, 27(1), [6] Faisal, M.N., Banwet, D.K., & Shankar, R. (2006). Supply chain risk mitigation: modeling the enablers. Busines s Process Management Journal, 12(4), [7] Govindan, K., Palaniappan, M., Zhu, Q., & Kannan, D. (2012). Analysis of third party reverse logistics provider using interpretive structural modeling. International Journal of Production Economics, 140(1), [8] Gorane, S. J., & Kant, R. (2013). Modelling the SCM enablers: an integrated ISM-fuzzy MICMAC approach. Asia Pacific Journal of Marketing and Logistics, 25(2), [9] Grover, D., Shankar, R., &Khurana, A. (2007). An interpretive structural model of corporate governance. International Journal of Business Governance and Ethics, 3(4), [10] Haleem, A., Sushil, Qadri, M. A., & Kumar, S. (2012). Analysis of critical success factors of world -class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process. Production Planning & Control, 23(10-11), [11] Jha, K. N., &Devaya, M. N. (2008). Modelling the risks faced by Indian construction companies assessing international projects. Construction Management and Economics, 26(4), [12] Kannan, G., &Haq, A. N. (2007). Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research, 45(17), [13] Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, conservation and recycling, 54(1), [14] Lee, A. H., Wang, W. M., & Lin, T. Y. (2010). An evaluation framework for technology transfer of new equipment in high technology industry. Technological Forecasting and Social Change, 77(1), Nameesh Miglani, Rajeev Saha, R.S. Parihar

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