ISM Model of Critical Success Factors of using alternative dispute solutions on Saudi Arabian construction projects

Document Type : Original Article

Author

CEO MISK CONSULTANT

Abstract

This paper discusses the Interpretive Structural Modelling (ISM) approach as a tool in the pyramid chart categories and the critical success factors of using alternative dispute solutions on Saudi Arabian construction projects. By defining the relationships, and investigating the interrelationships between the eleven important progressing critical success factors and MICMAC analysis, an advantageous tool for allowing practitioners to understand the critical factors of success in the alternative dispute solutions of construction projects can be created. It can be stated here that the main aim of this study is to develop a model of critical success factors for alternative dispute solutions, thus serving to analyse the interaction of the major critical success factors, and helping to improve dispute resolutions in Saudi Arabian
2 Introduction
.construction projects
In 1973, Warfield proposed the ISM-based approach which can be considered as a tentative theoretical framework, in that it encapsulates the way that subject matter experts understand and explain the phenomenon of study (Warfield, 1974). ISM is also useful in that it can summarize and find relationships amongst specific variables, thus defining an issue or problem (Sage, 1977). According to Von Winterfeldt (1980), ISM is a useful tool for the formal representation of a decision-based problem, in that it employs graph and matrix theory notions. Furthermore, Saxena and Vrat (1990) observe that MICMAC analysis is utilized extensively as a way of identifying and analysing variables in accordance to their dependence power and driving power; where the aim of MICMAC is to make analyse driver (influencer) power, and
dependence (reliance) power of the factors involved (Mandal and Deshmukh, .)1994 Numerous studies have utilised the ISM approach in the past. Nishat et al. (2006) used the ISM method to find out the interrelationships found between various elements linked to a particular problem. Conversely, (2009) and Sagheer et al. (2009) utilised ISM to find out and analyse the critical factors that affect standards compliance and their level of effect in the developing world’s food industry. Manoharan et al. (2010) employed ISM to analyse theinterrelationships of factors of performance appraisal, and to further plan a training programme for employees.

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References

  • References

    • Sagheer, S., Yadav, S. S., & Deshmukh, S. G. (2009). An application of interpretative structural modeling of the compliance to food standards. International Journal of Productivity and Performance Management, 58(2), 136-159.
    • Agarwal, A., Shankar, R., & Tiwari, M. K. (2007). Modeling agility of supply chain. Industrial marketing management, 36(4), 443-457.
    • Attri, R., Dev, N., & Sharma, V. (2013). Interpretive structural modelling (ISM) approach: an overview. Research Journal of Management Sciences, 2(2), 3-8.
    • Crowther, D. & Lancaster, G. (2012). Research Methods. London: Routledge.
    • D.W. Malone, An introduction to the application of interpretive structural modeling, Proc. IEEE 63 (3) (1975) 397–404.
    • 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), 3831-3852.
    • Lin, Y. T., Lin, C. L., Yu, H. C., & Tzeng, G. H. (2011). Utilisation of interpretive structural modelling method in the analysis of interrelationship of vendor performance factors. International Journal of Business Performance Management, 12(3), 260-275.
    • Mandal, S.G. Deshmukh, Vendor selection using interpretive structural modeling (ISM), Int. J. Oper. Prod. Manage. 14 (6) (1994) 52-59.
    • Manoharan, T.R., Muralidharan, C. and Deshmukh, S.G. (2010), “Analyzing the interaction of performance appraisal factors using interpretive structural modeling”, Performance Improvement, Vol. 49 No. 6, pp. 25-35.
    • Mishra, S., Datta, S., & Mahapatra, S. S. (2012). Interrelationship of drivers for agile manufacturing: An Indian experience. International Journal of Services and Operations Management, 11(1), 35-48.
    • Nishat Faisal, M., Banwet, D. K., & Shankar, R. (2006). Supply chain risk mitigation: Modeling the enablers. Business Process Management Journal, 12(4), 535-552.
    • Sage, A. P. (1977). Methodology for large-scale systems. McGraw-Hill College.
    • Saxena J.P., Sushil and Vrat P., The impact of indirect relationships in classification of variables: A MICMAC analysis for energy conservation, System Research, 7(4), 245-253 (1990)
    • Von Winterfeldt, D. (1980). Structuring decision problems for decision analysis. Acta Psychologica, 45(1-3), 71-93.
    • Warfield JW (1974) developing interconnected matrices in structuralmodeling. IEEE Transcr Syst Men Cybern 4(1):81–87.

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