A Model for Measuring Institutionalization Level of SMEs

Abstract

Institutionalization help an organization to gain legitimacy, increase resources and maintain survival. In other words, institutionalization is realized by developing appropriate and meaningful behaviors with the environment to gain legitimacy and conformity and transferring them to next generations. It is a crucial issue especially for small and medium sized enterprises (SMEs) to adopt themselves according to the changes in the environment, and sustain competitive. In this study, fuzzy hybrid multi-criteria decision making approach is used in order to measure institutionalization level of SMEs. For achieving this, first of all, criteria that indicate the institutionalization level of SMEs are determined. Then cause and effect interaction among main criteria is determined by fuzzy DEMATEL method. According to the inter influence derived from fuzzy DEMATEL, fuzzy analytic network process (ANP) is implemented in order to obtain the weights of the criteria. Expert opinions and group decision making approach are utilized during both fuzzy DEMATEL and fuzzy ANP methods. After acquiring the weights, several SMEs are evaluated according to the criteria predefined and VIKOR method is implemented for measuring the level of institutionalization of the SMEs

Keywords

• Institutionalization, multi-criteria decision making, Fuzzy DEMATEL, Fuzzy ANP, VIKOR

References

1. Alpay G., Bodur, M., Yılmaz, C., Çetinkaya, S., Arıkan, L., (2008). Performance implications of institutionalization process in family-owned businesses: Evidence from an emerging economy. Journal of World Business, 43, 435-448.
2. Apaydın, F., (2009). Kurumsal teori ve işletmelerin kurumsallaşması. C.Ü. İktisadi ve İdari Bilimler Dergisi, 10.
3. Apaydın, F., Coşkun, A. (2008). Organizational Institutionalization and Corporate Performance Results: A Study on Small and Medium Size Enterprises in Turkey, In: Proceeding of 1st International Conference on Management and Economics, Tirane, Albania.
4. Buckley, J.J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17, 233-247.
5. Büyüközkan, G., & Çifçi, G. (2012). A. novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications 39, 3000-3011
6. Chang, D.Y. (1992). Extent analysis and synthetic decision, optimization techniques and applications (Vol. 352). Singapore: World Scientific.
7. Chang, D.Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649-655.
8. Cheng, C.H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96, 343-350.

9. Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21, 215-231.
10. Ho, W.-R.J., Tsai, C.-L., Tzeng, G.-H., Fang, S.-K., (2011). Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM. Expert Systems with Applications, 38, 16–25.
11. Kahveci, T.C., (2007). İmalat işletmelerinde kurumsallaşma ve kurumsal modelleme, PhD thesis, Sakarya University.
12. Karpuzoğlu, E. (2004). Büyüyen ve Gelişen Aile Şirketlerinde Kurumsallaşma. Hayat Yayınları.
13. Korkmaz, M., (2003). Kurumsallaşma nedir ve niçin gereklidir?. Dünya Gazetesi.
14. Lee, W.-S., Huang, A.Y., Chang, Y.Y., Cheng, C.M., (2011). Analysis of decision making factors for equityinvestment by DEMATEL and Analytic Network Process. Expert Systems with Applications, 38, 8375–8383.
15. Leung, L.C., Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124, 102-113.
16. Lin, Chi-Jen, Wu, Wei-Wen (2008). A causal analytical method for group decision making under fuzzy environment. Expert Systems with Applications, 34, 205-213.
17. Lin, Chi-Jen, Wu, Wei-Wen (2004). A fuzzy extension of the DEMATEL method for group decision making. European Journal of Operational Research, 156, 445-455.
18. Lin, C.-L., Hsieh, M.-S., Tzeng, G.-H., (2010). Evaluating vehicle telematics system by using a novel MCDM techniques with dependence and feedback. Expert Systems with Applications, 37, 6723–6736.
19. Liou, J.J.H, Chuang, Y.T., (2010). Developing a hybrid multi-criteria model for selection of outsourcing providers. Expert Systems with Applications, 37, 3755–3761.
20. Mikhailov, L. (2004). A fuzzy approach to deriving priorities from interval pairwise comparison judgments.European Journal of Operational Research, 159, 687-704.
21. Mohanty, R.P., Agarwal, R., Choudhury, A.K., Tiwari, M.K., 2005. A fuzzy ANP based approach to R&D projectselection: A case study. International Journal of Production Research, 43(24), 5199–5216.
22. Opricovic, S., (1998). Multicriteria optimization of civil engineering systems (in Serbian, Visekriterijumskaoptimizacija sistema u gradjevinarstvu). Faculty of Civil Engineering, Belgrade.
23. Opricovic, S., Tzeng, G.H. (2002). Multicriteria planning of post-earthquake sustainable reconstruction. ComputerAided Civil and Infrastructure Engineering, 17, 211–220.
24. Opricovic, S., Tzeng, G.H. (2003). Defuzzification within a multicriteria decision model, International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems, 11(5), 635-652
25. Saaty, T.L. (1996). Decision Making with Dependence and Feedback: Analytic Network Process, RWS Publications, Pittsburgh.
26. Saaty, T.L., Vargas, L.G., (1998). Diagnosis with dependent symptoms: Bayes theorem and the analytic network process. Operations Research, 46(4), 491–502.
27. Sanayei, A., Mousavi, S.F., Yazdankhah, A., (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37, 24–30.
28. Selznick, P., (1996). Institutionalism old and new. Administrative Science Quarterly, 41, 270-277.
29. Yager, R.R., Filev, D.P. (1994). Essentials of fuzzy modeling and control. New York: John Wiley & Sons.
30. Yüksel, İ., Dağdeviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firmExpert Systems with Applications, 37, 1270-1278.
31. Van Laarhoven, P.J.M., Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11, 229-241.
32. Wu, Wei-Wen (2008). Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications 35, 828–835.
33. Yang, H.W., Chang, K.F., (2012). Combining means-end chain and fuzzy ANP to explore customers’ decision process in selecting bundles. International Journal of Information Management, 32, 381– 395.
34. Yang, J. L., Tzeng, G.-H., (2011). An integrated MCDM technique combined with DEMATEL for a novel clusterweighted with ANP method. Expert Systems with Applications 38, 1417–1424
35. Zeleny, M. (1982). Multiple Criteria Decision Making, Mc-Graw-Hill, New York.