An Analysis of Job Change Decision Using a Hybrid Mcdm Method: A Comparative Analysis
DOI:
https://doi.org/10.18533/ijbsr.v6i3.935Keywords:
Fuzzy AHP, Fuzzy TOPSIS, job change decision, multi criteria decision making.Abstract
This paper investigates the decision process relating to job change which mostly depends on individual’s expectations about a job. Failing to fully understand the factors shaping these expectations leads to dissatisfaction and poor work performance; which produces unwanted consequences for both individuals and businesses. Since job change decision is defined as a multiple criteria decision making (MCDM) problem. This study uses a hybrid approach as a methodology combining fuzzy Analytic Hierarchy Analysis (AHP) and fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for the job change decision of a faculty working in a university. In this approach, while the use of fuzzy AHP method helps determine the weight of the decision criteria; fuzzy TOPSIS enables the evaluation of the alternatives. In order to investigate the methods’ applicability in multiple dimensions of decision problem space, a comparison analysis is conducted with the three methodologies; fuzzy AHP, fuzzy TOPSIS and the proposed hybrid approach (named fuzzy AHP-TOPSIS) in the same decision making context. Four factors are considered for the comparison: adequacy to changes of criteria or alternatives; agility in the decision process; computational complexity; and the number of criteria and alternatives. Analysis shows that three methods achieve the same results. This verifies their robustness and indicates that MCDM methods are viable in job change decisions. However; comparison analysis shows that based on the four factors; the proposed hybrid fuzzy AHP-TOPSIS method provide more consistent results than fuzzy AHP and fuzzy TOPSIS methods. Thus the proposed hybrid fuzzy AHP-TOPSIS method is more appropriate to use on a wide range of job change decision problems.
References
Abo-Sinna, M. A., & Amer, A. H. (2005). Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems. Applied Mathematics and Computation, 162(1): 243–256. http://doi.org/10.1016/j.amc.2003.12.087
Alp, S., & Özkan, T. K. (2015). Job choice with multi-criteria decision making approach in a fuzzy environment. International Review of Management and Marketing, 5(3): 165–172.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4): 141–164. http://doi.org/10.1287/mnsc.17.4.B141
Boran, F. E., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5): 493–503. http://doi.org/10.1002/hfm.20252
Boswell, W. R., Roehling, M. V., LePine, M. A., & Moynihan, L. M. (2003). Individual job-choice decisions and the impact of job attributes and recruitment practices: a longitudinal field study. Human Resource Management, 42(1): 23–37.
Breaugh, J. A. (2008). Employee recruitment: current knowledge and important areas for future research. Human Resource Management Review, 18(3): 103–118. http://doi.org/10.1016/j.hrmr.2008.07.003
Breaugh, J. A., Macan, T. H., & Grambow, D. M. (2008). Employee Recruitment: Current Knowledge and Directions for Future Research. In G. P. Hodgkinson & J. K. Ford (Eds.), International Review of Industrial and Organizational Psychology. John Wiley & Sons, Ltd. pp. 45–82. http://doi.org/10.1002/9780470773277.ch2
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3): 233–247. http://doi.org/10.1016/0165-0114(85)90090-9
Chan, F. T. S., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4): 417–431. http://doi.org/10.1016/j.omega.2005.08.004
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649–655.
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1): 1–9. http://doi.org/10.1016/S0165-0114(97)00377-1
Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2): 289–301. http://doi.org/10.1016/j.ijpe.2005.03.009
Chen, L. H., & Hung, C. C. (2010). An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D. International Journal of Production Research, 48(24): 7483–7506. http://doi.org/10.1080/00207540903365308
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(2): 343–350. http://doi.org/10.1016/S0377-2217(96)00026-4
Cheng, C. H., & Mon, D. L. (1994). Evaluating weapon system by analytical hierarchy process based on fuzzy scales. Fuzzy Sets and Systems, 63(1): 1–10. http://doi.org/10.1016/0165-0114(94)90140-6
Csutora, R., & Buckley, J. J. (2001). Fuzzy hierarchical analysis: the lambda-max method. Fuzzy Sets and System, 120(2): 181–195.
Dubois, D. (2011). The role of fuzzy sets in decision sciences: old techniques and new directions. Fuzzy Sets and Systems, 184(1): 3–28. http://doi.org/10.1016/j.fss.2011.06.003
Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6): 4324–4330. http://doi.org/10.1016/j.eswa.2009.11.067
Ertuğrul, İ., & Karakaşoğlu, N. (2007). Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. The International Journal of Advanced Manufacturing Technology, 39(7-8): 783–795. http://doi.org/10.1007/s00170-007-1249-8
Fields, D., Dingman, M. E., Roman, P. M., & Blum, T. C. (2005). Exploring predictors of alternative job changes. Journal of Occupational and Organizational Psychology, 78(1): 63–82. http://doi.org/10.1348/096317904X22719
Gati, I., & Asher, I. (2001). Prescreening, in-depth exploration, and choice: from decision theory to career counseling practice. The Career Development Quarterly, 50(2): 140–157. http://doi.org/10.1002/j.2161-0045.2001.tb00979.x
Groysberg, B., & Abrahams, R. (2010). Managing yourself: five ways to bungle a job change. Harvard Business Review, January–February. Retrieved January 16, 2015, from https://hbr.org/2010/01/managing-yourself-five-ways-to-bungle-a-job-change
Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 36(2): 4067–4074. http://doi.org/10.1016/j.eswa.2008.03.013
Hadi-Vencheh, A., & Mohamadghasemi, A. (2011). A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications, 38(4): 3346–3352. http://doi.org/10.1016/j.eswa.2010.08.119
Halaby, C. N. (1988). Action and information in the job mobility process: the search decision. American Sociological Review, 53(1): 9-25. http://doi.org/10.2307/2095729
Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications : A State-Of-The-Art Survey. Berlin; New York: Springer-Verlag.
Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation, 181(2): 1544–1551. http://doi.org/10.1016/j.amc.2006.02.057
Jing, L., Chen, B., Zhang, B., Li, P. (2013). A hybrid stochastic-interval analytic hierarchy process approach for prioritizing the strategies of reusing treated wastewater. Mathematical Problems in Engineering, 2013: 1-10. http://doi.org/10.1155/2013/874805, 10.1155/2013/874805
Kahraman, C. (2008). Multi-criteria decision making methods and fuzzy sets. In C. Kahraman (Ed.), Fuzzy Multi-Criteria Decision Making. Springer US pp. 1–18. http:// doi.org/10.1007/978-0-387-76813-7_1
Kahraman, C., Ateş, N. Y., Çevik, S., Gülbay, M., & Erdoğan, S. A. (2007). Hierarchical fuzzy TOPSIS model for selection among logistics information technologies. Journal of Enterprise Information Management, 20(2): 143–168. http://doi.org/10.1108/17410390710725742
Kilic, H. S. (2013). An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling, 37(14–15): 7752–7763. http://doi.org/10.1016/j.apm.2013.03.010
Lee, Y. C., Li, M. L., Yen, T. M., & Huang, T. H. (2011). Analysis of fuzzy decision making trial and evaluation laboratory on technology acceptance model. Expert Systems with Applications, 38(12): 14407–14416. http://doi.org/10.1016/j.eswa.2011.04.088
Lima-Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21(August): 194–209. http://doi.org/10.1016/j.asoc.2014.03.014
Lin, C. J., & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1): 205–213. http://doi.org/10.1016/j.eswa.2006.08.012
Liou, J. J. H., & Tzeng, G. H. (2012). Comments on “multiple criteria decision making (MCDM) methods in economics: an overview.” Technological and Economic Development of Economy, 18(4): 672–695. http://doi.org/10.3846/20294913.2012.753489
Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – two decades review from 1994 to 2014. Expert Systems with Applications, 42(8): 4126–4148. http://doi.org/10.1016/j.eswa.2015.01.003
Montazer, G. A., Saremi, H. Q., & Ramezani, M. (2009). Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection. Expert Systems with Applications, 36(8): 10837–10847. http://doi.org/10.1016/j.eswa.2009.01.019
Murtagh, N., Lopes, P. N., & Lyons, E. (2011). Decision making in voluntary career change: an other-than-rational perspective. Career Development Quarterly, 59(3): 249–263.
Önüt, S., Kara, S. S., & Efendigil, T. (2008). A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing, 19(4): 443–453. http://doi.org/10.1007/s10845-008-0095-3
Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications, 39(3): 2822–2841. http://doi.org/10.1016/j.eswa.2011.08.142
Pitz, G. F., & Harren, V. A. (1980). An analysis of career decision making from the point of view of information processing and decision theory. Journal of Vocational Behavior, 16(3): 320–342.
Rajput, H. C., Milani, A. S., & Labun, A. (2011). Including time dependency and ANOVA in decision-making using the revised fuzzy AHP: A case study on wafer fabrication process selection. Applied Soft Computing, 11(8): 5099–5109. http://doi.org/10.1016/j.asoc.2011.05.049
Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill, NewYork.
Tuzkaya, G., Gülsün, B., Kahraman, C., & Özgen, D. (2010). An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Systems with Applications, 37(4): 2853–2863. http://doi.org/10.1016/j.eswa.2009.09.004
U.S. Bureau of Labor Statistics. (2015). Number of jobs held, labor market activity, and earnings growth among the youngest baby boomers: results from a longitudinal survey (News Release No. USDL-15-0528) p. 12. Retrieved January 25, 2016, from http://www.bls.gov/news.release/pdf/nlsoy.pdf
Vahdani, B., & Hadipour, H. (2010). Extension of the ELECTRE method based on interval-valued fuzzy sets. Soft Computing, 15(3): 569–579. http://doi.org/10.1007/s00500-010-0563-5
van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3): 199–227. http://doi.org/10.1016/S0165-0114(83)80082-7
Vasant, P., Bhattacharya, A., & Abraham, A. (2008). Measurement of level-of-satisfaction of decision maker in intelligent fuzzy-MCDM theory: a generalized approach. In C. Kahraman (Ed.), Fuzzy Multi-Criteria Decision Making. Springer US pp. 235–261. http:// doi.org/10.1007/978-0-387-76813-7_9
Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers & Mathematics with Applications, 53(11): 1762–1772. http://doi.org/10.1016/j.camwa.2006.08.037
Wang, Y. M., & Elhag, T. M. S. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications, 31(2): 309–319. http://doi.org/10.1016/j.eswa.2005.09.040
Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186(2): 735–747. http://doi.org/10.1016/j.ejor.2007.01.050
Yavuz, V. A., Pilli, R., & Pasham, P. R. (2014). A fuzzy analytic hierarchy process model for the evaluation of print advertisement designs. In K. O. Oruç & H. Demirgil (Eds.),. Proceedings of the 15th International Symposium on Econometrics, Operations Research and Statistics, Isparta, Turkey pp. 624–643. http://www.eyi2014.org/docs/Bildiriler-Kitabi.pdf
Yazdani-Chamzini, A. (2014). An integrated fuzzy multi criteria group decision making model for handling equipment selection. Journal of Civil Engineering and Management, 20(5): 660–673. http://doi.org/10.3846/13923730.2013.802714
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3): 338–353. http://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12): 1061–1078. http://doi.org/10.1002/int.21676
Zimmermann, H. J. (2001). Fuzzy sets-basic definitions. In Fuzzy Set Theory and Its Applications. Springer Netherlands pp. 11–21. http:// doi.org/10.1007/978-94-010-0646-0_2
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