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Volume 5 • Issue 2 | July 2021

TOPSIS model based on entropy and similarity measure for market segment selection and evaluation

Truong Thi Thuy Duong, and Nguyen Xuan Thao

01/11/2021 109 0 0
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Multi-criteria decision-making Market segment selection Neutrosophic set Entropy Similarity measure

Abstract:

Purpose
The paper aims to propose a practical model for market segment selection and evaluation. The paper carries out a technique of order preference similarity to the ideal solution (TOPSIS) approach to make an operation systematic dealing with multi-criteria decision- making problem.

Design/methodology/approach
Introducing a multi-criteria decision-making problem based on TOPSIS approach. A new entropy and new similarity measure under neutrosopic environment are proposed to evaluate the weights of criteria and the relative closeness coefficient in TOPSIS model.

Findings
The outcomes show that the TOPSIS model based on new entropy and similarity measure is effective for evaluation and selection market segment. Profitability, growth of the market, the likelihood of sustainable differential advantages are the most important insights of criteria.

Originality/value
This paper put forward an effective multi-criteria decision-making dealing with uncertain information.

References:

  1. Aghdaie, M.H. (2015), “Target market selection based on market segment evaluation: a multiple attribute decision making approach”, International Journal of Operational Research, Vol. 24, pp. 262-278.
  2. Aghdaie, M.H., Zolfani, S.H. and Zavadskas, E.K. (2013), “Market segment evaluation and selection based on application of fuzzy AHP and COPRAS- G methods”, Journal of Business Economics and Management, Vol. 14, pp. 213-233.
  3. Broumi, S. and Smarandache, F. (2013), “Several similarity measures of neutrosophic sets”, Neutrosophic Sets and Systems, Vol. 1 No. 1, pp. 54-62.
  4. Chang, D.Y. (1992), “Extent analysis and synthetic decision”, Optimization Techniques and Applicatons, Vol. 1, pp. 352-355.
  5. Chiu, C.-Y., Chen, Y.-F. and Kuo, I.-T.K. (2009), “An intelligent market segmentation system using k-means and particle swarm optimization”, Expert Systems with Applications, Vol. 36, pp. 4558-4565.
  6. Dat, L.Q., Phuong, T.T., Kao, H.P., Chou, S.Y. and Nghia, P.V. (2015), “A new integrated fuzzy QFD approach for market segments evaluation and selection”, Applied Mathematical Modelling, Vol. 39, pp. 3653-3665.
  7. Duong, T.T.T., Phong, L.T., Hoi, L.Q. and Thao, N.X. (2020), “A novel model based on similarity measure and quality function deployment on interval neutrosophic sets for evaluation and selection market segments”, Journal of Intelligent and Fuzzy Systems, Vol. 38, pp. 5203-5214.
  8. Freytag, P. and Clarke, A. (2001), “Business to business market segmentation”, Industrial Marketing Management, Vol. 3 No. 6, pp. 473-486.
  9. Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Hooshmand, R. and Antuchevičienė, J. (2017), “Fuzzy extension of the CODAS method for multi-criteria market segment evaluation”, Journal of Business Economics and Management, Vol. 8 No. 1, pp. 1-19.
  10. Hwang, C.L. and Yoon, K. (1981), Multiple Attribute Decision Making Methods and Application: A State of the Art Survey, Springer Verlag.
  11. McDonald, M. and Dunbar, I. (2004), Market Segmentation How to Do it How to Do Profit from it, Elsevier Butterworth- Heinemann.
  12. Quinn, L. and Dibb, S. (2010), “Evaluating market-segmentation research priorities: targeting re-emancipation”, Journal of Marketing Management, Vol. 26 Nos 13-14, pp. 1239-1255.
  13. Simkin, L. and Dibb, S. (1998), “Prioritizing target markets”, Marketing Intelligence and Planning, Vol. 16, pp. 407-417.
  14. Smarandache, F. (1998), Neutrosophy: Neutrosophic Probability, Set, and Logic. Analytic Synthesis Synthetic Analysis. American Research Press.
  15. Smith, W. (1956), “Product differentiation and market segmentation as alternative marketing strategies”, Journal of Marketing, Vol. 21 No. 1, pp. 3-8.
  16. Thao, N.X. and Duong, T.T.T. (2019), “Selecting target market by similar measures in interval intuitionistic fuzzy set”, Technological and Economic Development of Economy, Vol. 25, pp. 934-950.
  17. Thao, N.X. and Smarandache, F. (2020), “Apply new entropy based similarity measures of single valued neutrosophic sets to select supplier material”, Journal of Intelligent and Fuzzy Systems, Vol. 39 No. 1, pp. 1005-1019.
  18. Tian, Z.P., Wang, J.Q. and Zhang, H.Y. (2018), “Hybrid single-valued neutrosophic MCGDM with QFD for market segment evaluation and selection”, Journal of Intelligent and Fuzzy Systems, Vol. 34, pp. 177-187.
  19. Wang, H., Smarandache, F., Zhang, Y. and Sunderraman, R. (2010), “Single valued SVNS sets”, Multisp Multistruct, Vol. 4, pp. 410-413.
  20. Wind, Y. (1978), “Issue and advances in segmentation research”, Journal of Marketing Research, Vol. 15, pp. 317-337.
  21. Zandi, F., Tavana, M. and O'Connor, A. (2012), “A Strategic cooperative game- theoretic model for market segmentation with application to banking in emerging economies”, Technological and Economic Development of Economy, Vol. 18, pp. 389-423.

JEL classification: C02,C44,D40,D81

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