Advertisement

International Journal of Fuzzy Systems

, Volume 20, Issue 5, pp 1523–1538 | Cite as

A Method for Evaluating Service Quality with Hesitant Fuzzy Linguistic Information

  • Hao Xu
  • Zhi-Ping Fan
  • Yang Liu
  • Wu-Liang Peng
  • Yin-Yun Yu
Article

Abstract

Service quality evaluation is vital to identifying the strengths and weaknesses of the services provided by service organizations. In practical situations, because of customers’ inherent uncertainty and hesitancy, hesitant fuzzy linguistic information is often employed by customers to assess expected and perceived services. The purpose of this paper is to propose a novel method for evaluating service quality based on the use of hesitant fuzzy linguistic term sets (HFLTS). In this method, customers’ comparative linguistic expressions concerning the expected service, perceived service, and attribute weights are transformed into HFLTS. By using a transformation formula, HFLTS are expressed as trapezoidal fuzzy numbers. Then, based on Gap 5 of the Parasuraman Zeithaml Berry service quality model, calculation formulas and a related theoretical analysis of the discrepancy degree between fuzzy perceived service and expected service are given. A fuzzy evaluation result for each dimension is determined by aggregating the discrepancy degrees of customers with respect to all attributes in the same dimension. Furthermore, a linguistic evaluation result for each dimension is achieved by calculating and comparing the similarity degrees between the fuzzy evaluation result and the predefined linguistic variables. On this basis, an overall service quality evaluation result is determined by aggregating the evaluation results for all dimensions. Finally, an example is used to illustrate the feasibility and effectiveness of the proposed method.

Keywords

Service quality evaluation PZB service quality model Hesitant fuzzy linguistic term sets (HFLTS) Trapezoidal fuzzy number Discrepancy degree Similarity degree 

Notes

Acknowledgements

The authors would like to thank the editor-in-chief and the anonymous referees for their insightful and constructive comments and suggestions that led to an improved version of this paper. This work was supported by the National Natural Science Foundation of China (Project Nos. 71201109, 71571039, 71771043 and 71671117), the China Postdoctoral Science Foundation Funded Project (Project No. 2014M551113), the Doctoral Start-up Foundation of Liaoning Province in China (Project No. 20141089), the Program for Liaoning Excellent Talents in University in China (Project No. LJQ2014024) and the Liaoning Bai QianWan Talents Program (Liaoning baiqianwan Project No. 2015[52]).

References

  1. 1.
    Bitner, M.J., Hubbert, A.R.: Encounter satisfaction vs. overall satisfaction and quality. In: Rust, R.T., Oliver, R.L. (eds.) Service Quality: New Directions in Theory and Practice, pp. 72–94. Sage Publications, New York (1994)CrossRefGoogle Scholar
  2. 2.
    Culiberg, B.: Identifying service quality dimensions as antecedents to customer satisfaction in retail banking. Econ. Bus. Rev. 12(3), 151–166 (2010)Google Scholar
  3. 3.
    Morash, E.A., Ozment, J.: Toward management of transportation service quality. Logist. Transp. Rev. 30, 115–140 (1994)Google Scholar
  4. 4.
    Hallikas, J., Immonen, M., Pynnönen, M., Mikkonen, K.: Service purchasing and value creation: towards systemic purchases. Int. J. Prod. Econ. 147(1), 53–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Lee, H., Kim, C.: Benchmarking of service quality with data envelopment analysis. Expert Syst. Appl. 41(8), 3761–3768 (2014)CrossRefGoogle Scholar
  6. 6.
    Parasuraman, A., Zeithaml, V.A., Berry, L.L.: Reassessment of expectation as a comparison in measuring service quality: implications for further research. J. Mark. 58(1), 111–124 (1994)CrossRefGoogle Scholar
  7. 7.
    Chien, C.J., Tsai, H.H.: Using fuzzy numbers to evaluate perceived service quality. Fuzzy Sets Syst. 116(2), 289–300 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Chou, C.C., Liu, L.J., Huang, S.F., Yi, J.M., Han, T.C.: An evaluation of airline service quality using the fuzzy weighted SERVQUAL method. Appl. Soft Comput. 11(2), 2117–2128 (2011)CrossRefGoogle Scholar
  9. 9.
    Tseng, M.L.: Using the extension of DEMATEL to integrate hotel service quality perceptions into a cause–effect model in uncertainty. Expert Syst. Appl. 36(5), 9015–9023 (2009)CrossRefGoogle Scholar
  10. 10.
    Bentez, J.M., Mart, J.C., Roma, C.: Using fuzzy number for measuring quality of service in the hotel industry. Tour. Manag. 28(2), 544–555 (2007)CrossRefGoogle Scholar
  11. 11.
    Li, W., Yu, S., Pei, H., Zhao, C., Tian, B.Z.: A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. J. Air Transp. Manag. 60, 49–64 (2017)CrossRefGoogle Scholar
  12. 12.
    Büyüközkan, G., Çifçi, G.: A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst. Appl. 39(3), 2341–2354 (2012)CrossRefGoogle Scholar
  13. 13.
    Stefano, N.M., Casarotto Filho, N., Barichello, R., Sohn, A.P.: A fuzzy SERVQUAL based method for evaluated of service quality in the hotel industry. Procedia CIRP 30, 433–438 (2015)CrossRefGoogle Scholar
  14. 14.
    Zhang, L., Zhang, L.P., Zhou, P., Zhou, D.: A non-additive multiple criteria analysis method for evaluation of airline service quality. J. Air Transp. Manag. 47, 154–161 (2015)CrossRefGoogle Scholar
  15. 15.
    Kuo, M.S., Liang, G.S.: Combining VIKOR with GRA techniques to evaluate service quality of airports under fuzzy environment. Expert Syst. Appl. 38(3), 1304–1312 (2011)CrossRefGoogle Scholar
  16. 16.
    Cid-Lópeza, A., Hornos, M.J., Carrasco, R.A., Herrera-Viedma, E.: SICTQUAL: a fuzzy linguistic multi-criteria model to assess the quality of service in the ICT sector from the user perspective. Appl. Soft Comput. 37, 897–910 (2015)CrossRefGoogle Scholar
  17. 17.
    Tsai, H.H., Lu, I.Y.: The evaluation of service quality using generalized Choquet integral. Inf. Sci. 176(6), 640–663 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Rodríguez, R.M., Mart´ınez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Fuzzy Syst. 20(1), 109–119 (2012)CrossRefGoogle Scholar
  19. 19.
    Li, P., Wei, C.P.: A case-based reasoning decision-making model for hesitant fuzzy linguistic information. Int. J. Fuzzy Syst. (2017).  https://doi.org/10.1007/s40815-017-0391-1 Google Scholar
  20. 20.
    Liu, H.B., Rodríguez, R.M.: A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Inf. Sci. 258(6), 220–238 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Wang, J., Wang, J.Q., Zhang, H.Y.: A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing. Comput. Ind. Eng. 99, 287–299 (2016)CrossRefGoogle Scholar
  22. 22.
    Liao, H.C., Xu, Z.S., Zeng, X.J.: Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Trans. Fuzzy Syst. 23(5), 1343–1355 (2015)CrossRefGoogle Scholar
  23. 23.
    Liao, H.C., Xu, Z.S.: Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Syst. Appl. 42, 5328–5336 (2015)CrossRefGoogle Scholar
  24. 24.
    Yavuz, M., Oztaysi, B., Onar, S.C., Kahraman, C.: Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Syst. Appl. 42, 2835–2848 (2015)CrossRefGoogle Scholar
  25. 25.
    Liao, H.C., Xu, Z.S.: A VIKOR-based method for hesitant fuzzy multicriteria decision making. Fuzzy Optim Decis. 12(4), 373–397 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Liao, H.C., Yang, L.Y., Xu, Z.S.: Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Appl. Soft Comput. 63, 223–234 (2018)CrossRefGoogle Scholar
  27. 27.
    Wu, H.Y., Xu, Z.S., Ren, P.J., Liao, H.C.: Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Comput. Ind. Eng. 115, 449–458 (2018)CrossRefGoogle Scholar
  28. 28.
    Liu, W.S., Liao, H.C.: A bibliometric analysis of fuzzy decision research during 1970–2015. Int. J. Fuzzy Syst. 19(1), 1–14 (2016)CrossRefGoogle Scholar
  29. 29.
    Liao, H.C., Xu, Z.S., Enrique, H.V., Francisco, H.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. Int. J. Fuzzy Syst. 12, 1–27 (2017)Google Scholar
  30. 30.
    Liao, H.C., Xu, Z.S., Zeng, X.J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf. Sci. 271(3), 125–142 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Grönroos, C.: Service Management and Marketing: Managing the Moments of Truth in Service Competition. Free Press, Lexington Books, Lexington (1990)Google Scholar
  32. 32.
    Grönroos, C.: Quality comes to service. In: Scheuing, E.E., Christopher, W.F. (eds.) The Service Quality Handbook, pp. 17–24. AMACOM, New York (1993)Google Scholar
  33. 33.
    Harvey, J.: Service quality: a tutorial. J. Oper. Manag. 16(5), 583–597 (1998)CrossRefGoogle Scholar
  34. 34.
    Parasuraman, A., Zeithaml, V.A., Berry, L.L.: A conceptual model of service quality and its implications for future research. J. Mark. 49(3), 41–50 (1985)CrossRefGoogle Scholar
  35. 35.
    Parasuraman, A., Zeithaml, V.A., Berry, L.L.: SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. J. Retail. 64(1), 12–40 (1988)Google Scholar
  36. 36.
    Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl. Based Syst. 76, 127–138 (2015)CrossRefGoogle Scholar
  37. 37.
    Xu, Z.S.: Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment. Inf. Sci. 168(1–4), 171–184 (2004)CrossRefzbMATHGoogle Scholar
  38. 38.
    Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic—Theory and Applications. Thomson Computer Press, New York (1991)zbMATHGoogle Scholar
  39. 39.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar
  40. 40.
    Zimmermann, H.J.: Fuzzy Set Theory and its Applications, 2nd edn. Kluwer Academic Publishers, Boston/Dordrecht/London (1991)CrossRefzbMATHGoogle Scholar
  41. 41.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: Direct approach processes in group decision making using linguistic OWA operators. Fuzzy Set Syst. 79, 175–190 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Fan, Z.P., Liu, Y.: A method for group decision-making based on multi-granularity uncertain linguistic information. Expert Syst. Appl. 37(5), 4000–4008 (2010)CrossRefGoogle Scholar
  43. 43.
    Das, S., Guha, D.: A Centroid-based ranking method of trapezoidal intuitionistic fuzzy numbers and its application to MCDM problems. Fuzzy Inf. Eng. 8, 41–74 (2016)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Xu, Z.Y., Shang, S.C., Qian, W.B., Shu, W.H.: A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers. Expert Syst. Appl. 37(3), 1920–1927 (2010)CrossRefGoogle Scholar
  45. 45.
    Liu, Y., Bi, J.W., Fan, Z.P.: Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf. Fus. 36, 149–161 (2017)CrossRefGoogle Scholar
  46. 46.
    Liu, Y., Bi, J.W., Fan, Z.P.: A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy TOPSIS. Int. J. Inf. Tech. Decis. 16(6), 1497–1522 (2017)CrossRefGoogle Scholar
  47. 47.
    Liu, Y., Bi, J.W., Fan, Z.P.: A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Inf. Sci. 394–395, 38–52 (2017)CrossRefGoogle Scholar
  48. 48.
    Liu, Y., Bi, J.W., Fan, Z.P.: Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms. Expert Syst. Appl. 80, 323–339 (2017)CrossRefGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hao Xu
    • 1
    • 2
  • Zhi-Ping Fan
    • 3
  • Yang Liu
    • 3
  • Wu-Liang Peng
    • 4
  • Yin-Yun Yu
    • 5
  1. 1.Department of Information Management and Information System, School of Economics and ManagementShenyang Ligong UniversityShenyangPeople’s Republic of China
  2. 2.Shenyang Institute of Computing TechnologyChinese Academy of SciencesShenyangPeople’s Republic of China
  3. 3.Department of Information Management and Decision Sciences, School of Business AdministrationNortheastern UniversityShenyangPeople’s Republic of China
  4. 4.Department of Business Administration, School of Economics and ManagementYantai UniversityYantaiPeople’s Republic of China
  5. 5.Department of Management Science and Engineering, School of ManagementShenyang University of TechnologyShenyangPeople’s Republic of China

Personalised recommendations