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


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.


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



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]).


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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

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