A Novel Decision Making Approach for Benchmarking the Service Quality of Smart Community Health Centers

Hao Li, Jinlin Li*, Helmut Dietl

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The purpose of this research is to develop a novel approach to benchmark smart community health centers in order to achieve continuous improvement of service quality. Three methods are presented: the fuzzy DEMATEL method is used to determine the criteria weights, the fuzzy ELECTRE III method is employed to obtain the ranking of smart community health centers, and IPA (Importance-Performance Analysis) is employed to formulate improvement strategies. The proposed approach clearly identifies the strengths and weakness of each smart community health center by ranking its performance with respect to a system of five service quality criteria. In addition, IPA is able to develop the most effective improvement strategies for each smart community health center. The proposed approach was applied to five smart community health centers in Beijing and service strengths and weakness are discussed. The proposed approach has three notable advantages. First, the novel approach can address ambiguity and uncertainty in the process of decision making. Second, interdependent relationships among the evaluation criteria are analyzed by the fuzzy DEMATEL method, so that the weights obtained are more in line with reality. Third, the fuzzy ELECTRE III method considers non-compensatory behavior for service quality comparisons among smart community health centers. The novel fuzzy-based approach presented in this article is a powerful and highly effective tool to benchmark smart community health centers and develop successful improvement strategies of service quality.

Original languageEnglish
Article number9257491
Pages (from-to)209904-209914
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Quality of service
  • decision making
  • fuzzy set theory
  • quality management

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