Structural equation modelling – artificial neural network based hybrid approach for assessing quality of university cafeteria services


TQM Journal, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1108/tqm-01-2022-0001
  • Journal Name: TQM Journal
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, EBSCO Education Source, Educational research abstracts (ERA), Food Science & Technology Abstracts, INSPEC
  • Keywords: Artificial intelligence, Service quality, Artificial neural networks, SEM-ANN hybrid approach, Student satisfaction, University cafeterias, STUDENTS SATISFACTION, DETERMINANTS, MANAGEMENT, ANTECEDENTS, INTEGRATION, ACCEPTANCE, LOYALTY
  • Eskisehir Osmangazi University Affiliated: Yes


© 2022, Emerald Publishing Limited.Purpose: This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach. Design/methodology/approach: An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach. Findings: Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked. Originality/value: Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.