Annals of Operations Research

, Volume 263, Issue 1–2, pp 565–585 | Cite as

A causal analytic approach to student satisfaction index modeling

  • Ali Turkyilmaz
  • Leyla Temizer
  • Asil Oztekin
Data Mining and Analytics


The increasing number of new higher education institutions (HEIs) has resulted in a fierce competition for attracting and retaining the best students. In line with these purposes, this study is aimed at devising a student satisfaction index (SSI) model for the HEIs. The SSI model is developed to measure the satisfaction of students in terms of different aspects such as image of the university, expectations, perceived quality, perceived value, and loyalty. The SSI model was developed using the Bayesian neural networks-based Universal Structure Modeling method. The results provide strategically valuable information for HEIs decision makers with regard to influential factors of student satisfaction and loyalty.


Student satisfaction index Higher education Bayesian neural networks Universal Structure Modeling Causal analytics Data mining 



Authors are thankful to the two anonymous reviewers for their constructive comments. They are also grateful to the Editor-in-Chief of Annals of Operations Research, Dr. Endre Boros, and managing editor, Katie D’Agosta, for their timely management of this manuscript’s submission.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Industrial EngineeringFatih UniversityBuyukcekmece, IstanbulTurkey
  2. 2.Department of Operations and Information Systems, Manning School of BusinessUniversity of Massachusetts LowellLowellUSA

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