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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 640-643 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.205

Measuring Service Quality Using Complement Methods for Gap Model

Zahra Mokhtary and Behrouz Pazhouh
Abstract—purpose of this study is to empirically assess three comparative approaches to measure service quality: gap model, TOPSIS, and loss function. Statically populations of this research are the insurants of five branches of social security organization in Tabriz that gathered in the second half of the year 2009. Empirically evidence obtained from a sample of four hundreds numbers from customer data in service quality of branches by SERVEQUAL questionnaire. Service quality evaluation obtained by these three distinct methods are compared and tested for their mutual agreement. We answered questions of the research by this data. Findings show that ranking obtained by these methods are Results show that branch three is the best and branch four is the worst of the branches.
This research provides profound concepts and is a framework for managers to improve service quality. This structure measures service quality gaps, selects an optimal combination of attribute levels to keep customer satisfied, and focuses on reducing the future loss caused by poor quality.

Index Terms—Measuring service quality, quality management, Gap analysis, SERVEQUAL.

The authors are with Mathematical Institution of Azar Pazhouhan, Tabriz, Iran (e-mail: zahra.mokhtary@gmail.com; behrouz.pazhouh@gmail.com).

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Cite:Zahra Mokhtary and Behrouz Pazhouh, "Measuring Service Quality Using Complement Methods for Gap Model," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 640-643, 2012.

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