A hybrid banking websites quality evaluation model using AHP and COPRAS-G: a Turkey case

Abstract

The quality of websites can directly influence bank's effectiveness. Therefore, the effective evaluation of the quality of bank websites is a point of concern for customers, owners, and researchers. The primary goal of the study is to propose a hybrid model of AHP and COPRAS-G methods for evaluating the website quality of banks. Furthermore, this study sheds light on understanding weights of evaluation criteria related to quality of bank websites. In this study, the weights of the evaluation criteria are computed by AHP method. Next, COPRAS-G is used to assess the quality levels of the websites and to rank them. A case study of evaluating the quality of bank websites of seventeen banks in Turkey is used to demonstrate the applicability and the effectiveness of this model. Empirical findings show that banks in Turkey utilize the Internet to its full potential to improve their websites. Furthermore, Garanti Bankasi has the best overall performance, followed by TEB, and Ziraat Bankasi. Additionally, the top-five evaluation criteria in order of importance are relevance, richness, understandability, navigability, and response time. Overall, the results show that this model provides a comprehensive and systematic approach that quantitatively measures a website's quality.

Keywords:

• website quality
• COPRAS-G
• AHP
• banking
• grey system theory

JEL Classification:

• C44
• C65
• G21

Additional information

Notes on contributors

Fatih Ecer

Fatih ECER is an Assistant Professor of Operations Research in the International Trade and Finance, Faculty of Economics and Business Administrations, University of Afyon Kocatepe, Turkey. He holds a BSc in Mathematics from Dokuz Eylul University, Izmir, Turkey, and a MSc and PhD in Business Administrations from University of Afyon Kocatepe, Turkey. His work has been published, or is forthcoming, in high quality international journals. His current research interests are in the Multiple Criteria Decision Making (MCDM), optimization, Artificial Neural Networks (ANNs), and data mining.