Measuring customer satisfaction with banking services using the process of network analysis and goal programing

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract
The goal of all service institutions, including banks, is to provide appropriate and satisfactory services to customers. To measure service quality, in fact, the difference between what customers feel they should receive and the service they actually received; must be measured. In this research, by studying the characteristics and models of service quality, five quality indicators were weighted through the process of network analysis. The prioritized weights, in turn, were used in the goal programing model to be used in selecting the best set of service quality measurement tools. The obtained results indicate that among the available options, considering the limitations that exist for the available hours of officials and managers; The best option is to survey customers in the bank in person.

Keywords


[1] Lee, M. C., & Hwan, S. (2005). Relationships among service quality, customer satisfaction and profitability in the Taiwanese banking industry. International journal of management22(4), 635.
[2] Gronroos, C. (1984). Strategic Management and Marketing in the Service Sector. Chartwell-Bratt.
[3] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of marketing49(4), 41-50. https://doi.org/10.2307/1251430
[4] Parasuraman, A. B. L. L., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. 64(1), 12-40. https://www.researchgate.net/publication/225083802
[5] Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation. Mcgraw-Hill, New York.
[6] Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922, No. 2). Pittsburgh: RWS publications.
[7] Chung, S. H., Lee, A. H., & Pearn, W. L. (2005). Analytic network process (ANP) approach for product mix planning in semiconductor fabricator. International journal of production economics96(1), 15-36. https://doi.org/10.1016/j.ijpe.2004.02.006
[8] Zarrinpoor, N., Amiri, M., & Nematolahi, M. H. (2021). The risk evaluation of green buildings using a hybrid procedure of DEMATEL and analytic network process. Journal of decisions and operations research6(1), 115-131. doi: 10.22105/dmor.2021.247961.1213
[9] Mousavi Arab, S. A., Homayounfar, M., & Ajalli, M. (2022). Balanced performance evaluation of B2C online stores with using a hybrid fuzzy ANP and fuzzy WASPAS approach. Journal of decisions and operations research6(Special Issue), 1-14. doi: 10.22105/dmor.2021.287084.1403
[10] Donyavi Rad, M., Sadeh, E., Amini Sabegh, Z., & Ehtesham Rasi, R. (2023). Introducing a fuzzy robust integrated model for optimizing humanitarian supply chain processes. Journal of Applied Research on Industrial Engineering10(3), 427-453. doi: 10.22105/jarie.2022.284946.1323
[11] Maghbouli, M., & Yekta, A. P. (2023). Undesirable Input in Production Process: A DEA-Based Approach. J. Oper. Strateg. Anal1, 46-54. https://doi.org/10.56578/josa010201