The Performance Evaluation of Tejarat Internet Bank Services (AHP and Eisenhower Matrix Methods)

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Kharazmi University, Tehran, Iran.

2 Department of Economics, Kharazmi University, Tehran, Iran.

Abstract

Online banking is becoming a popular choice for many clients who value convenience and efficiency. However, it is also important to evaluate how well internet banks perform in terms of client satisfaction. The aim of this study is to measure the performance of Tejarat internet bank services, one of the largest Iranian banks that offers online banking services to its clients. We used two methods: The Importance-Performance Matrix and the analytical Hierarchy Process. The Importance-Performance Matrix helped us rank the identified factors according to their importance and urgency for improving the bank’s performance. The analytical hierarchy process helped us compare and weigh the relative importance of various criteria and sub-criteria that affect the bank’s performance. We collected data from a sample of 169 Tejarat online financial services users through a questionnaire survey. Our results showed that the bank’s performance was influenced by five factors: efficiency, ease of use, security, speed, and support. We also found that security and ease of use were the most critical and urgent factors that required immediate attention from the bank. Therefore, we recommend that Tejarat internet bank services focus on enhancing security and ease of use as their top priorities to improve their performance. We also suggest that they improve the quality of service in the client support section by being more responsive and fast. These insights are not only beneficial for Tejarat Internet Bank Services but also for other banks that want to improve their performance in the online banking sector.

Keywords

Main Subjects

References

Abbasi, B. N., Umer, M., Sohail, A., Tang, J., Ullah, I., & Abbasi, H. (2020). Service Quality, Customer Satisfaction and Loyalty in Banking Sector of Pakistan. International Journal of Management & Entrepreneurship Research, 1(1), 1–8. Retrieved from https://doi.org/10.51594/IJMER.V1I1.1
Al-Masaeed, S., Al Nawayseh, M. K., AlFawwaz, B. M., Maqableh, M., Alnabhan, M., Masa’deh, R., & AL-Shatnawi, A. (2022). Factors Affecting Consumers’ Intention to Use Mobile Ride Hailing Services in Developing Countries. International Journal of Interactive Mobile Technologies, 16(11), 207–223. Retrieved from https://doi.org/10.3991/IJIM.V16I11.30579
Almaiah, M. A., Al-Rahmi, A. M., Alturise, F., Alrawad, M., Alkhalaf, S., Lutfi, A., Al-Rahmi, W. M., & Awad, A. B. (2022). Factors Influencing the Adoption of Internet Banking: An Integration of ISSM and UTAUT with Price Value and Perceived Risk. Frontiers in Psychology, 13, 4811. Retrieved from https://doi.org/10.3389/FPSYG.2022.919198/BIBTEX
Alsmadi, A. A., Shuhaiber, A., Alhawamdeh, L. N., Alghazzawi, R., & Al-Okaily, M. (2022). Twenty Years of Mobile Banking Services Development and Sustainability: A Bibliometric Analysis Overview. Sustainability, 14(17), 1-14. Retrieved from https://doi.org/10.3390/SU141710630
Amiri, M., Hashemi-Tabatabaei, M., Keshavarz-Ghorabaee, M., Antucheviciene, J., Šaparauskas, J., Keramatpanah, M. (2023). Evaluation of Digital Banking Implementation Indicators and Models in the Context of Industry 4.0: A Fuzzy Group MCDM Approach. Retrieved from https://doi.org/10.3390/ axioms12060516
Anouze, A. L. M., & Alamro, A. S. (2020). Factors Affecting Intention to Use E-Banking in Jordan. International Journal of Bank Marketing, 38(1), 86–112. Retrieved from https://doi.org/10.1108/IJBM-10-2018-0271/FULL/PDF
Bach, L. M., Mihaljevic, B., & Zagar, M. (2018). Comparative Analysis of Blockchain Consensus Algorithms. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, 1545–1550. Retrieved from https://doi.org/10.23919/MIPRO.2018.8400278
Chen, W. C., Chen, C. W., & Chen, W. K. (2019). Drivers of Mobile Payment Acceptance in China: An Empirical Investigation. Information, 10(12), 1-20. Retrieved from https://doi.org/10.3390/INFO10120384.
Elshaer, I. A., Alrawad, M., Lutfi, A., & Azazz, A. M. S. (2024). Social Commerce And Buying Intention Post COVID-19: Evidence from a Hybrid Approach Based on SEM – fsQCA. Journal of Retailing and Consumer Services, 76, 1-15. Retrieved from https://doi.org/10.1016/j.jretconser.2023.103548
Ezzi, S. W. (2014). A Theoretical Model for Internet Banking: Beyond Perceived Usefulness and Ease of Use. Archives of Business Research, 2(2), 31–46. Retrieved from https://doi.org/10.14738/ABR.22.184
Fan, J., Shao, M., Li, Y., & Huang, X. (2018). Understanding Users’ Attitude Toward Mobile Payment Use: A Comparative Study between China and the USA. Industrial Management and Data Systems, 118(3), 524–540. Retrieved from https://doi.org/10.1108/IMDS-06-2017-0268
Fox, G., Clohessy, T., van der Werff, L., Rosati, P., & Lynn, T. (2021). Exploring the Competing Influences of Privacy Concerns and Positive Beliefs on Citizen Acceptance of Contact Tracing Mobile Applications. Computers in Human Behavior, 121, 1-18. Retrieved from https://doi.org/10.1016/J.CHB.2021.106806
George, A. (2018). Perceptions of Internet Banking Users—A Structural Equation Modelling (SEM) Approach. IIMB Management Review, 30(4), 357–368.
Gull, H., Saeed, S., Iqbal, S. Z., Bamarouf, Y. A., Alqahtani, M. A., Alabbad, D. A., Saqib, M., Qahtani, S. H. Al, & Alamer, A. (2022). An Empirical Study of Mobile Commerce and Customers Security Perception in Saudi Arabia. Electronics, 11(3), 293-310. Retrieved from https://doi.org/10.3390/ELECTRONICS11030293
Hewa, T., Ylianttila, M., & Liyanage, M. (2021). Survey on Blockchain Based Smart Contracts: Applications, Opportunities and Challenges. Journal of Network and Computer Applications, 177, 102857. Retrieved from https://doi.org/10.1016/J.JNCA.2020.102857
Hsu, C. L., & Lin, J. C. C. (2023). Understanding the User Satisfaction and Loyalty of Customer Service Chatbots. Journal of Retailing and Consumer Services, 71, 103211. Retrieved from https://doi.org/10.1016/J.JRETCONSER.2022.103211
Hu, Y. C., & Liao, P. C. (2011). Finding Critical Criteria of Evaluating Electronic Service Quality of Internet Banking Using Fuzzy Multiple-Criteria Decision Making. Applied Soft Computing Journal, 11(4), 3764–3770. Retrieved from https://doi.org/10.1016/J.ASOC.2011.02.008
Katiyar, R., & Badola, S. (2018). Modelling the Barriers to Online Banking in the Indian Scenario: An ISM Approach. Journal of Modelling in Management, 13(3), 550–569. Retrieved from https://doi.org/10.1108/JM2-01-2018-0006/FULL/XML
Kaur, S. J., Ali, L., Hassan, M. K., & Al-Emran, M. (2021). Adoption of Digital Banking Channels in an Emerging Economy: Exploring the Role of In-Branch Efforts. Journal of Financial Services Marketing, 26(2), 107. Retrieved from https://doi.org/10.1057/S41264-020-00082-W
Li, C., & Li, Y. (2023). Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis. Sustainability (Switzerland), 15(5), 3939. Retrieved from https://doi.org/10.3390/SU15053939/S1.
Lin, W. R., Wang, Y. H., & Hung, Y. M. (2020). Analyzing the factors influencing adoption intention of internet banking: Applying DEMATEL-ANP-SEM approach. PloS one, 15(2), e0227852. Retrieved from https://doi.org/10.1371/journal.pone.0227852.
Liu, Y. W., Kwon, Y. J., & Kang, B. Do. (2007). A Fuzzy AHP approach to evaluating e-commerce websites. Proceedings - SERA 2007: Fifth ACIS International Conference on
Software Engineering Research, Management, and Applications, 114–122. Retrieved from https://doi.org/10.1109/SERA.2007.16.
Mansouri Rad, H., & Bagherian, B. (2023). Importance-performance analysis (IPA) of banking factors affecting the improvement of business environment and prevention of corporate bankruptcy through the IPA model. Int. J. Nonlinear Anal. Appl, 14, 2008–6822. Retrieved from https://doi.org/10.22075/ijnaa.2022.26860.3429.
Meyer-Waarden, L., Bruwer, J., & Galan, J. P. (2023). Loyalty programs, loyalty engagement and customer engagement with the company brand: Consumer-centric behavioral psychology insights from three industries. Journal of Retailing and Consumer Services, 71, 103212. Retrieved from https://doi.org/10.1016/J.JRETCONSER.2022.103212.
Miranda, F. J., Chamorro, A., Murillo, L. R., & Vega, J. (2010). An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions. Journal of Service Science and Management, 03(02), 227–234. Retrieved from https://doi.org/10.4236/JSSM.2010.32028.
Munusamy, J., Chelliah, S., & Mun, H. W. (2010). Service Quality Delivery and Its Impact on Customer Satisfaction in the Banking Sector in Malaysia. International Journal of Innovation, Management and Technology, 1(4), 398.
Naeem, M., & Ozuem, W. (2021). The Role of Social Media in Internet Banking Transition During COVID-19 Pandemic: Using Multiple Methods and Sources in Qualitative Research. Journal of Retailing and Consumer Services, 60, 102483. Retrieved from https://doi.org/10.1016/j.jretconser.2021.102483.
Namahoot, K. S., & Laohavichien, T. (2018). Assessing the Intentions to Use Internet Banking: The Role of Perceived Risk and Trust as Mediating Factors. International Journal of Bank Marketing, 36(2), 256–276. Retrieved from https://doi.org/10.1108/IJBM-11-2016-0159
Nazaritehrani, A., & Mashali, B. (2020). Development of E-banking Channels and Market Share in Developing Countries. Financial Innovation, 6(1), 1–19. Retrieved from https://doi.org/10.1186/S40854-020-0171-Z/TABLES/5
Raza, S. A., Umer, A., Qureshi, M. A., & Dahri, A. S. (2020). Internet Banking Service Quality, E-Customer Satisfaction and Loyalty: The Modified E-SERVQUAL Model. TQM Journal, 32(6), 1443–1466. Retrieved from https://doi.org/10.1108/TQM-02-2020-0019/FULL/HTML.
Rodriguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant Fuzzy Linguistic Term Sets for Decision Making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. Retrieved from https://doi.org/10.1109/TFUZZ.2011.2170076
Saaty, T. L. (1988). What is the Analytic Hierarchy Process. Mathematical Models for Decision Support. Boston: Springer.
Salehnia, M., Saki, M., Eshaghi, A., & Salehnia, N. (2014). A Model of E-Loyalty and Word-Of-Mouth Based on e-trust in E-banking services (Case Study: Mellat Bank). 8th International Conference on e-Commerce in Developing Countries: With Focus on e-Trust, Retrieved from https://ieeexplore.ieee.org/abstract/document/6836764
Samani, Z. N., & Bashi, M. R. K. (2020). Reliable Resource Allocation and Fault Tolerance in Mobile Cloud Computing. Journal of Information Systems and Telecommunication (JIST), 2(26), 2-96. Retrieved from https://doi.org/10.7508/JIST.2019.02.002.
Sasono, I., Jubaedi, A. D., Novitasari, D., Wiyono, N., Riyanto, R., Oktabrianto, O., Jainuri, J., & Waruwu, H. (2021). The Impact of E-Service Quality and Satisfaction on Customer Loyalty: Empirical Evidence from Internet Banking Users in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 465–473. Retrieved from https://doi.org/10.13106/JAFEB.2021.VOL8.NO4.0465.
Seçme, N. Y., Bayrakdaroǧlu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36(9), 11699–11709. Retrieved from https://doi.org/10.1016/J.ESWA.2009.03.013.
Shamshur, A., & Weill, L. (2019). Does Bank Efficiency Influence The Cost Of Credit? Journal of Banking and Finance, 105, 62–73. Retrieved from https://doi.org/10.1016/j.jbankfin.2019.05.002.
Ilo, B. M., Soyebo, Y. A., & Olaiya, I. K. (2024). Emerging Technology Adoption and Financial Performance of Deposit Money Banks in Nigeria. Iranian Economic Review, 28(2), 607-617. Retrieved from https://ier.ut.ac.ir/article_97726_89fd9f11569fcefdf8139680c442880e.pdf
Szopiński, T. S. (2016). Factors Affecting the Adoption of Online Banking in Poland. Journal of Business Research, Retrieved from https://doi.org/10.1016/j.jbusres.2016.04.027
Taherabady, A., Khotanlu, M., & Nosrati, A. R. (2010). Explaining Efficiency in Electronic Banking. SSRN, Retrieved from https://ssrn.com/abstract=1709028
Tang, M., Hu, Y., Corbet, S., Hou, Y. G., & Oxley, L. (2024). Fintech, Bank Diversification, and Liquidity: Evidence from China. Research in International Business and Finance, 67(Part A), 102082. Retrieved from https://doi.org/10.1016/j.ribaf.2023.102082
Wang, X., Liu, W., Yang, H., Wang, D., Procedia, J. Y.-T. R. (2019). Customer Behavioural Modelling of Order Cancellation in Coupled Ride-Sourcing and Taxi Markets. Retrieved March 24, 2023, from https://www.sciencedirect.com/science/article/pii/S2352146519300535
Xi, Y., & Li, Q. (2022). Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment. Advances in Multimedia, 2022, Retrieved from https://doi.org/10.1155/2022/9588486
Yazdani, M., Fomba, S., & Zarate, P. (2017). Decision Support System for Multiple Criteria Decision Making Problems. In 17th International Conference on Group Decision and Negotiation (GDN 217) (67-75). Retrieved from http://oatao.univ-toulouse.fr/19142
Iranian Economic Review
Volume 29, Issue 3 - Serial Number 3
September 2025
Pages 819-840

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