Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

Designing a Smart Banking Implementation Model Based on a Mixed-Methods Methodology

Document Type : Excerpt from master's thesis

Authors
ali Saffari Darberazi 1
Mohsen esmaeili sadrabadi 2
Fatemeh Zamzam 3
1 Assistant Professor, Department of Industrial Engineering, Higher Education Complex of Bam, Bam, Iran .
2 Master’s graduate, Depatrment of Management, Management, Science and Arts university, Yazd, Iran
3 PhD in Industrial Management, Department of Industrial Management, Yazd University, Yazd, Iran
JABM.3.2.15564.351256.3257.36244
Abstract
In today’s highly dynamic and competitive environment, becoming intelligent and acting intelligently is one of the most important guarantees of success for organizations and institutions. In recent years, business intelligence and smart transformation have attracted considerable attention in the banking industry. Accordingly, the aim of this study is to develop a model for the implementation of smart banking in the branches of Bank Saderat in Yazd, Iran. This research is applied in terms of purpose and descriptive–survey in terms of research method. The statistical population consists of employees of Bank Saderat branches in Yazd, and the required data were collected through a questionnaire. The results indicate that available resources, as the foundational element of the smart banking model, significantly contribute to the improvement of information and communication technology (ICT) infrastructure as well as to management support and commitment. Moreover, the use of appropriate and advanced technological infrastructures in the branches of Bank Saderat in Yazd facilitates the integration of existing systems and enhances their compatibility with future systems implemented for branch smartization. In addition, adequate technological infrastructure plays a significant role in improving knowledge and information transfer among branch employees. Such infrastructure also positively affects employee training for smart banking implementation and their perceived usefulness of smart systems. Finally, management support and commitment exert a significant influence on training, perceived usefulness, knowledge-sharing culture, system compatibility, and system integration. The novelty of this study lies in adopting an integrated approach that combines Interpretive Structural Modeling (ISM) and Structural Equation Modeling (SEM) to identify causal relationships and empirically test the factors affecting the implementation of smart banking at the branch level. This approach not only explains the hierarchical structure of the influencing factors but also examines the strength and significance of the relationships among them.
Keywords
Smartization
Smart Banking
Interpretive Structural Modeling (ISM)
Structural Equation Modeling (SEM)
Abbasian,E. and Ghiyami Azad,E. (2024). Developing a Smart Strategic Management Model for Banks' Electronic Financing Using Deep Reinforcement Learning Algorithms. Journal of Intelligent Strategic Management, 3(4), 179-212. doi: bumara.3.2.15564.35879843739
Adamik, A., & Sikora-Fernandez, D. (2021). Smart Organizations as a Source of Competitiveness and Sustainable Development in the Age of Industry 4.0: Integration of Micro and Macro Perspective. Energies, 14(6), 1572. https://doi.org/10.3390/en14061572
Ai, D., Kwon, S.-D., Lee, S.-C., Ko, M.-H., & Lee, B.-H. (2017). A Study on Trust Transfer in Traditional Fintech of Smart Banking. Management & Information Systems Review, 36(3), 167–184.
Alatailat M, Elrehail H, Emeagwali OL (2019), "High performance work practices, organizational performance and strategic thinking: A moderation perspective". International Journal of Organizational Analysis, Vol. 27 No. 3 pp. 370–395, doi: https://doi.org/10.1108/IJOA-10-2017-1260
Alghizzawi, M., Ahmed, E., Albanna, H., Alkhlaifat, B.I., Jadu, K. (2024). The Relationship Between Business Intelligence and Digital Banking Services in Jordanian Islamic Banks. In: Mansour, N., Bujosa, L. (eds) Islamic Finance. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-48770-5_5.
ALHOSANI, F. A., & TARIQ, M. U. (2020). Improving Service quality of smart banking using quality management methods in UAE. International Journal of Mechanical Production Engineering Research and Development (IJMPERD), 10(3), 2249–8001. http://doi.org/10.24247/ijmperdjun2020199
Almahadin, G., Sawant, P. D., Ali, S., Anjum, A., Ibrahim, S. K., & Naser, S. J. (2023). Enabling Smart Banking AI and IoT: Challenges and Opportunities. Paper presented at the Proceedings of the 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023. 10.1109/ICSES60034.2023.10465372
Alshrafi, S., & Al Shobaki, M. J. (2020). The Role of Organizational Leadership in Achieving the Characteristics of Smart Organization.
Alassuli, A. (2025). Impact of artificial intelligence using the robotic process automation system on the efficiency of internal audit operations at Jordanian commercial banks. Banks and Bank Systems, 20(1), 122.
Ayesh Albugami, M. (2022). Developing a Hierarchical Model for the Drivers of Digital Banking–an Interpretive Structural Modelling Approach. Zagreb International Review of Economics & Business, 25(2), 85-96. https://doi.org/10.2478/zireb-2022-0016.
Bach, M. P., Čeljo, A., & Zoroja, J. (2016). Technology acceptance model for business intelligence systems: Preliminary research. Procedia Computer Science, 100, 995–1001. https://doi.org/10.1016/j.procs.2016.09.270
bahrami, T., Ghanizadeh, S., & Jahandideh, H. (2020). the effect of make smart On Efficiency Staff in the Organization. NAJA HUMAN RESOURCES, 11(60), 135-159.
Bamakan, S. M., Malekinejad, P., Ziaeian, M., & Ziaeian, M. (2021). Explain the Strategies of University Incubators in order to Production Mutation. Journal of Strategic Management Studies, 12(46), 139-156.  20.1001.1.22286853.1400.12.46.8.4
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: personal computer adoption ans use as an Illustration.
Bucko, J. (2017). Security of smart banking applications in Slovakia. Journal of Theoretical and Applied Electronic Commerce Research, 12(1), 42–52. 10.4067/S0718-18762017000100004
Campus, M. I. T., & Adyar, M. (2018). Measuring the Effect Size of Coefficient of Determination and Predictive Relevance of Exogenous Latent Variables on Endogenous Latent Variables through PLS-SEM. International Journal of Pure and Applied Mathematics, 119(18), 39–48.
Hajiheydari N, Delgosha MS, Wang Y, Olya H (2021), "Exploring the paths to big data analytics implementation success in banking and financial service: an integrated approach". Industrial Management & Data Systems, Vol. 121 No. 12 pp. 2498–2529, doi: https://doi.org/10.1108/IMDS-04-2021-0209.
Hakimi, H., Divandari, A., Keimasi, M. and Haghighi kaffash, M. (2019). Development of Retail Banking Customer Experience Creation Model from Manageable Factors by Organization Using Interpretive Structural Modeling (ISM). Journal of Business Management, 11(3), 565-584. doi: 10.22059/jibm.2019.267812.3285.
Hammoud, J., Bizri, R. M., & El Baba, I. (2018). The impact of e-banking service quality on customer satisfaction: Evidence from the Lebanese banking sector. SAGE Open, 8(3), 2158244018790633.
Hassan R (2025), "Barriers to e-banking adoption in Egypt: an expert-based interpretive structural modeling analysis". Digital Policy, Regulation and Governance, Vol. 27 No. 6 pp. 746–765, doi: https://doi.org/10.1108/DPRG-09-2024-0232.
Hrytsenko, L., Pakhnenko, O. Kuzior, A. & Kozhushko, I. (2024). Smart technologies in banking. Financial Markets, Institutions and Risks, 8(1), 81-93. https://doi.org/10.61093/fmir.8(1).81-93.2024.
Hou, C.-K. (2014). User acceptance of business intelligence systems in Taiwan’s electronics industry. Social Behavior and Personality: An International Journal, 42(4), 583–596. 10.2224/sbp.2014.42.4.583
Hung, S.-Y., Huang, Y.-W., Lin, C.-C., Chen, K., & Tarn, J. M. (2016). Factors Influencing Business Intelligence Systems Implementation Success in the Enterprises. PACIS, 297. https://aisel.aisnet.org/pacis2016/297
Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43, 305–318. 10.1016/j.ijinfomgt.2018.08.017
Jun, B. (2017). An Empirical Study on the Adoption of Smart Banking in Korea. International Information Institute (Tokyo). Information, 20(5B), 3471–3478.
Kalna, F., & Belangour, A. (2019). A Meta-model for Diverse Data Sources in Business Intelligence. American Journal of Embedded Systems and Applications, 7(1), 1–8.10.11648/j.ajesa.20190701.11.
Kansal, P. (2015). Banking Smart: Factors Affecting Intentions to Use. In AK Vashisht, G. Suri & R. Kaur (Eds.), Innovation Management Addressing Gaps in Theoretical and Empirical Interpretations, 114–122. https://ssrn.com/abstract=2571181
khashei varnamkhasti, V., Ebrahimi, M., Khalil Nezhad, S., & Motahari nejad, F. (2024). Generative mechanisms of digital banking ecosystem evolution. Business Intelligence Management Studies, 12(48), 33-81. 10.22054/ims.2024.76327.2394.
Kouhzadi,F. , Mohammad Karimi,Y. and Najafpour Kordi,A. (2025). Drawing a Fuzzy Cognitive Map of the Intelligent Banking System Based on the Requirements of the Digital Economy. Journal of Intelligent Strategic Management, 4(2), 651-680. doi: bumara.3.2.15564.35887873.63083325
Lande, R. S., Meshram, S. A., & Deshmukh, P. P. (2018). Smart banking using IoT. 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), 1–4. IEEE. 10.1109/RICE.2018.8627903
Laursen, G. H. N., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
Lautenbach, P., Johnston, K., & Adeniran-Ogundipe, T. (2017). Factors influencing business intelligence and analytics usage extent in South African organisations. South African Journal of Business Management, 48(3), 23–33.  https://doi.org/10.4102/sajbm.v48i3.33
Law, L., & Fong, N. (2020). Applying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English. Journal of English for Academic Purposes, 46, 100884. http://dx.doi.org/10.1016/j.jeap.2020.100884
Liu, J., Yi, Y., & Wang, X. (2020). Exploring factors influencing construction waste reduction: A structural equation modeling approach. Journal of Cleaner Production, 276, 123185. 10.1016/j.jclepro.2020.123185
Lukman, T., Hackney, R., Popovič, A., Jaklič, J., & Irani, Z. (2011). Business intelligence maturity: the economic transitional context within Slovenia. Information Systems Management, 28(3), 211–222. org/10.1080/10580530.2011.585583
Luo, G., Li, W., & Peng, Y. (2020). Overview of intelligent online banking system based on HERCULES architecture. IEEE Access, 8, 107685-107699. 10.1109/ACCESS.2020.2997079.
Mabrouk, N. (2020). Interpretive structural modeling of critical factors for buyer-supplier partnerships in supply chain management. Uncertain Supply Chain Management, 8(3), 613–626. 10.5267/j.uscm.2020.2.002
Mahmood, Y. N. (2019). The Impact of Quality Service Factors on Banking Service Sector Case study in Erbil banks. Tikrit Journal of Administrative and Economic Sciences, 2(42), 1.
Mahmoud Banat, H. F., Alotoum, F. J., & Nael Hashem, T. (2024). Smart Banking Services and Their Impact on the Customers' Intentions to Use Mobile Banking in Commercial Banks: Evidence from UAE. Paper presented at the 2nd International Conference on Cyber Resilience, ICCR 2024.
Mani, Z., & Chouk, I. (2018). Smart banking: Why it’s important to take into account consumers’ concerns?
Mehdiabadi, A., Shahabi, V., Shamsinejad, S., Amiri, M., Spulbar, C., & Birau, R. (2022). Investigating Industry 5.0 and Its Impact on the Banking Industry: Requirements, Approaches and Communications. Applied Sciences, 12(10), 5126. https://doi.org/10.3390/app12105126.
Mei, H., Bodog, S. A., & Badulescu, D. (2024). Artificial intelligence adoption in sustainable banking services: the critical role of technological literacy. Sustainability, 16(20), 8934. 10.3390/su16208934
Mehrabi, A., Hajikarimi, B. and Najafi, A. (2025). Presenting a paradigm model of financial performance in the banking industry for smart support of entrepreneurship and innovation in small and medium-sized enterprises. Journal of Intelligent Marketing Management, 6(1), 210-223.  JABM.3.2.15564.351256.325754510
Moharrak, M., & Mogaji, E. (2025). Generative AI in banking: empirical insights on integration, challenges and opportunities in a regulated industry. International Journal of Bank Marketing, 43(4), 871-896. 10.1108/IJBM-08-2024-0490
Nayanajith, G., Damunupola, K. A., & Pastor, C. K. L. (2020). E-Service Quality, Technology Self-Efficacy and Smart Banking Adoption in Sri Lanka. ASEAN Multidisciplinary Research Journal, 4(1), 25–32. https://www.paressu.org/online/index.php/aseanmrj/article/view/178
Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-05-2017-0170
Rahman M, Ming TH, Baigh TA, Sarker M (2023), "Adoption of artificial intelligence in banking services: an empirical analysis". International Journal of Emerging Markets, Vol. 18 No. 10 pp. 4270–4300, doi: https://doi.org/10.1108/IJOEM-06-2020-0724.
rajabi farjad,H. (2024). Social responsibility and organizational commitment in smart banking. Journal of Intelligent Marketing Management, 5(1), 45-65. doi: JABM.3.2.15564.3515434.64565
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. In An Updated Guide and Practical Guide to Statistical Analysis. Pearson.
Ravangard, F., Ronaghi, M. H., & ebrahimi, A. (2023). Digital Banking Challenges in Banking Industry. Journal of International Business Administration, 6(1), 201-225.10.22034/jiba.2023.52292.1908.
Rezaie, S., Mirabedin, S. J., & Abtahi, A. (2018). Factors Affecting the Implementation of Business Intelligence in Iran Banking Industry. Business Intelligence Management Studies, 6(23), 33-81. doi: 10.22054/ims.2018.8851.
Rezaie, S., Mirabedini, S. J., & Abtahi, A. (2017). Identifying key effective factors on the implementation process of business intelligence in the banking industry of Iran. Journal of intelligence studies in business, 7(3). https://doi.org/10.37380/jisib.v7i3.276.
Rohit, K., Kumari, P., Singh, N., & Alofaysan, H. (2025). Smart banking chatbots and consumer engagement: the role of trust and privacy in AI-driven banking. Journal of Strategic Marketing, 1-18.
Singhal, A., Dube, P., & Jain, V. K. (2022). Modelling barriers of artificial intelligence in banking sectors using total interpretive structural modelling. International journal of applied decision sciences, 15(3), 311-335. https://doi.org/10.1504/IJADS.2022.122649.
Sangar, A. B., & Iahad, N. B. A. (2013). Critical factors that affect the success of business intelligence systems (BIS) implementation in an organization. Intelligence, 12(2), 14–16.
Shamshad M, Sarim M, Akhtar A, Tabash MI (2018), "Identifying critical success factors for sustainable growth of Indian banking sector using interpretive structural modeling (ISM)". International Journal of Social Economics, Vol. 45 No. 8 pp. 1189–1204, https://doi.org/10.1108/IJSE-10-2017-0436
Shiri, G., Hashemi, S. M. and Tootian Esfahani, S. (2025). Proposing a Value Creation Model for the Banking Industry with an Emphasis on Artificial Intelligence. Journal of Intelligent Marketing Management, 6(4), 237-257. JABM.3.2.15564.3514545.2594721
Takeda, F., Takeda, K., Takemura, T., & Ueda, R. (2021). The impact of information technology investment announcements on the market value of the Japanese regional banks. Finance Research Letters, 41, 101811. https://doi.org/10.1016/j.frl.2020.101811.
Utami Tjhin, V., & Eka Riantini, R. (2021). A study for the implementation of Banking 4.0 in Indonesia. Paper presented at the Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City.
Veisi,V. , Namamian,F. , Hasani,S. R. and Kahrizi,O. (2026). Designing and developing a digital banking market penetration model in Sepah Bank using the Interpretive Structural Modeling Method. Journal of Intelligent Strategic Management, 5(1), 421-458. doi: 10.87453/bumara.2026.372301.4808
Vugec, D. S., Vukšić, V. B., Bach, M. P., Jaklič, J., & Štemberger, M. I. (2020). Business intelligence and organizational performance. Business Process Management Journal.
Wanke, C. G. (2020). Acceptance of business intelligence technology in Austrian organizations/submitted by Wanke Christian BSc. Universität Linz.
Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, (1), 81–87. 10.1109/TSMC.1974.5408524
Yadav, A., & Sagar, M. (2021). Modified total interpretive structural modeling of marketing flexibility factors for Indian telecommunications service providers. Global Journal of Flexible Systems Management, 22(4), 307–330. org/10.1007/s40171-021-00278-6
Zaied, A. N. H., Grida, M. O., & Hussein, G. S. (2018). Evaluation of critical success factors for business intelligence systems using fuzzy AHP. Journal of Theoretical and Applied Information Technology, 96(19), 6406–6422.
Zarei, A., Modarresi, M., & Ebrahimi, A. (2024). Investigating the factors affecting the use of blockchain technology in the management of electronic banking services' (Mellat bank). Quarterly journal of Industrial Technology Development, 10.22034/jtd.2024.71269.