Structural–Interpretive Model of Customer Confusion Management Based on the Internet of Things in the Banking Industry

Authors

    Vajihe Balipour Babadi Department of Business Management, Arv.C., Islamic Azad University, Abadan, Iran
    Asghar Rashnoudi * Department of Maritime Business Management, Faculty of Economics and Management, Khorramshahr, University of Marine Science and Technology, Khorramshahr, Iran Asghar.rashnoodi@kmsu.ac.ir
    Fereydoun Omidi Department of Business Management, Arv.C., Islamic Azad University, Abadan, Iran.

Keywords:

Customer Confusion, Internet of Things , Banking Industry, Thematic Analysis, Interpretive Structural Modeling

Abstract

The present study was conducted with the aim of developing a structural–interpretive model (ISM) for customer confusion management based on the Internet of Things (IoT) in the banking industry. This research is applied in nature and qualitative in approach, employing thematic analysis and the structural–interpretive modeling method to explain the relationships among the factors influencing customer confusion management. The statistical population consisted of 17 managers and experts from Bank Mellat branches in Tehran, who were selected through purposive and snowball sampling methods. Data were collected using semi-structured interviews and analyzed with MAXQDA 2018 software. The results of the thematic analysis led to the identification of 77 indicators categorized into 14 main components and six key dimensions. These dimensions include sources of confusion, consequences of customer confusion, internet infrastructures, customer characteristics affecting the increase in confusion, organizational learning management approaches for IoT, and organizational factors influencing confusion management. The findings of the interpretive structural model indicated that the organizational learning management approach for IoT has the highest level of influence, while the sources and consequences of confusion occupy the lowest level. The results further revealed that employing IoT in the banking industry—through enhancing technological infrastructures, automating service processes, and implementing targeted customer education—can effectively reduce confusion and improve customer experience. Therefore, strengthening organizational learning, developing modern technologies, and increasing informational transparency are regarded as fundamental requirements for the effective management of customer confusion in banks.

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References

Ariosh, R., Saeednia, H., Mehrani, H., & Kavousi, E. (2023). Designing a Paradigm Model of Customer Dithering in Brand Selection with a Focus on the Perceived Value of Services: Application of the Grounded Theory Approach. Scientific Quarterly of Business Management Perspectives, 4(1), 1. https://asm.pgu.ac.ir/article_701287.html?lang=en

Bagheri, S. N. M. A. (2021). Identifying the Effective Components of the Internet of Things on Human Resource Management Processes. Journal of Applied ICT Innovations, 1(1), 75-85. https://ait.ihu.ac.ir/article_206663.html?lang=en

Butt, M. M., Wilkins, S., Hazzam, J., & Marder, B. (2024). Rollover service contracts: the influences of perceived value, convenience, confusion and switching costs on consumer satisfaction and service loyalty. Journal of Strategic Marketing, 1-21. https://doi.org/10.1080/0965254X.2024.2319831

Chauhan, V., & Sagar, M. (2021). Consumer confusion: A systematic review and research directions. Journal of Consumer Marketing, 38(4), 445-456. https://doi.org/10.1108/JCM-03-2020-3705

Esmaili Ranjbar, K., Hariri, N., Salajegheh, M., & Bab al-Hawaajeh, F. (2022). Presenting a Model for the Use of the Internet of Things in Iranian Knowledge-Based Companies (An Approach to Enhancing Productivity in These Companies). Productivity management, 16(4(63)), 217-243. https://journals.iau.ir/article_697213.html?lang=en

Gaiardelli, P., & Songini, L. (2021). Successful business models for service centres: an empirical analysis. International Journal of Productivity and Performance Management, 70(5), 1187-1212. https://doi.org/10.1108/IJPPM-05-2019-0230

Jalali Nazari, S. A. H. K. H., & Saeednia, H. (2023). Modern Customer-Centric Banking: Approaches, Challenges, and Models. Investment Knowledge, 12(46), 583-612. https://www.magiran.com/paper/2530701/new-customer-oriented-banking-approaches-challenges-and-patterns?lang=en

Johnson, V. L., Woolridge, R. W., & Bell, J. R. (2021). The Impact of Consumer Confusion on Mobile Self- Checkout Adoption. Journal of Computer Information Systems, 61(1). https://doi.org/10.1080/08874417.2019.1566802

Khalilzadeh Talat Tapeh, M., Nasehifar, V., Ghobadi Lamouki, T., & Asghari Sarem, A. (2022). Analyzing Factors Affecting Customer Confusion in Life Insurance Services Using a Mixed-Method Approach.

Kim, S. H., & Yang, Y. R. (2025). The Effect of Digital Quality on Customer Satisfaction and Brand Loyalty Under Environmental Uncertainty: Evidence from the Banking Industry. Sustainability, 17(8), 3500. https://doi.org/10.3390/su17083500

Minhaj, S. M., & Khan, M. A. (2025). Dimensions of E-Banking and the Mediating Role of Customer Satisfaction: A Structural Equation Model Approach. International Journal of Business Innovation and Research, 36(1), 42-57. https://doi.org/10.1504/IJBIR.2025.143944

Momivand, B., Gholami Jamkarani, R., Maleki, M. H., & Jahangirnia, H. (2022). Presenting a Framework for Identifying Effective Drivers on the Future of the Banking Industry with an Emphasis on the Role of Financial Technology.

Muhammad, A., Fahad, Z., Sharjeel Ahmad, S., & Zahir, S. (2025). Unlocking Mobile Banking Adoption: The Interplay of Interface Design, System Quality, Service Quality, Security, and Customer Involvement. The Critical Review of Social Sciences Studies.

Nisa, N. U., Mendoza, S. A. J., & Shamsuddinova, S. (2022). The Concept of Greenwashing and its Impact on Green Trust, Green Risk, and Green Consumer Confusion: A Review-Based Study. JABS, 8(3), 1-18. https://doi.org/10.20474/jabs-8.3.1

Patel, P. H., Rathod, C. K., & Zaveri, K. (2021). Green Internet of Things (IoT) and Machine Learning (ML): The Combinatory Approach and Synthesis in the Banking Industry. Green Internet of Things and Machine Learning: Towards a Smart Sustainable World, 297-316. https://doi.org/10.1002/9781119793144.ch11

Rostami, M., & Ghorchibeigi, E. (2022). Presenting a Model for Customer Relationship Management (CRM) in Discount Chain Stores Using the Internet of Things and Big Data. Marketing Management, 17(55), 111-128. https://sanad.iau.ir/en/Journal/jomm/Article/811421/FullText

Sharma, A., Singh, J., & Prakash, G. (2023). Consumer confusion and its consequences in the e-hospitality marketplace: the mediating role of negative emotions. Journal of Service Theory and Practice, 33(4), 488-510. https://doi.org/10.1108/JSTP-11-2022-0264

Shoaee Astaneh, S. M. S., Rahim Pour, H., & Hosseinzadeh, A. (2022). Investigating Smart Marketing Scenarios Based on IoT in the Banking Industry. Management, 3(1), 227-240. https://asm.pgu.ac.ir/article_706486_en.html

Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020). Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 521-524. https://doi.org/10.1016/j.dsx.2020.04.041

Yan, X., Li, Y., Nie, F., & Li, R. (2025). Bank Customer Segmentation and Marketing Strategies Based on Improved DBSCAN Algorithm. Applied Sciences, 15(6).

Yeo, S. F., Tan, C. L., Kumar, A., Tan, K. H., & Wong, J. K. (2022). Investigating the impact of AI-powered technologies on Instagrammers' purchase decisions in digitalization era-A study of the fashion and apparel industry. Technological Forecasting and Social Change, 177, 121551. https://doi.org/10.1016/j.techfore.2022.121551

Zecevic, M. G. P., Žabkar, V., & Kos Koklič, M. (2022). Consumer Confusion Caused by Nutrition Apps in Product Healthiness Evaluation. Economic and Business Review, 24(2), 101-110. https://doi.org/10.15458/2335-4216.1300

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Published

2026-04-01

Submitted

2025-06-23

Revised

2025-10-16

Accepted

2025-10-26

Issue

Section

Articles

How to Cite

Balipour Babadi, V. ., Rashnoudi, A., & Omidi, . F. . (2026). Structural–Interpretive Model of Customer Confusion Management Based on the Internet of Things in the Banking Industry. Digital Transformation and Administration Innovation, 1-13. https://www.journaldtai.com/index.php/jdtai/article/view/217

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