Explaining the Role of Artificial Intelligence Acceptance Factors in Perceived Quality and Continuous Omnichannel Usage Intention: The Moderating Role of Relationship Proneness

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Business Management, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Prof., Department of Business Management, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

10.22059/jibm.2025.387579.4896

Abstract

Objective
The adoption of artificial intelligence (AI) in the banking industry, along with the factors influencing its acceptance, has emerged as a key driver of customers’ intentions to use omnichannel services, promising a significant transformation in financial service delivery. This transformation not only enhances customer experience but also enables banks to strengthen their competitive position through the provision of innovative and efficient services. Consequently, the future of the banking industry, supported by AI technologies, appears more dynamic and promising than ever before. Accordingly, the present study aims to explain the role of AI acceptance factors in shaping perceived quality and customers’ intentions toward the continued use of omnichannel services in the banking industry, while considering the moderating role of relationship proneness.
 
Methodology
This study is positivist in terms of paradigm, deductive in terms of research strategy, cross-sectional in terms of time, applied in terms of purpose, and descriptive in terms of method. The statistical population of this study is the customers of banks in Isfahan who have used various service delivery channels at least once. Using the Cochran formula, 400 people were selected. Data were collected using a structured questionnaire. The measurement items for perceived usefulness, perceived ease of use, and perceived enjoyment were adapted from Hassan et al. (2023). Items measuring perceived performance and perceived sociability were adopted from Pais (2019), while perceived quality items were derived from Kabadayi, Carlson, O’Cass, and Ahrholdt (2017). The items related to intention to continue using omnichannel services were adapted from Lee and Kim (2021), and the relationship proneness scale was adopted from Gao et al. (2022). The validity of the instrument was assessed through content and construct validity, while reliability was evaluated using Cronbach’s alpha, factor loadings, and composite reliability. Structural equation modeling (SEM) was employed for data analysis using SPSS 25 and SmartPLS 3.0 software.
 
Findings
The results of structural equations show that perceived usefulness, ease of use, enjoyment, performance, and sociability have a positive and significant effect on perceived quality. Perceived quality has a positive and significant impact on satisfaction, and satisfaction has a positive and significant effect on the intention to continue using the omnichannel. Relationship orientation was found to moderate the relationship between customer satisfaction and the intention to continuously use omnichannel services.
 
Conclusion
The banking industry should focus on optimizing the factors of usefulness, ease of use, enjoyment, and performance of artificial intelligence to improve perceived quality and customer satisfaction, and thus have a positive effect on the intention to use omnichannel. In addition, paying attention to customers' relationship orientation can help strengthen the intention to continue using these channels and lead to creating a unique experience in financial interactions. In this regard, banks need to continuously review and improve their services and utilize customer feedback to establish their position in a competitive market and be recognized as leaders in providing smart financial services.

Keywords

Main Subjects

References

References
Abasi Nami, H. & Asayesh, A. (2021). Omnichannel Strategy: The Impact of Integrated Interaction Quality and Perceived Fluency on Customer Attitudinal Loyalty Through Positive Experience. Journal of Business Management Perspective, 20(48), 87-113.
 (in Persian)
Accenture (2020). Banking Technology Vision 2020. Retrieved from https://www.accenture.com/_acnmedia/PDF-120/Accenture-Banking-Technology-Vision-2020.pdf
Bailey, A. A., Pentina, I., Mishra, A. S. & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: An extended TAM. International Journal of Retail & Distribution Management, 45(6), 626–640.
Balakrishnan, J. & Dwivedi, Y. K. (2024). Conversational commerce: entering the next stage of AI-powered digital assistants. Annals of Operations Research, 333(2), 653-687.‏
Calvo, A. V., Franco, A. D. & Frasquet, M. (2023). The role of artificial intelligence in improving the omnichannel customer experience. International Journal of Retail & Distribution Management, 51(9/10), 1174-1194.‏
Carlson, J., O’Cass, A. & Ahrholdt, D. (2015). Assessing customers’ perceived value of the online channel of multichannel retailers: A two country examination. Journal of Retailing and Consumer Services, 27, 90-102.‏
Cho, V., Cheng, T. E. & Lai, W. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-227.‏
Christian, M., Yulita, H., Sunarno, S., Halim, F. & Arifin, P. (2024). Omnichannel Banking Services: Perceived Value and Integration Quality on Customer Loyalty. In The AI Revolution: Driving Business Innovation and Research: Volume 2 (pp. 81-93). Cham: Springer Nature Switzerland.‏
Cotarelo, M., Fayos, T., Calderón, H. & Mollá, A. (2021). Omni-channel intensity and shopping value as key drivers of customer satisfaction and loyalty. Sustainability, 13(11), 5961.‏
Dağhan, G. & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211.
Dahana, W. D., Miwa, Y. & Morisada, M. (2019). Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market. Journal of Business Research, 99, 319-331.‏
Dandis, A. O. & Al Haj Eid, M. B. (2022). Customer lifetime value: investigating the factors affecting attitudinal and behavioural brand loyalty. The TQM Journal, 34(3), 476-493.
Darapu, K. & Marukukula, M. (2025). Fraud Detection and Prevention in Finance and Banking Using Artificial Intelligence. In Real-World Applications of AI Innovation (pp. 213-232). IGI Global Scientific Publishing.‏
Dehghani Ghahnavieh, A., Seifollahi, N., Bashekouh Ajirloo, M. and Zarei, G. (2024). Explaining the role of dimensions of the characteristics of the omnichannel about the Customer-brand engagement and brand value co-creation according to the moderating role of the brand reputation. Journal of Business Management, 17(2), 359- 388.
(in Persian)
Durongkadej, I., Hu, W. & Wang H. E. (2025). How artificial intelligence incidents affect banks and financial services firms? A study of five firms, Finance Research Letters, 70, 106279.
Elkhatibi, Y., Guelzim, H. & Benabdelouahed, R. (2024). Factors Influencing the Adoption of AI-Powered Chatbots in the Moroccan Banking Sector: An Extended UTAUT Model. Journal of Logistics, Informatics and Service Science, 11(7), 559-585.‏
 Gao, L., Li, G., Tsai, F., Gao, C., Zhu, M. & Qu, X. (2022), The impact of artificial intelligence stimulion customer engagement and value co-creation: the moderating role of customer abilityreadiness, Journal of Research in Interactive Marketing, 17 (2), 317-333.
Grewal, D., Gauri, D. K., Das, G., Agarwal, J. & Spence, M. T. (2021). Retailing and emergent technologies. Journal of Business Research, 134, 198-202.‏
Hamouda, M. (2019). Omni-channel banking integration quality and perceived value as drivers of consumers’ satisfaction and loyalty. Journal of Enterprise Information Management, 32(4), 608-625.‏
Hasan, S., Godhuli, E. R., Rahman, M. S. & Al Mamun, M. A. (2023). The adoption of conversational assistants in the banking industry: is the perceived risk a moderator? Heliyon, 9(9).‏
Heerink, M., Kröse, B., Evers, V. & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The almere model. International Journal of Social Robotics, 2(4), 361-375. DOI:10.1007/s12369-010-0068-5
Hu, X., Qiu, J., Zhao, J. & Li, Y. (2023). Can in-store recommendations for online-substitutive products integrate online and offline channels? Journal of Retailing and Consumer Services, 70, 103142.‏
Huré, E., Picot-Coupey, K. & Ackermann, C. L. (2017). Understanding omni-channel shopping value: A mixed-method study. Journal of retailing and consumer services, 39, 314-330.
Irani, H. R., Ebrahimi, E. and Somi, B. (2024). Clarification and Prioritization of Omnichannel Marketing Antecedents: A Meta-Synthesis Approach. Journal of Business Management, 16(4), 909-948. (in Persian)
Jayasingh, S., Girija, T. & Arunkumar, S. (2022). Determinants of omnichannel shopping intention for sporting goods. Sustainability, 14(21), 14109.‏
Kabadayi, S., Loureiro, Y. K. & Carnevale, M. (2017). Customer value creation in multichannel systems: The interactive effect of integration quality and multichannel complexity. Journal of Creating Value, 3(1), 1-18.‏
Karthiga, D. R., Ananthi, S., Kaur, R., Das, D. K., Natarajan, S. & Dhinakaran, D. P. (2024). Impact of artificial intelligence in the banking sector, YUGATO, 76(1). 701-714.‏
Keng, C. J., Huang, T. L., Zheng, L. J., & Hsu, M. K. (2007). Modeling service encounters and customer experiential value in retailing: An empirical investigation of shopping mall customers in Taiwan. International Journal of Service Industry Management, 18(4), 349-367.
Khashei Varnamkhasti, V. and Farsi, S. (2024). A Model for Creating and Implementing Ambidextrous Innovation in Iranian Banking. Journal of Business Management, 16(4), 1002-1028 (in Persian).
Lazaris, C. & Vrechopoulos, A. (2014, June). From multichannel to “omnichannel” retailing: review of the literature and calls for research. In 2nd International Conference on Contemporary Marketing Issues,(ICCMI) (Vol. 6, pp. 1-6).‏
Lee, K. H. & Kim, B. Y. (2021). Service satisfaction and continuous use intention on omnichannel-based pickup service. Journal of Distribution Science, 19(10), 5-15.‏
Mohammadzadeh Vanestan, S. & Abedi, R. (2024). Investigating the Role of Artificial Intelligence Enablers and Companies' Readiness in Adopting an Artificial Intelligence-integrated Customer Relationship Management System. Journal of Business Management, 16(1), 34-58. (in Persian)
Narteh, B. (2018). Service quality and customer satisfaction in Ghanaian retail banks: the moderating role of price. International Journal of Bank Marketing, 36(1), 68-88.‏
Natarajan, T. & Raghavan, D. R. V. (2023). How integrated store-service quality promotes omnichannel shoppers’ word-of-mouth behaviors: the moderating role of perceived relationship investment and the shopper’s perceived value. The TQM Journal, 36(4), 1113-1144.
Obaze, Y., Xie, H., Prybutok, V.R., Randall, W. and Peak, D.A. (2023), Contextualization of relational connectedness construct in relationship marketing, Journal of Nonprofit and Public Sector Marketing, 35(2), 111-143.
Pais, A. R. F. (2019). Artificial Intelligence and Marketing Strategy: Drivers of Consumers' Acceptance of Automatic Conversational Agents (Chatbots) as Human/Tech Interfaces.‏
Parsaei, Z., Eslami, G. & Rahimnia, F. (2023). The Role of Omnichannel in Increasing Customer Satisfaction and Loyalty. Commercial Surveys, 21(119), 75-98 (in Persian)
Qian, W. & Mao, J. (2023). Exploring the influential factors of personal media bloggers on followers’ continuous following intention based on relationship marketing theory. Behavioral Sciences, 13(5), 416.
Quach, S., Barari, M., Thaichon, P. & Moudrý, D. V. (2023). Price promotion in omnichannel retailing: how much is too much? Asia Pacific Journal of Marketing and Logistics, 35(1), 198-213.‏
Roy, S. K., Balaji, M. S., Quazi, A. & Quaddus, M. (2018). Predictors of customer acceptance of and resistance to smart technologies in the retail sector. Journal of Retailing and Consumer Services, 42, 147-160.‏
Sazegarnejad, A., Ahmadi, A. M., abedinzadeh, Z., Fatemi, F., Roknadini, F., Mirzaei, M., Nazifkar, M., khajepoor, B., Fatemi, H. & Miremadi, S. I. (2024). Generative artificial intelligence. Journal of Science and Technology Policy, 17(0), 1-100. (in Persian)
Sethuraman, P. & Thanigan, J. (2019). An empirical study on consumer attitude and intention towards online shopping. International Journal of Business Innovation and Research, 18(2), 145-166.
Sharma, D., Panda, T. K. & Sahadev, S. (2025). Predicting Brand Loyalty to Manufacturer-Sponsored Brands in an Omnichannel Environment: A Configurational Perspective. Indian Journal of Marketing, 55(1), 8-28.‏
Shen, X. L., Li, Y. J., Sun, Y. & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61-73.‏
Song, H. G. & Jo, H. (2023). Understanding the continuance intention of omnichannel: Combining TAM and TPB. Sustainability, 15(4), 3039.‏
Sousa, B. M. & Alves, G. M. (2019). The role of relationship marketing in behavioural intentions of medical tourism services and guest experiences. Journal of Hospitality and Tourism Insights, 2(3), 224-240.‏
Thaichon, P., Quach, S., Barari, M. & Nguyen, M. (2024). Exploring the role of omnichannel retailing technologies: Future research directions. Australasian Marketing Journal, 32(2), 162-177.‏
Torres, E. N. & Kline, S. (2006). From satisfaction to delight: a model for the hotel industry. International Journal of contemporary hospitality management, 18(4), 290-301.‏
Venkatesh, V., Thong, J. Y. & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
Verhoef, P. C., Kannan, P. K. & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of retailing, 91(2), 174-181.‏
Walter, J. & Abendroth, B. (2020). On the role of informational privacy in connected vehicles: A privacy-aware acceptance modelling approach for connected vehicular services. Telematics and Informatics, 49, 101361.
 Yao, P., Sabri, M. F., Osman, S., Zainudin, N. & Li, Y. (2023). Consumers’ Continued Intention to Use Online-to-Offline (O2O) Services in Omnichannel Retail: Differences between To-Shop and To-Home Models. Sustainability, 15(2), 945.
Yen, Y. S. (2023). Channel integration affects usage intention in food delivery platform services: the mediating effect of perceived value. Asia Pacific Journal of Marketing and Logistics, 35(1), 54-73.
Zha, D., Marvi, R. & Foroudi, P. (2023). Synthesizing the customer experience concept: A multimodularity approach. Journal of Business Research, 167, 114185.‏
Zhang, M., Li, Y., Sun, L. & Moustapha, F. A. (2022). Integrated store service quality measurement scale in omni-channel retailing. International Journal of Retail & Distribution Management, 50(7), 839-859.
Zhang, M., Ren, C., Wang, G. A. & He, Z. (2018). The impact of channel integration on consumer responses in omni-channel retailing: The mediating effect of consumer empowerment. Electronic commerce research and applications, 28, 181-193.
Journal of Business Management
Volume 18, Issue 2
2026
Pages 447-474

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