IDEAS home Printed from https://ideas.repec.org/a/ibn/ijbmjn/v19y2024i5p172.html

Artificial Intelligence in the Saudi Arabian Banking Sector: Role in Customer Satisfaction and Its Implementation Challenges

Author

Listed:
  • Abdulaziz Alotaibi

Abstract

The study aimed to examine the impact of artificial intelligence on customer satisfaction and the challenges Saudi Arabian banks face in implementing this cross-cutting technology. The study used a survey design and collected responses from 100 participants, mainly bank customers and bank officials. The result revealed that artificial intelligence (AI) is positively and significantly correlated with customer satisfaction (CS). This suggests that customer satisfaction tends to rise in tandem with the application of AI in banking. The mediation analysis result showed that Ease of Use only mediates 9.82% of the relationship between AI and CS, and it is not statistically significant (β=0.0607 (95% Cl- -.0246, .146), z=1.39, p=0.163. The study provides practical insights for Saudi Arabian banks, highlighting the need to enhance the adoption of AI to promote customer satisfaction. It also outlines frameworks for minimizing challenges and barriers against the implementation of AI, including promoting data security and customer privacy.

Suggested Citation

  • Abdulaziz Alotaibi, 2024. "Artificial Intelligence in the Saudi Arabian Banking Sector: Role in Customer Satisfaction and Its Implementation Challenges," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(5), pages 172-172, September.
    • Handle: RePEc:ibn:ijbmjn:v:19:y:2024:i:5:p:172
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ijbm/article/download/0/0/50547/54758
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ijbm/article/view/0/50547
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. María Teresa Ballestar & Pilar Grau-Carles & Jorge Sainz, 2019. "Predicting customer quality in e-commerce social networks: a machine learning approach," Review of Managerial Science, Springer, vol. 13(3), pages 589-603, June.
    2. Hu, Han-fen & Krishen, Anjala S., 2019. "When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective," Journal of Business Research, Elsevier, vol. 100(C), pages 27-37.
    3. Saeed Badghish & Yasir Ali Soomro, 2024. "Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework," Sustainability, MDPI, vol. 16(5), pages 1-24, February.
    4. Krijger, Joris, 2023. "Operationalising ethics for AI in the financial industry: Insights from the Volksbank case study," Journal of Digital Banking, Henry Stewart Publications, vol. 8(3), pages 220-241, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Weiwei & Zhang, Yifan, 2025. "Artificial intelligence innovation and environmental performance: Unraveling the complex roles of application and method innovation across enterprise sizes," Technological Forecasting and Social Change, Elsevier, vol. 218(C).
    2. Stefan Hoffmann & Tom Joerß & Robert Mai & Payam Akbar, 2022. "Augmented reality-delivered product information at the point of sale: when information controllability backfires," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 743-776, July.
    3. Yang Liu & Xingchen Ding & Maomao Chi & Jiang Wu & Lili Ma, 2024. "Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach," Information Technology & Tourism, Springer, vol. 26(1), pages 59-87, March.
    4. Arroyabe, Marta F. & Arranz, Carlos F.A. & Fernandez De Arroyabe, Ignacio & Fernandez de Arroyabe, Juan Carlos, 2024. "Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment," Technology in Society, Elsevier, vol. 79(C).
    5. Panteha Farmanesh & Niloofar Solati Dehkordi & Asim Vehbi & Kavita Chavali, 2025. "Artificial Intelligence and Green Innovation in Small and Medium-Sized Enterprises and Competitive-Advantage Drive Toward Achieving Sustainable Development Goals," Sustainability, MDPI, vol. 17(5), pages 1-20, March.
    6. Huňady Ján & Pisár Peter & Khawaja Sarwar & Qureshi Fayyaz Hussain, 2024. "The Digital Transformation of European Union Countries before and during COVID-19," Business Systems Research, Sciendo, vol. 15(1), pages 22-45.
    7. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    8. Marco Cioppi & Ilaria Curina & Barbara Francioni & Elisabetta Savelli, 2023. "Digital transformation and marketing: a systematic and thematic literature review," Italian Journal of Marketing, Springer, vol. 2023(2), pages 207-288, June.
    9. Dasom Jeong & Dongeon Kim & Jinzhe Yan & Jiaen Li, 2025. "Effect of Aspect-Level Emotion Expression of Online Restaurant Reviews on Perceived Helpfulness: An Aspect-Based Sentiment Analysis Perspective," SAGE Open, , vol. 15(2), pages 21582440251, May.
    10. Bedggood, Rowan & Russell-Bennett, Rebekah & McAndrew, Ryan & Glavas, Charmaine & Dulleck, Uwe, 2023. "Challenging the social-power paradigm: Moving beyond consumer empowerment to an energy ecosystem of shared value," Energy Policy, Elsevier, vol. 173(C).
    11. Aw, Eugene Cheng-Xi, 2020. "Understanding consumers’ paths to webrooming: A complexity approach," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    12. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    13. Manuel Muth & Michael Lingenfelder & Gerd Nufer, 2025. "The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review," Management Review Quarterly, Springer, vol. 75(3), pages 2759-2802, September.
    14. Yang, Yang & Zhao, Jichang & Li, Yashuai, 2025. "More than enough is too much: Curvilinear relationship between anchor body movements and sales in live streaming e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    15. Pantano, Eleonora & Viassone, Milena & Boardman, Rosy & Dennis, Charles, 2022. "Inclusive or exclusive? Investigating how retail technology can reduce old consumers’ barriers to shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    16. Saranya Devi Nair Pavithran & Tan Owee Kowang & Muhammad Harith Zainal Abidin, 2025. "The Mediating Effects of Process Innovation on AI Adoption and Operational Performance in Malaysia’s Electrical and Electronics Small and Medium Enterprise Manufacturers: A Conceptual Framework," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 3854-3864, August.
    17. Piotr Sawicki & Hanna Sawicka & Marek Karkula & Krzysztof Zajda, 2025. "Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem," Energies, MDPI, vol. 18(2), pages 1-48, January.
    18. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    19. Tassiello, Vito & Tillotson, Jack S., 2020. "How subjective knowledge influences intention to travel," Annals of Tourism Research, Elsevier, vol. 80(C).
    20. Khan, Imran, 2023. "Customer engagement outcomes in mobile applications: Self-congruence as a moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijbmjn:v:19:y:2024:i:5:p:172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.