Perceived Usefulness of AI Across Digital Marketing Stages: An Empirical Study of ‎Adoption Intention in Saudi Retail SMEs

  • Authors

    • Maha Ali Ateeq AlGhamdi Graduate School of Management, Post Graduate Centre, Management and Science University (MSU), University Drive, Off Persiaran Olahraga, Section 13, 40100, Selangor, Malaysia
    • Asad Ur Rehman Muhammad Sadiq Graduate School of Management, Post Graduate Centre, Management and Science University (MSU), University Drive, Off Persiaran Olahraga, Section 13, 40100, Selangor, Malaysia
    • Wail Alhakimi Graduate School of Management, Post Graduate Centre, Management and Science University (MSU), University Drive, Off Persiaran Olahraga, Section 13, 40100, Selangor, Malaysia
    https://doi.org/10.14419/vamvmv23

    Received date: November 19, 2025

    Accepted date: December 30, 2025

    Published date: January 2, 2026

  • Perceived Usefulness; AI Adoption Intention; Digital Marketing; RACE Model; TAM; Retail Sector, ‎SMEs, Saudi Arabia

  • Abstract

    The landscape of digital marketing is transformed by Artificial Intelligence (AI) technologies that ‎improved personalization, efficiency, and customer engagement. However, research on AI ‎adoption is limited, especially in Saudi retail small and medium-sized enterprises (SMEs). This ‎study addresses this gap by examining how the perceived usefulness of AI in different digital ‎marketing stages influences adoption intention. Drawing on the Technology Acceptance Model ‎‎(TAM) and the RACE framework (Plan, Reach, Act, Convert, Engage), five hypotheses were ‎developed to test the effects of perceived usefulness on adoption intention across these stages. Data ‎were collected through surveys from 450 decision-makers in Saudi retail SMEs. Structural ‎Equation Modeling (SmartPLS) was used for analysis. The results support four hypotheses. ‎Perceived usefulness in the Plan, Reach, Convert, and Engage stages positively influenced ‎adoption intention (H1, H2, H4, H5). The Act stage showed no significant effect (H3). These ‎findings highlight that AI’s perceived value differs across marketing stages. The study contributes ‎to theory by extending TAM in a multi-stage marketing context. It shows that adoption intention is ‎shaped by how useful AI is perceived across customer journey stages. Practically, the results guide ‎managers to focus AI investments in stages where perceived usefulness drives stronger adoption, ‎especially Reach, Convert, and Engage. For policymakers, the findings emphasize the need for ‎targeted support and training to enhance AI readiness in Saudi SMEs‎.

  • References

    1. Ahmed, M. I. A. A. (2023). The Contribution of Modern Technologies of Artificial Intelligence in the Marketing of Sports Tourist Services in the Kingdom of Saudi Arabia. Journal for ReAttach Therapy and Developmental Diversities, 6(1), 665–679.
    2. Alnajim, R. A., & Fakieh, B. (2023). A Tourist-Based Framework for Developing Digital Marketing for Small and Medium-Sized Enterprises in the Tourism Sector in Saudi Arabia. Data, 8(12), 179. https://doi.org/10.3390/data8120179.
    3. Aloufi, H. A., Al Atif, M. A., & Abdul Aziz, A. H. A. (2021). Artificial Intelligence and Its role in Supporting Marketing. International Multilingual Academic Journal, 2(1), Article 1. https://aasrc.org/aasrj/index.php/imaj/article/view/2096.
    4. Al-Tayyar, R. S. D., Abdullah, A. R. B., Rahman, A. A., & Ali, M. H. (2021). Challenges and obstacles facing SMEs in the adoption of e-commerce in developing countries; A case of Saudi Arabia. Studies of Applied Economics, 39(4), Article 4. https://doi.org/10.25115/eea.v39i4.4644.
    5. Basri, W. (2020). Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium En-terprises: Toward Effective Business Management in the Saudi Arabian Context: International Journal of Computational Intelligence Systems, 13(1), 142. https://doi.org/10.2991/ijcis.d.200127.002.
    6. Ben Khalifa, W. A., Alshorman, B. A., Seddik, W. A. S., Esseket Zahou, A. M., Torky, M. Sh., Mona Fathi Rizk, Wafaa A. Mostafa Hussein, Mo-hamed H Rabie, & Yousif, M. A. A. B. (2023). The Reality of Using Artificial Intelligence Applications in Developing E-Marketing in the Kingdom of Saudi Arabia. Migration Letters, 20(S3), Article S3. https://doi.org/10.59670/ml.v20iS3.3768
    7. Cambridge.org. (2024, September 11). Perception. https://dictionary.cambridge.org/dictionary/english/perception
    8. Chaffey, D. (2024, January 17). The RACE Framework: A practical digital marketing strategy framework. Smart Insights. https://www.smartinsights.com/digital-marketing-strategy/race-a-practical-framework-to-improve-your-digital-marketing/
    9. Chaffey, D., & Smith, P. (2022). Digital Marketing Excellence: Planning, Optimizing and Integrating Online Marketing (6th ed.). Routledge. https://doi.org/10.4324/9781003009498.
    10. Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880.
    11. Chen, L., & Aklikokou, A. K. (2020). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850–865. https://doi.org/10.1080/01900692.2019.1660989.
    12. Dam, H. K., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2019). Towards Effective AI-Powered Agile Project Management. 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), 41–44. https://doi.org/10.1109/ICSE-NIER.2019.00019.
    13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008.
    14. Dilami, Z., Hosseini, S., & Ahmadi, H. (2021). Evaluation of the digital marketing Strategy of the Bushehr Province Mining export Companies using RACE model. Journal of International Business Administration, 4(2), 21–41.
    15. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203.
    16. Kar, S. (2023). Impact of Artificial Intelligence on Digital Marketing. Interantional Journal of Scientific Research in Engineering And Management, 07(07). https://doi.org/10.55041/IJSREM25001.
    17. Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The Internation-al Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/IJILT-05-2020-0090.
    18. Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models (5th ed.). McGraw-Hill/Irwin.
    19. Monsha’at. (2024). Digital Transformation for SMEs. https://www.monshaat.gov.sa.
    20. Nevalainen, I. (2020). Digital marketing development in a B2B context.
    21. O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6.
    22. Palos-Sanchez, P. R., Saura, J. R., Martin, M. Á. R., & Aguayo-Camacho, M. (2021). Toward a Better Understanding of the Intention to Use mHealth Apps: Exploratory Study. JMIR mHealth and uHealth, 9(9), e27021. https://doi.org/10.2196/27021.
    23. Park, D. Y., & Kim, H. (2023). Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability, 15(1), Article 1. https://doi.org/10.3390/su15010872.
    24. Rautela, S. (2021). Social media for new product launch: A study of social media platforms across the RACE planning framework. International Journal of Interactive Mobile Technologies, 15(5). https://doi.org/10.3991/ijim.v15i05.18147.
    25. Reuters. (2023, May 31). Saudi population at 32.2 million, 63% of Saudis under 30 years old, census shows. Reuters. https://www.reuters.com/world/middle-east/saudi-population-322-mln-median-age-29-years-old-general-authority-statistics-2023-05-31/.
    26. Rojalin, K. (2020). Search engine optimization for an international business to business company in the beauty industry: Measuring the results with web analytics. Jyväskylä University of Applied Sciences.
    27. Saudi-US. (2024). SMEs in Saudi Arabia: How the Supreme Economic Council Can Support SME Growth in the Kingdom | SUSTG.com – News, Analysis, and Features on all things Saudi Arabia. https://www.sustg.com/smes-in-saudi-arabia-supporting-growth-sme/.
    28. Stylidis, K., Wickman, C., & Söderberg, R. (2015). Defining Perceived Quality in the Automotive Industry: An Engineering Approach. Procedia CIRP, 36, 165–170. https://doi.org/10.1016/j.procir.2015.01.076.
    29. Wilson, N., Keni, K., & Tan, P. H. P. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers’ loyalty in China. Gadjah Mada International Journal of Business, 23(3), 262–294. https://doi.org/10.22146/gamaijb.32106.

  • Downloads

  • How to Cite

    AlGhamdi , M. A. A. ., Sadiq, A. U. R. M. ., & Alhakimi, W. . (2026). Perceived Usefulness of AI Across Digital Marketing Stages: An Empirical Study of ‎Adoption Intention in Saudi Retail SMEs. International Journal of Basic and Applied Sciences, 14(8), 643-651. https://doi.org/10.14419/vamvmv23