The Influence of Perceived Usefulness of AI within The RACE Framework on Adoption Intention and Digital Marketing Performance: A Comprehensive Literature Review
DOI:
https://doi.org/10.14419/hyym2k22Published
29-08-2025Keywords:
Saudi Arabia, Artificial Intelligence, Digital Marketing, Marketing Performance, Perceived Usefulness, Adoption IntentionAbstract
Artificial Intelligence (AI) is changing how businesses run various digital marketing activities. Many studies have explored AI adoption and its benefits. Most of these studies focus on AI perceived usefulness, the influence of AI adoption, marketing results, or RACE as a planning framework. Though there is limited understanding of how perceived AI’s usefulness influences the adoption intention of AI, and hence the expected marketing performance. This is especially true in emerging markets such as Saudi Arabia. Understanding AI’s perceived usefulness in each stage of the RACE (Reach, Act, Convert, and Engage) framework is also important. This paper reviews previous research on AI adoption and adoption intention, the RACE framework, and marketing performance. It identifies a gap in using the technology acceptance model (TAM) model to explain how perceived usefulness of AI influences adoption intention and, in turn, digital marketing performance. It also highlights the lack of studies that apply a full marketing planning framework while analyzing these relationships. The study calls for empirical research that tests the AI adoption through the lens of an extended TAM version at each stage of RACE. Future work should use data to test this relationship. This will help marketers understand how AI adoption improves results. A stage-by-stage view helps show where AI is perceived as more useful, where it is not.
References
Acatrinei, C. (2025). To Use or Not to Use Artificial Intelligence (AI) in Marketing: Insights from Qualitative Research on Adopters and Non-adopters. In N. A. Pop (Ed.), Marketing Theory and Practice in Romania (pp. 253–267). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-93875-7_19
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.
Al Khaldy, M. A., Al-Obaydi, B. A. A., & al Shari, A. J. (2023). The Impact of Predictive Analytics and AI on Digital Marketing Strategy and ROI. In S. G. Yaseen (Ed.), Cutting-Edge Business Technologies in the Big Data Era (pp. 367–379). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42455-7_31
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
Aloufi, H. A., Al Atif, M. A., & Abdul Aziz, A. H. A. (2021). Artificial Intelligence and Its role in Supporting Marketing. International Multilin-gual Academic Journal, 2(1), Article 1. https://aasrc.org/aasrj/index.php/imaj/article/view/2096
Alqasa, K. M. A., & Afaneh, J. A. A. (2022). A Study On Digital Marketing And SME Performance During Covid-19: A Case Saudi SMEs. Inter-national Journal of eBusiness and eGovernment Studies, 14(1), 344–360.
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). https://doi.org/10.25115/eea.v39i4.4644
Arghashi, V., & Yuksel, C. A. (2022). Interactivity, Inspiration, and Perceived Usefulness! How retailers’ AR-apps improve consumer engagement through flow. Journal of Retailing and Consumer Services, 64, 102756.
Babatunde, S. O., Odejide, O. A., Edunjobi, T. E., & Ogundipe, D. O. (2024). The Role Of Ai In Marketing Personalization: A Theoretical Explo-ration Of Consumer Engagement Strategies. International Journal of Management & Entrepreneurship Research, 6(3), Article 3. https://doi.org/10.51594/ijmer.v6i3.964
Badghish, S., & Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864
Basri, W. (2020). Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: 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
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 King-dom of Saudi Arabia. Migration Letters, 20(S3), Article S3. https://doi.org/10.59670/ml.v20iS3.3768
Benkert, C.-L. M. (2019). Ethics & AI: Identifying the ethical issues of AI in marketing and building practical guidelines for marketers [B.S. thesis]. University of Twente.
Bhardwaj, S., Sharma, N., Goel, M., Vandana, Sharma, K., & Verma, V. (2024). Enhancing Customer Targeting in E-Commerce and Digital Mar-keting through AI-Driven Personalization Strategies. In Advances in Digital Marketing in the Era of Artificial Intelligence. CRC Press.
Broklyn, P., Olukemi, A., & Bell, C. (2024). AI-Driven Personalization in Digital Marketing: Effectiveness and Ethical Considerations (SSRN Scholarly Paper No. 4906214). Social Science Research Network. https://doi.org/10.2139/ssrn.4906214
Bryan, J. D., & Zuva, T. (2021). A Review on TAM and TOE Framework Progression and How These Models Integrate. Advances in Science, Technology and Engineering Systems Journal, 6(3), 137–145. https://doi.org/10.25046/aj060316
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/
Chaffey, D., & Ellis-Chadwick, F. (2019). Digital Marketing. Pearson UK.
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive ad-vantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013
Chaudhary, R. K. (2024). How SEO Impacts on Websites Rank? Multi-Disciplinary Explorations: The Kasthamandap College Journal, 2(2), Article 2. https://doi.org/10.3126/mdekcj.v2i2.74195
Chen, L., & Aklikokou, A. K. (2020). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and per-ceived ease of use. International Journal of Public Administration, 43(10), 850–865.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Dilami, Z., Hosseini, S., & Ahmadi, H. (2021). Evaluation of the digital marketing Strategy of the Bushehr Province Mining export Companies us-ing RACE model. Journal of International Business Administration, 4(2), 21–41.
Drydakis, N. (2022). Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandem-ic. Information Systems Frontiers, 24(4), 1223–1247. https://doi.org/10.1007/s10796-022-10249-6
Durmus Senyapar, H. N. (2024). Artificial Intelligence in Marketing Communication: A Comprehensive Exploration of the Integration and Impact of AI. Technium Social Sciences Journal, 55, 64–81. https://doi.org/10.47577/tssj.v55i1.10651
Dwivedi, D. N., & Mahanty, G. (2025). Leveraging Personalized AI Recommendations to Enhance User Experience in Streaming Services (OTT Platform): An Empirical Evaluation of Recommendations. In Transforming Cinema with Artificial Intelligence (pp. 275–300). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-3916-9.ch011
Ganesh, C., Podila, N., Vamsi, G. B. K., Rao, C. M., & Bhardwaj, N. (2024). AI-Enhanced Content Marketing for Sustainability: A Theoretical Perspective on Eco-friendly Communication Strategies. MATEC Web of Conferences, 392, 01045. https://doi.org/10.1051/matecconf/202439201045
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization. Sage Open, 13(4), 21582440231210759. https://doi.org/10.1177/21582440231210759
Giri, A., Chatterjee, S., Paul, P., & Chakraborty, S. (2019). Determining the impact of artificial intelligence on’developing marketing strategies’ in organized retail sector of West Bengal, India. International Journal of Engineering and Advanced Technology, 8(6), 3031–3036.
Gonçalves, A. R., Pinto, D. C., Rita, P., & Pires, T. (2023). Artificial Intelligence and Its Ethical Implications for Marketing. Emerging Science Journal, 7(2), 313–327. https://doi.org/10.28991/ESJ-2023-07-02-01
Hasan Emon, M. M., Hassan, F., Hoque Nahid, M., & Rattanawiboonsom, V. (2023). Predicting Adoption Intention of Artificial Intelligence ChatGPT. AIUB Journal of Science and Engineering (AJSE), 22(2), 189–199. https://doi.org/10.53799/ajse.v22i2.797
Helmold, M. (2022). Artificial Intelligence (AI) and Cyber Tools in Marketing and Sales. In M. Helmold (Ed.), Performance Excellence in Market-ing, Sales and Pricing: Leveraging Change, Lean and Innovation Management (pp. 235–245). Springer International Publishing. https://doi.org/10.1007/978-3-031-10097-0_22
Ifekanandu, C. C., Ezirim, A. C., & Asemota, K. U. (2023). Artificial Intelligence Adoption and Marketing Performance of Quoted Manufacturing Firms in Nigeria. International Journal of Innovative Science and Research Technology, 8(8).
Joshi, S., Bhattacharya, S., Pathak, P., Natraj, N. A., Saini, J., & Goswami, S. (2025). Harnessing the potential of generative AI in digital marketing using the Behavioral Reasoning Theory approach. International Journal of Information Management Data Insights, 5(1), 100317. https://doi.org/10.1016/j.jjimei.2024.100317
Iyelolu, T. V., Agu, E. E., Idemudia, C., & Ijomah, T. I. (2024). Leveraging Artificial Intelligence for Personalized Marketing Campaigns to Im-prove Conversion Rates. International Journal of Engineering Research and Development, 20(8), 253–270.
Jarek, K., & Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2), 46–55. https://doi.org/10.18267/j.cebr.213
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
Khosroshahi, F. T. (2024). The Future of Project Management: Embracing artificial intelligence for enhanced efficiency, data-driven decision-making, and collaborative teamwork. https://gupea.ub.gu.se/handle/2077/82467
Krishnan, C., Gupta, A., Gupta, A., & Singh, G. (2022). Impact of Artificial Intelligence-Based Chatbots on Customer Engagement and Business Growth. In T.-P. Hong, L. Serrano-Estrada, A. Saxena, & A. Biswas (Eds.), Deep Learning for Social Media Data Analytics (pp. 195–210). Springer International Publishing. https://doi.org/10.1007/978-3-031-10869-3_11
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: An exploration of solutions. Journal of Information, Communica-tion and Ethics in Society, 22(1), 124–144. https://doi.org/10.1108/JICES-05-2023-0068
Lewrick, M., & Hatamleh, O. (2024). AI and Innovation: How to Transform Your Business and Outpace the Competition with Generative AI (1st edition). Wiley.
Lim, C. V., Zhu, Y.-P., Omar, M., & Park, H.-W. (2024). Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models. Digital, 4(1), 244–270. https://doi.org/10.3390/digital4010013
Mashat, R. M. A. (2020). The Effect of the Use and Knowledge of AI on the Advanced Entrepreneurship in Saudis Small Business and Startups. International Journal of Business and Management, 15(12), 35. https://doi.org/10.5539/ijbm.v15n12p35
Noranee, S., & Bin Othman, A. K. (2023). Understanding Consumer Sentiments: Exploring the Role of Artificial Intelligence in Marketing. JMM17 : Jurnal Ilmu Ekonomi Dan Manajemen, 10(1), 15–23. https://doi.org/10.30996/jmm17.v10i1.8690
Olha, V., Potwora, M., Lipych, L., & Saienko, V. (2024). The Use of Artificial Intelligence in Marketing Strategies: Automation, Personalization and Forecasting. http://rps.chtei-knteu.cv.ua:8585/jspui/handle/123456789/2054
Panigrahi, R. R., Shrivastava, A. K., Qureshi, K. M., Mewada, B. G., Alghamdi, S. Y., Almakayeel, N., Almuflih, A. S., & Qureshi, M. R. N. (2023). AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country. Sustainability, 15(18), 13743. https://doi.org/10.3390/su151813743
Papastefanou, C. T., & Papaioannou, E. (2024). The acceptance and adoption of artificial intelligence tools by marketing executives in Greek busi-nesses. Proceedings of the International Conference on Contemporary Marketing Issues. https://doi.org/10.12681/iccmi.7594
Patel, S. N. (2024). Analysing the Influence of Digital Marketing on Consumer Perceptions and Actions Within the UK’s Mobile Technology Sec-tor [Masters, University of Wales Trinity Saint David]. https://repository.uwtsd.ac.uk/id/eprint/3299/
Pereira, L., Tomás, D., Dias, Á., Costa, R. L. da, & Gonçalves, R. (2023a). How artificial intelligence can improve digital marketing. International Journal of Business Information Systems, 44(4), 581–624.
Pereira, L., Tomás, D., Dias, Á., Costa, R. L. da, & Gonçalves, R. (2023b). How artificial intelligence can improve digital marketing. International Journal of Business Information Systems, 44(4), 581–624.
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).
Salah, O. H., & Ayyash, M. M. (2024). E-commerce adoption by SMEs and its effect on marketing performance: An extended of TOE framework with ai integration, innovation culture, and customer tech-savviness. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100183. https://doi.org/10.1016/j.joitmc.2023.100183
Sanjna, P., Sujith, D., Vinodh, K., & Vasavi, B. (2023). AI and Human Cognizance. In Artificial Intelligence and Knowledge Processing. CRC Press.
Sestino, A., Prete, M. I., Piper, L., & Guido, G. (2021). The future of online marketing strategies and digital tools: New challenges and contribu-tions to RACE framework. International Journal of Electronic Trade, 1(1), 1. https://doi.org/10.1504/IJETRADE.2021.10042594
Sestino, A., Prete, M. I., Piper, L., & Guido, G. (2023). The future of online marketing strategies and digital tools: New challenges and contribu-tions to RACE framework. International Journal of Electronic Trade, 1(1), 1. https://doi.org/10.1504/IJETRADE.2021.10042594
Setiawan, D., Lenawati, M., & Andria, A. (2023). Analisis dan Perencanaan Race Framework Digital Marketing Strategy Pada CV. Pandawa Digi-tal Media. Fountain of Informatics Journal, 7(3), 7–11. https://doi.org/10.21111/fij.v7i3.9406
Shaffi, S. S., & Muthulakshmi, I. (2022). Search Engine Optimization by using Machine Learning for Web Page Classification. 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 342–349. https://doi.org/10.1109/ICAISS55157.2022.10011123
Shanmugam, G., Rajendran, D., Thanarajan, T., Murugaraj, S. S., & Rajendran, S. (2023). Artificial Intelligence as a Catalyst in Digital Marketing: Enhancing Profitability and Market Potential. Ingénierie Des Systèmes d Information, 28(6), 1627–1636. https://doi.org/10.18280/isi.280620
Sharma, K. K., Tomar, M., & Tadimarri, A. (2023). Optimizing Sales Funnel Efficiency: Deep Learning Techniques for Lead Scoring. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 2(2), Article 2. https://doi.org/10.60087/jklst.vol2.n2.p274
Singh, N. (2023). AI-Driven Personalization in eCommerce Advertising. International Journal for Research in Applied Science and Engineering Technology, 11(12), 1692–1698. https://doi.org/10.22214/ijraset.2023.57695
Singh, P., Das, L., Bahubal, D., Rani, S., & Kumar, A. (2023). AI/ML Revolutionizing Social Media and Business: A Comprehensive Study. In Security and Risk Analysis for Intelligent Cloud Computing. CRC Press.
Suleiman, D. A., Awan, T. M., & Javed, M. (2021). Enhancing digital marketing performance through usage intention of AI-powered websites. IAES International Journal of Artificial Intelligence (IJ-AI), 10(4), Article 4. https://doi.org/10.11591/ijai.v10.i4.pp810-817
Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research. Computers, 12(2), Article 2. https://doi.org/10.3390/computers12020037
Theodoridis, P. K., & Gkikas, D. C. (2019). How artificial intelligence affects digital marketing. In Strategic Innovative Marketing and Tourism (pp. 1319–1327). Springer.
Vision 2030. (2025). 2030: Strategic objectives and vision realization programs.
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wang, C., Ahmad, S. F., Ayassrah, A. Y. A. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empiri-cal evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18349
Wijayanto, G., Rivai, Y., Alvionita, A., Wildah, S. W., & Jushermi, J. (2023). Evaluation of the Effect of Chatbot in Improving Customer Interac-tion and Satisfaction in Online Marketing in Indonesia. West Science Business and Management, 1(04), 304–310. https://doi.org/10.58812/wsbm.v1i04.248
Yu, X., Zhu, Y., Jia, C., Lu, W., & Xu, H. (2024). The analysis of green advertisement communication strategy based on deep factorization ma-chine. Expert Systems, 41(5), e13258. https://doi.org/10.1111/exsy.13258
Zhang, Y. (2021). Prediction of Customer Propensity Based on Machine Learning. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), 5–9. https://doi.org/10.1109/ACCTCS52002.2021.00009