Artificial Intelligence Marketing Capability & Decision-Making Effectiveness: Evidence from Marketing Professionals in Saudi Arabia
- 1. University of Business & Technology, Jeddah, Saudi Arabia
Description
Purpose: This study examines the effects of Artificial Intelligence Marketing Capability on Marketing Decision-Making Effectiveness and Quality of Data-Driven Insights. It further tests the effect and mediating role of Quality of Data-Driven Insights, and the moderating role of Ethical AI Governance.
Design/methodology/approach: A mixed-methods design was employed using survey responses from 150 marketing professionals in Saudi Arabia and interviews with marketing and analytics practitioners. PLS-SEM was used to test the proposed relationships, while interview data were analysed thematically.
Findings: The findings indicate that Artificial Intelligence Marketing Capability contributes to more effective marketing decision-making. Quality Data-Driven Insights emerged as an important mechanism through which AI capability supports decision effectiveness. The qualitative evidence further highlighted faster responses, improved decision confidence, actionable insights, and the importance of ethical governance. Overall, the results suggest that the value of AI depends not only on technological capability but also on the quality of insights and governance practices.
Research Implications: The study extends the Resource-Based View by linking AI marketing capability with decision-making effectiveness through the quality of data-driven insights. It also brings ethical AI governance into the proposed framework as a contextual factor influencing the value of AI capability. By combining quantitative and qualitative evidence, the study provides a more integrated explanation of how technological capability, information quality, and governance interact in AI-supported marketing decisions.
Social Implications: Managers should focus on developing AI capabilities that produce reliable, relevant, and actionable marketing insights rather than adopting AI tools solely for technological advancement. Organisations should also strengthen employee expertise, data practices, transparency, accountability, and fairness in AI use. Clear governance procedures can improve confidence in AI-supported decisions while reducing potential risks. These measures can help organisations translate AI investments into more timely and informed marketing decisions.
Originality / Value: The study offers an integrated framework connecting AI Marketing Capability, Quality of Data-Driven Insights, Marketing Decision-Making Effectiveness, and Ethical AI Governance. Its distinctive contribution lies in examining insight quality as a mediating mechanism while considering ethical governance as a moderating factor. The mixed-methods design further combines statistical evidence with managerial perspectives, providing a broader view of how organisations can derive decision-making value from AI-enabled marketing capabilities.
Keywords:
JEL: M31, M15, O33, D81.
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References
- 1. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A.,, Garcia, S., Gil-Lopez, S., Malina, D., Benjamins, R., Chatila, R. & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 2. Borimnejad, H. & Borimnejad, V. (2025). Emerging trends, challenges and research opportunities in artificial intelligence applications in marketing. Discover Artificial Intelligence, 6, Article 9. https://doi.org/10.1007/s44163-025-00705-y 3. Castelo, N., Bos, M. W. & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788 4. Cruz, R. N. & Rosário, A. T. (2025). Data-driven decision-making in marketing: A systematic literature review of emerging themes and research Gaps. Systems, 13(12), 1114. https://doi.org/10.3390/systems13121114 5. Davenport, T., Guha, A., Grewal, D. & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0 6. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Babdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M.A., Al-Busaidi, A. S. Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L. & Wright, R. (2023). Opinion paper: "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 7. Elgammal, I., Selem, K. M., Khan, M. A. & Shoukat, M. H. (2025). Unperplexing the nexus between e-commerce website assistance and online brand advocacy: Roles of self-presentation and hedonic value. Journal of Organizational Computing and Electronic Commerce, 35(1), 33-50. https://doi.org/10.1080/10919392.2024.2398884 8. Elgammal (2025). Exploring AI and consumer decision-making in tourism and marketing. IGI Global Scientific Publishing. https://doi.org/10.1080/10919392.2024.2398884 9. Glikson, E. & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057 10. Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P. & Suman, R. (2022). Artificial intelligence applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005 11. Haverila, M., Haverila, K., Gani, M. O., & Mohiuddin, M. (2024). The relationship between the quality of big data marketing analytics and marketing agility of firms: the impact of the decision-making role. Journal of Marketing Analytics, 13(1), 162-179. https://doi.org/10.1057/s41270-024-00301-6 12. Huang, M.-H. & Rust, R. T. (2021). Engaged to a Robot? The Role of AI in service. Journal of Service Research, 24(1), 3–20. https://doi.org/10.1177/1094670520902266 13. Ma, L. & Sun, B. (2020). Machine learning and AI in marketing—Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005 14. Manis, K. T. & Madhavaram, S. (2023). AI-enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues. Journal of Business Research, 157, 113485. https://doi.org/10.1016/j.jbusres.2022.113485 15. Mehta, P., Jebarajakirthy, C., Maseeh, H. I., Anubha, A., Saha, R. & Dhanda, K. (2022). Artificial intelligence in marketing: A meta-analytic review. Psychology & Marketing, 39(11), 2013–2038. https://doi.org/10.1002/mar.21716 16. Naz, H. & Kashif, M. (2025). Artificial intelligence and predictive marketing: an ethical framework from managers' perspective. Spanish Journal of Marketing-ESIC, 29(1), 22-45. https://doi.org/10.1108/SJME-06-2023-0154 17. Ngai, E. W. T., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35–48. https://doi.org/10.1016/j.jbusres.2022.02.049 18. Nguyen, K. M., Bui, T. T. M., Pham, H. T. T., Nguyen, L. X., Pham, H. T. X., Le Gia, L. & Nguyen, N. T. (2025). Fostering employees' AI adoption in strategic marketing planning and decision making: A mixed-method study in Vietnam. Cognition, Technology & Work, 28(169-202). https://doi.org/10.1007/s10111-025-00838-1 19. Peltier, J. W., Dahl, A. J. & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: A conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54-90. https://doi.org/10.1108/JRIM-01-2023-0030 20. Puntoni, S., Reczek, R. W., Giesler, M. & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131-151. https://doi.org/10.1177/0022242920953847 21. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141. https://doi.org/10.1007/s11747-019-00710-5 22. Rasul, T., Nair, S., Palamidovska-Sterjadovska, N., Ladeira, W. J., Santini, F. D. O. & Elgammal, I. (2024). The evolution of customer engagement in the digital era for business: A review and future research agenda. Journal of Global Scholars of Marketing Science, 34(3), 325-348. https://doi.org/10.1080/21639159.2023.2275798 23. Reed, C., Wynn, M. & Bown, R. (2025). Artificial intelligence in digital marketing: Towards an analytical framework for revealing and mitigating bias. Big Data and Cognitive Computing, 9(2), 40. https://doi.org/10.3390/bdcc9020040 24. Sidra, S. & Wagan, S. M. (2025). How artificial intelligence-enabled marketing activities will change consumer behavior. Discover Artificial Intelligence, 6, 17. https://doi.org/10.1007/s44163-025-00735-6 25. Soliman, M., Fatnassi, T., Elgammal, I., & Figueiredo, R. (2023). Exploring the major trends and emerging themes of artificial intelligence in the scientific leading journals amidst the COVID-19 era. Big Data and Cognitive Computing, 7(1), 12. https://doi.org/10.3390/bdcc7010012 26. Sun, B. (2025). Data-driven personalized marketing strategy optimization based on user behavior modeling and predictive analytics: Sustainable market segmentation and targeting. PLOS ONE, 20(7), e0328151. https://doi.org/10.1371/journal.pone.0328151 27. Ujwary-Gil, A., & Florek-Paszkowska, A. (2025). Artificial intelligence, analytics, and strategic decision-making: Concepts, practice, and future research directions. In A. Ujwary-Gil & A. Florek-Paszkowska (Eds.), AI, analytics and strategic decision-making (pp. 1–21). Routledge. https://doi.org/10.4324/9781003507840-1 28. Volkmar, G., Fischer, P. M., & Reinecke, S. (2022). Artificial intelligence and machine learning: Exploring drivers, barriers, and future developments in marketing management. Journal of Business Research, 149, 599–614. https://doi.org/10.1016/j.jbusres.2022.04.007 29. Xie, H., Nie, Y., Liu, L., & Chen, Y. (2025). Machine learning-based research of AI marketing: topic analysis and model construction. Future Business Journal, 11(1), 272. https://doi.org/10.1186/s43093-025-00686-5