Artificial Intelligence for Digital Marketing Strategies (AI4DMS): An Explanatory PLS-SEM Study of Higher Education
Abstract
This study proposes and empirically tests the AI4DMS (Artificial Intelligence for Digital Marketing Strategies) framework to examine how AI Impact Perceptions (AIB), AI Knowledge (AKM), Analytical-Digital Skills (ADS), AI Influence on Decisions (AID), and AI Marketing Applications (AIM) interact across seven hypotheses. Grounded in absorptive capacity and dynamic capability perspectives, the model was tested using PLS-SEM (SmartPLS 4.1.1.2) with data from 258 digitally active higher education students (pre-organizational population with incipient exposure to AI-related activities in business contexts). The measurement model achieved satisfactory reliability and validity (α, CR > 0.70; AVE > 0.50; HTMT < 0.85), and all hypotheses were supported. The strongest paths were AIB → AKM (β = 0.771) and ADS → AIM (β = 0.610). AI4DMS follows a recursive logic in which perceptions enhance knowledge, knowledge supports analytical-digital skills, and these shape perceived readiness for AI-supported marketing decision processes. While conceptually linked to digital marketing, the findings reflect pre-organizational perceptions of AI-related capabilities, rather than firm-level adoption or actual consumer behavior.
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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
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Vangalla, R. (2024). The Role of AI in Personalizing Digital Marketing Campaigns: A Technical Analysis. International Journal of Research in Computer Applications and Information Technology, 7(2), 2535–2546. https://doi.org/10.34218/IJRCAIT_07_02_190
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
World Economic Forum. (2020, 20 de octubre). The future of jobs report 2020. https://www.weforum.org/publications/the-future-of-jobs-report-2020
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.2307/4134351
Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
Balakrishnan, T., Chui, M., Hall, B., & Henke, N. (2020, November 17). The state of AI in 2020. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2020
Brewis, C., Dibb, S., & Meadows, M. (2023). Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms. Technological Forecasting and Social Change, 190, Artículo 122402. https://doi.org/10.1016/j.techfore.2023.122402
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, Artículo 120880. https://doi.org/10.1016/j.techfore.2021.120880
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Fornell, C. L., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515
Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, Artículo 1323512. https://doi.org/10.3389/frai.2024.1323512
Gurjar, K., Jangra, A., Baber, H., Islam, M., & Sheikh, S. A. (2024). An analytical review on the impact of artificial intelligence on the business industry: Applications, trends, and challenges. IEEE Engineering Management Review. https://doi.org/10.1109/EMR.2024.3355973
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2023). Advanced issues in partial least squares structural equation modeling. Sage. https://doi.org/10.1057/s41270-023-00279-7
Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) 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
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Mejía-Trejo, J. (2023). Estadística Multivariante. TOMO II. Técnicas Interdependientes con SPSS en las Ciencias Sociales. AMIDI Biblioteca. https://doi.org/10.55965/abib.2023.9786075939797
Oraif, G. (2024). AI-Driven business analytics: Its impact on strategic decision-making-An empirical study on educational institutions in the Kingdom of Saudi Arabia. Journal of Ecohumanism, 3(8), 9712–9732. https://doi.org/10.62754/joe.v3i8.5581
Organisation for Economic Co-operation and Development (OECD). (2024). Fostering an inclusive digital transformation as AI spreads among firms. OECD Publishing. https://www.oecd.org/en/publications/fostering-an-inclusive-digital-transformation-as-ai-spreads-among-firms_5876200c-en.html
Organisation for Economic Co-operation and Development, & Eurostat. (2018). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD Publishing. https://doi.org/10.1787/9789264304604-en
Rabby, F., Chimhundu, R., & Hassan, R. (2021). Artificial intelligence in digital marketing influences consumer behaviour: a review and theoretical foundation for future research. Academy of Marketing Studies Journal, 25(5), 1–7. https://www.researchgate.net/publication/354198906
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
United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. Naciones Unidas. https://sdgs.un.org/2030agenda
Vangalla, R. (2024). The Role of AI in Personalizing Digital Marketing Campaigns: A Technical Analysis. International Journal of Research in Computer Applications and Information Technology, 7(2), 2535–2546. https://doi.org/10.34218/IJRCAIT_07_02_190
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
World Economic Forum. (2020, 20 de octubre). The future of jobs report 2020. https://www.weforum.org/publications/the-future-of-jobs-report-2020
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.2307/4134351
Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
Mejía-Trejo, J. (2026). Artificial Intelligence for Digital Marketing Strategies (AI4DMS): An Explanatory PLS-SEM Study of Higher Education. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 15, e33354. https://doi.org/10.14201/adcaij.33354
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