Artificial Intelligence in Digital Marketing: Qualitative Data Analysis of Strategic, Ethical, and Organizational Dynamics

Morad FAREH; Khadija IDRISSI

doi:10.23882/ijdam.26269

Authors

Morad FAREH Hassan First University, Settat, Morocco
Khadija IDRISSI Hassan First University, Settat, Morocco

DOI:

https://doi.org/10.23882/ijdam.26269

Keywords:

Artificial intelligence, digital marketing, personalization, consumer behavior, qualitative research, marketing strategy

Abstract

Artificial intelligence (AI) has fundamentally transformed digital marketing practices, yet the strategic implementation processes and stakeholder perspectives remain underexplored through qualitative inquiry.This study explores how marketing professionals perceive, implement, and evaluate AI technologies in digital marketing strategies, with particular attention to challenges, opportunities, and ethical considerations.We conducted semi-directive interviews with 13 marketing professionals (6 marketing managers, 4 digital marketing specialists, 2 AI consultants, and 1 CMO) from diverse industries between September and October 2025. Thematic analysis was employed using an inductive coding approach to identify emergent themes.Five major themes emerged: (1) AI-driven personalization as a competitive imperative, (2) operational challenges in AI integration, (3) ethical tensions between personalization and privacy, (4) evolving consumer expectations and trust dynamics, and (5) organizational learning and skill development needs. Participants emphasized that successful AI implementation requires balancing technological capabilities with human oversight, ethical considerations, and authentic consumer relationships.

AI in digital marketing represents both a transformative opportunity and a complex challenge requiring careful strategic planning, ethical frameworks, and continuous organizational learning. The findings highlight the need for transparent AI practices, consumer education, and interdisciplinary collaboration to realize AI's potential while mitigating risks.

References

Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., &Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49.

Aguirre, E., Roggeveen, A. L., Grewal, D., &Wetzels, M. (2016). The personalization-privacy paradox: Implications for new media. Journal of Consumer Marketing, 33(2), 98-110.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

Babadoğan, B. (2024). Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing. Journal of Digital Marketing, 4(2), 112-128. https://doi.org/10.62802/fj43xy18

Bleier, A., &Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669-688.

Baxter, G., &Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4-17.

Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595.

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(1), 24-42.

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., et al. (2024). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.32920/25475263

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Grand View Research. (2023). Artificial Intelligence in Marketing Market Size Report, 2023-2030. Retrieved, from https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market

Kumar, V., Rajan, B., Venkatesan, R., &Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.

Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.

Mani, A. (2024). Applying Artificial Intelligence to the Digital Marketing: Opportunities and Challenges for the Marketer. International Journal of Business and Management Review, 12(2), 56-74. https://doi.org/10.37745/ijbmr.2013/vol12n25674

G, P. (2023). The effects of artificial intelligence on digital marketing. ShodhKosh Journal of Visual and Performing Arts, 4(1SE), 89-102. https://doi.org/10.29121/shodhkosh.v4.i1se.2023.431

Potwora, M., Vdovichena, O., Semchuk, D., et al. (2024). The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting. Journal of Marketing World, 2024(2), 45-62. https://doi.org/10.53935/jomw.v2024i2.275

Tanwar, P. S., Antonyraj, S. M., &Shrivastav, R. (2024). A Study of "Rise of AI in Digital Marketing". International Journal of Multidisciplinary Research and Studies, 7(5), 57-68. https://doi.org/10.15680/ijmrset.2024.0705057

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

Freeman, R. E. (1984). Strategic Management: A Stakeholder Approach. Pitman.

Eason, K. (2014). Afterword: The past, present and future of sociotechnical systems theory. Applied Ergonomics, 45(2), 213-220.

Nguyen, M., Malik, A., & Sharma, P. (2021). How to motivate employees to engage in online knowledge sharing? Differences between posters and lurkers. Journal of Knowledge Management, 25(7), 1811-1831.

Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714-12719.

Saadati, F., &Jafari, M. (2023). Strategies for Enhancing Customer Engagement Using Artificial Intelligence Technologies in Online Markets. Journal of Technology and Entrepreneurship Management, 2(1), 45-62. https://doi.org/10.61838/kman.jtesm.2.1.6

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.

Van der Kaa, H., &Krahmer, E. (2014). Journalist versus news consumer: The perceived credibility of machine written news. Proceedings of the Computation + Journalism Conference, New York, NY.

Kietzmann, J., Paschen, J., &Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267.

Ramamoorthy, V. H., &Maruteesh, K. (2022). Explanatory and Descriptive Study Using Digital Marketing and Artificial Intelligence. Shanlax International Journal of Management, 9(S1-Mar), 78-92. https://doi.org/10.34293/management.v9is1-mar.4900

Jarek, K., &Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2), 46-55.

Tam, K. Y., &Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865-890.

Sundar, S. S., &Marathe, S. S. (2010). Personalization versus customization: The importance of agency, privacy, and power usage. Human Communication Research, 36(3), 298-322.

Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155.

Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1), 100-126.

Baruh, L., Secinti, E., &Cemalcilar, Z. (2017). Online privacy concerns and privacy management: A meta-analytical review. Journal of Communication, 67(1), 26-53.

Longoni, C., Bonezzi, A., &Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.

Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809-825.

Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835-850.

Barocas, S., &Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671-732.

Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966-2981.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62.

Susser, D., Roessler, B., &Nissenbaum, H. (2019). Technology, autonomy, and manipulation. Internet Policy Review, 8(2), 1-22.

Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.

European Commission. (2021). Proposal for a Regulation on Artificial Intelligence. COM(2021) 206 final.

Wachter, S., Mittelstadt, B., &Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law, 7(2), 76-99.

Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1), 118-132.

Martin, K. D. (2019). Ethical issues in the big data industry. MIS Quarterly Executive, 14(2), 67-85.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1), 1-17.

DiCicco-Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical Education, 40(4), 314-321.

Kallio, H., Pietilä, A. M., Johnson, M., &Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954-2965.

Rabionet, S. E. (2011). How I learned to design and conduct semi-structured interviews: An ongoing and continuous journey. The Qualitative Report, 16(2), 563-566.

Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237-246.

McHugh, M. L. (2012). Interrater reliability: The kappa statistic. BiochemiaMedica, 22(3), 276-282.

Lincoln, Y. S., &Guba, E. G. (1985). Naturalistic Inquiry. Sage.

Redman, T. C. (2018). If your data is bad, your machine learning tools are useless. Harvard Business Review, March 2.

Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. Doubleday.

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