The Long-Term Consumer Impact of AI-Generated Content in Digital Marketing
Explore the long-term consumer impact of AI-generated content in digital marketing. Discover innovative research topic ideas that enhance your understanding of marketing strategies in the age of AI.
QUANTITATIVE AND QUALITATIVE RESEARCH
Realyn Manalo
5/27/20252 min read
In the fast-paced landscape of digital marketing, artificial intelligence (AI) is increasingly being used not only to automate tasks but also to create content that drives consumer interaction. From personalized recommendations to emotionally resonant posts, AI-generated content is reshaping how brands communicate. However, while short-term engagement spikes are often observed, less is known about the lasting impact of AI-driven storytelling on consumer trust, loyalty, and decision-making. As generative models evolve to mimic human tone and nuance more closely, marketing strategies must also evolve to critically assess how these tools influence brand authenticity, retention, and consumer cognition over time.
Who Can Use These Topics
This research is ideal for students and professionals pursuing the following courses or strands:
College Programs:
BSBA Marketing Management
BSBA Advertising and Public Relations
BS in Digital Marketing and E-Commerce
BS in Entrepreneurship
Senior High School Strands:
Accountancy, Business, and Management (ABM)
General Academic Strand (GAS)
Why This Topic Needs Research
This area warrants further study due to several unresolved questions in the literature:
Unmeasured long-term effects of AI content: Although Vargas (2024) found that AI-generated content boosts engagement, it did not evaluate whether this engagement translates into sustained loyalty or repeated consumer interaction, especially when users are aware that the content is machine-made.
Lack of platform-level behavioral insights: Gamage et al. (2025) designed warning labels for synthetic content, but the study did not investigate how user trust or purchase intent evolves across time or platforms when these labels are visible at scale.
Cross-cultural variability untested: Ozcan (2024) emphasized that user perception of authenticity in AI content is influenced by AI literacy, yet cultural differences in literacy and media consumption remain understudied, limiting marketing adaptation across markets.
Cognitive impact of condensed AI content: Kim et al. (2024) showed that AI video summaries increase clicks but warned of potential risks to comprehension and critical thinking. No long-term study yet explores how repeated exposure affects content processing habits or trust.
Limited empirical validation in e-commerce: Shevchyk (2024) concluded that user engagement with AI content was comparable to human content, but real-world evidence across demographics and product categories is lacking, particularly regarding emotional tone and product fit.
Feasibility & Challenges by Target Group
Get Your Free Thesis Title
Finding a well-structured quantitative research topic can be challenging, but I am here to assist you.
✔ Expertly Curated Topics – Not AI-generated, but carefully developed based on existing academic studies and research trends.
✔ Comprehensive Research Support – Includes an existed and updated research gaps, explanation of variables as well as SDG relevance.
✔ Personalized for Your Field – Get a thesis title tailored to your academic requirements and research interests.
Prefer video content? Subscribe to my YouTube Channel for expert insights on research topics, methodologies, and academic writing strategies.
References
Gamage, D., Sewwandi, D., Zhang, M., & Bandara, A. (2025). Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media. arXiv preprint arXiv:2503.05711.
Kim, A., Lu, Y., Ma, T., & Tan, Y. (2024). Less Is More? Impact of AI-Generated Summaries on User Engagement of Video-Sharing Platforms. Impact of AI-Generated Summaries on User Engagement of Video-Sharing Platforms (November 13, 2024).
OZCAN, A. K. (2024). Exploring the Perceptual Boundaries Of AI-Generated Content In Modern Content Marketing.
Shevchyk, Y. (2024). Generative AI in e-commerce: a comparative analysis of consumer-brand engagement with AI-generated and human-generated content. PhD diss.
Vargas, N. (2024). Testing AI's potential: can AI-generated content increase social media engagement?.