Tate, M. A. (2019). *Web wisdom: How to evaluate and create information quality on the Web* (3rd ed.). CRC Press.
This book presents authoritative criteria—accuracy, authority, objectivity, currency, and coverage—for evaluating online information. As a course text, it meets academic standards, and the author’s expertise ensures its authority and reliability. These criteria are foundational for judging the credibility of digital content, which supports evaluating sources in digital marketing research involving AI.
Suraña‑Sánchez, C., & Aramendia‑Muneta, M. E. (2024). *Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda.* *International Journal of Consumer Studies*.
This bibliometric review of 190 articles examines how AI affects consumer and ad engagement, identifying research trends and gaps. It is peer-reviewed and methodologically sound, meeting credibility standards. It supports analysis of how AI enhances engagement—relevant to content creation and consumer behavior in digital marketing.
Srivastav, S. K., Habil, M., & Thakur, P. (2025). *Evaluating the effects of artificial intelligence and digital marketing on consumer behaviour: A bibliometric approach.* *Golden Ratio of Marketing and Applied Psychology of Business, 5*(2), 517–540.
This 2025 study analyzes over 600 articles to identify AI trends in consumer behavior and marketing. Published in a peer-reviewed journal with rigorous methodology, it meets credibility and currency criteria. It is essential for understanding AI’s evolving impact on consumer engagement and decision-making.
Basu, R., Aktar, N., & Kumar, S. (2024). *The interplay of artificial intelligence, machine learning, and data analytics in digital marketing.* *Journal of Marketing Analytics*.
This bibliometric analysis identifies themes such as personalization and branding automation. Peer-reviewed and methodologically transparent, it provides valid insights. It supports your project by structuring the scope of AI’s use in personalization and branding.
Gowri, D. P. (2024). *Impact of AI in personalized digital marketing: Boosting customer engagement through tailored content.* *Journal of Contemporary Marketing*, 2024(334).
This conceptual paper explores NLP and predictive analytics in personalized content. As a peer-reviewed source with grounded theory, it meets evaluation standards. It supports your webpage’s focus on ethical content creation and personalization strategies.
Rrijm Editorial Team. (2024). *AI-powered personalization in digital marketing.* *RRIJM*, 9(11), 026.
This review article explores advanced analytics and personalization with ethical implications. Peer-reviewed and recent, it aligns with Web Wisdom’s standards of objectivity and accuracy. It supports your discussion on AI ethics and responsible marketing communication.
Aghaei, R., Kiaei, A. A., Boush, M., et al. (2025). *Harnessing the potential of large language models in modern marketing management: Applications, future directions, and strategic recommendations.* *arXiv preprint*.
This preprint outlines uses of LLMs in content generation and bias mitigation. Though not peer-reviewed, it is current, well-sourced, and transparent about limitations. It’s relevant for AI’s applications in content creation and ethics in digital marketing.
Vidrih, M., & Mayahi, S. (2023). *Generative AI‑driven storytelling: A new era for marketing.* *arXiv preprint*.
Explores personalized marketing narratives powered by generative AI, including brand case studies. As a preprint, it lacks peer review but demonstrates practical examples and ethical discussions. It is useful for sections on storytelling, content innovation, and AI responsibility.
