AI-driven marketing tactics and consumer behavior: a bibliometric review and systematic literature review
Swati Ahuja1, Amit Dutt2, Nancy Sahni1
1Mittal School of Business, Lovely Professional University, Phagwara, Punjab, India.
Objective:
This study synthesizes the literature on AI-driven marketing tactics and their influence on consumer behavior. It identifies dominant theories, contexts, methodological approaches, and key antecedents that explain how AI-driven marketing tactics shape consumers' cognitive, emotional, and behavioral responses. This study also provides intellectual structure, publication trends and future research directions within this field.
Methods:
This study reviewed 155 articles, retrieved from the Scopus database and analyzed using bibliometric analysis, SPAR-4-SLR, and the TCM-ADO framework. The review covers publications from 2016 to 2026 and examines theoretical, contextual, and methodological approaches as well as factors influencing consumer responses to AI-driven marketing tactics.
Findings:
The findings reveal a significant growth in research publications related to AI-driven marketing tactics over the past decade. The analysis identifies dominant theories, research contexts, and methodological approaches employed in the field. Furthermore, a five-category antecedent framework was proposed, including AI characteristics, consumer psychological and behavioral traits, consumer attitudes and responses, marketing and communication factors, and contextual and situational factors. The results highlight that AI-driven marketing tactics influence consumer decision-making through technological, psychological, marketing and contextual mechanisms.
Conclusion:
The research on AI and consumer behavior has been expanded; however, literature remains fragmented. Most of the prior studies have examined individual AI tactics, such as chatbots, personalized recommendations and targeted advertisements in isolation instead of as an integrated AI-driven marketing tactic. Moreover, earlier studies focused on factors such as technology adoption and purchase intention, while fewer studies examined cognitive, emotional and behavioral mechanisms through which diverse AI-driven marketing tactics influence consumer behavior.
Implications:
The study provides a structured synthesis of literature and highlights gaps for future research. It suggests the need for greater theoretical integration, cross-cultural investigations, and longitudinal studies examining consumer responses to AI-driven marketing tactics. The findings provide valuable insights for researchers to advance the field and for practitioners aiming to design effective AI-enabled marketing strategies that enhance consumer engagement and decision-making.
