Universitat Ramon Llull. Esade
2025-06-13
This study presents a comprehensive analysis of the influence of conversational artificial intelligence (AI)—a subset of AI that enables machines to simulate human-like conversations through natural language processing (NLP)—on consumer decision-making within the digital marketing landscape. Through a systematic literature review and advanced clustering techniques, we offer a novel perspective on the evolving research in this field. Our methodology combines TF-IDF vectorization with K-means clustering and silhouette analysis to identify and examine five distinct thematic clusters: Consumer Behavior and Engagement, Sentiment Analysis and NLP in E-Commerce, Artificial Intelligence in Marketing, Trust and Technology Adoption, and Big Data and Predictive Analytics. This clustering approach provides valuable insights into the temporal, disciplinary, and geographical dimensions of the research landscape. By synthesizing findings from 78 scholarly articles, we highlight the transformative potential of conversational AI in shaping marketing strategies and enhancing consumer experiences. Our analysis reveals emerging trends, critical gaps, and future directions for research, offering decision-makers in both academia and industry a structured framework for understanding and leveraging conversational AI in consumer-centric marketing initiatives. The principal contribution of this article lies in its data-driven approach to mapping the research landscape to identify key thematic clusters, emerging trends, and underexplored areas in the field. By integrating computational clustering methods with a systematic literature review, we provide a more structured and granular understanding of the field, identifying key thematic intersections and underexplored areas. This study not only advances theoretical knowledge but also offers practical insights for businesses and researchers seeking to optimize AI-driven consumer engagement strategies.
Artículo
Versión publicada
Inglés
Conversational AI; Cluster analysis; Predictive analytics; Consumer Decision-Making
15 p.
SAGE Publications
International Journal of Engineering Business Management, Vol. 17
Review article
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