The impact of conversational AI on consumer decision-making: A systematic review and cluster analysis
David López-López & Marc Bara Iniesta
What the paper says
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.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.