Resum
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 mar-
keting landscape. Through a systematic literature review and advanced
clustering techniques, we offer a novel perspective on the evolving re-
search in this field. Our methodology combines TF-IDF vectorization
with K-means clustering and silhouette analysis to identify and exam-
ine 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 Predic-
tive 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 high-
light the transformative potential of conversational AI in shaping market-
ing 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 understand-
ing of the field, identifying key thematic intersections and underexplored
areas. This study not only advances theoretical knowledge but also of-
fers practical insights for businesses and researchers seeking to optimize
AI-driven consumer engagement strategies
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 mar-
keting landscape. Through a systematic literature review and advanced
clustering techniques, we offer a novel perspective on the evolving re-
search in this field. Our methodology combines TF-IDF vectorization
with K-means clustering and silhouette analysis to identify and exam-
ine 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 Predic-
tive 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 high-
light the transformative potential of conversational AI in shaping market-
ing 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 understand-
ing of the field, identifying key thematic intersections and underexplored
areas. This study not only advances theoretical knowledge but also of-
fers practical insights for businesses and researchers seeking to optimize
AI-driven consumer engagement strategies
| Idioma original | Anglès |
|---|---|
| Número d’article | 18479790251351889 |
| Nombre de pàgines | 15 |
| Revista | International Journal of Engineering Business Management |
| Volum | 17 |
| DOIs | |
| Estat de la publicació | Publicada - 1 de gen. 2025 |
SDG de les Nacions Unides
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