Decoding Conversations: Bibliometric Insights into AI and Marketing Communication Research

DOI

10.2991/978-94-6463-978-0_31How to use a DOI?

Keywords

Artificial Intelligence; Marketing Communication; Bibliometric Analysis; Machine Learning; Digital Marketing

Abstract

The rapid integration of artificial intelligence technologies in marketing communication has created a dynamic research landscape requiring systematic examination to understand emerging trends and theoretical developments. Despite growing scholarly interest, a comprehensive bibliometric assessment mapping the intellectual structure and evolutionary patterns of AI-marketing communication research remains largely unexplored, creating a significant knowledge gap regarding research trajectories and future directions. This study addresses this gap by conducting a comprehensive bibliometric analysis of 1,724 peer-reviewed journal articles retrieved from Scopus database using a meticulously curated search protocol under the disciplines of business and management. Employing R package Biblioshiny and VOSviewer software, this research utilized performance analysis and science mapping techniques to examine publication patterns, collaboration networks, and thematic evolution. The analysis revealed four distinct research clusters: (1) Virtual Influencers and Generative AI Marketing Applications, (2) AI-Powered Content Generation and Algorithmic Marketing Systems, (3) Consumer Psychology and AI Ethics in Digital Marketing, and (4) AI-Driven Personalization and Strategic Marketing Integration. Each cluster demonstrates unique evolutionary trajectories from foundational AI applications to sophisticated ethical and strategic implementations. The study contributes to academic discourse by providing a structured intellectual framework for understanding AI-marketing communication convergence and offers strategic research directions for scholars, practitioners, and policymakers navigating this rapidly evolving field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kapil Khandeparkar
AU  - Amol S. Dhaigude
PY  - 2025
DA  - 2025/12/31
TI  - Decoding Conversations: Bibliometric Insights into AI and Marketing Communication Research
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 352
EP  - 368
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_31
DO  - 10.2991/978-94-6463-978-0_31
ID  - Khandeparkar2025
ER  -