Lim, C.V.; Zhu, Y.-P.; Omar, M.; Park, H.-W. Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models. Digital2024, 4, 244-270.
Lim, C.V.; Zhu, Y.-P.; Omar, M.; Park, H.-W. Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models. Digital 2024, 4, 244-270.
Lim, C.V.; Zhu, Y.-P.; Omar, M.; Park, H.-W. Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models. Digital2024, 4, 244-270.
Lim, C.V.; Zhu, Y.-P.; Omar, M.; Park, H.-W. Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models. Digital 2024, 4, 244-270.
Abstract
In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry.
The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.
Keywords
generative AI advertising; artificial intelligence advertising; machine learning advertising; bibliometrics; full counting; co-occurrence network analysis; co-author network analysis
Subject
Business, Economics and Management, Marketing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
