AI advertising: An overview and guidelines

Ford, John; Jain, Varsha; Wadhwani, Ketan; Gupta, Damini Goyal

doi:10.1016/j.jbusres.2023.114124

Cited by 19 publications

(2 citation statements)

References 81 publications

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“…The selection of keywords in the search string was based on the relevance of the research topic so we could retrieve and assess a holistic set of papers that examine the intersection between AI and consumer behavior. AIrelated keywords such as 'artificial intelligence,' 'big data,' 'machine learning,' 'neural network,' and 'deep learning' were included because these are vital technologies associated with AI and are widely recognized in the academic literature (Ford et al, 2023;Mariani et al, 2022;Mariani et al, 2023). Similarly, consumer behavior-related keywords such as 'online consumer behavior,' 'consumer attitude,' 'consumer perceptions,' 'consumer culture,' 'consumer purchase decision,' 'consumer buying behavior,' 'consumer motivation,' 'consumer decision making and 'consumer information processing' were used to ensure the search captured studies focused on various dimensions of consumer behavior (Hoyer et al, 2016;Jain & Sheth, 2019).…”

Section: Identificationmentioning

confidence: 99%

Artificial intelligence consumer behavior: A hybrid review and research agenda

Jain,

Wadhwani,

Eastman

2023

J of Consumer Behaviour

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The advancement of artificial intelligence (AI) technology and its applications has drastically transformed consumer behavior (CB). As consumers interact with these applications on multiple platforms and touchpoints, it becomes crucial to understand how these interactions affect consumer behavior and its components, including personality, attitude, engagement, decision‐making, and trust. The research on the relationship between artificial intelligence and consumer behavior (hereafter referred to as AI CB) revolves around these topics and has grown exponentially in recent years. A rigorous review is required to provide directions for future studies by comprehending the extensive literature, understanding research gaps, and identifying the future directions for scholarly work. This article aims to address this research gap by analyzing 107 AI CB articles using the bibliometric and framework‐based methodology to provide insights into publication trends, dominant theories, methods, antecedents, decisions, and outcomes in the AI CB literature. Most importantly, the review identifies clusters of research fronts and provides a thematic framework for current research. These clusters or themes relate to AI interaction with consumer behavior dimensions, including consumer acceptance and trust, consumer interaction and engagement, attitude and personality, decision‐making, and adoption. This thematic framework and TCM‐ADO analysis offer future research directions to advance theory development and have implications for industry and society.

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Section: Identificationmentioning

confidence: 99%

Artificial intelligence consumer behavior: A hybrid review and research agenda

Jain,

Wadhwani,

Eastman

2023

J of Consumer Behaviour

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“…Against this backdrop, personalized advertising research has become a prominent topic in the field of marketing, as businesses increasingly recognize the effectiveness of attracting and retaining target audiences by meeting individualized needs. Personalized advertising research aims to construct more intelligent and personalized advertising communication systems by leveraging advanced technologies such as big data (L. Li & Zhang, 2021;Zhu, 2021), artificial intelligence (Ford et al, 2023), and deep learning (X. Liu, 2023).…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

Zhang,

Qu,

Long

et al. 2024

Journal of Organizational and End User Computing

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With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.

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How generative AI is (will) change consumer behaviour: Postulating the potential impact and implications for research, practice, and policy

Mogaji,

Jain

2024

J of Consumer Behaviour

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This article sheds light on the profound impact of technology on consumer behavior, specifically focusing on the rise of generative AI tools. It highlights how these advancements have revolutionized consumer engagement, purchase decision‐making, and technology interaction. The article underscores the transformative potential of generative AI in shaping consumer behavior through personalized recommendations and interactive shopping experiences. It emphasizes the need for continued research and exploration to comprehend and effectively navigate the ever‐evolving landscape of consumer behavior influenced by generative AI. Additionally, the article identifies implications for research and practice, offers valuable strategies for brands, and presents a comprehensive research agenda to delve deeper into this field. Ultimately, it provides valuable insights into the challenges and opportunities presented by generative AI in consumer behavior, serving as a guiding resource for advancing theory, practice, and policy in this domain.

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AI advertising: An overview and guidelines

Cited by 19 publications

References 81 publications

Artificial intelligence consumer behavior: A hybrid review and research agenda

Artificial intelligence consumer behavior: A hybrid review and research agenda

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

How generative AI is (will) change consumer behaviour: Postulating the potential impact and implications for research, practice, and policy

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