Ad generation modalities and response to in-app advertising – an experimental study

模式 广告 合理行为理论 模态(人机交互) 价值(数学) 独创性 心理学 产品(数学) 动作(物理) 计算机科学 社会心理学 业务 社会学 人工智能 创造力 数学 物理 机器学习 量子力学 社会科学 几何学
作者
Charunayan Kamath,Sivakumar Alur
出处
期刊:Global knowledge, memory and communication [Emerald Publishing Limited]
被引量:3
标识
DOI:10.1108/gkmc-04-2024-0245
摘要

Purpose The widespread use of mobile apps in marketing has resulted in in-app advertising to promote products and services. Research on in-app advertising has focused on several dimensions but not on the modality of ad generation. The use of artificial intelligence (AI) and memes as advertisements has paved the way for multiple ways to create them. This study aims to understand the effect of various advertisement generation modalities on an individual’s trust, attitude toward the advertisement, subjective norms, intentions and use of a particular product. Design/methodology/approach Using the theoretical lens of reasoned action and trust, the authors explored through an experimental study (five treatments-AI-generated ad and meme, human-created ad and meme and user-generated meme, and ( n = 300) the consumer’s intention to purchase a fictitious shampoo brand based on in-app advertising. The respondents were exposed to one of the treatments without knowledge of the ad generation modality. Findings Trust differed significantly across all the experimental conditions. Furthermore, the authors observe that the theory of reasoned action holds for all advertising generation modalities. Originality/value The use of AI in advertising is increasing exponentially, and brands are using AI-generated content to engage with their audiences on various platforms. To the best of the authors’ knowledge, this is one of the first studies to attempt to understand the effects of various ad generation modalities on the trust, attitude and behavior of individuals. Furthermore, this study examines both AI and human-created memes and their effects. The authors suggest optimizing the prompt engineering to develop AI-generated images.
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