脉冲(物理)
广告
业务
社会化媒体
心理学
计算机科学
万维网
物理
量子力学
出处
期刊:Young Consumers: Insight and Ideas for Responsible Marketers
[Emerald Publishing Limited]
日期:2025-03-08
卷期号:26 (5): 765-785
被引量:13
标识
DOI:10.1108/yc-10-2024-2297
摘要
Purpose This study aims to explore the largely under-researched area of user perceptions of artificial intelligence (AI)-driven recommendations and how the visibility of AI-driven labels influences impulsive buying behavior on social media platforms. Addressing a gap in existing literature, it further examines the moderating effects of generational differences and the mediating role of consumer knowledge, providing new insights into how these factors shape consumer responses to AI-driven content in social media. Design/methodology/approach This study used a quantitative approach, using consumer surveys to explore the direct and indirect impacts of AI-driven recommendations and the visibility of AI-driven labels on impulsive buying behavior through social media. A conditional process analysis was conducted to examine how generational differences act as a moderator and how consumer knowledge of AI serves as a mediator in these relationships. This analysis integrated these factors into a single framework to provide a detailed understanding of how consumers respond to AI-driven personalization in social media. Findings This study confirms that AI-driven personalization effectively nudges impulsive buying on social media, with personalized recommendations impacting behavior more subtly than AI labels. Consumer knowledge does not mediate this effect, while generational differences emerge as a significant moderator; Millennials are found to be more responsive to both recommendations and labels in comparison to Gen Z, possibly due to less familiarity with AI technologies present in social media. Research limitations/implications This study identifies limitations such as its reliance on consumer perceptions from a questionnaire rather than direct interactions with AI-driven features. While insightful, future research should incorporate actual user data, like clickstreams, and include a wider range of generational cohorts to deepen the understanding of AI’s impact on impulsive buying behavior. Originality/value This study bridges gaps in the literature by examining the combined effects of AI-driven recommendations and visibility of AI-driven labels on impulsive buying behavior, offering new insights into the role of generational differences and consumer knowledge in AI-based social media marketing.
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