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Artificial Intelligence and Consumer Behaviour in Social Media: Systematic Literature Review and Future Research Agenda

影响力营销 系统回顾 社会化媒体 消费者行为 透视图(图形) 生成语法 交叉口(航空) 人工智能 社会学 实证研究 知识管理 心理学 协议(科学) 人工智能应用 计算机科学 潜意识 大数据 情绪分析 管理科学 社会学习 消费(社会学) 社会影响力
作者
Andrea Morales‐Muñoz,Mª. Ángeles Iniesta‐Bonillo,Antonia Estrella‐Ramón,Sara Herrada‐Lores
出处
期刊:International Journal of Consumer Studies [Wiley]
卷期号:50 (1)
标识
DOI:10.1111/ijcs.70173
摘要

ABSTRACT The rise of artificial intelligence (AI) and, more recently, generative AI (GAI) has transformed digital marketing, particularly within social media. However, academic research on this intersection remains dispersed, requiring a structured synthesis to identify prevailing trends and gaps. Given the increasing integration of AI in digital marketing, understanding its implications for consumer behaviour is crucial for both researchers and practitioners. This study conducts a systematic literature review (SLR) following the SPAR‐4‐SLR protocol to analyse existing research on AI, GAI, social media, and consumer behaviour. In addition, the 5W1H framework is used to organise information and answer questions that arise. Specifically, it examines how AI is portrayed in social media and consumer behaviour literature, whether as an enabler, risk, or neutral factor, the perspective taken by the studies, and the application given to it. Findings show that AI is primarily framed as a driver of personalisation, engagement, and analytics, yet notable concerns about ethical risks like algorithmic bias and privacy persist. Research perspectives vary, spanning consumer, business, and integrative views that reflect the complex AI influence on user experience and organisational strategy. Empirical studies mainly treat AI as a core subject, focusing on applications such as chatbots, recommendation systems, and virtual influencers (VIs). A smaller number employ AI methodologically for social media data analysis through machine learning (ML) and natural language processing (NLP). Despite growth, significant gaps remain in understanding AI's long‐term effects, cross‐cultural nuances, and theoretical integration. Ethical issues highlight the need for responsible AI frameworks balancing innovation and fairness. This review synthesises current knowledge and outlines future research directions, aiming to guide academic inquiry and responsible implementation of AI in digital consumer contexts.
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