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January 14, 2026International Journal of Consumer Studies5 citationsOpen Access

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

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AMAndrea Morales‐MuñozMIMª. Ángeles Iniesta‐BonilloAEAntonia Estrella‐Ramón

Key Points

  • The aim is to synthesize existing research on the intersection of AI and consumer behaviour in social media.
  • Conducted a systematic literature review following the SPAR-4-SLR protocol
  • Utilized the 5W1H framework to organize findings
  • Analyzed perspectives on AI's role as an enabler or risk in marketing
  • AI is primarily seen as a driver of personalisation and engagement in marketing
  • Ethical concerns include algorithmic bias and privacy issues
  • Growth in empirical studies focusing on chatbots, recommendation systems, and VIs

Abstract

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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Cite This Study

Morales‐Muñoz et al. (2026) studied this question.

synapsesocial.com/papers/6966f2f013bf7a6f02c003b8https://doi.org/10.1111/ijcs.70173
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