Algorithmic personalisation on TikTok and its effect on Gen Z's self-concept and consumer behaviour

• Main Authors: Yupar, Tin Win, Tan, Misa Li Sin
• Other Authors: Kang Hyunjin
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Other; Algorithms
• Online Access: https://hdl.handle.net/10356/174489
• Institution: Nanyang Technological University

TikTok’s recommendation algorithm has created a novel user experience. Guided by existing literature on algorithmic personalisation, this study investigated the relationship between algorithmically-curated content on TikTok, Gen Z TikTok users’ self-concepts, and their consumer behaviours. Based on a series of in-depth interviews with 18 Gen Z TikTok users, this study delved into how TikTok’s algorithmically-curated content reflects and influences Gen Z’s self concept. The study also explored how the algorithmically-influenced self-concept in turn influences Gen Z TikTok users’ consumer behaviours. It was found that many Gen Z TikTok users were keenly aware of the algorithm and its workings. They attempted to influence the algorithm such that it better aligns with their self-concept. Their algorithmically-curated content feeds reflected various aspects of their self-concepts, with their interactions with the content playing a role in influencing their feed. Algorithmically-curated content had a notable influence on their self-concepts, though some perceived otherwise. It was also found the algorithmically-influenced self-concept played a role in influencing consumer behaviour, particularly purchase behaviour. These findings advance our understanding of the dynamics between algorithms, self-concept and consumer behaviour, and highlight the power of algorithms in shaping users’ internal perceptions and external behaviours.