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What Is the Draw of the Metaverse?: Personality Correlates of Zepeto Use Motives and Their Associations With Psychological Well-Being.

Eun-Ju LeeWonjae LeeInhwan Bae
Published in: Cyberpsychology, behavior and social networking (2023)
Despite the hype surrounding the metaverse, there is scant empirical research that examines who uses the service, for what specific purposes, and with what consequences. Based on a survey of current Zepeto users ( N  = 200), a popular metaverse application that enables people to create avatars and socialize while exploring the virtual spaces, we investigated (a) the key motives of Zepeto use, (b) how Big Five personality traits predict specific motives of Zepeto use, and (c) how specific motives of Zepeto use are associated with users' psychological well-being. Overall, users were largely driven by the desire to explore the virtual world and enjoy unique experiences, but such a tendency was stronger among those higher on openness and agreeableness. Extroverts were more likely to use Zepeto for functional purposes, while those higher on neuroticism turned to Zepeto to escape from reality. As for psychological consequences, while those using Zepeto for functional and escaping purposes reported higher levels of loneliness, those who used Zepeto for social and experiential goals were less lonely. The experiential and escape motives predicted perceived social support in the opposite directions. Moreover, by comparing Zepeto users' responses with those of non-users ( N  = 200), we found that (a) non-users overestimated users' motives of Zepeto use, especially social and escape motives, (b) Zepeto users were higher on extraversion and openness than non-users, and (c) users reported higher levels of loneliness than non-users with no significant difference in perceived social support. Implications of the findings and future directions are discussed.
Keyphrases
  • social support
  • depressive symptoms
  • mental health
  • healthcare
  • public health
  • physical activity
  • machine learning
  • big data
  • mass spectrometry
  • sleep quality
  • single molecule