Abstract

Customised avatars are a powerful tool to increase identification, engagement and intrinsic motivation in digital games. We investigated the effects of customisation in a self-competitive VR exergame by modelling players and their previous performance in the game with customised avatars. In a first study we found that, similar to non-exertion games, customisation significantly increased identification and intrinsic motivation, as well as physical performance in the exergame. In a second study we identified a more complex relationship with the customisation style: idealised avatars increased wishful identification but decreased exergame performance compared to realistic avatars. In a third study, we found that 'enhancing' realistic avatars with idealised characteristics increased wishful identification, but did not have any adverse effects. We discuss the findings based on feedforward and self-determination theory, proposing notions of intrinsic identification (fostering a sense of self) and extrinsic identification (drawing away from the self) to explain the results.
Original languageEnglish
Title of host publicationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, USA
PublisherAssociation for Computing Machinery
Pages1-16
Number of pages16
Volume2020-April
ISBN (Electronic)9781450367080
Publication statusAcceptance date - 16 Jan 2020

Publication series

NameCHI Conference on Human Factors and Computing Systems
PublisherACM
ISSN (Electronic)1062-9432

Projects

Datasets

Supplement for "Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame"

Jeffery, Z. (Creator), Koulouris, J. (Creator), Best, J. (Creator), Lutteroth, C. (Creator) & O'Neill, E. (Creator), University of Bath, 15 Jan 2020

Dataset

Cite this

Koulouris, J., Jeffery, Z., Best, J., O'Neill, E., & Lutteroth, C. (Accepted/In press). Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Vol. 2020-April, pp. 1-16). (CHI Conference on Human Factors and Computing Systems). Association for Computing Machinery.