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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Predicting proteus effect via the user avatar bond: a longitudinal study using machine learning
Behaviour & information technology, Vol.44(8), pp.1595-1611
09/05/2025
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Abstract
The impact of an avatar on real-world behaviors of users is known as the Proteus Effect. Different user avatar bond (UAB) aspects, including identifying, immersing, and compensating via the avatar, influence an individual's Proteus Effect propensity. This study aimed to use machine learning (ML) classifiers to automate the prediction of those likely to experience Proteus Effect, based on their reports of identifying, immersing, and compensating with their avatar. Participants were 565 gamers (Mage = 29.3 years; SD = 10.6), assessed twice, six months apart, using the User-Avatar-Bond Scale and the Proteus Effect Scale. Tuned and untuned ML classifiers showed ML models could accurately identify individuals with higher Proteus Effect propensity, informed by a gamer's reported UAB, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests performed better than other MLs, with avatar identification as the strongest predictor. This suggests higher Proteus Effect propensity for those with a stronger user-avatar bond, informing gamified health applications to introduce adaptive behavioral changes via the avatar. Prevention and practice implications are discussed.
Details
- Title
- Predicting proteus effect via the user avatar bond: a longitudinal study using machine learning
- Creators
- Mohammed Qasim Latifi - RMIT UniversityDylan Poulus - Southern Cross UniversityMichaella Richards - Mighty Serious Health Games Studio (Victoria, Australia)Yang Yap - RMIT UniversityVasileios Stavropoulos - RMIT University
- Publication Details
- Behaviour & information technology, Vol.44(8), pp.1595-1611
- Publisher
- Taylor & Francis
- Number of pages
- 17
- Identifiers
- 991013209890102368
- Copyright
- © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- Academic Unit
- Faculty of Health
- Language
- English
- Resource Type
- Journal article