Influence of Avatar Identification on the Attraction of Virtual Reality Games: Survey Study
Bibliographic record
Abstract
BACKGROUND: In gaming, the embodied interaction experience of avatars serves as a key to emotional sublimation in artistic creation. This presents the emotional expression of art in a more vivid form, which is a critical factor in the high attractiveness of virtual reality (VR) games to players. Intertwined with players' physiological and psychological responses, immersion is an essential element for enhancing gaming attractiveness. OBJECTIVE: This study aims to explore how to help players establish a sense of identity with their embodied avatars in VR game environments and enhance the attractiveness of games to players through the mediating effect of immersion. METHODS: We conducted a structured questionnaire survey refined through repeated validation. A total of 402 VR users were publicly recruited through the internet from March 22, 2024, to April 13, 2024. Statistical analysis was conducted using the SPSS and Amos tools, including correlation analysis, regression analysis, and mediation effect verification. We divided the self-differentiation theory into 4 dimensions to validate their impact on avatar identification. Subsequently, we correlated the effects of avatar identification, game immersion, and game attractiveness and proposed a hypothetical mediating model. RESULTS: Regression analysis of the predictor variables and the dependent variable indicated a significant positive predictive effect (P<.001); the variance inflation factor values for each independent variable were all <5. In the hypothesis testing of the mediating effect, the total mediating effect was significant (P<.001). Regarding the direct impact, both the effect of avatar identification on immersion and the effect of immersion on game attractiveness were significant (P<.001). However, the direct effect of avatar identification on game attractiveness was not significant (P=.28). Regarding the indirect impact, the effect of avatar identification on game attractiveness was significant (P<.001). The results indicate a significant positive correlation between different dimensions of the self-differentiation theory and identification with avatars. Moreover, immersion in the game fully mediated the relationship between identification with avatars and game attractiveness. CONCLUSIONS: This study underscores that the embodiment of avatar identification is influenced by dimensions of self-differentiation, and the impact of identification with avatars on game attractiveness is contingent upon full mediation by immersion. These findings deepen our understanding of the role of avatar identification in VR gaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".