Fig 1.
Conceptual model of research hypotheses.
Table 1.
Dataset characteristics and hypothesis mapping.
Table 2.
Distribution of emotion attributions and intensity ratings.
Fig 2.
Associations Between Digital Art Exposure and User Perception.
(a) Aesthetic perception; (b) Social value perception. ArtEmis dataset, N = 118,339. Error bars represent SE. One-way ANOVA: Aesthetic perception F(2, 118336)=187.42, p < 0.001; Social value perception F(2, 118336)=94.67, p < 0.001.
Table 3.
Digital art consumption behavior patterns.
Fig 3.
Behavioral engagement patterns by emotion category.
(a) Viewing duration by emotion; (b) Sharing intention by emotion. Viewing duration: VR Eye-tracking dataset, n = 152; Sharing intention: ArtEmis dataset, n = 389,247. One-way ANOVA: Viewing duration F(7, 144)=8.73, p < 0.001; Sharing intention χ2(7)=12847.32, p < 0.001.
Table 4.
Path analysis results for social cognitive outcome model.
Fig 4.
Contribution of pathways to social cognitive outcomes.
Table 5.
User characteristics and digital art engagement patterns.
Fig 5.
Heterogeneous associations of digital art exposure across user groups.
(a) Age moderation; (b) Technology proficiency moderation; (c) Cultural background moderation. ArtEmis dataset, N = 152,120. Moderation analysis: Age F(3, 152116)=18.47, p < 0.001; Technology F(2, 152117)=31.84, p < 0.001; Culture F(1, 152118)=24.63, p < 0.001.