Filters uncovered: Investigating the impact of AR face filters and self-view on videoconference fatigue and affect

Abstract

The rise of videoconferencing amidst the COVID-19 pandemic has brought about a new phenomenon, videoconference fatigue (VF), which refers to the emotional and physical exhaustion felt after videoconference meetings. Features of videoconference platforms, such as the self-view function and small screen size, increases self-awareness and cognitive load, resulting in increased negative affect and VF. However, AR face filters can soften facial expressions to reduce self-awareness and increase positive affect. Drawing from the theory of objective self-awareness, this study thus assesses the influence of AR face filters and self-view on users’ affect and perceived VF, through a 2 × 2 dyadic between-subjects experiment (N = 154). Our findings do not support the theory of objective self-awareness. Using AR face filters led to higher VF, but neither AR face filters nor self-view was significantly associated with affect. An alternative theory such as the expectancy violations theory may explain such results. Theoretical and practical implications are discussed.

Keywords

• Videoconference fatigue
• AR face filters
• Self-view
• Videoconferencing
• Affect