Mixed-method analysis examines how spatial variables influence visitor engagement in cultural exhibitions, suggesting design improvements.
The integration of virtual reality, digital twins, and spatial behavior-tracking technologies is reshaping cultural exhibition architecture, shifting the design focus from functional efficiency to immersive, user-centered experiences. However, the behavioral dynamics within these interactive environments remain insufficiently addressed. This study proposes a behavior-oriented spatial typology grounded in Bitgood’s attention–value model, which maps the psychological stages—Attraction, Hold, Engagement, and Exit—onto four spatial categories: Threshold Space, Transitional Space, Narrative Focus Space, and Closure Space. Each represents a distinct phase of perceptual and behavioral response along the exhibition sequence. A mixed-method approach was employed, combining eye-tracking experiments with structured questionnaires to capture both physiological reactions and subjective evaluations. Key spatial variables—enclosure (X1), visual corridors (X2), spatial scale (X3), and light–shadow articulation (X4)—were analyzed using multiple regression to assess their impact on interest and dwell time. The results show that enclosure (α = −0.094; β = −0.319) and light–shadow articulation (α = −0.057; β = 0.156), respectively, decreased interest and increased dwell time, while spatial scale (α = 0.042; β = 0.186) positively affected both. Visual corridors had minimal influence (α = −0.007; β = 0.022). These spatial effects align with the proposed typology: Threshold Spaces support rapid orientation and exploratory behavior, while Transitional Spaces aid navigation but reduce sustained attention. Narrative Focus Spaces enhance cognitive engagement and decision making, and Closure Spaces foster emotional resolution and extended presence. These findings validate the proposed typology and establish a quantifiable link between spatial attributes and visitor behavior, offering a practical framework for optimizing immersive exhibition sequences.
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Chen et al. (2025) studied this question.
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