Paper Number
ICIS2025-2405
Paper Type
Complete
Abstract
Digital content platforms use compelling images to present products. Prior research shows that visual attributes shape consumer decisions. While most studies focus on individual product features, products are often shown in combinations. The effects of these groupings—and interactions among products—remain underexplored. Drawing on Gestalt theory and limited-capacity attention models, this study examines how intensity and variability of visual features within combinations influence click-through decisions. Using a large-scale dataset from a Chinese digital reading platform, we extract seven features—capturing color, composition, and figure-ground properties—to analyze their effects on clicks. Results show that visual intensity boosts click-through rates, while visual variability has dimension-specific effects: color variability enhances engagement, and structural variability reduces it. Image position moderates these effects—a strong final image recaptures attention, activating a “residual attention window”. This study offers a perspective on how visual features of combinations shape both the set and components, extending IS research on visual perception.
Recommended Citation
zhu, yanyan; LIU, JIN; and li, yongjun, "Seeing the Whole How Visual Features of Image Combinations Influence Consumer Click Behavior" (2025). ICIS 2025 Proceedings. 15.
https://aisel.aisnet.org/icis2025/da_bus/da_bus/15
Seeing the Whole How Visual Features of Image Combinations Influence Consumer Click Behavior
Digital content platforms use compelling images to present products. Prior research shows that visual attributes shape consumer decisions. While most studies focus on individual product features, products are often shown in combinations. The effects of these groupings—and interactions among products—remain underexplored. Drawing on Gestalt theory and limited-capacity attention models, this study examines how intensity and variability of visual features within combinations influence click-through decisions. Using a large-scale dataset from a Chinese digital reading platform, we extract seven features—capturing color, composition, and figure-ground properties—to analyze their effects on clicks. Results show that visual intensity boosts click-through rates, while visual variability has dimension-specific effects: color variability enhances engagement, and structural variability reduces it. Image position moderates these effects—a strong final image recaptures attention, activating a “residual attention window”. This study offers a perspective on how visual features of combinations shape both the set and components, extending IS research on visual perception.
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07-DataAnalytics