{"ID":2885851,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04541","arxiv_id":"2508.04541","title":"Measuring Information Richness in Product Images: Implications for Online Sales","abstract":"A common challenge for e-commerce sellers is to decide what product images to display on online shopping sites. In this paper, we propose and validate a novel metric, k-value, to quantify the information richness of an image set, and we further investigate its effect on consumers' purchase decisions. We leverage patch-level embeddings from Vision Transformers (ViT) and apply k-means clustering to identify distinct visual features, defining k-value as the number of clusters. An online experiment demonstrates that k-value aligns with human-perceived information richness, validating the metric. A simulated online shopping experiment further reveals a significant yet counterintuitive result: while an image set with a higher k-value (richer information) shortens decision time, it paradoxically reduces purchase propensity. Our findings illuminate the complex relationship between visual information richness and consumer behavior, providing sellers a quantifiable tool for image selection.","short_abstract":"A common challenge for e-commerce sellers is to decide what product images to display on online shopping sites. In this paper, we propose and validate a novel metric, k-value, to quantify the information richness of an image set, and we further investigate its effect on consumers' purchase decisions. We leverage patch-...","url_abs":"https://arxiv.org/abs/2508.04541","url_pdf":"https://arxiv.org/pdf/2508.04541v2","authors":"[\"Zhu Yuting\",\"Cao Xinyu\",\"Su Yuzhuo\",\"Ma Yongbin\"]","published":"2025-08-06T15:29:56Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}