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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04541 |
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| _version_ | 1866913979695104000 |
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| author | Yuting, Zhu Xinyu, Cao Yuzhuo, Su Yongbin, Ma |
| author_facet | Yuting, Zhu Xinyu, Cao Yuzhuo, Su Yongbin, Ma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measuring Information Richness in Product Images: Implications for Online Sales Yuting, Zhu Xinyu, Cao Yuzhuo, Su Yongbin, Ma Human-Computer Interaction 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. |
| title | Measuring Information Richness in Product Images: Implications for Online Sales |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.04541 |