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Abstract
In e-commerce, product photos are a major component of product presentations that aid consumers' understanding of products. In this study, we investigate the impact of the background of product photos on consumers' interest. Drawing upon the attention theories of visual perception, we propose a contrast-composition-distraction framework to understand the product photo background's impact. We conduct an empirical study using a clothing dataset collected from a major fashion product website in China. After differentiating photos' foreground and background and generating features using machine learning, we apply a hierarchical Bayesian model and find that consumers prefer clothing products to be shown on a darker and simpler background. The product should be located in the center of the photo with a slight horizontal offset. It is preferable to use a blurred background and reduce the use of human model faces. These findings are of substantial theoretical and practical value to e-commerce. © 2023 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Article number | 114124 |
| Journal | Decision Support Systems |
| Volume | 178 |
| Online published | 15 Nov 2023 |
| DOIs | |
| Publication status | Published - Mar 2024 |
Funding
This research is partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11503115), and the Natural Science Foundation of China (71572169).
Research Keywords
- E-commerce
- Machine learning
- Consumer behavior
- Product photo
- Attention
RGC Funding Information
- RGC-funded
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Dive into the research topics of 'A Contrast-Composition-Distraction Framework to Understand Product Photo Background’s Impact on Consumer Interest in E-commerce'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Product Photographic Presentation and Online Consumer Behavior: The Role of Image Features in E-commerce
LI, X. (Principal Investigator / Project Coordinator), CHAU, P. Y. K. (Co-Investigator) & Chen, Y. (Co-Investigator)
1/10/15 → 23/03/20
Project: Research
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