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Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context

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Abstract

Photos play a critical role in online shopping. To examine their impact on consumers, most previous studies rely on human assessments to develop measures for photos. Such an approach limits the number of dimensions and samples that can be investigated in one study. This study exploits image-processing techniques to tackle this challenge. We develop a framework and differentiate two types of computer-generated measures, aggregative and decompositive measures, which may be used in different ways in empirical research. We review the major image-processing technologies that have potential to be used in consumer behavior research. To showcase the feasibility of the framework, we conduct an example study on product photos’ impact on consumer click-through. Moreover, we conduct a simulation to investigate the robustness of the framework under the attack of image-processing algorithm errors. We find that image-processing techniques with 90~95% accuracy will be sufficient for empirical research.

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Notes

  1. Image-processing and the related computer vision area are one major field in computer science related to artificial intelligence, machine learning, and pattern recognition. In this paper, we use image-processing to represent the family of computer science techniques to understand and derive meanings from images.

  2. Recently, computers can achieve higher performance than humans on certain tasks, such as face recognition. But humans perform better on most tasks.

  3. The image-processing field has grown dramatically in the past two decades. High-quality papers in the field are often published in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), and European Conference on Computer Vision (ECCV). Advanced algorithms and novel topics can also be found at the Neural Information Processing Systems (NIPS) and ACM SIGKDD conferences on knowledge discovery and data mining (KDD). Their top journal is IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).

  4. We use a code name to keep the company’s identity confidential.

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Acknowledgments

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).

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  1. GoldMining Quant (Shenzhen) Technology Co Ltd, Shenzhen, Guangdong, China

    Mengyue Wang

  2. City University of Hong Kong, Hong Kong, China

    Xin Li

  3. University of Nottingham Ningbo China, Ningbo, China

    Patrick Y. K. Chau

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Wang, M., Li, X. & Chau, P.Y.K. Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context. Inf Syst Front 23, 607–626 (2021). https://doi.org/10.1007/s10796-020-09981-8

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