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

Mengyue Wang (), Xin Li and Patrick Y. K. Chau
Additional contact information
Mengyue Wang: GoldMining Quant (Shenzhen) Technology Co Ltd
Xin Li: City University of Hong Kong
Patrick Y. K. Chau: University of Nottingham Ningbo China

Information Systems Frontiers, 2021, vol. 23, issue 3, No 8, 607-626

Abstract: 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.

Keywords: Image-processing; Empirical study; Online shopping; Simulation; Econometrics (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10796-020-09981-8

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