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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Mengyue Wang
  • Xin Li
  • Patrick Y. K. Chau

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)607–626
Journal / PublicationInformation Systems Frontiers
Volume23
Issue number3
Online published10 Jan 2020
Publication statusPublished - Jun 2021

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.

Research Area(s)

  • Econometrics, Empirical study, Image-processing, Online shopping, Simulation