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

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

    Research output: Journal PublicationArticlepeer-review

    10 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)607-626
    Number of pages20
    JournalInformation Systems Frontiers
    Volume23
    Issue number3
    DOIs
    Publication statusPublished - Jun 2021

    Keywords

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

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Software
    • Information Systems
    • Computer Networks and Communications

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