A 2020 perspective on “How to derive causal insights for digital commerce in China? A research commentary on computational social science methods”

David C.W. Phang, Kanliang Wang, Qiuhong Wang, Robert J. Kauffman, Maurizio Naldi

    Research output: Journal PublicationArticlepeer-review

    Abstract

    Cyber-physical data from wearable and other data-sensing devices have been rapidly changing the landscape of opportunity for the conduct of computational social science (CSS) studies. We now have the opportunity to include in our research wearable healthcare data sensors, global positioning system (GPS) data, as well as a range of other digital data via mobile phones and other kinds of easily deployed sensors. The result is a dramatic new set of measurement opportunities for management scientists, marketing research staff, and policy analysts, who can now apply a range of approaches to such data capture and analysis, including machine learning of patterns, and causal inference methods for relevant policy analytics conclusions.

    Original languageEnglish
    Article number100975
    JournalElectronic Commerce Research and Applications
    Volume41
    DOIs
    Publication statusPublished - 1 May 2020

    Keywords

    • Causal inference
    • Computational social science (CSS)
    • Cyber-physical sensing
    • Data analytics
    • Machine learning
    • Wearable devices

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computer Networks and Communications
    • Marketing
    • Management of Technology and Innovation

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