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: Contribution to journalComment/debateResearchpeer-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
Issue numberMay-June
Number of pages2
ISSN1567-4223
DOIs
Publication statusPublished - 2020

Keywords

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

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