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

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2020

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.

Keywords

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

Discipline

Asian Studies | Databases and Information Systems | E-Commerce

Research Areas

Information Systems and Management

Publication

Electronic Commerce Research and Applications

Volume

41

First Page

1

Last Page

2

ISSN

1567-4223

Identifier

10.1016/j.elerap.2020.100975

Publisher

Elsevier

Embargo Period

5-27-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.elerap.2020.100975

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