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
Citation
PHANG, David C. W..
A 2020 perspective on "How to derive causal insights for digital commerce in China? A research commentary on computational social science methods". (2020). Electronic Commerce Research and Applications. 41, 1-2.
Available at: https://ink.library.smu.edu.sg/sis_research/5970
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.elerap.2020.100975