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 language | English |
|---|---|
| Article number | 100975 |
| Journal | Electronic Commerce Research and Applications |
| Volume | 41 |
| DOIs | |
| Publication status | Published - 1 May 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Free 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
Fingerprint
Dive into the research topics of 'A 2020 perspective on “How to derive causal insights for digital commerce in China? A research commentary on computational social science methods”'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver