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

• Main Authors: PHANG, David C.W., WANG, Kanliang, WANG, Qiu-hong, KAUFFMAN, Robert John, NALDI, Maurizio
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Big data; Business insights; Causal inference; Causal methods; Computational social science (CSS); Consumer behavior; China; Data analytics; Digital economy; E-commerce; Emerging markets; Empirical research; Information systems (IS) research; Machine learning (ML); M-commerce; Policy analytics; Research design; Secondary data; Sensor data; Streaming data; Social insights; Theory testing; Databases and Information Systems; E-Commerce
• Online Access: https://ink.library.smu.edu.sg/sis_research/4418; https://ink.library.smu.edu.sg/context/sis_research/article/5421/viewcontent/Derive_Causal_D_Commerce_China_ECRA_2019_av.pdf
• Institution: Singapore Management University

The transformation of empirical research due to the arrival of big data analytics and data science, as well as the new availability of methods that emphasize causal inference, are moving forward at full speed. In this Research Commentary, we examine the extent to which this has the potential to infl...