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 influence how e-commerce research is conducted. China offers the ultimate in data-at-scale settings, and the construction of real-world natural experiments. Chinese e-commerce includes some of the largest firms involved in e-commerce, mobile commerce, social media and social networks. This article was written to encourage young faculty and doctoral students to engage in research that can be carried out in near real-time, with truly experimental or quasi-experimental research designs, and with the clear intention of establishing causal inferences that relate the precursors and drivers of observable outcomes through various kinds of processes. We discuss: the relevant data sources and research contexts; the methods perspectives that are appropriate which blend Computer Science, Statistics and Econometrics, how the research can be made relevant for China; and what kinds of findings and research directions are available. This article is not a tutorial on big data analytics methods in general though, nor does it cover just those published works that demonstrate big data methods and empirical causality in other disciplines. Instead, the empirical research covered is mostly taken from Electronic Commerce Research and Applications, which has published many articles on Chinese e-commerce. This Research Commentary invites researchers in China and the Asia Pacific region to expand their coverage to bring into their empirical work the new methods and philosophy of causal data science.