Combining machine-based and econometrics methods for policy analytics insights

作者：

Highlights：

• Computational social science supports study of e-commerce policy analytics issues.

• Advances have been made for the discovery of new policy-related insights.

• Areas covered are: business, consumer and social public and private settings.

• Machine-based methods are combined with explanatory econometrics and statistics.

• Empirical illustrations are in four contemporary e-commerce research issue areas.

• Mobile phone stock trading, music popularity, TV viewing, video-on-demand services.

摘要

•Computational social science supports study of e-commerce policy analytics issues.•Advances have been made for the discovery of new policy-related insights.•Areas covered are: business, consumer and social public and private settings.•Machine-based methods are combined with explanatory econometrics and statistics.•Empirical illustrations are in four contemporary e-commerce research issue areas.•Mobile phone stock trading, music popularity, TV viewing, video-on-demand services.

论文关键词：Causality,Computational Social Science,Data analytics,Econometrics,E-commerce,Empirical research,Fintech,Fusion analytics,Music popularity,Stock trading,Policy analytics,TV viewing,Video-on-demand (VoD)

论文评审过程：Received 19 March 2017, Accepted 17 April 2017, Available online 25 April 2017, Version of Record 27 October 2017.

论文官网地址：https://doi.org/10.1016/j.elerap.2017.04.004