Combining machine-based and econometrics methods for policy analytics insights

Kauffman, R, Kim, K, Lee, S, Hoang, P and Ren, J 2017, 'Combining machine-based and econometrics methods for policy analytics insights', Electronic Commerce Research and Applications, vol. 25, pp. 115-140.

Document type: Journal Article
Collection: Journal Articles

Title Combining machine-based and econometrics methods for policy analytics insights
Author(s) Kauffman, R
Kim, K
Lee, S
Hoang, P
Ren, J
Year 2017
Journal name Electronic Commerce Research and Applications
Volume number 25
Start page 115
End page 140
Total pages 26
Publisher Elsevier BV
Abstract Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well as individual consumer, social and public issues. Recent developments and shifts in the scientific study of technology-related phenomena and Social Science issues in the presence of historically-large datasets prompt new forms of research inquiry. They include blended approaches to research methodology, and more interest in the production of research results that have direct application to industry contexts. This article showcases the methods shifts and several contemporary applications. They discuss: (1) feedback effects in mobile phone-based stock trading; (2) sustainability of top-rank chart popularity of music tracks; (3) household TV viewing patterns; and (4) household sampling and purchases of video-on-demand (VoD) services. The range of applicability of the ideas goes beyond the scope of these illustrations, to include issues in public services, healthcare, product and service deployment, public opinion and elections, electronic auctions, and travel and tourism services. In fact, the coverage is as broad as for-profit and for-non-profit, private and public, and governmental and non-governmental institutions.
Subject Econometric and Statistical Methods
Marketing Management (incl. Strategy and Customer Relations)
Consumer-Oriented Product or Service Development
Keyword(s) Causality
Computational Social Science
Data analytics
Empirical research
Fusion analytics
Music popularity
Policy analytics
Stock trading
TV viewing
Video-on-demand (VoD)
DOI - identifier 10.1016/j.elerap.2017.04.004
Copyright notice © 2017 Published by Elsevier B.V.
ISSN 1567-4223
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
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