Deadline Extended: Management Science Special Issue in Data Driven Prescriptive Analytics
Call for Papers
Management Science
Special Issue on Data-Driven Prescriptive Analytics
New Submission Deadline: May 31, 2019
Data-science algorithms and models changed the way we search for information on products and services, make payments, procure and trade, and along the way, changed how firms use individual-level consumer data, and how business transactions are created, documented, regulated, and analyzed.
The emergence of data-intensive environments and algorithms also challenges management science research in many ways. It affects how knowledge is organized, produced, and assessed; how we search for answers to management questions, analyze information or validate insights and findings; and it challenges how we discover, design, describe, motivate, and replicate solutions. More importantly, it demands new theory to enable the development of models that exploit the type and volume of data available.
The Special Issue on Data-Driven Prescriptive Analytics seeks to publish papers that leverage predictive and descriptive analytics to (i) derive effective solutions to business problems, or (ii) develop new methods associated with the integration of data and models in decision problems. To be considered, papers should examine significant decision making problems in any of the application areas represented by Management Science such as finance, healthcare, marketing, and operations, including-but not limited to-financial technology, online advertising, consumer search, product recommendations, dynamic assortment and pricing, clinical trials, experimental design, portfolio management, website design, product ranking, and others. For more information, click here
Coeditors
Kay Giesecke, Stanford University, Stanford, California 94305, giesecke@stanford.edu;
Gui Liberali, Erasmus University, 3062 PA Rotterdam, Netherlands, liberali@rsm.nl;
Hamid Nazerzadeh, University of Southern California, Los Angeles, California 90089, nazerzad@marshall.usc.edu;
George Shanthikumar, Purdue University, West Lafayette, Indiana 47907, shanthikumar@purdue.edu;
Chung Piaw Teo, National University of Singapore, Singapore 119245, bizteocp@nus.edu.sg
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David Simchi-Levi
Professor of Engineering Systems
Massachusetts Institute of Technology
Cambridge MA
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