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.

Additional Metadata
Persistent URL dx.doi.org/10.1287/mnsc.2018.3120, hdl.handle.net/1765/111950
Journal Management Science
Citation
Giesecke, K., Liberali, G, Nazerzadeh, H., Shanthikumar, G., & Teo, C.P. (2018). Special Issue on Data-Driven Prescriptive Analytics. Management Science, 64(6), 2972–2972. doi:10.1287/mnsc.2018.3120