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Data-Enabled Analytics: DEA for Big Data by Joe Zhu 9783030751647

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Description

This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.

About the Author
Joe Zhu is a Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA), and his research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published and co-edited several books focusing on performance evaluation and benchmarking using DEA and developed the DEAFrontier software. With more than 130 journal articles, books, and textbooks along with over 20,000 Google Scholar citations, he is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index.

Vincent Charles is a Professor of Management Science and the Director of Research at Buckingham Business School, University of Buckingham, UK. He has published over 110 research works with Pearson Education, Cambridge ScholarsPublishing, UK and other publishers. His area of research includes productivity, quality, efficiency, effectiveness, competitiveness, innovation, and design thinking. He has the following industry exposure for research and consultancy purposes: advertising, agriculture & agribusiness, transportation, consumer products, banking, education, electronics, and manufacturing.


Book Information
ISBN 9783030751647
Author Joe Zhu
Format Paperback
Page Count 364
Imprint Springer Nature Switzerland AG
Publisher Springer Nature Switzerland AG

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