Description
Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting.
Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.
Reviews and Testimonials
Researchers in business analytics, management, and other business fields describe methods for analyzing and predicting productivity and performance. Their topics include structural equation modeling algorithm and its application in business analytics, new product development and manufacturability techniques and analytics, analytics overuse in advertising and promotion budget forecasting, mastering business process management and business intelligence in global business, a conceptual and pragmatic review of regression analysis for predictive analytics, and an analytical employee performance evaluation in office automation and information systems.
– Protoview Reviews
Author's/Editor's Biography
Madjid Tavana (Ed.)
Madjid Tavana is Professor and Distinguished Chair of Business Analytics at La Salle University, where he serves as Chairman of the Business Systems and Analytics Department. He also holds an Honorary Professorship in Business Information Systems at the University of Paderborn in Germany. Dr. Tavana is Distinguished Research Fellow at the Kennedy Space Center, the Johnson Space Center, the Naval Research Laboratory at Stennis Space Center, and the Air Force Research Laboratory. He was recently honored with the prestigious Space Act Award by NASA. He holds an MBA, PMIS, and PhD in Management Information Systems and received his Post-Doctoral Diploma in Strategic Information Systems from the Wharton School at the University of Pennsylvania. He has published 12 books and over 250 research papers in international scholarly academic journals. He is the Editor-in-Chief of
International Journal of Applied Decision Sciences,
International Journal of Management and Decision Making,
International Journal of Communication Networks and Distributed Systems,
International Journal of Knowledge Engineering and Data Mining,
International Journal of Strategic Decision Sciences, and
International Journal of Enterprise Information Systems.
Kathryn Szabat (Ed.)
Kathryn A. Szabat is Associate Professor in the Business Systems and Analytics Department at La Salle University. Her instructional responsibilities include teaching of business statistics and management science to undergraduate and MBA students. Her current interests include promoting the inclusion of business analytics in business school curriculums and the development of analytical capabilities of business students. She is actively involved in several academic and professional associations. She received her PhD in Statistics, with cognate field in Operations Research, from the Wharton School of University of Pennsylvania.
Kartikeya Puranam (Ed.)
Kartikeya Puranam is an Assistant Professor of Business Systems and Analytics at La Salle University. He received his PhD in Supply Chain Management from Rutgers Business School. He received his Master’s and bachelor’s degrees in Mechanical Engineering from the Indian Institute of Technology in Bombay. His research interests include bidding strategies in auctions, learning in sequential auctions, inventory management, marketing and operations interface, Markov chains and Markov decision processes, and supply chain management. He has published in
Operations Research Letters and
European Journal of Operational Research.