Biography
Dr. Pratyush Nidhi Sharma is Associate Department Head and Associate Professor of Information Systems in the Department of Information Systems, Statistics and Management Science, in the University of Alabama’s Culverhouse College of Business. He received a PhD in Information Systems from the University of Pittsburgh, master's in Computer Science from the University at Buffalo, and bachelor's in Computer Science & Engineering from Visvesvaraya Technological University, India.
His interdisciplinary research interests focus on the development, application, and the impact of Information Systems. On the technology supply side, his work investigates how online collaboration communities can better develop technological artifacts such as open source software. On the demand side, his research investigates factors that affect individual and organizational adoption of information technologies, and the effect on user satisfaction and firm performance. In addition, he focuses on developing predictive-analytic tools to better utilize the strengths of the prediction-oriented approach (vis-à-vis the explanation-oriented approach) to create robust theory and policy. This work has helped introduce next-generation prediction metrics in composite-based path modeling.
Dr. Sharma has published in distinguished management research journals including the Journal of the Association for Information Systems, Journal of Retailing, Decision Sciences, Public Management Review, AIS Transactions on Human-Computer Interaction, Communications of the Association for Information Systems, Government Information Quarterly, Journal of Information Systems, Journal of Business Research, International Journal of Accounting Information Systems, and Journal of International Marketing. Dr. Sharma has also written several research-oriented book chapters and presented at premier conferences such as ICIS, AMCIS, Academy of Management, INFORMS, Academy of Marketing Science, IBM Frontiers in Service, and SCECR.
His research has been cited more than 2,000 times per Google Scholar. He was awarded a research grant by the Association of Certified Fraud Examiners (ACFE) Research Institute to build money laundering detection models in collaboration with Dr. Rachel Chung (William & Mary). His work on the development of predictive analytic techniques has been incorporated in the popular commercial statistical software, SmartPLS, and is available for broader use by academics and practitioners (for more information please see here & here). It has also been featured in Latest Thinking. He is the recipient of the William R. Darden Best Research Methodology Paper Award at the prestigious Academy of Marketing Science Annual Conference (2019).
He has served as an associate editor for ICIS, session chair at INFORMS, and is a member of the scientific advisory committee for the 2020 International Conference on Partial Least Squares Structural Equation Modeling.
Dr. Sharma has taught both graduate and undergraduate courses. He currently teaches MIS courses to Doctoral, Executive MBA, STEM/CREATE MBA, Traditional MBA, and MS-MIS students in the Manderson Graduate School at the Culverhouse College of Business. In the past, he has taught Database Design & Implementation, Concepts of Programming Languages, Excel Spreadsheet Modeling, and Introduction to Information Systems.
His interdisciplinary research interests focus on the development, application, and the impact of Information Systems. On the technology supply side, his work investigates how online collaboration communities can better develop technological artifacts such as open source software. On the demand side, his research investigates factors that affect individual and organizational adoption of information technologies, and the effect on user satisfaction and firm performance. In addition, he focuses on developing predictive-analytic tools to better utilize the strengths of the prediction-oriented approach (vis-à-vis the explanation-oriented approach) to create robust theory and policy. This work has helped introduce next-generation prediction metrics in composite-based path modeling.
Dr. Sharma has published in distinguished management research journals including the Journal of the Association for Information Systems, Journal of Retailing, Decision Sciences, Public Management Review, AIS Transactions on Human-Computer Interaction, Communications of the Association for Information Systems, Government Information Quarterly, Journal of Information Systems, Journal of Business Research, International Journal of Accounting Information Systems, and Journal of International Marketing. Dr. Sharma has also written several research-oriented book chapters and presented at premier conferences such as ICIS, AMCIS, Academy of Management, INFORMS, Academy of Marketing Science, IBM Frontiers in Service, and SCECR.
His research has been cited more than 2,000 times per Google Scholar. He was awarded a research grant by the Association of Certified Fraud Examiners (ACFE) Research Institute to build money laundering detection models in collaboration with Dr. Rachel Chung (William & Mary). His work on the development of predictive analytic techniques has been incorporated in the popular commercial statistical software, SmartPLS, and is available for broader use by academics and practitioners (for more information please see here & here). It has also been featured in Latest Thinking. He is the recipient of the William R. Darden Best Research Methodology Paper Award at the prestigious Academy of Marketing Science Annual Conference (2019).
He has served as an associate editor for ICIS, session chair at INFORMS, and is a member of the scientific advisory committee for the 2020 International Conference on Partial Least Squares Structural Equation Modeling.
Dr. Sharma has taught both graduate and undergraduate courses. He currently teaches MIS courses to Doctoral, Executive MBA, STEM/CREATE MBA, Traditional MBA, and MS-MIS students in the Manderson Graduate School at the Culverhouse College of Business. In the past, he has taught Database Design & Implementation, Concepts of Programming Languages, Excel Spreadsheet Modeling, and Introduction to Information Systems.