Dr. Pratyush Nidhi Sharma is the H. Taylor Morrissette Endowed Faculty Fellow, 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 was selected as a Fellow for the University of Alabama’s prestigious 2024-25 Leadership U program and received the 2025 Manderson MBA Student Impact Teaching Award. He will also serve as the director of the forthcoming AI Lab at the Institute of Data Analytics.

Dr. Sharma 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.

Dr. Sharma’s interdisciplinary research encompasses several cutting-edge areas. His recent work investigates the impact of artificial intelligence on student learning outcomes, software design, human-AI collaboration, and associated compliance and ethical issues. Additionally, his research examines the development of information systems artifacts through online collaboration communities such as open-source software production. Dr. Sharma also investigates the impact of information systems adoption and use on both individuals and firms, covering digital marketing (social media, e-commerce, mobile-commerce), technology-enabled positive outcomes (such as satisfaction, trust, and knowledge sharing), and negative outcomes (including occupational fraud and money laundering). 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. His recent article proposes the EP-Mixed framework for enhancing the replicability of MIS research using predictive analytic techniques.  

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, International Journal of Information Management,Journal of Information Systems, Journal of Business Research, and others. 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 3,000 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). His paper with PhD students, Sethu Das and Heba Taer, was nominated in the top 25% completed papers award category at AMCIS 2024. He has presented his research at several globally-renowned universities such as Ludwig Maximilians University in Munich, Hamburg University of Technology, and Trinity College Dublin.

Dr. Sharma is also a regular peer-reviewer for top journals in the field. He has served as an associate editor for ICIS, session chair at INFORMS, and was a member of the scientific advisory committee for the 2020 International Conference on Partial Least Squares Structural Equation Modeling. He is a member of the core scientific committee of the 2025 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. He has played a key role in leading an extensive overhaul of the MIS Ph.D. curriculum by introducing several new MIS Ph.D. seminars. He also helped develop the AI for Leaders Badge course for the Executive-MBA program. In the past, he has taught Database Design & Implementation, Concepts of Programming Languages, Excel Spreadsheet Modeling, and Introduction to Information Systems.

As Associate Department Head for the Information Systems Area, Dr. Sharma assists the Department Head, Dr. Jose Dula, in several key responsibilities, including planning and preparing course schedules, developing faculty-to-course assignments, allocating teaching assistants, curriculum administration (such as catalog updates and curriculum reviews), assessment of learning reports, conducting annual peer teaching evaluations, handling journal petitions, coordinating new faculty hiring, addressing MIS-specific strategic issues, and planning graduate student lines and doctoral student support.

Dr. Sharma is actively involved in several key service roles within the University of Alabama. He serves on the Faculty Executive Board (2023-2025), Dean Kay Palan’s AI Research Committee, the “Cultures and Societies” subcommittee of the General Education Core Curriculum Implementation Committee, and the Culverhouse Strategic Task Committee for Improving and Measuring Teaching. He has chaired search committees for MIS tenure-track and clinical professor positions and contributed to multiple committees, including the MIS Master’s Program Committee, ISM Strategic Committees on Collaboration and Faculty & Student Research Impact, the MIS598 Research Paper Committee, and the Doctoral Admissions Committee.