Carnegie Bosch Institute Junior Chair and Associate Professor of Business Technologies
David A. Tepper School of Business
Carnegie Mellon University
Pittsburgh, PA 15213
Param Vir Singh is Carnegie Bosch Junior Chair and Associate Professor of Business Technologies at the David A Tepper School of Business, Carnegie Mellon University. Professor Singh investigates the use of social technologies (such as social media, crowdsourcing, blogging, enterprise 2.0, social networks, open source) to solve business problems. Specifically, he models user behavior on social technologies to provide policy and design implications to foster innovation and knowledge sharing.
Profressor Singh is recipient of Information System Society's Sandra slaughter early career award. He is Associate editor for Management Science (2015- ) and Information Systems Research (2013- ). He has served as the co-chair for the Conference of Information Systems and Technology, 2012, the Workshop on Information Systems and Economics, 2012, and the first and second Workshops on Structural Modeling Applications of Research on Technology (2014-15). He has served as the Information Systems Society cluster chair for the Informs Annual Meeting, 2011, as an Associate Editor for the International Conference on Information Systems (2011-14) as program committee member for Conference on Information Systems and Technology (2009, 2010, 2011) and ACM Conference on Electronic Commerce 2009, and as session chair for CORS-Informs 2009, and Informs Annual Meeting 2009. Professor Singh holds a PhD in Business Administration from the Foster School of Business. His research has appeared in leading business journals such as Management Science, Marketing Science, Information Systems Research, Organization Science, and Management Information Systems Quarterly.
Prof Singh's studies on blogs have addressed important questions regarding governance of employee blogs by firms. His research shows that prohibiting employees from posting content that criticizes the firm or promotes the rivals, or posting non work related content or encourgaing lurkers to blog are suboptimal policies with negative consequences for organizations. His work on crowdsourcing ideation provides policies for firms to govern their crowdsourcing initiatives for getting highly valuable new product ideas from consumers. He is presently investigating how to optimally design crowdsourcing contests for innovation. His work on open source has highlighted inefficiencies in the choice of licensing options and using social network methods have provided guidelines on successful team formations. Professor Singh's work on social networks have highlighted dynamics in network formation in online communities. He shows why certain network structures evolve (core-periphery) and how to design systems to make salient structures that discourage knowledge sharing. His work on big data provides methods for predicting demand by employing real time large scale social media data from diverse sources such as Twitter, Google Trends, Wikipedia, Review Sites and Blogs.
Much of his work uses big data sources such as social networks, blogs, online help forums, and online crowdsourcing platforms to provide a fine grained and rich analysis. To deal with and benefit from the volume and variety of data, for methodology Professor Singh employs a variety of skills including structural models, text mining, social networks, Bayesian methods and reduces form analysis. Professor Singh is an early proponent for the use of structural modeling methods to answer policy and system design questions in the field of Information Systems.