Professor of Management
Thomas J. Howatt Chair in Management
Management Information Systems
Ph.D., Information Systems, Carnegie Mellon University
M.S., Electrical and Computer Engineering, Carnegie Mellon University
M.Phil., Public Policy and Management, Carnegie Mellon University
B.E., Electrical and Electronics Engineering, REC
- Wu, J., Tawarmalani, M., and Kannan, K. (2019). Cardinality Bundles with SpenceMirelees Reservation Prices. Management Science, vol. 65 (4), 1455-1947.
- Khern-am-nuai, W., Kannan, K., and Ghasemkhani, H. (2018). Extrinsic versus Intrinsic Rewards to Participate in a Crowd Context: An Analysis of a Review Platform. Information Systems Research, vol. 29 (4), 871-892.
- Gupta, A., Kannan, K., and Sanyal, P. (2018). Research Commentary: Experimental Economics in Information Systems Research. MIS Quarterly, vol. 42 (2), 595-606.
- Hashim, M., Kannan, K., and Wegener, D. (2018). Nudging the Digital Pirate: A Central Role for Moral Obligations in Determining Intentions to Engage in Digital Piracy. Journal of Management Information Systems, vol. 30 934-963.
- Hashim, M., Kannan, K., and Maximiano, S (2017). Information Feedback, Targeting, and Coordination: An Experimental Study. Information Systems Research, vol. 28 (2), 289-308.
- Kannan, K., Rahman, M., and Tawarmalani, M (2016). Implications of Restricted Patch Distribution. Management Science, vol. 62 (11), 3161-3182.
- Sha, Z., Kannan, K., and Panchal, J (2015). Behavioral Experimentation and Game Theory in Engineering Systems Design. Journal of Mechanical Design, vol. 137 (5),
- Overby, E., and Kannan, K (2015). Reduced Search Costs Bidder Distribution Auctions. Management Science, vol. 61 (6), 1398-1420.
- Hashim, M., Kannan, K., Maximiano, S., and Rees, J (2014). Digital Piracy, Teens, and the Source of Advice: An Experimental Study. Journal of Management Information Systems, vol. 31 (2), 211-244.
- Wang, T., Kannan, K., and Rees, J (2013). The Textual Contents of Media Reports of Information Security Breaches and Profitable Short-Term Investment Opportunities. Journal of Organizational Computing and Electronic Commerce, vol. 23 (3), 200-223.
- Wang, T., Kannan, K., and Rees, J (2013). The Association between the Disclosure and the Realization of Information Security Risk Factors. Information Systems Research, vol. 24 (2), 201-218.
- Kim, A., Balachander, S., and Kannan, K (2012). Optimal Number of Slots in Search Auctions. Marketing Letters, vol. 23 (3), 851-868.
- Kannan (2012). Effects of Revelation Policies under Cost Uncertainty. Information Systems Research, vol. 23 (1), 75-92.
- Cason, T., Kannan, K., and Siebert, R (2011). An Experimental Study of Information Revelation Policies in Sequential Auctions. Management Science, vol. 57 (4), 667-688.
- Balachandar, Kannan, & Schwartz (2010). A Theoretical and Empirical Analyses of Alternate Auction Policies for Search Advertisements. Review of Marketing Science, vol. 7 (1),
- Kannan (2010). Declining Prices in Sequential Auctions with Complete Revelation of Bids. Economic Letters, vol. 108 (1),
- Kannan (2010). Effects of Revelation Policies under Cost Uncertainty. Information Systems Research,
- Greenwald, A., Kannan, K., and Krishnan, R. (2009). On Evaluating Information Revelation Policies in E-marketplaces: A Markov Decision Process Approach. Information Systems Research, vol. 20 (4),
- Tawarmalani, M., Kannan, K., and De, P. (2009). Allocating Objects in a Network of Caches: Centralized and Decentralized Analyses. Management Science, vol. 55 (1),
- Arora, A., Greenwald, A., Kannan, K. and Krishnan, R. (2007). Effects of Information Revelation Policies under Market Structure Uncertainty. Management Science, vol. 53 (8),
- Kannan, K., Rees, J., and Sridhar, S. (2007). Market Reactions to Information Security Breach Announcements: An Empirical Analysis. International Journal of Electronic Commerce, vol. forthcoming
- Kannan, K., & Telang, R. (2005). Market for Vulnerabilities? Think Again. Management Science, vol. 51 (5), 726-740.
How can we make sure that algorithms are fair?
Machine and human intelligences bring different strengths to the table. Researchers like me are working to understand how algorithms can complement human skills while at the same time minimizing the liabilities of relying on machine intelligence.
When Donald Trump defeated Hillary Clinton in the 2016 U.S. presidential election, many voters and political pundits were surprised by the outcome. But the campaign strategies used by the opposing candidates were entirely predictable, says Karthik Kannan, a professor at the Purdue University Krannert School of Management and an expert in big data who studies systems that exploit instincts and biases to nudge human behavior.
When the augmented reality (AR) game Pokémon Go made its debut in 2016, it quickly became the most popular mobile game in U.S. history with more than 20 million active users daily. It’s now at the top of the menu in an industry that last year generated more than $1 billion in revenues. According to research from Purdue University’s Krannert School of Management, however, tech companies aren’t the only businesses getting a taste of the profits.
The obvious strategy for a clothing retailer is to have as much product on the sales floor as possible to yield high sales. For women, however, that turns out to be a counterproductive strategy, says Purdue researcher Karthik Kannan, a professor of management information systems.
Design for Instincts
Karthik Kannan Design for Instincts