My recent research applies data and analytics to product and service innovation. A stylized process for innovation and design has three steps: identify a problem, generate a solution, and iteratively test the solution. Data has long played a role in step three, iterative testing. Recent advances in artificial intelligence bring data and analytics into step two, transforming products and services. My research focuses on step one, developing analysis tools to mine what people say and do to reveal problems as opportunities to innovate.
Service innovation.
The automation and digitization of human activity creates a stream of digital exhaust. By analyzing patterns in these activity streams, we can discover new opportunities and define optimal performance to prioritize and select which opportunities to pursue first. This recent stream of research includes car sharing and transportation networks, consumer purchase patterns, and service process redesign in hospital emergency rooms.
Product innovation.
Whether designing new products or recommending existing products to new users, the basic underlying fundamentals are the same: Identify user needs and align those needs to actionable product or service specifications. In this stream of research, I develop and apply methods for mining user reviews to alternately learn user needs, product attributes, and/or relationships between the two.
Service innovation.
The automation and digitization of human activity creates a stream of digital exhaust. By analyzing patterns in these activity streams, we can discover new opportunities and define optimal performance to prioritize and select which opportunities to pursue first. This recent stream of research includes car sharing and transportation networks, consumer purchase patterns, and service process redesign in hospital emergency rooms.
- Kahlen, M., W. Ketter, T. Lee, and A. Gupta, "Optimal Prepositioning and Fleet Sizing to Maximize Profits for One-Way Transportation Companies," International Conference on Information Systems, December 2017.
- Lee, T. and K. Yoshihara, " Getting to Why: Semi-supervised Topic Modeling of Customer Purchase Histories," Workshop on Information Technology and Systems, December 2015.
- Lee, T. and E. Chen, "Mining Patient Orders to Rank Point of Care Tests in Emergency Department Operations," Workshop on Information Technology and Systems, Dec 2012.
- Lijie Song, Esther Chen, Nicole DeHoratius, Thomas Y. Lee, and Tava Olsen, "Point of Care Testing: Reducing Emergency Department Service Time and Waiting Time through Process Redesign," INFORMS 2012.
- Chen, E, DeHoratius, N, and T. Lee, “Point of Care Test Selection to Reduce Emergency Department Length of Stay,” University of Utah, Winter Information Systems Conference, 1 – 3 March 2012.
Product innovation.
Whether designing new products or recommending existing products to new users, the basic underlying fundamentals are the same: Identify user needs and align those needs to actionable product or service specifications. In this stream of research, I develop and apply methods for mining user reviews to alternately learn user needs, product attributes, and/or relationships between the two.
- Lee, T. and E. Bradlow, "Automated Marketing Research Using Online Customer Reviews," Journal of Marketing Research. 48(5) 2011. Finalist for the Paul E. Green Award for Best Paper in the Journal of Marketing Research, also presented at University of Utah, Winter Information Systems Conference, 10 – 12 March 2011.
- Chou, C., S. Kimbrough, and T. Lee, "Ideation for the Problem of Component Placing," IEEE International Conference on Industrial Engineering and Engineering Management, December 2010.
- Lee, T., "Adaptive Text Extraction for New Product Development," ASME Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2009.
- Lee, T., "Automatically Learning User Needs from Online Reviews for New Product Design," America Conference on Information Systems, 2009.
- Lee, T., S. Li, and R. Wei, "Needs-based Searching and Ranking Based on Customer Reviews," IEEE Conference on Electronic Commerce 2008.
- Lee, T., "Needs-based Analysis of Online Customer Reviews," International Conference on Electronic Commerce, August 2007.
- Lee, T., "Ontology Induction from Online Customer Reviews" Group Decision and Negotiation, 16(3) 2007.
- Lee, T. and E. Bradlow, "Automatic Induction of Conjoint Attributes Using Customer Reviews," INFORMS, 2005.
- Chen, G., S. Kimbrough, and T. Lee, "A note on automated support for product application discovery," Workshop on Information Technology and Systems, Dec 2004.
- Lee, T., "Use-Centric Mining of Customer Reviews," Workshop on Information Technology and Systems, Dec 2004.
