Yan Huang (黄彦)
Status Update I am a job market candidate. I will attend ICIS/ WISE 2012 (December 15-19, Orlando, FL)		Ph.D. Candidate in Information Systems H. John Heinz III College Carnegie Mellon University Email: yanhuang@andrew.cmu.edu Voice: 412-268-7848 (O); 412-608-3721 (M) Web: andrew.cmu.edu/user/yanhuang Address: Carnegie Mellon University HBH 3003, 5000 Forbes Avenue Pittsburgh, PA 15213
Teaching Research CV(.pdf,.html) Links Last Updated on October 18, 2012	Biography I am a fourth year doctoral candidate in Information Systems at Heinz College, Carnegie Mellon University. My research concentration is the economics of information technology (IT). I am a quantitative modeler interested in problems related to technological innovation. My research addresses the problems facing practitioners as they leverage crowdsourcing and social media internally and externally to improve their productivity and profitability. My work is among the first to look into the economic processes that shape participants’ behavior in various forms of enterprise social media and crowdsourcing initiatives. In my research, I combine economic theories, econometrics, structural modeling and Bayesian modeling methodologies and delve into individual decision-making mechanism in different social media contexts. Decision primitives estimated from the data are used to simulate effects of changes in the platform design and introduction of new policies, based on which I recommend policies that should lead to greater effectiveness in enterprise use of social media.  In my job market paper "Crowdsourcing new product ideas under consumer learning" (under preparation for second round review at Management Science), I study crowdsourced ideation, an innovative approach that firms use to solicit new product ideas directly from their consumers. In such initiatives, individuals can contribute ideas and vote on others’ ideas. The firm then decides which ideas to implement. Although popular, crowdsourced ideation faces increasing criticism as the number of ideas generated often declines over time, and the idea implementation rates are quite low. Using a structural model, I show that the downward trend in the number of ideas contributed in fact reflects market efficiency rather than failure. I find that individuals tend to significantly underestimate the costs to the firm for implementing their ideas but overestimate the potential of their ideas in the initial stages of the crowdsourcing process. Hence, the “idea market” is initially overcrowded with ideas that are less likely to be implemented. However, individuals learn about both their abilities to come up with high-potential ideas and the cost structure of the firm from their peers’ voting and the firm’s response to contributed ideas. Individuals learn rather quickly about their abilities to come up with high-potential ideas, but their learning of the firm’s cost structure is quite slow. Over time, contributors of low-potential ideas eventually drop out, while the high-potential idea contributors remain active, and the crowdsourcing market becomes more efficient.