| Yan Huang
密歇根大学助理教授
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【主讲】密歇根大学助理教授Yan Huang
【时间】2015年6月17日（周三）10.30-12.00
Yan Huang is an Assistant Professor of Technology and Operations in the Stephen M. Ross School of Business at the University of Michigan. She received her Ph.D. in Information Systems and Management from Carnegie Mellon University and holds a Bachelor’s degree from Tsinghua University.
Yan is interested in studying how firms can use information technology innovatively in their business processes. She is especially interested in topics related to crowdsourcing, social media, business analytics, data-driven decision making. Yan's recent research focuses on addressing the challenges facing practitioners as they leverage crowdsourcing and social media internally and externally to improve productivity and profitability. In her research, she applies quantitative methods to analyze the economic processes that shape participants’behavior in various forms of enterprise social media and crowdsourcing initiatives, and recommends policies that should lead to greater effectiveness in enterprise use of social technologies.
Yan Huang, Assistant Professor, University of Michigan: Crowdsourcing New Product Ideas Under Consumer Learning
【Speaker】Yan Huang, Assistant Professor, University of Michigan
【Title】Crowdsourcing New Product Ideas Under Consumer Learning
【Time】Wednesday, June 17, 10.30-12.00
【Venue】Room 453, Weilun Building, Tsinghua SEM
【Abstract】We propose a dynamic structural model that illuminates the economic mechanisms shaping individual behavior and outcomes on crowdsourced ideation platforms. We estimate the model using a rich data set obtained from IdeaStorm.com, a crowdsourced ideation initiative affiliated with Dell. We find that, on IdeaStorm.com, 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. Therefore, 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 as well as the cost structure of the firm from peer voting on their ideas and the firm's response to contributed ideas. We find that individuals learn rather quickly about their abilities to come up with high-potential ideas, but the learning regarding the firm's cost structure is quite slow. Contributors of low-potential ideas eventually become inactive, whereas the high-potential idea contributors remain active. As a result, over time, the average potential of generated ideas increases while the number of ideas contributed decreases. Hence, the decrease in the number of ideas generated represents market efficiency through self-selection rather than its failure. Through counterfactuals, we show that providing more precise cost signals to individuals can accelerate the filtering process. Increasing the total number of ideas to respond to and improving the response speed will lead to more idea contributions. However, failure to distinguish between high- and low-potential ideas and between high- and low-ability idea generators leads to the overall potential of the ideas generated to drop significantly.
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