Dr. Yue Han’s research span three areas: the optimization of crowdsourcing in creativity generation, the prediction of virality in social media, and the design of knowledge reuse in online communities. Crowdsourcing has been promoted by previous studies as an alternative source of creativity for organizations. Yue’s research aims to identify the conditions under which the generic crowd is more/less creative. Leveraging online experiments and semantic analysis, Yue and her coauthors find that the crowd is more creative than professionals in solving generalist tasks and less innovative than professionals in solving specialist tasks. However, crowd can gain relevant knowledge by exposing themselves to each other’s ideas and optimize the crowdsourcing outcomes.
Yue’s second research stream is the study of virality in social media platforms. Prior research on virality primarily focuses on two types of factors: content-based factors and creator-based factors. Yue and her coauthors aim to draw attention towards a relativity ignored set of constructs–the interactions between content characteristics and creator characteristics. Their paper suggests that adding nuanced content-creator interactions to the nomological network for virality will add conceptual richness and improve predictive validity of future studies.
The third research focus of Yue’s studies is knowledge reuse in online communities. Prior to Yue’s dissertation, little work focused on the importance and measurement of collective exploration in a design space for creative or complex tasks. In Yue’s research, she introduced a multivariate dispersion measurement from Biometrics to compare the coverage of collective exploration in a design space among different crowd groups. She proposed the importance of linking remixing–reusing work from self or others for new creations, with computer search algorithms, a novel approach to conduct a human-based innovation search in a design space for innovation. Based on this initial work, her studies focused on human-based remixing algorithms, which are used to organize crowd members to collectively explore a design space in a tree structure following the graph data structure searching algorithms in computer science such as breath-first search and depth-first search, and that work continues today.
About Dr. Yue Han
Yue Han is an Assistant Professor of Information Systems at the Madden School of Business, Le
Moyne College. Dr. Han holds a Ph.D. and an M.S from Stevens Institute of Technology. Her
research interests include crowdsourcing, collective intelligence, social media, and online
communities. Dr. Han has taught several courses at Le Moyne including Introduction to
Management Information Systems; Business Intelligence; Crowds, Social Media and Digital
Collaboration; Database Management Systems; and CNY SCORE Learning Initiative.
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Contact Dr. Han
Information Systems at Le Moyne College
Madden School of Business