QIAO Dandan

COM2-04-15

660 13789

www.qiaodandan.com

Assistant Professor

Ph.D. (Tsinghua University)
B.S. (Beijing University of Posts and Telecommunications)

QIAO Dandan is an assistant professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore (NUS). Prior to joining NUS, She earned her PhD in Information Systems from Tsinghua University and visited the University of Texas at Austin for two years. Two signature topics in her work include incentivized reviews and darkweb economies. Her research can be summarized as three key perspectives, which mainly focus on digital platform design and innovation. • The first stream involves analyzing individual behavior to assess the influence of various UGC and FGC artifacts, such as designing incentive policies in prosocial contexts and adding question-and-answer features in e-commerce settings. • The second stream delves into the societal impact of platform designs, emphasizing fostering greater social well-being. This includes endeavors such as reducing hate crimes through the sharing gigs and enforcement of crime on the dark web. • The third stream focuses on artificial intelligence and seeks to create practical predictive analytics tools to support platform businesses. This involves extracting representative information and making predictions based on persuasion techniques. Methodologically, her research combines econometric analysis with computational methods, aiming to not only provide causal insights but also actionable strategies for digital platform design.

RESEARCH AREAS

Digital Transformation, Platforms & Innovation

Data Science & Business Analytics

RESEARCH INTERESTS

Economics of Information Systems
Economics of Dark Web
Online Altruism and Crowd Wisdom
Competitive Intelligence and Predictive Analytics

RESEARCH PROJECTS

Decoding Persuasion on Crowdfunding Platforms

This research proposes a novel prediction framework based on persuasion theory to enhance crowdfunding success predictions. It aims to identify persuasive elements in project descriptions and improve the prediction accuracy so as to provide guidance for crowdfunding project initiators. The study integrates computational methods and deep learning to not only boost prediction performance but also offer interpretable insights, contributing to information systems and crowdfunding design.

Data Science & Business Analytics, Computational Social Science

Understanding Online Contribution: Impact Evaluation and Crowd Wisdom Extraction

With the massive amount of online contributions shaping individual and organizational decision-making, this project delves into utilizing and understanding this valuable resource. By combining econometrics and natural language processing, the research seeks innovative insights to leverage these vast information pools and assess their impacts on commerce and society.

Data Science & Business Analytics

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

Qiu, L., Qiao, D., Tan, B.C.Y., Whinston, A.B., (2023) Leading the Horse to Water? Investigating the Impact of Ride-hailing Services on Hate Crimes. Production and Operations Management. Forthcoming.
Chan, J., He, S., Qiao, D., and Whinston, A. (2023) Shedding Light on the Dark: Would Police Arrests Exert a Deterrence Effect on Darknet Transactions. Information Systems Research. Forthcoming
Khern-am-nuai, W., Ghasemkhani, H., Qiao, D., and Kannan, K. (2023) The Impact of Online Q&As on Product Sales: The Case of Amazon Answer. Information Systems Research. Forthcoming.
Qiao, D., Rui, H. (2022). Text Performance on Vine Stage? The Effect of Incentive on Product Review Text Quality. Information Systems Research. Forthcoming.
Qiao, D., Lee, S. Y., Whinston, A. B., & Wei, Q. (2021). Mitigating the Adverse Effect of Monetary Incentives on Voluntary Contributions Online. Journal of Management Information Systems, 38(1), 82-107.
Wang, L., Zhang, J., Chen, G., & Qiao, D. (2021). Identifying comparable entities with indirectly associative relations and word embeddings from web search logs. Decision Support Systems, 141, 113465.
Qiao, D., Lee, S. Y., Whinston, A. B., & Wei, Q. (2020). Financial incentives dampen altruism in online prosocial contributions: A study of online reviews. Information Systems Research, 31(4), 1361-1375.
Guo, X., Wei, Q., Chen, G., Zhang, J., & Qiao, D. (2017). Extracting Representative Information on Intra-Organizational Blogging Platforms. MIS Quarterly, 41(4), 1105-1127.
Wei, Q., Qiao, D., Zhang, J., Chen, G., & Guo, X. (2016). A novel bipartite graph based competitiveness degree analysis from query logs. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(2), 1-25.
Qiao, D., Zhang, J., We, Q., Chen, G., 2017. Finding Competitive Keywords from Query Logs to Enhance Search Engine Advertising. Information & Management, 54(4), pp.531-543.