Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers’ level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers’ perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers’ trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the time-order effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets.
- BS HK
- PhD Arizona
Michael Chau is currently an Associate Professor in the HKU Business School and the Warden of Lee Chi Hung Hall at the University of Hong Kong. He received a Ph.D. degree in Management Information Systems from the University of Arizona and a B.Sc. degree in Computer Science (Information Systems) from the University of Hong Kong. His research interests include business analytics, artificial intelligence, web mining, social media, electronic commerce, healthcare informatics, and security informatics.
He is the author of more than 140 articles and has been ranked in a research productivity study as the #14 most productive researcher in the field of information science in the period 1998-2007.
Michael has taught a wide range of courses at HKU, including database management, computer networking, business analytics and big data, artificial intelligence, project management, spreadsheet modelling, and computer programming.
- Business analytics
- Artificial intelligence
- Web mining and social media
- Electronic commerce
- Healthcare informatics
- Security informatics
- Deng, B. and Chau, M. “The Effect of the Expressed Anger and Sadness on Online News Believability,” Journal of Management Information Systems (JMIS), 38(4), pp. 959-988, 2021.
- Chau, M., Li, W., Yang, B., Lee, A., and Bao, Z. “Incorporating the Time-Order Effect of Feedback in Online Auction Markets through a Bayesian Updating Model,” MIS Quarterly (MISQ), 45(2), pp. 985-1006, 2021.
- Chau, M., Li, T. M. H., Wong, P. W. C., Xu, J. J., Yip, P. S. F., and Chen, H. “Finding People with Emotional Distress in Online Social Media: A Design Combining Machine Learning and Rule-based Classification,” MIS Quarterly (MISQ), 44(2), pp. 933-955, 2020.
- Xu, J. J. and Chau, M. “Cheap Talk? The Impact of Lender-Borrower Communication on P2P Lending Outcomes,” Journal of Management Information Systems (JMIS), 35(1), pp. 53-85, 2018.
- Xu, J. J., Chau, M., and Tan, B. “The Development of Social Capital in the Collaboration Network of Information Systems Scholars,” Journal of the Association for Information Systems (JAIS), 15(12), pp. 835-859, 2014.
- Fang, X., Hu, P. J., Chau, M., Hu, H., Yang, Z., and Sheng, O. R. L. “A Data-Driven Approach to Measure Web Site Navigability,” Journal of Management Information Systems (JMIS), 29(2), pp. 173-212, 2012.
- Chau, M. and Xu, J. “Business Intelligence in Blogs: Understanding Consumer Interactions and Communities,” MIS Quarterly (MISQ), 36(4), pp. 1189-1216, 2012.
- Chau, M. “Visualizing Web Search Results Using Glyphs: Design and Evaluation of a Flower Metaphor,” ACM Transactions on Management Information Systems (ACM TMIS), 2(1), pp. 1-27, 2011.
- Cheng, R., Chau, M., Garofalakis, M., and Yu, J. X. “Mining Large Uncertain and Probabilistic Databases,” IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 22(9), 1201-1202, 2010.
- Roussinov, D. and Chau, M. “Combining Information Seeking Services into a Meta Supply Chain of Facts,” Journal of the Association for Information Systems (JAIS), 9(3), 175-199, 2008.
- Xu, J., Wang, G., Li, J., and Chau, M. “Complex Problem Solving: Identity Matching Based on Social Contextual Information,” Journal of the Association for Information Systems (JAIS), 8(10), 525-545, 2007.
- Schroeder, J., Xu, J., Chen, H., and Chau, M. “Automated Criminal Link Analysis Based on Domain Knowledge,” Journal of the American Society for Information Science and Technology (JASIST), 58(6), 842-855, 2007.
- Chen, H., Chung, W., Xu. J., Wang, G., Qin, Y., and Chau, M. “Crime Data Mining: A General Framework and Some Examples,” IEEE Computer, 37(4), 50-56, 2004.
Dr. Chau’s research has appeared in more than 150 publications. Please refer to https://pweb.fbe.hku.hk/~mchau/publications.html for a complete list.
- AIS Sandra Slaughter Service Award
- HKU Outstanding Young Researcher Award
- HKU Faculty Knowledge Exchange Award
- IEEE ISI Best Conference Paper Runner-up (2016)
- PACIS Best Conference Paper (2006)
- Keynote speaker/Invited speaker at more than 10 conferences and workshops
- AIS Sandra Slaughter Service Award (2016)
Dr Michael Chau’s study uses the machine learning and rule-based classification components of artificial intelligence to find people online who are in emotional distress. Using these techniques may help health professionals locate struggling individuals faster, getting help to them before it’s too late.
Many people face problems of emotional distress. Early detection of high-risk individuals is the key to prevent suicidal behavior. There is increasing evidence that the Internet and social media provide clues of people’s emotional distress. In particular, some people leave messages showing emotional distress or even suicide notes on the Internet. Identifying emotionally distressed people and examining their posts on the Internet are important steps for health and social work professionals to provide assistance, but the process is very time-consuming and ineffective if conducted manually using standard search engines. Following the design science approach, we present the design of a system called KAREN, which identifies individuals who blog about their emotional distress in the Chinese language, using a combination of machine learning classification and rule-based classification with rules obtained from experts. A controlled experiment and a user study were conducted to evaluate system performance in searching and analyzing blogs written by people who might be emotionally distressed. The results show that the proposed system achieved better classification performance than the benchmark methods and that professionals perceived the system to be more useful and effective for identifying bloggers with emotional distress than benchmark approaches.
The health and medical needs of an ageing population mean Hong Kong has to store up increasing amounts of fresh blood products. This year alone, the Hong Kong Red Cross Blood Transfusion Service (BTS) needs 3.4 per cent more units of whole blood, plasma and platelets than it collected last year, when demand increased by 4.4 per cent. Added to that growing demand is the fact that donations from first-time donors have fallen, in part because the revised school curriculum means Form 7 students are now spread out in universities and the workplace, rather than easily targeted in a school. So where can additional donors be found? To help find an answer, the BTS has been turning to Dr Michael Chau, Associate Professor in the Faculty of Business and Economics, whose research focuses on data mining and analysis.
It’s no secret that private bloggers tend to speak freely in their conversations about firms and their products. But how can firms tap into blogs and build their Business Intelligence?