- Doctor of Business Administration (DBA), Marketing, Harvard Business School
- B.S., Massachusetts Institute of Technology (MIT)
Dr. Tuan Q. Phan is an Associate Professor in Marketing and Innovation & Information Management at the University of Hong Kong (HKU), Director of the Representative Office of HKU in Vietnam, Associate Director of the Asia Case Research Centre, and founding and current President of Vietnam Association for Information Systems. His research interests in social networks, privacy, education, digital economies, mobility, and retailing uses large and population-size datasets to investigate consumer insights across disciplines using econometrics, social network analysis, machine learning, natural language processing, image analysis, artificial intelligence, GIS, simulations, game theory, and experimental methods. He received a doctorate from the Harvard Business School and undergraduate at the Massachusetts Institute of Technology (MIT).
Dr. Phan has published in top academic journals such as the Proceedings of the National Academy of Sciences (PNAS), Information Systems Research (ISR), Marketing Science, Journal of Marketing Research (JMR), IEEE Transactions on Engineering Management, Journal of the Association of Information Systems (JAIS) as well as leading cases through Harvard Business School Publishing (HBSP) and the Asia Case Research Center (ACRC). He has also published several book chapters, and edited a book on information systems in Vietnam. Prior to HKU, he was at the National University of Singapore (NUS) Department of Information Systems & Analytics in the School of Computing, and in the Department of Analytics & Operations at the Business School where he received tenure. Dr. Phan was the research lead at the NUS Institute of Applied Learning Sciences and Educational Technology where he lead a team to conduct research on education. He has directly advised 6 PhD students, and a committee member for 11 students. Dr. Phan is also an entrepreneur, investor, expert witness to Singapore Prime Minister Lee Hsien Loong, board member, and frequently consults industry leaders. He was also ranked in the top 10 in competitive ballroom dancing in North America.
- Big Data Consumer Analytics
- Technology Innovations in Retail Banking & Consumer Finance
- Marketing Analytics
- Social Network & Social Media
- FinTech & Retail Banking
- Retailing and E-commerce
- Computational Social Science
- Phan, Tuan Q. and Sandy Ong. “Puma’s Maya: Southeast Asia’s First Virtual Influencer.” Asia Case Research Centre, 2021. https://www.acrc.hku.hk/Case/Detail/1081
- Phan, Tuan Q. and Minyi Huang. “FinVolution.” Asia Case Research Centre, 2021. https://www.acrc.hku.hk/Case/Detail/1077
- Ferreira, Kris, Joel Goh, Dawn Lau, and Tuan Phan. “GHN and AhaMove: Last-Mile Delivery in Vietnam.” Harvard Business Publishing, June 4, 2019. https://hbsp.harvard.edu/product/619051-PDF-ENG
- Han, Yoon, Khim Yong Goh, Seung Hyun Kim, and Tuan Q. Phan. “The Effect of Ad Image’s Sentiment Scores and Mobile Device Attributes on Mobile Ad Response Behavior.” IEEE Transactions on Engineering Management (forthcoming). https://doi.org/10.1109/TEM.2022.3157125.
- Oh, Hyelim, Khim Yong Goh, and Tuan Q. Phan. “Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing.” Information Systems Research (forthcoming). https://doi.org/10.1287/isre.2022.1112.
- Xu, Haifeng, Tuan Q. Phan, and Bernard C. Y. Tan. “Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction.” Journal of the Association for Information Systems 23, no. 3 (2022): 806-837. https://doi.org/10.17705/1jais.00735.
- Mou, Jian, J. Christopher Westland, Tuan Q. Phan, and Tianhui Tan. “Microlending on Mobile Social Credit Platforms: An Exploratory Study Using Philippine Loan Contracts.” Electronic Commerce Research 20, no. 1 (2020): 173-196. https://doi.org/10.1007/s10660-019-09391-2.
- Bhattacharya, Prasanta, Tuan Q. Phan, Xue Bai, and Edoardo M. Airoldi. “A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks.” Information Systems Research 30, no. 1 (2019): 117-132. https://doi.org/10.1287/isre.2018.0790.
- Phan, Tuan Q., and David Godes. “The Evolution of Influence Through Endogenous Link Formation.” Marketing Science 37, no. 2 (2018): 259-278. https://doi.org/10.1287/mksc.2017.1077.
- Chen, Xi, Ralf Van Der Lans, and Tuan Q. Phan. “Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies.” Journal of Marketing Research 54, no. 2 (2017): 187-201. https://doi.org/10.1509/jmr.12.0511.
- Cavusoglu, Huseyin, Tuan Q. Phan, Hasan Cavusoglu, and Edoardo M. Airoldi. “Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook.” Information Systems Research 27, no. 4 (2016): 848-879. https://doi.org/10.1287/isre.2016.0672.
- Phan, Tuan Q., and Edoardo M. Airoldi. “A Natural Experiment of Social Network Formation and Dynamics.” Proceedings of the National Academy of Sciences 112, no. 21 (2015): 6595-6600. https://doi.org/10.1073/pnas.1404770112.
- 2022-2023, HKD 9.5M, Collaborative Research Fund of HK, Second Round One-off CRF COVID-19 and Novel Infectious Disease (NID) Research Exercise, #C7105-21GF, Co-Principal Investor, “Spatial Exposure Notification”
- 02/2021-02/2023, HKD 4.5M, Collaborative Research Fund of HK, One-off CRF Coronavirus Disease and Novel Infectious Disease (NID) Research Exercise, #CRF7105-20GF, Co-Principal Investor, “Leveraging Mobility and Digital Trace Big Data to Model COVID-19 Risk & Socio-Economic Recovery”
- 04/2020-04/2021, RMB 500,000, Natural Science Foundation of China, Special Grant of COVID-19, #72042009, Co-Investigator, “Monitoring, early warning and response to major infectious diseases based on big data.”
- 02/2020-02/2022, HKD 150,000, HKU University Research Committee, “Investigating Consumer Behavior in Online & Offline Social Networks: an Empirical and Methodological Approach in Marketing, Education, Mental Health, and FinTech”
- 2018, Health Information Traceability Foundation Awards, Global Finalist for best proposal for use of blockchains & healthcare, Zurich, Switzerland
- 2016, International Conference on Information Systems (ICIS), Dublin, Ireland. Most Innovative Research-In-Progress Paper Award
- 2014, International Conference on Information Systems (ICIS), Auckland, New Zealand. Best Research-in-Progress Award, runner up
As coronavirus rages across the globe, online business is still booming, with data and analytics driving this trend. People now marooned at home for the foreseeable future are finding the daily goods they need from online stores, solace in conferencing apps, and entertainment provided by streaming platforms. The world is revolving increasingly online with lockdowns in place, and data is being even further highlighted as an undisputable source of wealth.
With the rapid growth of online social network sites (SNSs), it has become imperative for platform owners and online marketers to quantify what factors drive content production on these platforms. Previous research identified challenges in modeling these factors statistically using observational data, where the key difficulty is the inability of conventional methods to disentangle the effects of network formation and network influence on content generation from the subsequent feedback effect of newly generated content on network structure. In this paper, we adopt and enhance an actor-oriented continuous-time statistical model that enables the joint estimation of the coevolution of the users’ social network structure and of the amount of content they produce, using a Markov chain Monte Carlo–based simulation approach. Specifically, we offer a method to analyze nonstationary and continuous-time behavioral data, typically recorded in social media ecosystems, in the presence of network effects and other observable and unobservable user-specific covariates. The proposed method can help disentangle network effects of interest from feedback effects on the network. We apply our model to social network and public posting data over six months to find that (1) users tend to connect with others that have similar posting behavior; (2) however, after doing so, these users tend to diverge in their posting behavior, and (3) peer influence effects are sensitive to the strength of the posting behavior. More broadly, the proposed method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. Our results lead to insights and recommendations for SNS platform owners on how to sustain an active and viable community.