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.
Academic & Professional Qualification
- Doctor of Business Administration (DBA), Marketing, Harvard Business School
- B.S., Massachusetts Institute of Technology (MIT)
Dr. Tuan Q. Phan’s research uses large and population-size datasets and spans multiple disciplines including economics, marketing, consumer behavior, computer science, and statistics. His expertise covers various industries including FinTech, retail and e-commerce, logistics and transportation, social media, news and video media, technology and consumer products, and education. His research has been published in leading scientific and management journals including the Proceedings of the National Academy of Science (PNAS), Marketing Science, Journal of Marketing Research (JMR), and Information Systems Research (ISR). Dr. Phan is currently on leave from the National University of Singapore (NUS) in the Department of Information Systems & Analytics (School of Computing), and the Department of Analytics and Operations (Business School), a Research Team Lead at the Institute of Application of Learning Science and Educational Technology, and affiliated with the Business Analytics Centre. He received his doctorate from Harvard Business School, and an undergraduate from MIT. He is also an entrepreneur, and frequently consults industry leaders. Dr. Phan was also amongst the top 10 competitive ballroom dancers in North America.
- Technology Innovations in Retail Banking & Consumer Finance
- Big Data Consumer Analytics
- Social Networks
- FinTech and Retail Banking
- Education & Mental Illness
- Big Data, AI & Economics
- Computational Social Science
Refereed Journal Publications
- 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, January 10, 2019. https://doi.org/10.1287/isre.2018.0790.
- Phan, Tuan Q., and David Godes. “The Evolution of Influence Through Endogenous Link Formation.” Marketing Science, March 12, 2018. doi: 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. doi: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. doi: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 (May 26, 2015): 6595–6600. doi:10.1073/pnas.1404770112.
Awards and Honours
Awards, Honors, and Fellowships
- 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
A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks
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.