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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.
社交媒體盛行，不少人都會在平台上分享新聞，令新聞的傳播效果更加顯著，企業亦因此十分留心社交媒體上的新聞討論熱點，以便更貼近「民情」，亦慎防公關危機的發生。 港大經管學院市場學、創新及資訊管理學副教授潘光俊，早前就大眾閱讀新聞和分享社交媒體的行為發表研究報告，發現文章內容對大眾閱讀新聞和社交媒體分享的行為有影響。他指出，讀者傾向在新聞網站閱讀負面新聞，但在社交媒體上，卻傾向分享正面報道，「目的是為『推廣』自己，予人一個正面形象，並非側重所分享的新聞本身。」研究並發現，較深的文章亦傾向多人分享，但實際閱讀的人數卻很少。
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.
Dr. Phan is a scholar, consultant, entrepreneur, and ballroom dancer. Believing that Hong Kong will be playing a significant role in the digital revolution between two AI giants, China & the USA, Dr. Phan has arrived with his expertise in AI and Fintech.
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.
