Tengteng Ma

I am an assistant professor in the School of Information Systems, Muma College of Business, University of South Florida. I earned my Ph.D. degree in Management Information Systems from University of Illinois Chicago.

My research interest lies in the intersection of Artificial Intelligence and Business Analytics. I study human behaviors on digital platforms using large-scale heterogeneous datasets and design interpretable artificial intelligence models to address complex business problems; I also conduct econometrics-oriented empirical analyses to investigate individual decision making on digital platforms, which are based on the valuable information extracted from semi-structured and unstructured data using machine learning, natural language processing, and computer vision.

CV  /  Google Scholar

Projects

We explore the relationship between passive and active engagement activities when one of the engagement channels is removed. By collecting a large-scale dataset from Youtube, we empirically examine the reinforcement and substitution effects between the two types of engagement behaviors.

We investigate the impact of inserting commercial endorsements in videos on influencers' popularity and reputation from an omni-dimensional perspective. Leveraging face recognition techniques, we further investigate how this effect can be moderated if content generators demonstrate stronger endorsement by showing their face.

We empirically examine how group size affects viewers' real-time engagement on online synchronous platforms and how moderators affect this relationship. The findings indicate a congestion effect of increasing group size viewer engagement in the synchronous communication setting and suggest the beneficial role of content moderators.

We design a novel Graph Neural Network based deep learning model called GACE to predict customer engagement of brand posts by exploiting large-scale content consumption information from the perspective of heterogeneous networks. In addition, we provide business insights regarding overall brand engagement performance and the change of customer preferences over time, which has practical value for customer relationship management.

We systematically evaluate evidence from controlled studies of interventions using virtual humans on their effectiveness in health-related outcomes. The design and implementation characteristics of these systems are also examined.