Understanding of the Dynamics of Mobile Reading: An HMM Model of User Engagement and Content Consumption

Abstract

Understanding consumers’ engagement and subsequent content consumption behavior in the mobile context is critical to mobile app providers. In this paper, we develop a Hidden Markov Model (HMM) to capture the dynamics of users’ engagement states and consumption decisions on reading Breadth, Depth, and Spending. Our method allows us to simultaneously capture three interdependent usage behaviors using a single integrated model and reveal the long-term impact of Network Delay, Recency, and Frequency on users’ content consumption. We calibrate the model using a tap stream data set of individual users’ reading activities on a mobile app. Our analysis reveals three distinct engagement states, a low state with inactive users, a medium state with the majority of users sampling books, and a high state with users reading intensively. Furthermore, we find that Network Delay and Recency have higher negative impacts on high-state users in state transitioning than medium-state users, whereas the effect of Frequency on users in state transitioning is always positive. Finally, our simulations quantify the shortened Network Delay and Recency on users’ engagement and content consumption dynamics, which provide app providers with important implications on the implementation of 5G networks and providers’ efforts to reactive users.

Keywords: User Engagement, Hidden Markov Model, Network Quality, Digital Content Consumption, Mobile Channels