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

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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 the number of books/chapters read and the amount of money spent. Our method allows us to simultaneously capture three interdependent usage behaviors using a single integrated model and identify the impact of content loading time and previous reading behavior on users’ engagement dynamics and 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 users sampling books, and a high state with users reading intensively. Furthermore, we find that content loading time has higher negative impacts on high-state users in state transitioning than medium-state users. In contrast, the days that elapsed since the last visit has a similar negative impact on the users in the high and medium states. The effect of usage frequency on users in state transitioning is always positive. We have also identified the weekend effect and social influence on users’ reading outcomes. Finally, our simulations quantify the shortened content loading time and the days elapsed since the last visit on users’ engagement dynamics and content consumption decisions, which generate important managerial implications for app providers.
Funding Information
  • National Natural Science Foundation of China (91846302, 72271058, 72033003)

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