This study aimed to answer the question of how media users will reallocate their sleep time when their main content channel changes from real-time broadcasting to selective viewing through the over-the-top (OTT) media streaming service. To draw a causal inference between OTT consumption and sleep patterns, the difference-in-difference (DID) estimation method was applied. With the DID approach, a clear distinction between treatment and control groups is essential because the main treatment effects can be screened by the compounding effects. While the conventional way of dividing two groups relies on the selection of limited variables, this study adopted random forest nearest-neighbor propensity score matching based on a machine learning algorithm to divide the two groups. This allows for meticulous matching of the two groups except for treatment. Results show that watching OTT late at night has a significant effect on reducing the total sleep duration on average by about 18-20 min (maximum about 30 min at 95% confidence level) and delaying bedtime by about 18 min (maximum about 26 min at 95% confidence level). This study showed that the selective viewing of content through OTT has the advantage of widening the range of content choices for media users and helping in arranging their time more autonomously, but watching content through OTT late at night leads to media users' departure from the existing sleep routine.
Keywords: Causal inference; Media behavior; Media panel data; OTT; Random forest; Sleep duration; Sleep pattern.
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