Learning When Reading: Evidence from an Online Mobile Reading Platform

Online reading platforms increasingly adopt a by-chapter purchase model and embed novel in-chapter commenting functions to alleviate consumer uncertainty about the quality of digital books. This study examines how these platform designs influence consumer learning and purchasing behavior. We develop a Bayesian learning model that captures how consumers form and update beliefs about book quality using both their own consumption experience and indirect signals derived from prior readers’ in-chapter comments. Our model estimation results show that in-consumption comments significantly influence consumers’ perceived quality and downstream consumption decisions, with varied effects depending on the topics of comments. Specifically, comments offering scene-based insights, evaluations of character behavior, and pleas for new chapters lead to upward revisions in perceived quality, whereas comments filled with happy emotional words or imagining hypothetical future scenes tend to reduce it. Through policy simulations, we further illustrate how various platform designs around in-consumption comments can alter consumers’ consumption decisions. Our work contributes to the literature on online business models by examining consumers’ decision-making process within the emerging pay-by-content model. It also advances research on in-consumption social listening and narrative participation by quantifying the topic-specific impacts of in-consumption social listening signals on consumer decision-making process.

History: Ram Gopal, Senior Editor; Hong Guo, Associate Editor.

Funding: This work was supported by the National Natural Science Foundation of China [Grant 72188101, Grant 92301259, and Grant 72091210].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.0465.