Monopoly Pricing with Participation‐Dependent Social Learning About Quality of Service

The quality of many online services (such as online games, video streaming, cloud services) depends not only on the service capacity but also on the number of users using the service simultaneously. For a new online service, the potential users are often uncertain about both the capacity and congestion level of the service, and hence are uncertain about the quality of service (QoS). In this study, we consider users’ participation‐dependent social learning (PDSL), that is, learning of the QoS through participants’ online reviews. The key difference from the traditional social learning mechanism is that the learning object (QoS) is not a fixed value, but instead, it depends on the number of review participants. We study how such a new learning process affects the service provider's dynamic pricing strategy in four different market scenarios, depending on whether the decisions are for two periods or infinite periods and whether users are aware of the congestion effect or not. Our analysis yields several key insights. First, the presence of PDSL significantly affects the provider's optimal pricing policy. In a two‐period market with congestion‐unaware users, the provider would always set a flat price when there is no PDSL; in contrast, when there is PDSL, the optimal price can either increase or decrease, depending on the capacity and the prior mean QoS belief. Second, users’ congestion‐awareness causes the provider to set a non‐decreasing pricing policy in the two‐period market, while the provider's steady‐state pricing policy in the infinite‐period market increases with the capacity and the prior QoS belief. Third, the existence of PDSL increases the provider's profit in all four market scenarios as long as the provider's capacity is larger than users’ prior mean QoS belief.