This dissertation studies three different problems service firms can face. The first chapter looks at the optimal way to price reservations and services when customers make reservations in advance, while they are uncertain about the future value of service, to avoid waiting on the day of service. We show that charging customers the full price as non-refundable deposit when they make reservations and charging zero for service when they show up to claim their reservations is optimal for the firm. When the firm faces very large potential market, then it is better for the firm to not take reservations and accept only walk-ins. The second chapter looks at a problem of how to mitigate worker demotivations due to fairness concerns, when workers have intrinsic difference in quality, and higher quality server tends to be overcrowded by customers willing to receive higher quality service. We suggest distributing workload fairly between workers and compensating workers per workload as potential remedies and show which remedy works well under what operational conditions. We show that compensating workers per customer they serve results in high customer expected utility and expected quality. However, when customers also care about fairness and dislike receiving inferior service compared to other customers, then there does not exist a single remedy that results in both high customer expected utilization and high expected quality. In the third chapter, we study how a service firm should choose its advertising strategy when the service quality is not perfectly known to the customers. We model customers' learning process using a Markov chain, and show that when customers do not perfectly learn the quality of service from advertisements, then the firm is better off by advertising actively when customers' initial belief about service quality is low. Oppositely, when customers initially believe the service quality to be high, then it is better for the firm to stay silent and not use advertisement to signal its quality. In all three chapters, we use game theory to model the interactions among the participants of the problem and find the equilibrium outcomes.