This paper studies how service-age information influences customers’ strategic joining decisions in an observable M/G/1 queue. We ask how such information is reflected in customers' strategies at equilibrium, and what would be the corresponding system throughput and social welfare. The managerial insights indicate that a revenue-maximizing provider should disclose the service-age information when congestion is high, while a social planner should disclose the information when congestion is low.

We study a problem where a fixed number of servers must be split between a shared and a dedicated track. High-priority customers can be served on the dedicated track or the shared track with non-preemptive priority over low-priority customers. Low-priority customers are strategic and choose to either join the shared track or balk from the system based on wait time information. We use a queueing game to study the capacity allocation decision under different information revealing policies.

Strategic servers may discreetly disclose or hide queue length information to manage customers' joining behavior. For customer-intensive services, the choice of service rate further complicates this queue observability decision by affecting the service quality and consequently customers’ service rewards. In this work, we provide an integral analysis of managing speed-quality trade-off with the joint use of pricing and queue disclosure strategies.

Online labor markets are growing rapidly, where the numerical rating to workers provides a critical metric of service quality. In this study, we propose a two-stage machine learning framework to predict such quality. We apply our method on a unique from a leading online labor platform. We provide an understanding of how various features impact service quality. Then, we discuss the value of prediction in decision-making and provide actionable insights to improve service quality in OLMs.

Service providers often offer multiple variants of a service to their customers simultaneously through different servers. These servers could differ in their service value and expected service times, making one server preferable to customers over the other. However, even when the preferred server is busy and the less preferred server is free, customers may choose to wait for the preferred server. This behavior is called "stalling". Our goal is to price the servers in order to maximize revenue.

Ride-hailing platforms are highly popular so their decisions can affect social satisfaction levels significantly. The price determined by the platform is so critical decision factor in managing their demand and pool of available drivers. However, consumers are affected by pricing in different ways due to their heterogeneity of price or waiting time sensitivity.

We analyze a waiting time differentiation pricing strategy using a game-theoretic model in both monopoly and duopoly environments.