No abstract

In many services, for example, website or landscape design, the value or quality derived by a customer depends upon the service time, and this valuation differs across customers. Customers procure the service based on the expected value to be delivered, prices charged, and the timeliness of service. We investigate the performance of the optimal pricing scheme as well as two commonly used pricing schemes (fixed fee and time‐based pricing) for such services on important dimensions such as revenue, demand served, and utilization. We propose a novel model that captures the above features and wherein both service rate and demand are endogenous and functions of the pricing scheme. In particular, service time is an outcome of the pricing scheme adopted and the heterogeneous valuations of customers, unlike in the queueing‐based pricing literature. We find that the service system may benefit from a greater variance in consumer valuations, and the performance of pricing schemes is impacted by the shape of the distribution of customers' valuation of service time and the responsiveness desired by customers. Both the fixed fee and time‐based schemes do well relative to the optimal pricing scheme in terms of revenue in many plausible scenarios, but there are substantial differences between the pricing schemes in some important operational metrics. For instance, the fixed fee scheme serves more customers and has higher utilization than the time‐based scheme. We also explore variants of the fixed and time‐based schemes that have better revenue performance and show that the two‐part tariff which is a combination of fixed and time‐based pricing can do as well as the optimal scheme in terms of revenue.

Problem definition: Games are the fastest-growing sector of the entertainment industry. Freemium games are the fastest-growing segment within games. The concept behind freemium is to attract large pools of players, many of whom will never spend money on the game. When game publishers cannot earn directly from the pockets of consumers, they employ other revenue-generating content, such as advertising. Players can become irritated by revenue-generating content. A recent innovation is to offer incentives for players to interact with such content, such as clicking an ad or watching a video. These are termed incentivized (incented) actions. We study the optimal deployment of incented actions. Academic/practical relevance: Removing or adding incented actions can essentially be done in real-time. Accordingly, the deployment of incented actions is a tactical, operational question for game designers. Methodology: We model the deployment problem as a Markov decision process (MDP). We study the performance of simple policies, as well as the structure of optimal policies. We use a proprietary data set to calibrate our MDP and derive insights. Results: Cannibalization—the degree to which incented actions distract players from making in-app purchases—is the key parameter for determining how to deploy incented actions. If cannibalization is sufficiently high, it is never optimal to offer incented actions. If cannibalization is sufficiently low, it is always optimal to offer. We find sufficient conditions for the optimality of threshold strategies that offer incented actions to low-engagement users and later remove them once a player is sufficiently engaged. Managerial implications: This research introduces operations management academics to a new class of operational issues in the games industry. Managers in the games industry can gain insights into when incentivized actions can be more or less effective. Game designers can use our MDP model to make data-driven decisions for deploying incented actions.

Problem definition: In many healthcare systems, general practitioners refer patients to specialists, who make treatment decisions under limited capacity. We evaluate the effectiveness of different payment schemes, both traditional ones, where the payer contracts separately with the providers, and bundled schemes, where the providers share a single bundled payment. A key feature considered is a performance-based payment component to coordinate the decisions of the general practitioner and specialist by a single payer. The providers are partially responsible for the patient outcomes and costs stemming from their treatment decisions. Academic/practical relevance: We propose and analyze a model to address how referral and specialist treatment decisions in healthcare impact each other and how payment schemes impact the coordination of care between the general practitioner and specialist. Our work is valuable to policymakers in understanding the trade-offs between using bundled and unbundled payment schemes in a referral-based healthcare system. Methodology: The underlying research method is the analysis of optimization models (with congestion effects embedded) to explore the effects of different payment schemes on different entities and various operational performance measures in a healthcare system. Results: A bundled scheme has higher referral rates, lower time spent with the patient by the specialist, higher specialist utilization relative to an unbundled system in most scenarios, and higher system costs. Our conclusions are robust to various model changes. Managerial implications: Our work provides specific managerial insights into the relative performance of bundled and unbundled payment systems on operational metrics in a referral-based healthcare system. It also sheds light on how the level of attributability of service outcomes to providers interacts with payment schemes to influence referral and specialist treatment decisions and impact various quality and cost metrics.

Service quality is an important attribute that is used to characterize many service systems. In this study, we examine a service system with two consecutive steps that have shared resources. The service process consists of a base service (first step in the process) followed by a second step that adds additional value. We first look at a social surplus maximizing service provider (SP) who decides the optimal service capacity and re‐optimizes in response to changes in the speed of service of the first step due to local innovations. Our main objective is to explore using simple and stylized models, the effect on the service system of local innovations in step 1 that decrease this step's service times. We find that the effect of such innovations can sometimes lead to the worsening of certain critical service quality measures when SPs are monopolists. Next, using a model of competition, we find that this effect continues to hold in settings where SPs compete for arrivals. Our results have interesting consequences for many service systems and may help explain some of the unintended effects of service innovations reported in the literature.