UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.

Essays on Macroeconomic Trends and Cycles

This dissertation describes two studies on macroeconomic trends and cycles. The first chapter studies the impact of Information Technology (IT) on the U.S. labor market. Over the past 30 years, employment and income shares of routine-intensive occupations have declined significantly relative to nonroutine occupations, and the overall U.S. labor income share has declined relative to capital. Furthermore, the decline of routine employment has been largely concentrated during recessions and ensuing recoveries. I build a model of unbalanced growth to assess the role of computerization and IT in driving these labor market trends and cycles. I augment a neoclassical growth model with exogenous IT progress as a form of Routine-Biased Technological Change (RBTC). I show analytically that RBTC causes the overall labor income share to follow a U-shaped time path, as the monotonic decline of routine labor share is increasingly offset by the monotonic rise of nonroutine labor share and the elasticity of substitution between the overall labor and capital declines under IT progress. Quantitatively, the model explains nearly all the divergence between routine and nonroutine labor in the period 1986-2014, as well as the mild decline of the overall labor share between 1986 and the early 2000s. However, the model with IT progress alone cannot explain the accelerated decline of labor income share after the early 2000s, suggesting that other factors, such as globalization, may have played a larger role in this period. Lastly, when nonconvex labor adjustment costs are present, the model generates a stepwise decline in routine labor hours, qualitatively consistent with the data. The timing of these trend adjustments can be significantly affected by aggregate productivity shocks and concentrated in recessions. The second chapter studies the implications of loss aversion on the business cycle dynamics of aggregate consumption and labor hours. Loss aversion refers to the fact that people are distinctively more sensitive to losses than to gains. Loss averse agents are very risk averse around the reference point and exhibit asymmetric responses to positive and negative income shocks. In an otherwise standard Real Business Cycle (RBC) model, I study loss aversion in both consumption alone and consumption-and-leisure together. My results indicate that how loss aversion affects business cycle dynamics depends critically on the nature of the reference point. If, for example, the reference point is status quo, loss aversion dramatically lowers the effective inter-temporal rate of substitution and induces excessive consumption smoothing. In contrast, if the reference point is fixed at a constant level, loss aversion generates a flat region in the decision rules and asymmetric impulse responses to technology shocks. Under a reasonable parametrization, loss aversion has the potential to generate asymmetric business cycles with deeper and more prolonged recessions.