This dissertation consists of three essays examining how and why firms set prices in markets. In particular, this dissertation shows how firms may utilize nonlinear pricing to price discriminate, how firms may experiment with the prices they set to learn about the demand function in the market they serve in later periods and the effects of these pricing strategies on consumer welfare. In Essay 1, I show how firms in the milk market use nonlinear price schedules -- quantity discounts -- to price discriminate and increase profits. I find that firms have a greater ability to price discriminate on their own ``private label'' products rather than regional branded that they sell alongside their own. Though some consumers benefit from a lower price as a result of the price discrimination, total consumer surplus is lower than if the store had to offer a fixed price per unit. Additionally, I compare my structural demand estimates, which using the Nielsen household panel data include consumer demographic information and actual household choices, to the standard approach in the literature on price discrimination that uses only market level data. By doing so I find that ignoring demographic information and actual consumer choices leads to biased parameter estimates. In the case of the milk market, the biased parameter estimates due to ignoring household demographic information and actual consumer choices lead to underestimating welfare harm to consumers on average. After finding that price discrimination harms consumers overall in this market, I quantify which consumer demographic are better off and which are worse off. I find that households with children and low income households with children are the only households to benefit from the price discriminatory practices of firms in this market. Since these groups are particularly vulnerable, I suggest that policymakers take no action to correct this market, as any action will directly hurt these consumer groups. In Essay 2, I study how firms learn about the demand in a new market by exploiting a significant change in Washington's state's liquor laws. In 2012, the state of Washington switched from a price-controlled state-store system of selling liquor to one in which private sellers could sell liquor with minimal restrictions on price and range of products. As a result, a heterogeneous group of firms entered the liquor market across the state with little knowledge of the regional demand for alcohol in the state of Washington across heterogeneous localities. Using the Nielsen retail scanner data I am able to observe the variation in pricing and offerings seasonally and over time to see if there is convergence in offerings and prices, and how quickly that convergence occurs across different localities depending on local demographics and competition. I also investigate the extent to which the variation is "experimentation'' by the firms, i.e., the firms purposely experimenting to learn more about demand and the extent that local demographics and competition can affect the experimentation and whether there are spill-overs from local competition (i.e. do firms learn from each other and does this effect how much they experiment and how quickly they learn). My main findings are that over time, firms within this market have learned better how to price discriminate over the holiday season; firms experiment more with prices for the pint sized products than the larger sizes; and that menu of options that firms have offered has been expanding but at a slower rate, suggesting that they are approaching a long-run steady state for the optimal menu of options.

This dissertation analyzes pricing under uncertainty focusing on the U.S. airline industry. It sets to test theories of price dispersion driven by uncertainty in the demand by taking advantage of very detailed information about the dynamics of airline prices and inventory levels as the flight date approaches. Such detailed information about inventories at a ticket level to analyze airline pricing has been used previously by the author to show the importance of capacity constraints in airline pricing. This dissertation proposes and implements many new ideas to analyze airline pricing. Among the most important are: (1) It uses information about inventories at a ticket level. (2) It is the first to note that fare changes can be explained by adding dummy variables representing ticket characteristics. Therefore, the load factor at a ticket level will lose its explanatory power on fares if all ticket characteristics are included in a pricing equation. (3) It is the first to propose and implement a measure of Expected Load Factor as a tool to identify which flights are peak and which ones are not. (4) It introduces a novel idea of comparing actual sales with average sales at various points prior departure. Using these deviations of actual sales from sales under average conditions, it presents is the first study to show empirical evidence of peak load pricing in airlines. (5) It controls for potential endogeneity of sales using dynamic panels. The first essay tests the empirical importance of theories that explain price dispersion under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand uncertainty and to identify which flights are peak and which ones are off-peak. It shows that different prices can be explained by the different selling probabilities. The second essay is the first study to provide formal evidence of stochastic peak-load pricing in airlines. It shows that airlines learn about the demand and respond to early sales setting higher prices when expected demand is high and more likely to exceed capacity.

There is a lack of studies on the current status of the use of on-demand learning in organizations and factors that may accelerate or hold back the acceptance and implementation of on-demand learning in organizations. The purpose of this study is to contribute to a better understanding of the adoption of on-demand learning in organizations in the United States. More specifically, this research was conducted to answer the following questions: 1) Are training professionals familiar with the concept of on demand learning? 2) What are the most commonly practiced on-demand learning applications in organizations? 3) What are the most commonly used on-demand learning devices? 4) Which subject areas are appropriate for applying on-demand learning? 5) What factors explain and predict the adoption of on-demand learning? 6) Does organizational nature (non-profit vs. for-profit) have an impact on the adoption of on-demand learning? 7) Does economic sector have an impact on the adoption of on-demand learning? 8) Does organizational size have an impact on the adoption of on-demand learning? and 9) Does training budget have an impact on the adoption of on-demand learning? Study results indicated that although many factors influence the adoption of on-demand learning in organizations, compatibility and top management support were the most significant determinants in general. The training budget was a moderator for the adoption of on-demand learning and it amplified the effects of top management support and organizational centralization on the adoption process. The adoption of on-demand learning among small organizations, non-profit organizations, or organizations with relatively small training budgets, was primarily determined by available organizational resources, such as technical infrastructure, financial resources for experimental innovations, professional development opportunities, and investment on training and development. But among for-profit organizations or large organizations (i.e., 1,000 employees and over), the adoption of on-demand learning was primarily determined by its compatibility with organizations and organizational openness. Moreover, perceived usefulness, perceived ease of use, costs, and customer demand were not significant determinants in this study. In short, organizational factors had a greater explanatory power than innovative, environmental, or individual variables. Recommendations were proposed for future studies.