Discrete choice models are the backbone of empirical analysis in many fields. These models are grounded in random utility maximization theory and generally require that fundamental properties, including stable and transitive preferences, be observed. Recent work by behavioral economists suggests that these properties may not be valid in all decision making contexts, and that failure to properly account for these and other behavioral biases may lead to poor forecasting accuracy. The objectives of this CAREER proposal are to: 1) integrate behavioral biases into discrete choice models to enhance our fundamental knowledge of individual and firm decision making; 2) investigate whether behavioral biases found to be important in current market conditions persist in future market conditions; 3) develop and validate theories of individuals, search and purchase behaviors; and, 4) develop probability modules for Georgia high school teachers based on state-mandated problem-based learning techniques that use datasets representative of engineering problems encountered in practice.
The research findings will be applied and tested in the airline industry, which affords ideal conditions for testing behavioral theories under a wide variety of market conditions. This is due to the ability to both track menus and decisions made by travelers in a developed industry that has flexible capacity. This proposal creatively integrates data from multiple online sources and complements this with online experiments conducted with the airline industry. The ability to analyze large volumes of data from naturally occurring online markets provides the opportunity to uncover new empirical insights that will drive the development of theoretical models.
The developments from this CAREER proposal will explicitly integrate individuals, search and purchase behaviors with firm decision-making. This approach results in a theoretical foundation that is unique from that of other researchers and reveals that demand and behavioral bias assumptions are not trivial and can result in forecasts opposite those predicted by classic models. The most important broader impact is the potential to change the nature of online markets to make them more interactive with customers, which will result in both increased profits for firms as well as better product and service offerings for customers.
Many people find air travel frustrating. Flight delays, missed connections, full flights, long security lines, and add-on fees are just a few reasons why air travel is so frustrating. The U.S. Department of Transportation is responsible for making air travel less frustrating by setting policies that help protect the rights of air travelers. However, it can be challenging for government agencies to design policies that best balance the needs of customers and airlines because policy makers have historically had little information about airline customers. The internet is providing us with new opportunities to design policies that better protect customers. Several firms have created databases of airlines’ prices from online sources, and sell this information to airlines. This research explored how researchers and policy makers can use these big data to better understand how customers respond to airline practices. As part of this grant, a dataset of online airline fares was published in Manufacturing &Service Operations Management. This dataset was used to develop models to estimate flight-level price elasticities which can be used to estimate consumer impacts associated with different airport congestion management policies (such as charging landing fees that are higher during peak take-off and landing periods). The dataset was also used to investigate how seat map displays influence customers’ purchases of premium coach seats (with extra legroom and early boarding privileges). Results show that customers avoid seating in middle seats and seats near the back of the plane by purchasing premium coach seats. In fact, customers are between 2 and 3.3 times more likely to purchase premium coach seats when there are no window or aisle seats that can be reserved for free. These results suggest that if an airline were to block certain rows of seats for premier customers, the airline could sell more premium coach seats and potentially increase seat revenues by 2 to 7 percent. This is one example of how big data can be used to better understand how customers respond to airline practices. Practices that are shown to mislead customers or prevent them from informed decisions when selecting flights can be prioritized as part of future government policies, thereby improving airline customers’ experiences.
