Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 39-41).

Includes bibliographical references (p. 48-49).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 114-118). Also available in electronic version. Access restricted to campus users.

The internet has become a more prominent part of people’s lives. Clickstream and other online data have enabled researchers to better understand consumers’ decision-making behavior in a variety of application areas. This dissertation focuses on using clickstream data in two application areas: the airline industry and the field of education. The first study investigates if airline passengers departing from or arriving to a multi-airport city actually consider itineraries at the airports not considered to be their preferred airport. It was found that customers do consider fares at multiple airports in multi-airport cities. However, other trip characteristics, typically linked to whether a customer is considered business or leisure, were found to have a larger impact on customer behavior than offered fares at competing airports. The second study evaluates airline customer search and purchase behavior near the advance purchase deadlines, which typically signify a price increase. Search and purchase demand models were constructed using instrumented two-stage least squares (2SLS) models with valid instruments to correct for endogeneity. Increased demand was found before each deadline, even though these deadlines are not well-known among the general public. It is hypothesized that customers are able to use two methods to unintentionally book right before these price increases: (1) altering their travel dates by one or two days using the flexible dates tools offered by an airline’s or online travel agency’s (OTA) website to receive a lower fare, (2) booking when the coefficient of variation across competitor fares is high, as the dynamics of one-way and roundtrip pricing differ near these deadlines. The third study uses clickstream data in the field of education to compare the success of the traditional, flipped, and micro-flipped classrooms as well as their impacts on classroom attitudes. Students’ quiz grades were not significantly different between the traditional and flipped classrooms. The flipped classroom reduced the impact of procrastination on success. In the end, it was found that micro-flipped was most preferred by students as it incorporated several benefits of the flipped classroom without the effects of a learning curve.

Demand forecasting is an important step of a company’s supply chain management process, allowing companies to project their needs for different components that are used in the final product. This is even more important in emerging industries with job order (or project-based) products where historical demands do not exist and components may not be readily available or may involve a long lead time. Developing a demand forecasting model which accurately projects the needs of components for a company can decrease costs while decreasing overall lead times of final products. This demand forecast model takes into account projected component needs along with the likelihood of successfully winning a project bid. The model is extended to four different demand forecasting formulas incorporating different use of the winning probabilities. Historical results are then used to compare the methods and their advantages and disadvantages are discussed. / text

No description available.

With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project.

No description available.

This thesis provides a unique perspective to the milk market relative to previous studies. While there is rich literature on the various sectors of the milk market, a contribution to this literature was made by providing a specific econometric analysis of the Promised Land brand. Previous work conducted by Capps and Salin (2010) provided the foundation for this thesis. The purpose of this thesis was threefold: 1. Estimate household demand functions for Promised Land white and chocolate milk using Nielsen homescan data. The demand functions were estimated using a tobit model to estimate the conditional and unconditional own-price and cross-price effects of Promised Land white and chocolate milk. 2. Assess the impacts of household demographic drivers of demand for Promised Land white and chocolate milk. 3. Use the empirical results derived from the tobit model to identify substitutes and complements of Promised Land white and chocolate milk and provide a profile of households that purchase Promised Land white and chocolate milk. The study concluded that households who purchased Promised Land white were insensitive to changes in the price of Promised Land white milk. The price of private label milk was the only cross-price that was significant in the quantity of Promised Land white milk purchased. The estimated elasticities showed that Promised Land white milk and private label white milk were substitutes. From the estimated elasticities, it was found that households that purchased Promised Land chocolate milk were largely insensitive to changes in the price of Promised Land chocolate milk. It was also found that the prices of Nesquick, Oak Farms, and private label chocolate milk had a significant effect on the quantity of Promised Land chocolate milk purchased. Of these significant prices, private label and Oak Farms chocolate milk were substitutes for Promised Land chocolate milk. The price of Nesquick chocolate milk demonstrated a complementary relationship with Promised Land chocolate milk.

With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project.