As consumers continue to adopt and adapt to newer technologies, firms face a common question adapted from the 1989 motion picture Field of Dreams: “If we build it, will they come?” Until recently, it has been assumed that the answer will always be “yes.” However, to properly expand upon this question, we look in this paper to the research proposed around the same time the aforementioned feature film was released to understand the application of Davis’s Technology Acceptance Model (1986) as it relates to newer emerging technologies such as Facial authentication as an alternative to the point-of-entry at public event spaces. The introduction of biometrics as a method of entry creates an opportunity to examine the tension between this technology and user acceptance of privacy. The option of mobile ordering juxtaposed with traditional point-of-sale and physically waiting in line provides insight into a second sub-setting to address technology adoption’s impact on revenue.The first study leverages secondary data collected from a pilot program conducted at three Major League Baseball stadiums in 2021 to examine the use and efficacy of Facial authentication as a point-of-entry solution and alternative to mobile ticketing. This study analyzed the data collected in pre- and post-participant surveys and transactional data from 21 events in 30 days (n=343). The study uses secondary data to test two hypothetical scenarios through variables such as the method of entry and patron traits to determine if a relationship exists between one or more of the variables. The second study focuses on understanding the use of a newly introduced technology within the sub-setting of concessions in the sports & entertainment setting. The data was collected from 45 consecutive events over 25832 individual transactions and examined the influence of mobile ordering on revenue and patron behavior (n=557). The setting provides an opportunity for examination of variables impacting the firm as juxtaposed with legacy technology. The study establishes setting and sub-settings related to sports & entertainment. The primary setting is the venue, which contains sub-settings permeated with emerging technologies. The two sub-settings examined are the first two settings patrons typically interact with on a given event level. Point-of-entry provided for examinations of patron use of a newly introduced facial authentication platform, which affirms TAM and UTAUT models. In the second study the count of items within a mobile ordering transaction were significant and led to creation of a new variable which measures average revenue per item per transaction. This new variable shed light into a key finding. Interestingly enough, the second sub-setting provided insight into consumer spending habits through the newly introduced technology that led to findings related to revenue and conclusions for the firm regarding future deployment and positioning of the technology. / Business Administration/Management Information Systems

This dissertation presents innovative demand management techniques for service systems with limited resources. The first study analyzes demand management policies of animal shelters with limited Kennel space as a set of interacting stochastic queueing systems. In practice, there are two main policies being used, which we call "Kill" and "No-Kill" policies. In a "Kill" system, animals may be euthanized if a shelter is full. Many shelters have moved to a "No-Kill" policy, where they avoid killing for space and adopt other approaches to reduce supply and demand mismatch. Our goal is to provide insights on how No-Kill policies, such as coordination, adoption and neutering campaigns, help reduce the animals' killing rate so that the shelter management can choose the way to effectively solve their problems. In the second part, we consider a topic of demand management for the Sports and Entertainment (S&E) industry, called "Scaling the house", i.e., how to divide seats into zones for different prices to maximize revenue across the venue. From the data obtained from several performance venues in the U.S., we find ticket demand is impacted by locations of seats as well as by price. We characterize closed-form solutions for the optimal two-dimensional zoning decision (with row and column cuts) and the one-dimensional decision (with row cuts), and explore when each model should be applied. The third study considers pricing as a tool to manage demand for the S&E tickets. We develop dynamic pricing with demand learning models where demand is also affected by time left until the show dates. Since the show's popularity is usually uncertain to the seller, we propose a method to learn the overall popularity via Bayesian updates. We perform computational experiments to understand properties of the model solutions and identify when demand learning is most beneficial.