Deciding on the appropriate level of customer service remains an important area of research. In the current service environment where competition is ubiquitous, the importance of identifying and retaining key customers is of paramount importance. As such, the concept of customer delight, which refers to a profoundly positive emotional state experienced by the customer, has developed. Unfortunately, much remains unknown regarding customer delight. In response to this dearth of research, the current study focuses on delight from multiple perspectives utilizing multiple methods. Thus, this dissertation adds to the emerging knowledge base of customer delight in three areas: first, assessing what delight represents to the employee; second, investigating its impact on the employee; and third, examining what delight represents to the customer. To gain this knowledge, three separate essays were written. A summary of each is below. In Essay 1 (Chapter 2), the goal was to gain an appreciation of delight from the employee’s viewpoint. Through the use of a qualitative technique where critical incidents were content analyzed, several themes emerged. First, employees evaluate delight differently then customers. Second, employees experience elevated affective states after providing delight. Finally, employees seem to exhibit customer-oriented behaviors after a delightful encounter. With these key themes in mind, Essay 2 (Chapter 3) utilized structural equation modeling, which is a quantitative method that helps investigate relationships among variables. Findings indicated that employees did in fact experience elevated levels of affect, as well as commitment, satisfaction, and customer-oriented behaviors. After investigating the effects of delight on the employee, it was necessary to evaluate what exactly delights the customer. Utilizing the aforementioned qualitative method, Essay 3 (Chapter 4) provides several themes regarding the customer perspective: first, there are both cognitive and affective routes to delight; second, both the disconfirmation paradigm and the needs-based model are appropriate for understanding delight; and third, employee affect and effort are key drivers of delight. Taken together, the findings provide a more complete understanding of the focal construct, as well, as articulating specific behaviors that lead to perceptions of delight. Finally, this dissertation evaluates the important employee outcomes that result from providing delight.

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Dynamics of social processes in populations, such as the spread of emotions, influence, opinions, and mass movements (often referred to individually and collectively as contagions), are increasingly studied because of their economic, social, and political impacts. Moreover, multiple contagions may interact and hence studying their simultaneous evolution is important. Within the context of social media, large datasets involving many tens of millions of people are leading to new insights into human behavior, and these datasets continue to grow in size. Through social media, contagions can readily cross national boundaries, as evidenced by the 2011 Arab Spring. These and other observations guide our work. Our goal is to study contagion processes at scale with an approach that permits intricate descriptions of interactions among members of a population. Our contributions are a modeling environment to perform these computations and a set of approaches to predict contagion spread size and to block the spread of contagions. Since we represent populations as networks, we also provide insights into network structure effects, and present and analyze a new model of contagion dynamics that represents a person\'s behavior in repeatedly joining and withdrawing from collective action. We study variants of problems for different classes of social contagions, including those known as simple and complex contagions. / Ph. D.

Network science emerged as an interdisciplinary field over the last 20 years, and played a central role to address fundamental problems in other fields, e.g., epidemiology, public health, and transportation, and is now part of most university curriculums. Network dynamics is a major area within network science where researchers study different forms of processes in networked populations, such as the spread of emotions, influence, opinions, flu, ebola, and mass movements. These processes often referred to individually and collectively as contagions. Contagions are increasingly studied because of their economic, social, and political impacts. Yet, resources for studying network dynamics are largely dispersed and stand-alone. Furthermore, many researchers interested in the study of networks are not computer scientists. As a result, they do not have easy access to computing and data resources. Even with the presence of software or tools, it is challenging to install, build, and maintain software. These challenges create a barrier for researchers and domain scientists. The goal of this work is the design and implementation of a research framework for modeling contagions on large networked populations. The framework consists of various systems and services that provide support for researchers and domain scientists at different stages of their research workflow. / Ph. D.