This dissertation consists of three studies that collectively investigate the factors influencing the consumer adoption intention towards emerging healthcare technologies. Essay 1 systematically reviews the extent literature on healthcare technology adoption and serves as the theoretical foundation of the dissertation. It investigates different models that have been previously applied to study healthcare technology acceptance. Meta-analysis method is used to quantitatively synthesize the findings from prior empirical studies. Essay 2 posits, develops, and tests a comprehensive biotechnology acceptance model from the end-user's perspective. Two new constructs, namely, perceived risk and trust in technology, are integrated into the unified theory of acceptance and use of technology. Research hypotheses are tested using survey data and partial least square – structural equation modeling (PLS-SEM). Essay 3 extends the findings from the Essay 2 and further investigates the consumer's trust initiation and its effect on behavioral adoption intention. To achieve this purpose, Essay 3 posits and develops a trust model. Survey data allows testing the model using PLS-SEM. The models developed in this dissertation reflect significant modifications specific to the healthcare context. The findings provide value for academia, practitioners, and policymakers.

Managers rely on analytics to make decisions and the choice of the analytical method can influence their decision-making. This dissertation considers three cases and examines how the choice of analytical methods influence interpretations and implications. These areas are communication for health-related information in social media, health information technology investment by hospitals as it relates to patient satisfaction, and health related expenditure policies of countries. These studies develop theoretical models and empirically test them on primary or secondary data, comparing the performance of popular analytical methods. The conduct of these three studies contributes to a better understanding about the choice of analytical methods and allow development of a heuristic approach by offering guidelines for selecting an appropriate methodology. They demonstrate the value of heuristic approaches for use with non-traditional and traditional statistical methods, as the information gained from non-traditional methods (NNs) provides insights into traditional statistical methods, similar to insights gained from exploratory data analysis. The studies also show the value in examining any dataset with multiple methods because they either confirm each other or fail to confirm, providing insights.

This dissertation is designed to answer the following questions: (1) Which measurement model is better to contribute to the research model in different areas? (2) Within a given model, how does the data size influence the performance of a neural network (NN) and some other methods? (3) Compared to partial least square (PLS), ordinary least square (OLS), XGBoost, how is the performance of NN? Essay 1 systematically compares PLS-SEM to ANN and builds the hybrid vehicle purchasing intention model (HVPIM). It investigates different models that have been previously applied to study the theory of planned behavior (TPB). The methods find those factors that significantly correlated with consumer purchase intention. Essay 2 posits, develops, and tests a PNN model with healthcare data. A research survey is designed and distributed to undergraduate students from a major research school in the U.S. southwest region. Research hypotheses are tested using PLS-SEM and PNN. Essay 3 targets on testing the performance of the NN model with panel data from the soccer transfer market. To achieve this purpose, the essay posits and develops an empirical test built on game theory. The NN model is tested and compared to OLS and XGBoost. As the research compares the …

This work embraces three essays on information privacy: 1) Measures of Personal Information Privacy in Social Networks: Information Control and Situation Awareness, 2) Care to Share your Personal Information? and 3) Privacy Breaches: How Resilient Are You? Every transaction made either online or offline, and every social interaction that is transferred or stored electronically in some way, are generally consumed as big data and ultimately drives the analytics from which consumers benefit. However, this raises some concerns about privacy and ethics. For example, should companies that consumers interact with be allowed to sell their personal information? Consumers derive certain benefits such as personalized content when they choose to offer their data to many websites. However, consumers providing personal data to websites subject themselves to possible privacy invasion when third parties purchase their data. In this case, since the consumer willfully gave away their personal information, is it genuinely personal, and should they retain some, if any, control over it? Theories such as privacy calculus and protection motivation theory (PMT) are a couple of prominent examples that focus on the privacy risks and benefits that drive consumer behavior. However, there is still a lack of research on the instantiation of privacy …

Social media platforms create rich social structures, expand users' boundaries of social networks and revolutionize traditional forms of communications, social interactions and social relationships. These platforms not only facilitate the creation and sharing of news and information, but they also drive various kinds of businesses models, processes and operations, knowledge sharing, marketing strategies for brand management and socio-political discourses essential for healthy and democratic functions. As such, social media has greater implications on organizations and society brought about by individuals' social media usage patterns, and therefore, calls for further investigations. The main objective of this dissertation is to explore and offer insights into such social media usage and information sharing behaviors via data driven examination of various theories. This dissertation involves three studies that focus on factors that explain individuals' three different social media usage behaviors. Essay 1 investigates individuals' perceived importance of online affiliation, self-esteem, self-regulation and risk-benefit structure as antecedents of users' geo-tagging behavior on social media. Essay 2 examines the role of online news quality, source credibility, individuals' perception towards online civic engagement, attitude towards news sharing and social influences to understand users' news sharing behavior on social media platforms. Essay 3 seeks to examine the individuals' …