The purpose of our research is to empirically test how system attributes of blockchain build trust through system and information components in blockchain food traceability systems. Findings showed that system attributes of blockchain are strong predictors of trust leading to intention to purchase. A sample of 358 responses were collected from college students through online survey. SmartPLS 3.0 is adopted for data analysis. We made contributions by building a new research model to guide future studies on trust formation in blockchain based systems as well as informing practice to adopt proven features of blockchain to create and capture values for customers.

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 study addresses the antecedents of individuals' disposition to use telemedicine applications, as well as the antecedents of their usage to provide insight into creating sustained usage over time. The theoretical framework of this research is Bhattacherjee's expectation-confirmation IS continuance model. By combining a series of key factors which may influence the initial and continued usage of telemedicine applications with key constructs of Bhattacherjee's IS continuance model, this study aims to provide a deeper understanding of barriers to telemedicine app usage and how to facilitate continued use of these apps. Online survey data was collected from college students who are telemedicine application users. A total of 313 responses were gathered, and data analysis was conducted using SmartPLS 3. This dissertation contributes by looking at the IS adoption and IS continuance research simultaneously to connect these two research streams as well as suggesting the usage context of some established IS theory being different with regard to healthcare applications.

The primary focus of this research is explicating the role of emotion in IT post-adoption behavior. Studied in the context of intelligent personal assistants (IPA), a class of conversational artificial intelligence (AI), the first study integrates elements from computer science, communications, and IS disciplines. The research identifies two constructs vital for speech-based technologies, natural language understanding, and feedback, and examines their role in use decisions. This work provides guidance to practice on how best to allocate R&D investments in conversational AI. The second essay examines the IT continuance through the theoretical lens of the expectation-confirmation model (ECM), incorportating cognitive and emotional satisfaction into the ECM framework. Empirical testing of the model suggests that it offers additional clarity on IT continuance phenomena and provides a significant improvement to the explanatory power of ECM in the context of an emerging technology. The third essay is one of the earliest efforts to conceptualize and test a theoretical model that considers emotional attachment in IT continuance behavior. This essay develops a novel model to investigate this phenomenon based on emotional attachment theory, and empirically validates the proposed model in the context of conversational artificial intelligence systems. While the existing theories of IT continuance focus …

This research examines the relationship between quality management (QM) practices and performance in the healthcare industry via the conduct of three studies. The results of this research contribute both to advancing QM theory as well as in developing a unique text mining method that is illustrated by examining QM in the healthcare industry. Essay 1 explains the relationship between operational performance and QM practices in the healthcare industry. This study analyzed the findings from the literature using meta-analysis. We applied confirmatory semantic analysis (CSA) to examine the Baldrige winners' applications. Essay 2 examines the benefits associated with an effective QM program in the healthcare industry. This study addressed the research question about how effective QM practice results in improved hospital performance. This study compares the performance of Baldrige Award-winning hospitals with matching hospitals, state average, and national average. The results show that the Baldrige Award can lead to an increase in patient satisfaction in certain periods. Essay 3 discusses the contribution of an online clinic appointment system (OCAS) to QM practices. An enhanced trust model was built on understanding the mechanism of patients' trust formation in the OCAS. Understanding the determinants related to patients' trust and willingness to use OCAS …

Phishing is a social engineering attack to deceive and persuade people to divulge private information like usernames and passwords, account details (including bank account details), and social security numbers. Phishers typically utilize e-mail, chat, text messages, or social media. Despite the presence of automatic anti-phishing filters, phishing messages reach online users' inboxes. Understanding the influence of phishing techniques and individual differences on susceptibility and detection accuracy is an important step toward creating comprehensive behavioral and organizational anti-phishing awareness programs. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to explore the nature of phishing threats that including identifying attack intentions, and psychological and design techniques of phishing attacks. Essay 2 seeks to understand the relative influence of attack techniques and individual phishing experiential traits on people's phishing susceptibility. Essay 3 seeks to understand an individual's cognitive and affective differences that differentiate between an individual's phishing detection accuracy.

Intelligent algorithm approaches that augment the analytical capabilities of traditional techniques may improve the evaluation and performance of decision making units (DMUs). Crises such as the massive COVID-19 pandemic-related shock to businesses have prompted the deployment of analytical tools to provide solutions to emerging complex questions with incredible speed and accuracy. Performance evaluation of DMUs (e.g., financial institutions) is challenging and often depends on the sophistication and robustness of analytical methods. Therefore, advances in analytical methods capable of accurate solutions for competitive real-world applications are essential to managers. This dissertation introduces and reviews three robust methods for evaluating and optimizing the decision-making processes of DMUs to assist managers in enhancing the productivity and performance of their operational goals. The first essay proposes a robust search field division method, which improves the performance of evolutionary algorithms. The second essay proposes a robust double judgment approach method that enhances the efficiency of the data envelopment analysis method. The third essay proposes a robust general regression neural network method to examine the effect of shocks on GDP loss caused by COVID-19 on the global economy. These three essays contribute to optimization methodology by introducing novel robust techniques for managers of DMUs to improve …

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 …

Services and service-based business are a major part of any economy. However, service-based supply chains require a greater level of interaction between provider and consumer than the traditional manufacturing or product-based supply chain. Therefore, they require optimization and resiliency models that acknowledge the constraints and goals unique to service-based industries. Value co-creation and service-dominant logistics (SDL) are relatively new to operations research. Existing literature in management science provides a framework for value co-creation but does not provide a model for optimizing value cocreation and resiliency in a complex or dynamic systems such as education supply chains (ESC). This dissertation addresses these knowledge gaps through 3 essays. The first essay establishes a method for optimizing investment in resiliency measures when utilizing parallel supply chains. The essay examines the intersection of value co-creation theory between higher education and service-dominant logistics (SDL) to understand the role of supply chain elements in value cocreation. The second essay provides a theoretical approach to incorporating resilience planning into the customer relationship management model. The final essay establishes a method for optimizing investment in resiliency measures when utilizing parallel service supply chains.

This dissertation contributes to IS research by explaining user intentions while using AR features in mobile social networking and retail app contexts. It consists of three essays, which use partial least squares modeling to analyze different consumer behavior models. The first essay examines the influence of quality, human, and environmental factors on AR reuse intention in a mobile social networking context. The second essay introduces position relevance, a new construct essential to AR research in e-commerce, and it looks at the influence of this construct and app involvement on user purchase intention, while using view-in-room features on mobile retail apps. The third essay examines the influence of service quality and visual quality on recommendation intention of mobile retail apps while using view-in-room features compared to shopping without using these AR features.