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.

I extended and adapted the current technology acceptance models and privacy research to the peculiar context of the COVID-19 pandemic to ascertain the effective "power" of IT in fighting such a pandemic. The research models developed for the purpose of this study contain peculiar modifications to the technological-personal-environmental (TPE) framework and privacy calculus model because of the unique technologies implemented and the peculiar pandemic scenario. I developed three studies that investigate the interaction between social, legal, and technical factors that affect the adoption of IoT devices and blockchain systems implemented to fight the spread of COVID-19. Essay 1 systematically reviews existing literature on the analysis of the social, legal, and technical components in addressing phenomena related to IoT architecture and blockchain technology. The employment of a comparable coding method allows finding which of the above components is prominent in relation to the study of IoT and blockchain. Essay 2 develops a technological acceptance model by integrating the TPE framework with new constructs, i.e., regulatory environment, epidemic ecosystem, pre-epidemic ecosystem, perceived social usefulness, and technical characteristics. Essay 3 further explores the interplay between social, legal, and technical factors toward the adoption of smart contracts in the context of the COVID-19 pandemic. …

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.

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 …

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.