In this work, we focus on enhancing the efficacy of predictive modeling in operational management decision making in two different settings: Essay 1 focuses on demand forecasting for the companies and the second study utilizes longitudinal data to analyze the illicit drug seizure and overdose deaths in the United States. In Essay 1, we utilize an operational system (newsvendor model) to evaluate the forecast method outcome and provide guidelines for forecast method (the exponential smoothing model) performance assessment and judgmental adjustments. To assess the forecast outcome, we consider not only the common forecast error minimization approach but also the profit maximization at the end of the forecast horizon. Including profit in our assessment enables us to determine if error minimization always results in maximum profit. We also look at the different levels of profit margin to analyze their impact on the forecasting method performance. Our study also investigates how different demand patterns influence maximizing the forecasting method performance. Our study shows that the exponential smoothing model family has a better performance in high-profit products, and the rate of decrease in performance versus demand uncertainty is higher in a stationary demand environment.In the second essay, we focus on illicit drug overdose …

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.

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 …

In 2012, the CIA World Fact Book showed that the service sector contributed about 76.6% and 51.4% of the 2010 gross national product of both the United States and Ghana, respectively. Research in the services area shows that a firm's success in today's competitive business environment is dependent upon its ability to deliver superior service quality. However, these studies have yet to address factors that influence customers to remain committed to a mass service in economically diverse countries. In addition, there is little research on established service quality measures pertaining to the mass service domain. This dissertation applies Rusbult's investment model of relationship commitment and examines its psychological impact on the commitment level of a customer towards a service in two economically diverse countries. In addition, service quality is conceptualized as a hierarchical construct in the mass service (banking) and specific dimensions are developed on which customers assess their quality evaluations. Using, PLS path modeling, a structural equation modeling approach to data analysis, service quality as a hierarchical third-order construct was found to have three primary dimensions and six sub-dimensions. The results also established that a country's national economy has a moderating effect on the relationship between service quality and …

Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research …

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.

The increasing demand for mobile apps is out the current capability of mobile app developers. In addition, the growing trend in smartphone ownership and the time people spend on mobile apps has raised several opportunities and risks for users and developers. The average time everyday a user spend on smartphones to use mobile apps is more than two hours. The worldwide mobile app revenue increase is estimated to grow 33%, $19 billion. Three quarter of the time used on mobile apps is solely for using game and social networking apps. To provide more customized services and function to users, mobile apps need to access to personal information. However, 80% of mobile apps put people's information privacy at risk. There is a major gap in the literature about the privacy concerns of mobile device users in the context of mobile apps. This dissertation addresses one fundamental research question: how does individuals' privacy change in the context of mobile apps? More precisely, the focus of this dissertation is on information privacy role in individuals' and mobile app developers' protective behaviors. We investigate the information sensitivity level influence on mobile app developers' emphasis on privacy across mobile app categories. The results show information …