Managers in organizations make investment decisions all the time. These decisions have an impact on the bottom-line profits and on the market penetration of the organization. Some decisions have more impact than others do and not all such decisions are evaluated for their impact. The Service-Profit Chain (SPC) framework brings together several components like operational attributes, customer perceptions, customer behavioral intentions and customer loyalty to evaluate the service operation. This research augments the SPC with another component — uncontrollable factors (environmental variables and competition) that are exogenous to the operation but definitely have an effect on the service delivery process. Further, this research develops a dynamic model to evaluate investments made in operational attributes (e.g. number of tellers in a bank, number of airline flight options to a particular city available to customers) and determine the behavior of customer perceptions, customer intentions, customer loyalty, profit, market penetration and marginal rate of return over time. The above is accomplished by incorporating a hill-climbing algorithm into the dynamic SPC model. This hill-climbing algorithm senses the current state of the system and compares it to a certain goal to determine the discrepancies and make additional interventions. The objective is to determine an optimal path to steady state and to evaluate if certain goals are realistic. Next, the Service Sustainability Chain is developed to be applicable to training services. This is accomplished by building key relationships specific to training services into separate modules. The Dynamic SPC module is based on the SPC framework. The Customer Base Growth module captures the structure for referrals and how this enables the growth of the customer base mimicking the infectious model for epidemic diseases in the literature. A methodology based on Chi-Square Automatic Interaction Detection (CHAID) and Structural Equation Modeling (SEM) developed to explore, uncover and identify relationships and mathematical equations is used to determine the structural input-output representation of the SPC. Next, the model and the methodology developed are applied to a case study in a training services organization, simulated and validated. / Ph. D.

The changes in the course of communication changed the world from different perspectives. Public participation on social media means the generation, diffusion, and exposure to a tremendous amount of user-generated content without supervision. This four-essay dissertation analyzes information diffusion through social media and its opportunities and challenges through management systems engineering and data analytics. First, we evaluate how information can be shared to reach maximum exposure for the case on online petitions. We use system dynamics modeling and propose policies for campaign managers to schedule the reminders they send to have the highest number of petition signatures. We find that sending reminders is more effective in the case of increasing the signature rate. In the second essay, we investigate how people build trust/ mistrust in science during an emergency. We use data analytics methods on more than 700,000 tweets containing keywords of Hydroxychloroquine and chloroquine, two candidate medicines, to prevent and cure patients infected with COVID-19. We show that people's opinions are concentrated in the case of polarity and spread out in the case of subjectivity. Also, they tend to share subjective tweets than objective ones. In the third essay, building on the same dataset as essay two, we study the changes in science communication during the coronavirus pandemic. We used topic modeling and clustered the tweets into seven different groups. Our analysis suggests that a highly scientific and health-related subject can become political in the case of an emergency. We found that the groups of medical information and research and study have fewer tweets than the political one. Fourth, we investigated fake news diffusion as one of the main challenges of user-generated content. We built a system dynamics model and analyzed the effects of competition and correction in combating fake news. We show that correction of misinformation and competition in fake news needs a high percentage of participation to be effective enough to deal with fake news. / Doctor of Philosophy / The prevalence of social media among people has changed information diffusion in several ways. This change caused the emergence of a variety of opportunities and challenges. We discuss instances of these in this dissertation in four main essays. In the first essay, we study online social and political campaigns. Considering the main goal of campaign managers is to gain the highest reach and signatures, we generate a model to show the effects of sending reminders after the initial announcement and its schedule on the final total number of signatures. We found that the best policy for online petition success is sending reminders when people are increasingly signing it rather than when people lose interest in it. In the second essay, we investigated how people build trust/ mistrust in scientific information in emergency cases. We used public tweets about two candidate medicines to prevent and treat patients infected with COVID-19 and analyzed them. Our results suggest that people trust and retweet the information that is based on emotions and judgments more than the one containing facts. We evaluated the science communication during the mentioned emergency by further investigating the same dataset in the third essay. We clustered all the tweets based on the words they used into seven different groups and labeled each of them. Then, we focused on three groups of medical, research and study, and political. Our analysis suggests that although the subject is a health-related scientific one, the number of tweets in the political group is greater than the other clusters. In the fourth essay, we analyzed the fake news diffusion through social media and the effects of correction and competition on it. In this context, correction means the reaction to misinformation that states its falsity or provides counter facts based on truth. We created a model and simulated it for the competition considering novelty as one influential factor of sharing. The results of this study reveal that active participation in correction and competition is needed to combat fake news effectively.

Although there have been warnings about the decline in the U.S. transportation infrastructure for the last two decades, the infrastructure's service condition remains barely above current requirements, and is insufficient to meet future demands of growth and economic development. This deterioration and obsolescence primarily derives from a lack of investment, inappropriate management, and growing travel demands. The objective of this research is to develop a model, using a system dynamics methodology, that serves as an instrumentality for generating scenarios for facilitating highway infrastructure management -- policy-making, planning, budgeting, and programming for the Virginia highway system. The Highway Management System (H~1S) model, developed in this research, is a system dynamics model equipped with capabilities for analyzing and solving the meta-problems related to highway infrastructure planning and management. The HMS consists of five subsystems: 1) Physical Subsystem (Pavement Management System, and Bridge Management System), 2) Evaluation Subsystem, 3) Functional Subsystem, 4) Financial Subsystem, and 5) Administration Subsystem. Through steady state analysis, an understanding of the relationship between state variables and decision variables can be obtained. The nomographs corresponding to the steady state solutions of the HMS are the analytically-based, manual means of gaining understanding by tracing paths from decision variables to state variables. Simulations were performed using the HMS, based on various budget-size and allocation scenarios for the I-81 corridor. The results indicate that the service condition of the highway will be diminished and will return to its pre-expansion condition in several years, if the maintenance budget is not increased according to the expansion. The HMS is a useful tool for decision-makers and engineers attempting to analyze and solve meta-problems related to transportation infrastructure management. The HMS presents a whole picture of the highway system according to various policy options. This systems approach to highway management also can be applied to the management of other infrastructure, and eventually it should be possible to achieve an integrated infrastructure management system. / Ph. D.

During the 20th century, the construction of dams and large reservoirs was a common approach by water managers to control hydrological variability and alleviate the effects of hydrological extremes (i.e. floods and droughts). However, the complex human-water relationships arising in response to reservoir construction can lead to unexpected (and often undesired) outcomes, which diverge from the original intentions. Sociohydrological models explicitly account for feedbacks between society, the environment, and water resources. As such, they provide opportunities to: i) uncover the dynamics of hydrological risks generated by the interplay of human and water systems, and ii) explore trade-offs in the management of water resources, thereby informing the decision-making process. This study develops a sociohydrological model for two diverse case studies in Australia, to explore the human-water interplays emerging from the occurrence of drought and flood events in an urban and rural context. The model is used to explore alternate water management scenarios with respect to trade-offs between: i) the environment vs. irrigation (Case Study 1); and ii) urban water supply vs. flood mitigation (Case Study 2). The model outcomes are compared to observed hydrological conditions, socioeconomic characteristics, and water management decisions. The findings show consistency between observations and simulations, and align with analysis from other studies, validating the model. Model results indicate the emergence of several sociohydrological phenomena in each case study. The comparison of the case study results shows: i) differences in the way rural and urban consumers respond to drought; and ii) that the role of reservoir operators is more significant in the urban setting. This study finds that the model can be a useful tool for water managers increase catchment understand, to explore the potential outcomes of alternative water management decisions, and to identify preferred trajectories across multiple hydrological and socioeconomic criteria. Keywords: sociohydrology, system dynamics

The global infrastructure gap has continually widened over the last few decades. Industry reports and academic publications suggest that, in terms of road infrastructure, both advanced and developing economies have not paid sufficient attention to modernize their infrastructure assets. A wider road infrastructure gap signifies that highway conditions have declined because governments have not had enough resources for maintenance and rehabilitation. In the same way, it also indicates that congestion levels have grown and the level of service in most road networks has dropped because public agencies have not had sufficient funds to generate new highways and expand existing corridors. This dissertation, therefore, provided insights into the difficulties associated with improving the existing highway assets and the barriers related to expanding the current roadway capacity through public-private partnerships (PPPs). The research involved three interdependent studies. In the first study, I examined the continuous deterioration of the US highway system through a system dynamics model, which focused on the dynamics of capital investments and maintenance expenditures in the US road infrastructure. The results confirmed that the American highway system is currently stuck in a capability trap. This makes it difficult for the system to improve at the rates required by the country's economic growth. In my second investigation, my attention shifted toward the governance challenges related to building new roads and expanding highway capacity through PPPs. I developed a systems map of governance variables informed by past-published evidence from actual projects. By specifically examining the shaping phase of public-private initiatives, the work uncovered the effects of feedback relationships and interdependencies on PPP feasibility. This offered insights about the relationship between governance mechanisms and successful PPP development. In the third study, I utilized variables and relationships identified in my second investigation to develop a management flight simulator in order to better explain governance difficulties in the procurement phase of PPP projects. The simulator was implemented during an educational exercise with graduate students of civil engineering. By doing so, I confirmed that the simulator has the potential to increase our understanding of PPP procurement processes. Results indicated that the simulation tool was a suitable instrument to explain how government capacity, project uncertainty, and technical complexity influence PPP tendering. Overall, my findings across the three studies illustrate different means to understand why closing the global road infrastructure gap is challenging. Together, the three inquiries indicate that examining the road infrastructure sector as a socio-technical system contributes to improve our understanding of the expenditure dynamics related to existing assets and to enhance our comprehension of the governance challenges associated with developing new roads. / Ph. D. / Over the last decades, the global infrastructure gap has continually expanded. In the road infrastructure sector, governments around the world have failed to meet the growing societal and economic demands for additional infrastructure investments. This suggests that highway conditions have declined because there are not enough resources to improve the existing roads and develop new highways. As a result, the size and quality of global road network keep deteriorating annually. This dissertation provided insights into the difficulties related to enhancing the quality conditions of the existing road network and the problems associated with increasing roadway capacity through public-private partnerships (PPPs). The research was conducted through three interdependent studies. In the first study, I developed a simulation model in order to understand the continuous deterioration of the US highway system. I concluded that the US government has been unable to achieve sustained improvements in the system because of prioritizing rehabilitation over preventive maintenance. In my second investigation, I conducted a literature review focused on analyzing governance-related concepts in PPP projects. I created a systems map that helps to understand how governance variables influence failure and success in the shaping phase of public-private agreements. In the third study, I developed an educational tool focused on explaining some of the challenges of conducting procurement processes in PPPs. This tool proved to be effective in terms of illustrating how government capacity, project uncertainty, and technical complexity influence PPP tendering. Overall, the three studies offer a socio-technical perspective of why closing the global road infrastructure gap is challenging. Together, they contribute to improve our understanding of the difficulties associated with enhancing the conditions of the current road network and developing new roads through PPPs.

Adopting model-based systems engineering (MBSE) tools signifies a shift in the complex nature of systems related to digital transformation. This study performs a comprehensive review and analysis to investigate organizational thinking toward training adult learners to use MBSE tools. Recognizing the wealth of experience and unique training needs characteristic of professionals, this study identifies and reflects on the prevalence of andragogy-based learning strategies to support future needs. By aligning MBSE tools with andragogy-based principles, this study seeks to contribute to the body of knowledge for both adult education and systems engineering, supporting a pathway for organizations to navigate and excel in a digitally transformed environment. This study found that organizations do not explicitly recommend a structured andragogy-based learning theory as a critical component to developing training for MBSE tools to support digital transformation. However, many of the principles ascribed to andragogy are commonly used by both organizations and researchers to promote MBSE training to adult learners. potential future development to further advance this work.

System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling. Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity. / Ph. D.

Highly deteriorated U.S. road infrastructure, major budgetary restrictions and the significant growth in traffic have led to an emerging need for improving efficiency and effectiveness of highway maintenance practices that preserve the road infrastructure so as to better support society's needs. Effectiveness and efficiency are relative terms in which the performance of a production unit or decision making unit (DMU) is compared with a benchmark (best practice). Constructing the benchmark requires making a choice between an "estimation approach" based on observed best practices (i.e., using data from input and output variables corresponding to observed production units (DMUs) to estimate the benchmark with no elaboration on the details of the production process inside the black box) or an "engineering approach" to find the superior blueprint (i.e., focusing on the transformation process inside the black box for a better understanding of the sources of inefficiencies). This research discusses: (i) the application of the estimation approach (non-parametric approach) for evaluating and comparing the performance of different highway maintenance contracting strategies (performance-based contracting versus traditional contracting) and proposes a five-stage meta-frontier and bootstrapping analytical approach to account for the heterogeneity in the DMUs, the resulting bias in the estimated efficiency scores, and the effect of uncontrollable variables; (ii) the application of the engineering approach by developing a dynamic micro-level simulation model for the highway deterioration and renewal processes and its coupling with calibration and optimization to find optimum maintenance policies that can be used as a benchmark for evaluating performance of road authorities. This research also recognizes and discusses the fact that utilization of the maintenance budget and treatments that are performed in a road section in a specific year directly affect the road condition and required maintenance operations in consecutive years. Given this dynamic nature of highway maintenance operations, any "static" efficiency measurement framework that ignores the inter-temporal effects of inputs and managerial decisions in future streams of outputs (i.e., future road conditions) is likely to be inaccurate. This research discusses the importance of developing a dynamic performance measurement framework that takes into account the time interdependence between the input utilization and output realization of a road authority in consecutive periods. Finally, this research provides an overview of the most relevant studies in the literature with respect to evaluating dynamic performance and proposes a classification taxonomy for dynamic performance measurement frameworks according to five issues. These issues account for major sources of the inter-temporal dependence between input and output levels over different time periods and include the following: (i) material and information delays; (ii) inventories; (iii) capital or generally quasi-fixed factors and the related topic of embodied technological change; (iv) adjustment costs; and (v) incremental improvement and learning models (disembodied technological change). In the long-term, this line of research could contribute to a more efficient use of societal resources, greater level of maintenance services, and a highway and roadway system that is not only safe and reliable, but also efficient. / Ph. D.

Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties. / Ph. D.

Yes / Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3-machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.