Doctor of Education , University of Dayton, 2024, Educational Administration

This research project aims to understand what elementary teachers need from internal communication systems to promote trust and engagement within their school and across the district. The data for this project was collected through a sequential mixed-method approach that included a survey of all elementary teachers in the school district, as well as 5 one-on-one interviews with elementary teachers. Reasons for understanding teachers' attitudes about communication within the organization include the impact on teachers' trust within the organization and teacher's engagement within the organization. Additionally, teachers' satisfaction with the amount of information being shared with them is shown to be positively associated with their attitudes about communication. Themes identified in the research speak to the importance of efficiency and clarity in communication, the desire of the team to be equipped with knowledge, and timeliness and transparency as being key to trust and engagement. These findings indicate the need for schools to establish clear communication channels, but to also engage teachers in sensemaking opportunities and collaborative message building.

Doctor of Organization Development & Change (D.O.D.C.), Bowling Green State University, 2024, Organization Development

This dissertation explores how state and local governments incorporate Generative Artificial Intelligence (GenAI) into their operations. As these technologies evolve, they offer opportunities to improve efficiency and decision-making but also bring challenges that require updated governance frameworks. The study uses content analysis to examine how well current governance frameworks manage the integration of GenAI, focusing on strategic alignment, risk management, data governance, and ethical considerations. The findings show that governments are at different stages of incorporating GenAI, emphasizing the need for improved governance frameworks to handle these new technologies effectively. This research helps understand how GenAI is changing public sector governance and suggests directions for future policies.

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Statistics

Deep learning, neural network, has been penetrating into almost every corner of data analyses. With advantages on computing power and speed, adding more layers in a neural network analysis becomes a common practice to improve the prediction accuracy. However, over depleting information in the training dataset may consequently carry data noises into the learning process of neural network and result in over-fitting errors. Neural Network has been used to predict the future time series data. It had been claimed by several authors that the Neural Network (Recurrent Neural Network) can predict the time series data, although time series models have also been used to predict the future time series data. This dissertation is thus motivated to investigate the prediction performances of neural networks versus the conventional inference method of time series analysis. After introducing basic concepts and theoretical background of neural network and time series prediction in Chapter 1, Chapter 2 analyzes fundamental structure of time series, along with estimation, hypothesis testing, and prediction methods. Chapter 3 discusses details of computing algorithms and procedures in neural network with theoretical adjustment for time series prediction. In conjunction with terminologies and methodologies in the previous chapters, Chapter 4 directly compares the prediction results of neural networks and conventional time series for the squared error function. In terms of methodology assessment, the evaluation criterion plays a critical role. The performance of the existing neural network models for time series predictions has been observed. It has been experimentally observed that the time series predictions by time series models are better compared to the neural network models both computationally and theoretically. The conditions for the better performances of the Time Series Models over the Neural Network Models have been discovered. Theorems have also been pro (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2024, Department of Computer Science and Information Systems

The field of coding education is rapidly evolving, with emerging technologies playing a pivotal role in transforming traditional learning methodologies. This thesis introduces Dr. Lego, an innovative framework designed to revolutionize the assessment and understanding of block-based coding through the integration of sophisticated deep learning models. Dr. Lego combines cutting-edge technologies such as MobileNetV3 (Howard, 2019), for visual recognition and BERT (Devlin et al., 2018), and XLNet (Yang et al., 2019) for natural language processing to offer a comprehensive approach to evaluating coding proficiency. The research methodology involves the meticulous curation of a diverse dataset comprising projects from the LEGO SPIKE app (LEGO Education, 2022), ensuring that the models are subjected to a broad range of coding scenarios. Leveraging the dynamic educational environment provided by the LEGO SPIKE app (LEGO Education, 2022), Dr. Lego empowers users to design and implement various coding projects, fostering hands-on learning experiences. This thesis delves into methodologies aimed at enhancing coding education by exploring model integration, data generation, and fine-tuning of pre-trained models. Dr. Lego not only evaluates coding proficiency but also provides cohesive and insightful feedback, enhancing the learning experience for users. The adaptability of the framework highlights its potential to shape the future of coding education, paving the way for a new era of interactive and engaging learning experiences.

Master of Computing and Information Systems, Youngstown State University, 0, Department of Computer Science and Information Systems

Bootstrap sampling serves as a cornerstone in statistical analysis, providing a robust method to evaluate the precision of sample-based estimators. As the landscape of data processing expands to accommodate big data, approximate query processing (AQP) emerges as a promising avenue, albeit accompanied by challenges inaccurate assessment. By leveraging bootstrap sampling, the errors of sample-based estimators in AQP can be effectively evaluated. However, the implementation of bootstrap sampling encounters obstacles, particularly in the computation-intensive resampling procedure. This thesis embarks on an exploration of various resampling methods, scrutinizing five distinct approaches: On Demand Materialization (ODM) Method, Conditional Binomial Method (CBM), Naive Method, Two-Step Poisson Random (TSPR), and Two-Step Poisson Adaptive (TSPA). Through rigorous evaluation and comparison of the execution time for each method, this thesis elucidates their relative efficiencies and contributions to AQP analyses within the realm of big data processing. Furthermore, this research contributes to the broader understanding of resampling techniques in statistical analysis, offering insights into their computational complexities and implications for big data analytics. By addressing the challenges posed by AQP in the context of bootstrap sampling, this thesis seeks to advance methodologies for accurate assessment in the era of big data processing.

Bachelor of Business Administration (BBA), Ohio University, 2024, Business Administration

This thesis presents Scoreboard for Excel, a pedagogical tool designed to improve student engagement in large undergraduate classes. Addressing the challenge of individualized learning in large settings, the study examines how Scoreboard for Excel integrates technology into education to foster engagement. The research highlights the limitations of current approaches and the unique benefits offered by Scoreboard for Excel, including real-time feedback, gamification, automated grading, and honesty controls. The methodology presents how Scoreboard was designed to be user-centered and aligned with pedagogical theory. The findings suggest that Scoreboard for Excel significantly enhances student motivation and learning outcomes by providing a more engaging, personalized, and interactive educational experience.

Doctor of Philosophy, Case Western Reserve University, 2024, Management

This dissertation synthesizes two studies at ambulatory clinics of an academic medical center to understand the transition from exclusive telemedicine use during the pandemic to a preference for in-person care and the varied telemedicine practices among clinical specialties and individual clinicians. I used a multi-method approach incorporating process and variance methods and multilevel analysis over time to examine telemedicine's evolution and impact on the sociotechnical (ST) system and work practices. ST systems are analyzed using a novel second-generation framework of ST fusion and punctuated change over time, combining three frameworks to emphasize their co-evolution going beyond a specific state of “fit” at a singular level. The first study utilizes a systems dynamics model to illustrate how clinical actions, accessibility, and digital options dynamically interact during aggressive telemedicine implementation, leading to different outcomes based on implementation decisions. Varying influences create feedback and feedforward loops, potentially pushing the system toward a state of constrained access and action due to the increasing shift to in-person visits post-pandemic, workflow burdens, divergent clinical actions, and the absence of necessary contextual information for care. The second study shows significant variability in telemedicine for the same diagnoses, with clinicians being the primary driver of this variability and clinicians' perceptions of telemedicine use contingent on its ability to improve access. Time-based visualizations indicate consistent trends in low telemedicine users and declining trends among medium, high users with distinct clinician typologies based on in-group characteristics: Traditionalists (low), Pragmatists (medium), and Empiricists (high). Traditionalists prefer in-person visits for their relational aspects and professionalism, pragmatists value flexible, patient-centered care with a utilitarian telemedicine approach, and emp (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2024, Aerospace Engineering

This research introduces an Origami-inspired dynamic spacecraft radiator, capable of adjusting heat rejection in response to orbital variations and extreme temperature fluctuations in lunar environments. The research centers around the square twist origami tessellation, an adaptable geometric structure with significant potential for revolutionizing radiative heat control in space. The investigative involves simulations of square twist origami tessellation panels using vector math and algebra. This study examines both a two-dimensional (2- D), infinitely thin tessellation, and a three-dimensional (3-D), rigidly-foldable tessellation, each characterized by an adjustable closure or actuation angle “φ”. Meticulously analyzed the heat loss characteristics of both the 2D and 3D radiators over a 180-degree range of actuation. Utilizing Monte Carlo Ray Tracing and the concept of “view factors”, the study quantifies radiative heat loss, exploring the interplay of emitted, interrupted, and escaped rays as the geometry adapts to various positions. This method allowed for an in-depth understanding of the changing radiative heat loss behavior as the tessellation actuates from fully closed to fully deployed. The findings reveal a significant divergence between the 2D and 3D square twist origami radiators. With an emissivity of 1, the 3D model demonstrated a slower decrease in the ratio of escaped to emitted rays (Ψ) as the closure/actuation angle increased, while the 2D model exhibited a more linear decline. This divergence underscores the superior radiative heat loss control capabilities of the 2D square twist origami geometry, offering a promising turndown ratio of 4.42, validating the model's efficiency and practicality for radiative heat loss control. Further exploration involved both non-rigidly and rigidly foldable radiator models. The non-rigidly foldable geometry, initially a theoretical concept, is realized through 3D modeling and physica (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

Additive Manufacturing (AM), particularly LASER Powder Bed Fusion (LPBF), has gained prominence for its flexibility and precision in handling complex metal structures. However, optimizing L-PBF for intricate designs involves analyzing over 130 process parameters, leading to prolonged duration and increased costs. This thesis proposes a novel approach by harnessing statistical and machine learning algorithms to predict manufacturability issues before the printing process. By performing a comparative analysis of the intended design with the machine produced result, the study introduces two machine learning and one artificial neural network (ANN) algorithm to forecast the printability of new designs accurately. This innovative method aims to reduce or eliminate the need for iterative printing, reducing productivity costs and optimizing the LPBF additive manufacturing process.

Doctor of Philosophy, The Ohio State University, 2023, Biomedical Sciences

This dissertation introduces the Tool-Assisted Research Discovery Informatics Surveillance System (TARDISS), a proof of concept of a research surveillance system (RSS). TARDISS aims to reduce the transaction costs of Knowledge Production by using templated analysis and automated data management, which allows for studies built using TARDISS to update when new data are released. TARDISS focuses on research that uses the 2012-2020 Centers for Medicare & Medicaid Services (CMS) Standard Analytical File (SAF) Claims datasets and aims to increase the velocity of information sharing by facilitating A/B comparisons between original and updated studies. By automatically updating studies, TARDISS can help identify when the findings of historical scholarship are no longer consistent with the present reality by helping researchers identify trends and discontinuities in large secondary datasets. The research employed a mixed-methods approach with quantitative analysis of literature replications identified by an environmental scan to validate that TARDISS functions as an RSS and qualitative interviews and user story mapping to understand researchers that might benefit from TARDISS and how they perceived the usefulness and useability of TARDISS. This dissertation also explores barriers to adopting research surveillance, including lack of code transparency, the low priority of replication research, and user adoption.

Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

This study will evaluate the organizational effectiveness of an artificial intelligence (AI)/machine learning (ML) recommender system at a higher education institution. It will determine the positive or negative net benefits (i.e., organizational effectiveness) of utilizing the D&M ISSM. Identifying the value and efficacy of information systems (IS) management actions and investments requires evaluating their success or effectiveness. A system's effectiveness is evaluated from the organizational perspective based on the degree to which it meets the goals of the organization. Although the pandemic has negatively impacted numerous lives and business activities, more leaders considered it an opportunity because it offered new opportunities for business innovation and entrepreneurship, despite it being viewed as the most significant crisis in the modern world. Considering the significant changes caused by the COVID-19 pandemic and the response to it, it is no longer simply considered an option to adopt and use AI/ML systems, but rather an obligation. There is a lack of understanding of the factors contributing to the success of recommendation systems, therefore, these systems are rarely used to their full potential. An analysis of the relationship between information quality, system quality, use/intention to use, and user satisfaction in recommender systems was conducted using a mixed-method approach based on the DeLone and McLean IS success model. Students enrolled in a two-year college who used a portal as part of their academic journey were the target population of this study, and a total of 8,559 participants were contacted to participate, and 305 of them completed the survey. The results of this study indicate that quality factors relate closely to the success of the recommender system as measured by organizational effectiveness. The results indicate that there are statistically significant relationships between the independent variables, Information Qu (open full item for complete abstract)

Doctor of Business Administration, Cleveland State University, 2023, Monte Ahuja College of Business

According to McKinsey Consulting (2016 and 2021), 70% of digital transformation efforts fail to reach their stated objectives, and the rate of software complexity continues to grow. At the same time, the degree of interdependencies in the workplace are increasing (Handke et al. 2022), and virtual teams are becoming more commonplace. As a result, IS managers seeking ways to foster successful outcomes among software development teams face formidable challenges. Software development teams must coordinate expertise, adhere to development methodologies, follow prescribed processes, and solve complex problems. Due to knowledge intensity, task interdependencies, and the degree of risk associated with software projects, the nature of shared cognition among these teams becomes a salient consideration for managers and researchers alike. Components grounded in theories of group psychology such as shared mental models and transactive memory have been shown in prior research to favorably influence team performance. Shared mental models are the collective, structural representation of a team's knowledge domain. Transactive memory is the interaction of individual memories and group processes. For software development teams however, it is unclear whether these factors translate into higher quality software development team outcomes. It is also unclear whether the implementation of formal software development processes foster the maturity of shared mental model similarity and transactive memory. Using an online experiment, this study implements group training and coding standard interventions to understand (a) the nature of relationships between these interventions and software development team performance (b) whether these interventions help strengthen the similarity of shared mental models in software development teams, and (c) whether collectively, stronger shared mental models and transactive memory positively influence software development team performance. Using results fro (open full item for complete abstract)

PHD, Kent State University, 2023, College of Business and Entrepreneurship, Ambassador Crawford / Department of Management and Information Systems

One of the most prolific research streams in individual level information systems (IS) research has been the study of technology adoption and use. The Technology Acceptance Model (TAM; Davis, 1989) has set the stage for much of the current study of technology adoption. Over the years, other theories such as the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) have improved upon TAM by incorporating additional key factors, but the goal has always remained the same: improving our understanding of factors impacting technology adoption. Research such as Jasperson et al. (2005) has pushed the research agenda further towards a more holistic view of technology use, by incorporating post-adoption behaviors such as the exploration and adaptation of particular technology features (e.g., Sun, 2012). Later researchers (e.g., Ahuja & Thatcher, 2005; Carter et al., 2020; Maruping & Magni, 2015; Sun, 2012) have investigated the exploration, exploitation, adaptation, and extension of IT features to better understand how individuals innovate with IT. Other researchers have studied IS use through the lenses of cognitive absorption (Agarwal & Karahanna, 2000), mindfulness (Thatcher et al., 2018), and flow (Csikszentmihalyi, 1990). My dissertation seeks to further contribute to the post-adoption literature, by specifically investigating how individuals and groups engage with technology. In the first essay, I investigate the current state of the engagement literature and summarize the key areas of research including two major literature streams (Khan, 1990; O'Brian and Toms, 2008). The key findings from this review point towards an incomplete understanding of individual level engagement in the literature, and an almost desolate research stream on group level engagement. I then propose a comprehensive model for simultaneously investigating engagement at both the individual and group levels with both a focal task and the technology used to support it. T (open full item for complete abstract)

Master of Science in Cyber Security (M.S.C.S.), Wright State University, 2023, Computer Science

Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems with a Zero Trust based approach to process authentication. This work also develops Path-Safe, a MAC security system that focuses on protecting filesystem access from unauthorized processes and malware. We show that our security system can mitigate real-world malware threats with low overhead and high accuracy.

Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

When autistic professionals in the United States secure employment, over half remain underemployed (Linden & Wiscarson, 2019). Underemployment impacts a significant portion of the U.S. population, as autism appears in 2.4% of males and 0.5% of females in the United States (Austin & Pisano, 2017). Even with a college degree, 85% of autistic Americans remain unemployed compared to 4.5% of the general U.S. population (Lyn Pesce, 2019). Employment programs have been developed for autistic people. However, these programs may stereotype autistic people as pattern-recognizing-savants and build autism employment programs centered on these stereotypes (Austin et al., 2017). Furthermore, autistic professionals, especially those lacking visible characteristics of autism, feel that a disclosure of a clinical autism diagnosis negatively affects their employability (McMahon, 2021). However, limited autism self-disclosures by prominent tech executives, such as Elon Musk (Musk, 2021), anecdotally demonstrate autistic professionals self-directing themselves to attain leadership roles. This study used qualitative research to determine 12 self-directed career growth success factors of autistic business leaders who served the U.S. Tech industry. A phenomenological approach with semi-structured interviews was used in the research to understand themes as data was collected to determine how autistic business leaders self-directed themselves to their leadership roles. The research study aims to empower autistic professionals to own their career development with or without third-party support.

Doctor of Philosophy, University of Toledo, 2023, Manufacturing and Technology Management

This research study aims to explore the factors responsible for social media addiction and examine its impact on work productivity among full-time employees. Grounded in the theoretical frameworks of Uses and Gratification Theory and Social Cognitive Theory, the study employed a survey instrument to collect data from a sample of 476 full-time employees. The collected data were subjected to analysis using the partial least squares structural equation modeling (PLS-SEM) approach through the SmartPLS 4.0 software package. The research model developed for this study incorporated various constructs and variables related to social media addiction. The analysis focused on investigating the relationships between these constructs and examining their effects on work productivity. The study specifically considered the antecedents to social media addiction, which encompassed enjoyment, social needs, information needs, network size, and object of addiction, based on existing research (Bulgurcu et al., 2010). The findings of this study revealed significant contributions of social needs and information needs as antecedents to social media usage among full-time employees. These findings suggest that employees turn to social media platforms to fulfill their social and informational needs within the workplace. However, contrary to expectations, enjoyment, network size, and object of addiction were not found to be significant factors influencing social media usage in the context of work productivity. Moreover, the study identified several determinants of social media addiction, including mindfulness, task distraction, technostress, health, and work productivity. These factors were found to be influential in shaping individuals' susceptibility to social media addiction within the work environment. The practical implications of this study highlight the importance of recognizing and addressing social media addiction in the workplace to enhance work productivity. O (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 2023, Management

Digital platforms, such as Facebook, Amazon, and Uber, are becoming crucial components of modern societies' infrastructure. In addition to driving innovation and economic growth, they shape political opinion and facilitate social change. Despite their pervasiveness, digital platforms are increasingly challenged with emerging uncertainty that stems from a variety of sources and affects a wide range of platform actors. Without a proper and prompt approach to navigate such uncertainty, digital platforms are susceptible to potential failures and business discontinuity. This dissertation provides a preliminary understanding of the emerging uncertainty in digital platforms. It focuses on uncertainty associated with negative events that range from incidents in the interactions between the platform's external users to major exogenous shocks that have a system-wide impact on the digital platform. Drawing on qualitative methods and interdisciplinary research, the dissertation is comprised of three independent studies. The first study utilizes a grounded theorizing approach to understand how users of digital platforms attribute blame for negative incidents. It follows media coverage of extreme incidents in two major platforms: YouTube and Airbnb. Findings show that the initial attribution of blame is transformed into a collectively distributed attribution through a retrospective sensemaking process. Study 2 seeks to understand how digital platforms organize for high reliability to manage uncertainty in negative incidents. An in-depth case study of the support function in a marketplace platform demonstrates evolving routine dynamics in the upstream (preventing incidents) and the downstream (resolving incidents) processes. Study 3 adopts a macro perspective on negative incidents by studying how digital platforms maintain operational resilience against major shocks. A longitudinal case study follows the response of a marketplace platform to the disruptions caused by the recent C (open full item for complete abstract)

MFA, Kent State University, 2023, College of Communication and Information / School of Visual Communication Design

I chose to focus on improving libraries to make them viable institutions in this age of quick information because I have noticed a gap in how libraries are perceived and utilized by the general public. As someone who works as a designer in the library system, I have observed a great emphasis on programming and events. However, not as much attention is given to the physical space and how it can be made more attractive to people of all ages and backgrounds. In today's fast-paced world, people have access to a wealth of information at their fingertips, making libraries appear to be less relevant than they were in the past. However, libraries still have an essential role to play in society, especially in fostering lifelong learning, community building, and providing access to resources that are not readily available to everyone. By rethinking how libraries are currently designed and used, we can ensure that they remain relevant and useful to people in the digital age. One of the key issues that I want to address is how to make the library a place that appeals to both adults and children. While libraries have traditionally been viewed as places for young children, there is a need to attract and engage adults with diverse interests and life commitments. By focusing on creating a space that is both visually appealing and offers resources and services that cater to adults, we can make libraries more attractive to a wider audience. Furthermore, I want to explore how the visual layout of libraries, including signage and wayfinding, can be optimized to help people navigate and better understand the resources available to them. Research has shown that people respond more quickly and accurately to visual stimuli, making it important to create clear and effective signage that can help people find what they need. By studying how visual cues can create an intuitive and easy-to-use library system, we can make libraries more user-friendly and accessible to all. In conclus (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

The performance of machine learning (ML) methods, including deep learning, for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. Therefore, finding the optimal values of these hyperparameters is integral to improving the prediction accuracy of a machine learning algorithm and the model selection. However, manually searching for the best configuration is a tedious task, and many AutoML (automated machine learning) frameworks have been proposed recently to help practitioners solve this problem. Hyperparameters are the values or configurations used to control the algorithm's behavior while building the model. Hyperparameter optimization is the guided process of finding the best combination of hyperparameters that delivers the best performance on the data and task at hand in a reasonable amount of time. In this work, the performance of two frequently used AutoML hyperparameter optimization frameworks, Optuna and HyperOpt, are compared on popular OpenML tabular datasets to identify the best framework for tabular data. The results of the experiments show that the performance score of Optuna is better than that of HyperOpt, while HyperOpt is the fastest for hyperparameter optimization.

MFIS, Kent State University, 2023, College of the Arts / School of Fashion

​ The fashion industry's overproduction, environmental impact, global sourcing, fast fashion business model, and labor exploitation make the current system unsustainable (Bick et al., 2018; EPA, 2023; WWF, 2023). The industry needs to shift toward supporting practices promoting environmental and social protection, to ameliorate these global problems. As competition for natural resources increases, executives will need the strategies of sustainability officers, experts, and research to guide the response toward ethical and equitable solutions. This study investigates how sustainable leaders in the fashion industry conceptualize and implement sustainable practices. Most research revolves around the importance of consumer perception, education, circular economy, and innovation toward advancing sustainable objectives. However, much of the current academic literature overlooks the sustainability leaders in the industry that set, communicate, and evaluate their companies' sustainability agendas. This disconnect suggests a research gap focused on how firm strategies and systems thinking support sustainability in the fashion sector. The research aims to identify how sustainable fashion leaders implement systems and best practices to achieve sustainable objectives through a knowledge discovery lens. A semi-structured qualitative interview method was used to explore the topic. The transcripts of the qualitative interviews were analyzed using NVivo software to facilitate the coding and analysis of data generated from the interviews. The targeted questions aim to identify Knowledge Discovery activities or strategies used to move sustainable objectives forward; the goals of this study are to explore: (1) What are the conceptualizations of sustainability in the fashion industry for sustainability leaders and their organizations? (2) How do those leaders and organizations discover the necessary knowledge to implement those conceptualizations? The findings suggest that sustainable (open full item for complete abstract)