Master of Arts in Psychology, Cleveland State University, 2021, College of Sciences and Health Professions

Previous job search research suggests a large proportion of jobs are acquired through contacting other people (i.e., networking; Granovetter, 1995). In recent years, research on networking has focused on determining the antecedents of the job search strategy (e.g., Wanberg et al., 2000). This study examined various perceived characteristics of networking, which included job seeker perceptions of autonomy, customizability, and social cost, as antecedents of networking intensity. In addition, this study examined extraversion, a personality trait that is predictive of networking behavior, as a moderator of the relationships between the perceived characteristics of networking and networking intensity. Results of this study have indicated that job seeker perceptions of autonomy, customizability, and social cost do not interact with extraversion for the prediction of networking intensity. However, job seeker perceptions of social cost were found to predict networking intensity. Additionally, job seeker perceptions of autonomy positively correlate with extraversion and networking comfort, which are predictors of networking intensity.

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science and Engineering

The demand for video content has skyrocketed in the past decade owing to the popularity of video streaming services such as YouTube and Netflix and the expanding usage of video analytics applications such as surveillance, telemedicine, and public safety. This tremendous demand for video has necessitated providing good quality of experience (QoE) to video application users. Although the concept of QoE is not new, the developments of new network architectures and computational paradigms have led to the need to design QoE-aware video communication frameworks that can handle the new challenges. The overall goal of this dissertation is to study video quality in emerging networks from the perspectives of both human users and video analytic tools and propose new QoE-aware video communication strategies to improve video quality in both cases. Content-Centric Networking (CCN) is a future Internet architecture that has been proposed to tackle the vast amount of global video traffic, provide better scalability, and allow more efficient bandwidth usage. A key feature of CCN is ubiquitous in-network caching, where popular contents are cached near the end-user for faster content fetching. However, this caching mechanism brings new challenges in maintaining QoE for video streaming. We investigated how in-network caching influences video content distribution and video streaming among CCN nodes. Then, we conducted human subjective tests to quantify the influence of the video stalling events on the overall QoE scores. After that, we proposed a new QoE-aware multi-source video streaming algorithm for CCN that aims to suppress the stalling resulted from switching between content sources. The exchange of video among different wireless communication entities has seen an uprise due to the popularity of wireless imaging applications and the Internet of Things (IoT) technology. Software-defined radio (SDR) is a promising technology to communicate across different wireless networ (open full item for complete abstract)

Master of Science (MS), Ohio University, 2021, Computer Science (Engineering and Technology)

The Interplanetary Internet (IPN) is an architecture for standardized communication between nodes located on or around different celestial bodies. The key concept of the IPN is to use standard Internet protocols within local high-bandwidth, low-latency networks and to interconnect these networks using an "interplanetary backbone" comprised of satellites and ground stations communicating using specialized protocols designed for use in low-bandwidth, high-latency networks. This thesis focuses on the performance within local networks constructed for use in an IPN setting. Delay Tolerant Networking (DTN) is a protocol designed to solve the challenges of IPN. This thesis studies the performance characteristics of the Interplanetary Overlay Network (ION), an implementation of the DTN protocol. A hardware test bench was constructed using two high-performance computers directly connected via a 10 Gbps link. A software tool was devised to test the throughput over this link under various configurations of ION. Through this testing, improvements to ION and configuration recommendations were found to increase the performance of ION in 10 Gbps networks. The main increases in performance were the result of locking the threads of ION to the same CPU core and increasing the shared memory allocation to convergence layer processes. Performance of ION was also studied on a test bench utilizing an ARM A53 processor which uses the same ARMv8 architecture used in the High Performance Spaceflight Computing architecture.

Master of Science (MS), Bowling Green State University, 2015, Computer Science

Benefits of Cloud Computing (CC) such as scalability, reliability, and resource pooling have attracted scientists to deploy their High Performance Computing (HPC) applications on the Cloud. Nevertheless, HPC applications can face serious challenges on the cloud that could undermine the gained benefit, if care is not taken. This thesis targets to address the shortcomings of the Cloud for the HPC applications through a platform called HPC as a Service (HPCaaS). Further, a novel scheme is introduced to improve the performance of HPC task scheduling on the Cloud using the emerging technology of Software-Defined Networking (SDN). The research introduces “ASETS: A SDN-Empowered Task Scheduling System” as an elastic platform for scheduling HPC tasks on the cloud. In addition, a novel algorithm called SETSA is developed as part of the ASETS architecture to manage the scheduling task of the HPCaaS platform. The platform monitors the network bandwidths to take advantage of the changes when submitting tasks to the virtual machines. The experiments and benchmarking of HPC applications on the Cloud identified the virtualization overhead, cloud networking, and cloud multi-tenancy as the primary shortcomings of the cloud for HPC applications. A private Cloud Test Bed (CTB) was set up to evaluate the capabilities of ASETS and SETSA in addressing such problems. Subsequently, Amazon AWS public cloud was used to assess the scalability of the proposed systems. The obtained results of ASETS and SETSA on both private and public cloud indicate significant performance improvement of HPC applications can be achieved. Furthermore, the results suggest that proposed system is beneficial both to the cloud service providers and the users since ASETS performs better the degree of multi-tenancy increases. The thesis also proposes SETSAW (SETSA Window) as an improved version of SETSA algorism. Unlike other proposed solutions for HPCaaS which have either optimized the cloud to make it more HPC-fr (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

Over the past ten years, cyberbullying has become a prevalent issue across various levels of education and society globally. This dissertation delves into the complex landscape of cyberbullying text detection. Through a thorough parametric analysis, it explores the intricacies of cyberbullying text detection research, presenting insights into potential solutions and strategies. A case study is conducted to investigate cultural variations and perceptions of offensiveness, particularly within Ghanaian culture, contributing to a deeper understanding of cyberbullying dynamics. The dissertation also explores strategies for prevention and fostering a safer online environment, along with examining cultural interpretations of technology features. Furthermore, this dissertation focuses on detecting cyberbullying in adversarial text content within social networking site, with a specific emphasis on identifying hate speech. Utilizing a deep learning-based approach with a correction algorithm, this dissertation yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.17%, and 91% respectively. The LSTM model's performance surpassed that of previous studies when compared. Additionally, the dissertation offers recommendations for defense strategies against adversarial attacks on AI-based models, providing valuable insights for future research endeavors.

Doctor of Education (Ed.D.) in Organizational Leadership , Franklin University, 2023, International Institute for Innovative Instruction

Declining student engagement has been an ongoing concern for educators, education researchers, school administration, and policymakers for the last 40 years (Axelson & Flick, 2011), with socioeconomically disadvantaged students being the most susceptible to disengagement (Finn, 1993; Jensen, 2013). The discourse surrounding declining student engagement includes exploring effective pedagogy as an intervention and influencing optimal academic development and successful school completion. Innovative pedagogical tools have been studied to provide a more engaging learning experience, evolving from chalkboards to tape recorders, projectors, computers, digital games, and the latest social networking sites (SNSs) (DeCoito & Vacca, 2020), such as Facebook, Twitter, and Instagram. Though the integration of SNSs into the curriculum in secondary education is still being fine-tuned, many educators have adopted them into their teaching strategies because of their growing popularity in society and, more recently, due to the pause in traditional learning caused by the coronavirus pandemic (Cavus et al., 2021; Iivari, 2020). However, concerns about a need for more guidelines on how SNSs should be integrated, their effectiveness, and teachers' preparedness are apparent (Pedler et al., 2020; Van Den Beemt et al., 2020). The study aimed to determine teachers' perceptions of the impact SNSs as pedagogical tools have on student engagement. A secondary purpose was to determine whether teacher perceptions of student engagement when SNSs were used as a pedagogical tool differed based on their years of experience, assignment integration, the type of SNS(s) used, and time spent per week using SNSs for varying assignments. Title 1 high school teachers were surveyed and answered a series of 5-point Likert scale questions rating their level of agreement with statements about SNSs as pedagogical tools and teachers' perception of their impact on different engagement indicators. The sum of the resp (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), University of Dayton, 2023, Educational Administration

In 2018, women earned 58% of all bachelor's degrees in higher education, yet only 36% of all STEM degrees (McCullough, 2019). Currently, 30% of the STEM workforce in the United States is made up on long-term, non-citizen residents and immigrants. Houssain and Robinson (2012) noted the importance of increasing the number of Americans pursuing STEM fields, as a lack of Americans in this field threatens the United States position as both a STEM and world leader. In the absence of more women in STEM fields, it will be hard to meet STEM workforce demands. Higher education is naturally a place to increase diversity and equity in STEM. Women academics in STEM leadership thereby hold essential information to understand what makes women successful in STEM. This qualitative research study used a constructivist, narrative inquiry method to investigate the lived experiences of women academics in STEM fields who have participated in the Executive Leadership in Academic Technology, Engineering, and Science (ELATES) program. ELATES is a leadership development program designed specifically for individuals who identify as women and advocates of women, who are mid-career professionals in STEM. ELATES provides leadership training for the purpose of advancing these professionals into leadership 4 positions on campuses across the United States and Canada. Utilizing an intersectional, feminist inquiry approach allowed the researcher to consider the multidimensional identities participants hold and how intersecting systems of power and oppression may impact their lives and careers. In addition, the researcher investigated participants' experiences with mentoring and networking, focusing on whether and how participants continued to use these skills and resources to advance other members of their campus community. The study's findings offer insight into the ELATES program and (1) participants' self-concepts as leaders, (2) mentoring and networking among women in STE (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), University of Dayton, 2023, Electrical Engineering

Emerging Artificial Intelligence (AI) techniques, including both Machine Learning algorithms and Deep Learning models, have become viable solutions to support network measurement and management. As the fundamental of network analytics, network traffic classification has recently been studied with the adoption of AI techniques. For example, widely studied AI-based traffic classifiers, developed based on artificial neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), have demonstrated high classification accuracy. However, their performance is limited to the coverage of the knowledge databases, which restricts their effectiveness in dealing with updated or new network applications. To address the limitations, model update mechanisms are introduced, which allow AI-based traffic classification models to sustain high performance by creating a new knowledge base. These mechanisms enable the AI-based network traffic classification models to adapt to those evolving network applications in dynamic network environments. Additionally, the dissertation discusses the challenges of AI performance in network security and resolves them by leveraging the proposed mechanisms.

Master of Science, Miami University, 2023, Computer Science and Software Engineering

In today's networked world, cybersecurity threats pose a significant challenge to the integrity and reliability of communication networks. One such threat is the crossfire attack, where adversaries exploit network vulnerabilities by injecting malicious packets into traffic flows. To address this, we present a novel crossfire detection scheme that solely inspects packet headers, reducing the computational overhead associated with packet inspection. Our proposed detection scheme includes both analysis of variance (ANOVA) and neural networks to identify anomalous packet behaviors indicative of crossfire attacks. To evaluate the effectiveness of our approach, we conducted experiments on a real ATT backbone topology, simulating a crossfire attack in the Mininet simulation environment. The results demonstrate that our detection scheme achieves an accuracy of 95.3\% in detecting adversarial packets, effectively mitigating the crossfire threat. Furthermore, we introduce a traffic optimization model to adapt routing decisions in response to crossfire or link flooding attacks. Leveraging the detection scheme's real-time analysis, our optimization model dynamically alters routing paths to minimize the impact of attacks on network performance. Overall, our research presents an innovative and comprehensive framework that combines efficient crossfire detection using packet headers, high-accuracy detection using ANOVA and neural networks, and an adaptive traffic optimization model.

Doctor of Education (Ed.D.) in Organizational Leadership , Franklin University, 2023, International Institute for Innovative Instruction

This explanatory sequential mixed methodology study examined the impact of mentorship on African American female accountants' advancement to executive leadership positions. The target sample included mentored African American female accountants in the Accountants of Color (AOC) Facebook group, Alliance of Black Women Accountants (ABWA), Black Women in Accounting and Finance Network LinkedIn group, and the Accountancy Board of Ohio. The goal of the mixed methods study was to understand the mentoring relationships, perceived mentorship benefits, and social networks experienced by African American female accountants. Due to the small sample size for the quantitative portion of the explanatory sequential mixed methods approach, multiple regression analysis was not performed. Phenomenological interviews were conducted for the qualitative approach to gain insight into participants' lived experiences regarding the benefits of connecting with a mentor, which contributed to the findings. Aside from the benefits of connecting with a mentor, the study's findings identified obstacles experienced by African American female accountants as they climbed the career ladder. The study's findings highlight a need to enhance the recruitment and retention of African American female accountants, diversity at the executive-level in leadership positions, and organizational strategies within accounting firms to help shatter glass and concrete ceilings in order for African American female accountants to obtain executive leadership positions. The study's research contributions highlight the benefits of mentoring relationships, networking, and the promotion of diversity initiatives within the organizational culture of accounting firms. The study enriched existing research as it focused exclusively on the lived experiences of African American female accountants to examine if mentorship influenced career advancement.

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

Individuals are exposed to viral fashion trends daily on social media channels like TikTok, Facebook, Snapchat, and Instagram. This study focuses specifically on Generation Z (Gen Z) and their purchase intentions regarding viral fashion trends on the popular social media channel TikTok. This study aims to better understand how the virality of a video on TikTok impacts the purchase intention of Generation Z(Gen Z) through examining parasocial interaction, perceived expertise, and trustworthiness of the content creator. A viral and nonviral video posted by two different influencers of the same gender/ethnicity was delivered to participants, followed by a survey regarding the participants' purchase intention guided by the influencer's trustworthiness, perceived expertise, and parasocial interaction with the audience. Approximately 421 Gen Z students at a large Midwest university were given a 26-question survey through the online platform, Qualtrics. The results are expected to not only identify the current questions surrounding Gen Z's purchase intentions based on trustworthiness, perceived expertise, and parasocial interaction with viral fashion trends viewed on TikTok but The results will also suggest a possible strategy or strategies to create viral marketing content that is critical for marketers to reach large audiences and build brand awareness.

Master of Arts, University of Toledo, 2023, Psychology - Clinical

Problematic social networking site use (PSNSU) has demonstrated associations with social anxiety symptom severity across the literature; however, less is known about transdiagnostic psychopathology-related variables that may mediate relationships between PSNSU and fear of evaluation. There is an especially prominent gap regarding mediating variables between PSNSU and fear of evaluation - involving difficulties in emotion regulation (DER) and intolerance of uncertainty (IU). The present study builds on recent research findings that fear of negative evaluation (FNE) and difficulties in emotion regulation are associated with PSNSU severity, and that intolerance of uncertainty is related to PSNSU severity and motives for addictive behavior. There is also a lack of literature regarding how fear of positive evaluation (FPE), a construct unique to social anxiety, relates to PSNSU severity. Given the current prevalence of SNS usage and the social nature of these sites, it is especially important to explore whether individuals who fear social evaluation use SNSs in a problematic way, and whether lesser-studied transdiagnostic constructs such as intolerance of uncertainty and difficulties in emotion regulation mediate the relationship between fear of evaluation and subsequent PSNSU. The present study explored this gap in the literature with a mediation model in which DER and IU explained relations between both FNE and FPE with PSNSU. Confirmatory factor analysis (CFA), structural equation modeling (SEM), and mediation analyses indicated that IU and DER mediated the relationship between FNE and PSNSU, but did not mediate the relationship between FPE and PSNSU. These findings highlight the role of IU in PSNSU for individuals experiencing social anxiety symptoms, which has not been previously established, and provide further support for the relationship of DER with FNE and PSNSU, where DER particularly functions as a mediator of this relationship.

Doctor of Philosophy (PhD), Ohio University, 2023, Instructional Technology (Education)

This mixed methods research study explores the university student learning experience of IP subnetting. Using a constructivist perspective, VR software was designed, written, and utilized in a learning process to gather both quantitative and qualitative data about the experience of learning IP subnetting. Participants underwent pre-test and post-test scoring to gather data about the depth of learning. NASA-TLX survey was implemented with all participants to gather quantitative data about situational learning factors. In addition, several participants were selected for interviews based on their scores. Overall, the pre-test to post-test Change Scores revealed that both Control and Experimental groups had significant learning gains from the learning process. However, the Experimental NASA-TLX scores showed significantly less cognitive load and stress when compared to Control groups. Interview data details provided a rich description and a more detailed perspective regarding the NASA-TLX results. Overall results from the research process indicate that learning using a VR application for IP subnetting allows for more rapid creation of mental schema for the subject matter.

Doctor of Psychology (Psy.D.), Xavier University, 2022, Psychology

Eating disorders are increasingly prevalent in young women, constituting a serious public health concern. Maladaptive use of social networking sites (SNSs) is associated with increased eating disorder risk factors and symptomology among young women, suggesting that eating disorder prevention programs targeting this behavior may be beneficial. The present study tested a modified version of the Body Project that was shortened to a single, 2-hour session to address attrition problems of previous versions and adapted to include elements specifically targeting maladaptive SNS use. Female undergraduates (N = 128) were blindly assigned to a high-dissonance intervention, a low-dissonance intervention, or a no-treatment control group during a two-step enrollment process. Using a repeated measures design to assess for differential change in outcomes (i.e., thin-ideal internalization, body dissatisfaction, dietary restraint, negative affect, eating disorder symptomology) between conditions across time from baseline to posttest and 1-month follow-up, a 3 (Time) x 3 (Condition) mixed factorial multivariate analysis of variance (MANOVA) identified a significant Time x Condition interaction. Notably, follow-up analyses indicated participants in both active conditions experienced significant decreases in eating disorder symptomology and multiple eating disorder risk factors across time. Against expectations, the active conditions did not produce significant reductions on any of the measured outcomes relative to controls at posttest or 1-month follow-up. Results are promising in that they suggest minimal exposure to this modified intervention can produce positive effects which may enhance the feasibility and accessibility of eating disorder prevention options for busy college students.

Master of Science in Engineering, University of Akron, 2022, Electrical Engineering

A decentralized framework for network optimization is presented for wireless sensing nodes. The wireless sensing nodes use a dynamic programming algorithm to choose optimal routes for data transmission from any network node to a specialized ‘gateway' node that provides access to the wider internet. The dynamic programming algorithm is a variation of the Bellman-Ford algorithm and allows for the wireless sensing nodes to make decisions based on locally available network information, resulting in a decentralized routing algorithm. Routing decisions depend on the cost it takes to communicate from a node to a gateway, either directly or indirectly, using neighboring nodes as relay points. Nodes constantly share information with neighbors and when something effects the cost of a path, such as a node failure or the discovery of a less costly route, all nodes upstream along the existing path are made aware and re-route accordingly. A sample network is used to illustrate and verify the functionality of the proposed algorithm. The network and node decisions are simulated to show the evolution of the network routing decisions, and the simulation consistently shows the network converging to an optimal configuration. The speed of convergence depends on the order in which the nodes are assumed to attempt to establish and optimize their connections.

Doctor of Philosophy (Ph.D.), University of Dayton, 2022, Electrical and Computer Engineering

Artificial Intelligence (AI)-based algorithms have demonstrated their robust capability to support networking and communication systems, such as network traffic classifier (NTC), intrusion detection systems, channel state information processing in massive multiple input multiple output (MIMO) wireless communication systems, etc. However, due to the relatively high-dimensional data input and limited computing resources, many of the existing AI implementations are too complicated for efficient processing in networking and communication systems. To address this issue, this dissertation explores a systematic approach that simplifies the AI-supported implementation for multiple networking and communication systems. The proposed approaches mainly evaluate the structure of AI implementations in different scenarios. In specific, for an AI-supported NTC development, an input feature contribution extraction scheme is developed to weigh each input feature based on both the significance and the uniqueness of the corresponding feature. The optimal set of input features is determined to minimize the complexity of targeting AI-based NTC while maintaining high performance in classification. Moreover, an autonomous update scheme is proposed to detect the changes in feature contribution and process updates. Evaluations of two fundamental AI-based classifiers demonstrated that the proposed scheme can significantly reduce the input features and accelerate NTC models by one to two magnitudes while maintaining high accuracy. The proposed autonomous update scheme can accurately detect a change in feature contributions and update the NTC models to sustain high accuracy. In addition, we further developed an adaptive pruning for MLP-based NTC to fit the different requirements of NTC due to network congestion. The results demonstrated that the lossless optimization and adaptive pruned network traffic classifier accelerate the baseline MLP-based NTC models by about 5 to 10 times based on the r (open full item for complete abstract)

Doctor of Education , University of Dayton, 2022, Educational Leadership

This study explores obstacles to career advancement faced by black female staff in Higher education. Through interviews and surveys, key factors impacting this population are identified and a solution is proposed in the form of an action plan. Interviews exposed the issue of isolation, lack of support, and a need for early training for both employees and supervisors. The proposed action plan adopts an Africentric model of community building to provide a structure to address the three identified themes. This study contributes to an area of research with limited attention. University diversity, equity, and inclusion (DEI) efforts routinely focus on diversifying faculty and student populations, while excluding staff. This mixed method research study addresses how the creation, implementation, and maintenance of a self-governing support structure is beneficial to the culture of the campus and expands the DEI recruitment and retention efforts ordinarily reserved for faculty and students.

Doctor of Education , University of Dayton, 2022, Educational Leadership

The purpose of this DiP is to explore existing fire service professional development programs, specifically identifying current program weaknesses and potential avenues for growth. The methods used for this action research included a document analysis of the selected sample departments and three interviews of command staff members of the selected population departments. The study was conducted using three different Ohio fire departments as the primary stage one population, with a secondary stage two survey to other Ohio fire departments. The result of this research supports earlier research that identified there is a direct relationship between participation in a structured professional development process program and success. The research established that to improve the likelihood for success, both for team members and departmentally, organizations must devote time and energy towards professional development. This research will add to the existing research foundation for future professional development research specific to the fire service. The quantitative data, specifically the results of survey data could be a baseline data set regarding any future research. This research could be utilized as a kickoff point for a future longitudinal long term studies regarding PLC impact on professional development.

Doctor of Philosophy (PhD), Wright State University, 2022, Computer Science and Engineering PhD

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting with TE and evaluating TE systems efficiently as this tool employs parallel processing to achieve high efficiency. The purpose of the simulator is two aspects: (1) We use it to understand traffic engineering, (2) we use it to formulate a new traffic engineering algorithm that is near-optimal and applicable in practice. We study the design of some important aspects of any TE system. In particular, the consequences of achieving optimal TE by solving the multi-commodity flow problem (MCF) and the consequences of choosing single-path routing over multi-path routing. With the help of the TE simulator, we compare many TE systems constructed by combining different paths selection techniques with two objective functions for rate adaptations: load balancing (LB) and average delay (AD). The results confirm that paths selected based on the theoretical approach known as Oblivious Routing combined with AD objective function can significantly increase the performance in terms of throughput, congestion, and delay.\par However, the new proposed system comes with a cost. The AD function has a higher complexity than the LB function. We show that this problem can be tackled by training deep learning models. We trained two models with two different neural network architectures: Mult (open full item for complete abstract)

Doctor of Business Administration (D.B.A.), Franklin University, 2022, Business Administration

The practical sense of business in female entrepreneurship as it relates to the concept of intrinsic and extrinsic creative behaviors of female entrepreneurs working in the United States is the main focus of this study. The field of female entrepreneurship is growing with the participation of women with or without full-time jobs in standard employment, with or without formal business education, and with or without equal access to financial resources compared to their male counterparts. This study aims to capture the definition of success and how female entrepreneurs perceive success. Utilizing convenience sampling, this qualitative study conducted semi-structured interviews with 15 successful female entrepreneurs in a major Midwest metropolitan area. With dual roles in work and family, the female entrepreneurs engage in a role of chaotic business management and self-branding with a quest for work/life balance. Their pursuit of a lifestyle business brings about a direction of working within an area of great interest, commonly called a passion. This passion allows for exploring what the female entrepreneur enjoys and a quest to produce a profit from that inspiration. Emergent themes resulting from this study are definitions of success, pandemic challenges, entrepreneurial credibility, social networking, business investment, brand management, creativity, innovation, profit design, and authentic leadership. One core result of this qualitative study is a theory called female entrepreneurial design. The female entrepreneur creates an organizational life unique to her personal style and business brand through personal self-care and professional investment.