Dissertation Defense Announcements

Candidate Name: Yelixza I. Avila
Title: EXPLORING CARRIER IMPACT ON IMMUNE RESPONSE TO NUCLEIC ACID NANOPARTICLES AND PROVIDING INSIGHTS INTO CONDITIONALLY ACTIVATED THERAPEUTIC NUCLEIC ACIDS
 April 15, 2024  10:00 AM
Location: Burson 116
Abstract:

In this work, the in vitro characterization profiles of delivery vehicles, specifically polyamidoamine (PAMAM) dendrimers, are assessed to investigate their impact on pre-established immune responses to immunostimulatory and immunoquiescent nucleic acid nanoparticles (NANPs). Isolated human peripheral blood mononuclear cells (PBMCs) were used as the universal model system for these investigations, providing detailed understanding of the impact delivery vehicles play on NANP recognition. Additionally, to further identify mechanisms of immune recognition of these novel formulations, several engineered reporter cell lines were employed to understand the involvement of pattern recognition receptors, relevant to nucleic acid detections in human cells.
Furthermore, we explore the design and in vitro assessment of conditionally activated reconfigurable nucleic acid nanoparticles (recNANPs). By further investigating dynamic recNANPs and their interactions with delivery vehicles and the immune system, we aim to gain deeper insights into these systems. This innovative platform will enable the development of refined design principles for therapeutic systems incorporating NANPs, allowing for the creation of more precise and optimized options.



Candidate Name: Hussein Ghnaimeh
Title: EXTENDING THE EXTENDED UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT2): The moderating Role of Information Privacy Concerns
 April 11, 2024  1:00 PM
Location: Zoom https://charlotte-edu.zoom.us/j/95020353532
Abstract:

This dissertation enhances the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by integrating information privacy concerns, examining their influence on the adoption of web-based healthcare portals. Through a survey of 298 U.S. residents using healthcare technologies, the study investigates the interplay between UTAUT2 predictors—Performance Expectancy, Effort Expectancy, Facilitating Conditions, Habit, Social Influence, and Hedonic Motivation—and the intention to use these technologies, while assessing how privacy concerns modulate these relationships. Regression analysis highlights the positive impact of Performance Expectancy, Effort Expectancy, and Habit on adoption intent, with privacy concerns significantly moderating the relationship between Effort Expectancy and usage intention.
The research enriches the UTAUT2 model by showcasing the pivotal role of privacy concerns, thus advancing theoretical understanding and enhancing model predictability in the context of healthcare technology. Practically, it offers insights for practitioners and policymakers on addressing privacy concerns to improve technology adoption. This synthesis of privacy concerns within the technology acceptance framework paves the way for targeted strategies to increase the uptake of healthcare technologies, marking a significant contribution to both academic discourse and practical application in healthcare technology management.



Candidate Name: Hussein Ghnaimeh
Title: EXTENDING THE EXTENDED UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT2): The moderating Role of Information Privacy Concerns
 April 11, 2024  1:00 PM
Location: Zoom
Abstract:

This dissertation enhances the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by integrating information privacy concerns and examining their influence on adopting web-based healthcare portals. Through a survey of 298 U.S. residents using healthcare technologies, the study investigates the interplay between UTAUT2 predictors—Performance Expectancy, Effort Expectancy, Facilitating Conditions, Habit, Social Influence, and Hedonic Motivation—and the intention to use these technologies while assessing how privacy concerns modulate these relationships. Regression analysis highlights the positive impact of Performance Expectancy, Effort Expectancy, and Habit on adoption intent, with privacy concerns significantly moderating the relationship between Effort Expectancy and usage intention.
The research enriches the UTAUT2 model by showcasing the pivotal role of privacy concerns, thus advancing theoretical understanding and enhancing model predictability in the context of healthcare technology. Practically, it offers insights for practitioners and policymakers on addressing privacy concerns to improve technology adoption. This synthesis of privacy concerns within the technology acceptance framework paves the way for targeted strategies to increase the uptake of healthcare technologies, marking a significant contribution to both academic discourse and practical application.



Candidate Name: Tianyang Chen
Title: SPATIALLY CONTEXT-AWARE 3D DEEP LEARNING FOR ENHANCED GEOSPATIAL OBJECT DETECTION
 April 11, 2024  11:00 AM
Location: McEniry 307
Abstract:

This dissertation explores the intersection of Geographic Information Science (GIScience) and Artificial Intelligence (AI), specifically focusing on the enhancement of 3D deep learning models by spatial principles for understanding 3D geospatial data. With the rapid advancement in geospatial technologies and the proliferation of 3D data acquisition methods, there is a growing necessity to improve the capability of AI models to interpret complex 3D geospatial data effectively. This work seeks to leverage spatial principles, particularly spatial autocorrelation, to address the challenges pertaining to 3D geospatial object detection.

The research is structured around three pivotal questions: the utility of spatial autocorrelation features for understanding 3D geospatial data, the approach to derive content-adaptive spatial autocorrelation features, and the enhancement of post-processing in the task of 3D geospatial object detection. Through a series of experiments and model developments, this dissertation demonstrates that incorporating spatial autocorrelation features, such as semivariance, significantly enhances the performance of 3D deep learning models in geospatial object detection. A novel spatial autocorrelation encoder is introduced, integrating spatial contextual features into the 3D deep learning workflow and thereby improving accuracy in detecting objects within complex urban and natural environments. Further, the dissertation delves into the challenges brought by data partitioning and sampling in large-scale 3D point clouds, as evidenced in the DeepHyd project focusing on the detection of hydraulic structures (i.e., bridge and its components). The findings highlight the critical role of spatial dependency patterns in optimizing object detection accuracy and pave the way for future improvement of the 3D deep learning frameworks.  



Candidate Name: Hesam Fallahian
Title: Synthesizing Contextually Relevant Tabular Data Using Context-Aware Conditional Tabular GAN (CA-CTGAN) and Transfer Learning
 April 10, 2024  2:00 PM
Location: https://charlotte-edu.zoom.us/j/96836215539
Abstract:

The Context-Aware Conditional Tabular Generative Adversarial Network (CA-CTGAN) introduces an innovative architecture for the generation of synthetic tabular data, distinguished by effectively incorporating context-specific elements into its generative process. This enables the production of synthetic datasets that not only accurately reflect real-world distributions but are also tailored to specific contexts across a variety of experimental domains, including laboratory, field, natural, and clinical experiments, as well as survey research. In many cases, CA-CTGAN can generate data suitable for research purposes, potentially reducing or eliminating the need for certain real-world experiments. By utilizing Transfer Learning the model effectively identifies and exploits complex semantic relationships within the data to ensure the implementation of rigorous contextual requirements and maintains high semantic integrity. Furthermore, a novel auxiliary classifier is implemented, which includes entity embedding and multi-class multi-label capabilities, enabling the creation of enhanced datasets that strictly adhere to the specified contextual requirements. These contributions position CA-CTGAN as a remarkably versatile and efficient tool across multiple scientific disciplines. Its ability to generate high-quality, contextually relevant synthetic data not only streamlines research processes and reduces associated costs but also addresses ethical concerns in sensitive studies. Consequently, CA-CTGAN emerges as an essential resource for researchers, facilitating more ethical, cost-effective, and data-informed experimental design and decision-making.



Candidate Name: Daisy Ortiz-Berger
Title: Understanding Consumers' Intention to Act on Social Media Influencers' Cosmetic Surgery Recommendations
 April 10, 2024  10:00 AM
Location: Zoom: https://charlotte-edu.zoom.us/j/98145672448
Abstract:

UNDERSTANDING CONSUMERS’ INTENTION TO ACT ON SOCIAL MEDIA INFLUENCERS’ COSMETIC SURGERY RECOMMENDATIONS

(Under the direction of Dr. Jared Hansen)

A growing concern is how social media is redefining how consumers view themselves and their choices to reshape their physical bodies. There is a stream of research that indicates that attractiveness is important to people. Some studies focus on the perceived benefits of attractiveness in their authenticity. A different stream has started to look at coolness. Other studies have focused on attractiveness and envy. This research combines all of these different reasons together, comparing how they work in tandem, with a new lens of focus: consumers’ views of the attractiveness, authenticity, and coolness of the social media influencer, and how those elements in tandem, in combination with envy, impact consumers' behavioral intention to do the things (e.g., cosmetic procedures or surgeries) recommended by the influencers. Additionally, it examines if potential envy antecedents of (a) attractiveness to improve job opportunities versus (b) attractiveness to ‘fit in' vary depending on the consumer life stage. I elaborate on implications for future research related to marketing and society, marketing managerial practice, and consumer well-being.

Keywords: Instagram; social media influencer; technology acceptance model (TAM); structural equation modeling; attractiveness; authenticity; coolness; envy; fitting in; career opportunities; cosmetic surgery



Candidate Name: Kimberly D. Turner
Title: THE EXPERIENCES OF BLACK WOMEN ADMINISTRATORS IN MID-LEVEL LEADERSHIP NAVIGATING THE SUPERWOMAN SCHEMA
 April 10, 2024  10:00 AM
Location: COED 259
Abstract:

This qualitative study examines how Black women mid-level leaders navigate the superwoman schema. The findings extend Woods-Giscombé’s (2010) work by exploring the schema’s impact on Black women working in mid-level leadership administrative positions at HWIs. A descriptive phenomenological study was employed to understand and describe the lived experiences of Black women mid-level leaders and how the superwoman schema impacts work, leadership style, and personal care. The research questions addressed were: (1) How do Black women mid-level higher education administrators experience the superwoman schema at HWIs?; and (2) How do Black women mid-level higher education administrators respond to the superwoman schema at HWIs? Semi-structured interviews were conducted with 10 Black
women who identified with the characteristics of the superwoman schema, worked at a HWI, and served in a mid-level leadership role. Data were analyzed utilizing Colaizzi’s seven-step descriptive, phenomenological data analysis process (Appendix F). Findings from one-on-one interviews indicate Black women mid-level leaders experience the exhaustion of misogynoir and use resistance responses focusing on their personal advocacy and joy. In relation to the superwoman schema, participants were aware of their emotions, exhausted from external pressures to succeed without the proper resources, and committed to the preservation of self and survival. There was consistent commitment to help others and preserve the Black community while also finding community for themselves.



Candidate Name: Kimberly D. Turner
Title: THE EXPERIENCES OF BLACK WOMEN ADMINISTRATORS IN MID-LEVEL LEADERSHIP NAVIGATING THE SUPERWOMAN SCHEMA
 April 10, 2024  10:00 AM
Location: COED 110
Abstract:

This qualitative study examines how Black women mid-level leaders navigate the superwoman schema. The findings extend Woods-Giscombé’s (2010) work by exploring the schema’s impact on Black women working in mid-level leadership administrative positions at HWIs. A descriptive phenomenological study was employed to understand and describe the lived experiences of Black women mid-level leaders and how the superwoman schema impacts work, leadership style, and personal care. The research questions addressed were: (1) How do Black women mid-level higher education administrators experience the superwoman schema at HWIs?; and (2) How do Black women mid-level higher education administrators respond to the superwoman schema at HWIs? Semi-structured interviews were conducted with 10 Black women who identified with the characteristics of the superwoman schema, worked at a HWI, and served in a mid-level leadership role. Data were analyzed utilizing Colaizzi’s seven-step descriptive, phenomenological data analysis process (Appendix F). Findings from one-on-one interviews indicate Black women mid-level leaders experience the exhaustion of misogynoir and use resistance responses focusing on their personal advocacy and joy. In relation to the superwoman schema, participants were aware of their emotions, exhausted from external pressures to succeed without the proper resources, and committed to the preservation of self and survival. There was consistent commitment to help others and preserve the Black community while also finding community for themselves.



Candidate Name: Lori Eberly
Title: Long-Term Care in the United States: Examining the Role of Socioeconomic Status
 April 10, 2024  9:00 AM
Location: CHHS 426
Abstract:

My research examined the association between socioeconomic status (SES) and informal versus formal care use, unmet care needs (UCN), and concordance between preferred and actual care used; exploring whether the middle class faces barriers accessing care. Each study involved a cross-sectional analysis using NHATS data. Guided by Andersen and Newman’s behavioral model of health, I explored the relationship between SES and each outcome of interest, controlling for predisposing, enabling, and need factors. Descriptive analysis characterized the sample; bivariate analysis examined the relationship between SES and each outcome of interest and associations between SES and the control variables. Logistic regression with backward elimination retained control variables with a p-value less than 0.10. Results were interpreted using adjusted odds ratios (AOR) and 95% confidence intervals (CI). The middle-SES group had decreased odds of using informal care compared to the low-SES group, but increased odds compared to the upper-SES group. The middle-SES group had increased odds of UCN compared to the low-SES group, but no significant association when compared to the upper-SES group. The middle-SES group had decreased odds of concordance between preferred care and actual care used when compared to the low-SES group, but increased odds of concordance when compared to the upper-SES group.