Dissertation Defense Announcements

Candidate Name: Amanda Sargent
Title: Intersectional Status Beliefs Transfer in Employee Referral Processes
 May 16, 2022  10:30 AM
Location: COLVARD 3120
Abstract:

Using employee referral programs is generally considered a best practice for organizations seeking top quality talent. However, research on whether or not these programs result in positive outcomes equally for all applicants is mixed. To data, most research examining employee referral programs focuses on how status characteristics (such as race and gender) of applicants can result in unequal outcomes (such as being hired or promoted) for applicants with different identities. Little is known, however, about the influence of referring employee’s status characteristics during hiring processes and whether or not decision makers’ biases toward certain referring employees may lead to different hiring process outcomes for the applicants they refer. Using Status Characteristics Theory and the theory of Status Beliefs Transfer, hypotheses were tested regarding how status characteristics of referring employees, namely race and gender, might lead to a transfer of evaluators’ status beliefs from the referring employee to the applicant and affect subsequent applicant evaluations. Four hundred and thirty-seven U.S. individuals with hiring experience served as participants for an online resume evaluation experiment where the only difference between resumes was the name of the referring employee noted at the top of the document. Referring employee names were selected via pre-test to signal the referrer was either a white man, black man, white woman, or black women. Results of quantitative analyses revealed a positive statistically significant difference in average ratings of competence, recommendations for interviews, and starting salary between referred and non-referred applicants, with participants rating referred applicants more favorably. In addition, a positive statistically significant effect of race, but not gender, was found in average ratings of competence, commitment, interview recommendations, and salary recommendations for black referring compared to white referring employees. Additional qualitative thematic analysis of open response data describing rationale for participant ratings revealed additional intersectional evaluative differences among applicants referred by employees with different race/gender statuses. Taken together, and viewed through the lens of intersectional theories, findings suggest evaluations of applicants may have been influenced by a status beliefs transfer process whereby the intersectional status characteristics of referring employees were transferred onto and used to evaluate the applicants they referred. Implications for theory, practice, and future research are discussed.



Candidate Name: Li Song
Title: Impacts of connected and autonomous vehicles on deep reinforcement learning controlled intersection systems
 May 16, 2022  11:00 AM
Location: EPIC 3344
Abstract:

Connected and autonomous vehicle (CAV) technologies could significantly change the car-following behaviors and affect the performance of the intersection systems. As it is expected to have a long transition time during which human driven vehicles (HDVs) and CAVs will coexist, it is important to investigate the impacts of CAVs on the intersection systems under different market penetration rates (MPRs). Also, the currently used Highway Capacity Manual does not consider the impacts of CAVs when calculating the intersection capacity. Though highly needed, a new guideline for estimating the intersection capacity under different MPRs of CAVs is becoming a critical issue for transportation planners and engineers. Furthermore, combining the intersection traffic signal control (TSC) systems with deep reinforcement learning (DRL) provides a new potential solution to improve the efficiency, safety, and sustainability of the intersection system. However, the training procedure of the DRL TSC system requires large samples and takes a long time to converge. Furthermore, it is common to have several intersections along corridors or in networks. A single DRL agent is unable to control several intersections as this may result in exponential explosion in the action space. Hence, a modification of the DRL TSC framework to improve the training efficiency and a multi-agent control framework to control several intersections are needed.
To better prepare and guide both intersection planning and operations under different MPRs of CAVs and traffic demands, this dissertation provides an intensive evaluation of the impacts of CAVs in several signal intersection systems, as well as an in-depth analysis on intersection capacity adjustments that consider varying MPRs of CAVs. Also, a transfer-based DRL TSC framework is proposed and tested at different MPRs of CAVs and traffic demand levels. A multi-agent DRL TSC with shared traffic states between downstream and upstream intersections is investigated in a corridor. It is concluded that 100% MPR of CAVs can increase the saturation flow rate of the through-only lane by 126.8%. Meanwhile, transfer-based models could significantly improve training efficiency and model performance. The multi-agent DRL TSC also enables coordination between intersections. The insights of this research should be helpful and valuable to transportation researchers and traffic engineers in calculating intersection capacity, designing intelligent intersections, improving intersection efficiency, and implementing DRL-controlled traffic signals under the mixed flow with CAVs.



Candidate Name: Shaojie Liu
Title: The impact of connected and autonomous vehicles on the superstreets
 May 18, 2022  2:00 PM
Location: EPIC Room 3344
Abstract:

Connected and autonomous vehicles (CAVs) are a type of emerging technology that has promising potentials in improving many aspects of the existing transportation infrastructure, including operations, safety, and the environment. With the capability of traveling on the roads with shorter headways and more stable speeds, CAVs can yield a larger road capacity compared to human-driven vehicles (HDVs). Additionally, since the CAVs run on the roads with the guidance of computers or algorithms, accidents caused by errors from human drivers may be prevented, which can greatly reduce significant economic and societal losses. Less speed fluctuations are also beneficial to decrease emissions and contribute to the environment.
Thanks to the rapid development of computer science and communication technology, CAVs have evolved from theoretical experiments in academic labs to reliable products by commercial companies. Since both academic and industrial professionals have high expectations for CAVs, many studies have been conducted to explore and identify the impacts of CAV technologies on the transportation performances in many scenarios. These scenarios included conventional intersections, highway segments, on/off ramps, and roundabouts. Through extensive investigations on CAVs in different scenarios, it can be concluded that CAVs can perform better overall than HDVs. Nevertheless, it has also been found that the performances of CAVs are affected by many factors such as communication range, acceleration capabilities, and market penetration rates. Improvement in operational performance has been confirmed by existing studies when the market penetration of CAVs reaches a certain rate.
Superstreet is one of the innovative intersection designs and was proposed to alleviate the road congestion especially where unbalanced traffic volumes from main street and minor street exist. Superstreets have been successfully implemented in numerous states. Nevertheless, how CAVs would affect the performances of superstreets has not been explored, even to a minimum extent. This research is designed to investigate how CAVs with different technologies perform in the environment of superstreets. To be specific, the following questions will be answered: (1) at what market penetration rate CAVs would bring benefits towards operational performances; (2) at what extent CAVs would bring benefits towards operational performances of superstreets; (3) how the impact of CAVs on the operational performance would vary across different traffic scales and market penetration rates.
To achieve the research goals, models for CAV platooning, trajectory planning, and signal optimization have been developed, respectively. The effects of these models are tested respectively in a simulation environment where relevant traffic measures are extracted to evaluate the performances. The finding of this research may also be applied to other innovative intersection designs which have similar geometric characteristics and traffic patterns.



Candidate Name: Shohreh Shadalou
Title: Dynamic Illumination Systems using Freeform Optics
 May 06, 2022  1:00 PM
Location: Grigg 238
Abstract:

ABSTRACT
SHOHREH SHADALOU. Dynamic Illumination Systems using Freeform Optics. (Under the direction of DR. THOMAS J. SULESKI)

Illumination systems that can create light patterns of varying sizes or shapes with high efficiency and uniformity are advantageous for a range of applications, including lighting, augmented/virtual reality, laser-based manufacturing, medicine/dermatology, and lithography. Previous approaches for continuous variable illumination have utilized longitudinal movement of the source or other optical components along the optical axis, which increases both system size and light pattern non uniformity. Liquid lenses with adjustable membranes have also been used for tunable illumination, but leakage and manufacturing complexity can be significant issues. Thus, new approaches that enable dynamically tunable illumination patterns in compact, robust packages are of interest.

Recent advances in design, production and metrology have enabled the use of freeform surfaces in a wide range of optical imaging applications. As one example, the Alvarez lens consists of a pair of cubic freeform surfaces that enable variable focal length with small lateral displacements between the two elements. Complex freeform surfaces are also regularly used in static illumination systems such as automotive headlights and luminaires.

The primary objectives of this dissertation are to explore and characterize dynamic freeform optical systems enabling continuously variable illumination. Results are addressed through three articles. The first article introduces the use of arrays of freeform Alvarez lenses with LED sources to enable tunable illumination. The second article builds from this work to present the design, manufacturing, and characterization of a compact tunable illumination system. The third article introduces a general design method using freeform optics to enable variable optical illumination between two arbitrary boundary conditions. These three articles demonstrate the methods and utility of freeform optics for dynamic illumination systems.



Candidate Name: Wendy C. Long
Title: UNDERSTANDING PERCEIVED OVERQUALIFICATION AT WORK: A SCALE DEVELOPMENT AND LATENT PROFILE ANALYSIS
 May 06, 2022  11:00 AM
Location: Zoom
Abstract:

Employee overqualification is becoming increasingly relevant in a post-pandemic world. While there have been theoretical advancements in the overqualification literature, several methodological issues remain unresolved. Specifically, the conceptualization and operationalization of perceived overqualification (POQ) are often not aligned. To date, the perception of overqualification is not yet fully understood. Thus, the main goal of this dissertation is to address these methodological limitations. In Study 1, I refined the scope of POQ by offering an explicit construct conceptualization grounded in person-job fit theory and developed a new scale to measure the multidimensional construct. In Study 2, I validated the psychometric properties of the Perceived Overqualification at Work Scale (POQWS) and explored the relationship of POQ with various work-related outcomes. Taking a person-centric approach, I used latent profile analyses (LPA) to identify different profiles of overqualified employees in Study 3 based on the POQWS dimensions. This study is the first to examine the process by which patterns of variables are identified in POQ profiles and how these combinations differentially relate to outcomes. Results from a series of exploratory and confirmatory factor analyses clearly supported a four-factor model. In the subsequent study, four distinct profiles emerged from the latent profile analyses. One-way analyses of variance (ANOVA) provided further criterion-related validity evidence for these four profiles. Taken together, the findings from this dissertation lay the grounds for future person-centered research.



Candidate Name: MiKayla Raines
Title: Customer Success and the Transformation of Customer Relationships
 April 11, 2022  9:00 AM
Location: Zoom
Abstract:

The construct of “customer satisfaction” has been used for several decades in marketing to achieve outcomes such as customer loyalty, word-of-mouth communication, resistance to competition, and customer equity. Recent research, however, has indicated little to no correlation between customer satisfaction and many of these outcomes. A more recent marketing construct is “customer delight,” where affective bonds and positive associations are the foundations for customer relationships. While customer delight has numerous advantages, an important limitation is that it can only be used with certain types of products and consumption situations.
This study introduces the academic construct of “customer success,” an objective tool that could redefine customer relationships, and define it as an objective and mathematically based strategic process to maximize customer-desired outcomes. A long-term customer success strategy is customer-driven and designed to be mutually beneficial to both an organization and its customers. While the construct of customer success has been sporadically used by practitioners in the past, the use of the term has often been arbitrary, and the construct has never been precisely defined.
First, drawing on the reverse logic framework (RLF) of relationship marketing, the customer valuation model, and return on relationships (ROR), this study will use Hunt’s indigenous theory, inductive realist approach to help build the initial theoretical framework for the construct of customer success. Then, this study uses this construct in a government-to-customer (G2C) market scenario to test a series of hypotheses to evaluate government-achieved customer success for COVID-19 pandemic response outcomes. This study will conclude with theoretical and managerial research contributions and provide directions for future research.



Candidate Name: siqi huang
Title: ANALYSIS AND ENHANCEMENT OF RESOURCE-HUNGRY APPLICATIONS
 April 07, 2022  9:00 PM
Location: Online
Abstract:

Resource-hungry applications play a very important role in people's daily lives, such as real-time video streaming applications and mobile augmented reality applications.
However, there are several challenges to satisfy the user Quality-of-Experience (QoE) requirements of resource-hungry applications. First, these applications usually require a vast amount of network bandwidth resources to support the data communication of different functionalities. However, only limited network bandwidth resources can be assigned to these applications which leads to long network latency and poor user QoE. In addition, artificial intelligent (AI) and machine learning (ML) models are widely adopted in these applications which significantly increases the computation complexity of these applications. Because of the limited computing resource on mobile devices, computation-intensive tasks are offloaded to edge servers located at the edge of the core network. However, additional network latency and bandwidth usage are introduced which may degrade user QoE. In this dissertation, the characteristics of popular resource-hungry applications are first analyzed. Then, based on the analyzed characteristics, we propose several specific ally designed algorithms to enhance the performance of several popular resource-hungry applications.



Candidate Name: Allura Pulliam
Title: RELATIONSHIPS BETWEEN INSITUTIONAL TYPE, PERCEIVED EXPERIENCES OF RACIAL AND ETHNIC MICROAGGRESSIONS, MULTICULTURAL COUNSELING COURSE EXPERIENCE AND SOCIAL JUSTICE ADVOCACY ORIENTION AMONG COUNSELORS IN TRAINING AND PROFESSIONALS
 April 05, 2022  2:00 PM
Location: Virtual
Abstract:

The manifestations of institutional and interpersonal racism have been linked to lower recruitment, retention and matriculation rates among ethnic minority students in predominantly white institutions (Harper, 2012). Those who experience racial and ethnic microaggressions have been impacted in numerous deleterious ways. Physical, mental, emotional and political outcomes have been examined in prior research (McGee & Stovall, 2016; Smith, Allen & Danley, 2007; Sue, 2010; ). In counselor training programs, specific coursework in multicultural education introduces counselors to the foundational aspects of the Multicultural and Social Justice Advocacy Competencies (Ratts et al, 2016) . Using Critical Race theory as a framework, a non-experimental, correlational survey design was used to explore the relationship between institutional type, perceived experiences of school-based racial and ethnic microaggressions, racialized experiences in multicultural coursework and social justice advocacy orientation among counseling students and professionals (N= 346). A standard multiple regression indicated a significant relationship between the school-based racial and ethnic microaggressions and racialized experience in multicultural coursework with social justice advocacy orientation. However, there was no significant relationship with social justice advocacy orientation and institutional type. Results from an independent sample t- test indicated there were significant differences between institutional type in experiences with school-based racial and ethnic microaggressions and racialized experience in multicultural counseling coursework. There however, was no significant difference between institutional type with regard to the social justice advocacy orientation of participants in this study.



Candidate Name: Sabrina M. Brown
Title: Heritage-seeking and its impact on Black HBCU students
 April 07, 2022  10:00 AM
Location: https://wustl.zoom.us/j/96241646240?pwd=bkNzN3pycERQY1VzNnljdFBzczZGUT09
Abstract:

SABRINA M. BROWN. HERITAGE SEEKING AND ITS IMPACT ON BLACK HBCU STUDENTS. (Under the direction of DR. LISA MERRIWEATHER)

Abstract
Study abroad is a high-impact practice in the college and university setting that can lead to increased student engagement and student success. While study abroad participation has increased, it is not a common practice across ethnic demographics or minority-serving institutions. Heritage-seeking is a form of study abroad that allows students of the ethnic minority to learn more about themselves in the context of another country. The purpose of this qualitative descriptive study was to understand what, if any, impact heritage-seeking study abroad had on Black, HBCU students. This study also identified the aspects of heritage-seeking that are important to include in the experience to encourage student success.

This study utilized interviewed six HBCU students who participated in a heritage-seeking experience in Haiti. At the conclusion of the interviews, it was found that heritage-seeking study abroad impacted the students in two ways; it nurtured their university relationships, and it instilled a greater sense of responsibility to the Black community. This study also found that there were three aspects of heritage-seeking instrumental to this type of study abroad program: creating opportunities for students to develop relationships, developing it as an immersive experience; and allowing students the space to self-reflect.



Candidate Name: Raghuveer Gouribhatla
Title: MODELING THE EFFECTS OF ADVANCED DRIVER ASSISTANCE SYSTEMS ON DRIVER BEHAVIOR
 April 13, 2022  2:00 PM
Location: EPIC 3344
Abstract:

Driver errors are the leading cause and contribute to about 94% of traffic crashes. To mitigate this issue, improve mobility, and enhance safety, automobile manufacturers are striving to develop various types of advanced driver assistance systems (ADAS). These ADAS are designed to assist or in some cases take over certain driving maneuvers. On the other hand, the acceptance levels of ADAS among drivers are questionable. Many surveys determined that drivers are unaware of the applications and limitations of ADAS. While ADAS are designed to enhance safer driving, their indirect effects on driver behavior have been seldom ventured and widely debated.

The focus of this research is on developing different driving scenarios that replicate real-world driving conditions using a driving simulator. Selected participants were prompted to interact with traffic within the simulation environment through a setup equipped with warning (lane departure warning, blind-spot warning, and over speed warning) or automated (lane keep assist and adaptive cruise control) features. The responses of participants when driving a vehicle with warning features, advanced features, and without ADAS in the simulation were captured, analyzed, and compared to understand their effects. The findings are valuable insights to automobile manufacturers as well as policymakers to better design ADAS such that their applicability is streamlined from both safety and user perspective.