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Three Essays On Customer Success Management: Unveiling Dynamics, Developing Measures, And Mitigating Challenges In Contemporary Business Practices
No full textThe emergence of Customer Success Management (CSM) as a pivotal aspect of contemporary business practices has spurred significant scholarly discourse and calls for deeper investigation. This dissertation endeavors to address this imperative through a comprehensive exploration of CSM across three essays. Essay 1 lays the groundwork by conducting a systematic literature review to assess the necessity for a tool to measure CSM and the susceptibility of Customer Success Managers (CSMs) to role-related stressors. The findings not only advocate for the development of a scale to evaluate CSM effectively but also underscore the prevalence of role stressors among CSMs, setting the stage for further inquiry. Building upon the understandings gathered from essay 1, essay 2 focuses on developing and validating a reliable and valid scale for CSM. Through meticulous scale development methods, this essay equips B2B suppliers with a potent mechanism for enhancing customer value-in-use (VIU) and strengthening their competitive position in the marketplace. In essay 3, drawing on self-determination theory, conservation of resources theory, and self-regulation theory, the dissertation delves into the impact of role conflict faced by CSMs on service sabotage. Through Structural Equation Modeling (SEM) analysis, this essay elucidates the intricate relationships between role conflict, job dissatisfaction, emotional intelligence, and service sabotage, offering practical recommendations for B2B suppliers to foster a conducive work environment favorable to championing customer success. Together, this dissertation not only contributes to exploring the existing gaps in academic research surrounding CSM but also offers actionable insights to enlighten strategic decision-making and enhance organizational performance in the dynamic landscape of B2B marketing
Gurevich\u27s Quizani Dialogs as an Example of Explainable Mathematics, and How This Is Related to Quantum Space-Time Ideas that Can Speed Up Computations
No full textEveryone talks about the need for Explainable AI -- when, to supplement a long difficult-to-understand sequence of computational steps leading to AI\u27s decision, we are looking for a shorter and understandable more-informal explanation for this decision. In this paper, we argue that this need is a particular case of what we call Explainable Mathematics -- when we want to supplement a long sequence of arguments and/or computations with a shorter and understandable more-informal explanation. Important instances of Explainable Mathematics are Yuri Gurevich\u27s Quizani dialogs that help explain complex results from theoretical computer science and physicists\u27 more-informal explanations of complex physical phenomena. We explain that in the physics\u27 case, since -- according to most physicists -- all physical theories are approximate, the use of approximate more-informal methods often makes more sense that the use of rigorous methods that implicitly assume that the current theories are absolute correct. We then apply this argument to one of the common uses of physics in theory of computation -- that limitation by the speed of light limits the computation speed. Specifically, we show that quantum space-time ideas potentially allow computations at the micro-level speed of light which can be higher than its usual macro-level value. This potential increase in possible communication speed can speed up computations
Why Interval-Valued (and Type-2) Fuzzy Methods Are Often More Effective
No full textInterval-valued and type-2 fuzzy techniques were designed to provide a more adequate representation of expert knowledge than the traditional (type-1) fuzzy techniques. Somewhat unexpectedly, they also often turn out to be more effective even when there is no expert knowledge at all -- when we are simply using fuzzy rules to fit experimental data. In precise terms, for the same number of parameters, interval-valued and type-2 systems often provide a better fit for the data and/or better quality control than traditional (type-1) fuzzy techniques. In this paper, we provide a theoretical explanation for this surprising phenomenon
Transnational Teachers on Both Sides Of The U.S.-Mexico Border: Exploring Their Identities and Conocimiento. A Phenomenological Study With A Comparative Stance
No full textThe transnationalism phenomenon is at the core of an increasingly globalized world. This phenomenological study with a comparative stance explores the lived experiences and perspectives of teachers with a transnational migration background with regards to how they [re]construct their identities and conocimiento. It compares and contrasts the histories and insights from participants in the borderlands with those from the heart of Mexico. Specifically, this study contributes to the under-explored body of literature which has taken a comparative stance in looking at transnationalism in education on both sides of the U.S.-Mexico border. Data derived from migration memory maps and in-depth, phenomenological interviews with a total of 10 participants (five participants in each of the two settings). Data analysis was guided by a three-pronged framework: transnationalism theory, theory of conocimiento, and identity theory, through both inductive and deductive data coding. Findings denote the different stages of conocimiento that the participants experienced, as well as borderlands epistemologies and imagined transnationalism
Computational Framework for Integrating Single Nucleotide Variant Scores to Identify Novel Genes in Cancers
No full textA quantitative integrated scoring function, iQ(G) was developed to assess the cumulative effects of nonsynonymous single nucleotide variants (SNVs) on the protein-coding genes with the goal to find novel candidate cancer-related genes from patients with acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and ovarian cancer (OC). With Genomic Data Commons as primary data resource for this project, whole-exome SNV data were extracted on patients with one of these three cancers. For each specific cancer, the iQ(G) function sums up the deleterious effects of individual SNVs with respect to the transcripts of the gene G in which they occur, weighted by the occurrence frequency difference between tumor and normal samples among patients and accounting for transcript lengths, to provide an overall cumulative pathogenic score for the gene. After obtaining the iQ(G) scores, the genes can be ranked accordingly, and the top-ranking genes are considered likely to be associated with the cancer. In this study, we applied iQ(G) scoring using four established SNV effect analyzers, namely FATHMM-XF, SIFT, PolyPhen, and CADD, as well as their averages. With a compiled list of known genes for each cancer type, we assessed the performance of iQ(G) when used with the individual analyzers, and with two integrative approaches that averaged the variant effects. The assessment results suggested that the integrated average approach had an overall advantage over using individual analyzers. Downstream bioinformatics analysis, including protein-protein interaction, gene ontology, and pathway analysis, performed on the top-scoring genes revealed similar carcinogenic pathways between the three cancers. This computational framework can be easily adapted to analyze SNV datasets for other cancers and to accommodate new SNV effect analyzers as they are developed in the future
Sustainable Disposal of Pecan Shells
No full textUrbanization acceleration has increased the consumption of natural resources and carbon footprint, pushing the need for sustainable development. Even though various construction materials are needed for infrastructure development, the most commonly used material is Ordinary Portland Cement for strength (OPC) and durability, while sand is used as a filler in the mixes prepared with OPC. The main goal of this study is to find a sustainable substitute for sand, which is locally available and currently being placed in landfills. One such material is pecan shells, and a thorough analysis of the literature led us to investigate the possible use of pecan shells in cement mortar mix. The pecan shells can be milled or ashed and then mixed in the cement mortar mix by substituting a portion of sand with pecan and tested as per ASTM- C109 guidelines, which provide the necessary ratios of OPC, sand, and water for a control mix. The study also sought whether adding pecan shells would improve the mortar\u27s compressive strength in fly ash presence. Under ASTM C109, the extent of this study concentrated on assessing the compressive strength of 2-inch mortar cubes. The sand proportions substituted with pecan shells ranged from 2% to 5% in increments of 0.25%. Additionally, the evaluations included varying curing times of 3, 7, 14, and 28 days to track strength gain over time. The average strength gains for specimens at varying substituted proportions were 389 psi from 3 to 7 days, 382 psi from 7 to 14 days, and 498 psi from 14 to 28 days for milled pecan shells (MPS). MPS specimens with fly ash showed average strength gains of 291 psi from 3 to 7 days, 478 psi from 7 to 14 days, and 391 psi from 14 to 28 days. Ashed pecan shells (APS) with fly ash resulted in average strength gains of 475 psi from 3 to 7 days, 448 psi from 7 to 14 days, and 573 psi from 14 to 28 days. APS only specimens resulted in strength gains averaging 450 psi from 3 to 7 days, 513 psi from 7 to 14 days, and 455 psi from 14 to 28 days. This study offers important new perspectives on the viability of pecan shells as a sustainable material in cement mortar, benefiting the building sector both in terms of waste reduction and resource economy. The analysis suggested that the milled pecan shells with and without fly ash lowered the compressive strength; thus, milled pecan shells are unsuitable for use in cement mortar mix. However, a mixture of ashed pecan shells and sand additions performed favorably compared to the control specimens by 500 psi and could partially replace sand
Gender And Ethnic Differences In Mental Health Outcomes In Sexual And Gender Minority (SGM) Youth And Young Adults In Texas And California, 2022-2024
No full textBackground: Depression and anxiety affect populations differently, particularly LGBTQ+, ethnic minorities, and youth with limited access to competent care. Risk factors include harassment, victimization, and discrimination. Purpose: To examine gender identity and ethnic differences in depression and anxiety among SGM youth in Texas and California. It is hypothesized that both Latinx and gender minority youth will report higher rates of depression and anxiety compared to non-Latinx and cisgender youth. Methods: Secondary analysis used data from the Family, Housing, and Me (FHAM) Project, following 83 LGBTQ+ youth from South Texas and California\u27s Inland Empire over 25 months. Data includes sociodemographic characteristics, adverse events, and mental health. Statistical analyses include descriptive statistics, ANOVA for monthly anxiety and depression differences, and Chi-square tests and logistic regression for associations between anxiety and depression and sociodemographic characteristics and adverse events. Multiple logistic regression assessed differences in depression and anxiety by cisgender identity and Latinx ethnicity, adjusting for significant factors. Results: Latinx participants did not have significantly higher depression (aOR: 1.30; 95% CI: 0.3, 6.6; p\u3e0.999) or anxiety (aOR: 1.6; 95% CI: 0.6, 4.6; p=0.946) compared to non-Latinx and Non-cisgender participants had significantly higher depression (OR: 7.2; 95% CI: 1.3, 40.5; p=0.022) but not anxiety (aOR: 2.0; 95% CI: 0.7, 5.8; p=0.111) compared to cisgender participants. Public Health Implications: Inclusive mental health policies are needed, as restrictive laws may worsen disparities among LGBTQ+ youth. Expanding gender-affirming care, legal protections, and targeted interventions can help mitigate these risks, particularly for marginalized groups
Finding Strength in the Struggle: The Impact of Resilience on Life Satisfaction Among Veterans with Mental Illness
No full textBackground: Veterans living with mental illness often face challenges that impact their overall life satisfaction and adjustment to civilian life. While resilience is widely considered a protective factor, limited research has examined its role in conjunction with personality traits, psychosocial factors, and demographic characteristics. Purpose: This cross-sectional study examined the associations between resilience and life satisfaction among U.S. veterans with mental illnesses, while accounting for personality dimensions, demographic variables, and psychosocial factors. Methods: Secondary data from 156 veterans diagnosed with mental illness were analyzed. Hierarchical linear regression was used to assess the relationship between resilience and life satisfaction, controlling for relevant covariates Results: Resilience demonstrated the strongest positive association with life satisfaction (β = .379, p \u3c .001). Additional significant associations were observed for agreeableness (β = .170, p = .040), marital or cohabitation status (β = .158, p = .024), and age, which was negatively associated (β = â??.188, p = .011). A gender difference in resilience was also noted, with male participants reporting higher resilience scores than females (p = .049). Discussion/Conclusion: Findings highlight resilience as a key factor linked to life satisfaction among veterans with mental illnesses, supporting the need for targeted, individualized interventions. Incorporating personality traits and gender-specific considerations into resilience-building programs may enhance their effectiveness and relevance. Recommendations: Future initiatives should integrate personalized, evidence-informed approaches to resilience development, to better address the diverse needs of veterans. Keywords: Veterans, resilience, life satisfaction, mental illness, personality, psychosocial factor
Former Foster Youth And The Impact Of Providing Basic Needs On Their Experience In College
No full textThis dissertation examines the experience of former foster youth as they enter college and attempt to earn a college degree. The Tuition Waiver Program is designed to cover the cost of college beyond any financial aid former foster students receive from the university they are attending. Over 3,000 former foster college students across Texas use the Tuition Waiver Program. However, less than 3% of former foster college students in Texas complete their college degrees (Watt & Faulkner, 2020). A successful college experience for former foster youth provides a firm foundation on which students can continue to grow and develop, gaining lifelong tools to assist them as they acquire their degree and become positive members of society. This case study is a qualitative study consisting of a one-on-one interview that documents the personal experiences of one former foster college student from the start of her college career to the completion of her bachelor\u27s degree. The information shared in this study shows the importance of providing support and resources to former foster youth that align with Maslow\u27s Hierarchy of Needs and Schlossberg\u27s Transition Theory. When this student was in a positive living environment and had the support from those she loved and trusted, she had a higher academic success rate than when she faced significant transitional periods in the absence of love, support, and resources. By assessing the interview presented in this study, a framework of high-impact practices already available at many colleges and universities can be identified and shared with former foster youth to assist with their college experience. This will provide the support required to this fragile population, positively impacting the completion rate of their college degrees
A Multi-Modal Method For Synthetic Data Generation In Social Network Analysis
No full textSocial network analysis (SNA) research is often rife with data collection pitfalls, frequently leading to incomplete and missing data. With the growing use of SNA-based research, researchers must address the challenge of missing data and synthetic data generation in these settings. Missing data occurs due to longitudinal non-response or lack of response to sensitive or difficult-to-answer questions. Synthetic data generation in SNA settings addresses the lack of representation that is often present in large-scale SNA studies. This dissertation investigates synthetic data generation methods to address these challenges and develops a novel algorithm that leverages information from multi-modal data, e.g., databases combining graphical data with participant-level survey data. The synthetic data generation methods incorporate latent variable and stochastic modeling approaches, as well as large language models, approaches well-suited to SNA settings. The proposed algorithm is assessed using a variety of synthetic data generation approaches to determine the quality and diversity of the synthetic data. This assessment employs a rigorous set of metrics that are fine-tuned to SNA multi-modal data settings. The results demonstrate that the LLM and stochastic modeling approach outperformed the two latent feature models examined. This outcome potentially stems from the variable mapping in the latent feature models