SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
1777 results
Search Results
Item Restricted THREE ESSAYS ON ENTREPRENEURIAL STRATEGY AND PERFORMANCE: EXPLORING ARTIFICIAL INTELLIGENCE (AI)-DRIVEN METHODS AND EMPIRICAL PATTERNS(Saudi Digital Library, 2025) Alrubaiaan, Omar; Chari, MuraliThis dissertation consists of three essays on entrepreneurial strategy and performance. Entrepreneurship is inherently risky, and understanding the factors that shape entrepreneurial outcomes remains a central concern in the field. The three essays highlight complementary themes: strategic adaptation through pivots and the use of advanced AI to detect them, the role of entrepreneurial experience in shaping performance, and the contextual conditions under which immigrant entrepreneurs achieve superior outcomes. The first essay introduces a novel approach to measuring post-launch pivots in startups using Large Language Models (LLMs). By systematically analyzing changes in firm descriptions over time, the study develops an automated method to capture strategic redirection at scale. While pivoting is widely discussed in entrepreneurship, prior research has largely relied on case studies of individual or small groups of ventures. This study advances the literature by leveraging CrunchBase data and prompting LLMs to assess description changes and benchmarking their performance against human raters. Results show that the top-performing LLM outperforms human accuracy (84% versus 79%), demonstrating the promise of LLMs as scalable tools for systematically studying pivots across large samples. Moreover, when human raters were subsequently exposed to the LLM’s assessments, their accuracy increased to 85%, demonstrating the potential of LLMs not only as scalable analytical tools but also as valuable decision-support systems that enhance human judgment. The second essay examines the performance of serial versus novice entrepreneurs, distinguishing between survival and financial outcomes. Using panel data, the study develops and tests hypotheses on differences in venture performance, arguing that survival and financial performance—often treated interchangeably—reflect distinct dimensions of entrepreneurial success. Findings reveal that the first ventures of serial entrepreneurs generate higher financial performance but shorter survival relative to those of novices. Furthermore, subsequent ventures of serial entrepreneurs survive longer than earlier ones, though their financial performance does not significantly improve. These results shed light on the nuanced relationship between entrepreneurial experience and venture outcomes. The third essay investigates the performance of self-employed immigrant entrepreneurs in the United States, asking when and under what conditions they outperform their non-immigrant counterparts. Drawing on longitudinal panel data from the PSID, the study tests four hypotheses, including the moderating roles of family size, wealth, and industry context. Findings show that immigrant entrepreneurs outperform their native-born counterparts on average, with family size strengthening this advantage and wealth diminishing it. Industry dynamics also matter: immigrants excel in blue-collar sectors but are less competitive in white-collar industries, reflecting both constraints and opportunities embedded in immigrant entrepreneurship. Taken together, these three essays contribute to entrepreneurship research by advancing methodological tools for measuring pivots, clarifying the performance implications of entrepreneurial experience, and uncovering the contextual factors that shape immigrant entrepreneurs’ outcomes. Collectively, they provide a multidimensional perspective on entrepreneurial strategy and performance.10 0Item Restricted Occupational Performance and Movement Disorders and the Use of the Canadian Occupational Performance Measure in Individuals With Parkinson Disease(Saudi Digital Library, 2025) ALSaeed, Abdullah Saad; Pickett, KristenMovement disorders (MDs) include a range of neurological conditions that affect both voluntary and involuntary movement, including Parkinson disease (PD), dystonia, essential tremor, and Huntington disease. MDs include hyperkinetic disorders that produce excessive movements, and hypokinetic disorders that reduced or slowed movements. MDs also involve non-motor symptoms which frequently appear before motor symptoms and significantly affect quality of life, daily activities, and caregiver burden. Occupational therapists (OT) play an important role in providing holistic, client-centered care through frameworks like the Person-Environment-Occupation (PEO) model. OT assess how motor and non-motor symptoms interact with environmental factors to effect participation in meaningful activities. However, most standardized assessment tools rely on predetermined task lists that fail to capture each person's unique priorities. The Canadian Occupational Performance Measure (COPM) fills this gap by allowing individuals to identify and rate their own performance and satisfaction in self-care, productivity, and leisure activities. Although the COPM has been used across various neurological populations, its use in MDs, particularly in individuals with PD, is limited. This dissertation systematically examined COPM application in MDs through two studies. Study one conducted a systematic review using PRISMA guidelines, identifying nine studies: seven in PD with 367 participants and two in functional neurological disorders (FNDs) with 144 participants. The review revealed heterogeneous COPM use with varying evidence levels. Study two compared COPM and MDS-UPDRS in twenty-nine individuals with PD identified 88 occupational problems across self-care, productivity, and leisure domains. Only 43 problems (48.9%) were captured by the MDS-UPDRS. These findings demonstrate that client-centered assessments like the COPM capture meaningful occupational problems that were overlooked by the traditional PD measure, emphasizing the need for comprehensive evaluation approaches in MDs.9 0Item Restricted Electrolyte-Driven Advances in pH Sensor Development, CO2 Conversion, and Concentrated Electrolyte Modeling(Saudi Digital Library, 2025) Bahdad, Abdullah Omar; Leonard, KevinElectrolytes are critical yet underexplored factors in electrochemical systems. This dissertation advances mechanistic understanding of electrolyte behavior at or near electrochemical interfaces, for improved control and design of electrochemical processes. The dissertation is organized into three research chapters. Chapter 2 focuses on developing potentiometric solid-state pH sensors. Anodically grown platinum oxide ultramicroelectrodes fabricated under alkaline conditions exhibited a near-Nernstian response and excellent stability in aqueous electrolyte environments, with fast temporal resolution when fabricated on nanoelectrodes. These findings highlight the crucial roles of electrolyte synthesis conditions and geometric scaling in enabling fast and reliable pH sensing, especially in challenging aqueous environments. Chapter 3 investigates the mechanistic role of ion pair formation in the electroreduction of CO2 to formate in KHCO3/KCl electrolytes. Through cathodic linear sweep voltammetry, bulk electrolysis, and Tafel analysis, KHCO3 ion pairs, not free bicarbonate or dissolved CO2, serve as the dominant electroactive species toward formate production. Electrolyte optimization led to the identification of 1.75 M KHCO3/2 M KCl as the optimum electrolyte, exhibiting the highest formate current density on planar tin electrodes (~20 mA.cm-2) at reduced overpotentials. These findings establish a new framework for electrolyte design, showing that ion pair speciation directly impacts selectivity and activity in CO2 electroreduction. Chapter 4 presents a modified Debye-Hückel framework that quantitatively links microscopic ion structuring with macroscopic properties in symmetrical and asymmetrical concentrated electrolytes. The derived mean ionic activity coefficients and effective water activities enable accurate prediction of electromotive force and conductivity over a wide concentration range. This analytically tractable, non-empirical model overcomes century-old limitations of classical theory, providing a unified, predictive foundation for electrolyte design and optimization. These contributions advance both the mechanistic and applied understanding of electrolyte role in electrochemical systems. They establish critical principles that guide innovation in electrolyte-driven technologies, including electrochemical sensing, selective electrosynthesis, and electrolyte design and control across diverse scientific and engineering applications.10 0Item Restricted Behavioral intentions of Saudi female students in the College of Computer Science and Engineering at Taibah University in Saudi Arabia toward using mobile computer devices in their learning(Saudi Digital Library, 2025) Alnehari, Naif Nasser; Goodson, Todd; Taylor, Kay AnnThe Kingdom of Saudi Arabia is making crucial progress and development in all fields, including education, as it develops higher education to compete globally. Among these strategies is the effective integration of technology into the educational environment, a key goal of Vision 2030. The spread of mobile computer devices among students has proven effective in student learning. However, there is still a lack of studies about the effectiveness of these mobile computer devices in higher education, especially among Saudi female students. Therefore, processes are involved before integrating any technology into the educational environment; one is to understand students' acceptance of these devices in their learning journey. Thus, the purpose of this study is to predict the behavioral intention of Saudi female students in the College of Computer Science and Engineering at Taibah University in Saudi Arabia to learn with mobile computer devices based on the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). This study employed a quantitative, non-experimental survey design to collect data. The number of responses received was 134; 127 were valid. The multiple linear regression analysis was administered to answer the research questions. The results revealed that effort expectancy and social influence were significantly associated with the female Saudi students' behavioral intentions toward using mobile computer devices in their learning (β = 0.444, p < 0.001; β = 0.174, p = 0.033). Performance expectancy had no statistically significant relationship with the behavioral intentions of Saudi female students regarding the use of mobile computer devices (β = 0.108, p = 0.232). In addition, multiple linear regression results were presented, showing that the model accounted for 39% of the variance in the behavioral intentions of Saudi female students regarding the use of mobile computer devices in their learning. The current study also revealed some challenges students face when using mobile computer devices for learning. These include classroom infrastructure issues, such as limited Internet access and low-quality hardware, as well as battery and device performance problems when downloading files and applications. Additionally, some educational websites and content were incompatible with these devices. Based on these findings, the current study provides recommendations for future research and for improving educational practices when integrating mobile computer devices.18 0Item Restricted FLUORIDE CONTENT OF INFANT FORMULA COMMERCIALLY AVAILABLE IN CENTRAL INDIANA(Saudi Digital Library, 2025) Altamimi, Ayman; Lippert, FrankBACKGROUND Fluorides have a well-established role in dental caries prevention. Fluoride content in infant formula has raised concerns about whether it is within safe levels for the developing teeth. There is a large number of products on the market with likely varying fluoride concentrations, and these products’ fluoride content will differ depending on whether, for example, fluoridated water was used during manufacturing or reconstitution. Several studies have been published on infant formula containing fluoride and the associated risk of developing enamel fluorosis. However, few recent studies in the US have determined whether liquid or powder infant formula fall within safe/recommended levels. Purpose: This study measured the fluoride content of infant formula sold in grocery stores in central Indiana, prepared using three types of water (Purified, Nursery, and Tap) to determine if they fall within safe levels. Alternative hypotheses: There is a significant difference in the concentration of fluoride between different brands of infant formula. Material & Methods: We analyzed twenty different infant formula products sold in grocery stores in the Indianapolis, Indiana area for their fluoride content. Samples were reconstituted with Nursery water (containing approx. 1.0 ppm fluoride), Tap water (approx. 0.7 ppm fluoride) and Purified water (negligible fluoride content). A sample for the tests was taken from each preparation and the concentrations of fluoride of all samples was determined using the fluoride microdiffusion method. The statistical analysis of results was carried out using two-way ANOVA. Results: When comparing the mean (SD) fluoride concentration among the three types of infant formula reconstitution with water, tap water had significantly higher fluoride concentration mean than both Nursery water and purified water (P <.001 at α=.050 level). Nursery water also had significantly higher fluoride concentration mean than purified water (P <.001 at α=.050 level). When the three types of water were used for reconstitution of the 20 infant formula brands, the overall highest fluoride concentration mean was seen when tap water was used for reconstitution (0.950) followed by nursery water (0.789) while the least fluoride concentration was in purified water (0.102). Conclusion: Within the study's limitations, it can be concluded that apart from one formula none of the tested infant formulas sold in central Indiana grocery stores when reconstituted with purified water were found to decrease the chance of infants exceeding UL levels for both age groups but were found to increases the chance exceeding the AI levels for infants aged 0–6 months. All tested infant formulas reconstituted with nursery and tap water were found to increase the chance of infants exceeding the UL, and the AI levels for both groups resulted in increasing the chance of fluoride concentrations exceeding the recommended/safe levels. Thus, the type of water used for reconstitution rather than the type of formula appears to be the determining factor for the levels of fluoride intake associated with infant formula. Clinical Significance: With the recent increase in the utilization of infant formula, different brands with varying fluoride concentrations and the different modes of reconstitution must be evaluated to determine if their fluoride concentrations will fall within safe/recommended levels and thus increase the risk of enamel fluorosis development.28 0Item Restricted EARNED INCOME TAX CREDIT 2009 EXPANSION: A NATIONAL AND STATE-LEVEL ANALYSIS OF CHILD POVERTY REDUCTION, TAX RETURNS, AND INFLATION-ADJUSTED BENEFITS, 2000-2022(ProQuest Dissertations & Theses, 2025) Aljohani, Mohammed; Gilleylen, JohnnyThis study investigates the impact of the Earned Income Tax Credit (EITC) 2009 on child poverty in the U.S. Census Bureau data shows child poverty has increased by 0.29 points each year, starting at 16.20 percent in 2000 to 19.00 percent in 2008. States with the highest child poverty rates, such as Mississippi (28.08 percent), Louisiana (26.67 percent), New Mexico (25.23 percent), Arkansas (23.86 percent), and West Virginia (23.78 percent), exceed the national average of 17.62 percent for those years. Prior research found that childhood poverty negatively affects the economy, health, and education. Previous research on the impact of the 2009 EITC policy change on child poverty reduction in the U.S.—including metrics such as EITC claims, average credit per household, and inflation-adjusted benefits—has been limited or inconclusive. This study investigates the effects of the EITC provisions introduced through the American Recovery and Reinvestment Act of 2009 on the U.S. child poverty. It also focuses on state-level EITC tax returns and average credit amounts in the five states with the highest and lowest child poverty rates, using a quantitative approach that combines interrupted time series and cross-sectional research designs with data from the U.S. Census Bureau and the IRS. Since 2000, the EITC has helped lift an average of 2.36 million children out of poverty each year. A 2009 policy change boosted that number by 21 percent, with 2.52 million children lifted annually. However, the impact declined during COVID-19, with noticeable drops in EITC claims, credits, and adjusted benefits. The 2009 expansion led to increased EITC use across both high- and low-poverty states, with the greatest gains seen in the most disadvantaged states— contradicting key assumptions of social construction theory. At the end, the study offers policy recommendations to enhance EITC’s equity and effectiveness.13 0Item Restricted How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, PaulaContext: Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek are transforming professional work across sectors by enhancing information processing and decision support. In public health, these technologies offer the potential to improve efficiency, analytical capacity, and data-driven decision-making. Yet, their integration raises concerns about workforce preparedness, evolving skill requirements, and ethical oversight. In Saudi Arabia, where Vision 2030 prioritizes digital transformation in healthcare, understanding how public health professionals adapt to these technologies is vital for workforce and policy planning. Method: This exploratory mixed-methods study examined the professional impact of LLMs and the preparedness of public health professionals for their integration. The validated Shinners Artificial Intelligence Perception (SHAIP) survey, adapted for LLMs and public health, was distributed to employees of the Saudi Public Health Authority, yielding 32 complete responses. Ten semi-structured interviews further explored four constructs: professional impact, preparedness, new essential skills, and obsolete skills. Quantitative data were analyzed descriptively, and qualitative data were coded using thematic analysis. Findings: Survey results indicated that LLMs positively influence efficiency and workflow but revealed gaps in training and ethical guidance. Interview themes reinforced these findings, identifying new essential skills such as prompt engineering, digital literacy, and critical oversight, while traditional tasks like manual data entry and report drafting were viewed as increasingly automated. Conclusion: LLMs are transforming the roles of public health professionals. Successful adoption requires structured training, institutional readiness, and ethical governance. The study offers actionable recommendations to align workforce development and recruitment strategies with Saudi Vision 2030, emphasizing capacity building and responsible AI integration in public health practice.13 0Item Restricted TOPOLOGICAL DATA ANALYSIS (TDA) AS A FEATURE EXTRACTION TOOL FOR EEG SIGNAL ANALYSIS IN SLEEP STAGING(Saudi Digital Library, 2025) Albidah, Hamad; Zhi-Hong, Mao; Dallal, AhmedSleep is a biological process essential for all living organisms. For humans, it plays a fundamental role in regulating emotions, memory consolidation, cognitive function, and overall physical health. Despite its importance, many individuals remain unaware of chronic sleep deficiencies until diagnosed—often after years of suffering. Accurate diagnosis of sleep disorders requires reliable tools and methods, particularly in clinical settings. Electroencephalography (EEG) remains a widely used technique in the study of sleep for capturing brain signals that contain rich physiological information. However, EEG data are inherently high-dimensional and complex, posing challenges for analysis and interpretation. To address this, the goal of this dissertation is to develop an explainable dual hierarchical feature selection and dimensionality reduction framework aimed at improving sleep stage classification. The proposed framework consists of two stages. The first stage is feature construction and selection. Specifically, we integrate Topological Data Analysis (TDA) to explore the intrinsic structure of the data and extract both traditional statistical features and TDA-based features as a supplement to model training. Then, we use Recursive Feature Elimination with Cross-Validation (RFECV) to optimize feature selection. The second stage is to further reduce the dimensionality of the feature space. Four dimensionality reduction techniques are considered: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Kernel Principal Component Analysis (KPCA). Our results indicate that manifold learning algorithms generally outperform PCA; among them, t-SNE achieves the highest classification accuracy at 78.9%. This improvement arises because the TDA-based features can extract global structural patterns from EEG signals that traditional spectral–temporal metrics cannot capture. Thus, this study demonstrates that a structured, theory-driven approach can enhance both the performance and interpretability of machine learning models in sleep-stage classification. It also provides a practical framework for processing complex biomedical signals, with potential implications for real-world clinical applications.11 0Item Restricted Analysis of No-Confounding Designs in 16 Runs for 10-14 Factors(Saudi Digital Library, 2025) Alqarni, Hanan; Montgomery, DouglasRegular two-level fractional factorial designs are widely used for factor screening experiments, where the objective is to efficiently identify the set of active factors from a larger initial group of factors. The 16-run designs are very popular for screening because they can accommodate a reasonably large number of factors and for 6 – 8 factors they are Resolution IV, while for larger numbers of factors they are Resolution III. Assuming that 3-factor and higher interactions are negligible the Resolution IV designs provide clear estimate of the main effects while aliasing all 2-factor interactions with each other and the Resolution III designs alias main effects and 2-factor interactions. Because of the aliasing, follow-up experiments are often required to obtain complete information about main effects and 2-factor interactions. However, there are many situations where follow-up experimentation isn’t possible. Nonregular fractional designs that do not have complete aliasing involving main effects and 2-factor interactions can be a good alternative for these situations. However, analysis methods for these designs is an ongoing area of research. This work investigate analysis methods for a class of non-regular 2-level fractional factorials for 8 – 14 factors in 16 runs. In these designs there is no complete aliasing between the main effects and the two-factor interactions, so these designs are useful alternatives to the regular Resolution III fractions. The analysis methods are forward stepwise regression, the least absolute shrinkage and selection operator (LASSO) and the Dantzig selector method. The results show that in most cases that for effect sizes of 2 and 3 standard deviations stepwise regression and the LASSO outperform the Dantzig selector in correctly identifying the set of active factors for situations where the number of active factors does not exceed approximately half of the number of degrees of freedom for thedesign. Lastly, additional approaches are explored: the two-stage stepwise regression method, design augmentation and other no-confounding designs with 20 and 24 runs, to examine differences in method performance.9 0Item Restricted A PARSESCIENCING INQUIRY ON FEELING SAD(Saudi Digital Library, 2025) Zain Aldeen, Aisha; Mario, OrtizThe investigation aimed to explore the universal humanuniverse living experience of feeling sad using Parsesciencing, which is a unique mode of inquiry within the humanbecoming paradigm. The historians were 10 English-speaking adults aged 18 and older, willing to share their experiences of feeling sad. The inquiry stance was: What is the discerning extant moment of the universal humanuniverse living experience of feeling sad? The major discovery of this Parsesciencing inquiry was the discerning extant moment: Feeling sad is profound discomfort with disengaging from affiliations surfacing with promising new insights.5 0