SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Explainable Goal Recognition Systems(Saudi Digital Library, 2025) Alshehri, Abeer; Vered, MorThis thesis explores human-centered approaches to explaining and understanding why goal recognition (GR) agents predict specific goal hypotheses. Goal recognition is the process of inferring an agent’s hidden goal from its observed behaviour, playing a crucial role in AI with various practical applications. Since the field’s inception, understanding the behaviour, decisions, and actions of ar- tificial intelligence (AI) agents has been a core focus of research. As these systems grow increasingly complex, their reasoning processes often become opaque to end users, raising significant challenges in high-stakes and collaborative environments. Lack of transparency can undermine trust and hinder effective decision-making. Enhancing au- tonomous agents’ explainability is vital, enabling users to comprehend and trust the reasoning behind these systems’ predictions. Understanding how humans generate, select, and convey explanations can serve as a ba- sis for developing effective explainable agents. Explaining the behaviour and predictions of GR agents engaged in sequential decision-making presents unique challenges. Tra- ditional approaches to explainability often focus on aligning an agent’s behaviour with an observer’s expectations or making the reasoning behind decisions more transparent. Building on insights from cognitive science and philosophy, this thesis delves deeper into understanding the nature of explanations within human cognition. The central contribution of this work is the introduction of the eXplainable Goal Recog- nition (XGR) model, a novel framework that generates counterfactual explanations for GR agents. The XGR model addresses “why” and “why not” questions by leverag- ing insights from two human-agent studies and proposing a conceptual framework for human-centred explanations of GR. Building on these foundations, the thesis extends the XGR model by introducing the Hypothesis-Driven XGR model, which integrates the emerging decision-making paradigm of Evaluative AI. Our empirical evaluations demon- strate that the proposed models enhance trust in GR agents and effectively support user decision-making, outperforming baseline approaches across key domains. This research presents the first systematic investigation into human-centred explanations for goal recognition systems in sequential decision-making domains. It advances the field of explainable AI and provides practical methods to improve user understanding and trust in GR systems.9 0Item Restricted The Impact of Generative AI on Teaching, Learning, and Integrity in Higher Education: A Systematic Review(Saudi Digital Library, 2025) Alghamdi, Abdullah Ali A; Kang, KyeongThis systematic review investigates the impact of Generative Artificial Intelligence (GenAI) on teaching practices, student learning, and academic integrity within higher education. This review conducted a qualitative thematic synthesis of 25 peer reviewed studies published 2020−2025, and using PRISMA 2020 framework for the review. The results will prove that GenAI will enable greater teaching efficiency, allow for more personalized learning routes, and will make education more accessible. GenAI tools are now being used by educators to automate feedback, create fresh assessments, and create differentiated instruction, while students use its AI powered platforms for academic’s support, language help, and creative exploration. The study also reveals some critical challenges. Misuse of GenAI can lead into superficial engagement and hinder the growth of critical thinking skills. The one major consideration is the development of the academic misconduct patterns, which GenAI-generated content is not being detected by traditional plagiarism detection tools. Higher education institutions may struggle to maintain academic integrity when there is robust human judgment, and redefined standards of academic authorship, in an AI enhanced environment. Responses to GenAI adoption by institutions are still uneven, going from proactive policy formulation to restrictive bans. Similar attitudes vary among disciplines, age, and tolerance for the prior digital exposure. The review highlights the necessity of universities to have clear, adaptive policy in place, incorporate AI literacy into curricula, redesign of assessments to encourage authentic learning processes, and university faculty development. To contribute to the growing dialogue on AI and education this study provides a synthesized thematic understanding of GenAI integration’s opportunities, risks, and institutional strategies. Second, it contends that GenAI must be embraced by higher education by both leveraging its benefits and mitigating its challenges for the sake of technology that offers no benefit and, even worse, threatens to undermine academic values.18 0Item Restricted Essays on Economic Growth, Natural Resources Rent, Economic Complexity and Environmental Pollution: Assessment Using Global Samples(Saudi Digital Library, 2025) Alnutayfat, Mohsen Qaynan; Zhu, RongAbstract This thesis presents three empirical essays that examine the complex interactions between economic growth, environmental sustainability, foreign direct investment (FDI), economic complexity, and natural resource rents, using diverse global samples and advanced econometric techniques. Collectively, the essays aim to provide a deeper understanding of the validity of prominent economic-environmental hypotheses, the determinants of capital inflows, and the resource–growth nexus, thereby offering insights with strong policy relevance for both developed and developing nations. The first essay revisits two prominent hypotheses in environmental economics—the Environmental Kuznets Curve (EKC) and the Pollution Haven Hypothesis (PHH)—for a global sample of 132 nations over the period 1995–2020. Using robust panel data models and heterogeneity tests, the analysis confirms an inverse U-shaped EKC at the global level, indicating that economic growth initially undermines environmental sustainability, though the effect diminishes at higher income levels. However, the PHH is not supported when all countries are considered together. Further disaggregation reveals that an N-shaped EKC better describes the growth–pollution relationship in developing nations, while no EKC form is valid for developed nations. In addition, the PHH holds some validity in developing countries but not in advanced economies. These results underscore the necessity of differentiated policy frameworks tailored to the structural and developmental characteristics of each country group. The second essay focuses on the role of economic complexity and uncertainty in shaping global FDI inflows, employing a balanced panel of 84 countries from 1995–2020. Using a fixed effects model and heterogeneity tests, the study finds that economic complexity positively influences inward FDI, while various forms of uncertainty—economic, political, and policy-related—significantly hinder capital inflows. These findings highlight that structural transformations, such as diversifying and upgrading export baskets, are critical for attracting sustainable FDI. At the same time, the results caution that exogenous shocks, institutional instability, and unpredictable policy environments can disrupt cross-border capital flows, with significant implications for growth strategies in both developed and developing contexts. The third essay investigates the impact of natural resource rents on economic growth, focusing on the top 15 oil-exporting nations from 1995–2020. By applying second-generation econometric diagnostics and the Method of Moments Quantile Regression (MMQR), the study shows that, in aggregate, resource rents contribute positively to economic growth, challenging the notion of a universal resource curse. Disaggregated analysis reveals that oil, natural gas, and forest rents stimulate growth, whereas coal rents exert no significant effect. The results also uncover heterogeneity in the influence of human capital across the growth distribution. Furthermore, globalization, renewable energy expansion, and rising carbon emissions are found to accelerate growth in these territories. These insights suggest that resource-rich nations can avoid the resource curse through policies that encourage economic diversification, renewable energy adoption, and human capital investment. Together, these three essays demonstrate the multifaceted relationships between growth, environment, investment, and resources. They provide evidence that global development challenges cannot be addressed through uniform strategies, but instead require nuanced, context-specific policies. The findings highlight the importance of sustainable industrialization pathways, export sophistication, institutional resilience, and efficient resource management in achieving long-term growth and environmental objectives.8 0Item Restricted Enhancing Supply Chain Resilience for SMEs in Import Logistics via Sea Freight(Saudi Digital Library, 2025) Almarshad, Norah; Liyanagamage, NellwThis research examines how small and medium-sized enterprises (SMEs) involved in import logistics can enhance their resilience to global sea freight disruptions. Although SMEs play a crucial role in international trade, they often lack the financial, technological, and structural resources to manage disruptions such as port strikes, geopolitical instability, and shipping cancellations. Despite these challenges, there is limited research focusing specifically on SME strategies within maritime logistics. To address this gap, the study adopts a qualitative case study approach, using Whale Logistics, an Australian freight forwarder that supports SMEs, as the focal firm. Data were collected through a virtual interview with the company’s General Manager, complemented by internal documents, industry reports, and academic literature. Thematic analysis identified key resilience strategies employed by Whale Logistics, including flexible pricing mechanisms, diversified shipping partnerships, strong financial safeguards, and coordinated internal communication. These practices align with the three stages of supply chain resilience: anticipation, resistance, and recovery. Based on these findings, the study recommends that SMEs implement structured risk management frameworks and knowledge management systems to improve their ability to detect, respond to, and recover from disruptions. These strategies can help SMEs maintain operational continuity and remain competitive in an increasingly unpredictable global logistics environment.11 0Item Restricted Using Drones for Traffic Analysis(Saudi Digital Library, 2025) Alzahrani, Ali Omar; Rocco, ZitoAccurate traffic data collection is essential for effective traffic modelling, congestion management, and road safety analysis. However, traditional methods such manual counting and fixed-location systems like SCATS are often limited including restricted coverage, high labour costs, and reduced adaptability in complex environments. The study here considers the application of drones (Unmanned Aerial Vehicles) as a contemporary method for traffic data collection and performance assessment at different traffic conditions, such as intersections and roundabouts. Drones can provide a broader perspective of intersections by filming from above, providing a better view of the dynamics of traffic flow, especially in places that are hard to observe from the ground. Three locations were selected in Adelaide based on traffic volume and site complexity to evaluate their effectiveness and these include: signalised intersection with high traffic, roundabout with medium traffic, and another roundabout with low traffic. Drone recordings were taken at peak hours and the traffic volumes were estimated. These volumes were then entered into the SIDRA software, in which the digital models of the respective sites were developed. Once the site layout was created, traffic data were introduced to the models and calibrated with real conditions. This difference emphasises the benefits of using a drone for data collection, especially in sites where conventional automated systems such as SCATS are not available or feasible. Using drones for traffic analysis shows a great potential since they provide higher perspective and larger coverage than the traditional ground-based methods do. This advantage is particularly clear for complicated environments, for example crossroads and roundabouts, since it can be difficult to collect full vehicle movements following recording analysis for ground-level observer. Although promising, the degree of improvement in SIDRA results due to drone-acquired data is yet to be fully understood. While the results indicate that drones may enhance the granularity and fidelity of traffic evaluations, it is still unclear if they can be consistently better than classical traffic sensors. However, drones are peculiar as, in several cases, are superior to the current state of the art in the context of traffic monitoring applications. Although issues related to regulatory compliance and weather conditions persist, the advantages drones offer in terms of spatial coverage, data accuracy, and cost efficiency position them as a transformative tool in traffic modelling.21 0Item Restricted Offshore Wind Farm Displacement due to Submarine Landslide(Saudi Digital Library, 2025) ALYASEEN, HUSSAIN MANSOUR H; Barari, AminThe fast expansion of offshore wind turbine (OWT) installations necessitates durable monopile foundation designs for saturated sandy seabed. This study investigates soil liquefaction and instability, as well as their impact on large-diameter monopile behaviour. NorSand was used to create a PLAXIS 3D numerical model that simulates non-linear, undrained soil responses. The monopile was modelled using Beam and Embedded Beam elements, with liquefaction-induced instability initiated by programmed displacement at the model base. The researchers observed pile head load settlement, pore pressure evolution, effective stress variations, and lateral displacement and rotation. Another finding reveal that excess pore pressure accumulates quickly, resulting in effective stress loss and extensive liquefaction, which has a major impact on pile performance. Load-settlement response was erratic and unstable, characterized by rapid settlement and sudden load-carrying capacity drops. The pile initially heaved 2mm before settling significantly. Sensitivity study demonstrated that increasing pile diameter had an effect on load and settlement, although L/D ratio variations showed comparable tendencies after liquefaction. For L/D = 5, the maximum lateral rotation occurred at mid-depth, but in other cases, the average rotation was -0.9° at the base. This work emphasizes the essential sensitivity of monopile foundations to liquefaction induced instability, highlighting the importance of improved soil-structure interaction modelling in offshore foundation design.17 0Item Restricted Critical Success-Related Factors Influencing the Adoption and Use of Artificial Intelligence in Saudi Small and Medium-Sized Enterprises(Saudi Digital Library, 2025) alsulami, jehan fahim; Catherine, LouThe current decade presents significant opportunities for businesses to harness the transformative power of artificial intelligence (AI). Nonetheless, organisations of all sizes, including small and medium-sized enterprises (SMEs), continue to encounter challenges related to the critical success factors influencing AI adoption. Understanding the interplay between AI adoption, its utilisation, and these success factors remains pivotal to enhancing technology-enabled business operations. Therefore, this thesis investigates the critical success factors affecting AI adoption and use within Saudi SMEs. To construct a robust conceptual framework, three theoretical perspectives are employed based on a structured evaluation of relevant literature: the Technology-Organisation-Environment (TOE) framework, the Human-Organisation-and-Technology Fit (HOT-FIT) model, and Institutional Theory. The resulting framework addresses the technical, social, organisational, and environmental contexts critical to supporting SMEs in adapting to and utilising AI. It identifies eight key factors: skilled personnel, organisational readiness, data strategies, security concerns, system quality, government regulation, AI vendors, and trust in AI. A quantitative research methodology is employed to collect data from a sample of 300 SMEs across Saudi Arabia, utilising a simple random sampling technique within a cross-sectional survey design. Various statistical techniques are used to analyse and validate the proposed framework, including partial least squares structural equation modelling (PLS-SEM), which facilitates the testing and validation of the conceptual framework for AI adoption and use among Saudi SMEs. The findings indicate that critical success factors significantly impact AI adoption and use by Saudi SMEs. Notably, skilled personnel, government regulation, AI vendors, and trust in AI are identified as primary determinants of AI adoption. Additionally, skilled personnel, organisational readiness, government regulation, AI vendors, and trust in AI emerge as essential for the effective and sustainable use of AI. A statistically significant variation in AI adoption and usage is observed among Saudi SMEs of different sizes. The primary contribution of this study lies in extending existing information technology adoption literature to encompass the context of critical success factors for AI. Moreover, the findings offer a comprehensive perspective on the organisational dynamics influencing AI adoption and use by integrating human, organisational, technological, environmental, and social dimensions into a single framework. From a practical standpoint, the research provides valuable insights for technology consultants, policymakers, and regulatory authorities. Specifically, Saudi SMEs can leverage these findings to effectively enhance their capacity to adopt and sustain AI-driven innovations effectively.12 0Item Restricted Usage of Cyber Security to Protect Women and Children(Saudi Digital Library, 2025) Alotibi, Bander; Al-Doghman, FirasDigital safety especially for women and children is crucial, and cybersecurity is a big part of that. Using feminist and intersectional perspectives, this research explores how Protection Motivation Theory (PMT) and Social Learning Theory (SLT) explain cybersecurity behaviors. Empirical findings suggest that women and children are disproportionately affected by harassment, doxing, and identity theft. The research further reveals knowledge holes in cybersecurity awareness, showing that younger people quickly adopt security measures but engage in high-risk behaviors, whereas older people emphasize safety but lag in technical implementation. Gender differences are also important, with women more concerned about cybersecurity but less confident in dealing with threats, and men more confident but more willing to take risks. The research emphasizes education, legal reforms, technological advancements, and community awareness programs to address these challenges. Future research should investigate AI-driven cybersecurity tools, inclusive security policies, and bridging the digital divide. To create a safer digital environment for women and children, collaboration between governments, tech companies, and advocacy groups is essential.18 0Item Restricted The Influence of TikTok Food-Related Content on the Eating Behaviour of Young Adults in Al-Jouf, Saudi Arabia(Saudi Digital Library, 2025) Alsharari, Khalid Hail S; Feng, JuanThis research set out to explore how food-related content on TikTok influences the eating behaviours of young adults in Al-Jouf, Saudi Arabia. Grounded in Social Cognitive Theory and informed by a structured review of global and regional literature, the study employed a quantitative design to examine behavioural trends, digital media engagement, and dietary motivations among a sample of 215 participants. Data were analysed using descriptive statistics and one-way ANOVA. The findings revealed that frequent TikTok users were significantly more likely to try new foods, discover unfamiliar cuisines, and follow diet-related content creators. For example, participants who used TikTok for more than three hours daily reported higher motivation to explore new diets (p = .0003) and greater confidence in changing their eating habits (p = .0011) but also showed stronger appeal toward unhealthy food content (p < .001). These results highlight TikTok’s dual role in shaping both health-promoting and indulgent dietary behaviours. By focusing on an underrepresented region, the study offers contextual insights into how algorithm-driven content influences food choices among Saudi youth and provides a basis for more targeted health communication strategies. By situating this investigation within the underrepresented context of Al-Jouf, the study offers region-specific insights into how global platforms like TikTok are reshaping dietary perceptions even in semi-urban environments. Through its thematic analysis and theoretical framing, the research affirms key constructs of Social Cognitive Theory—namely observational learning, reinforcement, and self-efficacy—as relevant mechanisms in digital food culture. Furthermore, it identifies areas where health communication strategies could engage more effectively with youth through culturally relevant and visually persuasive content. While the study acknowledges its limitations, including the use of a non-random sample and reliance on self-reported data, its contributions are clear. It provides a foundation for future research on algorithm-driven health messaging in the Gulf region and highlights the growing need to understand how young people interact with food content in digital spaces. Ultimately, this research underscores the importance of aligning public health efforts with the evolving media environments that shape the everyday lives of young adults in Saudi Arabia and beyond.34 0Item Restricted The Impact of Job Burnout on the Performance of Non-Academic Staff(Saudi Digital Library, 2025) Alzahrani, Ahmed; O’Loughlin, TimJob burnout has emerged as a critical issue affecting employee well-being and organisational performance, particularly within the higher education sector. Despite substantial international research, limited studies have explored the relationship between burnout and performance among non-academic staff in Saudi Arabian universities. This study addresses this gap by investigating how job burnout influences the performance of non-academic employees at the public universities in Riyadh. The research aims to examine the extent and nature of burnout experienced by staff, identify its main causes, and assess its impact on individual performance outcomes. A quantitative research design was employed, using a structured survey distributed to a sample of non-academic staff across various administrative departments at the public universities. The survey collected information on demographic characteristics, burnout experiences (across emotional exhaustion, depersonalisation, reduced personal accomplishment, job stress, work-life balance, and social support at work), and self-perceptions of job performance. Data analysis involved descriptive statistics, correlation analysis, and regression modelling to examine the relationships between burnout dimensions and performance outcomes. The findings revealed a moderate level of overall job burnout among participants, with a weighted mean of 57.6%, alongside a moderate level of job performance effectiveness, with a weighted mean of 56.8%. Statistical analysis confirmed a significant negative impact of overall job burnout on job performance (R² = 0.756, p < 0.05). Among the burnout dimensions, job stress demonstrated the strongest negative association with job performance (correlation coefficient = 0.797, p < 0.05). These results highlight the critical need for organisational strategies to address job burnout, particularly by managing work-related stress and enhancing support systems for non-academic staff. Implementing initiatives such as workload management, employee engagement programs, and professional development opportunities may contribute to improved staff well-being and greater institutional effectiveness. Future research could benefit from longitudinal studies and broader cross-institutional comparisons within the Saudi higher education sector.27 0