Saudi Cultural Missions Theses & Dissertations
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Item Restricted The impact of artificial intelligent (AI) on inventory management and cost efficiency in the supply chain.(Saudi Digital Library, 2025) almasaeed, murtadha; fang, liuThis dissertation discusses the impact of artificial intelligence on inventory management and cost efficiency. The research shows how AI improves in the supply chain such as forecasting, replenishment, and warehouse operations. At the same time, it shows the challenges facing the small and medium sized enterprises (SMEs). Furthermore, the research investigates the importance of trust and satisfaction that can affect the success of AI adoption. The study uses a quantitative survey to collect data from professionals in the supply chain from different organisations and industries. Various analyses were employed including descriptive statistics, reliability testing, t-tests, correlation, regression, and ANOVA tests. According to the findings, AI adoption has improved the inventory turnover, reduced delays, and lowered labour and logistics costs. However, there are some unexpected findings such as demand forecasting and automated replenishment, which did not show statistically significant evidence. This shows that system integration and the maturity of adoption are important to achieve all the benefits. The findings also show that trust and satisfaction have an important role. The trust showed to reduction in stockouts and the satisfaction improved as the company size and usage time increased. These findings match the Technology Acceptance Model (TAM) which shows user perception affects the adoption and the outcome of AI. In conclusion, the study shows improvement in the supply chain operations after AI adoption. However, the success of AI depends on some factors such as the company's resources, the level of adoption and how employees trust the technology. These results give the manager the full view of how to use AI effectively in the operations.9 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 Metadata-Centric Cybersecurity Classification: A Fair Benchmark of LLMs and Classical Models(Saudi Digital Library, 2025) Binothman, Elyas; Chaudhry, Umair BilalCybersecurity breach classification supports triage and risk response but is hindered by heterogeneous reporting, class imbalance, and limited semantic coverage in traditional pipelines. Prior work has relied on rule-based heuristics and classical models (SVM, Random Forest) with heavy feature engineering, while recent LLM studies rarely evaluate breach metadata under identical, fair splits; severity labels are often absent or not reproducibly constructed. We present a metadata-centric benchmark on the Privacy Rights Clearinghouse chronology spanning two tasks: breach-type classification and severity tiering in three and five labels, with severity derived reproducibly from native fields using a Breach Level Index style mapping. All models share one preprocessing recipe and a single stratified 80/20 train–test split. We compare parameter-efficient transformers (DistilBERT and T5 with LoRA) against tuned tabular baselines (Linear SVM, Random Forest, compact ANN). On breach type, DistilBERT achieves the strongest results (Accuracy 0.943; Macro– F1 0.840), surpassing tabular baselines. For severity, a classweighted ANN on TF–IDF and categorical features attains the highest Macro–F1 at both granularities, while T5 shows high accuracy but low Macro–F1, indicating majority-class bias. The study contributes a unified PRC schema with transparent severity construction, a fair head-to-head comparison under identical conditions, and an efficiency-oriented training recipe suitable for modest hardware.11 0Item Restricted Exploring the Impact of Talent Management Strategies on AI Adoption in Saudi Arabia’s Emerging Tech Startups: The Mediating Role of Knowledge Sharing(Saudi Digital Library, 2025) Abuhaimed, Mohammad Saad; Abdoulrahman Aljounaidi Mhd RamezSaudi Arabia's Vision 2030 emphasizes AI-driven digital transformation, yet tech startups struggle to scale AI beyond pilots. Purpose: This study examines how talent management (TM) strategies—attracting-selecting (AST), developing (DT), empowering (ET), retaining (RT), and career succession (CS)—shape AI adoption, and whether knowledge sharing (KS) mediates this relationship. Method: Using probability-based systematic random sampling of employees (n=337, N=2,308) across Saudi AI-adopting startups, the model was analyzed with PLS-SEM (SmartPLS 4). Findings: AST, DT, and ET positively affect AI adoption; RT shows no effect; CS exhibits a negative effect. KS partially mediates AST, DT, ET, and CS effects, indicating TM practices influence adoption primarily through knowledge institutionalization. Implications—Industrial: Startup leaders should integrate KS infrastructures with TM initiatives. Recommended practices: (1) cross-functional AI taskforces with rotating membership; (2) peer-learning sessions where early adopters mentor colleagues; (3) searchable repositories (wikis, Confluence) documenting implementation lessons and troubleshooting guides; (4) succession systems prioritizing collaborative knowledge transfer (mentoring, communities of practice) to prevent silos. Empirical evidence shows succession planning without KS scaffolding correlates negatively with adoption (β = -0.182, p < .01), highlighting knowledge-hoarding risks. Academic: The study extends technology-acceptance theory by integrating human-capital antecedents and positioning KS as the pivotal mediating mechanism in resource-constrained startups. Testing 16 structural paths across five TM dimensions addresses three gaps: (1) mechanistic under-specification, (2) construct aggregation bias, and (3) non-Western context neglect. The mediation framework—validated through bootstrapped indirect effects—provides a replicable blueprint for future research examining causality, moderators (industry velocity, founder literacy), and boundary conditions.27 0Item Restricted SMART TOURISM IN SAUDI ARABIA: EXPLORING THE INTEGRATION OF AI IN CULTURAL HERITAGE DESTINATIONS(Saudi Digital Library, 2025) Alotaibi, Hussain; Buhalis, DimitriosIn line with Saudi Arabia’s Vision 2030, the tourism sector is undergoing rapid transformation, with smart tourism emerging as a key pillar of innovation and development. This study investigates the integration of Artificial Intelligence (AI) technologies in cultural heritage tourism, with a focus on three significant heritage destinations: Al-Ula, Diriyah, and Historic Jeddah. While innovative tourism technologies such as AI-powered recommendation systems, augmented reality (AR), and sentiment analysis have the potential to enhance tourist experiences, increase visitor satisfaction, and support heritage preservation, their adoption within Saudi Arabia’s heritage sector remains underexplored. This research aims to assess international tourists’ perceptions of AI usefulness, satisfaction, and trust, and to examine their behavioural intentions and willingness to pay for AI-enhanced services. A quantitative survey method was employed, with a sample of 306 international tourists who interacted with AI services at the selected heritage sites. Data were analysed using frequency distribution, descriptive statistics, reliability analysis, ANOVA, and correlation tests. The findings are expected to provide empirical insights into the effectiveness of AI technologies in enhancing cultural tourism experiences while preserving authenticity. The study offers practical implications for tourism authorities, technology developers, and policymakers on how to strengthen innovative heritage tourism strategies in Saudi Arabia.25 0Item Restricted The Role of Artificial Intelligence in Monitoring the Quality of Construction Project (Case Study) in Al-Jouf Region(Saudi Digital Library, 2025) Alsharari, Abdulmohsen; AlBtoush, MuhammedThis study investigates the current status and potential of Artificial Intelligence (AI) applications in Construction Engineering and Management (CEM) in the Al-Jouf region of Saudi Arabia. AI has demonstrated significant promise in enhancing resource use, project performance, and quality control in a number of industries; however, Al-Jouf's implementation of AI is beset by particular difficulties, including a lack of funding, a lack of skilled AI specialists, and limited infrastructure. The study uses a descriptive-analytical methodology to fill this gap, combining a review of the literature with primary data gathered from stakeholders in ongoing construction projects in Al-Jouf using a structured questionnaire. The approach includes both secondary sources (academic literature, journals, and reports) and primary sources (field responses from engineers, project managers, and administrative personnel). Data analysis was conducted using SPSS software to evaluate AI awareness, current applications, challenges, and impacts on construction quality dimensions—namely technical compliance, time performance, cost control, and customer satisfaction. The structured questionnaire was designed based on a pilot test and adapted to reflect local construction sector dynamics. Non-probability purposive sampling was used to ensure the selection of knowledgeable participants. The study offers practical insights and recommendations to policymakers, engineers, and industry stakeholders for facilitating AI adoption, aligning it with local project requirements, and supporting sustainable development in the construction sector. The study results indicate a generally positive impression of AI in the construction sector in Al-Jouf region, with a high average score of 3.76. The field “Impact of AI on the Quality of Construction Projects” received the highest score (M = 3.97), while the second field, “Challenges of Applying AI in Construction Projects” received a high score (M = 3.93). The field “Using AI in Project Quality Control” also received a high score (M = 3.76). The lowest-rated field was “Awareness of AI in the Construction Sector” (M = 3.37). The study recommended promoting a culture of artificial intelligence in the sector through collaboration between government agencies and educational institutions with construction companies to ensure regular workshops, seminars, and technical training courses on the fundamentals of artificial intelligence and its applications in the construction sector.21 0Item Restricted A CLOUD-BASED AI SYSTEM FOR SKILL GAP ANALYSIS AND TRAINING PATH RECOMMENDATION IN HR DEPARTMENTS(Saudi Digital Library, 2025) Alanazi, Abdullah Ramadan; AlYamani, AbdulghaniThis dissertation presents the development of a cloud-based artificial intelligence (AI) system designed to automate skill gap analysis and provide personalised training recommendations in Human Resource (HR) departments. The system integrates employee profiles, job role requirements, and training histories to identify competency gaps using a decision tree algorithm. The AI model achieved an accuracy of 0.86 and demonstrated strong interpretability and efficiency in recommending relevant training paths. Usability testing with HR professionals confirmed the system’s practicality and reliability in supporting workforce development and data-driven training strategies. The research contributes to the field of HR analytics by combining Human Capital Theory with Knowledge Discovery in Databases (KDD) to provide an explainable, scalable, and cloud-enabled HR decision-support framework.10 0Item Restricted Resilience of Saudi Financial Institutions Against AI-Driven Cyber Threats(Saudi Digital Library, 2025) ALshammar, Rushud; Adamos, VasileiosArtificial intelligence (AI) is increasingly exploited by cybercriminals, creating advanced threats that challenge the security of financial institutions. Saudi banks, central to Vision 2030’s digital transformation, face heightened risks from AI-driven attacks such as phishing, fraud detection evasion, and adversarial machine learning. The aim of this research was to evaluate the resilience of six major Saudi banks (NCB, Al Rajhi, SABB, Riyad Bank, BSF, and ANB)against AI-enabled cyber threats, with a focus on identifying gaps in current frameworks, assessing employee awareness, and recommending improvements. A quantitative, cross-sectional survey was employed, gathering data from banking professionals across cybersecurity, compliance, and risk management roles. The findings show that while AI-driven threats are widely recognised, frameworks are inconsistently applied, AI-powered defences are rare, and employee training lacks AI-specific content. These shortcomings reduce institutional agility and leave human awareness as the weakest layer of defence. The study is limited by its reliance on survey data, which restricts depth of institution-specific insights. It recommends mandatory AI-focused training, adoption of automated defence systems, and contextualised national frameworks. Future research should include longitudinal studies, case-specific analyses, and simulation-based testing to strengthen resilience in evolving threat environments.17 0Item Restricted Advancing narcolepsy diagnosis: Leveraging machine learning to identify novel neuro-biomarkers(Saudi Digital Library, 2024) Orkouby, Hadir; Bartsch, UllrichNarcolepsy is a rare neurological disorder with a well-identified pathophysiology that manifests as a sudden onset of sleep during wake behaviour. The current diagnostic pathways for narcolepsy involve complex assessments of sleep neurophysiology, including polysomnography and the multiple sleep latency (MSLT) test. These are cumbersome and work-intensive, and with limited resources within the NHS, this has led to increased waiting times for diagnosis and treatment of narcolepsy. This project harnessed the power of digital neuro-biomarkers and Artificial Intelligence (AI) to develop novel diagnostic markers for narcolepsy. Leveraging an open-source dataset of labelled archival polysomnography (PSG) recordings, including electroencephalography (EEG), I created a data analysis and classification pipeline to enhance diagnostic decision-making in clinical settings. This pipeline combines comprehensive data preprocessing and feature extraction with XGBoost and Random Forest (RF) classification models. The feature extraction process included selected time- series analysis features, spectral frequency ratios, cross-frequency coupling and moment-based statistical features of Intrinsic Mode Functions (IMFs) derived from empirical mode decomposition (EMD). The RF classifier emerged as the best model, achieving an accuracy of 82.5%, with a specificity of 82.5% and a sensitivity of 92.86%, by combining and averaging these feature sets and incorporating sleep stage labels during model training. These results underscore the potential of a novel approach using single-channel sleep EEG data from wearable devices. This innovative method simplifies the lengthy and costly pathway for narcolepsy diagnosis and also paves the way for developing new tools to diagnose sleep disorders automatically in non-clinical environments.14 0Item Restricted أثر الذكاء االصطناعي في أداء إدارة المخاطر في المؤسسات الحكومية السعودية دراسة تحليلية تطبيقية في هيئة الزكاة والضريبة والجمارك السعودية(Saudi Digital Library, 2025) Alruwaili, Mohmed Theyab; المبيضين, باسم احمدهدفت الدراسة إلى التعرف على أثر الذكاء الاصطناعي في أداء إدارة المخاطر في المؤسسات الحكومية السعودية، مع تطبيق تحليلي على هيئة الزكاة والضريبة والجمارك السعودية .اعتمدت الدراسة على المنهج الوصفي التحليلي، واستخدمت الاستبانة كأداة رئيسية لجمع البيانات الأولية من عينة الدراسة. تمثل مجتمع الدراسة في جميع العاملين بهيئة الزكاة والضريبة والجمارك السعودية البالغ عددهم 12700 موظف وموظفة، وتمثلت عينة الدراسة في 323 مفردة تم اختيارهم بطريقة العينة العشوائية. تناولت الدراسة الذكاء الاصطناعي كمتغير مستقل بأبعاده: النظم الخبيرة، الشبكات العصبية، والوكلاء الأذكياء، بينما كان أداء إدارة المخاطر هو المتغير التابع. أظهرت النتائج أن هناك اثر إيجابيا وذو دلالة احصائية لأبعاد الذكاء الاصطناعي ( النظم الخبيرة، الشبكات العصبية، الوكلاء الأذكياء )على أداء إدارة المخاطر في هيئة الزكاة والضريبة والجمارك السعودية كما كشفت النتائج عن مستوى مرتفع جدا للموافقة على تطبيق أبعاد الذكاء الاصطناعي ومستوى مرتفع لأداء ادارة المخاطر في الهيئة أوصت الدارسة بناء على هذه النتائج بضرورة تعزيز الاستثمار في البنية التحتية للذكاء الاصطناعي، وتطوير وتطبيق نظم خبيرة متقدمة، والاستفادة القصوى من الشبكات العصبية في التحليل التنبؤي للمخاطر، وتفعيل دور الوكلاء الأذكياء في المراقبة وأتمتة الاستجابة الأولية. كما أكدت على أهمية تأهيل وتدريب الكوادر البشرية، ووضع إطار حوكمة واضح الاستخدام الذكاء الاصطناعي في إدارة المخاطر، وتبادل الخبرات مع الجهات الرائدة. The study aimed to identify the impact of artificial intelligence on risk management performance in Saudi government institutions, with an analytical application to the Saudi Zakat, Tax and Customs Authority. The study adopted a descriptive analytical approach, and a questionnaire was used as the primary tool to collect primary data from the study sample. The study population comprised all employees of the Saudi Zakat, Tax and Customs Authority, totaling 12,700 male and female employees. The study sample consisted of 323 individuals selected through a random sampling method. The study examined artificial intelligence as an independent variable with its dimensions: expert systems, neural networks, and intelligent agents, while risk management performance was the dependent variable. The results showed a positive and statistically significant impact of AI dimensions (expert systems, neural networks, and intelligent agents) on risk management performance at the Saudi Zakat, Tax, and Customs Authority. The results also revealed a very high level of approval for the implementation of AI dimensions and a high level of risk management performance at the Authority. Based on these results, the study recommended the need to enhance investment in AI infrastructure, develop and implement advanced expert systems, maximize the use of neural networks in predictive risk analysis, and activate the role of intelligent agents in monitoring and automating initial responses. It also emphasized the importance of qualifying and training human resources, establishing a clear governance framework for the use of AI in risk management, and exchanging expertise with leading entities.34 0