Saudi Cultural Missions Theses & Dissertations
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Item Unknown 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.26 0Item Unknown 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.Item Unknown 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.Item Unknown 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.Item Unknown 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.Item Unknown 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.Item Unknown 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.Item Restricted Predicting Delayed Flights for International Airports Using Artificial Intelligence Models & Techniques(Saudi Digital Library, 2025) Alsharif, Waleed; MHallah, RymDelayed flights are a pervasive challenge in the aviation industry, significantly impacting operational efficiency, passenger satisfaction, and economic costs. This thesis aims to develop predictive models that demonstrate strong performance and reliability, capable of maintaining high accuracy within the tested dataset and showcasing potential for application in various real-world aviation scenarios. These models leverage advanced artificial intelligence and deep learning techniques to address the complexity of predicting delayed flights. The study evaluates the performance of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and their hybrid model (LSTM-CNN), which combine temporal and spatial pattern analysis, alongside Large Language Models (LLM, specifically OpenAI's Babbage model), which excel in processing structured and unstructured text data. Additionally, the research introduces a unified machine learning framework utilizing Gradient Boosting Machine (GBM) for regression and Light Gradient Boosting Machine (LGBM) for classification, aimed at estimating both flight delay durations and their underlying causes. The models were tested on high-dimensional datasets from John F. Kennedy International Airport (JFK), and a synthetic dataset from King Abdulaziz International Airport (KAIA). Among the evaluated models, the hybrid LSTM-CNN model demonstrated the best performance, achieving 99.91% prediction accuracy with a prediction time of 2.18 seconds, outperforming the GBM model (98.5% accuracy, 6.75 seconds) and LGBM (99.99% precision, 4.88 seconds). Additionally, GBM achieved a strong correlation score (R² = 0.9086) in predicting delay durations, while LGBM exhibited exceptionally high precision (99.99%) in identifying delay causes. Results indicated that National Aviation System delays (correlation: 0.600), carrier-related delays (0.561), and late aircraft arrivals (0.519) were the most significant contributors, while weather factors played a moderate role. These findings underscore the exceptional accuracy and efficiency of LSTM-CNN, establishing it as the optimal model for predicting delayed flights due to its superior performance and speed. The study highlights the potential for integrating LSTM-CNN into real-time airport management systems, enhancing operational efficiency and decision-making while paving the way for smarter, AI-driven air traffic systems.Item Restricted Exploring the Impact of Artificial Intelligence on Risk Management Practices in Project Management within Small and Medium-Sized Enterprises (SMEs) in the IT Sector of the UK(Saudi Digital Library, 2025) Alburaq, Huda; Rutherford, CarrieThis dissertation investigates the impact of artificial intelligence (AI) on risk management practices in project management within small and medium-sized enterprises (SMEs) in the UK IT sector. The study addresses a gap in understanding how AI adoption influences risk identification, response time, and overall project performance. Using a positivist philosophy and quantitative design, data were collected through an online survey of 75 professionals from UK IT SMEs. Statistical analysis showed that AI implementation significantly improves risk identification effectiveness and reduces response times, confirming two research hypotheses. However, no direct link was found between AI adoption and overall project risk performance, suggesting that successful outcomes depend on additional factors such as organisational readiness, integration strategies, and complementary capabilities. The findings provide both theoretical and practical contributions, emphasising that SMEs should prioritise AI for risk identification and response while building comprehensive integration strategies. This research offers guidance for SMEs seeking to leverage AI in project management and highlights areas for future investigation.Item Restricted ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING AND LEARNING: LEVERAGING AI FOR LEARNER SUPPORT AND TEACHER DEVELOPMENT(Saudi Digital Library, 2025) Alyobi, Mazen; Egbert, JoyThis dissertation explores the emerging role of artificial intelligence (AI) technologies in English language teaching and learning. The dissertation comprises two complementary studies. The first study is a systematic review, utilizing the PRISMA model, that examines empirical research studies on the use of intelligent personal assistant (IPA) tools in an English as a foreign language (EFL) context. It focuses on the types of IPAs implemented, the language skills the studies target, IPA effectiveness for language learners, and the challenges students encountered. The findings revealed that the most commonly utilized IPAs in the EFL context were Google Assistant and Alexa. It also highlights that IPA use helped EFL learners improve their oral, listening, and pronunciation skills in several studies. The analysis found that speaking and listening skills were the most frequently targeted in the included studies, with positive effects, as well as students’ overall positive perceptions. However, the systematic review shed light on some limitations of IPA use, including errors in detecting pronunciation, students’ accents, and other technological issues. The second study is an exploratory case study that examines three English language educators’ usage and experiences with an automated feedback tool to support reflective teaching (RT). It investigates whether those experiences led to changes in their teaching practices and what changes were made. Data were collected from background surveys, self-reflection questions, semi-structured interviews, and automated feedback tool reports. The findings indicated that participants had a positive perception of using automated feedback to support RT, and they primarily used the automated feedback to increase their awareness of classroom interactions. The data revealed a measurable change in reducing teacher talk time and increasing student talk time for two of the teachers, while other instructional strategies showed mixed results. However, EL teachers expressed concerns regarding the accuracy of automated feedback in detecting nuanced interactions. In sum, while AI integration in these two studies showed some positive outcomes, the reported AI limitations may hinder its use due to limitations such as AI detection accuracy for diverse language classrooms. However, the two studies holistically provide insights into AI integration in English language teaching and learning, and they contribute to the growing body of knowledge on AI in language education.