Saudi Cultural Missions Theses & Dissertations
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Item Embargo Integrating Emerging Technologies and Data Analytics Skills into Saudi Accounting Curricula: An Institutional Logics Perspective(Saudi Digital Library, 2025-07-01) Bin Mibrad, Ibrahim; Xiao, LingThis study investigates the integration of emerging technologies and data analytics skills into Saudi accounting curricula. Leading organisations, such as the Big Four accounting firms, AICPA, and AACSB, have called for these skills to be embedded in accounting education to keep pace with evolving professional demands. However, the integration process remains slow or insufficient both internationally and within Saudi Arabia. Moreover, despite the importance of this area, there has been little research in developing and MENA countries, including Saudi Arabia, which have distinctive cultural, economic, and political systems. This research seeks to understand the drivers and barriers to integrating these skills in Saudi accounting curricula and explores how these obstacles can be addressed. Grounded in a systematic literature review, the study conducted 34 semi-structured interviews with accounting educators and representatives of professional accounting bodies, both nationally and internationally, alongside document analysis. The study found that while Saudi and Western accounting educators share some common factors influencing technology integration, cultural and religious norms in Saudi Arabia uniquely impact the process. This research also provides novel insights into the integration of emerging technologies in accounting curricula within a developing, MENA context, using the institutional logics perspective (ILP). Through ILP, the study examines how accounting educators navigate two co-existence competing logics: traditional and modernisation. These varied responses from faculty members, including compliance, defiance, combination and compartmentalisation, are shaped by factors such as adherence, the religious principle of Al-Amanah, age and retirement considerations, and strong job security. Furthermore, the findings offer valuable implications for decision-makers, including department heads, and accounting educators, as they consider integrating emerging technologies into accounting curricula.5 0Item Restricted Unlocking Profitability: The Role and Importance of Data Analytics in Construction Financial Management(University College London, 2025) Abussaud, Laian; Krystallis, IliasThe construction industry faces persistent challenges in financial management, including cost overruns, tight profit margins, and unpredictable project variables. This dissertation investigates the transformative role of data analytics in addressing these challenges and enhancing profitability in construction projects. By comparing projects that implement data-driven financial strategies with those using traditional methods, the study employs a quantitative, quasi-experimental design to assess key financial performance indicators such as cost variance, profitability, and return on investment. Drawing from secondary datasets and real-world case studies, the research demonstrates how historical and real-time data, predictive modeling, and integrated analytics tools contribute to improved cost estimation, resource allocation, and risk mitigation. Findings indicate that comprehensive utilization of data analytics significantly enhances financial outcomes, providing a compelling case for its broader adoption in the industry. The study not only bridges a critical research gap but also offers practical recommendations for construction firms, project managers, and policymakers seeking to harness analytics for sustainable financial success.15 0Item Restricted Utilizing Data Analytics for Fraud Detection and Prevention in Online Banking Systems of Saudi Arabia(University of Portsmouth, 2024-09) Almotairy, Yazeed; Jiacheng, TanThis thesis addresses the critical issues of online banking and online banking fraud in Saudi Arabia. The thesis focusses on the older methodologies of the online banking systems in Saudi Arabia. The frauds are discussed in detail that are occurring in the online banking systems and are causing inconvenience to the users and account holders of the online banks and applications. In this thesis, online banking frauds are discussed thoroughly, and the traditional fraud detection methods are elaborated as well. The vulnerabilities in the current systems are explored. It discusses how the older systems are not performing well and why the new system encompasses the power of data analytics and machine learning. The methods proposed use a set of data analytics and machine learning algorithms and techniques to detect fraud or any fraudulent activity that a scammer or fraudster may perform. The results of this study explain how the proposed system can outperform the traditional methodologies being used in Saudi Arabian online banking systems. The proposed system can also enhance the user experience. The possible privacy and ethical concerns are also discussed. In the end, it is also discussed what the future prospects are for the researchers who are looking to enhance this research or want to work in the field of data analytics and machine learning to improve the security of the security of online banking applications. In conclusion, this thesis not only contributes to the body of knowledge on online banking frauds in Saudi Arabia and their detection but also features future research topics for new researchers.51 0Item Restricted AN AGILE DATA ANALYTICS FRAMEWORK TO IMPROVE HEALTHCARE PROCESS PERFORMANCE IN INFECTIOUS DISEASE PROPAGATION(Binghamton University, State University of New York, 2024-05-17) Asiri, Mohammed Ali A; Lu, SusanThe recent COVID-19 pandemic has highlighted the importance of responding quickly and efficiently in the healthcare industry, especially when dealing with rapidly spreading infectious diseases. This dissertation presents an Agile Data Analytics Framework that aims to address the critical challenges observed during the COVID-19 crisis. These challenges include the need for early detection of the disease progression, which was difficult due to the initial surge in cases that outpaced the system's responsiveness. There were also limitations in the support system, which made it challenging for healthcare professionals to make triage decisions and classify the severity of cases, which is essential for operational decision-making. Finally, there were issues with process workflow monitoring, where bottlenecks of the patient treatment journey unknowingly led to delays in patient care and process inefficiencies. This framework aims to tackle those challenges to enhance the healthcare process performance and improve patient outcomes. The research has three primary objectives. Firstly, it aims to develop a conceptual agile framework that will use healthcare big data for rapid disease pattern detection, facilitate cross-departmental cooperation, and minimize manual data processing. Secondly, the research implements analytical tools, including machine learning algorithms and time series forecasting, to improve clinical decisions and risk classification. Thirdly, process mining techniques are integrated as performance indicators for healthcare processes, enabling more effective and timely healthcare delivery. The research presented within this dissertation has yielded two substantial contributions to the healthcare industry. Firstly, it has formulated an agile framework marked by its adaptability, expeditious response capabilities, and potential to enhance the responsiveness of healthcare processes in the context of infectious diseases. Secondly, it emphasizes the strategic advantages of integrating big data technology, significantly improving healthcare performance through more informed decision-making processes, and facilitating superior care quality. This dissertation comprehensively explores the Agile Data Analytics Framework's development, implementation, and potential impact on healthcare processes. It presents a transformative approach to healthcare outcomes during health crises, suggesting a novel path for leveraging agility and data analytics in combating infectious diseases' challenges.20 0Item Restricted Leveraging Potentials of Big Data for Better Decision-Making and Value Creation in Nonprofit Organisations(Saudi Digital Library, 2023-07-21) Alsolbi, Idrees; Prasad, Mukesh; Agarwal, Renu; Unkelhar, BhuvanIn Nonprofit Organisations, analysing and understanding donor behaviour remain critical and challenging. While big data and machine learning techniques promise technical solutions to address this problem, how to design and build an intelligent decision support system based on these technologies remains unclear. The literature reveals certain challenges for analysing donor behaviour. The researcher adopted a design science framework which helped to create an artefact (an intelligent decision support system) to analyse donor behaviour. The results show that (by analysing public big data sets of donors from different sources) certain variables are essential to analyse donor behaviour in nonprofit organisations. These variables are the total amount of donations, the number of donations, gender, age, social level of income, educational level, and the frequency of donations which assist the researcher in choosing the appropriate analysis model, from classification to predictions, and deciding the most beneficial machine learning techniques. The researcher aims to provide a theoretical foundation design for developing an intelligent decision support system for analysing donor behaviour. The research contributes to decision support and data analytics research by presenting the capabilities of data analytics and machine learning techniques in the context that face the difficulty of understanding donor behaviour.85 0