Saudi Cultural Missions Theses & Dissertations
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Item Restricted AN EXPLORATORY STUDY OF EMPLOYEE PERCEPTIONS OF BIG DATA ANALYTICAL COMPETENCY AMONG LEADERS AND ITS IMPACTS ON DECISION- MAKING QUALITY(Saudi Digital Library, 2025) Alqahtani, Amal; Vaccaro, Christian AThe requirement for leaders to have strong abilities in data analysis has increased due to the growing integration of big data analytics in corporate decision-making. This study explores the leaders' and data scientists' perceptions of data analytical competency among leaders and its relationship to the quality of decision-making in enterprises with a particular emphasis on quickly changing technology environments around the globe. Leaders are concentrating more on using big data to improve the ability of their firms to make decisions (Dahiya, 2021). The true value of big data lies in its effective utilization, requiring leaders to orchestrate multiple resources, thereby fostering dynamic capabilities and enhancing the company's capacity for big data decision-making. I conducted a qualitative study that involved interviews with leaders and data scientists. To investigate the perspectives of data scientists and leaders, three study questions were created regarding their respective roles and responsibilities, as well as the importance of collaboration, data literacy, and adapting to new technology. The data were collected from interviews with 10 Saudi participants, including 4 leaders and 6 data scientists from different big data organizations. Preliminary findings suggest that both leaders and data scientists perceive the importance of having analytical competencies, and they agree that leaders should focus on hiring experts to provide them with data-driven insights to inform their decision making as the ability to lead their organizations within the context of a data analytic environment. Both leaders and data scientists perceive it is important for data scientists to analyze incoming data regularly and to gain knowledge of the mission and vision of the organization.6 0Item Restricted Early Prediction of Cancer Using Supervised Machine Learning: A Study of Electronic Health Records From The Ministry of National Gurad Health Affairs(University College London (UCL), 2024-08) Alfayez, Asma; Lai, Alvina; Kunz, HolgerEarly detection and treatment of cancer can save lives; however, identifying those most at risk of developing cancer remains challenging. Electronic health records (EHR) provide a rich source of "big" data on large patient numbers. I hypothesised that in the period preceding a definitive cancer diagnosis, there exist healthcare events, such as a history of disease, captured within EHR data that characterise cancer progression and can be exploited to predict future cancer occurrence. Using longitudinal phenotype data from the EHR of the Ministry of National Guard Health Affairs, a large healthcare provider in Saudi Arabia, I aimed to discover health event patterns present in EHR data that predict cancer development in periods prior to diagnosis by developing predictive models using supervised machine learning (ML) algorithms. I used two different prediction periods: six months and one year prior to cancer diagnosis. Initially, the thesis focused on the prediction of both malignant and benign neoplasms, before moving on to predicting the future risk of malignant neoplasms (cancer), since predicting life-threatening illness remains the most important clinical challenge. To refine the approach for specific cancer types, predictive models were built for the top three malignancies in this population: breast, colon, and thyroid cancers. ML predictive models were developed using the following algorithms: (1) logistic regression; (2) penalised logistic regression; (3) decision trees; (4) random forests; (5) gradient boosting; (6) extreme gradient boosting; (7) k-nearest neighbours; and (8) support vector machine. Model performance was assessed using k-fold cross-validation and area under the curve—receiver operating characteristics (AUC-ROC). After developing different models, their performance was compared with and without hyperparameter tuning using tree-based pipeline optimization (TPOT) and GridSearch. This study provides novel proof-of-principle that ML algorithms can be applied to EHR data to develop models that can be used to predict future cancer occurrence.40 0Item Restricted Digital Technologies in Accounting Firms: Adoption, Impact and New Avenues for Future Research(University of East Anglia, 2023-06) Alsahlawi, Saja; Guven-Uslu, Pinar; Dewing, IanPaper 1 Purpose – The purpose of this paper is to review the literature on the impact of digital technologies on accounting practice and accountants' roles in the context of accounting firms. Design/methodology/approach – A scoping review of academic studies was used to achieve the study's purpose. Findings – Only thirteen empirical papers on the impact of digital technologies on accounting practice and accountants' roles in the context of accounting firms were identified. Furthermore, the review revealed that discussion papers and anecdotal claims dominate the literature. It is important for future research to consider to what extent the accounting profession, and accounting firms in particular, are embracing digital technology and how it is impacting accounting practice and accountants' roles. The findings also reveal that the challenges and risks associated with digital technologies are unaccounted for and ignored in the literature. Originality/value – This paper contributes to the field of accounting research by providing an overview of emergent literature on the usage of digital technologies and its impact on accounting and accountants' roles in accounting firms context. It is also the first to synthesise and discuss the challenges/risks, as well as the opportunities/benefits associated with digital technologies within that context while also aiming to serve as a catalyst for future research.142 0