Saudi Cultural Missions Theses & Dissertations
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Item Restricted رسالة دكتوراه ( قدرة الجماعات الارهابية على استخدام مواقع التواصل في السعودية لتنجيد الشباب ونشر الايدلوجيا المتطرفة - دراسة تحليلية لبناء السرديات الجهادية في تويتر - )(Universidad De Alcala, 2024) Alghamdi, Saleh Saeed; Pertu, Julian LaThe rise of jihadi discourse on Twitter (now known as X) has represented an issue of increasing concern with its influence on online audiences and sociey. Despite counter strategies and global initiatives to curb extremist discourse and online radicalisation the influence of jihadi narratives on social media and Twitter remains a pervasive threat. The widespread adoption of social media and Twitter in Saudia and as a major power in the Middle East, Saudia Arabia has been the target of jihadi discourse and susceptible to extremist influence with far reaching regional and global implications. Given the rise and dissemination of jihadi discourse on Twitter relating to or emanating from Saudi Arabia this research investigates the ability of extremist groups to spread their ideologies through the social media platforms in Saudi Arabia on Twitter in the period 2015-2106. A systematic grounded discourse analysis of Twitter data was employed to comprehensively examine jihadi discourse on the platform. The findings reveal a deliberate and diverse narrative structure that integrates ideological, historical, religious, and socio-political elements, enhancing its persuasive power through strategic framing techniques. The communication strategies on Twitter, including rapid dissemination, amplification, and the use of multimedia, significantly enhance the discourse’s influence. This discourse shapes perceptions, behaviors, and identities, creating strong emotional and cognitive impacts. In Saudi Arabia, Twitter's role in propagating jihadi narratives is particularly impactful, linking messages to current events and leveraging multimedia to resonate with the audience, thereby posing a persistent threat to societal stability. This contributes a novel strategic framework for analysing online extremist communication that extends theory and supports policy guidance to counter online extremism by analysing jihadi discourse's persuasive power and influence on Twitter.140 0Item Restricted THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING KPIS AND OPTIMIZATION OF HUMAN RESOURCE MANAGEMENT(The Hague University, 2024-09-28) Alsamhan, Khulud; Le Fever, HansMANAGEMENT SUMMARY This thesis explores how artificial intelligence (AI) can enhance human resource management (HRM), particularly in recruitment and onboarding. The study focuses on LinkedIn's AI tools, aiming to understand their effectiveness in improving key performance indicators (KPIs) and optimizing HR processes. The research draws on a broad literature review, examining the evolution of AI in HR. AI has shown potential in automating tasks like candidate screening and onboarding, but there are challenges, including biases in AI systems and the need for continuous improvement. Using Saunders' research onion framework, a mixed-methods approach was adopted, combining surveys and interviews with HR professionals who use LinkedIn's AI tools. This approach provided a comprehensive view of AI's impact on HRM. The results indicate that AI tools significantly enhance effectiveness by automating repetitive tasks and improving candidate matching, thus reducing the time-to-hire and increasing accuracy. However, some challenges remain, such as occasional inaccuracies and the need for better user training. It's clear that refining AI algorithms and incorporating human oversight can help address these issues. In onboarding, AI tools have been successful in automating administrative tasks and personalizing the onboarding experience. Feedback suggests that AI-driven processes help new hires feel more supported and prepared. The study concludes with recommendations for further research and practical steps for implementation. It highlights the need for ongoing refinement of AI tools, better integration practices, and comprehensive training for HR professionals. Future research should focus on long-term impacts and best practices for AI in HRM. In summary, AI has the potential to transform HRM by enhancing KPIs and optimizing processes. However, a balanced approach that combines technology with human judgment is essential for maximizing these benefits. This thesis provides a foundation for future advancements in using AI in HRM.24 0Item Restricted Balancing Efficiency and Ethics: The Impact of AI-Driven Recruitment on Employee Satisfaction in the Tech Industry(University of Sussex, 2024-09) Bin Amer, Malak; Pede, Graziana DiThis dissertation investigates the integration of artificial intelligence (AI) in recruitment processes, focusing on its impact on employee satisfaction within Saudi Arabia’s technology sector. As AI becomes central to recruitment, particularly in data-driven tasks like resume screening and candidate assessments, it promises efficiencies but also raises ethical challenges, including concerns about algorithmic bias and privacy. This research examines how AI-driven tools in recruitment affect perceptions of fairness, transparency, and employee satisfaction, using a qualitative approach that incorporates interviews with employees and HR professionals in the tech industry. Findings highlight that while AI significantly streamlines recruitment, the depersonalisation of interactions and potential biases challenge its effectiveness, especially in contexts requiring cultural sensitivity. The study recommends a balanced approach that combines AI-driven efficiencies with human oversight to enhance employee satisfaction and ensure ethical recruitment practices. These insights contribute to the broader discourse on AI in human resource management and offer actionable strategies for ethical AI implementation in recruitment.23 0Item Restricted On Evaluating Cyber Defense by Humans and Reinforcement Learning Agents(Saudi Digital Library, 2023-12-09) Aljohani, Asmaa; Jones, JamesA significant factor that drives the investment in/integration of a cyber defense technique is its proven effectiveness, in terms of performance, usability, and security, against an intended adversary. Measuring the effectiveness of security defenses is complex, mainly because evaluation is a never-ending process, a natural outcome of evolving technologies and adversarial capabilities. To facilitate cyber defense evaluation, it is, thus, imperative to examine novel methods through which researchers could gain a deeper understanding of adversarial behavior from both technical and cognitive perspectives. To this purpose, the first part of this dissertation investigated the applicability of using two popular paid crowdsourcing platforms to run hacking experiments (i.e., Capture-the-Flag challenges). Paid crowdsourcing platforms can potentially facilitate studies in the Oppositional Human Factors (OHF), a field that considers attackers’ cognitive biases when examining the effectiveness of defensive techniques. Such platforms are unique in that they offer schemes eliciting real-world adversarial behaviors (e.g., maliciousness) while minimizing the constraints imposed by typical recruitment methods. Findings showed that the platforms vary significantly in data quality and workers’ technical skills. As a result, I examined the possibility of using Prolific to conduct a randomized study to analyze attackers’ cognitive biases and limitations when they encounter deception under an incentive-compatible scheme (i.e., monetary) and a design different from prior studies. Findings showed consistent behavioral patterns between the populations under consideration but revealed some shortcomings of online cybersecurity experimentation. As online experimentation with qualified human participants remains challenging, the third part of this research examined another alternative in which datasets from experimental psychology studies designed to analyze deficits in decision-making processes (e.g., gambling tasks) are used to guide the design of human-like cyber agents. More specifically, I investigated the role memory systems and settings play in predicting the behavior of healthy and unhealthy populations. The motivation behind this work came from the realization that healthy individuals and individuals with pathological tendencies—the latter are more inclined to commit cyber crimes [5]—exhibit different risk attitudes that could be partially attributed to how memories are processed and used to guide future decisions. The results demonstrated differences in the role memory systems and settings play in predicting actions taken by the populations under consideration. Having examined the role of memory in predicting real-world behavior, I augmented reinforcement learning agents with similar settings, analyzed the agents’ behavior in simulated network environments, and observed interactions between the type of memory systems used to guide the agents’ learning process, the type of experiences used in the learning process, the underlying learning strategy, and the observation spaces. The results highlighted the importance of analyzing the aforementioned factors when designing predictive models for adversarial decision-making.72 0