SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted THE INFLUNCE OF DIVERSITY OF CULTURE ON COMMUNICATION IN PROJECT MANAGEMENT WITHIN THE PRIVATE DIVISION IN THE KINGDOM OF SAUDI ARABIA(DeMontfort University, 2024-09) Alotaibi, Fahad; Oyinlola, AdewaleWith the expansion of businesses in the entire world, they need a huge range of commitments from multiple sectors in which some complexities like cultural diversity in the relevant context of Hofstede's Cultural Dimension Theory and Hall's high-context and low-context communication theory. This study is conducted to figure out the influence of cultural diversity on communication patterns of project management in the private sector mainly in Saudi Arabia. Through a detailed review of previous studies, we analyse it effects on management and the economy, it also identified a few obstacles with advantages and the efficient tactics to resolve them. This research study was based on Saunders’s research onion which provides a practical framework with the necessary tools and techniques to achieve study objectives by utilising quantitative research techniques. The findings of this conduct reveal that communication styles are found significant while others like cultural relativism, hierarchical structures, and time zone differences are found insignificant. In conclusion, this study seems credible in identifying the influence of cultural diversity on project management.21 0Item Restricted Evaluating CAMeL-BERT for Sentiment Analysis of Customer Satisfaction with STC (Saudi Telecom Company) Services(The University of Sussex, 2024-08-15) Alotaibi, Fahad; Pay, JackIn the age of informatics platforms such as Twitter (X) plays a crucial role for measuring public sentiment, especially in both private and public sectors. This study explores the application of machine learning, particularly deep learning, to perform sentiment analysis on tweets about Saudi Telecom Company (STC) services in Saudi Arabia. A comparative analysis was conducted between pre-trained sentiment analysis models in English and in Arabic to assess their effectiveness in classifying sentiments. In addition, the study highlights a challenge in existing Arabic models, which are based on English model architectures but trained on varied datasets, such as Modern Standard Arabic and Classical Arabic (Al-Fus’ha). These models often lack the capability to handle the diverse Arabic dialects commonly used on social media. To overcome this issue, the study involved fine-tuning a pre-trained Arabic model using a dataset of tweets related to STC services, specifically focusing on the Saudi dialect. Data was collected from Twitter (X), focusing on mentions of the Saudi Telecom Company (STC). Both English and Arabic models were applied to this data, and their performance in sentiment analysis was evaluated. The fine-tuned Arabic model (CAMeL-BERT) demonstrated improved accuracy and a better understanding of local dialects compared to its initial version. The results highlight the importance of model adaptation for specific languages and contexts and underline the potential of CAMeL-BERT in sentiment analysis for Arabic-language content. The findings offer practical implications for enhancing customer service and engagement through more accurate sentiment analysis of social media content in the service providers sector.15 0Item Restricted A Rigorous Analysis Template Process to Capture the Safety Properties of Self-Driving Vehicle Systems(University of Southampton, 2024-03-28) Alotaibi, Fahad; Butler, Michael; Hoang, SonSelf-Driving Vehicles (SDVs) are seen as a significant advancement in the automotive domain, hinting at a future where human drivers might be rendered obsolete. However, even with the advancements in SDV technology, the need for human drivers is still recognised. The incorporation of human drivers into SDVs introduces unique and significant challenges. The significance of human driver and SDV interactions cannot be overstated, especially when the SDV relies on the human driver as a fallback option during hazardous driving events. To address this critical aspect, this thesis presents a methodology termed the Rigorous Analysis Template Process (RATP). RATP establishes an analytical journey to develop a comprehensive framework ensuring safety and optimal cooperation between human drivers and SDV systems. It represents an evolution in existing work on analysing system safety and provides a more rigorous systematic strategy for SDV systems. It involves both systematic analysis and formal methods to evaluate safety in SDV systems. Drawing strength from a combination of both systematic analysis and formal methods, RATP adeptly identifies high-level safety requirements and develops a rigorous model to investigate issues and assumptions that may arise during the operations of SDV systems. One of the key benefits of RATP is its modularity, offering researchers and developers the ability to systematically analyse system behaviours from a high-abstraction view down to a more detailed view. The conclusion of this research presents a robust set of modelling patterns that act as a blueprint for the future development of SDV systems. RATP is demonstrated with a case study that explores the various functionalities of an SDV system to evolve the methodology into a mature state. Finally, this thesis presents a discussion on future improvements that could be undertaken to develop the methodology further.39 0