Saudi Cultural Missions Theses & Dissertations
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Item Restricted Exploring Factors Influencing the Adoption of AI Tools in Auditing: A Mixed-Methods Study(Virginia Commonwealth University, 2024-07-12) Alsudairi, Fahad; Yoon, Victoria; Osei-Bryson, Kweku-Muata; Etudo, Ugochukwu; Senechal, JesseArtificial Intelligence's (AI) rise has created value for organizations and society, prompting scholars to study its spread across many areas. However, the impact of AI adoption on governmental organizations still needs to be explored. Governmental entities face unique challenges distinct from private organizations, and existing research often focuses on the perspectives of AI experts or senior management, neglecting the insights of lower-level employees who will use the system daily. This study investigates the multifaceted factors influencing the intention to adopt AI tools within a governmental auditing bureau in Saudi Arabia. To the best of our knowledge, no previous study has specifically delved into AI adoption within the context of governmental auditing in the literature. This study employs an exploratory mixed-method approach based on IS guidelines by Venkatesh et al. (2013, 2016). This research combines qualitative and quantitative methods to comprehensively investigate the factors influencing the intention to adopt AI tools in auditing. Initially, the study identifies key factors and develops a conceptual model grounded in qualitative data and theoretical background. The model is then validated and tested through a survey using a larger sample within the governmental bureau. The findings support many hypotheses, emphasizing the significance of technological factors such as AI complexity, perceived scalability, relative advantage, and security in the intention to adopt AI tools in auditing. The study also highlights the need to align governmental auditing tasks and AI tools, and the importance of Task Technology fit. Organizational factors, such as leadership support and strategic AI implementation, are crucial for successfully adopting AI. Additionally, environmental factors underscore the pivotal role of higher authorities in facilitating and supporting AI adoption in governmental organizations. This study offers several contributions. It extends organizational AI adoption literature by broadening the understanding of AI adoption factors, emphasizing the value of studying government organizations due to their unique nature, and providing insights into the factors affecting AI adoption from the end-user's viewpoint. It offers practical benefits for the governmental auditing agency and similar governmental organizations. Educationally, this dissertation functions as a rich case study within the Information Systems (IS) field, providing a valuable educational resource. Possible limitations include sample selection constraints, sample size in Phase I, and the limited contextual scope of the study. Directions for future research include examining the dynamics of AI implementation over time through longitudinal studies, testing the conceptual model across different governmental sectors and similar cultural and socio-political contexts, and investigating how AI tools affect auditors' compensation and job satisfaction.73 0Item Restricted Leveraging Technologies in Milk Traceability to Improve Supply Chain Performance: A Qualitative Study of the Saudi Dairy Industry(Victoria University, 2024-05-31) Alessa, Afyaa; Shee, Himanshu; DeVass, TharakaChallenges related to food safety and fraud have caused a decrease in consumer confidence, emphasising the need for trustworthy and efficient traceability technologies to prevent and rapidly address these issues. In an industry intrinsically linked to public health, the dairy sector has a lot to gain from adopting such technologies. However, the dimensions that influence firms' intentions to adopt these technologies remain under researched. This study investigates the Technological, Organisational, and Environmental (TOE) aspects that encourage dairy companies in Saudi Arabia to adopt traceability technologies into their operations and supply networks. It further delves into the role of firm's culture, which is significantly influenced by top management, in potentially affecting the intention to adopt these technologies. The use of the TOE framework and in-depth interviews with senior production and distribution managers, and the analysis that was conducted using NVivo revealed that government policies primarily drive the intention for technology adoption. Conversely, employee resistance emerges as a significant barrier, and surprisingly, the complexity of the technology was not seen as an obstacle. The study also uncovered that workforce localisation initiatives, such as the "Saudization" policy, could hinder the adoption of such technologies. Findings indicate a gap in the adoption of new traceability technologies within the Saudi dairy sector, a stance that is inconsistent with the ambitions of Saudi Vision 2030. Furthermore, the COVID-19 pandemic had a minor influence on the decisions made by companies regarding the adoption of these technologies, likely due to governmental support and a sense of satisfaction with their current technologies. From a practical standpoint, this study enhances industry practices, improves policymaking, and promotes sustainable development goals. It provides critical insights for industry practitioners and policymakers and contributes to several United Nations' SDGs by advocating sustainable production, reducing environmental impact, and identifying areas for improvement in the dairy supply chain. From a theoretical perspective, this research functions as a form of theory elaboration. It strengthens the Technological, Organisational, and Environmental (TOE) framework by offering empirically grounded insights gained through semi-structured interviews with senior managers in the Saudi dairy industry. Additionally, the research introduces the concept of workforce localisation and probes the pivotal yet often overlooked role of cultural factors in influencing the adoption of traceability technologies.20 0