SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Critical Success-Related Factors Influencing the Adoption and Use of Artificial Intelligence in Saudi Small and Medium-Sized Enterprises(Saudi Digital Library, 2025) alsulami, jehan fahim; Catherine, LouThe current decade presents significant opportunities for businesses to harness the transformative power of artificial intelligence (AI). Nonetheless, organisations of all sizes, including small and medium-sized enterprises (SMEs), continue to encounter challenges related to the critical success factors influencing AI adoption. Understanding the interplay between AI adoption, its utilisation, and these success factors remains pivotal to enhancing technology-enabled business operations. Therefore, this thesis investigates the critical success factors affecting AI adoption and use within Saudi SMEs. To construct a robust conceptual framework, three theoretical perspectives are employed based on a structured evaluation of relevant literature: the Technology-Organisation-Environment (TOE) framework, the Human-Organisation-and-Technology Fit (HOT-FIT) model, and Institutional Theory. The resulting framework addresses the technical, social, organisational, and environmental contexts critical to supporting SMEs in adapting to and utilising AI. It identifies eight key factors: skilled personnel, organisational readiness, data strategies, security concerns, system quality, government regulation, AI vendors, and trust in AI. A quantitative research methodology is employed to collect data from a sample of 300 SMEs across Saudi Arabia, utilising a simple random sampling technique within a cross-sectional survey design. Various statistical techniques are used to analyse and validate the proposed framework, including partial least squares structural equation modelling (PLS-SEM), which facilitates the testing and validation of the conceptual framework for AI adoption and use among Saudi SMEs. The findings indicate that critical success factors significantly impact AI adoption and use by Saudi SMEs. Notably, skilled personnel, government regulation, AI vendors, and trust in AI are identified as primary determinants of AI adoption. Additionally, skilled personnel, organisational readiness, government regulation, AI vendors, and trust in AI emerge as essential for the effective and sustainable use of AI. A statistically significant variation in AI adoption and usage is observed among Saudi SMEs of different sizes. The primary contribution of this study lies in extending existing information technology adoption literature to encompass the context of critical success factors for AI. Moreover, the findings offer a comprehensive perspective on the organisational dynamics influencing AI adoption and use by integrating human, organisational, technological, environmental, and social dimensions into a single framework. From a practical standpoint, the research provides valuable insights for technology consultants, policymakers, and regulatory authorities. Specifically, Saudi SMEs can leverage these findings to effectively enhance their capacity to adopt and sustain AI-driven innovations effectively.12 0Item Restricted Saudi Primary Teachers' Perceptions of Artificial Intelligence in Education: A Qualitative Investigation Through the TPACK-C Framework(Saudi Digital Library, 2025-06-28) Alshehri, Fahad; Marc, PruynSaudi Arabia's Vision 2030 identifies education as the principal driver of economic diversification and situates the Human Capability Development Program at the core of workforce preparation. Notwithstanding these ambitions, the 2018 PISA cycle showed that more than 50 percent of Saudi learners failed to attain minimum reading proficiency. The COVID-19 crisis subsequently demonstrated the system's capacity for rapid digital adaptation, characterised by agility and innovation. In this milieu, artificial intelligence has shifted from peripheral curiosity to policy imperative. No published study has yet employed the Technological Pedagogical Content Knowledge-Contextualised (TPACK-C) model enriched with Islamic constructs to investigate Saudi primary classrooms. This qualitative study explores five primary teachers' AI perceptions through TPACK-C, TAM, and SDT frameworks. Semi-structured Arabic interviews with teachers from diverse infrastructural contexts were analyzed using reflexive thematic analysis, revealing six key themes: instructional-efficiency catalyst, the infrastructure divide, variable student digital readiness, professional identity re-negotiation, fragmented professional learning, and the necessity of ethical-cultural safeguarding. Findings reveal a novel Threshold-Trajectory Model requiring sequential progression through digital-infrastructure, competency development, and ethical-cultural alignment phases. Results extend TPACK-C with Student Technological Knowledge (STK) and a culturally specific Ethical Knowledge domain, while providing actionable recommendations supporting Vision 2030 objectives.16 0Item Restricted A Systematic Review of User Consent, Transparency, and Secure Data Transmission and Storage(University of Technology Sydney (UTS), 2024-11-03) Alharbi, Sultanah; Hussain, Farookh KhadeerSmart home technology is revolutionizing residential environments by connecting devices to enhance comfort, safety, and energy efficiency. However, these advancements raise significant privacy concerns, particularly in data collection, transmission, and storage. This systematic review examines user consent, transparency, and secure data handling in smart homes, identifying challenges and innovative solutions such as blockchain and AI integration. The review highlights deficiencies in current consent mechanisms, the complexity of GDPR compliance, and practical barriers to implementation, offering insights for future research and practical privacy frameworks.30 0Item Restricted Audit Data Analytics, the Transformation within the Audit Profession: Perspectives from the Kingdom of Saudi Arabia. A(Royal Melbourne Institute of Technology (RMIT) University, 2024-04-18) Alharbi, Yousef; Phan, Duc; Kend, MichaelEmerging and advanced audit data analytics (ADA) technologies such as big data analytics (BDA) and artificial intelligence (AI) algorithms that can analyse vast amounts of structured, semi-structured, and unstructured data are changing the auditing industry's practices, processes, and evidence collection processes. This research will investigate factors that encouraged or discouraged Saudi audit firms from investing capital in emerging and advanced (ADA) technologies such as BDA and AI tools. Also examined here are new forms of audit evidence generated by these emerging and advanced technologies and factors that facilitate or impede the collection of such audit evidence. Furthermore, this study will explore the differences between listed and unlisted audit clients regarding the audit processes when advanced or emerging technologies are deployed. Diffusion of innovation (DOI) theory, technological-organisational-environmental (TOE) framework, and socio-technical (ST) theory will serve as the basis for the theoretical framework for this study. The study will use two methodologies to collect data: firstly, conducting semi-structured interviews with participants who have knowledge and experience of emerging and advanced ADA technologies for auditing, and secondly, reviews of the documentary data sources generated by audit firms on this topic. In this study, empirical findings from Saudi Arabia will be presented on the transformation occurring in the audit profession where emerging and advanced ADA technologies are being used. This study presents four contributions. The present study is one of the first to reveal capital investment decisions in emerging and advanced ADA technologies since it provides empirical knowledge aimed at enhancing academic perceptions in this area. It is also one of the first studies to provide empirical evidence about the new forms of audit evidence and the differences in audit processes between listed and unlisted clients when using ADA tools. For practical contribution, this study provides a comprehensive capital investment decision-making model in ADA technologies that consists of three pillars (i.e., technological, organisational, and external environmental), which allows audit firms to make informed capital investment decisions in emerging and advanced ADA technologies. In terms of theoretical contributions, this study is among the first to combine diffusion of innovation (DOI) withtechnological-organisational-environmental (TOE) theories to interpret findings about RQ1 and RQ2. This study is the first to utilize ST system theory to investigate the phenomenon of RQ3 since this topic has not been addressed previously. As a methodological contribution, this study utilized two qualitative data collection methods (i.e., semi-structured interviews and documentary sources) in conjunction with interpretivism philosophy in order to support and strengthen its findings. The findings reveal that KSA audit firms have many reasons (i.e., technological, organisational, and external environmental) for investing or not investing in ADA technologies. Technological factors are relative advantages, compatibility, complexity, trialability, observability, uncertainty vs. certainty, and trust in such technologies. Several organisational factors lead KSA audit firms to invest in ADA technologies: improvements of their operations, leadership support, the readiness of KSA audit firms, and technological competencies of auditors and other relevant employees. External environmental factors that encourage or discourage KSA audit firms from investing in such technologies are the country’s regulations and regulators, international auditing standards, competitive pressures, and trading partners' or clients’ requirements. It is not necessary for each audit firm to consider all these factors before deciding to invest or delay investment in audit technologies. However, it is more beneficial that KSA audit firms consider all these factors before deciding to invest in modern audit techniques, as they can look at matters from many angles and in detail, which gives them a better opportunity to make informed decisions. The findings about the new forms of audit evidence have been interpreted based on DOI and TOE theories, and they KSA audit firms did generate new forms of audit evidence by analysing the entire population of clients' data using AI and BDA, Radio Frequency Identification (RFID), drones, and sensors. The technical-based factors that lead KSA audit firms to generate new forms of audit evidence are relative advantages, compatibility, complexity, and simplicity. Organisational aspects that simplify the generation of new forms of audit evidence are leadership commitment and support and seeking to improve how audit firms operate with reference to external auditing practices. Finally, collecting new forms of audit evidence has been influenced significantly by external environmental factors such as government regulations and audit standards, audited clients, and competitive pressures. The findings about the differences in audit processes between listed and unlisted audit clients to collect new forms of evidence using modern technologies are driven by the six elements that comprise the ST system framework. These six elements drive four factors that establish the differences in audit processes and the use of ADA technologies between listed and unlisted clients: risk levels, regulations, accounting standards, and differences in the quality and quantity of the data provided by each client category (i.e., listed, or unlisted).81 0Item Restricted Audio-to-Video Synthesis using Generative Adversarial Network(University of New South Wales, 2024-01-23) Aldausari, Nuha; Mohammadi, Gelareh; Sowmya, Arcot; Marcus, NadineVideo generation is often perceived as stringing several image generators. However, in addition to visual quality, video generators must also consider motion smoothness and synchronicity with audio and text. Audio plays a crucial role in guiding visual content, as even slight discrepancies between audio and motion can be noticeable to human eyes. Thus, audio can be a self-supervised signal for learning the motion and building correlations between the audio and motion. While there are attempts to build promising audio-to-video generation models, these models typically rely on supervised signals such as keypoints. However, annotating keypoints as supervised signals takes time and effort. Thus, this thesis focuses on audio-based pixel-level video generation without keypoints. The primary goal of this thesis is to build models that generate a temporally and spatially coherent video from audio inputs. The thesis proposes multiple audio-to-video generator frameworks. The first proposed model, PhonicsGAN, uses GRU units for audio to generate pixel-based videos. The subsequent frameworks, each, address particular challenges while still pursuing the main objective. To improve the spatial quality of the generated videos, a model that adapts the image fusion concept to video generation is proposed. This model incorporates a multiscale fusion model that combines images with video frames. While the spatial quality of the video is important, the temporal aspect of the video frames should also be considered. To address this, a shuffling technique is proposed presenting each dataset sample with varied permutations to improve the video's temporal learning. We propose a new model that learns motion trajectory from sparse motion frames. AdaIN is utilised to adjust the motion in the content frame to the target frame to enhance the learning of video motion. All the proposed models are compared with state-of-the-art models to demonstrate their ability to generate high-quality videos from audio inputs. This thesis contributes to the field of video generation in several ways: Firstly, by providing an extensive survey on GAN-based video generation techniques. Secondly, by proposing and evaluating four pixel-based frameworks for enhanced audio-to-video generation output that each addresses important challenges in the field. Lastly, by collecting and publishing a new audio/visual dataset that can be used by the research community for further investigations in this area.24 0