SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Understanding the Factors Influencing the Acceptance and Adoption of Mobile Health Applications by Physicians during the COVID-19 Pandemic: The Case of Saudi Arabia(University of Technology Sydney, 2025-01) Alsahli, Sultan; Lam, Mary; Hor, Su-yin; Rogers, KrisBackground: The rapid evolution of mobile health applications has become increasingly crucial in enhancing healthcare delivery, particularly during the COVID-19 pandemic. Despite the critical role of these technologies, acceptance and adoption rates among physicians in developing countries, especially Saudi Arabia, have remained relatively low. This highlights the need to explore the determinants of their acceptance and adoption. Aim: This thesis aimed to investigate the key factors influencing Saudi physicians’ intentions toward using mHealth applications during the COVID-19 pandemic. Methods: This mixed methods research was conducted in three phases, each addressing specific objective and research question. In phase 1, a systematic review was conducted to present all available evidence of mHealth acceptance and adoption from the perspectives of physicians. Phase 2 applied a quantitative design based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate key factors influencing physicians’ behavioural intentions to adopt mHealth apps. Data were collected via an online survey and analysed using structural equation modeling. Phase 3 employed a qualitative design, exploring additional context-specific factors not accounted for by the UTAUT model through semi- structured interviews. The qualitative data were analysed using template analysis. Results: The systematic review identified technological, individual, and organizational factors affecting physicians’ acceptance of mHealth apps during the pandemic. The quantitative study found that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influenced physicians’ intention to use mHealth applications. Qualitative findings highlighted additional factors unique to the Saudi context, such as concerns about data privacy, patient engagement, compatibility with religious and cultural norms, and the impact of COVID-19 pandemic. These factors shaped physicians’ perceptions and adoption behaviours, emphasizing the need for tailored strategies to promote mHealth in Saudi Arabia. Conclusions: This thesis extends the UTAUT model by incorporating context-specific factors relevant to developing countries like Saudi Arabia during the COVID-19 pandemic. The findings emphasize the need for investments in infrastructure, targeted training programs, and policies that address both technological and cultural concerns. By fostering an environment that supports the integration of mHealth applications into routine practice, healthcare organizations can improve both healthcare delivery and patient outcomes during health crises and beyond. The study provides critical insights for policymakers and healthcare managers seeking to enhance the acceptance and use of mHealth technologies in similar global contexts. Future research should examine the perspectives of other healthcare workers and patients for a comprehensive understanding of mHealth adoption while also exploring its long-term impact on patient outcomes and healthcare professionals.26 0Item Restricted Factors Driving Individuals’ Usage Intention of Artificial Intelligence (AI) Assistants in E-commerce: Perspectives of Users and Non-Users(University of Technology Sydney, 2024) Alnefaie, Ahlam Eid Awad; Kang, Kyeong; Sohaib, OsamaThe ongoing revolution of e-commerce has brought about significant transformations in the global retail landscape, redefining how consumers interact with online platforms. In response to this transformative trend, businesses increasingly adopt and integrate artificial intelligence (AI) technologies, particularly AI assistants. AI assistants have gained significant traction to enhance customer engagement, improve personalised experience, and streamline various aspects of the e-commerce process. Companies across diverse industries and geographical regions have recognised the potential of AI assistants in fostering more profound connections with customers, providing real-time support, and bolstering sales through intelligent recommendations. Consequently, investment in AI research and development has surged, leading to remarkable advancements in AI assistants’ features and functionalities. Despite the growing interest of the scientific community and business stakeholders in the topic, scholarly research on the factors influencing e-commerce consumers’ attitudes and intentions toward using AI assistants is still limited and provides contradictory evidence regarding some factors. Moreover, no comparative studies in the e-commerce context empirically investigated the attitudes of non-users and users toward AI assistant use. Also, several consumers' demographics have been excluded from prior research, with no previous empirical research on AI assistant use across different cultural backgrounds. For these reasons, the study aimed to comprehend the factors influencing consumers' behavioural intention to utilise AI assistants and to recognise the significant user differences based on multiple perspectives. This study employed a unique research model based on the technology acceptance model. It extended it with external factors of AI assistants’ capabilities that still need to be tested together in AI assistant adoption for e-commerce consumers. This research conducted a mixed-method approach. In the first phase (Phase A), a quantitative method was employed to investigate the relationships between the constructs in the study model, and the Partial Least Square Structural Equation Modelling (PLS-SEM) and several statistical techniques were adopted. Furthermore, to account for cross-cultural differences and identify potential variations in usage intentions towards using AI assistants between Eastern and Western cultures, a multi-group analysis (MGA) was conducted. In the second phase (Phase B), a qualitative approach was conducted by applying machine learning and natural language processing techniques to analyse reviews of the Louis Vuitton brand's e-commerce applications. The objective of this stage was to obtain supporting evidence for the results of the VI first study and to gain deeper insights into consumer attitudes and experiences. Subsequently, the results were integrated to provide multiple insights to answer the research questions and strengthen the findings. This study has confirmed some previous studies' results and provided new findings. The attitude factor was the significant predictor of the intention to use AI assistants in non-users and users, with a direct and positive effect. Perceived usefulness was found to be the statistically significant predictor of attitudes in both non-users and users of AI assistants. The additions to the original TAM model, specifically incorporating interactive communication and personalisation, were statistically significant predictors of the attitudes of non-users and users to use AI assistants with positive effects. In contrast, perceived ease of use was a nonsignificant predictor of the non-users’ attitudes and positively impacted the users’ attitudes towards using AI assistants. Furthermore, no significant differences existed in the relationships among the primary factors influencing the intention to utilise AI assistants in e-commerce when comparing Western and Eastern cultural groups. This study contributes to both theory and practice by extending the TAM model with two external factors enabling the assessment of the factors affecting the intention to use AI assistants from consumer, social, and marketing perspectives and providing new empirical data on this topic in technology adoption studies. The study also enables further research on this topic and comparing study results, thus improving understanding of the phenomenon. It also provides various e-commerce practitioners with valuable information and recommendations regarding AI assistant use, enabling them to make better decisions in developing and implementing AI assistant technologies.10 0Item Restricted Blockchain Technology Adoption in Saudi Arabia’s Higher Education Sector(University of Technology Sydney, 2023-08-08) Alalyan, Mohrah Saad; Hussain, Farookh; Q.Gill, AsifBlockchain technology is a decentralised, digital ledger that records transactions in a secure and transparent way using cryptography. It allows for a trustless system where no central authority is needed to validate transactions. Originally used as a cryptocurrency mechanism, blockchain has since found applications in many fields and industries, although its applications in education are still emerging. Guided by 1) the importance assigned to innovative technologies for higher education development in Saudi Arabia and 2) the lack of blockchain adoption knowledge in this field, this study aims to develop a framework for blockchain adoption in Saudi higher education institutions. Using the Design Science Approach as a basis, this study 1) designs an original framework based on theoretical and empirical literature on blockchain adoption; 2) presents the results of the framework analysis and refinement by industry experts; and 3) demonstrates the results of the framework evaluation based on a large-scale survey of higher education professionals. The Blockchain Adoption Framework for Saudi Higher Education Institutions developed in this study is, to the best knowledge of the researcher, the first of its kind. It includes five dimensions: Technology, Organisation, Environment, Quality and Barriers. The model demonstrates a high level of validity with 11 out of 16 factors demonstrating a statistically significant relationship to blockchain adoption. The framework can serve as a practical tool for institutional decision makers in developing a plan for blockchain adoption in colleges and universities in Saudi Arabia. The framework is supplemented with a questionnaire tool that helps identify adoption enablers and barriers specific to each institution. The framework can also be used as a foundation for further research on blockchain adoption both in the context of higher education and related industries. Despite its rigorous research approach, the study still had some limitations regarding geographic and industry context, data collection and sampling methods. Therefore, future studies are recommended to explore the framework's applicability to other sectors and national contexts as well as using different methodologies to test its validity.42 0