Saudi Cultural Missions Theses & Dissertations
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Item 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.7 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.38 0Item Restricted Impediments To Adopting Building Information Modeling In Saudi Arabian Infrastructure Projects(Texas A&M University, 2023) Alsofiani, Mohammed; Caffey, Stephen; Lewis, Michael; Dooley, Kim; Escamilla, EdelmiroThe present research investigates the impediments that hinder the infrastructure sector in the Kingdom of Saudi Arabia (KSA) from adopting innovative technological solutions that can enhance communication and collaboration, ultimately minimizing and preventing construction project delays. The focus is particularly on Building Information Modeling (BIM) and its role within KSA infrastructure projects. BIM serves as a pivotal technology in the Industry 4.0 era, facilitating data sharing among stakeholders throughout the lifecycle of built assets. However, the adoption of these technologies in KSA has encountered significant impediments. Thus, the current research aims to investigate the obstacles preventing the adoption of BIM in KSA infrastructure projects and suggests strategic approaches to overcome these hurdles. To accomplish the stated aim, the investigation process employs a combination of qualitative and quantitative research methods, including a systematic review and stakeholder surveys. Specifically, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol was utilized, and 149 AEC (Architecture, Engineering, and Construction) professionals from both the private and public sectors in KSA, who possess awareness of BIM technologies, participated in the survey. The results indicate that key impediments to adopting BIM include a lack of training and education, unclear business value, absence of adoption initiatives, limited demand from clients, resistance to change, a lack of standardization, and cost considerations. However, participants generally concur that the current communication and collaboration practices in the context of KSA infrastructure projects need improvement. To overcome the identified impediments, the research emphasizes the need for a BIM mandating strategy, which includes developing standards, guidelines, and regulations alongside promoting BIM education and training programs. Raising awareness among stakeholders through workshops and incentive programs is also crucial. This strategy should involve the active participation of governmental bodies and industry organizations with clear roles and responsibilities. By addressing the identified challenges and implementing the recommended strategies, infrastructure projects in KSA can benefit from BIM technologies to enhance the successful and efficient delivery of these projects.55 0Item Restricted EXPLORING SUCESS FACTORS OF ADOPTING ADVANCED MANUFACTURING TECHNOLOGY FOR ELECTRICAL VEHICLES INDUSTRY IN SAUDI ARABIA APPLYING THE TECHNOLOGY ACCEPTANCE MODEL (TAM)(Al Fatais, Abdullah, 2023) Al Fatais, Abdullah Mohammed; Korwowski, WaldemarBased on the Technology Acceptance Model (TAM), the study explores the success factors of adopting Advanced Manufacturing Technology (AMT) for the Electrical Vehicles (EVs) industry in Saudi Arabia. The study assesses the impact of eight factors on AMT adoption and implementation success. The dimensions include Training & Education, Planning, Management, Technology, Business, Economic, Policies & Regulations, and Social. The study analyzes the sample including people with careers related to advanced manufacturing in Saudi Arabia, either in the public sector, private sector, industrial sector, and academia. Furthermore, an online questionnaire was used to collect data from the participants. Additionally, a Systematic Literature Review (SLR) was conducted to analyze the existing literature in addition to the utilization of TAM for data analysis. This study aims to evaluate the readiness of the Saudi industrial sector to adopt EVs manufacturing technologies. Moreover, this study is expected to use a reasonable sample size for analysis purposes which can result in solid conclusions and practical recommendations.18 0Item Restricted A Value-Cost Model for Cryptocurrency Adoption(2023) Abo-oleet, Saeed Saad; Fang, XiaowenThe wide adoption of cryptocurrency is predicted to change the world’s economy. Cryptocurrency mass adoption can provide several advantages to societies, such as decentralization of trust, lower transaction fees, financial inclusion for unbanked and underbanked individuals, increased innovation and improved economic growth. Thus, empirical research on predictors of cryptocurrency adoption is essential to guide practitioners and policymakers in discovering what factors influence the adoption. Previous research relies on existing adoption and human behavioral intentions theories, such as the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), to name a few. However, these theories fall short in explaining technology-oriented behaviors which are in some way related to economic outcomes. Without such considerations, the aforementioned models are rendered insufficient when attempting to explain cryptocurrency adoption. This study helps to address this gap by developing a Value-Cost Model for Cryptocurrency Adoption. The model is based on Transaction Cost Economics Theory (TCE) and the concept of perceived value. In this study, both cryptocurrency users (n = 173) and non-users (n = 140) completed an online survey assessing their perceptions of cryptocurrency’s perceived value, transaction costs and other related attitudes. The Partial Least Squared Structural Equation Modeling (PLS-SEM) was used to validate the Value-Cost Model. The findings supported the proposed model for both groups, revealing that adoption behavior is explained by perceived value. The users’ model demonstrated significant explanatory power, showing that perceived value accounts for 74% of the variance in the users’ continuous adoption of cryptocurrency—similar explanatory power was found for non-users, with perceived value explaining 63% of the variation in non-users’ intentions to adopt. Perceived economic and non-economic benefits significantly contributed to users’ and non-users’ perceived value. In both groups, the subsequent decrease in the perceived value R2 would be substantial if perceived economic benefit was excluded from the model, suggesting that perceived economic benefit plays a crucial role in explaining the variance in perceived value. The results also showed that users’ and non-users’ perceptions of transaction costs had a negative impact on their overall assessment of the value of cryptocurrencies. This impact was, however, negligible for both groups, as economic and non-economic benefits more strongly influenced the perceived value. Both uncertainty and asset specificity were shown to significantly and positively influence the perceived transaction costs of users and non-users. However, asset specificity was discovered to have a stronger impact on users’ and non-users’ perceptions of transaction costs than uncertainty. Finally, the results showed that transaction frequency was significantly linked with lower perceived transaction costs for users, although the impact of this predictor on the transaction costs was small in magnitude. The Value-Cost Model for cryptocurrency adoption considers the economic characteristics of cryptocurrency and its resemblance to other financial instruments such as stocks. Such consideration required support from preexisting economic models, which allowed for the use of TCE to understand the role of transaction costs in the adoption behavior. While TCE has been applied to investigate technology adoption in the past, it had not yet been utilized in the context of cryptocurrency, highlighting the economic factors affecting adoption behaviors. The proposed model also provides a novel conceptualization of the economic benefit relevant to cryptocurrency. By referencing financial instruments such as stocks, the proposed model introduces two variables (liquidity and prospect of growth) that can be utilized to measure cryptocurrency’s perceived economic benefits. Overall, this study advances our knowledge of the valuation process of cryptocurrencies and how practitioners can create value in them. The study concludes with several recommendations proposed based on the findings of the Value-Cost Model for cryptocurrency adoption.16 0