COVID-19 has impacted educational processes in most countries: some educational institutions have closed, while others, particularly in higher education, have converted to online learning systems, due to the advantages offered by information technologies. This study analyzes the critical factors influencing students’ satisfaction with their continuing use of online learning management systems in higher education during the COVID-19 pandemic. Through the integration of social cognitive theory, expectation confirmation theory, and DeLone and McLean’s IS success model, a survey was conducted of 181 UK students who engaged with learning management systems. It was found that, during the pandemic, service quality did not influence students’ satisfaction, although both information quality and self-efficacy had significant impacts on satisfaction. In addition, the results revealed that neither self-efficacy nor satisfaction impacted personal outcome expectations, although prior experience and social influence did. The findings have practical implications for education developers, policymakers, and practitioners seeking to develop effective strategies for and improve the use of learning management systems during the pandemic.

In this study, we aim to contribute to the existing literature on the implementation of artificial intelligence (AI) in education. We explore the factors that impact the behavior and attitude of students toward the use of AI in higher education. We employed a quantitative approach using a wide range of adoption theories and models, including the unified theory of acceptance and use of technology model. We formulated hypotheses and verified the conceptual model. A questionnaire was used to collect data from 350 students. The structural equation model (SEM) was applied to estimate the relationship between dependent and independent variables. Based on SEM results, we found that despite perceived risk negatively impacting students’ attitudes, the factors of performance expectancy and facilitating conditions significantly influenced students’ attitudes and their behavioral intention to use AI in education. The results also show that effort expectancy does not significantly influence attitudes toward AI use in higher education. Research limitations are discussed at the end of this study.

Information explosion and pressures are leading organizations to invest heavily in information security to ensure that information technology decisions align with business goals and manage risks. Limited studies have been done using small- and-medium-sized enterprises (SMEs) in the manufacturing sector. Furthermore, a small number of parameters have been used in the previous studies. This research aims to examine and analyze the effect of security organizational practices on information security management performance with many parameters. A model has been developed together with hypotheses to evaluate the impact of organizational practices on information security management performance. The data is collected from 171 UK employees at manufacturing SMEs that had already implemented security policies. The structure equation model is employed via the SPSS Amos 22 tool for the evaluation of results. Our results state that security training, knowledge sharing, security education, and security visibility significantly impact information security performance. In addition, this study highlights a significant impact of both security training and knowledge sharing on trust in the organization. Business leaders and decision-makers can reference the proposed model and the corresponding study results to develop favourable tactics to achieve their goals regarding information security management.

As chatbot technology becomes increasingly prevalent across a wide range of industries, it is crucial to explore the factors that shape user satisfaction with this AI-driven innovation. This research provides insights into how age and gender impact user perceptions and engagement with AI-driven health technologies in Saudi Arabia. The information systems success model has been utilised to determine the effect of age and gender on user satisfaction. A self-administered questionnaire was distributed in two hospitals in Makkah City, Saudi Arabia, and 527 responses were collected from chatbot users. Structural equation modelling via analysis of moment structures validated the model constructs. The findings revealed that the privacy issue on user satisfaction has been significantly greater with males than with females. However, the correlation between user satisfaction and continuance usage intention, as well as net benefits, has been much higher among the females. Also, notable differences were found between user satisfaction and net benefits and continuance usage intention and net benefits, especially when comparing younger and older participants. Across all age groups, user satisfaction consistently emerged as a central driver of continuance usage intention and net benefits, underscoring the importance of fostering satisfaction to enhance the effectiveness of AI-driven chatbots in digital health services. This study can serve as a guide to highlight the importance of chatbot user satisfaction and provide implications, limitations, and future research opportunities.

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Knowledge management systems (KMS) have gradually become pivotal to supporting information access, collaboration, and decision-making in higher education settings. Despite the widespread adoption of KMS by universities, students’ use of them is not always optimal. Moreover, although several universities invest heavily in KMS, digital repositories, and knowledge-sharing platforms, limited empirical research investigates how specific KMS dimensions influence students’ academic decision-making quality. This study investigates the relationships between system quality, knowledge quality, knowledge sharing, KMS usage, and decision-making quality among business students in a Saudi Arabian context. Using a quantitative survey of undergraduate and postgraduate business students (n= 376), the proposed conceptual framework was tested using partial least squares structural equation modeling. Findings show that system quality, knowledge quality, and knowledge sharing significantly enhance KMS usage, which in turn leads to improved decision-making quality. While knowledge quality and knowledge sharing show direct positive effects on decision-making quality, system quality also exerts both direct and indirect effects through KMS usage. The study contributes to the growing literature on knowledge management in higher education by demonstrating how KMS functionalities shape academic decision behaviors in digitally transforming universities. The findings extend KMS research by demonstrating that the benefits of digital knowledge environments are realized primarily through active usage and collaborative engagement.