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Novel extension of the UTAUT model to understand continued usage intention of learning management systems: the role of learning tradition
The key objective of this study was to reveal the key factors that impact university students’ continued usage intentions with respect to Learning Management Systems (LMSs). Given the context-dependent nature of e-learning, the Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied and extended with constructs principally related to LMSs. The newly added constructs include learning tradition, self-directed learning, and e-learning self-efficacy. The extended model, which measures continued usage intentions with respect to LMSs, was validated with empirical data collected via an online survey questionnaire completed by a sample of 590 higher education students in three private universities in Jordan. PLS-SEM- “Partial least squares structural equation modelling” was employed to examine the various hypotheses introduced in the model. The results demonstrated that: (1) performance expectancy, e-learning self-efficacy, effort expectancy, facilitating conditions, and social influence have a direct positive influence on continued usage intentions, (2) effort expectancy has a direct positive effect on performance expectancy, (3) performance expectancy partially mediates the relationship between effort expectancy and continued usage intentions, and (4) self-directed learning and learning tradition have direct negative effects on continued usage intentions. The outcomes of this study have valuable theoretical and practical implications for researchers, higher education institutions (HEIs), and developers of LMSs.
Journal Article
Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning
This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing one of the most reliable measures of perceived eased of use, the System Usability Scale. 345 university students participated in the study. The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.
Journal Article
Machine learning and deep learning techniques in wireless and mobile networking systems
\"This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems\"-- Provided by publisher.
Book
Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic
The provision and usage of online and e-learning system is becoming the main challenge for many universities during COVID-19 pandemic. E-learning system such as Blackboard has several fantastic features that would be valuable for use during this COVID-19 pandemic. However, the successful usage of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems. There is lack of agreement about the critical challenges and factors that shape the successful usage of e-learning system during COVID-19 pandemic; hence, a clear gap has been identified in the knowledge on the critical challenges and factors of e-learning usage during this pandemic. Therefore, this study aims to explore the critical challenges that face the current e-learning systems and investigate the main factors that support the usage of e-learning system during COVID-19 pandemic. This study employed the interview method using thematic analysis through NVivo software. The interview was conducted with 30 students and 31 experts in e-learning systems at six universities from Jordan and Saudi Arabia. The findings of this study offer useful suggestions for policy-makers, designers, developers and researchers, which will enable them to get better acquainted with the key aspects of the e-learning system usage successfully during COVID-19 pandemic.
Journal Article
Factors predicting online university students' use of a mobile learning management system (m-LMS)
This study analyzed the relationships among factors predicting online university students' actual usage of a mobile learning management system (m-LMS) through a structural model. Data from 222 students in a Korean online university were collected to investigate integrated relationships among their perceived ease of use, perceived usefulness, expectation-confirmation, satisfaction, continuance intention and actual usage of m-LMS. Results showed that perceived ease of use predicted perceived usefulness, but expectation-confirmation was not related to perceived usefulness. Perceived usefulness and expectation-confirmation predicted satisfaction. Perceived usefulness and satisfaction predicted continuance intention, but perceived ease of use was not related to continuance intention. Continuance intention predicted actual usage of m-LMS.
Journal Article
Antecedents of continued usage intentions of web-based learning management system in Tanzania
Purpose
– The purpose of this paper is to examine factors that predict students’ continued usage intention of web-based learning management systems (LMS) in Tanzania, with a specific focus on the School of Business of Mzumbe University. Specifically, the study investigated major predictors of actual usage and continued usage intentions of e-learning system, and challenges of using the e-learning system.
Design/methodology/approach
– Data were collected through a questionnaire survey of 300 third year undergraduate students, with a rate of return of 77 per cent. A total of 20 faculty members were also interviewed. The unified theory of acceptance and use of technology (UTAUT) was utilized in the study.
Findings
– The results show that actual usage was determined by self-efficacy, while continued usage intentions of web-based learning system was predicted by performance expectancy, effort expectancy, social influence, self-efficacy, and actual usage. Challenges for using web-based LMS were related to information and communications technology (ICT) infrastructure barrier, LMS user interface was not user friendly, weak ICT policies, management and technical support, limited skills, lack of awareness, resistance to change, and lack of time to prepare e-content and use the e-learning system.
Practical implications
– The study findings are useful to e-learning managers and university management to identify important factors and develop appropriate policies and strategies to encourage long-term usage of e-learning systems for future studies and lifelong learning.
Originality/value
– By using UTAUT in the context of continued usage intentions and the integration of an additional construct (“self-efficacy”), the extended UTAUT model fits very well in the web-based learning systems in Tanzania, in particular where such studies are scant. The findings can be used in other institutions with similar conditions in investigating the continued usage intentions of e-learning systems.
Journal Article