Although learning management systems (LMS) have been widely adopted by higher educational institutions in many countries, they are considered an emerging technology in Saudi Arabia. Furthermore, research has demonstrated that the students’ use of them is not always satisfactory. This quantitative study investigated the factors that affect the students use of LMS in higher education by extending the technology acceptance model (TAM) and adapting eight external variables. Based on the probability multi-stage cluster sampling technique, online surveys were sent by email to 2000 students registered in three public universities in Saudi Arabia. 851 responses were submitted by participants, and 833 responses were used for data analysis. Using Partial Least Squares Structural Equations Modeling (PLS-SEM), the results revealed that perceived ease of use is affected by six factors (content quality, system navigation, ease of access, system interactivity, instructional assessment and system learnability). The findings confirmed that perceived usefulness has five determinants (content quality, learning support, system interactivity, instructional assessment and perceived ease of use). This research is relevant to researchers, decision makers and e-learning systems designers working to enhance students’ use of e-learning systems in higher education, in particular where there is not yet widespread adoption.

Due to rapid advancements in the field of information and communication technologies, mobile health (mHealth) has become a significant topic in the delivery of healthcare. Despite the perceived advantages and the large number of mHealth initiatives, the success of mHealth ultimately relies on whether these initiatives are used; their benefits will be diminished should people not use them. Previous literature has found that the adoption of mHealth by users is not yet widespread, and little research has been conducted on this problem. Therefore, this study identifies the antecedents of the intention to use mHealth and proposes a general model that might prove beneficial in explaining the acceptance of mHealth. The authors performed a quantitative meta-analysis of 49 journal papers published over the past 10 years and systematically reviewed the evidence regarding the most commonly identified factors that may affect the acceptance of mHealth. The findings indicate that the proposed model includes the seven most commonly used relationships in the selected articles. More specifically, the model assumes that perceived usefulness positively affects perceived ease of use and user behavioral intention to use mHealth is commonly influenced by five factors: perceived usefulness, perceived ease of use, attitude toward behavior, subjective norms, and facilitating conditions. The results of this work provide important insights into the predictors of mHealth acceptance for future researchers and practitioners.

The market for wearable health monitoring technology is promising globally and in Saudi Arabia particularly. The country has a very high prevalence of chronic diseases that can be managed using wearable health monitoring technology. However, wearable devices are not fully advantageous if people do not accept them. Due to the parsimony of studies on the acceptance of wearable health monitoring technology, understanding the key drivers of using wearable health monitoring technology remains uncertain. This cross-sectional study extends the extended unified theory of acceptance and use of technology (UTAUT2) to explain the variance in the adoption intention of wearable health monitoring technology. A total of 256 responses were analyzed using the partial least squares structural equation modeling technique, in addition to the importance-performance map analysis. The results indicate that performance expectancy (PE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM) and habit (HA) significantly impact users’ behavioral intention (BI) to adopt wearable health monitoring technology. The results also demonstrate that effort expectancy (EE), price value (PV), government health policy (GHP) and trust (TR) are not important. Based on the findings, this research presents a set of recommendations for decisions makers, managers and system developers in the healthcare sector to enhance the use and quality of wearable technology.

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