Integrating teachers’ TPACK levels and students’ learning motivation, technology innovativeness, and optimism in an IoT acceptance model

The growing use of the Internet of Things (IoT) around the world has encouraged researchers to investigate how and why the IoT is implemented in colleges and universities. Previous studies have focused on individual attitudes rather than the integration of attitudes from two different perspectives. Furthermore, other studies have investigated the use of the IoT in non-educational settings, ignoring the effect of the IoT related to the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) model. The present work aims to address this research gap by determining the main factors that influence acceptance of the IoT, leading to increased awareness in collaborative learning, where technology forms the core tool in enhancing the use of the IoT. A questionnaire was used to collect data from teachers and students from colleges and universities in Oman and the United Arab Emirates (UAE). The data were analyzed through the structural equation modeling (SEM) method. The findings indicated that there are two levels of positive effects on the intention to use IoT. The first level is technology features, which are represented by technology optimism and technology innovation; these factors are crucial to using the IoT. The second level is learning motivation, which has a close relationship with teachers’ knowledge, and content pedagogy, which has a significant effect on the familiarity with IoT tools and applications. TAM constructs have a positive and direct impact on the intention to use IoT. The practical and managerial implications show that teachers, educators, and students can obtain benefits from these results to help IoT features to suit users’ needs

Measuring institutions’ adoption of artificial intelligence applications in online learning environments: integrating the innovation diffusion theory with technology adoption rate

Artificial intelligence applications (AIA) increase innovative interaction, allowing for a more interactive environment in governmental institutions. Artificial intelligence is user-friendly and embraces an effective number of features among the different services it offers. This study aims to investigate users’ experiences with AIA for governmental purposes in the Gulf area. The conceptual model comprises the adoption properties (namely trialability, observability, compatibility, and complexity), relative advantage, ease of doing business, and technology export. The novelty of the paper lies in its conceptual model that correlates with both personal characteristics and technology-based features. The results show that the variables of diffusion theory have a positive impact on the two variables of ease of doing business and technology export. The practical implications of the current study are significant. We urge the concerned authorities in the governmental sector to understand the significance of each factor and encourage them to make plans, according to the order of significance of the factors. The managerial implications provide insights into the implementation of AIA in governmental systems to enhance the development of the services they offer and to facilitate their use by all users

Determinants influencing the continuous intention to use digital technologies in Higher Education

There is increasing evidence that the lack of access to digital information and technologies is not widely considered in the educational sectors when focusing on the perceived experience, tutor quality and students’ satisfaction. In this paper, we report on an evaluation of a project aiming to bridge the use of digital information in the educational sector by proposing an integrated model that measures teachers’ quality, uncertainty avoidance effects and students’ satisfaction concerning TAM constructs and the perceived experience of digital information in education (DIE). The model and hypotheses were validated using data collected from a survey of 553 students at a college level. The results revealed that users may perceive the importance of DIE based on several external factors that enhance their learning and teaching experiences. The personal characteristics of the user including his/her readiness to use technology are crucial in correlation with the perceived ease of use. In addition, the high quality of the tutor in some cultures may enhance the perceived usefulness of the technology. Other factors such as flow of information, uncertain avoidance and satisfaction may strongly assess the continuous intention to use the technology

The Impact of Personality Traits Towards the Intention to Adopt Mobile Learning

Mobile devices have become increasingly more common in the digitally connected world. Mobile learning as a model of e-learning refers to the acquisition of knowledge & skills utilizing mobile technologies. The aim of this study is to identify the extrinsic influential factors for the adoption of mobile learning. This study proposes the use of an extended technology acceptance model (TAM) theory that includes variables of personality traits such as perceived enjoyment and computer self-efficiency. The participants of this study were 351 students at University Technology Malaysia who had experiences in e-learning. The study found that perceived usefulness as an extrinsic factor has the highest influence on students’ intention to adopt mobile learning through an investigation of technology acceptance toward mobile learning. Personality traits such as perceived enjoyment and self-efficacy have impact on behavior intention to adopt mobile learning

Examining the effect of Knowledge Management factors on Mobile Learning adoption through the use of Importance-Performance Map Analysis (IPMA)

A tremendous amount of research indicated that KM factors have significant impacts on different technologies at the organizational level. What is not yet clear is the influence of these factors on technology adoption at the individual level. On the other hand, the understanding of students’ behavioral intention to use m-learning systems is still an ongoing research issue. Thus, the main theoretical contribution of this study is to investigate the impact of KM factors (i.e., acquisition, sharing, application, and protection) on m-learning adoption at the individual level, and to identify the importance and performance of each factor using the importance-performance map analysis (IPMA) technique through SmartPLS. A total of 319 IT undergraduate students enrolled at Al Buraimi University College in Oman took part in the study by the medium of online survey. In terms of importance, the empirical data analysis through IPMA exhibited that knowledge protection is the most important factor in determining the students’ behavioral intention to use m-learning. Concerning performance, the findings also triggered out that both knowledge sharing and knowledge protection perform well in determining the students’ behavioral intention to use m-learning