This study introduces a multilayered trust model within the Internet of Things (IoT) smart home ecosystem, addressing the intricate dynamics of user trust. Utilizing a convenience sample of 159 respondents in the UAE, familiar with smart home technology, the research employed an online survey to evaluate the model's components: Benevolence, Competence, Integrity, Situational Normality, Structural Assurance, Trust Stance, and Willingness to Depend. The findings reveal a nuanced trust landscape. Notably, while Benevolence, Competence, and Integrity did not significantly influence Willingness to Depend, Situational Normality showed a strong positive effect. Furthermore, Structural Assurance and Trust Stance emerged as foundational elements, significantly bolstering other trust dimensions. The model's robustness is underscored by the strong linkage between Willingness to Depend and Trusting Intentions. These insights are pivotal for stakeholders in the IoT smart home sector, offering a comprehensive framework to cultivate and measure user trust effectively.

Instagram, as many social media platforms, has been increasingly used by users to shop for goods and products from business or other individuals. Recently, studies have shed lights on acceptance and usage of Insta shopping from users’ perspectives by following popular technology models, such as technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT). However, more rich and in-depth insights about using Instagram for commercial purposes within a certain context are yet to be discovered. Therefore, this study aims at discovering experiences and interactions with Insta shopping, the factors and the drivers that impact users’ acceptance of Insta shopping, the weight of each factor (degree of consensus among participants), and their direction (positive, negative, or both). The study followed a qualitative approach, by creating four homogeneous focus groups (six participants each) of IT students in United Arab Emirates (UAE) universities. The data analysis approach considered is an axial coding technique as part of the grounded theory, which includes open coding, axial coding, and selective coding stages. The results revealed that the time factor, trust in Insta shops (and its drivers such as reviews, word of mouth, trading license, and others), distrust (and its drivers such as fake comments and reviews, extremely low prices, and others), and the associated risks (financial for losing money, security because of online payments, and some privacy issues) can impact users’ behaviors toward Insta shopping. Also, the study classified participants’ viewpoints and experiences’ themes into advantages, disadvantages, and issues that are associated with Insta shopping. The study indicated theoretical and practical implications and suggests future research directions.

Educational systems are based on students and professors who are linked in an organized manner to achieve learning outcomes. Recently, however, academic institutions are experiencing very few, yet ineffective, ways of communication among these parties. Therefore, a University ‘Sidekick’ Application has been developed in one of the educational institutions in the Gulf region to tackle the lack of communication among students and academics in a university community. The methodology followed to develop this application is the waterfall SDLC. Therefore, the application was planned for, the requirements were gathered, modeled and analyzed, the interfaces and databases were designed, and the coding and testing took place subsequently. Sidekick application is a friendly virtual assistant and intuitive to use. It provides booking for amenities, receiving alerts and notifications, a live chat which makes it easier to communicate on and off campus. Moreover, the virtual assistant can strengthen the relationships between “students and instructor”, “alumni students and current students” and “instructor and alumni students”. Furthermore, Sidekick is compatible on IOS and Android. The study concluded with implications and future development directions.

Social media addiction became a serious concern that has received more attention from the public health sector due to its addictive features and its correlated psychological consequences. Thus, this study aimed to analyze the direct effect of SMA on academic performance, and the indirect effects on psychological reactions among university students in Jordan. A random survey in two universities was conducted in a sample of 510 university students; 31.4% were males and 68.6% were females. Their average age was 21.38 years (SD = 2.12). To test and validate the research model, advanced data analysis (Structural Equation Modelling-Partial Least Squares [SEM-PLS]) was applied in this research. Findings revealed that social media addiction had an indirect effect on academic performance, however; it has a direct impact on students’ stress and anxiety levels. The stress could influence anxiety levels, which could directly affect students’ academic performance. Further, students’ stress levels had a direct effect on anxiety, which could result in depression. Given the importance of social media addiction and its potentially substantive effects on students’ psychological reactions and impact of these reactions on academic performance, similar studies are recommended in other universities various fields to obtain a more conclusive result.

Abstract Smart meters are a form of expert system with performance features beyond energy‐consumption record keeping, to include monitoring, analysing, and estimating meter readings. Although smart meters have great capabilities, this technology is still in its infancy in many developing countries, and little is known about the kinds of risks associated with smart meters from residents' perspectives. This research therefore aims to fill this gap by examining the influence of four different types of perceived risk on residents' intentions to use smart meters in Jordan. By following a quantitative approach, 242 survey responses were tested by using structural equation modelling–partial least squares. The statistical results indicated that perceived security and technical risks have a significant and negative impact on residents' intentions to use smart meters. However, perceived privacy and health risks, surprisingly, were found to have no significant negative influence on intention to use. Theoretical and practical implications are indicated, and directions of future research are subsequently specified.

Smart homes are one of the recent Internet of Things (IoT) applications that offer control and automation to residents to allow them to connect and manage home appliances intelligently. Despite its potential, the success of smart home heavily depends on accepting them by residents. Thus, this study aims to investigate the factors that influence residents acceptance and usage of smart home. By extending the technology acceptance model, this research focused on the role of smart home attributes on the intention to use them and investigate their impact quantitatively by using Structural Equation Modeling (SEM) and Partial Least Squares (PLS). Results show that service personalization, perceived usefulness, and perceived ease of use significantly influence attitude towards smart home which, with the cost and availability factors, impact the intention to use.

A university-student relationship has been viewed as a key to the success and continuity of private universities. Although keeping students loyal is a critical objective of relationship marketing, there is a limited theoretical and practical knowledge on which antecedents could be used to achieve this aim. The essence of this research is the development of a more comprehensive model of university-student relationship that could reflect a best practice, on the basis of a review of the literature, and the empirical investigation of this model by using a mixed method of qualitative (focus groups) and quantitative (surveys) approach. By sampling students in one private Jordanian university, results reveal that relationship strength and students' satisfaction can impact students' loyalty towards their universities. In addition, three relational bonds (financial, social and structural) can influence students' satisfaction. Finally, the study indicates academic and in-practice implications within the education sector, and suggests some future research guidelines.