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Although several different learning technologies have been integrated into the face-to-face (F2F) learning approach, the effective implementation of mobile learning (m-learning) is still at an early stage. This may be due to the lack of understanding factors that affect learners' acceptance of m-learning.

Considering the unified theory of acceptance and use of technology (UTAUT) as a theoretical background, this research investigates m-learning adoption in Saudi Arabia. The research framework extends the UTAUT by including intrinsic motivation, mobile learning self-efficacy and perceived satisfaction. A total of 200 higher education students voluntarily participated in the research. The application of the partial least square technique indicated that the proposed model can predict m-learning adoption adequately in Saudi Arabia (R² = 0.73.8).

The research outcomes are significant for educational institutions and teachers to implement m-learning effectively. Many recommendations can be suggested to help enhance learners' willingness to adopt m-learning technology: Teachers need to design their courses in an interactive way and provide several different activities to ensure that learners obtain real benefits in their learning outcomes. When learners have positive perceptions about the use of this technology in the learning process, such perceptions should be supported by providing beneficial courses.

This research examines the effectiveness of the extended UTAUT to predict m-learning adoption in Saudi Arabia higher education. This could provide a significant contribution to the existing evidence about the validity of the model in different learning cultures and contexts.

The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

The COVID-19 pandemic has resulted in social isolation; nevertheless, universities will proceed throughout this trying period with the assistance of technology. As such, this paper seeks to develop a conceptual framework to investigate the continued intentions of students to use mobile learning during COVID-19 under different cultural contexts expanding upon the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation-Confirmation Model (ECM) under different cultural contexts.

The suggested model is empirically tested with 1,206 students from different universities in three societies (i.e. Saudi Arabia, Egypt and the UK) using SEM/PLS.

Performance expectancy, satisfaction, social influence, facilitating conditions and instructors' competencies positively influence students' continued intentions to use mobile learning. In addition, the findings of the current research indicate that student's isolation negatively impact the continuous usage behavior. Furthermore, the findings indicated that a “one-size-fits-all” approach is insufficient in capturing the heterogeneity of students' intentions to use mobile learning across countries.

To the best of the authors' knowledge, this is the first study that has been conducted to understand the main determinants of students' continued intentions to use mobile learning under different cultural contexts.

This study investigates the relationship between social commerce adoption determinants, perceived trust and purchase behaviour among pastry product consumers.

The research data were collected using a five-point Likert from 409 pastry consumers through an online survey. Partial least square-structural equation modelling (PLS-SEM) was utilised to test the study model and hypotheses.

The study findings indicate that pastry consumers perceived social commerce as effortless, engaging, enjoyable, can be trusted, simple to use and time-saving, motivating them to consume pastry products via social commerce platforms.

This study provides insights and implications for social commerce researchers and marketers related explicitly to perishable products.

This study is one of the few studies that predict and explain the consumer acceptance of social commerce in the pastry products realm. As only a handful of research has endeavoured to research social commerce, this study findings contribute to the conceptualisation of technology acceptance theory by understanding the determinants of social commerce adoption among consumers.

The purpose of this study is to examine the factors influencing the usage of cloud-based accounting information systems (AIS) in the crisis era (i.e. the COVID-19 pandemic) by expanding the unified theory of acceptance and use of technology (UTAUT) with new related critical factors.

A quantitative research approach based on a cross-sectional online questionnaire was used for collecting empirical data from 438 potential and current users of cloud-based AIS. Structural equation modeling based on analysis of a moment structures 25.0 was applied in the data analysis.

The outcome of the structural path revealed that performance expectancy, social motivation, COVID-19 risk (COV-19 PR) and trust (TR) were significantly influencing users’ behavioral intention (BI) toward using cloud-based AIS and explained 71% of its variance. While, contrary to what is expected, the impact of effort expectancy and perceived security risk (SEC) on BI was insignificant. In addition, BI was revealed to influence the actual usage behaviors and explained 74% of its variance. The outcome factors: communication quality (CQ) and decision quality (DQ) were significantly influenced by the usage of cloud-based AIS.

The current research would be valuable for small- and medium-sized enterprises officials and policymakers to illustrate the relatively low rates of cloud-based AIS and formulate strategies to boost the acceptance and use of cloud-based AIS by Jordanian users, where cloud-based services are still deemed as an innovation.

To the best of the authors’ knowledge, the current study is the first academic paper that extends the UTAUT by integrating additional factors: TR, SEC and COV-19 PR. In addition to two outcome variables: CQ and DQ, to study the cloud-based AIS in the Jordanian setting beyond the COVID-19 pandemic. The current research contributes to the academic knowledge on information technology information system adoption by considering cloud accounting acceptance and use and integration into the work practices of users through the BIs and actual use of cloud-based AIS in Jordan.

This study aims to provide new insights into the relationship between individual characteristics, particularly personality traits and mature students' intention to use (ITU) mobile learning (m-learning).

The research model was constructed by integrating the Big Five personality traits into the unified theory of acceptance and use of technology (UTAUT) model. The data were collected from mature students at a university research center in Macau. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and test the proposed hypotheses.

The results reveal that personality traits play a significant role in determining mature students' ITU m-learning technology. In particular, social influence (SI) mediates the relationship between agreeableness (AGB) and ITU.

This study examines how personality traits collectively influence mature students' receptiveness and intentions toward m-learning. As mature learners' motivations and preferences remain underexplored, insights into trait-technology links could address current gaps and optimize mobile educational support tailored to their distinct characteristics and needs.

Available research on mobile learning all stand on the viewpoint of teachers. However, as mobile learning is learner-centric, learners' roles must be understood clearly from the learners' viewpoint, particularly considering its self-directed learning feature. It is well-known learners for self-directed learning must bear some teachers' core responsibilities. The knowledge gap on this important issue inspires the present work which tries to answer two questions: which “teacher roles” can learners play and how will learners play their “teacher roles” in a mobile learning context. This study aims to discuss the aforementioned objectives.

A novel research approach integrating an action research model and a teaching presence scale analysis is proposed to answer the questions. The mobile learning courses conducted by the present authors for engineering undergraduate students, during the COVID-19 pandemic, provided the experimental data for this study.

The experimental results reveal that the learners could play a number of “teacher roles” actively for mobile learning. Some of this research studies are consistent with available studies but a discrepancy is also observed. Discussion is conducted for such discrepancy.

The findings will contribute to improve the pedagogy of mobile learning theoretically and practically.