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Although mobile devices are ubiquitous among employees, their awareness and readiness to use mobile technologies for competence development is still not widespread and therefore requires further exploration. The purpose of this study is to propose a conceptual model based on the unified theory of acceptance and use of technology (UTAUT) to explain the determinants that affect employees’ intention to use mobile devices and software for knowledge transfer during the process of knowledge management.

A conceptual model based on the UTAUT with new variables concerning relative usability (RU) and user autonomy (UA) and new connections between the determinants was developed as a result of a subject matter literature review. A structural equation modelling approach was used to validate the model on the basis of data collected via a survey collected from 371 employees from 21 sectors, both public and private.

The UTAUT model extended by new variables like RU and UA explains employee acceptance of mobile technologies for knowledge transfer reasonably well. New proposed variables highlighted that the usability of technology compared to other solutions and user autonomy in the selection and the use of applications have the strongest impact on the employees’ intention to use mobile devices and software for knowledge transfer.

This model explains the 55 per cent behavioral intention of employees to use mobile technologies for knowledge transfer. Even though it is quite high in terms of acceptance theories, some new variables should be explored. Furthermore, study does not verify whether m-learning acceptance for knowledge transfer is sector-specific.

Mobile technologies used for knowledge transfer by employees should allow for high UA through their ability to select solutions that they find convenient, use of preferred platforms, personalize applications and utilize devices and software in various environments. They should not be simplified and should have the same functionality and efficiency of use as alternative solutions like web and desktop applications, even if additional effort to learn them would be required. Mobile technologies that take into account UA and RU support the process of employees capturing, distributing and effectively using knowledge.

The elaborated model provides a valuable solution with practical implications for increasing mobile technologies acceptance for knowledge transfer. The study results contribute both to knowledge management and technology acceptance research fields by introducing two new determinants for the acceptance of technologies in knowledge transfer, such as UA and RU with several additional connections between existing UTAUT variables.

In parallel with technological developments, mobile devices have become an important part of our daily lives. Nowadays, people, particularly generation Z, actively engage with the Internet and mobile technologies, including smartphones. The new technologies have also made electronic learning (e-learning) and mobile learning (m-learning) tempting for learners in higher education. Despite the growing use of m-learning, particularly in developed countries, its utilization in higher education is still at its early stage and far from its potential. This study investigates the university students' acceptance of using m-learning with smartphones in a developing country context.

Quantitative data collected through conducting questionnaires with 405 higher education students in Turkey were analyzed with structural equation modelling.

The findings show that the acceptance of using m-learning is affected by several factors, including perceived enjoyment, complexity and facilitating conditions. The findings are particularly relevant in the current context, where the COVID-19 pandemic has pushed many higher education institutions to adapt m-learning systems to enhance student learning experiences.

This study's findings offer fresh and important insights that can be used by m-learning developers and educators for designing m-learning systems and using m-learning applications in enhancing students' experience and performance with m-learning.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0516.

The mobile technology-pedagogy nexus has been instrumental in shifting the focus of information and communications technology in education from e-learning to mobile learning (ML). Learning with mobile technologies is more accessible, flexible, situated, personal, collaborative and lifelong. The use of technologies for educational purposes is the primary focus of higher education institutes. The successful implementation of ML rests on its acceptance by higher education teachers. It is, therefore, pertinent to determine the factors that impact higher education teachers’ acceptance of ML. This study aims to identify these factors and develop a reliable and valid instrument to measure higher education teachers’ acceptance of ML.

A sequential exploratory research design, a type of mixed method research was used for the study. A sound conceptual framework and rigorous scale development process provided the background for data collection and validation. Probability proportionate to size sampling technique was used to gather data from 212 higher education teachers from 42 higher education institutes. Teachers gave their responses on five-point Likert type items. The responses obtained were subjected to exploratory factor analysis, which provided a nine-factor solution. The factors were further validated through confirmatory factor analysis.

Teachers’ mobile learning (ML) acceptance questionnaire (TMLAQ), a 32-item questionnaire was developed to measure acceptance of ML among higher education teachers. A detailed literature review, interviews and focus group discussions with teachers facilitated the identification of nine constructs or antecedents of ML acceptance. These constructs were named as: perceived usefulness, ease of use, self-enhancement, constructivist belief (CB), technological barriers, attitude and behavioral intention. The scale possesses sound psychometric properties such as reliability, face validity, content validity and construct validity.

This instrument can serve as an authentic, valid and reliable measure of higher education teachers’ ML acceptance. It can be used by organizations to assess teachers’ perceptions and aid in the successful ML implementation.

There is a lack of measurement instrument that caters to wide angle view of teachers’ perception toward ML in the Indian context. This comprehensive scale will bridge this gap. Two new research constructs: CB and self-enhancement were found to be crucial from the teachers’ point of view. These constructs have not been explored in previous technology acceptance studies. To the best of the knowledge, such a comprehensive study has not been undertaken yet in the Indian context. This study can serve as a model for conducting similar kinds of studies in other developing nations.

Mobile learning is a unique form of learning which uses the distinct features of mobile devices. The purpose of this paper is to investigate the present state of student and faculty perception towards m-learning at open and distance educational institutes in Pakistan.

The paper presents a conceptual model based on TAM, which explains factors influencing student and faculty perception towards m-learning acceptance. M-learning acceptance mainly depends on personal attitude, so this study focusses on individual context. Primary data from students and faculty including tutors (n=612, students =448, faculty/tutors=162) was collected through a properly designed questionnaire by using purposive convenient sampling technique during Autumn 2015 semester. Structural equation modelling was used to analyse the collected data.

The results indicate that student and faculty skill readiness and self-efficacy influence perceived ease of use and perceived usefulness, where these two factors along with prior experience positively influence behavioural intension (BI) to accept mobile learning. Furthermore study results specifically provide factors which positively influence BI either directly or indirectly.

The study was limited to AIOU.

The study specifically provides factors which influence BI either directly or indirectly.

Due to the eruption of the COVID-19 pandemic, many universities were forced to shift from the traditional learning practices to digital learning. Hence, the purpose of this study is to evaluate the factors that affect the university student's adoption of mobile technologies for mobile learning (m-learning) in their learning process.

Technology acceptance model (TAM) is incorporated to study the adoption of mobile learning by university students. Quantitative research technique is used as core research approach in this study. Structural equation modelling (SEM), which is a part of quantitative research method, was employed on the congregated data via a set of questionnaire from 268 University students. SEM is used to explore the relationships among the hypothesized constructs. SPSS and AMOS software were used for the analysis of data.

This study validated the updated TAM model and assessed the students' adoption of mobile technologies for m-learning during COVID-19. All the constructs of proposed model were found to be significant with more than 50% average variance extracted. It was found that two external constructs mobile system efficacy and mobile service efficacy appended in technology acceptance model show the direct positive effect on perceived usefulness and perceived ease of use constructs. However, hypothesized relationships were found to be unsupported among perceived usefulness and perceived ease of use. Furthermore, perceived usefulness and ease of use during m-learning impact the students' usage attitude which consequently impact the students' adoption behaviour towards adoption of mobile technology.

Six constructs were considered for this study; however, mobile information quality for mobile learning was not included which could affect students' adoption criteria. Additionally, this study is limited to a country where future study needs validation of propose constructs in different demographic settings.

No study allied to the students' adoption of mobile technology for m-learning has accomplished in the context of India during COVID-19. Furthermore, TAM model has been updated with regard to the students' adoption of mobile learning during COVID-19 in Indian higher education setting.

This study aims to improve the Unified Theory of Acceptance and Use of Technology (UTAUT) model by examining technological anxiety and other influential factors on international students' adoption of mobile learning (m-learning) during COVID-19 emergency remote teaching (ERT).

This study utilized the modified UTAUT framework to test hypotheses through a cross-sectional survey method. Participants were university students studying Chinese as a foreign language who were selected using a convenience sampling approach. An online questionnaire was then administered. The data collected from the surveys were analyzed using the partial least squares method with SmartPLS 4 software.

The study examined 16 hypotheses and found support for six of them. The results confirmed that performance expectancy (PE) is a significant predictor of behavioral intention (BI), and anxiety influences both PE and effort expectancy. The negative effect of social influence on anxiety was found to be significant, while facilitating conditions had a negative impact on learners' self-efficacy. The model fit indices indicated a good overall fit for the model.

This study presents a valuable contribution to the literature on m-learning in emergency education by incorporating technological anxiety into the enhanced UTAUT model. Examining the relationships between the key factors of the model provides a better understanding of learners' intentions and can inspire researchers to establish new theoretical foundations to evaluate the roles of these factors in diverse educational settings.

The study found that performance expectations are linked to learners' intentions, and anxiety indirectly affects BIs to use mobile learning platforms. Thus, these platforms should be designed to meet learners' expectations with minimum effort and eliminate anxiety triggers to facilitate ease of use. Language curriculum developers and policymakers should incorporate mobile learning applications to support diverse language skills, address students' needs and encourage their use through professional development opportunities for instructors.

Social factors have been found to significantly influence anxiety levels among learners. Therefore, it is crucial for teachers and family members to play an essential role in mitigating anxiety's adverse effects. Discussing related issues can enhance the quality of mobile learning and stimulate social initiative by providers, ultimately improving the learning experience for all learners, regardless of their location or circumstances. This can also contribute to the growth and development of society.

This study contributes to the originality of m-learning development by proposing an enhanced UTAUT model that considers anxiety and emphasizes the critical role of foreign learners' BIs. It provides fundamental guidelines for designing and evaluating m-learning in ERT contexts.

This paper aims to focus on contextualized features for mobile language learning apps. The scope of this paper is to explore students’ perceptions of contextualized mobile language learning.

An extended Technology Acceptance Model was developed to analyze the effect of contextual app features on students’ usage intention. The suggested app concept applies context-triggered push notifications to initiate learning sessions based on a location-aware vocabulary. Partial least squares structural equation modeling (PLS-SEM) was used for an empirical validation of the proposed research model.

The results of the analysis revealed, that students perceived the proposed app as beneficial for their learning endeavors. The location-aware feature is essentially relevant to improve the perceived usefulness of the system, as it may increase the learning effectiveness of the app in their everyday life.

The study was conducted in quite a homogenous population. The sample size of the survey was rather small (n = 45). Further research is necessary to confirm the promising results of the research.

The results give some first evidence that the integration of innovative contextual features in mobile language learning apps may increase the usage intention and motivation to engage in a learning activity.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

The study aims to validate a mobile learning readiness scale through the technology readiness and acceptance model (TRAM), thereby assessing students' readiness to adopt m-learning in teaching and learning, including its acceptance.

A structured questionnaire was administered to open and distance learning (ODL) students in Odisha, India, to assess their readiness and acceptance of m-learning. 665 valid responses were collected, and collected data was analysed using statistical packages for social sciences (SPSS) and SmartPLS.

The findings of the study reveal that optimism contributes positively to perceived ease of use (PEOU) and perceived usefulness (PU) of m-learning (β = 7.921, p < 0.001; β = 2.123, p < 0.05), whereas innovativeness positively contributes to PEOU of m-learning (β = 2.227, p < 0.05), but not PU of m-learning. ODL student's optimism improves his/her PEOU and PU of m-learning, but innovativeness improves only his/her PEOU. Further, the impact of innovativeness is higher than that of optimism in the TRAM and innovativeness is the strong predictor to adopt m-learning. It also shows that the PU of m-learning positively influences behavioural intention to use m-learning (β = 4.757, p < 0.001). Integrating technology readiness (TR) with technology acceptance model (TAM) to predict students' acceptance of m-learning is very useful.

The paper will help decision-makers to adopt and use m-learning in higher educational institutions.

This paper is the first to explore the readiness and acceptance of m-learning in higher education in India.

Learning management systems (LMSs) have been embraced for their potential to create a ubiquitous learning that is free from time and space constraints. Mobile devices afford enhanced mobility that enables flexible learning with LMSs. Thus, understanding students’ use of mobile devices to interact with LMSs and the influencing factors is essential. This paper aims to examine the factors that influenced students’ behavioural intention in using Web-based LMSs via mobile phones and compared the factors with those that affect students’ general acceptance of Web-based LMSs.

This study surveyed 356 university students and interviewed 17 students on the various factors that might affect their LMS adoption. Structural equation modelling was used to analyse the survey data.

This study identified that perceived usefulness, perceived ease of use, social influence and facilitating conditions were significant determinants of students’ usage intention in both contexts. However, social factors exerted greater influence on students’ behavioural intentions of mobile access than the attitudinal factors. The results also pinpointed some sociocultural and tempo-spatial factors that might have minimized the influence of perceived usefulness in the mobile context.

The study calls for special attention to the potential influences of sociocultural norms and tempo-spatial circumstances of mobile use in shaping the nature of learners’ voluntary mobile use of LMSs.