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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 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.

This study aims to use the novel approach of applying the behavioural reasoning theory (BRT) to understand the relative influence of reason for and reason against the adoption of mobile learning applications (M-learning apps) among information technology (IT) and information technology enabled services (ITeS) employees.

This study surveys 680 employees of IT and ITeS companies in India to examine the adoption of M-learning apps for learning using the BRT and the primary data analysis was done using the partial least squares-structural equation modelling technique.

It is found that the context-specific adoption factors for M-learning apps are hedonic motivation, self-efficacy, learning autonomy, ubiquitous and relative advantage, whereas the reasons against adoption of the M-learning apps are traditional barrier, usage barrier and image barrier. It is also found that values of openness to change positively affect the reasons for adoption and do not significantly affect reasons against adoption of M-learning. Values of openness to change affect the attitude towards M-learning apps and attitude affects the adoption intention of M-learning apps for learning.

This cross-sectional study was conducted only in the Indian IT/ITeS firms and future research can be conducted in other sectors and countries to generalize the results.

This research uniquely highlights the adoption factors both for and against, which should be considered while developing marketing strategies for M-learning apps’ adoption. It is imperative for training managers to consider these factors during the selection of M-learning apps and for designers while designing the M-learning apps.

This study provides new insights towards the use of mobile apps for learning with the employees’ perspective using the BRT theory and it highlights the reason for adoption and reason against adoption of M-learning apps.

This study aims to contribute to literature on mobile learning (m-learning) by proposing four research clusters whereby scholars can expand m-learning research to facilitate effective learning experiences for students.

This study reviews student-centric literature on m-learning since 2010 and presents insights on m-learning while applying well-established bibliometric techniques. Consequently, 722 articles published in the past decade were evaluated by identifying key research areas, most influential authors, countries, journals and organisations. Most influential studies based on number of citations were also examined.

Through article co-citation analysis, four clusters representing m-learning literature were identified: concept of m-learning, application of m-learning in education, designing framework for model learning/acceptance and emerging technologies.

As mobile learning (m-learning) has undergone an evolution from being an emerging field to a significant teaching and research tool, it is pertinent to explore and identify the trends of m-learning research.

The purpose of this study was to investigate factors that influence the intention to use mobile learning (m-learning) by learners in developing countries such as Thailand. This study integrated two theories; namely, the unified theory of acceptance and use of technology (UTAUT), which focuses on technology, and uses and gratifications theory (UGT), which involves studying learners’ motivation.

Applying a quantitative research method, this study conducted a survey of 359 undergraduates. The partial least squares methods and a statistical analysis technique based on the structural equation modelling (SEM), were used to analyse the data.

The results revealed that the performance expectancy, cognitive need, affective need and social need had significant effect on intention to use m-learning. Furthermore, this study found a significant effect of the cognitive need on the performance expectancy and social need on effort expectancy.

This research model has provided guidelines for the effective development of educational applications for use on mobile devices. The findings can be applied as guidelines for public organizations to develop educational strategies to further encourage the development of online learning.

This research closed a gap of understanding from previous studies by integrating UTAUT and UGT. The method derived from the theoretically integrated model could be applied to study the intentions for the implementation the mobile learning application from the context of developing countries such as Thailand.

This paper investigates if the existing degree of students' acceptance and use of mobile or m-learning may face the online shift determined by SARS-CoV-2. Based on the extended unified theory of acceptance and use of technology (UTAUT2), a new comprehensive model, SD-UTAUT (social distancing-UTAUT), is developed to better understand relationships between the original constructs, plus personal innovativeness (PI) and information quality (IQ). It identifies the key factors affecting behavioral intention (BI) and use by examining the influence of revaluated hedonic motivation (HM) and learning value (LV) importance as mediators.

The paper opted for an exploratory study involving 311 learners, using partial least squares structural equation modeling (PLS-SEM).

SD-UTAUT can be a new m-learning model in higher education. It has high predictive power and confirmed 15 out of 16 hypotheses. The most powerful relationship is between performance expectancy (PE) and HM. IQ affected LV the most, since HM the behavioral use (BU). HM impacts the use behavior (UB) more than LV, but habit (HT) affects it the most.

Because of the pandemic context, output may lack generalizability and reproducibility.

To improve usage, staff must provide better support, course creators emphasize the objectives and competencies and developers integrate innovation. The joy and pleasure of m-learning use may stimulate the LV through interesting and interactive content, like incorporating gamification.

The model set-up and circumstances are previously unseen. SD-UTAUT confirms ten new hypotheses and introduces the student's grade point average (GPA) as a moderator.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-01-2021-0017

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.

The main purpose of this study was to combine the extended technology acceptance model (TAM) with the innovation diffusion theory (IDT) to examine how learners' beliefs affected their usage intention of mobile learning (m-learning) and explore whether the relationships between learners' beliefs and their usage intention of m-learning changed under different levels of personal innovativeness regarding the new information technology (IT).

Sample data for this study were collected from Taiwanese mobile phone users, a total of 750 questionnaires were distributed, and 486 usable questionnaires were analyzed in this study, with a usable response rate of 64.80 per cent. Collected data were analyzed using structural equation modeling, multiple group analysis, and hierarchical moderated regression analysis.

Perceived usefulness (PU), perceived ease of use (PEOU), perceived enjoyment (PE), and compatibility can play essential roles in affecting learners' intention to use m-learning. Personal innovativeness can moderate the effects of PU, PEOU, and compatibility on intention to use m-learning except the effect of PE on intention to use m-learning.

Based on the views of the extended TAM with the IDT, this study incorporates intrinsic motivator (i.e. PE) along with conventional extrinsic motivators (i.e. PU and PEOU) into its analysis of m-learning acceptance for a more robust analysis and exhibits explicit results indicating that the effects of learners' beliefs on their usage intention of m-learning depend largely on their innovative predisposition regarding the new IT.

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.

Based on the literature on technology readiness, online learning readiness, and mobile computer anxiety, the purpose of this paper is to develop and validate a mobile learning readiness (MLR) scale which can be used to assess individuals’ readiness to embrace m-learning systems.

Based on previous literature, this study conceptualizes the construct of MLR and generates an initial 55-item MLR scale. A total of 319 responses are collected from a three-month internet-based survey. Based on the sample data, this study provides an empirical validation of the MLR construct and its underlying dimensionality, and develops a generic MLR scale with desirable psychometric properties, including reliability, content validity, criterion-related validity, convergent validity, discriminant validity, and nomological validity.

This study develops and validates a 19-item MLR scale with three dimensions (i.e. m-learning self-efficacy, optimism, and self-directed learning). A tentative norm of the MLR scale is presented, and the scale’s theoretical and practical applications are also discussed.

This study is a pioneering effort to develop and validate a MLR scale. The results of this study are helpful to researchers in building m-learning theories and to educators in assessing and promoting individuals’ acceptance of m-learning systems.