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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.

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.

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.

The purpose of this paper is to investigate undergraduate nursing students’ use of mobile learning (m-learning) and the factors contributing to their use of m-learning.

In total, 586 nursing students from three universities in Ghana participated in this study. Survey questionnaires were used to collect data. Descriptive statistics, sample t-test and multiple regression were used to analyze the data.

The research found that most students owned smartphones. Mobile technology was mainly used for doing homework. The result indicates that gender differences exist in terms of perceived usefulness of m-learning. In addition, age differences exist with regard to the perceived ease of use of m-learning. Furthermore, students showed positive attitudes toward the use of technology. Finally, perceived usefulness and attitudes toward the use of technology predicted students’ intention to use m-learning.

Despite the abundance of research on nursing education in other countries, there is a lack of research on nursing students’ use of m-learning and factors influencing their implementation of m-learning in higher learning institutions in Ghana. This study is important because it provides a clear description of nursing students’ use of m-learning and factors affecting their use in schools. Also, the author suggests that information from this study assists school administrators and nursing educators to understand students’ positions regarding m-learning in classroom.

This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.

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.

The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods.

This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research.

After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments.

This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account.

The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

The purpose of this paper is to explore barriers; the mediating role of usability; and the moderating effects of subjective norms, personal innovativeness, self-efficacy and perceived image on users’ attitudes toward loyalty to Internet banking (IB) in Iran.

Based on the consumer data collected from a survey, structural equations modeling and path analysis were used to test the research model.

The results revealed that “low perceived usefulness” and “low perceived ease of use” both had negative effects on users’ attitudes. “Low awareness” and “low system compatibility” were found to be the main factors impeding users’ attitudes toward loyalty to IB. “Perceived usefulness” showed no mediating role in the relationship between ease of use and users’ attitudes. At last, all concerned moderators moderated the relationships between ease of use/usefulness and users’ attitudes.

The sample was only composed of IB users of one Iranian bank, and non-users were not studied.

Past studies have seldom examined the role of individual drivers such as personal innovativeness and self-efficacy and social drivers such as subjective norms and perceived image as moderating variables in the context of developing countries.

The purpose of this paper is to explore barriers, the mediating role of usability and the moderating effects of self-efficacy and perceived image on consumers’ attitudes toward use of mobile banking (MB) in Iran.

Based on the consumer data collected through a survey, structural equations modeling and path analysis were employed to test the research model.

The results revealed that “system compatibility” was found to be the main factor affecting users’ attitudes toward use of MB. “Resistance” showed a significant negative effect on both ease of use and usefulness. “Perceived usefulness” mediated the relationship between ease of use and users’ attitudes. At last, contrary to self-efficacy which showed no significant effect, perceived image moderated the relationships between usefulness and attitude.

The sample was only composed of MB users and non-users were not studied.

Past studies have seldom examined the role of individual drivers like self-efficacy and social drivers like perceived image as moderating variables in the context of developing countries.

Mobile learning has emerged as one of the main methods for training and academic activities in the present era. It is, also, highly relevant in the wake of the COVID-19 pandemic whereupon digitization of mobile learning has made it possible for many students to continue with their education. This study investigated attitudes and intentions towards the adoption of mobile learning in vocational education.

This is a quantitative study based on cross-sectional empirical data. In targeting vocational students throughout Pakistan, the study used a survey questionnaire with a convenience sampling method. From the responses to the questionnaire, 320 samples were used to obtain the study outcomes.

The structural equation modeling’s (SEM) findings reveal that learning autonomy (LA), mobile device self-efficacy (MDSE), task-technology fit (TTF), perceived ease of use (PEOU), perceived usefulness (PUS) and perceived enjoyment (PE) have a positive and significant effect on mobile usage attitudes (MUA) and intentions to adopt mobile learning (ITAML). Moreover, this study’s findings confirm, also, MUA’s predictive power on ITAML.

Further, this study’s findings encourage individuals to use mobile devices to properly promote knowledge in society. In addition, this study’s findings support vocational institutions’ operators’ and policymakers’ development of online education and training strategies to resist the complications arising from the transmission of COVID-19. Moreover, this study’s findings open new doors when conducting similar research studies on students’ perceptions and learning behaviors.

The empirical investigation of attitudes and intentions to adopt mobile learning in the context of COVID-19 helps potential adopters to test the likely behaviors.