PurposeDue 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.Design/methodology/approachTechnology 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.FindingsThis 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.Research limitations/implicationsSix 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.Originality/valueNo 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 COVID-19 pandemic has transformed the paradigm of the higher education sector and has instigated a speedy consumption of a diverse range of mobile learning software systems. Many universities were adhering to online modes of education during the pandemic; however, some of the universities are now following hybrid modes of learning, termed h-learning. Higher education students spent two years of taking their classes online during the COVID-19 pandemic and have experienced various challenges. Simultaneously, the main challenge for higher education institutions remains how to consistently offer the best quality of students’ perceived m-learning and maintain continuance for the new shift towards hybrid learning. Hence, it becomes essential to determine the m-learning quality factors that would contribute to maintaining superior m-learning quality in higher education during the COVID-19 pandemic and afterwards via a hybrid mode of learning. Thus, the m-learning quality (MLQual) framework was conceptualized through an extensive review of the literature, and by employing survey-based quantitative research methods, MLQual was validated via structural equation modeling (SEM) techniques. The outcome of this research yielded the MLQual framework used to evaluate the students’ perceived m-learning quality and will offer higher education practitioners the chance to upgrade their higher education policies for h-learning accordingly. With the preceding discussion, it is evident that evaluation of the students’ perceived m-learning quality factors in higher education is always a question that should be researched adequately. Determination of such m-learning quality factors is essential in order to offer significant directions to the higher education practitioners for improving both the quality and delivery of m-learning and h-learning. Consequently, the present study embraces two key objectives: First, to identify and evaluate the m-learning quality factors which could be employed to improve the quality of m-learning. Second, to propose the MLQual framework for the evaluation of students’ perceived m-learning quality.

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