Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model

Abstract

The COVID-19 pandemic has prompted the adoption of an e-Learning pedagogy. This forced teachers and students to shift to online learning and thus was compelled to adopt online educational technology. Educational institutes have been facing challenges like insufficient infrastructure and a shortage of quality teachers. Online learning can help to address these challenges as online classes can accommodate more students. However, before implementing e-Learning technology management of institutes wants to be sure whether students will adopt new technology. Therefore, the purpose of this study was to unveil which factors are important to adopt new technology if implemented mandatorily. We tested the most popular technology acceptance model the UTAUT to understand students’ intentions to continue using the e-Learning system in a mandatory environment. The study used a quantitative approach of research. The participants for this study were selected from a private university in India. The questionnaire for the study was adapted from previous studies. The survey was conducted by sharing an online link while students were attending classes online during the pandemic. Thus, the study utilized a convenience sampling technique. The data were analyzed using structural equation modelling. The findings revealed that the UTAUT model can partially explain the forceful adoption of technology. The study found ‘Performance expectancy’ and the ‘availability of resources’ as significant indicators of ‘intention for continued usage’. This study recommends educational institutes should ensure students attain academic goals by using e-Learning platforms and ensuring the availability of essential resources to use the e-Learning technology.

Lecturers' Adoption of E-Learning from the TAM Perspective: Transitioning from Lecture Halls to Virtual Classrooms

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“…The second sort of validity, known as discriminant validity (Patil & Undale, 2023), evaluates the degree to which one concept differs from all other constructs in the research model. The Fornell-Larcker criterion (i.e., the square root of AVE), cross-loadings, and the Heterotrait-Monotrait ratio of correlations (HTMT) are used to evaluate discriminant validity.…”

Human-machine dialogues unveiled: an in-depth exploration of individual attitudes and adoption patterns toward AI-powered ChatGPT systems

“…This framework used "performance expectancy, effort expectancy, social influence and facilitating conditions" to explore individuals' intentions regarding various technologies (Venkatesh et al, 2012). The researchers have adopted performance and effort expectancy as a construct based on prior literature recommendations (Patil and Undale, 2023). The rationale behind selecting performance expectancy is to examine the usefulness of ChatGPT for individuals.…”

“…Besides, Strzelecki (2023) reported that EE significantly influences BI. However, several studies found that EE insignificantly impacts ATT (Chavez Herting et al, 2023) and BI (Patil and Undale, 2023). Hence, the authors proposed the following hypotheses:…”

“…, 2012). The researchers have adopted performance and effort expectancy as a construct based on prior literature recommendations (Patil and Undale, 2023). The rationale behind selecting performance expectancy is to examine the usefulness of ChatGPT for individuals.…”