Understanding college students’ continuous usage intention of asynchronous online courses through extended technology acceptance model

Abstract

Asynchronous online learning has gained great popularity in higher education, especially due to the recent COVID-19 pandemic. However, few studies have investigated how to maintain students’ continuous usage intention of asynchronous online courses in the context of higher education. This study incorporated four key factors (intrinsic motivation, extrinsic motivation, perception of multiple sources, and cognitive engagement) associated with students’ continuous usage intention of asynchronous online courses into technology acceptance model (TAM) to identify the influencing factors on students’ continuous usage intention. A survey with 325 college students was conducted to explore their continuous usage intention of asynchronous online courses and structural equation modeling analysis was carried out to analyze the relationships between the key influencing factors and students’ continuous usage intention. The results showed that cognitive engagement was the only factor that directly related to continuous usage intention. Intrinsic motivation, extrinsic motivation, and perception of multiple sources indirectly correlated with students’ continuous usage intention through different pathways. The results of the study have several theoretical and practical implications. Theoretically, the study verified what key learning factors incorporated into TAM and in what way they relate to the continuous usage intention of asynchronous online courses. Practically, the present study indicated that it is required to take intrinsic motivation, extrinsic motivation, perception of multiple sources, cognitive engagement and TAM into consideration when designing and conducting asynchronous online learning courses to ensure college students’ continuous usage intention of asynchronous online courses.

Applying the Technology Acceptance Model to Explore Nursing Students' Behavioral Intention to Use Nursing Information Smartphones in a Clinical Setting

“…As for the external variables in this study, only the educational program and the clinical course significantly influenced perceived usefulness and perceived ease of use. This result is in line with that of Lu et al 16 Under the educational program, the perceived ease of use of students enrolled in 2-year programs was similar to that of students in 4-year programs. However, students enrolled in 2-year programs reported lower perceived usefulness than those in the 4-year programs.…”

“…Perceived ease of use reinforces perceived usefulness, which influences attitudes toward using and behavioral intention to use, and finally, actual use 13 . External variables such as age, sex, educational program, and the clinical course will impact users' perceived ease of use and perceived usefulness of technologies 15–17 . The TAM is widely used to evaluate students' opinions on technology acceptance 11 …”

Modelling the effects of emotional engagement and peer interaction on the continuous intention to use asynchronous e-learning

“…It refers to the support that learners provide to each other in the environment of e-learning ( Goh et al, 2017 ). Lu, Pang & Shadiev (2023) conducted a study in which they extended TAM to examine the impacts of particular factors—extrinsic and intrinsic motivations, cognitive engagement and multiple types of resources—on students’ continuous intention to use asynchronous e-learning. The authors found that cognitive engagement directly affected students’ intention to utilise asynchronous e-learning.…”

“…The authors found that cognitive engagement directly affected students’ intention to utilise asynchronous e-learning. Lu, Pang & Shadiev (2023) recommended extending TAM and examining the effects of emotional engagement and peer interaction on students’ continuous intention to use asynchronous e-learning. It is not yet clear whether these two factors have an effect on students’ continuous intention to use asynchronous e-learning.…”

Evaluating the Determinants of Young Runners' Continuance Intentions toward Wearable Devices

“…Moreover, the TAM was chosen as a key theoretical framework for its extensive applicability in analyzing user adoption across diverse technologies [17][18][19][20][21][22]. Previous research on TAM covered a wide range of technologies, including tablets [23], smartphones [24,25], long-term evolution (LTE), cloud technology [26], and e-books [27].…”