Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning

By Ronnie Chu Ting Cheung, D. Vogel. Comput. Educ., 2013

853 citations recorded by the source corpus.

Subfields: Communication, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1c9211f08b79e1b121d3eeb43b6a67d0bf5a102d

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Citations in the corpus

Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model (2019, neutral): “Accessibility 5 [49], [55], [61] Actual use 4 [55], [93], [94] Attitude towards use 4 [27], [35], [37], [46] Behavioral intention to use 4 [35], [37] Computer playfulness 5 [11], [64] Computer self-efficacy 5 [35], [37], [49], [59] Content quality 5 [36]–[38], [71] Perceived enjoyment 5 [14], [40], ”
Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service (2018, neutral): “TAM has been extensively used to explain and predict user behavior of information technology applications (Cheung and Vogel, 2013; Hung et al., 2003; Keat and Mohan, 2004; Kim et al., 2016; Legris et al., 2003).”
Learning with mobile technologies - Students' behavior (2017, background): “The convergent validity “shows the degree towhich the items of a certain instrument are related” (Cheung & Vogel, 2013).”
Technology acceptance model: a literature review from 1986 to 2013 (2014, methodology): “Park et al. [66] and Farahat [28] have tested the application of the original TAM, while Gong et al. [33], Zhang et al. [95], and Cheung and Vogel [16] have used the extended TAM in the context of e-learning.”
Factors Influencing Preservice Teachers' Intention to Use Technology: TPACK, Teacher Self-efficacy, and Technology Acceptance Model (2018, methodology): “Scholars have discussed how to improve users’ positive perception of technology and intention to use technology by adopting the Technology Acceptance Model (TAM) (Cheung & Vogel, 2013; Teo, Su Luan, & Sing, 2008).”
Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model (2021, background): “Previous studies on e-learning acceptance have indicated attitude as a determinant factor of behavioural intention toward e-learning usage (e.g., Cheung & Vogel, 2013; Tosuntaş et al., 2015).”
Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) (2017, background): “Our results contradict the ﬁndings of Venkatesh et al. (2003) and most of previous research (e.g. Al-Gahtani 2016; Cheung and Vogel 2013; Merhi 2015; Park 2009).”
The imperative of influencing citizen attitude toward e-government adoption and use (2015, background): “…(e.g. Gefen, Elena, & Straub, 2003; Pavlou and Fygenson; 2006), mobile commerce (e.g. Fang, Chan, Brzezinski, & Xu, 2006), e-learning (e.g. Cheung & Vogel, 2013; Mohammadi, 2015), mobile government (e.g. Liu et al., 2014; Wang, 2014) and e-government (e.g. Al-Hujran et al., 2013; Alomari…”