Impact of media richness and flow on e-learning technology acceptance

By SuHoun Liu, Hsiu-Li Liao, Jean A. Pratt. Comput. Educ., 2009

646 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/01c167943d53685dc19405b7172a7e4bef5a2e12

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Citations in the corpus

Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis (2016, background): “Furthermore, challenge [61, 100, 141], aesthetics and design [120, 139, 142, 143] and credibility features [68, 73], referring to features that inculcate a feeling of trust, familiarity [97, 139, 144], and the provision of guidance or tutorials [68, 106, 145] have been hypothesised to positively…”
The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology (2009, background): “…is synonymously with an affect (i.e. a positive, indifferent, or negative feeling) and further that affect (as attitude or satisfaction) in prior e-learning studies is found to be an important predictor of intentions concerning use of e-learning (e.g., Chiu et al., 2005; Liu et al., 2009).”
Social Robots for Language Learning: A Review (2018, background): “This excitement can result in a so-called novelty effect on learning: Learners enjoy the new technology so much that their initial interest leads to higher learning outcomes, which would not have been attained if learners had been more familiar with the robot (cf. S. Liu, Liao, & Pratt, 2009).”
Perceived Ease of Use and Perceived Usefulness of Social Media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis (2016, background): “Many researchers have applied TAM in e-learning studies and have found that the perceived ease of use and the perceived usefulness have significant effects on an individual's behavioral intention to use e-learning systems [29].”
An expectation-confirmation model of continuance intention to use mobile instant messaging (2016, methodology): “A technology with good user interface enhances the total user experience (Flavián et al., 2006; Harrison et al., 2013; Karapanos, 2013; Zviran et al., 2006), and increases user satisfaction (Legris et al., 2003; Liu et al., 2009; Zviran et al., 2006).”
Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics (2021, background): “TAM is tailored to information systems contexts, and it is the most recognised model by far in terms of research on user acceptance behaviour (Liu, Liao, and Pratt 2009).”
Antecedents and consequences of e‐learning acceptance (2011, methodology): “…technologies and Web-based applications have created unprecedented opportunities for conducting learning (Liaw et al ., 2008; Liu et al ., 2009), and this phenomenon has led to the exponential growth of electronic learning (e-learning) in recent years (Baylari & Montazer, 2009; Liu et al ., 2009).”