An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning

By Sung Youl Park. J. Educ. Technol. Soc., 2009

1,766 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f2e97c5834f9ce006aeb2398c13c502acdf6bcbc

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Citations in the corpus

Examining the impact of gamification on intention of engagement and brand attitude in the marketing context (2017, methodology): “It has been asserted that TAM appears to be able to account for 40% to 50% of user acceptance (Park, 2009).”
Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India (2020, background): “If the SME authority perceives that use of a technology (here use of SMM) would considerably enhance the productivity of the SME, that SME would not hesitate to use that technology (Park, 2009).”
Exploring Students Acceptance of E-Learning Using Technology Acceptance Model in Jordanian Universities. (2013, background): “Moreover, exploring students’ intentions and investigating the factors that impact on students’ beliefs about e-learning can help management to create new methods for attracting a larger number of students who are willing to be involved in e-learning systems (Park, 2009).”
Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model (2019, background): “According to the existing literature, perceived accessibility is found to have a significant effect on perceived ease of use [38], [49], [51], [55], [61], [62] and perceived usefulness [38], [42], [51], [61] of e-learning system.”
Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors (2016, background): “Moghadam and Bairamzadeh (2009) E-learning system Students 155 YES 0.350 NR Park (2009) E-learning system Students 628 YES 0.422 6.78** NR Pituch and Lee (2006) E-learning system Students 259 YES 0.318 p .05 Lee, Hsiao and Purnomo (2014) CSE * E-learning system Students 326 YES 0.268 p 0.001 Lee,…”
Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers’ Technology Acceptance (2011, background): “However, Park (2009) found that e-learning self-efficacy was the most important construct, followed by subjective norm, in influencing the behavioral intention to use e-learning.”
What Drives Experiential Loyalty Toward Smart Restaurants? The Case Study of KFC in Beijing (2018, unknown): “, 2007) and technology development (Chinomona, 2013; Jahangir & Begum, 2008; Morosan, 2011, 2014; Park, 2009).”
Using the Technology Acceptance Model in Understanding Academics' Behavioural Intention to Use Learning Management Systems (2014, background): “The linkage between the different variables has been proven by different studies on e-learning and LMS usage [31, 53-56].”
Determinants of social media adoption by B2B organizations (2015, unknown): “Consistent with TAM2, later evidence (e.g. Park, 2009; Pentina, Koh, & Le, 2012) highlighted the impact of social influences on both perceived usefulness and adoption intention.”
Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM) (2021, result): “The findings in this study are in line with other studies like (Chen 2010; Compeau and Higgins 1995; Park 2009; Venkatesh 2000; Wong et al. 2010).”
Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model (2017, background): “Many studies have recently supported the relationship between BI and Actual Usage (AU) in the context of e-learning (Chang & Tung, 2008; Liu et al., 2010; Park, 2009; Tarhini et al., 2015; Teo, 2010).”
Evaluating E-learning systems success: An empirical study (2020, background): “…e-learning success factors to maximize the effectiveness of these systems (e.g., Ali & Ahmad, 2011; Fathema, Shannon, & Ross, 2015; Islam, 2013; B. C. Lee, Yoon, & Lee, 2009; J. K. Lee & Lee, 2008; M. C. Lee, 2010; Mohammadi, 2015; Mtebe & Raphael, 2018; Park, 2009; Wahab, 2008; Wang, 2003)).”
Using the UTAUT model to understand students’ usage of e-learning systems in developing countries (2021, background): “Like the theory of reasoned action (TRA), behavioural intention (IU) helps capture the reasons underlying students’ acceptance of technology (Maldonado et al., 2011; Park, 2009).”