Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model

By Mun Yong Yi, Yujong Hwang. Int. J. Hum. Comput. Stud., 2003

1,412 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/86bd68dfcdf6f19f3ee54b97112aa3d3ec8b781c

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Citations in the corpus

Integrating Service Quality with System and Information Quality: An Empirical Test in the E-Service Context (2013, background): “…the IS community because of the hedonic aspects of the Internet and web-based systems (e.g., Koufaris 2002; Shang et al. 2005; Van der Heijden 2004; Yi and Hwang 2003); the construct is therefore appropriate for capturing a hedonic perspective of service (Childers et al. 2001; Fiore et al. 2005)…”
Explaining the adoption of transactional B2C mobile commerce (2008, background): “In the past two decades, TAM attracted much attention and has been established as a parsimonious yet powerful model for explaining and predicting usage intentions and acceptance behavior ( Yi and Hwang, 2003”
The acceptance and use of computer based assessment (2011, methodology): “Yi & Hwang, 2003 used Enjoyment which explains that using a computer system is perceived to be personally enjoyable in its own right aside from the instrumental value of the technology (Davis et al., 1992).”
Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept (2012, methodology): “It is an adaptation of Fishbein and Azjen’s (1975) theory of reasoned action (TRA), which has been successful in predicting and explaining behaviour in general (Malhotra & Galletta, 1999; Yi & Hwang, 2003).”
Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model (2016, background): “Self-efficacy plays a significant role in in IS acceptance, electronic-based services, and webbased IS use (Hasan, 2006; Hsu and Chiu, 2004; Yi and Hwang, 2003).”
Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence (2021, methodology): “All the scales used a 7 ‐ point Likert scale ranging from strongly disagree to strongly agree (see Table 1) and all items used exceeded the Cronbach ɑ value threshold of .60, showing reliability (Malhotra et al., 2010).”