Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet

By Se-Joon Hong, James Y. L. Thong, Kar Yan Tam. Decision Support Systems, 2006

1,007 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f940916d784182590ca92c6f9cf664da5e71ee88

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

What drives continued knowledge sharing? An investigation of knowledge-contribution and -seeking beliefs (2009, background): “In particular, infrequent use of the IT may result in a considerable waste of an organization's resources and efforts in its development and implementation processes [32].”
Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective (2013, methodology): “The post-adoption behaviour has been examined in different contexts within the ﬁ eld of information systems, such as online banking [13], mobile Internet services [33], self-service information systems [62], online services [34], and social networking sites [63].”
An investigation of users’ continuance intention towards mobile banking in China (2016, methodology): “Since TAM is a very parsimonious and generic model which can be used to study both initial and continued IS adoption (Hong et al., 2006), we incorporate perceived ease of use into the basic framework of ECM to better explain users’ continuance intention.”
Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context (2011, background): “…performance of a system has been found to be important for inﬂuencing technology acceptance across a wide range of technologies and user populations (e.g. Thong, 1999; Venkatesh et al ., 2003), it is not always the strongest predictor (see Venkatesh, 1999; Hong et al ., 2006; Thong et al ., 2006).”
The impact of customer experience and perceived value on sustainable social relationship in blogs: An empirical study (2015, background): “According to the expectation–confirmation model, a positive causal relationship exists between the satisfaction and CI of users of ISs and technology (Hong et al., 2006; Thong et al., 2006; Bhattacherjee et al., 2008).”
Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives (2016, methodology): “Besides, a significant body of research in the areas of IS and marketing suggest that user satisfaction is a reliable predictor of continued IS use intention (Thong et al., 2006; Wang et al., 2010).”
E-LOYALTY IS NOT ALL ABOUT TRUST, PRICE ALSO MATTERS: EXTENDING EXPECTATION-CONFIRMATION THEORY IN BOOKSELLING WEBSITES (2013, methodology): “When deeper explanation of customer loyalty is desired, it allows other factors to be incorporated easily into this basic model [Hong et al. 2006].”
Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model (2010, methodology): “In addition, the items measuring perceived enjoyment and concentration were adapted from Moon and Kim (2001), and the continuance intention, satisfaction, confirmation were adapted from Bhattacherjee(2001a, 2001b).”
Modeling users’ acceptance of mobile services (2012, background): “When deeper explanation of user adoption intention is desired, it allows other factors to be incorporated easily into its basic model [34].”