The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance

By James Y. L. Thong, Se-Joon Hong, Kar Yan Tam. Int. J. Hum. Comput. Stud., 2006

1,391 citations recorded by the source corpus.

Subfields: Information Systems and Management, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bfdda5fc5a05492fd08b40b3ced08e5a8860e3ad

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Citations in the corpus

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).”
An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation-confirmation model (2010, methodology): “Empirical evidence on IS continuance supports that user satisfaction is a major factor of IS continuance intention (Kim & Han, 2009b; Thong et al., 2006).”
The Role of Augmented Reality for Experience-Influenced Environments: The Case of Cultural Heritage Tourism in Korea (2018, background): “Thus, consistent with the theory of reasoned action (TRA) and the theory of planned behavior (TPB), IS adoption literature also asserts that satisfaction with IS determined by users’ beliefs (Thong, Hong, and Tam 2006).”
Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model (2018, methodology): “Recently, to study post-acceptance behaviour at the individual level, the ECM has been adopted by several IS researchers (e.g. Bhattacherjee (2001b), Lin et al. (2005), Thong et al. (2006), Lee (2010), Albashrawi and Motiwalla (2017)).”
Modeling Customer Loyalty from an Integrative Perspective of Self-Determination Theory and Expectation–Confirmation Theory (2009, background): “Researchers (e.g., LaTour and Peat 1980) have concluded that expectation based on consumers’ direct experiences is a main predictor of consumer satisfaction (Thong et al. 2006).”
"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media (2017, background): “At its core, this is consistent with both the ECM [2, 31] and Lapointe and Rivard’s resistance framework [24], as they center around continued confirmation of expectation satisfaction to keep users using a system.”
The influence of tourism website on tourists' behavior to determine destination selection: : A case study of creative economy in Korea (2015, methodology): “…behavior is defined as consumers' belief on attributes of products/services (Churchill and Surprenant, 1982), the perceived usefulness, one of the most influential cognitive factors for IT acceptance, is a reasonable surrogate for expectation (Thong et al., 2006).”
What drives purchase intention for paid mobile apps? - An expectation confirmation model with perceived value (2015, background): “For example, Thong et al. (2006) proposed a new variable (perceived enjoyment) to study mobile internet service continuance intention.”
The role of task-technology fit as users' motivation to continue information system use (2009, background): “…been to focus on IT-centric variables with, e.g., user satisfaction and perceived usefulness as the key to success (success meaning ‘‘strong intention among users to continue their use of an IS”) (Chiu, Sun, Sun, & Ju, 2006; Premkumar & Bhattacherjee, 2006; Roca et al., 2006; Thong et al., 2006).”
Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality (2019, background): “Hedonic motivation is defined as the enjoyment derived from the use of technology and plays an important role in technology acceptance and use [60], directly influencing technology acceptance and use [12,61,62].”
Culture-Technology Fit: Effects of Cultural Characteristics on the Post-Adoption Beliefs of Mobile Internet Users (2007, background): “Although Thong, Hong, and Tarn developed and empirically tested the post-adoption model for understanding the continued usage of the mobile Internet [93], the model does not include the antecedents of users' post-adoption beliefs, which are the cultural characteristics of indi vidual users in this s”
Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services (2011, background): “First, Bhattacherjee’s was a representative study whose results were veriﬁed by several other studies (Lin et al. 2005, Thong et al. 2006, Min and Shenghua 2007, Limayem and Cheung 2008, Vatanasombut et al. 2008).”
An expectation-confirmation model of continuance intention to use mobile instant messaging (2016, methodology): “Specifically, it demonstrates a positive relationship with adoption intention (Chang et al., 2014b; Thong et al., 2006; Venkatesh, 2000), continuance use intention (Agarwal and Karahanna, 2000; Bhattacherjee, 2001; Bhattacherjee and Premkumar, 2004), satisfaction (Bhattacherjee, 2001; Limayem et al.”
Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives (2012, methodology): “The model of IS continuance intention has been tested in different cases, such as the continued involvement of open-source software developers (Wu et al., 2007), mobile Internet services (Thong et al., 2006) and web-based learning systems (Liao et al., 2009).”
Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction (2019, background): “According to ECM studies, satisfaction is an important factor influencing CI (e.g. Hong et al., 2006; Thong et al., 2006; Bhattacherjee and Barfar, 2011; Chen et al., 2013).”
Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology (2010, background): “…the two most common technological attributes that contribute to positive user attitudes and user satisfaction in work and consumer contexts (e.g., Brown et al., 2008; Hong et al., 2002; Thong, 1999; Thong et al., 2006; Venkatesh et al., 2003), including mandated use situations (Brown et al., 2002).”