- Abstract
- Introduction
- Theoretical Background
- Expectation-Confirmation Model
- Perceived Knowledge Update
- Research Model and Hypotheses
- Method
- Discussion and Conclusion
- References
- About the Authors
This chapter is from the book
This chapter is from the book
This chapter is from the book
Expectation-Confirmation Model
Individuals are likely to engage with a new technology if they perceive that the new system has benefits for them. If potential users understand that the technology is useful for them, they will more likely accept and use it in the future (Bhattacherjee, 2001). In the literature, the intent to adopt again is referred to as continuance intention. The ECM has been widely used to examine continuance intention. Originally, ECM was drawn upon expectation-confirmation theory (Oliver, 1980), the technology acceptance model (TAM; Davis, 1989; Davis, Bagozzi, & Warshaw, 1989), and the theory of planned behavior (TPB; Ajzen & Fishbein, 1980). The aforementioned theories focus on the motivations of users in accepting a new technology, instead of continual usage of that technology. Deriving from these theories and consumer behavior literature, ECM focuses on three main variables—expectation, satisfaction, and confirmation in determining continued usage intention (Bhattacherjee, 2001). This model suggests that initial use does not automatically result in continued use, which has a more vital role in determining the success of a system than initial use. Confirmation indicates a cognitive belief that is salient to IS usage. It is defined as the degree to which an individual’s initial expectation about the performance of a system is being confirmed after having an experience with the system (Bhattacherjee, 2001). Confirmation describes individual’s affective state and is the consequence of a cognitive assessment of the potential discrepancy between initial expectation and experienced performance. Individuals in the later stage form a level of satisfaction based on their degree of confirmation and expectation on which that confirmation was established. Finally, all these interactions may lead to continued and repeated usage of a system. In sum, the difference between expectations (pre-usage) and perceived benefits (post-usage) determines the confirmation or disconfirmation level, which consequently affects satisfaction and usage continuance behavior.
The ECM and its adaptations have been applied to various technology-related contexts (Brown, Venkatesh, & Goyal, 2012; Stone & Baker-Eveleth, 2013). Moreover, the ECM has been used to improve our understanding of the role of the technology on learning and adoption. For example, Limayem and Cheung (2008) included IS habits in ECM and studied their interaction with continuance intentions in context of Internet-based learning technologies. In an e-learning context, Chiu et al. (2005) found that the components of perceived performance, usability, quality, and value influence satisfaction and consequently intention to continuance and ultimately intention in an e-learning environment. On realizing the earlier mentioned applications of the ECM from the literature, we adopted and contextualized the main relationships of ECM elements into our study’s setting, an ERP simulation game.