Expectation confirmation research in general, and in information systems (IS) in particular, has produced conflicting results. In this paper, we discuss six different models of expectation confirmation: assimilation, contrast, generalized negativity, assimilation-contrast, experiences only, and expectations only. Relying on key constructs from the technology acceptance model (TAM), we test each of these six models that suggests different roles for expectations and experiences of the key predictor—here, perceived usefulness—and their impacts on key outcomes—here, behavioral intention, use, and satisfaction. Data were collected in a field study from 1,113 participants at two points in time. Using polynomial modeling and response surface analysis, we provide the analytical representations for each of the six models and empirically test them to demonstrate that the assimilation-contrast is the best existing model in terms of its ability to explain the relationships between expectations and experiences of perceived usefulness and important dependent variables—namely, behavioral intention, use, and satisfaction—in individual-level research on IS implementations.
We propose a model to study expectation confirmation in information systems. The proposed model is based on the assimilation-contrast model and prospect theory, and suggests that both are needed to account for the magnitude and direction of the deviations between experiences and expectations. Using the technology acceptance model's (TAM) primary construct-namely, perceived usefulness-expectations and experiences were conceptualized and operationalized to test our model. Data were collected in a field study from 1,113 participants at two points in time. Using polynomial modeling and response surface analysis, we demonstrated that our model offers a good explanation of the relationship among information systems expectations, experiences, and use. We discuss theoretical and practical implications.
Individual-level information systems adoption research has recently seen the introduction of expectation-disconfirmation theory (EDT) to explain how and why user reactions change over time. This prior research has produced valuable insights into the phenomenon of technology adoption beyond traditional models, such as the technology acceptance model. First, we identify gaps in EDT research that present potential opportunities for advances--specifically, we discuss methodological and analytical limitations in EDT research in information systems and present polynomial modeling and response surface methodology as solutions. Second, we draw from research on cognitive dissonance, realistic job preview, and prospect theory to present a polynomial model of expectation-disconfirmation in information systems. Finally, we test our model using data gathered over a period of 6 months among 1,143 employees being introduced to a new technology. The results confirmed our hypotheses that disconfirmation in general was bad, as evidenced by low behavioral intention to continue using a system for both positive and negative disconfirmation, thus supporting the need for a polynomial model to understand expectation disconfirmation in information systems.