Research Note—Using Expectation Disconfirmation Theory and Polynomial Modeling to Understand Trust in Technology

Trust in technology is an emerging research domain that examines trust in the technology artifact instead of trust in people. Although previous research finds that trust in technology can predict important outcomes, little research has examined the effect of unmet trust in technology expectations on trusting intentions. Furthermore, both trust and expectation disconfirmation theories suggest that trust disconfirmation effects may be more complex than the linear expectation disconfirmation model depicts. However, this complexity may only exist under certain contextual conditions. The current study contributes to this literature by introducing a nonlinear expectation disconfirmation theory model that extends understanding of trust-in-technology expectations and disconfirmation. Not only does the model include both technology trust expectations and technology trusting intention, it also introduces the concept of expectation maturity as a contextual factor. We collected data from three technology usage contexts that differ in expectation maturity, which we operationalize as length of the introductory period. We find that the situation, in terms of expectation maturity, consistently matters. Using polynomial regression and response surface analyses, we find that in contexts with a longer introductory period (i.e., higher expectation maturity), disconfirmation has a nonlinear relationship with trusting intention. When the introductory period is shorter (i.e., expectation maturity is lower), disconfirmation has a linear relationship with trusting intention. This unique set of empirical findings shows when it is valuable to use nonlinear modeling for understanding technology trust disconfirmation. We conclude with implications for future research.

• 저널Information Systems Research · 27(1) · 197–213
• 토픽Technology Adoption and User Behaviour · Information Systems and Management
• DOI10.1287/isre.2015.0611
• 저자Nancy K. Lankton, D. Harrison McKnight, Ryan Wright, Jason Bennett Thatcher