From Failing to Exceeding User Expectations: A Large-Scale Study Exploring the Role of Expectation Gap Magnitude and Direction in Robot Adoption Intention

The mismatch between user expectations and robot performance—the expectation gap—is common in human–robot interaction. Although related research is limited, preliminary evidence suggests that the expectation gap has a considerable impact on user adoption of robots. The present study examined how failing, confirming, and exceeding user expectations and the extent to which robot performance differs from expectations predict users’ adoption intention. A sample of 234 participants completed pre-interaction expectation measures and post-interaction robot performance ratings after completing a drawing activity with a humanoid robot (Pepper). Results showed that considering both the magnitude and direction of the expectation gap (signed gap values) consistently yielded stronger associations and predictive power for adoption intention than considering the magnitude alone (absolute gap values) across four expectation dimensions, with expectation gaps related to Relative Advantage emerging as the strongest predictor. Overall, the findings highlight that failing to meet expectations consistently predicted lower adoption intention compared to both confirming and exceeding expectations, whereas evidence for whether exceeding expectations provides additional benefits beyond confirming them was mixed.