Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
The current study sought to extend the Affective Technology Acceptance (ATA) model to human-robot interactions. We tested the direct relationship between affect and technology acceptance of a security robot. Affect was measured using a multi-method approach, which included a self-report survey, as well as sentiment analysis, and response length of written responses. Results revealed that participants who experienced positive affect were more likely to accept technology. However, the significance and direction of the relationship between negative affect and technology acceptance was measurement dependent. Additionally, positive and negative sentiment words accounted for unique variance in technology acceptance, after controlling for self-reported affect. This study demonstrates that affect is an important contributing factor in human-robot interaction research, and using a multi-method approach allows for a richer, more complete understanding of how human feelings influence robot acceptance.
Recommended Citation
Jessup, Sarah; Willis, Sasha M.; and Alarcon, Gene, "Extending the Affective Technology Acceptance Model to Human-Robot Interactions: A Multi-Method Perspective" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/cl/human-robot_interactions/3
Extending the Affective Technology Acceptance Model to Human-Robot Interactions: A Multi-Method Perspective
Online
The current study sought to extend the Affective Technology Acceptance (ATA) model to human-robot interactions. We tested the direct relationship between affect and technology acceptance of a security robot. Affect was measured using a multi-method approach, which included a self-report survey, as well as sentiment analysis, and response length of written responses. Results revealed that participants who experienced positive affect were more likely to accept technology. However, the significance and direction of the relationship between negative affect and technology acceptance was measurement dependent. Additionally, positive and negative sentiment words accounted for unique variance in technology acceptance, after controlling for self-reported affect. This study demonstrates that affect is an important contributing factor in human-robot interaction research, and using a multi-method approach allows for a richer, more complete understanding of how human feelings influence robot acceptance.
https://aisel.aisnet.org/hicss-56/cl/human-robot_interactions/3