This study aimed to validate the Robot Acceptance Questionnaire (RAQ), a self-report instrument designed to assess user acceptance toward social robots. Originally structured around four theoretical domains—pragmatic, hedonic (identity and feelings), and attractiveness—the RAQ was empirically found to converge into two robust and inversely related dimensions: Positive Attitude (PA) and Negative Attitude (NA). A total of 208 participants (mean = 43.1; S.D. = 21.4) viewed a short video of a humanoid robot (Pepper) and completed the RAQ. Factorial structure (Principal Component Analysis), internal reliability (Cronbach’s alpha), and construct validity were assessed. Results showed excellent internal consistency for both PA and NA (α = 0.93), and intuitive associations with independent measures of ease of use, mastery, and willingness to interact. The RAQ thus offers a concise and reliable tool for assessing general robot acceptance, especially suitable for remote and large-scale studies.

Development and Validation of the Robot Acceptance Questionnaire (RAQ)

Cordasco G.;
2025

Abstract

This study aimed to validate the Robot Acceptance Questionnaire (RAQ), a self-report instrument designed to assess user acceptance toward social robots. Originally structured around four theoretical domains—pragmatic, hedonic (identity and feelings), and attractiveness—the RAQ was empirically found to converge into two robust and inversely related dimensions: Positive Attitude (PA) and Negative Attitude (NA). A total of 208 participants (mean = 43.1; S.D. = 21.4) viewed a short video of a humanoid robot (Pepper) and completed the RAQ. Factorial structure (Principal Component Analysis), internal reliability (Cronbach’s alpha), and construct validity were assessed. Results showed excellent internal consistency for both PA and NA (α = 0.93), and intuitive associations with independent measures of ease of use, mastery, and willingness to interact. The RAQ thus offers a concise and reliable tool for assessing general robot acceptance, especially suitable for remote and large-scale studies.
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4918062
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