# Preliminary Validation of the Italian Version of the Artificially Intelligent Device Use Acceptance (AIDUA-IT) Scale: Cross-Cultural Adaptation and Psychometric Evaluation

## TL;DR

This study validates an Italian version of a scale to measure acceptance of AI devices, showing it is reliable and valid for use in Italy.

## Contribution

The study provides the first validated Italian version of the AIDUA scale for assessing AI device acceptance.

## Key findings

- The AIDUA-IT scale showed excellent fit and strong psychometric properties in structural validity and internal consistency.
- Convergent and discriminant validity, as well as test–retest reliability, were supported across subscales.
- The AIDUA-IT is a reliable instrument for assessing AI acceptance in Italian-speaking populations.

## Abstract

Background: Artificial intelligence (AI) is increasingly integrated into healthcare and public services, making user acceptance a key prerequisite for safe and effective implementation. The Artificially Intelligent Device Use Acceptance (AIDUA) model provides a multidimensional framework for evaluating acceptance of intelligent systems, yet no validated Italian instrument is currently available. Objectives: This study aimed to translate, culturally adapt, and preliminarily validate the Italian version of the AIDUA scale (AIDUA-IT) following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations. Methods: A two-phase cross-sectional design was used. Phase one included forward–backward translation, expert review (n = 7), and cognitive debriefing (n = 8). Phase two assessed structural validity, internal consistency, convergent and discriminant validity, and short-term test–retest reliability in a convenience sample of Italian-speaking adults (N = 140), with a subsample completing the test–retest assessment (n = 32). Results: The hypothesized eight-factor measurement model demonstrated excellent fit (Comparative Fit Index [CFI] = 0.984; Tucker–Lewis Index [TLI] = 0.981; Root Mean Square Error of Approximation [RMSEA] = 0.041; Standardized Root Mean Square Residual [SRMR] = 0.056), with strong standardized loadings (β range: 0.64–0.96) and good internal consistency (Cronbach’s α and McDonald’s ω range: 0.82–0.90). Convergent and discriminant validity were supported, and test–retest reliability was good to excellent across subscales (Intraclass Correlation Coefficient [ICC] range: 0.81–0.90). Conclusions: These findings provide initial evidence that the AIDUA-IT is a reliable and valid instrument for assessing acceptance of AI-enabled services in Italy. Further validation in larger and more diverse samples is recommended.