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Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model

Interdisciplinary Studies

Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model

P. Mantello, M. Ho, et al.

Dive into the intriguing world of affect recognition technology (ART) with this research by Peter Mantello, Manh-Tung Ho, Minh-Hoang Nguyen, and Quan-Hoang Vuong. This study sheds light on the behavioral factors influencing attitudes towards emotional AI, revealing how familiarity with technology and social media habits can ease apprehension toward ART. Discover cultural insights and implications for policymakers in this rapidly evolving field!

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Playback language: English
Abstract
This paper investigates behavioral factors influencing attitudes toward affect recognition technology (ART), a type of emotional AI that extracts data from a person's non-conscious state. The study extends the Technology Acceptance Model (TAM) with insights from the mindsponge model and Bayesian multi-level analysis. A multinational survey of 1015 young adults reveals that familiarity with AI, perceived utility, and restrained social media behavior correlate with less apprehension toward ART. The findings highlight the role of culture and suggest implications for policymakers in regulating ART.
Publisher
Humanities and Social Sciences Communications
Published On
Jul 19, 2023
Authors
Peter Mantello, Manh-Tung Ho, Minh-Hoang Nguyen, Quan-Hoang Vuong
Tags
affect recognition technology
emotional AI
Technology Acceptance Model
behavioral factors
cultural implications

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