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

Abstract

The rise of emotional AI signals a new era in human-machine relations where intelligent machines not only feel but also feed on human emotions as statistical fodder with the goal of reshaping our behavior. Unlike many smart technologies, emotion-recognition systems sense, monitor, harvest and analyze data extracted from a person’s non-conscious or psycho-physical state, often without their knowledge or consent. As a far more invasive manner of surveillance capitalism, the technological adoption of emotional AI is problematized by a myriad of legal, ethical, cultural, and scientific issues. To better understand the behavioral factors determining an individual’s attitude towards this emerging technology, we first identify five major tensions that may impinge on adoption. Second, we extend the Technological Acceptance Model (TAM) (Davis, 1989) model with insights from the mindsponge model of information filtering (Vuong and Napier, 2015) along with quantitative affordances offered by the Bayesian computational approach. Our analysis was conducted based on a multi-national dataset surveying perceptions of 1015 young adults (age 18–27) regarding emotional AI applications and their socio-cultural characteristics such as income, region, religiosity, and home country politics. These characteristics are fed into our Bayesian multi-level models as varying intercepts so that we can systematically measure and compare the effects of various behavioral determinants on the attitudes of respondents towards non-conscious data harvesting by government and private sector actors. Critically, this study finds respondents who feel more familiar with, and perceive more utilities in AI technologies, as well as rate themselves as more restrained from heated arguments on social media, feel less threatened by the practice of non-conscious data harvesting by both government and private sector actors. Our findings offer a fertile platform for further exploration of the intersection between psychology, culture, and emotion-recognition technologies as well as important insights for policymakers wishing to ensure design and regulation of the technology serve the best interests of society.

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

“…The final direct relationship tested, H 5 , was supported (see Table 12), suggesting that AW significantly affects BI. This is aligned with prior studies investigating AW as one of the key factors affecting intention [61,62,[72][73][74][75]. Since the results confirm the determinacy of AW towards BI, it is advisable for ERS practitioners to further emphasize the development of new ERS applications and incorporate plans to share information about their innovations.…”

“…Hence, it is important as it is one of the factors determining whether the structural environment of ERS in this country is supported, and encouragement is needed to accelerate the adoption of ERS. Lastly, familiarity and awareness are possible factors in readiness, highlighted in many previous studies [61,62]. A thorough search was performed to support the findings on awareness and the fact that it is one of the key factors that determine technology adoption.…”

Effects of water scarcity awareness and climate change belief on recycled water usage willingness: Evidence from New Mexico, United States

“…The Bayesian Mindsponge Framework (BMF) analytics is a method combining the strengths of Mindsponge Theory and Bayesian analysis for analyzing data in cognitive, psychological, and social research (Nguyen et al, 2022;Vuong et al, 2022). It is particularly useful in studying psychological processes and mechanisms in several fields, including but not limited to mental health, psychological adaptation, and environmental psychology (Asamoah et al, 2023;Khuc, Dang et al, 2023;Khuc et al, 2022;Khuc, Tran, et al, 2023;Mantello et al, 2023;Nguyen et al, 2021;Vuong et al, 2023) BMF was applied in this research for several reasons. First, the method combines the reasoning strengths of Mindsponge Theory with the inferential advantages of Bayesian analysis (Nguyen et al, 2022).…”

The potential of incorporating mindsponge into the Technological Acceptance Model

“…In a recent study titled "Machines that feel: behavioral determinants of attitude towards affect recognition technology -upgrading technology acceptance theory with the mindsponge model," published in the journal Humanities and Social Sciences Communications [1], the authors incorporated mindsponge framework [2,3] into the Technological Acceptance Model (TAM), a useful well-established theory [4]. It aimed to extend and strengthen TAM using the dynamic informationprocessing-based approach to study complex and constantly changing problems in the new digital infosphere.…”