ReviewMethodsX2023
An analytical framework for studying attitude towards emotional AI: The three-pronged approach.
Review in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- What makes university students accept generative artificial intelligence? A moderated mediation model.BMC psychology · 2025Article
- Article
- State-of-the-arts methods for studying factors driving the utilization of open science resources.MethodsX · 2025Review
- Cognitive bias in generative AI influences religious education.Scientific reports · 2025Article
- Understanding dimensions of trust in AI through quantitative cognition: Implications for human-AI collaboration.PloS one · 2025Article
- Attitudes toward artificial intelligence and robots in healthcare in the general population: a qualitative study.Frontiers in digital health · 2025Article
- Top-down norms and psychological empowerment: Explaining Chinese public acceptance of autonomous driving.PloS one · 2025Article
- Culturally-attuned AI: Implicit learning of altruistic cultural values through inverse reinforcement learning.PloS one · 2025Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotional artificial intelligence (AI) is a narrow, weak form of an AI system that reads, classifies, and interacts with human emotions. This form of smart technology has become an integral layer of our digital and physical infrastructures and will radically transform how we live, learn, and work. Not only will emotional AI provide numerous benefits (i.e., increased attention and awareness, optimized productivity, stress management, etc.), but in sensing and interacting with our intimate emotions, it seeks to surreptitiously modify human behaviors. This study proposes to bring together the Technological Acceptance Model (TAM) and the Moral Foundation Theory to study determinants of emotional AI's acceptance under the analytical framework of the Three-pronged Approach (Contexts, Variables, and Statistical models). We argue that to quantitatively study the acceptance of new technologies, it is necessary to leverage two intuitions. The first is the degree of acceptance increases with how users of smart technology perceive its utilities and ease of use (formalized in the TAM). The second is the degree of acceptance decreases with the user's perception of threat or affirmation posed by the technology in relation to social norms and values (formalized in the Moral Foundation Theory). This study begins by mapping the ecology of current emotional AI use in various contexts such as workplace, education, healthcare, personal assistance, etc. It then provides a brief review and critique of current applications of the TAM and the Moral Foundation Theory in studying how humans judge smart technologies. Finally, we propose the Three-pronged Analytical Framework, offering recommendations on how future studies of technological acceptance could be conducted from the questionnaire design to building statistical models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.