ArticleFrontiers in artificial intelligence2024
The technology acceptance model and adopter type analysis in the context of artificial intelligence.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Navigating the Human Side of AI: A Socio-Technical Model of Employee Attitudes Toward Algorithmic Recruitment.Behavioral sciences (Basel, Switzerland) · 2026Article
- Article
- Beyond Ease of Use: Dynamics of Technology Adoption and Cognitive Load in AI-Assisted Programming for Non-Technical Students.Behavioral sciences (Basel, Switzerland) · 2026Article
- How social influence and innovativeness shape technology perceptions and usage intentions: a TAM extension.BMC psychology · 2026Article
- Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.Journal of medical Internet research · 2026Article
- Perceptions of Over-the-Counter Hearing Aids: A Qualitative Study Informed by the Technology Acceptance Model.Journal of the American Academy of Audiology · 2026Article
- Article
- Behavioural and institutional drivers of green technology adoption: an extended technology acceptance model for sustainability transitions.Frontiers in psychology · 2026Article
- Acceptance of AI-based tools in consumer financial decision-making: An application of the extended technology acceptance model.PloS one · 2026Article
- Clinical nursing interns' perceptions of artificial intelligence-assisted tools in human-AI collaboration: a qualitative persona-based study.Frontiers in public health · 2026Article
- When self-control is no longer a protective factor: the moderating effect of institutionalized identity on attitudes toward artificial intelligence.Frontiers in psychology · 2026Article
- Examining the association between AI-enhanced education and medical students' self-directed learning using an integrated TAM-UTAUT2 model.Frontiers in medicine · 2026Article
- Digital transformation in restaurants: key aspects of service robot deployment from project initiation to evaluation.Frontiers in robotics and AI · 2026Article
- The collaboration code: how humans and AI work together across millions of conversations on job tasks.Frontiers in artificial intelligence · 2026Article
- A process model of psychological acceptance in technology-based service innovation: a symbolic interactionism perspective.Frontiers in psychology · 2026Article
- Perinatal depression and anxiety in Ghana: a qualitative study of women's perspectives on AI-driven interventions.BMC pregnancy and childbirth · 2025Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Applying the technology acceptance model to examine factors influencing chatbot use in HPV prevention education among nursing students.BMC nursing · 2025Article
- Status and perceptions of ChatGPT utilization among medical students: a survey-based study.BMC medical education · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial Intelligence (AI) is a transformative technology impacting various sectors of society and the economy. Understanding the factors influencing AI adoption is critical for both research and practice. This study focuses on two key objectives: (1) validating an extended version of the Technology Acceptance Model (TAM) in the context of AI by integrating the Big Five personality traits and AI mindset, and (2) conducting an exploratory k-prototype analysis to classify AI adopters based on demographics, AI-related attitudes, and usage patterns. Methods: A sample of Results: The psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage ( Discussion: The findings highlight the importance of perceived usefulness and AI mindset in shaping attitudes toward AI adoption. The clustering results provide a nuanced understanding of AI adopter types, aligning with established innovation diffusion theories. Implications for AI deployment strategies, policy-making, and future research directions are discussed.
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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.