The technology acceptance model and adopter type analysis in the context of artificial intelligence
Publication date
2025-01-16
Document type
Forschungsartikel
Author
Organisational unit
Publisher
Frontiers Research Foundation
Series or journal
Frontiers in Artificial Intelligence
ISSN
Periodical volume
7
Article ID
1496518
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
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 N = 1,007 individuals individuals (60% female; M = 30.92; SD = 8.63 years) was collected. Psychometric data were obtained using validated scales for TAM constructs, Big Five personality traits, and AI mindset. Regression analysis was used to validate TAM, and a k-prototype clustering algorithm was applied to classify participants into adopter categories.
Results
The psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage (β = 0.34, p < 0.001), followed by AI mindset scale growth (β = 0.28, p < 0.001). Additionally, openness was positively associated with perceived ease of use (β = 0.15, p < 0.001). The k-prototype analysis revealed four distinct adopter clusters, consistent with the diffusion of innovations model: early adopters (n = 218), early majority (n = 331), late majority (n = 293), and laggards (n = 165).
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.
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 N = 1,007 individuals individuals (60% female; M = 30.92; SD = 8.63 years) was collected. Psychometric data were obtained using validated scales for TAM constructs, Big Five personality traits, and AI mindset. Regression analysis was used to validate TAM, and a k-prototype clustering algorithm was applied to classify participants into adopter categories.
Results
The psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage (β = 0.34, p < 0.001), followed by AI mindset scale growth (β = 0.28, p < 0.001). Additionally, openness was positively associated with perceived ease of use (β = 0.15, p < 0.001). The k-prototype analysis revealed four distinct adopter clusters, consistent with the diffusion of innovations model: early adopters (n = 218), early majority (n = 331), late majority (n = 293), and laggards (n = 165).
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.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0)
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2024.1496518/full#supplementary-material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2024.1496518/full#supplementary-material
Cite as
Ibrahim F, Münscher J-C, Daseking M and Telle N-T (2025) The technology acceptance model and adopter type analysis in the context of artificial intelligence. Front. Artif. Intell. 7:1496518. doi: 10.3389/frai.2024.1496518
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