Ibrahim Fabio, Münscher Johann-Christoph, Daseking Monika, Telle Nils-Torge
Faculty of Humanities and Social Sciences, Helmut-Schmidt-University/University of the Armed Forces, Hamburg, Germany.
Independent Researcher, Hamburg, Germany.
Front Artif Intell. 2025 Jan 16;7:1496518. doi: 10.3389/frai.2024.1496518. eCollection 2024.
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.
A sample of = 1,007 individuals individuals (60% female; = 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.
The psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage ( = 0.34, < 0.001), followed by AI mindset scale growth ( = 0.28, < 0.001). Additionally, openness was positively associated with perceived ease of use ( = 0.15, < 0.001). The k-prototype analysis revealed four distinct adopter clusters, consistent with the diffusion of innovations model: early adopters ( = 218), early majority ( = 331), late majority ( = 293), and laggards ( = 165).
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.
人工智能（AI）是一项变革性技术，正在影响社会和经济的各个领域。了解影响人工智能采用的因素对于研究和实践都至关重要。本研究聚焦于两个关键目标：（1）通过整合大五人格特质和人工智能思维模式，在人工智能背景下验证技术接受模型（TAM）的扩展版本；（2）进行探索性k原型分析，以根据人口统计学、与人工智能相关的态度和使用模式对人工智能采用者进行分类。
收集了1007名个体的样本（60%为女性；平均年龄=30.92岁；标准差=8.63岁）。使用经过验证的量表获取关于TAM构念、大五人格特质和人工智能思维模式的心理测量数据。回归分析用于验证TAM，k原型聚类算法用于将参与者分类为采用者类别。
心理测量分析证实了扩展TAM的有效性。感知有用性是对人工智能使用态度的最强预测因素（β=0.34，p<0.001），其次是人工智能思维模式量表增长（β=0.28，p<0.001）。此外，开放性与感知易用性呈正相关（β=0.15，p<0.001）。k原型分析揭示了四个不同的采用者群体，与创新扩散模型一致：早期采用者（n=218）、早期多数群体（n=331）、晚期多数群体（n=293）和落后者（n=165）。
研究结果突出了感知有用性和人工智能思维模式在塑造对人工智能采用态度方面的重要性。聚类结果提供了对人工智能采用者类型的细致理解，与既定的创新扩散理论相符。讨论了对人工智能部署策略、政策制定和未来研究方向的启示。
