The technology acceptance model and adopter type analysis in the context of artificial intelligence

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 (beta = 0.34, p < 0.001), followed by AI mindset scale growth (beta = 0.28, p < 0.001). Additionally, openness was positively associated with perceived ease of use (beta = 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.