The technology acceptance model and adopter type analysis in the context of artificial intelligence
Unlocking AI Adoption: Key Insights
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 (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 (p = 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.
Accelerate Your Enterprise AI Adoption
Despite the transformative potential of Artificial Intelligence (AI), understanding the specific factors that influence its adoption remains a critical challenge. Existing technology acceptance models (TAM) often overlook the nuanced interplay of personality traits and individual mindsets, leading to incomplete insights into user behavior. Furthermore, a clear classification of AI adopter types, crucial for targeted deployment strategies, is often absent, making it difficult for organizations to optimize AI integration and maximize return on investment.
This study addresses these gaps by: 1) validating an extended Technology Acceptance Model (TAM) for AI, incorporating Big Five personality traits and an AI mindset scale (AIMS) to better predict AI adoption attitudes; and 2) employing k-prototype clustering to identify and characterize distinct AI adopter types (innovators, early adopters, early majority, late majority, laggards). The findings provide a comprehensive framework for understanding AI acceptance, offering actionable insights for developing user-centric AI solutions, tailoring adoption strategies, and informing policy-making to foster a more symbiotic human-AI interaction.
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Extended TAM Validity
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Predictor of AI Attitude
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Distinct Adopter Types
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The study validated an extended Technology Acceptance Model (TAM) in the context of AI. Perceived usefulness was identified as the strongest predictor of attitudes towards AI usage (β = 0.34, p < 0.001), followed by AI mindset scale growth (p = 0.28, p < 0.001). Perceived ease of use also significantly influenced attitude towards use and perceived usefulness. Subjective norm had a weak association with attitude towards use. Attitude toward the technology strongly predicted behavioral intention (β = 0.73, p < 0.001).
β=0.34
Perceived Usefulness (Beta)
| Factor | Influence on Attitude Toward Use |
|---|---|
| Perceived Usefulness | Strongest predictor (β=0.34, p<0.001) |
| AI Mindset (Growth) | Significant predictor (β=0.28, p<0.001) |
| Perceived Ease of Use | Significant predictor (β=0.22, p<0.001) |
| Subjective Norm | Weak association (β=0.16, p<0.001) |
Openness was positively associated with perceived ease of use (β = 0.15, p < 0.001) and AI mindset growth (β = 0.15, p < 0.001). Neuroticism showed a negative relationship with AI mindset scales (non-deskilling and growth). Conscientiousness had a weak positive association with perceived ease of use (β = 0.08, p = 0.02) and a positive association with non-deskilling (β = 0.15, p < 0.001). Agreeableness was positively correlated with subjective norm (β = 0.11, p < 0.001). Extraversion and emotional stability did not significantly predict TAM constructs or AI mindset.
β=0.15
Openness (Beta)
Impact of Openness on AI Adoption
Individuals with higher openness, characterized by curiosity and a motivation for new knowledge, demonstrated a positive association with perceived ease of use and AI mindset growth. This suggests that open-minded individuals are more likely to find AI technologies user-friendly and perceive AI as a tool for personal development. This insight is crucial for targeting early adopters who are naturally inclined to explore and integrate new technologies.
Openness correlates positively with AI adoption and growth mindset, indicating these individuals are prime candidates for innovative AI solutions.
K-prototype analysis revealed four distinct AI adopter clusters: early adopters (n = 218), early majority (n = 331), late majority (n = 293), and laggards (n = 165), aligning with the diffusion of innovations model. Early adopters showed the highest AI use frequency, perceived usefulness, and AI mindset. Late majority had the youngest age but lowest AI mindset, perceiving AI as complicated. Laggards were older, predominantly male, with lowest perceived usefulness and ease of use, and moderate-low AI mindset, using AI least frequently for informational purposes.
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Distinct AI Adopter Types
Enterprise Process Flow
Innovators (2.5%)
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Early Adopters (13.5%)
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Early Majority (34%)
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Late Majority (34%)
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Laggards (16%)
| Adopter Type | Key Characteristics | AI Engagement |
|---|---|---|
| Early Adopters | Lowest mean age (24.09), mostly students with A-levels, high computer experience | High AI use frequency, highest perceived usefulness and AI mindset, primarily for information purposes |
| Early Majority | Slightly older (32.90), full-time job, Master's degree, mixed gender | Moderate AI mindset, low perceived usefulness & ease of use, uses AI for work |
| Late Majority | Youngest (25.58), mostly students, Bachelor's degree, male-dominated | Lowest AI mindset, perceives AI as complicated, primarily for work/information purposes |
| Laggards | Highest mean age (46.04), full-time job, Master's degree, male-dominated, moderate-low computer experience | Lowest perceived usefulness & ease of use, moderate-low AI mindset, uses AI least frequently for information |
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