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The technology acceptance model and adopter type analysis in the context of artificial intelligence
Fabio Ibrahim1, Johann-Christoph Münscher1, Monika Daseking1
1Faculty of Humanities and Social Sciences, Helmut-Schmidt-University/University of the Armed Forces, Hamburg, Germany.
Frontiers in Artificial Intelligence
|January 31, 2025
Summary
This study found perceived usefulness and AI mindset are key drivers of AI adoption. Four distinct adopter groups were identified, from early adopters to laggards, offering insights into technology diffusion.
Area of Science:
- Psychology
- Computer Science
- Sociology
Background:
- Artificial Intelligence (AI) adoption is rapidly expanding across sectors.
- Understanding user acceptance factors is crucial for effective AI integration.
- Existing models need extension to capture AI-specific nuances.
Purpose of the Study:
- To validate an extended Technology Acceptance Model (TAM) for AI, incorporating personality traits and AI mindset.
- To classify AI adopters into distinct categories using demographic, attitudinal, and usage data.
- To provide insights into AI diffusion patterns and user segmentation.
Main Methods:
- Surveyed 1,007 individuals using validated scales for TAM, Big Five personality traits, and AI mindset.
- Employed regression analysis to test the extended TAM.
- Utilized k-prototype clustering to identify AI adopter segments.
Main Results:
- Perceived usefulness (β=0.34) and AI mindset growth (β=0.28) significantly predicted AI adoption attitudes.
- "Openness" personality trait positively influenced perceived ease of use (β=0.15).
- Four adopter clusters emerged: early adopters (n=218), early majority (n=331), late majority (n=293), and laggards (n=165).
Conclusions:
- Perceived usefulness and AI mindset are critical determinants of AI acceptance.
- AI adopter segmentation aligns with innovation diffusion theory, revealing distinct user groups.
- Findings inform targeted AI implementation strategies and future research.
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