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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI for Human Learning and Behavior Change
Volume 7 - 2024 | doi: 10.3389/frai.2024.1496518
This article is part of the Research Topic Human-Centered Artificial Intelligence in Interaction Processes View all 7 articles

The Technology Acceptance Model and Adopter Type Analysis in the Context of Artificial Intelligence

Provisionally accepted
Fabio Ibrahim Fabio Ibrahim *Johann-Christoph Münscher Johann-Christoph Münscher Monika Daseking Monika Daseking Nils-Torge Telle Nils-Torge Telle
  • Helmut Schmidt University, Hamburg, Germany

The final, formatted version of the article will be published soon.

    Artificial Intelligence (AI) is a disruptive technology that affects all areas of society and economy. The focus of this study is on two aspects: First, the validation of the extended technology acceptance model (TAM) in the context of AI, incorporating the Big Five personality traits and the AI mindset; and second, an exploratory k-prototype analysis to classify AI adopters based on demographics, AI attitudes, and AI usage. The sample comprised a total of N = 1007 individuals (60% female; M = 30.92; SD = 8.63 years). Psychometric results supported the TAM, with perceived usefulness as the strongest predictor of attitudes towards AI use (β = .34, p < .001) and AI mindset scale growth as the second strongest predictor (β = .28, p < .001). Openness showed a positive relationship with perceived ease of use (β = .15, p < .001). Exploratory k-prototype analysis identified four clusters consistent with diffusion of innovations model types: early adopters (n = 218), early majority (n = 331), late majority (n = 293), and laggards (n = 165). The implications and possible explanations of the results are discussed.

    Keywords: Artificial Intelligence1, Technology Acceptance Model2, big five3, AI mindset4, early adopter5, Late adopter6

    Received: 14 Sep 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 Ibrahim, Münscher, Daseking and Telle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fabio Ibrahim, Helmut Schmidt University, Hamburg, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.