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Identifying Barriers Hindering the Acceptance of Generative AI as a Work Associate, measured with the new AGAWA scale

Published: December 29, 2025 | arXiv ID: 2512.23373v1

By: Łukasz Sikorski , Albert Łukasik , Jacek Matulewski and more

Potential Business Impact:

Helps companies understand how workers feel about AI coworkers.

Business Areas:
Artificial Intelligence Artificial Intelligence, Data and Analytics, Science and Engineering, Software

The attitudes of today's students toward generative AI (GenAI) will significantly influence its adoption in the workplace in the years to come, carrying both economic and social implications. It is therefore crucial to study this phenomenon now and identify obstacles for the successful implementation of GenAI in the workplace, using tools that keep pace with its rapid evolution. For this purpose, we propose the AGAWA scale, which measures attitudes toward an artificial agent utilising GenAI and perceived as a coworker. It is partially based on the TAM and UTAUT models of technology acceptance, taking into account issues that are particularly important in the context of the AI revolution, namely acceptance of its presence and social influence (e.g., as an assistant or even a supervisor), and above all, resolution of moral dilemmas. The advantage of the AGAWA scale is that it takes little time to complete and analyze, as it contains only four items. In the context of such cooperation, we investigated the importance of three factors: concerns about interaction with GenAI, its human-like characteristics, and a sense of human uniqueness, or even superiority over GenAI. An observed manifestation of the attitude towards this technology is the actual need to get help from it. The results showed that positive attitudes toward GenAI as a coworker were strongly associated with all three factors (negative correlation), and those factors were also related to each other (positive correlation). This confirmed the relationship between affective and moral dimensions of trust towards AI and attitudes towards generative AI at the workplace.

Page Count
23 pages

Category
Computer Science:
Computers and Society