Acceptance of Generative AI in Knowledge Work
Koponen, Kati (2024)
2024
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2024-01-19
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202401171577
https://urn.fi/URN:NBN:fi:tuni-202401171577
Tiivistelmä
Generative Artificial Intelligence will have a major impact on the ways of working. The groundbreaking technology has emerged for creating diverse content, generating text, images, and audio. These models, pretrained on vast datasets and fine-tuned iteratively, demonstrate an exceptional capacity to generate human-like, high-quality outputs. Notable Generative AI tools like ChatGPT, Bard, GitHub Copilot, Microsoft Bing, and Microsoft Copilot have become invaluable assistants in a wide array of tasks. These technologies are developing fast and provoke a discussion of the future of work.
Knowledge work, encompassing the efforts of experts, researchers, specialists, and managers, is characterized by its autonomous, complex, and often ambiguous nature. Generative AI tools hold the promise of enhancing knowledge work by increasing productivity and reducing the time spent on repetitive tasks. These tools enable the creation of content such as emails, articles, summaries, code writing and debugging, and information retrieval, while also boosting creativity and learning rates. Nevertheless, Generative AI has its limitations, including potential biases and inaccuracies, which prevent it from replacing knowledge work requiring critical thinking and deep expertise.
This study investigates the acceptance of Generative AI among knowledge workers in Finland, aiming to identify the factors influencing its adoption. Employing a modified UTAUT model, the research was conducted as a survey and using quantitative methods. The respondents included students and professionals from fields like software development, consultancy, and management. Seven factors emerged as determinants of Generative AI acceptance: Performance Expectancy, Effort Expectancy, Peer Social Influence, Superior Social Influence, Attitude, Trust, and Behavioral Intention to Use. The conceptual model was tested using structural equation modelling.
The results indicate that Attitude exerts the most substantial influence on the intention to use Generative AI, followed by Performance Expectancy, Effort Expectancy, and Peer Social Influence. Trust, Performance Expectancy, Effort Expectancy, and Peer Social Influence significantly impact Attitude. On contrary to Peer Social Influence the Superior Influence was found to be significant. In general, respondents expressed positive attitudes towards Generative AI, finding it enjoyable and useful for enhancing productivity and task completion. The research highlights the importance of incorporating Attitude into the UTAUT model and contributes to developing the model to explain the factors impacting utilization and acceptance of technology.
Knowledge work, encompassing the efforts of experts, researchers, specialists, and managers, is characterized by its autonomous, complex, and often ambiguous nature. Generative AI tools hold the promise of enhancing knowledge work by increasing productivity and reducing the time spent on repetitive tasks. These tools enable the creation of content such as emails, articles, summaries, code writing and debugging, and information retrieval, while also boosting creativity and learning rates. Nevertheless, Generative AI has its limitations, including potential biases and inaccuracies, which prevent it from replacing knowledge work requiring critical thinking and deep expertise.
This study investigates the acceptance of Generative AI among knowledge workers in Finland, aiming to identify the factors influencing its adoption. Employing a modified UTAUT model, the research was conducted as a survey and using quantitative methods. The respondents included students and professionals from fields like software development, consultancy, and management. Seven factors emerged as determinants of Generative AI acceptance: Performance Expectancy, Effort Expectancy, Peer Social Influence, Superior Social Influence, Attitude, Trust, and Behavioral Intention to Use. The conceptual model was tested using structural equation modelling.
The results indicate that Attitude exerts the most substantial influence on the intention to use Generative AI, followed by Performance Expectancy, Effort Expectancy, and Peer Social Influence. Trust, Performance Expectancy, Effort Expectancy, and Peer Social Influence significantly impact Attitude. On contrary to Peer Social Influence the Superior Influence was found to be significant. In general, respondents expressed positive attitudes towards Generative AI, finding it enjoyable and useful for enhancing productivity and task completion. The research highlights the importance of incorporating Attitude into the UTAUT model and contributes to developing the model to explain the factors impacting utilization and acceptance of technology.