Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model

Area of Science:

• Artificial Intelligence Adoption
• Technology Acceptance Models
• Organizational Behavior

Background:

• Generative AI is rapidly transforming industries, yet empirical research on its adoption is limited.
• Understanding technology acceptance is crucial for successful digital transformation and innovation.
• Existing research often overlooks the specific context of generative AI in business settings.

Purpose of the Study:

• To investigate the factors influencing the adoption and usage of generative AI systems in Korean firms.
• To apply the Unified Theory of Acceptance and Use of Technology (UTAUT) model to the context of generative AI.
• To identify key determinants of behavioral intention and usage behavior for generative AI.

Main Methods:

• Survey of 300 employees across large and small enterprises in South Korea.
• Application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model.
• Statistical analysis to determine the significance of various factors on AI adoption.

Main Results:

• Effort expectancy and social influence significantly impact behavioral intention to use generative AI.
• Effort expectancy is critical in early adoption stages; social influence (supervisor/peer support) strongly drives adoption.
• Performance expectancy and facilitating conditions showed no significant impact; age and experience influence adoption differently.

Conclusions:

• Effort expectancy and social influence are primary determinants for generative AI adoption in Korean companies.
• Tailored strategies considering employee demographics (age, experience) are essential for effective AI implementation.
• Findings provide strategic insights for organizations aiming to integrate AI technologies successfully.