Tingwei Lu, Dongmei Luo

Tingwei Lu

May 30, 2025

SECI theory, Generative artificial intelligence, Acceptance scale, Accounting education.

This study employs the SECI model(socialization, externalization, combination, and internalization) as the theoretical foundation to investigate the acceptance level of generative artificial intelligence among Accounting Students in application-oriented universities, and its efficacy in facilitating knowledge creation processes. By integrating the four stages of the SECI model with the four dimensions of the Technology Acceptance Model (TAM), the research constructs a theoretical framework for assessing the acceptance of generative AI. Using a highly reliable and valid five-point Likert scale questionnaire, the study surveyed 88 accounting students from an applied university in China. The results indicate that students exhibit higher acceptance levels during the explicit knowledge integration phases (“Combination and Internalization”), with “Performance Expectancy” at 94.48% and “Effort Expectancy” at 77.73%. In contrast, acceptance is lower in the tacit knowledge transformation stages (“Socialization and Externalization”), with “Facilitating conditions” at 75.76% and “Social influence” at 61.82%. The findings suggest that generative AI tools demonstrate advantages in facilitating explicit knowledge management but exhibit limitations in supporting interpersonal interaction and critical thinking, which are crucial for tacit knowledge conversion.

© 2025, the Authors. Published by ISCCAC

This is an open access article distributed under the CC BY-NC license