Factors shaping the adoption of large language models among hospital administrative staff: A cross-sectional survey study

Objective The use of large language models (LLMs) in hospital administration is increasing, yet the factors influencing LLM adoption among administrative staff remain insufficiently explored. This study aims to identify the key determinants of LLM adoption among hospital administrative staff. Methods An integrated model was developed by incorporating constructs from the Technology Acceptance Model (TAM) along with task–technology fit (TTF), trust (TR), social influence (SI), and work stress (WS). A questionnaire survey was carried out among administrative staff in 40 public hospitals in Guangxi, China, and 1,377 valid responses were collected using a stratified cluster sampling approach. The integrated model and hypothesised relationships were then tested using partial least squares structural equation modelling (PLS-SEM). Results TTF was the strongest predictor of perceived usefulness (PU). Perceived ease of use (PEOU) influenced behavioral intention (BI) primarily indirectly through PU. SI showed positive associations with both PU and BI. TR was an important predictor of BI and also of use behavior (UB). WS was positively related to UB; however, it did not moderate the relationship between BI and UB. Conclusion This study suggests that the adoption of LLMs among hospital administrative staff is shaped not only by the core elements of the TAM, but also by TTF, TR, SI and WS. These findings provide practical insights for public hospitals seeking to support the effective use of LLMs in administrative work.