Influencing factors of innovative leadership of clinical nurses: a cross-sectional study

Frequent public health events, the reporting of chronic diseases, and digital transformation are driving profound changes in nursing models. The role of nurses in resource integration, interdisciplinary collaboration, and process innovation is becoming increasingly prominent. Innovative leadership has become one of the important leadership capabilities to enhance the quality of nursing services. Therefore, this study aims to assess the level of innovative leadership among clinical nurses in China and explore its key influencing factors.

This was a cross-sectional study of 887 clinical nurses from 10 tertiary hospitals in Henan Province, China, between March and May 2024. The variables included sociodemographic information (sex, age, etc.), the Chinese version of the Organizational Innovation Climate Scale (Cronbach's alpha for reliability is 0.837, validity is 0.962), and the Chinese version of the Innovative Leadership Scale (Cronbach's alpha for reliability is 0.940, validity is 0.938).

A total of 887 valid questionnaires were recovered. The mean total score of nurses' innovative leadership was 158.04 ± 16.96. The results of multiple linear regression analysis showed that professional title, employment type, personality type, participation in leadership training, and nurses' perception of organizational innovation climate were independent influencing factors of nurses' innovative leadership (P < 0.05). This regression model could explain 33.9% of the total variation in nurses' innovative leadership.

The innovative leadership of nurses is at a moderate level. Therefore, nursing managers should actively monitor the innovative ideas and behaviors of nurses, and carry out targeted interventions in a timely manner to improve the innovative leadership of clinical nurses and enhance the quality of service. Due to the cross-sectional study design, we can not directly infer causal relationships between variables. Additionally, the relatively small sample size may affect the reliability and generalizability of the results; therefore, caution is advised when interpreting the study's conclusions.

Not applicable.

Not applicable.