Chen Chin-Hung, Lee Wan-I
College of Management, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan.
Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology (First Campus), Kaohsiung City, Taiwan.
J Adv Nurs. 2025 Jul;81(7):3739-3752. doi: 10.1111/jan.16495. Epub 2024 Sep 28.
This study examines how social influence, human-machine trust and perceived job stress affect nurses' behavioural intentions towards AI-assisted care technology adoption from a new perspective and framework. It also explores the interrelationships between different types of social influence and job stress dimensions to fill gaps in academic literature.
A quantitative cross-sectional study.
Five hospitals in Taiwan that had implemented AI solutions were selected using purposive sampling. The scales, adapted from relevant literature, were translated into Chinese and modified for context. Questionnaires were distributed to nurses via snowball sampling from May 15 to June 10, 2023. A total of 283 valid questionnaires were analysed using the partial least squares structural equation modelling method.
Conformity, obedience and human-machine trust were positively correlated with behavioural intention, while compliance was negatively correlated. Perceived job stress did not significantly affect behavioural intention. Compliance was positively associated with all three job stress dimensions: job uncertainty, technophobia and time pressure, while obedience was correlated with job uncertainty.
Social influence and human-machine trust are critical factors in nurses' intentions to adopt AI technology. The lack of significant effects from perceived stress suggests that nurses' personal resources mitigate potential stress associated with AI implementation. The study reveals the complex dynamics regarding different types of social influence, human-machine trust and job stress in the context of AI adoption in healthcare.
This research extends beyond conventional technology acceptance models by incorporating perspectives on organisational internal stressors and AI-related job stress. It offers insights into the coping mechanisms during the pre-adaption AI process in nursing, highlighting the need for nuanced management approaches. The findings emphasise the importance of considering technological and psychosocial factors in successful AI implementation in healthcare settings.
No Patient or Public Contribution.
本研究从一个新的视角和框架出发，考察社会影响、人机信任和感知工作压力如何影响护士采用人工智能辅助护理技术的行为意向。它还探讨了不同类型的社会影响与工作压力维度之间的相互关系，以填补学术文献中的空白。
定量横断面研究。
采用立意抽样法选取台湾地区五家已实施人工智能解决方案的医院。从相关文献中改编的量表被翻译成中文，并根据实际情况进行了修改。2023年5月15日至6月10日，通过滚雪球抽样法向护士发放问卷。使用偏最小二乘结构方程建模方法对总共283份有效问卷进行了分析。
从众、服从和人机信任与行为意向呈正相关，而依从与行为意向呈负相关。感知工作压力对行为意向没有显著影响。依从与工作不确定性、技术恐惧和时间压力这三个工作压力维度均呈正相关，而服从与工作不确定性相关。
社会影响和人机信任是护士采用人工智能技术意向的关键因素。感知压力缺乏显著影响表明，护士的个人资源减轻了与人工智能实施相关的潜在压力。该研究揭示了医疗保健领域采用人工智能背景下，不同类型的社会影响、人机信任和工作压力之间的复杂动态关系。
本研究超越了传统的技术接受模型，纳入了组织内部压力源和与人工智能相关的工作压力的视角。它为护理中人工智能预适应过程中的应对机制提供了见解，强调了需要细致入微的管理方法。研究结果强调了在医疗保健环境中成功实施人工智能时考虑技术和心理社会因素的重要性。
无患者或公众贡献。
