探索护士采用人工智能技术的行为意向:社会影响、感知工作压力和人机信任的视角

IF 3.8 3区 医学 Q1 NURSING
Chin‐Hung Chen, Wan‐I Lee
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引用次数: 0

摘要

目的本研究从一个新的视角和框架出发,探讨社会影响、人机信任和感知到的工作压力如何影响护士对采用人工智能辅助护理技术的行为意向。本研究还探讨了不同类型的社会影响和工作压力维度之间的相互关系,以填补学术文献的空白。方法本研究采用目的性抽样方法,在台湾选择了五家已实施人工智能解决方案的医院。根据相关文献改编的量表被翻译成中文,并根据实际情况进行了修改。调查问卷于 2023 年 5 月 15 日至 6 月 10 日通过滚雪球式抽样向护士发放。结果遵从、服从和人机信任与行为意向呈正相关,而服从与行为意向呈负相关。感知到的工作压力对行为意向没有明显影响。顺从与工作不确定性、技术恐惧症和时间压力这三个工作压力维度都呈正相关,而服从与工作不确定性相关。感知到的压力没有明显影响,这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。这项研究揭示了在医疗保健领域采用人工智能的背景下,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。 影响这项研究通过纳入组织内部压力源和人工智能相关工作压力的观点,超越了传统的技术接受模型。研究深入探讨了护理人员在适应人工智能前过程中的应对机制,强调了采取细致入微的管理方法的必要性。研究结果强调了在医疗机构成功实施人工智能过程中考虑技术和社会心理因素的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Nurses' Behavioural Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job Stress and Human–Machine Trust
AimThis 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.DesignA quantitative cross‐sectional study.MethodsFive 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.ResultsConformity, 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.ConclusionSocial 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.ImpactThis 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.Patient or Public ContributionNo Patient or Public Contribution.
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来源期刊
CiteScore
6.40
自引率
7.90%
发文量
369
审稿时长
3 months
期刊介绍: The Journal of Advanced Nursing (JAN) contributes to the advancement of evidence-based nursing, midwifery and healthcare by disseminating high quality research and scholarship of contemporary relevance and with potential to advance knowledge for practice, education, management or policy. All JAN papers are required to have a sound scientific, evidential, theoretical or philosophical base and to be critical, questioning and scholarly in approach. As an international journal, JAN promotes diversity of research and scholarship in terms of culture, paradigm and healthcare context. For JAN’s worldwide readership, authors are expected to make clear the wider international relevance of their work and to demonstrate sensitivity to cultural considerations and differences.
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