Exploring Nurses' Behavioural Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job...

Area of Science:

• Healthcare technology adoption
• Nursing informatics
• Human-computer interaction

Background:

• Nurses' behavioral intentions toward AI-assisted care technology are influenced by social factors and trust.
• Understanding the interplay of social influence, human-machine trust, and job stress is crucial for AI adoption in nursing.

Purpose of the Study:

• To examine the effects of social influence, human-machine trust, and perceived job stress on nurses' behavioral intentions regarding AI-assisted care technology.
• To explore interrelationships between social influence types and job stress dimensions in the context of AI adoption.

Main Methods:

• Quantitative cross-sectional study involving 283 nurses from five Taiwanese hospitals.
• Data collected via questionnaires adapted from existing literature and analyzed using partial least squares structural equation modeling.

Main Results:

• Conformity, obedience, and human-machine trust positively correlated with behavioral intention; compliance showed a negative correlation.
• Perceived job stress (job uncertainty, technophobia, time pressure) did not significantly impact behavioral intention.
• Compliance was positively associated with all job stress dimensions, while obedience correlated with job uncertainty.

Conclusions:

• Social influence and human-machine trust are key drivers for nurses' AI technology adoption intentions.
• Nurses' personal resources may buffer the impact of job stress related to AI implementation.
• Findings highlight the need for considering technological and psychosocial factors for successful AI integration in healthcare.