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Aim The purpose of this study is to investigate how the use of artificial intelligence is associated with the retention of elderly caregivers. Background The turnover of elderly caregivers is high and increasing. Elderly care institutions are beginning to use artificial intelligence to support caregivers in their work, and the use of technology is critical to staff retention. Empowerment of elderly caregivers has been neglected by managers and researchers. Methods This cross-sectional study involved 511 elderly caregivers in 25 elderly institutions. Six validated standardized scales were used for data collection, and the software SPSS and SmartPLS were used for data analysis. Results The quality of artificial intelligence has a significant positive effect on empowerment. Artificial intelligence psychological empowerment (β = .355, p < .001) and artificial intelligence structural empowerment (β = .375, p < .001) both had positive effects on retention intention, and the jointly explained variance (R2) was 42.6%. Conclusions The results show that a significant relationship exists between artificial intelligence empowerment and retention intention. Elderly caregivers with more structural empowerment have higher retention intention. Implications for Nursing Management Artificial intelligence suppliers need to pay attention to the role of product quality in elderly care services, continuously improve artificial intelligence quality, and strengthen the application and routine maintenance of artificial intelligence technologies. Elderly care institution managers should pay special attention to artificial intelligence structural empowerment (such as artificial intelligence-related education and training, learning and development opportunities, and resource support).
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Ying Wang
Jiangnan University
Chenze Xie
Hefei University of Technology
Changyong Liang
Ministry of Education of the People's Republic of China
Journal of Nursing Management
Hefei University of Technology
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Wang et al. (Fri,) studied this question.
synapsesocial.com/papers/6a208f11fdf8ac6477c63ee0 — DOI: https://doi.org/10.1111/jonm.13823
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