Factors of healthcare robot adoption by medical staff in Thai government hospitals
© 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature. The patients increasing number and growing shortage of medical staff are acute problems that face the healthcare industry today. Healthcare robots are being installed to solve this problem, since they have sufficient potential...
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| Format: | Article |
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2020
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| Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/59885 |
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| Institution: | Mahidol University |
| Summary: | © 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature. The patients increasing number and growing shortage of medical staff are acute problems that face the healthcare industry today. Healthcare robots are being installed to solve this problem, since they have sufficient potential to solve the problems. The healthcare robot initiative success is not only based on the executives’ decisions and robot designers but also on medical staff members’ willingness to adopt healthcare robots. Nowadays, there are gaps in our understanding about the evaluation of staff changes in using robots. This study investigated the factors involved in the robots using in Thai government hospitals based on the results of 466 questionnaire respondents. The medical staff was selected randomly for data collection. The Confirmatory factor analysis (CFA) and a structural equation modeling (SEM) are tools used in data analysis. The findings confirmed that all four UTAUT constructs of the study, namely, the facilitating conditions, social influence, effort expectancy, performance expectancy, and concerns about safety, significantly predicted the use of robots (p <.01). Medical practitioners under 35 years of age tended to accept the technology better than their more senior counterparts. The staff’s intentions and facilitation of support played a key role in adopting and using robots. Lack of technical knowledge was perceived as a barrier to technology adoption. The results also indicate a significant negative effect in the relationship between the medical staff’s behavioral intention and barrier/resistance to the healthcare robot using. This study also identifies key factors for medical staff to make acceptance decisions in relation to healthcare robots. |
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