Investigating Employee Acceptance of Automated Technologies in Healthcare: A Case Study on Automated Bed Washing Systems

Lukas Niemann Frandsen, Hans Henrik Lundvang, Máté Hidegföldi, Justina Karpavičė*

*Corresponding author for this work

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

The transition from manual to automated processes has become a central challenge that hinders the successful adoption of technologies, resulting in heightened resistance and anxiety among employees regarding technological changes. Therefore, this research aims to investigate individuals’ perceptions of automated technologies and identify variables impacting technology adoption. The research is based on a cross-sectional case study that examined the employees’ acceptance of the automated bed washer at three regional hospitals within the Midtjylland region in Denmark. This study extends the TAM by incorporating facilitating conditions, self-efficacy, social influence, anxiety, and trust. A mixed-method approach was applied for data collection, including insights from observations and results from interviews, complemented with a TAM-based questionnaire. Drawing from the results, facilitating conditions, self-efficacy and trust were identified as the key determinants impacting PU and PEOU.

Original languageEnglish
Title of host publicationHCI International 2024 Posters. HCII 2024
EditorsConstantine Stephanidis, Margherita Antona, Stavroula Ntoa, Gavriel Salvendy
Number of pages13
Place of publicationCham
PublisherSpringer
Publication date2024
Pages321-333
ISBN (Print)9783031619656
DOIs
Publication statusPublished - 2024
Event26th International Conference on Human-Computer Interaction, HCII 2024 - Washington, United States
Duration: 29 Jun 20244 Jul 2024

Conference

Conference26th International Conference on Human-Computer Interaction, HCII 2024
Country/TerritoryUnited States
CityWashington
Period29/06/202404/07/2024
SeriesCommunications in Computer and Information Science
Volume2119 CCIS
ISSN1865-0929

Keywords

  • Automated Technologies
  • Employees Acceptance
  • Healthcare
  • Technology Acceptance Model

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