{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:41:56Z","timestamp":1653482516011},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>Mental workload and technology acceptance are relevant factors that relate to use behavior and performance. Studies show a potential moderating effect of mental workload on predictors of technology acceptance. Aim of this study was the investigation of predictors of technology acceptance (UTAUT) related to clinical information systems and their relation to mental workload. This quasi-experimental study with 48 participants used the following measures: NASA TLX and UTAUT questionnaire. Participants had to perform three tasks on a clinical information system as well as four task-levels of the n-back task with increasing difficulty. Analyses show a high level of technology acceptance (M=3.82, SD=.76) and confirm performance expectancy as the most relevant predictor of behavioral intention (\u03b2=.48, p&lt;.001). A linear regression showed that a high level of mental workload has an influence on performance expectancy (F1,46=8.438, p&lt;.05). The study shows an influence of mental workload on acceptance, the strength and role of which (e.g. moderation) needs to be further investigated, especially in the context of other determinants.<\/jats:p>","DOI":"10.3233\/shti220576","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:16Z","timestamp":1653481036000},"source":"Crossref","is-referenced-by-count":0,"title":["How Does Mental Workload Influence the Adoption of Clinical Information Systems: An Exploratory Study"],"prefix":"10.3233","author":[{"given":"Lisanne","family":"Kremer","sequence":"first","affiliation":[{"name":"Faculty of Health Care, Niederrhein University of Applied Sciences, Krefeld, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann-Kathrin","family":"Schwarz","sequence":"additional","affiliation":[{"name":"Faculty of Health Care, Niederrhein University of Applied Sciences, Krefeld, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rainer","family":"R\u00f6hrig","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, Medical Faculty of RWTH Aachen University, Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Breil","sequence":"additional","affiliation":[{"name":"Faculty of Health Care, Niederrhein University of Applied Sciences, Krefeld, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220576","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:17Z","timestamp":1653481037000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220576","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}