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Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model
Mini Dissertation (MBA)--University of Pretoria, 2020.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613447282229248 |
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| access_status_str | Open Access |
| author2 | Marks, Jonathan |
| author_browse | Marks, Jonathan |
| author_facet | Marks, Jonathan |
| collection | Thesis |
| dc_rights_str_mv | © 2020 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MBA)--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/75255 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:17.608Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/75255 Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model Marks, Jonathan ichelp@gibs.co.za Johnson, Robyn UCTD Mini Dissertation (MBA)--University of Pretoria, 2020. The Electronic Health Record (EHR) has the potential to promote understanding or awareness of healthcare knowledge among patients and healthcare providers to facilitate collaboration between various key stakeholders to improve the quality of healthcare. The technology is also expected to provide global health communities with benefits, from improved health outcomes, reduced medical errors, and a reduction in healthcare expenditure. These benefits will not be realised unless the key stakeholders and consumers of the technology are willing to accept, adopt, and use the EHR. The purpose of this study is to identify crucial factors influencing clinicians’ adoption of the EHR in South Africa’s healthcare system by expanding the Unified Theory of Acceptance and Use of Technology (UTAUT) model to include the additional constructs Resistance to Change and Attitude Towards Organisational Change. A cross-sectional online questionnaire was used to gather data from 168 clinicians employed at various private and public healthcare facilities across South Africa. Performance expectancy and facilitating conditions were found to have a statistically significant positive impact on clinicians’ behavioural intention, whereas effort expectancy and social influence had no similar result. Resistance to change had a statistically significant negative influence on behavioural intention, and a negative attitude towards organisational change positively influenced resistance to change. The findings of this study can be used by government bodies, the private sector and technology vendors to better understand clinicians’ perceptions of the EHR in order to guide policy and effect implementation strategies accordingly. Gordon Institute of Business Science (GIBS) MBA Unrestricted 2020-07-15T13:09:11Z 2020-07-15T13:09:11Z 2020/09/30 2020 Mini Dissertation Johnson, R 2020, Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model, MBA Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/75255> http://hdl.handle.net/2263/75255 en © 2020 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title | Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title_full | Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title_fullStr | Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title_full_unstemmed | Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title_short | Predicting clinicians’ intentions towards the electronic health record (EHR) : an extended UTAUT model |
| title_sort | predicting clinicians intentions towards the electronic health record ehr an extended utaut model |
| topic | UCTD |
| url | http://hdl.handle.net/2263/75255 |