{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:13:31Z","timestamp":1751516011490,"version":"3.41.0"},"reference-count":0,"publisher":"South African Institute of Computer Scientists and Information Technologists","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"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":["S AFR COMP J"],"abstract":"<jats:p>The healthcare sector is progressively recognising the potential of big data analytics to revolutionise patient care, operational efficiency and decision-making processes. However, rural-based hospitals face challenges in embracing big data analytical tools to improve their service delivery to their patients. The adoption of big data analytics will assist rural-based hospitals in predicting and making informed decisions to manage the limited resources that they have and allocate them appropriately. However, there is a lack of literature that explores and identifies the factors that affect the adoption of big data analytics in rural-based hospitals to improve service delivery. This research employs a quantitative research methodology using surveys to identify the factors that affect the adoption of big data analytics in rural-based hospitals. This study found that rural-based hospitals prefer a hybrid method to collect data and it impedes the adoption of big data analytics in rural-based hospitals. Therefore, there is a need to implement modern infrastructure to integrate various data collection methods to promote the adoption of big data analytics in rural-based hospitals.<\/jats:p>","DOI":"10.18489\/sacjv37i1\/17830","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T17:07:50Z","timestamp":1751476070000},"source":"Crossref","is-referenced-by-count":0,"title":["Identifying factors affecting the adoption of big data analytics in South African rural-based hospitals to improve service delivery"],"prefix":"10.18489","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0510-0644","authenticated-orcid":false,"given":"Thifhindulwi Maxwell","family":"Rambau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6287-8829","authenticated-orcid":false,"given":"Willard","family":"Munyoka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-5462","authenticated-orcid":false,"given":"Nkhangweni Lawrence","family":"Mashau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7945","published-online":{"date-parts":[[2025,7,2]]},"container-title":["South African Computer Journal"],"original-title":[],"link":[{"URL":"https:\/\/sacj.org.za\/article\/download\/17830\/24407","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/sacj.org.za\/article\/download\/17830\/24407","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T17:07:50Z","timestamp":1751476070000},"score":1,"resource":{"primary":{"URL":"https:\/\/sacj.org.za\/article\/view\/17830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,7,2]]}},"URL":"https:\/\/doi.org\/10.18489\/sacjv37i1\/17830","relation":{},"ISSN":["2313-7835","1015-7999"],"issn-type":[{"value":"2313-7835","type":"electronic"},{"value":"1015-7999","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7,2]]}}}