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dc.contributor.authorNasseef, O.A.
dc.contributor.authorBaabdullah, A.M.
dc.contributor.authorAlalwan, A.A.
dc.contributor.authorLal, Banita
dc.contributor.authorDwivedi, Y.K.
dc.date.accessioned2021-09-07T10:50:28Z
dc.date.accessioned2021-09-30T08:25:57Z
dc.date.available2021-09-07T10:50:28Z
dc.date.available2021-09-30T08:25:57Z
dc.date.issued2022-10
dc.identifier.citationNasseef OA, Baabdullah AM, Alalwan AA et al (2022) Artificial Intelligence-based Public Healthcare Systems: G2G Knowledge-based Exchange to Enhance the Decision-making Process. Government Information Quarterly. 39(4): 101618.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18594
dc.descriptionYesen_US
dc.description.abstractWith the rapid evolution of data over the last few years, many new technologies have arisen with artificial intelligent (AI) technologies at the top. Artificial intelligence (AI), with its infinite power, holds the potential to transform patient healthcare. Given the gaps revealed by the 2020 COVID-19 pandemic in healthcare systems, this research investigates the effects of using an artificial intelligence-driven public healthcare framework to enhance the decision-making process using an extended model of Shaft and Vessey (2006) cognitive fit model in healthcare organizations in Saudi Arabia. The model was validated based on empirical data collected using an online questionnaire distributed to healthcare organizations in Saudi Arabia. The main sample participants were healthcare CEOs, senior managers/managers, doctors, nurses, and other relevant healthcare practitioners under the MoH involved in the decision-making process relating to COVID-19. The measurement model was validated using SEM analyses. Empirical results largely supported the conceptual model proposed as all research hypotheses are significantly approved. This study makes several theoretical contributions. For example, it expands the theoretical horizon of Shaft and Vessey's (2006) CFT by considering new mechanisms, such as the inclusion of G2G Knowledge-based Exchange in addition to the moderation effect of Experience-based decision-making (EDBM) for enhancing the decision-making process related to the COVID-19 pandemic. More discussion regarding research limitations and future research directions are provided as well at the end of this study.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.giq.2021.101618en_US
dc.rights© 2022 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectAIen_US
dc.subjectPublic healthcareen_US
dc.subjectCognitive fit modelen_US
dc.subjectG2G knowledge-based exchangeen_US
dc.subjectExperience-based decision-makingen_US
dc.subjectDecision-makingen_US
dc.titleArtificial Intelligence-based Public Healthcare Systems: G2G Knowledge-based Exchange to Enhance the Decision-making Processen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-07-30
dc.date.application2021-08-09
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2021-09-07T10:50:30Z
refterms.dateFOA2021-09-30T08:26:17Z
dc.openaccess.statusopenAccessen_US


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