Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process |
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| Institution: | 1. Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia;2. Al Balqa’ Applied University, Amman College for Financial & Managerial Science, Jordan;4. School of Management, University of Bradford, UK;5. Emerging Markets Research Centre (EMaRC), School of Management, Room #323, Swansea University, Bay Campus, Fabian Bay, Swansea, SA1 8EN Wales, UK;6. Department of Management, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra, India;1. Department of Organization, University of Zagreb, Faculty of Organization and Informatics Vara?din, Pavlinska 2, 42 000 Vara?din, Croatia;2. College of Arts and Sciences, Carlow University, 3333 Fifth Avenue, Pittsburgh, PA 15213, United States;1. Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, Brazil;2. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil;1. Associate Professor and Director, School of Planning, Public Policy, and Management, University of Oregon, 263 Hendricks Hall, 1209 University of Oregon, Eugene, OR 97403, United States of America;2. Associate Professor, School of Urban Affairs, Cleveland State University, Cleveland, OH 44115, United States of America;1. Aston Business School, Aston University, Birmingham B4 7ET, UK;2. College of Arts and Sciences, University of Wisconsin, USA;3. Birmingham Business School, The University of Birmingham, Edgbaston, Birmingham B14 2TY, UK;4. Division of Technology Services, University of Wisconsin – River Falls, 410 S. 3rd Street, River Falls, WI 54022, USA;5. Department of Physics and Mathematics, University of Hull, Hull HU6 7RX, UK;1. Mid Sweden University, Faculty of Science, Technology and Media, Department of Information systems and Technology, Forum for Digitalization, Holmgatan 10, Sundsvall 851 70, Sweden.;2. University of South Africa, Department of Information Science, Preller Street, Muckleneuk Ridge, Pretoria. |
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| Abstract: | With 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. |
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