Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (14:45:15 CEST)
How to cite:
Salem, A.M.; Eyupoglu, S.Z.; Ma’aitah, M.K. Navigating Digital Government Transformation: The Influence of Machine Learning on Rational Decision-Making within E-government. Preprints2024, 2024040539. https://doi.org/10.20944/preprints202404.0539.v1
Salem, A.M.; Eyupoglu, S.Z.; Ma’aitah, M.K. Navigating Digital Government Transformation: The Influence of Machine Learning on Rational Decision-Making within E-government. Preprints 2024, 2024040539. https://doi.org/10.20944/preprints202404.0539.v1
Salem, A.M.; Eyupoglu, S.Z.; Ma’aitah, M.K. Navigating Digital Government Transformation: The Influence of Machine Learning on Rational Decision-Making within E-government. Preprints2024, 2024040539. https://doi.org/10.20944/preprints202404.0539.v1
APA Style
Salem, A.M., Eyupoglu, S.Z., & Ma’aitah, M.K. (2024). Navigating Digital Government Transformation: The Influence of Machine Learning on Rational Decision-Making within E-government. Preprints. https://doi.org/10.20944/preprints202404.0539.v1
Chicago/Turabian Style
Salem, A.M., Serife Zihni Eyupoglu and Mohammad Khaleel Ma’aitah. 2024 "Navigating Digital Government Transformation: The Influence of Machine Learning on Rational Decision-Making within E-government" Preprints. https://doi.org/10.20944/preprints202404.0539.v1
Abstract
In the field of business environments, the integration of novel artificial intelligence (AI) techniques has been identified in the literature and theoretical discourse as an essential catalyst for enhancing the decision-making processes. In the contemporary landscape of digital transformation, the application of these techniques becomes essential, despite encountering various hurdles that impede their seamless integration into the rational decision-making process within e-government, which constitutes a critical component of digital government initiatives. This study explores whether similar advancements can be observed within the e-government sector and investigates the impact of using machine learning (ML) as a technique of artificial intelligence on rational decision-making (RDM). Employing an empirical research approach, the study utilized a quantitative methodology, relying on an electronically structured questionnaire survey administered to 163 employees in the e-government sector in Jordan through purposive random sampling. Data analysis was conducted using the SPSS v25 program, complemented by a media-tion analysis using AMOS v23 software. The research findings revealed a significant contribution of using machine learning to enhance rational decision-making and trust levels, with trust positively impacting RDM. Trust was identified as a beneficial mediator in the relationship between machine learning and rational decision-making. Despite certain limitations, this study's outcomes offer substantial insights for researchers and government policy-makers alike, contributing to the advancement of sustainable practices in the e-government domain.
Keywords
Machine Learning; Artificial intelligence; Rational Decision-Making; Trust; Digital Government; e-Government; Jordan
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
