Empowering deep learning based organizational decision making: A Survey

Abstract

The advent of deep learning has revolutionized the landscape of organizational decision-making by offering powerful tools for data analysis and prediction. In this comprehensive survey, we explore the intersection of deep learning and organizational decision-making, elucidating the theoretical underpinnings, empirical evidence, and practical implications of this synergy. Theoretical foundations and research hypotheses are rigorously examined, providing a solid framework for understanding the role of deep learning models in enhancing decision-making processes. We delve into the systematic survey, which encompasses a wide spectrum of applications across various industries and domains, showcasing how deep learning empowers decision support systems, augments data-driven decision-making, and refines decision-making frameworks. Drawing inspiration from the Egyptian Vision 2030, we explore the implications of deep learning-based decision-making on national development strategies and policy implementation. Our analysis sheds light on the transformative potential of these technologies, offering insights into how organizations, particularly in Egypt, can harness these advancements to achieve their developmental goals. Finally, we outline future directions in this field, highlighting emerging trends, technological advancements, and potential areas for further research. As the digital age continues to reshape the landscape of decision-making, this survey serves as a valuable resource for researchers, policymakers, and practitioners seeking to leverage deep learning for empowered, data-driven, and informed organizational decisions.