Journal of Decision Systems Taylor & Francis Impact Factor

Journal of Decision Systems - Taylor and Francis | 2024 Impact Factor: 4.3 | Cite Score:6.3 | Q1

Journal of Decision Systems With Cite Score

Cite Score and Journal Rank of Journal of Decision Systems

About: The Journal of Decision Systems (JDS) is a peer-reviewed journal dedicated to advancing the study and application of decision systems across various fields. It covers a wide range of topics related to decision-making processes, methodologies, tools, and technologies used to support and enhance decision-making in complex environments. The journal aims to publish high-quality research that contributes to the development of innovative decision support systems and methodologies.
Objective:
The primary objective of JDS is to advance the field of decision systems by disseminating significant research findings, theoretical advancements, and practical applications. The journal seeks to provide a platform for researchers, practitioners, and policymakers to share their work on decision support tools, systems, and processes. JDS aims to improve the effectiveness of decision-making across various domains through the publication of cutting-edge research and insights.
Interdisciplinary Approach:
JDS adopts an interdisciplinary approach, inviting contributions from fields such as operations research, management science, computer science, economics, and artificial intelligence. This approach ensures a comprehensive exploration of decision systems topics, integrating diverse perspectives and methodologies. By promoting interdisciplinary research, the journal aims to address complex decision-making challenges and develop integrated solutions that enhance decision support.
Impact:
The journal has a significant impact on both academic research and practical applications in the field of decision systems. It is widely cited by researchers, practitioners, and decision-makers interested in the latest developments and advancements in decision support systems. The research published in JDS contributes to the development of new tools, methodologies, and frameworks that improve decision-making processes. The journal also serves as a valuable resource for professionals involved in designing, implementing, and evaluating decision systems.
Significance:
JDS plays a crucial role in advancing the study and practice of decision systems by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative decision support tools and methodologies that address current and future challenges in decision-making. The journals interdisciplinary focus and commitment to excellence make it an essential resource for anyone involved in decision systems research, development, and application. Through its rigorous scholarship and broad coverage, JDS helps shape the future of decision systems.

Journal Home: Journal Homepage

Editor-in-Chief: Ciara Heavin

Scope: The Journal of Decision Systems focuses on the development and application of decision support systems and methodologies across various domains. Its scope includes, but is not limited to:
Decision Support Systems (DSS): Research on the design, implementation, and evaluation of systems that aid decision-making processes in organizations, including computer-based systems and tools.
Decision Theory: Studies on the theoretical foundations of decision-making, including decision analysis, game theory, and decision-making under uncertainty.
Optimization: Exploration of optimization techniques and methodologies used to improve decision-making, including linear programming, integer programming, and heuristic methods.
Data Analytics and Visualization: Research on methods and tools for analyzing and visualizing data to support decision-making, including data mining, statistical analysis, and visual analytics.
Modeling and Simulation: Studies on modeling and simulation techniques used to evaluate and support decision-making processes, including system dynamics, agent-based modeling, and scenario analysis.
Risk Management: Exploration of methods for assessing and managing risks in decision-making, including risk analysis, risk modeling, and mitigation strategies.
Knowledge Management: Research on the role of knowledge management in decision support, including knowledge capture, storage, and dissemination.
Artificial Intelligence and Machine Learning: Studies on the application of AI and machine learning techniques in decision support systems, including predictive modeling, pattern recognition, and automated decision-making.
Human Factors and Decision-Making: Exploration of human factors that influence decision-making, including cognitive biases, decision fatigue, and user interface design.
Multi-Criteria Decision Analysis (MCDA): Research on techniques for evaluating and prioritizing multiple criteria in decision-making, including AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and others.
Decision Support in Specific Domains: Studies on the application of decision support systems in specific domains such as healthcare, finance, logistics, and environmental management.
Decision-Making Processes: Exploration of decision-making processes within organizations, including decision-making frameworks, collaborative decision-making, and decision support workflows.
Computational Intelligence: Research on computational methods for decision support, including genetic algorithms, neural networks, and fuzzy systems.