DECISION SUPPORT SYSTEMS

Scope & Guideline

Transforming Insights into Actionable Solutions

Introduction

Delve into the academic richness of DECISION SUPPORT SYSTEMS with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0167-9236
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1985 to 2024
AbbreviationDECIS SUPPORT SYST / Decis. Support Syst.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'Decision Support Systems' focuses on the intersection of decision-making processes and technological advancements, particularly in the realms of data analytics, artificial intelligence, and systems integration. It aims to facilitate better decision-making through innovative methodologies and frameworks that address complex problems across various domains.
  1. Decision-Making Frameworks and Models:
    The journal emphasizes the development and application of decision-making frameworks and models that enhance the quality and effectiveness of decisions in uncertain environments. This includes multi-criteria decision-making, predictive modeling, and risk assessment methodologies.
  2. Integration of AI and Machine Learning:
    A significant focus is on integrating artificial intelligence and machine learning techniques into decision support systems to improve predictive capabilities and automate decision processes.
  3. Data Analytics and Visualization:
    The journal covers advancements in data analytics and visualization techniques that help in understanding complex datasets, enabling more informed decision-making.
  4. Behavioral Insights in Decision-Making:
    Research exploring the behavioral aspects of decision-making, including the influence of social dynamics, emotions, and cognitive biases on decision outcomes, is a core area of interest.
  5. Sector-Specific Applications:
    The journal also highlights applications of decision support systems across various sectors, including healthcare, e-commerce, finance, and public policy, showcasing how tailored solutions can address specific industry challenges.
The journal has seen a rise in interest in several emerging themes, reflecting current trends in technology and societal needs. These themes indicate the direction in which research and practical applications are heading, highlighting the importance of adaptability and innovation in decision support systems.
  1. Ethical AI and Responsible Decision-Making:
    There is an increasing emphasis on ethical considerations in AI and decision-making processes, focusing on transparency, fairness, and accountability in systems that influence critical decisions.
  2. Integration of Blockchain Technology:
    Research on the application of blockchain technology in decision support systems is emerging, particularly in enhancing trust and security in data transactions and decision processes.
  3. Dynamic and Adaptive Decision Systems:
    The trend towards dynamic and adaptive decision systems is gaining momentum, where systems evolve based on real-time data and changing conditions, reflecting the need for flexibility in decision-making.
  4. Human-AI Collaboration:
    A notable increase in research regarding human-AI collaboration in decision-making processes is evident, exploring how AI can augment human capabilities rather than replace them.
  5. Sustainability and Resilience in Decision-Making:
    There is a growing trend towards incorporating sustainability and resilience into decision-making frameworks, addressing global challenges such as climate change and resource management.

Declining or Waning

As the field of decision support systems evolves, certain themes that were once prominent are showing a decline in focus. This shift may indicate changing priorities in research and application areas, as well as the influence of emerging technologies and methodologies.
  1. Traditional Statistical Methods:
    There appears to be a waning interest in traditional statistical methods for decision-making, as more advanced machine learning and AI techniques gain traction for their predictive capabilities.
  2. Standalone Decision Support Systems:
    The focus has shifted away from standalone decision support systems to integrated systems that combine multiple technologies, such as AI, IoT, and big data, reflecting a trend toward holistic solutions.
  3. Basic Data Collection Techniques:
    Research on basic data collection methodologies is becoming less prominent, as there is a growing emphasis on advanced data analytics and real-time data processing techniques.
  4. Generic Decision-Making Models:
    There is a decline in research focused on generic decision-making models without context-specific applications, as the demand for tailored solutions that address unique challenges in particular sectors increases.

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