International Journal of Decision Support System Technology

Scope & Guideline

Fostering Collaboration for Enhanced Decision-Making.

Introduction

Welcome to the International Journal of Decision Support System Technology information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of International Journal of Decision Support System Technology, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1941-6296
PublisherIGI GLOBAL
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2009 to 2024
AbbreviationINT J DECIS SUPPORT / Int. AJ. Decis. Support Syst. Technol.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240

Aims and Scopes

The International Journal of Decision Support System Technology focuses on advancing the field of decision support systems (DSS) through innovative methodologies, applications, and theoretical frameworks. The journal emphasizes the integration of various decision-making approaches, including multi-criteria decision-making (MCDM), fuzzy logic, machine learning, and collaborative decision-making, to enhance the effectiveness and efficiency of decision processes across diverse domains.
  1. Multi-Criteria Decision-Making (MCDM):
    The journal frequently publishes research on MCDM techniques, exploring various models and frameworks to assist decision-makers in selecting the best alternatives based on multiple conflicting criteria.
  2. Fuzzy Logic and Intuitionistic Fuzzy Measures:
    A significant focus is placed on fuzzy logic methodologies and intuitionistic fuzzy measures, which are employed to handle uncertainty and vagueness in decision-making processes.
  3. Collaborative Decision-Making:
    Research on collaborative decision-making processes, particularly in emergency and complex scenarios, is prominent, showcasing the importance of involving multiple stakeholders in making informed decisions.
  4. Machine Learning Applications in Decision Support:
    The incorporation of machine learning techniques to enhance decision-making capabilities is a notable area of interest, particularly for predictive analytics and optimization in various sectors.
  5. Sustainability and Efficiency in Decision-Making:
    The journal emphasizes themes related to sustainability and efficiency in decision-making, particularly in industrial and environmental contexts, reflecting the growing importance of these issues in contemporary research.
Recent publications in the journal reflect a shift towards emerging themes that resonate with current technological advancements and societal needs. These trends indicate a growing interest in integrating innovative methodologies and addressing contemporary challenges through decision support systems.
  1. Integration of Machine Learning in Decision Support:
    There is a significant uptick in research utilizing machine learning algorithms to enhance decision support systems, particularly for predictive modeling and real-time decision-making applications.
  2. Sustainability and Environmental Decision-Making:
    Emerging themes around sustainability, particularly in industrial applications and urban planning, are gaining traction, reflecting a broader societal push towards environmentally responsible decision-making.
  3. Advanced Fuzzy Logic Techniques:
    The use of advanced fuzzy logic techniques, including intuitionistic fuzzy measures and hybrid approaches, is increasingly prominent, showcasing a trend towards more nuanced decision-making frameworks.
  4. Healthcare Decision Support Systems:
    A growing focus on healthcare-related decision support systems is evident, particularly in the context of pandemic responses and chronic disease management, highlighting the importance of DSS in public health.
  5. Collaboration in Decision-Making Processes:
    Research on collaborative decision-making frameworks is on the rise, emphasizing the importance of stakeholder engagement and collective intelligence in complex decision environments.

Declining or Waning

While the journal continues to explore a wide array of themes, certain areas have shown a decline in prominence, indicating a shift in focus towards more contemporary topics that align with current technological and societal trends.
  1. Traditional Decision Support Systems (DSS) without Advanced Techniques:
    Research centered solely on conventional DSS without the integration of advanced methodologies like machine learning or fuzzy logic appears to be decreasing, as the field evolves towards more sophisticated approaches.
  2. General Optimization Techniques:
    There is a noticeable decline in publications focusing on generic optimization techniques that do not incorporate specific decision-making frameworks or real-world applications, suggesting a move towards more tailored and context-specific methodologies.
  3. Static Decision-Making Models:
    The interest in static decision-making models that do not account for dynamic environments or changing variables is waning, as researchers increasingly recognize the need for adaptive and flexible decision support systems.
  4. Narrowly Focused Case Studies:
    The frequency of narrowly focused case studies lacking broader implications or theoretical contributions has diminished, as the journal seeks to promote research with wider applicability and relevance.

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