Journal Information
Decision Support Systems (DSS)
Impact Factor:

Call For Papers
Decision Support Systems welcomes contributions on the concepts and operational basis for DSSs, techniques for implementing and evaluating DSSs, DSS experiences, and related studies. In treating DSS topics, manuscripts may delve into, draw-on, or expand such diverse areas as artificial intelligence, cognitive science, computer supported cooperative work, data base management, decision theory, economics, linguistics, management science, mathematical modeling, operations management psychology, user interface management systems, and others. The common thread of articles published in the journal will be their relevance to theoretical, technical DSS issues.

Authors planning to submit papers to the journal should ensure that their work is relevant to the topics normally considered to be part of the field of decision support systems.

The Journal's research papers tend to fall into the following six topic departments:

1. DSS Foundations e.g. DSS principles, concepts, and theories; frameworks, formal languages, and methods for DSS research; tutorials about the nature of DSS; assessments of the DSS field.

2. DSS Development-Functionality e.g. methods, tools, and techniques for developing the underlying functional aspects of a DSS; solver/model management; data management in DSSs; rule management and AI in DSSs; coordinating a DSS's functionality within its user interface.

3. DSS Development-Interfaces e.g. methods, tools, and techniques for developing the overt user interface of a DSS; managing linguistic, presentation, and user knowledge in a DSS; DSS help facilities; coordinating a DSS's interface events with its functionality events.

4. DSS Impacts and Evaluation e.g. DSS economics; DSS measurement; DSS impacts on individual users, multiparticipant users, organizations, and societies; evaluating/justifying DSSs.

5. DSS Reference Studies e.g. reference discipline tutorials for DSS researchers; emerging technologies relevant to DSS characteristics or DSS development; related studies on such topics as communication support systems, computer supported cooperative work, negotiation support systems, research support systems, task support systems.

6. DSS Experiences, Management, and Education e.g. experiences in developing or operating DSSs; systems solutions to specific decision support needs; approaches to managing DSSs; DSS instruction/training approaches.
Last updated by Dou Sun in 2019-11-24
Special Issues
Submission Date: 2020-07-15

A main application of data science is to support and improve decision-making processes. Today, companies heavily invest in developing analytical and technological capabilities to enable the collection, storage and analysis of data. Their data science roadmap typically contains applications falling under descriptive, predictive and prescriptive analytics. Many businesses rely on advanced statistical and machine learning algorithms to support operational decision making across various business domains and processes, including credit risk, customer retention, human resource management, finance, fraud detection, inventory management, fleet management, and digital marketing. However, investments in improving data science capabilities are not always reflected in additional revenues or decreased costs. Decision makers are often reluctant to rely on statistical or machine learning models if it is not immediately clear how their outcomes are obtained. Decision makers contrast the data science outcomes with their own business logic and intuitions, while underlying drivers help in personalizing their decision-making strategies. Nowadays, companies are collecting a wide variety of information resulting in both high dimensional data in terms of the number of observations and variables, and a combination of structured and unstructured like text, audio, and image data. The prevalent focus on the data and technology has resulted in a strong emphasis on the data science practice itself, while neglecting the interpretability, explainability and actionability of the resulting outcomes to the business users. Still today, interpretability is often a key prerequisite for management to trust and deploy data science models and the lack of it could lead to diminishing practical relevance for business decision makers. Current streams of research as reported in the literature mainly focus on investigating the beneficial impact of data preprocessing methods, new data sources like text or audio, sophisticated and scalable algorithmic developments or novel statistical evaluation metrics. Although these innovations are highly relevant in the front-end of the data science pipeline, we see a practical need and challenging opportunities for more research in bringing the outcomes of the data science pipeline closer to the needs of business decision makers. Therefore this call for submissions to a special issue on novel research on enhanced decision making through interpretable data science. Below is an indicative list of research topics of interest: innovative visualization methods of preprocessing, processing and post-processing results experimental field tests and applications of interpretable machine learning methods fordecision making Innovative approaches for opening black box models and/or applications The development of new business-centric evaluation metrics The incorporation of (human) domain knowledge in preprocessing, processing or postprocessing methods The alignment of analytical models and operational decision processes Informed feature engineering or feature learning Aspects of the data collection process that affect interpretation
Last updated by Dou Sun in 2020-03-27
Special Issue on Decision Support Systems on Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks
Submission Date: 2020-12-30

The recent outbreak of coronavirus (Covid-19) reminded the world of the devastating impact of epidemic and pandemic outbreaks. The outbreak hit China hard, and continues to spread around the globe. Epidemics can be rapidly spread by a group of infectious agents through several methods, threatening the health of a large number of people in a very short time (Medina 2018). The threat to global healthcare from emerging and reemerging epidemics remains critical, and the capacity of pandemic preparedness to confront such threats needs to be strengthened. There is a need for research in the effectiveness of preparedness systems, and in epidemic monitoring to help stabilize economic activities and reduce systematic risks. This would be greatly aided by high-performance decision support systems to keep track of verified events with known or possible impacts on public health or financial market, providing useful data analytic capacities and suggesting proper and efficient reactions. Data Analytics and Artificial Intelligence (AI) based decision support technologies has also shown its potential in the analysis of epidemic diseases, including effectively pre-empting, preventing and combating the threats of infectious disease epidemic; facilitating understanding of health-seeking behaviors and control of public emotions during epidemics (Ginsberg et al., 2009). Today we have a great deal of health data, but utilizing this data in an effective manner is highly challenging. AI offers new tools for public health practitioners and policy makers to revolutionize healthcare and population health through focused, context-specific interventions (Wu et al. 2016, Nam and Seong. 2019, Wu et al. 2019, Chaudhuri and Bose, 2020, Müller-Peltzer et al. 2020, Liu et al. 2020). This call for papers of Decision Support Systems on the theme of “Data Analytics and Decision-Making Systems” is intended to publish insights and viewpoints from scholars regarding risk and data analytics in healthcare decision systems. Authors are encouraged to submit their articles addressing the theme of this special issue which are main focus on decision support system in epidemic and pandemic healthcare systems and cases. Topics of Interest: The special issue aims to address the following, but not limited to, potential topics in decision support system and data analytics and their applications: AI-based behaviors pattern cognition of epidemic outbreak Social-networks analysis applied to healthcare systems Data Analytics and recommendation system Decision making in emergence healthcare Covid-19 effect on operations and financial information systems Text and sentiment analysis of pandemic Innovative decision-making approaches/methods and technologies applied to epidemic control Information systems impact on health risk propagation control Intelligent system for emergence resource allocation of healthcare systems Emergency workflow management during epidemics and pandemics Other topics related to data analytics and decision support as applied to epidemics and pandemics
Last updated by Dou Sun in 2020-06-25
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