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 2021-03-07
Special Issues
Special Issue on Business and Government Applications of Text Mining & Natural Language Processing (NLP) for Societal Benefit
Submission Date: 2021-06-30

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that helps computers to understand, process, and analyze large amounts of natural human language data (Kang et al., 2020). The concepts of AI, machine learning (ML), and human-to-machine interactions have been prevalent for several decades. A growing number of businesses have been using advanced analytics and ML methods for resolving their business concerns, but NLP is emerging as one of the most popular and indispensable technologies for businesses today. NLP and text mining are generally used synchronously for achieving different goals. NLP has the ability to work with all natural human communication variables including text, audio, and video, whereas text mining deals with the analysis of textual datasets to discover novel and useful patterns and trends. The most common business application that concurrently uses NLP and text mining is social media monitoring, where businesses rely on these technologies to understand the sentiments (i.e. mood and the emotions) of the customers by analyzing the user generated data (Kang et al., 2020). Smart use of NLP empowers businesses to gain a competitive edge over their rivals in modern market spaces. According to a recent estimate, around 80% of business data is non-actionable due to being unstructured in nature (Bahja, 2020). As the unstructured data volume overloads several sectors in business and government (e.g. finance, e-commerce, healthcare, hospitality sectors in business, and business classification, request for proposals (RFP), trust and confidence, public comments in government areas), NLP and other AI technologies are becoming increasingly crucial for business growth and survival. Several emerging NLP business applications have the ability to revamp and revitalize struggling businesses, by demystifying obscure statistical business reports into precise actionable data, leading to streamlined action plans and higher revenues in the long run. One of the biggest challenges faced by businesses is to understand human behaviors. NLP and text mining have enabled e-commerce platforms to extract attributes and hence improve product searches to target the right customers. Recently, Amazon claimed to generate 35% of its revenue by providing targeted product recommendations (Bahja, 2020). NLP recommendation algorithms have shifted from traditional keyword paradigms to taking customer’s internet search history, location, context, and personalized affiliations into consideration. These real time insights have aided retailers to personalize product recommendations to an individual customer, to understand how consumers are using their products, while simultaneously helping online buyers to see relevant products matching their requirements (Nguyen et al., 2020). NLP has played a vital role in improving the performance of healthcare systems. ML and NLP are being integrated with traditional healthcare practices, like clinical diagnosis and proposed treatments, creating a digitalized healthcare that is highly intuitive and robust. NLP has proven to be extremely successful in improving the healthcare process by effectively interpreting clinical notes. Data is collected and interpreted from various diagnostic reports, symptoms, EHR, patient discharge summaries, lab reports and doctors’ prescriptions, and presented to medical consultants for a sound decision making (Mandelbaum et al., 2018; Ahsen, Ayvaci and Raghunathan, 2019; Bahja, 2020). The impact of online reviews on businesses has grown significantly during the last few years in several sectors such as e-governance, e-commerce, and hospitality industry (Wu, Lou, et al., 2019; Kang et al., 2020; Lu et al., 2020; Wu et al., 2020). Governments across the world are using text mining to decrease the interaction gap between citizens and the government and improve government services. Governments also collect a huge amount of textual data in the form of applications for permits, website feedback, stakeholder interviews, and social media responses. NLP techniques could help governments better analyze feedback, increase regulatory compliance, and enhance policy analysis – all of which could benefit society (Cuffe et al., 2019, Jung and Suh, 2019; Kang et al., 2020). Another application of NLP technology is to identify fake news propagators. In recent years, fake news created by manipulated images, text, audio, and videos has become a global phenomenon due to its explosive distribution, particularly on social media (Papanastasiou, 2020). Some people or groups often spread fake news through social media platforms to influence elections, initiate propaganda, spread violence, incite riots, or to humiliate others. Several governments and businesses are using NLP, text mining, machine learning, and deep learning techniques to fight the fake news menace and to identify the fake news propagators (Papanastasiou, 2020). The aim of this special issue is to highlight novel and high-quality research in data science and business analytics, and to examine the current and future impact of NLP, text mining, big data analytics, and related technologies including machine learning and deep learning in businesses, government and society. We wish to bridge the gap between managerial and technical perspectives, and to publish articles that make a significant research contribution to NLP and text mining applications in business industries, government and society by taking a strategic point of view on AI. All managerial, technical and strategic perspectives and methods are welcome, including (but not to limited to) strategic, behavioral, statistical and economic analysis approaches. Methodologically, we embrace a variety of methods, including applied research, field experiments, quantitative research, and secondary data analysis. NLP and text mining applications in healthcare sector to mine the EHR records, clinical trials, and clinical notes for predicting patient outcomes NLP applications in healthcare sector to predict early disease diagnosis and treatment Impact of NLP on organizational performance Impacts of NLP on decision-making quality Moral and ethical aspects of NLP NLP and dissemination of knowledge within firms NLP and text mining applications to understand the behaviour of opinion spammers and to identify fraudulent reviewers NLP and text mining applications to detect and fight neural fake news and fake news propagators NLP applications in e-commerce sector to understand customer shopping behaviour, to predict product demand, and to monitor trends for making better marketing strategies NLP applications in retail sector to provide product recommendations to online customers and to improve personalized customer experiences NLP and text mining applications to understand the role of social media platforms for influencing elections Computational analysis of political texts using text mining Applications of text mining and sentiment analysis for predicting box office revenue and movie success NLP applications in cyber-crime prevention NLP applications in fraud detection through claims investigation NLP and text mining applications in contextual advertising NLP applications in market research to find what consumers value most Text mining applications to analyze customer complaints data and identify new product ideals Text mining applications on investment decisions in crowdfunding Text mining applications to identify and analyze job satisfaction factors from online employee reviews NLP and text mining applications to determine user satisfaction in public services Text mining-based decision support system for e-governance Text mining applications to solve government issues and improve regulatory compliance, enhance policy analysis, and reduce operations expenditure
Last updated by Dou Sun in 2021-01-04
Special Issue on VSI on Data Analytics and Decision-Making Systems: Implications of the Global Outbreaks
Submission Date: 2021-07-30

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 VSI 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.
Last updated by Dou Sun in 2021-02-03
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