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.

Special Issue on Empowering Bright Internet and Bright Artificial Intelligence
Día de Entrega: 2024-07-31

The rapid evolution of technology with the established interconnectedness of our global society has led us to an unprecedented era of opportunities. Concurrently, the negative aspects of information and communication technologies (ICT) are also on the rise. More recently, as Artificial Intelligence (AI) systems are becoming prevalent in our daily lives and organizations' processes, the topic of AI is a subject of intense debate, encompassing both its potential benefits and negative consequences it might inflict upon individuals, organizations, society, and governance. Accordingly, two prominent issues that require intensive research are the intersection between cybersecurity and AI. CIOs in the United States regard cybersecurity and privacy as the most essential organizational issue in the last ten consecutive years. In this respect, previous studies have shed light on the dual nature of AI, demonstrating its capacity to yield positive outcomes alongside detrimental impacts within organizational contexts (Mikalef et al., 2022). However, there is no promising vision of mitigating the cybersecurity community as vaccines can preventively overcome an impending AI-driven pandemic.

AI has become popular since AlphaGo won the human champion in 2016. The recent advancements in generative AI (GAI), such as OpenAI's ChatGPT and Google's Bard, have sparked the promise of revolutionizing many management processes. It appears that Large Language Models and GAI have demonstrated their potential and give the high expectation of a revolutionary change in human intellectual jobs in many aspects and various domains. For example, it will provide positive value for human society regarding automating tasks that humans cannot perform well and economically. However, it will also give the adverse threats of deep fake and changing the robot-manipulated weapons of crimes and wars.

Given this context, this special issue pays attention to the Principles of Bright Internet, which aims to preventively mitigate the threat sources from the origins (Lee, 2015; Lee et al., 2020). For instance, AI and intelligent models can be used to build spam filtering models for inbound and outbound spam mail. This can be regarded as AI-enabled Bright Internet. Similarly, we can look at AI with the Principle of Bright Internet: Origin Responsibility, Deliverer Responsibility, Identifiable Anonymity, Privacy Protection, and Global Collaboration to prevent such risks (Lee 2015; Lee et al. 2018; Lee et al. 2020). Note that let us call this perspective of research Bright AI, but the themes of Bright AI do not intend to limit these principles and perspectives, although it can be a useful framework. From a comprehensive view, it can cover relevant high-level principles, such as fairness, transparency, accountability, social responsibility, and privacy, to ensure the responsible development and execution of AI systems (De Cremer 2020; Mikalef et al., 2022). 

Special Issue on Generative AI: Transforming Human, Business, and Organizational Decision Making
Día de Entrega: 2024-11-30

Generative Artificial Intelligence (GenAI) represents the next level of machine learning by going beyond recognizing patterns and making inferences to generating new content that mimics the training data of a variety of forms including text, audio, image, video, music, artwork, simulation, and even programming codes [1,5,9]. Hence, GenAI uses algorithms to enable users to generate new content based on a variety of inputs (prompts) that can be in multiple forms such as text, audio, image, video, and musical notes [4]. Given that prompts serve as the bridge between human intent and machine understanding, the ability to generate and use effective prompts has become an essential skill for users to maximize the outcomes of the human-GenAI interaction.

The rapid rise of GenAI technologies has raised both unprecedented opportunities and challenges to human, business, and organizational decision making for the future of work [4,7]. The impact of GenAI models, such as ChatGPT, is far-reaching, and its applications keep growing, ranging from large language models (LLMs) to learning from available data to predict the response of a target group to advertisements and marketing campaigns to creating new advertisements and marketing campaigns for specific target groups, and from generating a travel itinerary to clinical decision support in screening, prevention, and treatment [3]. Gartner identified GenAI as one of the most impactful and rapidly evolving technologies in the productivity revolution in its report on Emerging Technologies and Trends Impact Radar for 2022 [6]. The Gartner report also predicts that by 2026, more than 90% of IT operations management vendors will have embedded GenAI capabilities in their products and/or services, up from less than 5% in 2023. On the one hand, GenAI tools or systems have the potential to transform the way people learn, create multimedia content, perform tasks, and make decisions [2]. They can help simplify organizational tasks and operations with efficiency across a variety of use cases. On the other hand, the newfound capability of GenAI also raises risks and concerns about misinformation, plagiarism, copyright infringements, harmful or offensive content, biases, deepfakes, etc. that may result in significant negative impact on individuals, organizations, and society at large [1,4,5,8]. Hence, responsible, trustworthy, and ethical GenAI regulation and content moderation need to be in place.

GenAI can transform or revolutionize decision making in a variety of ways. For example, the predictive and generative prowess of GenAI enables not only more accurate forecasts but also accelerates data-driven design and decision making based on trends and discoveries from large datasets. GenAI can help generate innovative solutions to problems and organizational designs that are too complex for traditional analytical methods and reduce repetitive manual processes by automating content creation tasks to enable real-time, on-the-fly decision making that can produce remarkable outcomes.