EURO Working Group on DSS

Managerial tasks carry latent needs for support to do a better job; classical DSS had at its core the approach to support, not replace. We worked out a DSS called Woodstrat for strategic planning and management and could verify—in full-scale implementation—the DSS characteristics Sprague worked out and most of the DSS benefits Keen had found. We also found that a relevant and useful DSS could help “self-confident professionals” to back away from predictions on future demand and competition that could not find support in facts. The digital disruption of the 2010s brought big data and the need for decision-making in almost real time. It also introduced analytics and faster, more effective algorithms developed in computational intelligence. The road map for DSS for the 2020s points to digital coaching systems that adapt to the cognitive levels of the users.

This chapter uses a bibliometric approach to examine the growth of and changes in the Decision Support Systems (DSS) field over the 30 years from 1990 to 2019. Bibliographic databases such as Web of Science (WOS) provide valuable information on academic disciplines as they contain both the articles published and the articles cited. The changing disciplinary balance in the DSS field is indicated by the topics of the articles published, and the disciplinary categorisation of the journals where they are published. The citation links of these papers illustrate the intellectual influences on the DSS field. Network analysis of the bibliographic network allows the identification of key papers, authors, and journals. We identify important papers and concepts within the period and identify when these concepts subsequently became less important.

A review of Decision Support Systems (DSS) research shows technology and DSS evolve in a synchronized fashion. As new technological tools are introduced, researchers leverage the tools to expand the capabilities of DSS. However, advances in DSS are often piecemeal, lacking synergies that could come from adopting a grand challenge. The future will exhibit a similar pattern of technological advances, with analytics and artificial intelligence being two technologies that can be expected to impact DSS design. Analytics and artificial intelligence are broad technologies that have the potential to make significant impacts on decision support. For DSS to have a meaningful impact on decision-making processes, DSS must get “smarter.” DSS can get smarter by having greater understanding the contexts in which they operate. Two grand challenges are proposed: expanding the model of context implicit in all DSS and implementing a model of shared context understanding for networks of DSS. Each grand challenge provides opportunities for DSS researchers in many specialty areas to contribute, while also moving the discipline forward in a significant way.

Originally founded by 24 participants of the ESI VI-EURO Summer Institute on DSS, in Madeira in 1989, the EURO Working Group on Decision Support Systems (EWG-DSS) is now considered as one of the most stable and active groups of EURO and a reference on DSS in Europe and Worldwide. The EWG-DSS membership has continuously grown since its creation. The group currently counts with over 250 registered members, 340 members in LinkedIn, and 174 Twitter followers. Besides organizing annual scientific events and publications, the EWG-DSS leads a long-term research project on the analysis of its research activity, the Collab-Net project that is currently at its version 5. This chapter aims to explore the life of the group along the 30 last years of its existence through the optics of the Collab-Net project, its outcomes and its analysis.

Social media and the huge amount of user-generated content offer new possibilities for decision-making in companies, as more data can be acquired easily without extra cost. However, a larger database does not automatically lead to better decisions, and volume, variety, and veracity of data from different sources are often overwhelming and challenging. This chapter provides two cases where we used big social data as basis for decision-making. The first case describes the incorporation of extracted information from social media to decision-making models. The second case focuses on the veracity challenges of social media data. By relying on these two cases, we derive guidelines for tackling the veracity of social media data and provide insights how decision-making can be influenced positively and negatively by social media and user-generated content. With the guidelines, it can be determined how much big social data has an influence on quality and rigor in the decision-making process.

We use the Scopus database and naïve Bayes text classification to identify almost a thousand and a half DSS papers targeting problems in agriculture during the last three decades. We then use bibliometric network analysis to establish the chronological trends regarding the methodologies, the technologies, the topics, and their interrelation. We also provide insights into the evolution of international research and academic community cooperation and specialization.

At the crossing of disciplines as Information Systems, Management, Decision Support Systems, Data Mining, and Data Visualization, Business Intelligence (BI) is understood in very different ways by the multiple concerned actors. This chapter aims to offer to all of them an integrated view on multiple perspectives. To this end, it first proposes a standard Business Intelligence approach. Then, it describes the main technical challenges addressed in the literature with a particular focus on those risen by the emergence of Big Data.

The importance of knowledge and knowledge management (KM) has been widely recognised, from the context of individuals, groups, organisations to the economy. KM has greatly evolved over the last few decades in terms of its processes, life cycles, boundary-spanning mechanisms and facilitating technologies. Knowledge mobilisation, as one of the key stages of the KM process and life cycle, holds the key to the success of organisations’ learning and innovation activities, especially in the context of crossing knowledge boundaries to support business decisions. This chapter provides a systematic literature review (SLR) of knowledge mobilisation and its support to business decision-making. The SLR process used includes five well-structured, transparent stages. Key findings from the SLR reveal some important trends of the topic along four key themes of knowledge mobilisation: knowledge boundaries, boundary-spanning mechanisms, facilitating ICT technologies and support for business applications. All these trends will certainly provide insights into future research in knowledge mobilisation and its potential use to improve business decisions.

Should we be concerned by the massive use of devices and algorithms which automatically handle an increasing number of everyday activities within our societies? This chapter makes a short overview of the scientific investigation around this topic, showing that the development, existence and use of such autonomous artefacts are much older than the recent interest in machine learning monopolised artificial intelligence. We then categorise the impact of using such artefacts to the whole process of data collection, structuring, manipulation as well as in recommendation and decision making. The suggested framework allows to identify a number of challenges for the whole community of decision analysts, both researchers and practitioners.

Early approaches to negotiation support viewed negotiations mainly from a decision perspective and extended decision support systems by a communication component that allowed negotiators to exchange highly structured offers. In this chapter, we argue, based on a comprehensive survey of negotiation research of the last decades, that negotiation processes are more complex and involve multiple dimensions of substantive issues, communication, and emotions. We review the development of empirical research on negotiations along these three dimensions and explore possibilities for a comprehensive support of negotiation processes. Finally, we discuss the necessity to consider these dimensions not only in isolation but also their interactions. A successful negotiation support system would need to guide users through the complex interactions of all dimensions.

Model-based Decision Support Systems (DSSs) employ various types of models, such as statistical, optimization, simulation, or rule-based. Models are used to assess and analyze the given decision situation, and on this basis advise the decision-maker. Generally, the DSS development process involves three steps: (1) model development, (2) implementing the model(s) in a DSS, and (3) using the DSS. In this chapter, we focus on two model development approaches: Data Mining and Expert Modeling. We advocate for combing the two in order to get better models and better DSSs in general. We illustrate some points and potential pitfalls using an example of the PD_manager DSS, which is aimed at supporting medication change decisions in the management of Parkinson’s disease. Based on the experience from PD_manager and some other DSS development projects, we propose the so-called 5C requirements for better DSS models: correctness, completeness, consistency, comprehensibility, and convenience. Finally, we summarize the lessons learned and give advice to DSS developers and researchers.

This paper discusses the trends for building DSS (Decision Support Systems) for Multicriteria Preference Modeling by using partial information to be obtained from DMs (Decision-makers). Also, it discusses the use of results from behavioral studies, including those that take a Decision Neuroscience approach, in order to modulate changes in the decision process and in the design of a DSS. The preference modeling is considered from two different perspectives: elicitation by decomposition and elicitation by holistic evaluations. This chapter focuses on a DSS that deals with Multicriteria Preference Modeling in the scope of MAVT (Multiattribute Value Theory) and describes the evolution of these DSSs in recent years. Finally, the trends in the decision aiding process using this kind of DSS for Preference Modeling with partial information is illustrated with the DSS for the FITradeoff method. The trends in the flexibility of this DSS is one of the features explored. It is shown how to combine two different paradigms for preference modeling: decomposition and holistic evaluations. Also, this chapter demonstrates how results from neuroscience experiments can be used to prompt the analyst to have insights when talking with and advising decision-makers (DMs) and how to improve the design of the DSS, both for the choice and the ranking problematic.

Decision support systems (DSS) began as a radical movement in opposition to the total management information systems (MIS) orthodoxy of the 1970s. MIS aimed to support all decisions for all managers in an organization while DSS were small-scale systems developed in an evolutionary, exploratory way to support a manager making an important decision. DSS has remained a significant part of managerial and executive work to this day. By 2020, large-scale business intelligence and analytics (BI&A) systems emerged as the major information technology (IT) expenditure in organizations—large-scale decision support had become mainstream. Using the dual process theory of decision cognition from behavioral economics as a theory lens, we analyze decision support history and identify which decisions in organizations can effectively be supported by different decision support approaches. Our analysis is at odds with IT vendors’ and consultants’ marketing narratives. We find that BI&A and data science are mainly appropriate for well-understood operational decisions, while DSS is the only approach that effectively supports strategic decision-making. We suggest that large-scale BI&A and small-scale DSS will continue to coexist into the future; the first controlled by IT departments, the second by business managers and executives.

Decision-making rarely involves the evaluation of a decision on a single criterion. On the contrary, decisions involve multiple criteria that very often may involve dimensions that are not easily quantified and moreover could include alternatives that have conflicting objectives. As a result, the field of Decision Support emerged with the purpose of assisting decision-makers to structure their problems and formalize the process on which the final decision will be based. The purpose of the chapter is to present one of the well-known decision aid methods: PROMETHEE. In the following pages, the method is presented starting from its mathematical foundation. Furthermore, the latest research trends and software applications are illustrated while finally, future research directions are explained and discussed.

We currently live in an era, in which data heavily, constantly, and globally flows into all areas of our activities. This mobile world is based on the concepts of the Internet of Things, which evolved by the digital transformation from Web 2.0 to 4.0, from a people-centric, participative, read-write web to a data-centered, semantic-oriented, and symbiotic web. It connects us at anytime with our conveniences and contacts, feeds our information needs, guides our shopping tendencies, and informs us about businesses and opportunities in a way that otherwise would be difficult to manage, due to the massive amount of data involved. Individuals and mainly organizations have to tackle the problem of how to process large amounts of data in support of their respective needs and operations, aiming at improving their handling and response efficiency. Big Data can be a strategic asset for organizations, but it is only valuable if used constructively and efficiently to deliver appropriate business insights. Moreover, we currently see special needs, like the one with the pandemic outbreak of COVID-19 that affected all the world, in which high-level technology and analytics tools for supporting decision-making have proven to be important allied components on the counter-attack and management of the overall crisis. Novel methods and technologies were required to be developed to enable decision-makers to understand and examine the massive, multidimensional, multi-source, time-varying information stream to make effective decisions, sometimes in time-critical situations. The current work evolves from the need and interest of board members of the EURO Working Group on Decision Support Systems EWG-DSS to tackle these emerging issues related to Big Data and Decision-Making. The authors discuss the importance of having appropriate technologies for Decision-Making and Decision Support Systems to exploit the potentiality of Big Data analytics, so that we can treat crisis management in a more effective way; and organizations can improve their productivity to face increased competition in this new era. Our aim is to unveil the main impacts and challenges posed to decision-makers in organizations, in the new era of Big Data availability. An illustrative conceptual model is introduced to support the Big Data Analytics for Decision-Making in cross-domain applications.

The evolution of the pig industry over time has shown a concentration of production to maintain profit levels and the rise of new organisational structures like pig supply chains (PSC). At the same time, computers are becoming common tools at any level and little by little, sensors and electronic devices are invading the sector. In this context, there is a need of integration of data and information at different stages of PSC. Decision support systems (DSS) are the natural framework where decision models should be included in order to support farmers, advisers or management specialists in the decision-making process. The lack of adoption of past DSS tools may change in the near future were cloud computing-based DSS and Internet of Things (IoT) make integration, automation and data analysis easier. Data science methodologies and Artificial Intelligence (AI) enlarge the range of modelling techniques available to develop smart pig DSS at the service of the pig industry. There is a challenge of preparing the infrastructure capable of integrating old and new DSS and interconnect the number of new devices and sensors to deliver useful information not just on demand, but also in a preventive, either intelligent, manner anticipating decisions in a smart way.

This chapter aims to contribute to the current debate on how to face the challenge of managing limited resources in a sustainable way, specifically addressing the issue of urban sustainability. In this context, and more in general about the broad field of sustainability, academic literature specifically emphasizes that computer simulation could provide a potentially useful tool. More in detail, several calls for more research point to the use of computer-based learning laboratories—the so-called Interactive Learning Environments (ILEs)—not only to enhance individual as well collective learning but also to facilitate decision-making with a forward-looking orientation in complex sustainability systems. Particularly, ILEs are seen as complementary tools to—if not even as an evolution of—existing Decisions Support Systems (DSSs), traditionally used to analyze available data and steer decision-making. Starting from these considerations, this study aims to: (1) outline the role that DSSs and ILEs may play in fostering learning acquisition and supporting decision-making in and about complex sustainability-related systems; (2) discuss the main results of an ILE-based project used to support learning and decision-making about an urban sustainability context. From a methodological and technical point of view, this study employs System Dynamics (SD) modeling principles and tools. Specifically, a System Dynamics computer model was used to portray the urban environment under analysis (i.e., the simulated city); the model was subsequently transformed into an ILE used to explore the effects of managerial decisions related to the concept of “urban metabolism.”

Customer satisfaction is assessed by various quantitative and qualitative methods. Several quantitative methods adopt a regression analysis procedure, including Multiple Criteria Decision Aid (MCDA) techniques. However, most of them are compensatory approaches, based on an additive model that assumes preference independence among customer satisfaction criteria. During the last years, several rule-based methods have been proposed in the customer satisfaction analysis problem. Such approaches do not assume an analytical aggregation formula, and thus they may offer an alternative in this problem. The fsQCA method focuses on linguistic summarization of “if-then” type rules. This method provides all necessary/sufficient combinations (rules) of satisfaction criteria, which lead to the output (overall satisfaction). In this context, the criteria (causal conditions) constitute the input variables, while the presence of overall satisfaction is the desired outcome. The main aim of this chapter is to present the current progress in advanced rule-based approaches applied in customer satisfaction analysis, as well as the future prospects of fsQCA. For this reason, the chapter presents the theoretical background of the alternative tool that can identify any non-linear and asymmetric relationship among attribute performance and overall satisfaction. The applicability is illustrated through a case study. The dataset is analyzed using the fsQCA method, and the results are compared with an additive value-based model (MUSA method). The results provide a more detailed and valid analysis of customer satisfaction data and indicate the complementary nature of the alternative approach. Finally, the chapter discusses the potential future research efforts, given that rule-based approaches have gained increasing attention during the last years in analyzing customer satisfaction data.

The publication of the White and Green European Transport Paper in 2011 highlighted the need of shifting the urban mobility planning, towards more sustainable means of transport (public transport, bicycle, and pedestrian trips). The new urban mobility planning aims in giving space to the human (citizen) rather than to the motorized vehicles (cars).

Since 2013, the European cities were encouraged to support the policy mentioned above, by developing local Sustainable Urban Mobility Plans (SUMPs) based on the specific procedure that was launched by the DG Move. Towards the end of 2019, the updated specifications were published, giving more detailed instructions for the SUMPs development as there was a low number of local authorities who managed to follow the cooperative philosophy of SUMP.

One of the most critical steps in this 12-step procedure is the assessment—with specific criteria—of all the alternative measures and infrastructures, which will be optimally combined, in order to better respond to the problems and the vision of each area and also covering their specific criteria and particularities. That was even more difficult in the European countries, like Greece, where the citizens’ attitude is not in favor of “green mobility” and the authorities are not familiar with co-planning and co-creative procedures.

The aim of the proposed article is to present a methodological framework based on the use of Multicriteria Analysis in order to enhance the implementation of the SUMP development as regards, mainly the evaluation of alternative measures and the selection of the most appropriate for each urban area according to their Sustainable Efficiency Index (SEI).