EURO Working Group on DSS: A Tour of the DSS Developments Over the Last 30 Years (Integrated Series in Information Systems)

Preface
Contents
Decision Support Systems: Historical Innovations and Modern Technology Challenges
	1 Introduction
	2 DSS for Strategic Management: The Woodstrat
	3 Support for Decision-Making
	4 Decision Support for the 2020s
	5 Computational Intelligence in Decision Support
	References
Thirty Years of Decision Support: A Bibliometric View
	1 Introduction
	2 Bibliographic Analysis
	3 DSS Published in 1990–1999
	4 DSS Published in 2000–2009
	5 DSS Published in 2010–2019
	6 Conclusion
	References
Two Grand Challenges for DSS Evolution
	1 Introduction
	2 DSS Evolution
	3 Two Technologies: Analytics and Artificial Intelligence
	4 Design Philosophy
	5 Context
	6 Two Grand Challenges for DSS
	7 Conclusion
	References
30 Years of the EWG-DSS Through the Lens of the Collab-NetProject
	1 30 Years of History
	2 Events and Publications
	3 Collab-Net: The History of the Project
		3.1 First Versions of the Project
		3.2 Collab-Net V3
		3.3 The Current Collab-Net V5
	4 Network Analyses
		4.1 Summary of Previous Studies on Network Analysis
		4.2 Summary of Previous Study on Topics Analysis
		4.3 Data Collection and Preparation for the 30 Years Analysis
		4.4 Authorship Evolution
		4.5 Co-Authorship Network in 2019
		4.6 Co-Authorship Network 30 Years Evolution
	5 The Future of Collab-Net: A DSS for Research Consortium Building
		5.1 The New Functionalities
		5.2 Future Works in Collab-Net
	6 Conclusion
	References
Decision Support in the Era of Social Media and User-GeneratedContent
	1 Introduction
	2 Related Work
	3 Case 1: Incorporating Different Social Media Data into the Decision-Making Process
		3.1 Results: Distribution of the Systems
	4 Case 2: Tackling Veracity in Big Social Media Data
		4.1 Analyzing Data from a Social Network
		4.2 Veracity Challenges
	5 Conclusions from the Case Studies
	References
The Evolution of Decision Support Systems for Agriculture: A Bibliometric Network Approach
	1 Introduction
	2 Methodology
		2.1 Naïve Bayes Text Classification
		2.2 Bibliometric Network Analysis
	3 Identification of the Relevant Literature
	4 Results
		4.1 Naïve Bayes Classification
		4.2 Bibliographic Network Analysis
	5 Discussion
	6 Conclusions
	References
30 Years Business Intelligence: From Data Analytics to Big Data
	1 A Brief History of Business Intelligence
	2 Inmon and Kimball\'s Approaches of Data Warehouses
	3 Global Architecture Typology
	4 BI Engineering
	5 Big Data Challenges
		5.1 Volume
		5.2 Velocity
		5.3 Varity
	6 Other Challenges
		6.1 Specific Domain
		6.2 Methodology
	7 Conclusion
	References
A Systematic Literature Review of Knowledge Mobilisation and Its Support for Business Decisions Over Two Decades (1999–2019)
	1 Introduction
	2 Review Method: SLR
	3 Descriptive Analysis and Findings
	4 Thematic Analysis and Findings
		4.1 Theme A: Knowledge Boundaries
		4.2 Theme B: Boundary-Crossing Mechanisms
		4.3 Theme C: Advanced ICT Technologies Facilitating Knowledge Mobilisation
		4.4 Theme D: Business Decision Applications
	5 Conclusions
	References
Social Responsibility of Algorithms: An Overview
	1 Motivations
	2 Historical Background
	History
	3 What Is the Problem?
		3.1 Fundamentals
		3.2 The Raw Material
		3.3 The Outcome
		3.4 The Process
		3.5 The Implementation
		3.6 The Impact to Society
	4 Examples
		4.1 Automatic Pricing
		4.2 Voting
	5 Conclusion
	References
Negotiation Support: Trends and Problems
	1 Introduction
	2 Dimensions of Negotiation Processes
	3 Process Models and Support
		3.1 Substantive Level
		3.2 Communication Dimension
		3.3 Emotional Level
	4 Integrated Perspective
	5 Conclusions and Research Topics
	References
From Data and Models to Decision Support Systems: Lessons and Advice for the Future
	1 Introduction
	2 Basic Definitions and Concepts
		2.1 Decision Support Systems
		2.2 Model-Driven, Knowledge-Driven, and Model-Based DSS
		2.3 Model and Model Development
	3 Example: PD_manager DSS on Medication Change
	4 The “5C Requirements” for DSS Models
		4.1 Correctness
		4.2 Completeness
		4.3 Consistency
		4.4 Comprehensibility
		4.5 Convenience
	5 Conclusions and Take-Home Messages
	References
DSS for Multicriteria Preference Modeling with Partial Information and Its Modulation with Behavioral Studies
	1 Introduction
	2 DSS for Multicriteria Preference Modeling with the Use of Partial Information
	3 DSS for the FITradeoff Method
	4 Different Perspective for Preference Modeling: Holistic Evaluation
	5 Modulating the Decision Process and the Design of a DSS with Decision Neuroscience
		5.1 Neuroscience Experiments
		5.2 Behavioral Results from the Neuroscience Experiments
	6 Conclusions
	References
From Radical Movement to Organizational Mainstream: A Behavioral Economics Perspective on DSS History
	1 Introduction
	2 What Is to Be Supported
	3 Business IT in the 1960s and 1970s
	4 The Radical Movement of Early DSS
	5 Joining the Organizational Mainstream
	6 The 2020 Landscape of Decision Support
	7 What Is Actually Supported, and What Can Be
	8 The Way Ahead
	References
The History and Future of PROMETHEE
	1 Introduction
	2 Methodology
		2.1 PROMETHEE I and II
			2.1.1 PROMETHEE I Partial Ranking
			2.1.2 PROMETHEE II Complete Ranking
		2.2 PROMETHEE Visualizations
	3 PROMETHEE Software
	4 PROMETHEE Applications
	5 Conclusions and Future Research
	References
On the Impact of Big Data Analytics in Decision-Making Processes
	1 Introduction
	2 Background on Big Data for Decision-Making
		2.1 The Big Data Revolution
		2.2 Big Data Value Chain
		2.3 Big Data Analytics for Decision-Making
		2.4 Big Data Tools and Approaches
	3 Decision-Making: A Consequence of Big Data Analytics
		3.1 Decision-Making: Why Is It Different Nowadays?
		3.2 Decision-Making: Some Application Areas Affected by Big Data Revolution
			3.2.1 Collaborative Decision-Making Applications
			3.2.2 Dynamic-Temporal-Spatial Applications
			3.2.3 Logistics and Supply Chain Management Applications
			3.2.4 Crisis Management, Risky, and Critical Applications
	4 Conceptual Model for Big Data in Decision-Making
		4.1 Background for the Proposed Model
		4.2 Proposed High-Level Conceptual Model
		4.3 Value of the Proposed High-Level Conceptual Model
	5 General Remarks and Conclusions
	References
The Evolution of DSS in the Pig Industry and Future Perspectives
	1 Introduction
	2 The Pig Supply Chain Structure
	3 Decision-Making Problems in the Pig Industry
		3.1 Main Decision Problems
		3.2 Interactions or Impact Over Other Activities
		3.3 Human Perception of the Decision Problems
	4 Computer-Based Systems for the Pig Industry Over Time
		4.1 Situation Before the 1990s
		4.2 The Arrival of Personal Computers on Farm
		4.3 Current Computer-Based Systems
	5 Research Questions Contributing to DSS Development in the Pig Sector
	6 Pitfalls and Lessons Learnt
		6.1 Models or a Simplified Real World?
		6.2 Standardisation and Integration
		6.3 Research and Society
		6.4 How Decisions Are Made?
	7 Emerging Technologies and Future Prospects
		7.1 The Forthcoming DSS, Swimming in (Big) Data Pool
		7.2 Whole Chain View and Digitalisation
		7.3 Externalities and Disturbing Shocks
		7.4 Advances in Other Fields Reverting in DSS
	8 Conclusions
	References
Game-Based Learning and Decision-Making for Urban Sustainability: A Case of System Dynamics Simulations
	1 Introduction
	2 Concept and Evolution of Decision Support Systems
	3 The Potentials of Interactive Learning Environments in Facilitating Learning and Decision-Making
		3.1 Defining an ILE
		3.2 Main Components of an ILE
		3.3 Key Features of an ILE
			3.3.1 Transparency
			3.3.2 Realism
			3.3.3 No-Threatening and Friendly-User Environment
		3.4 Typical Applications of ILEs
	4 Research Design
	5 Results: the SD Model and the ILE
	6 Discussion, Limitations, and Further Research
	References
Advanced Rule-Based Approaches in Customer Satisfaction Analysis: Recent Development and Future Prospects of fsQCA
	1 Introduction
	2 Fuzzy-Set Qualitative Comparative Analysis (fsQCA)
		2.1 Step 1
		2.2 Step 2
		2.3 Step 3
		2.4 Step 4
		2.5 Step 5 (Causal Combinations)
		2.6 Step 6 (Surviving Causal Combinations)
		2.7 Step 7 (Actual Causal Combinations)
		2.8 Step 8 (Complex and Parsimonious Solutions)
		2.9 Step 9 (Intermediate Solutions)
		2.10 Step 10 (Coverage)
	3 An Illustrative Case Study
		3.1 Dataset
		3.2 MUSA Results
		3.3 fsQCA Results
		3.4 Complex Solution
		3.5 Parsimonious Solution (Minimal Prime Implicants)
	4 Robustness Analysis
	5 Conclusions and Future Research
	Appendix 1: Necessary Conditions Analysis
	Appendix 2: Indicative Calculations for Consistency
	Appendix 3: Indicative Calculations for Coverage and Consistency of the Solution Terms
	Appendix 4: Minimization using the Carnaugh Maps (Complex Solution)
	References
Use of Multicriteria Analysis for Enchancing Sustainable Urban Mobility Planning and Decision-Making
	1 Introduction
	2 Use of MCDA Methods for Decision-Making In Mobility Planning of Urban Areas
	3 Developing an MCDA-Based Methodological Framework for Evaluating Specific Sustainable Mobility Measures: A Greek Example
		3.1 The Status of the Sustainable Urban Mobility Planning Implementation in Greek Municipalities
		3.2 Selection of Sustainable Mobility Measures Using MCDA
	4 Calculating the Weights and Ranking the Objectives and Result Indicators
		4.1 Collecting the Relative Data
		4.2 Ranking of the Objectives and Result Indicators
		4.3 Ranking of the Easiness of Implementation Parameters
		4.4 Main Conclusions of the Ranking Procedure
	5 Conclusions
	References
Index