Handbook on Decision Support Systems 1 (eBook)

Preface 7
Impetus and Roots 7
Organization and Content 13
References 17
Acknowledgements 18
Table of Contents 21
Contributors to Volume 1 30
PART I Foundations of Decision Support Systems 50
CHAPTER 1 The Decision-Making Process in a Complex Situation 51
1 Introduction 51
2 Understanding and Exploring Complex Situations 55
3 Human Additive Factors 57
4 Preparing for the Decision Process 58
5 Mechanisms for Influencing Complex Situations 63
6 The Challenge 66
7 References 67
CHAPTER 2 Decisions and Knowledge 69
1 Introduction 69
2 Basic Trends 70
3 Decision Making – A Knowledge-Intensive Endeavor 73
4 Knowledge Management 86
5 Conclusion 97
References 98
CHAPTER 3 Decision Making and Sensemaking 102
1 Introduction 102
2 Decision Making 103
3 Sensemaking 104
4 Reconciling Decision Making and Sensemaking 105
5 Seeking a Metalevel Reconciliation in Design Thinking 108
6 Conclusion 109
References 110
CHAPTER 4 Data Quality and Decision Making 111
1 Introduction 111
2 Data Quality and Decision-Making 112
3 The InfoQual Framework: A Semiotic Framework for Data Quality 115
4 Improving Data Quality for Effective Decision- Making 120
5 Improving Decision Making Using Data Quality Tags 122
6 Conclusion 125
Acknowledgements 126
References 126
CHAPTER 5 Decision Makers and Their Need for Support 129
1 Introduction 129
2 Organizational Decision-Making 130
3 In Consideration of Different Decision-Making Perspectives 135
4 The Need for Multiple-Perspective Decision Support 141
5 Conclusion 144
6 References 144
CHAPTER 6 Decision Support Through Knowledge Management 149
1 Introduction 149
2 The Foundations of Decision Support Through Knowledge Management 151
3 Case 1: A KWS System for Improving Meteorological Forecasting 156
4 Case 2: A KW Support System for Improving Operations and Performance 158
5 Design Knowledge and Principles for KW Support Systems 161
6 Conclusions and Further Research 162
7 References 163
CHAPTER 7 Decision Support Systems: A Historical Overview 167
1 Introduction 167
2 Decision Support Systems Origins 168
3 Theory Development 170
4 DSS Applications Development 172
5 Conclusions: Looking Ahead 177
References 179
Appendix A. Decision Support Systems Time Line 184
CHAPTER 8 Reference Disciplines of Decision Support Systems 187
1 Introduction 187
2 Data and Research Methodology 188
3 Intellectual Relationship Between DSS and Business Disciplines 189
4 Intellectual Relationship Between DSSs and Non- business Disciplines 192
5 Conclusion 199
References 200
PART II Decision Support System Fundamentals 206
CHAPTER 9 DSS Architecture and Types 207
1 Introduction 207
2 The Generic Architecture for Decision Support Systems 209
3 A Brief History Generic Architecture’s Evolution 212
4 DSS Variations 214
5 Summary 229
Acknowledgements 229
References 230
CHAPTER 10 Integrated Document Management for Decision Support 234
1 Introduction 234
2 Document Environment Characteristics 236
3 Integrative Approach to Strategic Solution 237
4 Core Capabilities – Document/Content Management Systems 238
5 Extended Capabilities – System Interfaces 240
6 Implementation Scenarios 243
7 Business Usage Scenarios 244
8 Conclusion 248
References 249
CHAPTER 11 Databases and Data Warehouses for Decision Support 250
1 Introduction 250
2 The Role of Data in Decision Support 251
3 Data Warehousing in Practice 252
4 Data Modeling in the Data Warehouse 257
5 Data Warehouse Architectures 260
6 Project Scale 263
7 Organizational Issues 264
8 Current Trends and the Future of Data Warehousing 266
9 Conclusion 270
References 271
CHAPTER 12 Model Management and Solvers for Decision Support 274
1 Introduction 274
2 Models and DSS Modeling 276
3 Model Management 279
4 Key Issues in Model Management 282
5 Solvers 287
6 Impact of the Web Environment on Model Management and Solvers 290
7 Conclusion 295
References 295
CHAPTER 13 Online Analytical Processing (OLAP) for Decision Support 302
1 Introduction 302
2 Background: Data Warehouses and Data Marts 303
3 OLAP Functionalities 309
4 Relational Versus Multidimensional Data Model 313
5 OLAP Categories 315
6 Summary 318
References 319
CHAPTER 14 Spreadsheet-Based Decision Support Systems 320
1 Introduction 320
2 Defining Spreadsheet-Based Decision Support Systems 321
3 Spreadsheet Features for DSS Development 323
4 Developing Spreadsheet-Based Decision Support Systems 330
5 Alternatives to Spreadsheets 340
6 Conclusion 340
Acknowledgements 341
References 341
CHAPTER 15 Multi- Criteria Decision Support 342
1 Introduction 342
2 DSS and MCDM 343
3 Multicriteria Paradigms 344
4 Multicriteria Applications 346
5 Conclusions 350
References 351
CHAPTER 16 Web- Based Decision Support 358
1 Introduction 358
2 Differences Between Web-Based DSSs and Desktop DSSs 359
3 Unique Opportunities for WB-DSSs 360
4 Unique Challenges to WB-DSSs 364
5 WB-DSS Research 367
6 Conclusion 375
References 375
PART III Multiparticipant Decision Support Systems 382
CHAPTER 17 Collaborative Technologies 383
1 Introduction 383
2 The Nature of Team Collaboration 384
3 Collaborative Technologies for Teams: A Review 385
4 Collaborative Technologies Trends 392
5 Conclusion 393
References 393
CHAPTER 18 A Motivational Model of Knowledge Sharing 397
1 Introduction 397
2 Background 398
3 Virtual Communities of Practice as DSSs 400
4 Motivations for Knowledge Sharing in Virtual Communities 401
5 A Motivational Model of Knowledge Sharing 406
6 Summary and Conclusions 408
References 409
CHAPTER 19 The Nature of Group Decision Support Systems 413
1 Introduction 413
2 Early GDSS Rooms 418
3 GDSS Hardware 422
4 GDSS Software 423
5 Status and Future of GDSS 428
References 430
CHAPTER 20 GDSS Parameters and Benefits 432
1 Introduction 432
2 Research Model 433
3 GDSS Outcome Metrics 436
4 Dynamics of Group Process 436
5 GDSS Technology Parameters 438
6 Group Parameters 444
7 Task Parameters 446
8 Context Parameters 447
9 Concluding Remarks 448
References 449
CHAPTER 21 The Nature of Organizational Decision Support Systems 456
1 Introduction 456
2 Definitions 458
3 Examples of ODSS 462
4 Implementation of ODSS 467
5 ODSS and ERP 468
6 The Scaling Issue and the Nature of ODSS 469
7 Summary and Conclusions 470
References 471
CHAPTER 22 Organizational Decision Support Systems: Parameters and Benefits 474
1 Introduction 474
2 Architectural Characteristics: Design Parameters 476
3 Organizational Characteristics: Contextual Parameters 487
4 Benefits of Organizational Decision Support Systems 496
5 Conclusion 506
References 506
CHAPTER 23 Negotiation Support and E-negotiation Systems 510
1 Introduction 510
2 Classifications 516
3 Negotiation Software Design Issues 521
4 Early Applications and Research 526
5 E-negotiation Systems and Research 533
6 Conclusions 542
Acknowledgements 543
References 543
PART IV Intelligent Decision Support Systems 550
CHAPTER 24 Advisory Systems to Support Decision Making 551
1 Introduction 551
2 Expert Systems 552
3 Advisory Systems 555
4 Comparing, Contrasting, and Classifying Expert and Advisory Systems 559
5 Future Research 563
6 Conclusion 564
References 564
CHAPTER 25 Using Autonomous Software Agents in Decision Support Systems 568
1 Introduction 568
2 Software Agent Definition 570
3 An Implementation of an Agent-Integrated DSS 577
4 Agent Benefits in DSS 585
5 A Framework for an Agent-Enabled DSS 588
6 Conclusion and Future Work 590
References 591
CHAPTER 26 Artificial Neural Networks in Decision Support Systems 595
1 Introduction 595
2 Neural Networks Explained 596
3 Applications of Neural Networks in DSS 603
4 An Exemplary Case: A Web-Based DSS for Hollywood Managers 604
5 Issues in Developing ANN-based DSS 609
6 Conclusions 610
References 612
CHAPTER 27 Data Mining and Data Fusion for Enhanced Decision Support 619
1 Introduction 619
2 Data Mining 620
3 Decision Support Systems 628
4 One-Number Forecasting 630
5 Data Fusion and Multi-Level Decision Superiority Model 632
6 Conclusion 639
Acknowledgements 639
References 640
CHAPTER 28 Decision Support via Text Mining 647
1 Introduction 647
2 Information Entropy 648
3 Applications 650
4 Technology Fundamentals 656
5 Challenges of Text Mining 663
6 A Closer Look at Survey Analysis 665
7 Conclusion 669
References 669
CHAPTER 29 Decision Support Based on Process Mining 674
1 Introduction 674
2 Process Mining 677
3 Process Mining Using ProM 685
4 Related Work 691
5 Conclusion 692
Acknowledgements 693
References 693
CHAPTER 30 Adaptive Decision Support Systems via Problem Processor Learning 695
1 Introduction 695
2 An Overview of Machine Learning 696
3 Decision Support Paradigms and Machine Learning 699
4 Adaptive Decision Support Systems 705
5 An Illustration: Adaptive DSSs for Scheduling in FMS Contexts 708
6 Conclusion 725
Acknowledgements 725
Appendix A: An Overview of Genetic Algorithms and Their Applications 726
References 727
CHAPTER 31 A Learning-Enhanced Adaptive Decision Support System Framework 733
1 Introduction 733
2 Adaptive DSS 736
3 Adaptive DSS with Learning 737
4 The Design Science Perspective 749
5 Discussion and Conclusion 751
References 751
PART V Effects of Computer-Based Decision Support 753
CHAPTER 32 Benefits of Decision Support Systems 754
1 Introduction 754
2 Typical DSS Benefits 756
3 Assessing Benefits Before Building a Decision Support System 759
4 Assessing Benefits After Implementing a Decision Support System 761
5 Conclusion 762
References 763
CHAPTER 33 Involvement and Decision-Making Satisfaction with a Decision Aid: The Influence of Social Multimedia, Gender, and Playfulness 766
1 Introduction 766
2 Theoretical Framework 768
3 Research Methodology 776
4 Results 780
5 Discussion 785
6 Limitations and Conclusion 788
References 790
Appendix A. Survey Scales 795
CHAPTER 34 Decision Support Systems Failure 797
1 Introduction 797
2 Success and Failure in DSS Projects 798
3 Critical Success Factors for DSS Projects 801
4 Two Case Studies of DSS Failure 805
5 Concluding Comments 819
References 820
CHAPTER 35 Could the Use of a Knowledge-Based System Lead to Implicit Learning? 825
1 Introduction 825
2 Knowledge-Based Systems and Implicit Learning 826
3 Method 830
4 Results 833
5 Discussion 836
References 839
Appendix A. Some Differences Among the Responses From the Two Interfaces 842
Appendix B. Knowledge Test Questions 843
Appendix C. Task Completed Through the System 844
CHAPTER 36 Group and Organizational Learning Effects from Multiparticipant DSS Usage 846
1 Introduction 846
2 MDSS Characteristics 847
3 Organizational Learning 849
4 MDSS and Learning Effects 854
5 Evidence from the Literature 856
6 Conclusion 860
References 861
Keyword Index 865