Decision Support (eBook)

Preface 5
Contents 9
Contributors 11
About the Authors 13
1 GDSS Past, Present, and Future 21
1.1 Introduction 21
1.2 Overview of GDSS by Paul Gray 21
1.2.1 The Forerunner 22
1.2.2 The Churchill War Room 23
1.2.3 Engelbart's Decision Room at SRI (ca. 1967) 24
1.2.4 University of Southern California (USC) 25
1.2.5 Southern Methodist University (SMU) 25
1.2.6 Claremont Graduate University 25
1.2.7 Other Rooms 26
1.3 The Role of Leaders in GDSS by Bob Johansen 27
1.4 Global Teams by Jay Nunamaker 30
1.4.1 Collaboration 30
1.4.2 Challenges Facing Global Virtual Teams 31
1.4.3 Trade-Offs 32
1.4.4 Effective Collaboration 33
1.4.5 Conclusions 33
1.5 The Entrepreneurial View by Gerald R. Wagner 34
1.5.1 The Planning Laboratory 35
1.5.2 Next Version 35
1.5.3 Vision Quest, Web IQ 36
1.5.4 Reincarnation of IFPS 37
1.5.5 Concluding Thoughts 37
1.6 Telepresence by Jeff Rodman 38
1.6.1 Definition 38
1.6.2 What Is in This Section 39
1.6.3 Telepresence Drivers, 2010--2030 39
1.6.4 The Telepresence Vision 40
1.6.5 Technical Enablers 42
1.6.6 Summary 44
2 Reflections on the Past and Future of Decision Support Systems: Perspective of Eleven Pioneers 45
2.1 Introduction 45
2.2 DSS Research and Development Timeline 46
2.3 Reflections on Decision Support Pioneers Research Project 49
2.4 Reflections of DSS Pioneers 50
2.4.1 Major Conclusions from Experiences with Computerized DSS 51
2.4.2 Continuing Issues Associated with Decision Support 54
2.5 Conclusions 62
Appendix: Brief Biographies of Interviewees 64
References 66
Further Readings 68
3 The Intellectual Structure of Decision Support Systems Research (1991-2004) 69
3.1 Introduction 69
3.2 Background 70
3.3 Data 71
3.4 Research Methodology 72
3.5 Results of Multivariate Analysis 73
3.6 Results of Multi-dimensional Scaling Analysis 80
3.7 Limitations 82
3.8 Conclusions 82
References 83
4 Ethical Decision-Making and Implications for Decision Support 89
4.1 Introduction 89
4.2 Background 89
4.2.1 The Ethical Decision-Making Process: A Jones Perspective 91
4.3 Measuring Ethical Decision-Making Components 94
4.3.1 Recognition of a Moral Issue 94
4.3.2 Make a Moral Judgment 95
4.3.3 Establish a Moral Intent 96
4.3.4 Engage in Moral Behavior 96
4.3.5 Moral Intensity and Other Factors 97
4.3.6 Measuring Ethical Decision Processes 97
4.4 Decision Support Considerations 99
References 100
5 Web and Mobile Spatial Decision Support as Innovations: Comparison of United States and Hong Kong, China 103
5.1 Introduction 103
5.1.1 Background 103
5.1.2 Issues, Controversies, Problems 105
5.2 Theories of Adoption and Diffusion 106
5.3 Research Propositions 109
5.3.1 There Are Significant Advantages to the Web and Mobile SDS over the Traditional SDS Approach 109
5.3.2 The WMSDS Innovation Helps the Organization Competitively 110
5.3.3 The Organization Can Adjust Well to the Innovation of WMSDS 110
5.3.4 The Innovation Is User-Friendly for Customers and Internal Users 110
5.3.5 In Development, the WMSDS Innovation Is Prototyped or Piloted in Small Segments 111
5.3.6 The WMSDS Innovation Is Visible Within and Outside the Organization 111
5.3.7 WMSDS Differs in Its Innovation Features Between the US and Hong Kong 111
5.4 Methodology 112
5.4.1 Case Studies 112
5.5 Findings 112
5.5.1 Decision Support 113
5.5.2 Findings on Differences Between US WMSDS and Hong Kong WMSDS for the Case Organizations 113
5.5.2.1 Difference in Systems Development of Web-Based SDS 121
5.5.2.2 Difference in WMSDS Application Areas 121
5.5.2.3 3-D Applications 122
5.6 Outcomes for the Research Propositions 123
5.6.1 There Are Significant Advantages to the Web and Mobile SDS Over the Old Approach? 123
5.6.2 WMSDS Innovation Helps the Organization Competitively 124
5.6.3 The Organization Can Adjust Well to the Innovation of WMSDS 124
5.6.4 The Innovation Is User-Friendly for Customers and Internal Users 124
5.6.5 In Development, the WMSDS Innovation Is Prototyped or Piloted in Small Segments 124
5.6.6 The WMSDS Innovation Is Visible Within and Outside the Organization? 125
5.6.7 WMSDS Differs in Its Innovation Features Between the US and Hong Kong 125
5.7 Case Findings and the Usefulness of the Research Models of Adoption and Use 126
5.8 Future Trends 127
5.8.1 Technology and Data 127
5.9 Conclusion 129
References 129
6 Knowledge Management Capability in Education 132
6.1 Introduction 132
6.2 Educational Background 133
6.3 Knowledge Management Capability Background 134
6.3.1 Defining Knowledge 134
6.3.2 Knowledge Processes 135
6.3.3 Knowledge Management Capability Constructs 136
6.3.4 Knowledge Management Capability Model 137
6.4 Expanding KMC into Educational Contexts 140
6.4.1 Department of Defense Field Study Results 141
6.5 Future Trends 143
6.5.1 Implementation Issues 143
6.5.2 Implications 145
6.6 Conclusion 145
References 146
7 Knowledge Warehouse for Decision SupportINTbreak in Critical Business Processes: Conceptual ModelingINTbreak
7.1 Introduction 149
7.2 Theoretical Background 150
7.2.1 Data Warehouse 151
7.2.2 Knowledge Management 152
7.2.3 Organizational Memory 152
7.2.4 Data Warehouses and Knowledge Management 153
7.3 Research Methodology 154
7.4 Knowledge Warehouse Conceptual Model 155
7.5 KW4DSCBP Architecture 158
7.6 KW4DSCBP Evaluation and Requirements Elicitation 159
7.7 Requirements Elicitation Tool 161
7.8 Conclusion and Future Work 163
References 164
8 Agent-Based Modeling and Simulation as a Tool for Decision Support for Managing Patient Falls in a Dynamic Hospital Setting 167
8.1 Introduction 167
8.2 Prior Research 169
8.3 Background 170
8.3.1 Description of the Hospital Floor Plan 170
8.4 Modeling and Simulation of Patient Falls Incident 173
8.4.1 Simulation Setup 174
8.5 Results 175
8.6 Conclusions and Future Work 177
References 178
9 Context-Aware Mobile Medical Emergency Management Decision Support System for Safe Transportation 181
9.1 Introduction 181
9.2 Challenges of Decision Support in Medical Emergency Transportation Management 183
9.2.1 Illustrative Scenario 184
9.3 The Need for Context-Awareness and Its Role in Decision Support 185
9.4 Importance of Applying Domain Ontology to DSS for Medical Emergency Transportation Management 186
9.5 The Architecture of a Context-Aware Decision Support Model for Safe Transportation in Medical Emergency Management 188
9.5.1 Knowledge Base 189
9.5.1.1 Context-Awareness 189
9.5.1.2 Domain Ontology 190
9.5.2 Decision Support Tools and User Interface 191
9.6 Implementation of the Proposed Architecture 193
9.6.1 An Overview of the Prototype of Context-Aware Agents for Healthcare Emergency Decision Support 193
9.7 Conclusions and Future Directions 195
References 197
10 General Motors Bailout Problem: A Teaching Case Using the Planners Lab Software 200
10.1 Introduction 200
10.2 The Assignment 200
10.2.1 Analysis of the GM Bailout Loan Agreement Using the Planners Lab 200
10.3 Instructions for Analyzing the Problem 201
10.4 The Model 205
10.4.1 Sales and Revenue 205
10.4.2 Costs and Expenses 206
10.4.3 Cash from Operating Activities 206
10.4.4 Cash from Investing Activities 207
10.4.5 Cash from Financing Activities 207
10.4.6 Net Cash Flow and Present Value 207
10.4.7 Bailout Activities 207
10.4.8 Market Capitalization 208
10.5 Appendix A: Introduction to the Planners Lab 208
11 Assessing Today: Determining the Decision ValueINTbreak of Decision Support Systems
11.1 Introduction 220
11.2 Background on Decision-Making, DSS, and Evaluation 221
11.2.1 Decision Making Process 221
11.2.2 Decision Support Systems 223
11.2.3 Evaluation of DSS 225
11.3 Integrated Design and Evaluation Framework for DSS 227
11.4 Application 228
11.4.1 Analytic Hierarchy Process (AHP) 228
11.4.2 Description of Application 228
11.4.3 Evaluation of Application 230
11.5 Future Trends 232
11.6 Conclusions 232
Appendix: Evaluation of DSS Compared to DSS with No AI Method The First Value Is Calculated for the DSS with IA and the Second for the DSS Without IA 234
References 235
Index 238