Quantitative Methodologies using Multi-Methods: Models for Social Science and Information Technology Research

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Half Title
Title Page
Copyright Page
Contents
Preface: Possible Uses of this Book
Introduction
SECTION I: Development of the Methodological Modules
	Chapter 1: Pre-Requisite General Questions
		Impact of the Assumption of Homogeneity of the Sample on Research Questions
		From a Basket of Apples to a Set of Systems (Decision-Making Units)
		From Systems to Systems in Context
	Chapter 2: Components of Multi-Method Methodologies
		Cluster Analysis (CA)
		Classification Decision Trees Induction (CDTI)
		Neural Networks (NNs)
		Association Rules Mining (ARM)
		Data Envelopment Analysis (DEA)
		Multiple Regression (MR)
	Chapter 3: Framework for Methodological Modules
SECTION II: Description of the Methodological Modules
	Chapter 4: A1: Homogeneous Sample – DEA and DTI
		Phase 1: DEA
		Phase 2: DTI
		Examples of Application of DEA and DTI
	Chapter 5: A2: Homogeneous Sample – DEA and ARM
		Phase 1: DEA
		Phase 2: ARM
		Examples of Application of DEA and ARM
	Chapter 6: B1: Heterogeneous Sample (Groupings Are Given) – DTI and ARM
		Phase 1: DTI
		Phase 2: ARM
		Examples of Application of DTI and ARM
	Chapter 7: B2: Heterogeneous Sample (Groupings Are Given) – DTI and MR
		Phase 1: DTI
			Option 1: DTI Using the Data Set Comprised of a Causal Model Only
			Option 2: DTI Using the Data Set without Causal Model
			Option 3: DTI Using the Complete Data Set
		Phase 2: MR
			Option 1: MR Using the Causal Model Only
			Option 2: MR Using the Adapted Causal Model – Contextual Independent Variable
			Option 3: Creating a New MR Using Contextual Independent Variables
		Example of Application of DTI and MR
	Chapter 8: B3: Heterogeneous Sample (Groupings Are Given) – DTI, DEA, and ARM
		Phase 1: DTI
			Option 1: The Data Set Is Comprised of the Variables of the DEAea MODel
			Option 2: The Data Set Contains Contextual Variables
		Phase 2: DEA
		Phase 3: ARM
			Option 1: ARM to Generate “If→ (Level of the Top-Split Variable(s))”
			Option 2: ARM to Generate “If→ (DEA Model’s Inputs)”
			Option 3: ARM to Generate “If→ (DEA Model’ s Outputs)”
			Option 4: ARM to Generate “If→ (Level of Averaged Relative Efficiency)”
			Option 5: ARM to Generate “If→ (Received Categorization)”
		Examples of Application of DTI, DEA, and ARM
	Chapter 9: B4: Heterogeneous Sample (Groupings Are Given) – DTI, DEA, and NN
		Phase 1: DTI
		Phase 2: DEA
		Phase 3: NN
			Step 1: Generate NN Model of Transformative Capacity
			Step 2: Generate Outputs of a Less Efficient Group Based on Transformative Capacity of a More Efficient Group
			Step 3: Generate Outputs of a More Efficient Group Based on Transformative Capacity of a Less Efficient Group
			Step 4: Compile the Generated Outputs in a New Data Set
		Phase 4: DEA
		Example of Application of DTI, DEA, and NN
	Chapter 10: C1: Heterogeneous Sample (Groupings Are Not Known) – CA and DTI
		Phase 1: CA
		Phase 2: DTI
		Examples of Application of CA and DTI
	Chapter 11: C2: Heterogeneous Sample (Groupings Are Not Known) – CA and ARM
		Phase 1: CA
		Phase 2: ARM
			Option 1: ARM Using Only Intrinsic Variables
			Option 2: ARM Using Only Contextual Variables
			Option 3: ARM Using Intrinsic and Contextual Variables
		Examples of Application of CA and ARM
	Chapter 12: C3: Heterogeneous Sample (Groupings Are Not Known) – CA, DTI, and MR
		Phase 1: CA
		Phase 2: DTI
			Option 1: Data Set Is Limited to Variables of the MR Model
			Option 2: Data Set Comprises Variables of the MR Model and Contextual Variables
		Phase 3: MR
		Example of Application of CA, DTI, and MR
	Chapter 13: C4: Heterogeneous Sample (Groupings Are Not Known) – CA, DTI, and ARM
		Phase 1: CA
		Phase 2: DTI
			Option 1: A Priori Target Variable
			Option 2: CA-based Target Variable
		Phase 3: ARM
			Step 1
			Step 2
			Step 3
			Step 4
		Examples of Application of CA, DTI, and ARM
	Chapter 14: C5: Heterogeneous Sample (Groupings Are Not Known) – CA and DEA
		Phase 1: CA
			Option 1: CA based on the DEA Model
			Option 2: CA based on the DEA Model and Contextual Variables
		Phase 2: DEA
		Examples of Application of CA and DEA
	Chapter 15: C6: Heterogeneous Sample (Groupings Are Not Known) – CA, DEA, and ARM
		Phase 1: CA
		Phase 2: DEA
		Phase 3: ARM
			Option 1: Complete Sample, # of Variables = the DEA Model
			Option 2: Complete Sample, # of Variables = the DEA Model + Contextual Variables
			Option 3: Sub-Sets of the Sample, # of Variables = the DEA Model
			Option 4: Sub-sets of the Sample, # of Variables = the DEA Model + Contextual Variables
		Examples of Application of CA, DEA, and ARM
	Chapter 16: C7: Heterogeneous Sample (Groupings Are Not Known) – CA, DTI, and DEA
		Phase 1: CA
		Phase 2: DTI
		Phase 3: DEA
		Examples of Application of CA, DTI, and DEA
	Chapter 17: C8: Heterogeneous Sample (Groupings Not Known) – CA, DTI, DEA, and NN
		Phase 1: CA
		Phase 2: DTI
		Phase 3: DEA
		Phase 4: NN
			Step 1: Creating an NN Model of “Low-Level” Cluster
			Step 2: Creating an NN Model of “High-Level” Cluster
			Step 3: Simulation of the Outputs of “Low-Level” Cluster Using NN Model of “High-Level” Cluster
			Step 4: Simulation of the Outputs of “High-Level” Cluster Using NN Model of “Low-Level” Cluster
		Phase 5: DEA
		Examples of Application of CA, DTI, DEA, and NN
SECTION III: Methodological Modules – Examples of Their Application
	Chapter 18: A Hybrid DEA/DM-based DSS for Productivity-Driven Environments
		Introduction
		Description of the DSS
			Externally Oriented Functionality
			Internally Oriented Functionality
			Architecture of the DSS
		An Illustrative Application
			Step 1: Is the Business Environment Homogeneous?
			Step 2: What Are the Factors Responsible for Heterogeneity of the Business Environment?
			Step 3: Do Groups of Competitors Differ in Terms of the Relative Efficiency?
			Step 4: What Are some of the Factors Associated with the Differences in Relative Efficiency?
			Step 5: Are There any Complementarities Between the Relevant Variables?
			Step 6: What Is a Better Way to Improve Production of Outputs?
		Conclusion
		Acknowledgment
		References
	Chapter 19: Determining Sources of Relative Inefficiency in Heterogeneous Samples: Methodology Using Cluster Analysis, DEA, and Neural Networks
		Introduction
		Description of the Methodology
			Description of Steps 3–5 of the Methodology
				Step 3: Generate a “Black Box” Model of Transformative Capacity of Each Cluster
				Step 4: Generate Simulated Sets of the Outputs for Each Cluster
				Step 5: Determine the Sources of the Relative Inefficiency of the DMUs in the Sample
			Motivation for Steps 3 and 5 of the Methodology
				Motivation for Step 3
				Motivation for Step 5
		Illustrative Example
			Description of the Illustrative Data Set
			Application of the Methodology on the Illustrative Data Set
				Results of Step 1: Evaluate the Scale Heterogeneity Status of the Data Set
				Results of Step 2: Determine the Relative Efficiency Status of Each DMU
				Results of Steps 3 and 4: Generate Simulated Sets of the Outputs for Each Cluster Based on Black Box Models Transformative Capacity Processes
				Results of Step 5
		Discussion and Conclusion
		Acknowledgment
		References
	Chapter 20: Exploring Context Specific Micro-Economic Impacts of ICT Capabilities
		Introduction
		Theoretical Framework and the Research Model
		The Methodology of the Study
			Phase 1: Application of Data Envelopment Analysis (DEA)
				Phase 1, Step 1
				Phase 1, Step 2
				Phase 1, Step 3
			Phase 2: Decision Tree-Based Analysis
				Phase 2, Step 1
				Phase 2, Step 2
		Description of the Data
		Results of the Data Analysis
			Results from Phase 1: Application of Data Envelopment Analysis (DEA)
				Phase 1, Step 1
				Phase 1, Step 2
				Phase 1, Step 3
			Results from Phase 2 – Decision Tree (DT) Based Analysis
		Conclusion
		Contributions to Theory
		Contributions to Practice
		Acknowledgment
		References
	Chapter 21: A Methodology for Identifying Sources of Disparities in the Socio-Economic Outcomes of ICT Capabilities in SSAs
		Introduction
		Research Framework
		Proposed Methodology
			A New Methodology: Benefits and Justifications
			Phase 1: Data Envelopment Analysis (DEA)
			Phase 2: Decision Tree Induction (DTI)
			Phase 3: Association Rule Mining (ARM)
		Research Questions and Null Hypotheses of the Study
		The Data
		Results of the Data Analysis
			Phase 1: Data Envelopment Analysis
			Phase 2: Decision Tree Induction
			Phase 3: Association Rule Mining
		Discussion of the Results
		Conclusion
		Acknowledgment
		References
	Chapter 22: Discovering Common Causal Structures that Describe Context-Diverse Heterogeneous Groups
		Introduction
		A Conceptualization of the Benchmarking Problem
		Research Problem and Research Questions of the Study
		The Proposed Methodology
			Description of the Methodology
			Justification & Benefits of the Methodology
		Illustrative Example – Application to Sub-Saharan Economies
			Phase 1: Define the Transformation Framework
			Phase 2: Partition the Set of Decision Making Units into Meaningful Groups
			Phase 3: Data Envelopment Analysis
			Phase 4: Decision Tree Induction (DTI)
			Phase 5: Association Rule Mining
		Conclusion
		Acknowledgment
		References
	Chapter 23: An Empirical Investigation of ICT Capabilities and the Cost of Business Start-up Procedures in Sub-Saharan African Economies
		The Research Framework and Research Questions
		Proposed Methodology
			Phase 1: Cluster Analysis (CA)
			Phase 2: Decision Tree Induction
			Phase 3: Data Envelopment Analysis
			Phase 4: Ordinary Least Squares Regression
			Phase 5: Association Rule Mining
		Data
		Results of the Data Analysis
			Phase 1: CA
			Phase 2: DTI
			Phase 3: DEA
			Phase 4: OLS
			Phase 5: ARM
		Interpretation of the Results of the Data Analysis
			Cluster Analysis
			Decision Tree Induction
			DEA
			Ordinary Least Squares (OLS)
			ARM
		Discussion of the Results of the Study
		Conclusion
		Acknowledgment
		References
	Chapter 24: Exploring the Socio-Economic Impacts of ICT-Enabled Public Value in Sub-Saharan Africa
		Introduction
		Research Framework of the Study
		Research Questions of the Study
		Methodology of the Investigation
			Phase 1: Cluster Analysis
			Phase 2: Decision Tree Induction
			Phase 3: Data Envelopment Analysis (DEA)
			Phase 4: Ordinary Least Squares (OLS) Regression
			Phase 5: Association Rule Mining (ARM)
		Data
		Results of the Data Analysis
			Phase 1: CA
			Phase 2: DTI
			Phase 3: DEA
			Phase 4: OLS
			Phase 5: ARM
		Discussion of the Results of the Study
		Conclusion
		Acknowledgment
		References
	Chapter 25: Contributing Factors to Information Technology Investment Utilization in Transition Economies: An Empirical Investigation
		Introduction
		Theoretical Framework
			Growth Accounting
			Theory of Complementarity
		Overview on the Data
		Methodology: Searching for the Determinants of the Efficiency of Utilization of Investments in Telecoms
			Phase 1: Data Envelopment Analysis
				Data Used to Perform DEA
			Phase 2: Cluster Analysis
				Data Used to Perform CA
			Phase 3: Decision Tree
				Data Used to Perform DT
		Results
			Results: DEA
			Results: Cluster Analysis
			Results: Decision Tree
		Contribution of the Study
		Summary and Conclusion
		Acknowledgment
		References
		Appendix A
	Chapter 26: Increasing the Discriminatory Power of DEA in the Presence of the Sample Heterogeneity with Cluster Analysis and Decision Trees
		Introduction
		The Proposed Methodology
			Overview of Data Set of Illustrative Example
			Description of the Methodology
				Step 1: Determine the Structural Homogeneity Status of the Data Set
				Step 2: Determine the Relative Efficiency Status of DMUs
				Step 3: Describe the Relative Efficiency Categories
		Conclusion
		Acknowledgment
		References
	Chapter 27: An Exploration of the Intrinsic Negative Socio-Economic Implications of ICT Interventions
		Introduction
		Socio-Economic Impact of ICT
			Tools, Machines, and ICT
			Routes of Elimination and Substitution
			Conditions for Elimination and Substitution
			Pragmatics and Ethics of Implementation
			Dimensions of Social Impact of ICT
			Platform, Message, and Target
			Competing with Others: Additional Implications
			Competing with Others: Social Implications
			Impact of Collaboration
			Investigating Negative Implications of ICT: What Is the Plan?
		Conclusion
		References
SECTION IV: Appendix X
	The Purpose and the Suggested Use of the Content in this Appendix
Appendix X1: Models of Economic Growth
	References
Appendix X2: A Model of the Socio-Economic Impact of ICT
	References
Index