Business Models: Innovation, Digital Transformation, and Analytics (Data Analytics Applications)

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
PREFACE
EDITORS’ BIOGRAPHIES
CONTRIBUTORS’ BIOGRAPHIES
ACKNOWLEDGMENTS
CHAPTER 1: INNOVATION IN BUSINESS MODELS
	Contents
		1.1. Introduction
		1.2. Development of the Business Model Concept
		1.3. Essence of Innovative Activities in the Business Model
		1.4. Typology of Innovative Activities in Business Models
		1.5. Conclusion
		References
CHAPTER 2: BUSINESS MODELS IN THE DIGITAL TRANSFORMATION ERA
	Contents
		2.1. Introduction
		2.2. Theoretical Framework
			2.2.1. Digital Companies
			2.2.2. Digital Transformation
			2.2.3. Agile Methodology
			2.2.4. Business Model
		2.3. Results and Analysis
			2.3.1. Interviews
				2.3.1.1. Analysis 1
				2.3.1.2. Analysis 2
				2.3.1.3. Analysis 3
				2.3.1.4. Interviews Analysis
		2.4. Conclusion
		Acknowledgment
		References
CHAPTER 3: VALUE COMPOSITION FOR BUSINESS MODELS OF HIGH-GROWTH ENTERPRISES
	Contents
		3.1. Introduction
		3.2. Elements of the Business Model Construction
		3.3. Value Composition in HGEs Business Models – Empirical Researches
		3.4. Conclusion
		References
CHAPTER 4: THE VARIETY OF ASPECTS OF BUSINESS MODELS IN THE HIGH-GROWTH AND HIGH-TECH ENTERPRISES: AN ESTONIAN CASE
	Contents
		4.1. Introduction
		4.2. The Concept of Business Model in the Context of Ecosystem
		4.3. Globalization in the Business Model
		4.4. Components of the Business Model Design
		4.5. Empirical Research
		4.6. Main Findings and Discussion
		4.7. Conclusion
		References
CHAPTER 5: EXTERNAL CONDITIONS OF PROFITABILITY OF BUSINESS MODELS OF HIGH-GROWTH ENTERPRISES
	Contents
		5.1. Geographical Conditions of the Functioning of High-Growth Enterprises
		5.2. Financial Condition of High-Growth Enterprises in Poland
		5.3. The Methodology of the Assessment of Profitability of Business Models Adopted by Polish High-Growth Enterprises
		5.4. Cost-Effectiveness of the Business Models Adopted by High-Growth Enterprises – A Multidimensional Analysis
		5.5. Discussion and Conclusion
		References
CHAPTER 6: ANALYZING THE EMPLOYER BRANDING BUSINESS MODELS BASED ON PRIMARY RESEARCH RESULTS
	Contents
		6.1. Introduction
			6.1.1. Veteran Generation
			6.1.2. Baby-Boomer Generation
			6.1.3. Generation X
			6.1.4. Generation Y
			6.1.5. Generation Z
		6.2. Characteristics of Generation "Z" as Workforce
		6.3. Method
		6.4. Findings
			6.4.1. What Does the Z Think about the Conditions of Entering the World of Work?
			6.4.2. What Does It Take to Have a Successful Career? "Is It Surely Only up to Us?"
			6.4.3. How Can an Employer Effectively Motivate Its Z Workers?
			6.4.4. The Ranking of Drivers Influencing the Selection of Workplace, According to the Surveyed Generation Z
		6.5. Conclusion and Recommendations
		References
CHAPTER 7: MODELS OF RESPONSIBLE BUSINESS: CSR FROM SOCIAL AND ECONOMIC PERSPECTIVE
	Contents
		7.1. Introduction
		7.2. Selected CSR Definitions in the Light of the Literature
		7.3. Models of Responsible Business in a Social Context
		7.4. CSR Models in the Economic Context
		7.5. Conclusion
		References
CHAPTER 8: CYBER PROTECTION: INDUSTRIALIZED ASSESSMENTS FOR ANALYZING CYBER RISK
	Contents
		8.1. Introduction
		8.2. Evaluation of Technical and Business Risk Features for a Machine Learning Approach
		8.3. Machine Learning Algorithms for Cyber Insurance Decision-Making Process
			8.3.1. Analysis with Regression Algorithms
			8.3.2. Linear Regression
			8.3.3. Ridge Regression (L2)
			8.3.4. Lasso Regression (L1)
			8.3.5. Analysis with Classification Algorithms
			8.3.6. Logistic Regression
			8.3.7. Support Vector Machines
			8.3.8. Linear Kernel SVM Classification
			8.3.9. Gaussian RBF Kernel SVM
			8.3.10. K-Nearest Neighbors
			8.3.11. Random Forest
		8.4. Model Selection
		8.5. Conclusion
		References
CHAPTER 9: APPLIED DATA ANALYTICS
	Contents
		9.1. Introduction
		9.2. Big Data
		9.3. Data Analytics Techniques
		9.4. Cyber Risk Assessments
			9.4.1. Analysis of Cyber Security-Related Data
			9.4.2. Linear Regression: Identifying Linear Dependencies
			9.4.3. Logistic Regression: When Managing Logical Parameters
			9.4.4. K-Nearest Neighbors Classifier
			9.4.5. Decision Tree
			9.4.6. Support Vector Machines
		9.5. Implementation of the Data Analytics Methods for the Forecast
		9.6. Implementation of the Data Analytics Methods for the Football Matches Forecasts
		9.7. Web-Oriented Tools for Data Analytics
		9.8. Ethics, Regulations, and Law Constraints for Data Analytics
		9.9. Conclusion
		References
INDEX