Sustainable Development Through Data Analytics and Innovation : Techniques, Processes, Models, Tools, and Practices

Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management. Preface Contents Contributors The Impact of Business Analytics on Industry, Education, and Professional Development 1 Introduction 2 Literature Review 3 Methodology 3.1 Information Sources and Selection Approach 3.2 The Selection Process 4 Results 4.1 Definitions of Business Analytics 4.2 Relationship of Business Analytics with Business and Other Fields 4.3 The Functionality of Business Analytics 4.4 Application of Business Analytics in Industry 4.5 Training and Education Situation 5 Discussion 5.1 Principal Findings 5.2 Strengths and Limitations of Study 5.3 Practical Limitations 5.4 Research Limitations 6 Conclusions References Green Purchasing Behavior on Social Media: A Goal-Framing Theory Perspective 1 Introduction 2 Literature Review 2.1 Social Media Persuasion and Goal-Fain Frame Theory 3 Methodology 3.1 Sampling Technique and Research Instrument 3.2 Pilot Testing and Data Screening 4 Results 4.1 Measurement Model 4.2 Structural Model 5 Conclusion and Implications 5.1 Limitations References Business Analytics and Sustainability 1 Introduction 2 What Is Business Analytics? 3 Types of Business Analytics 4 Popular Analytics Tools 5 Sustainability in Business 5.1 What is Corporate Sustainability? 6 How Does Corporate Sustainability Differ from Corporate Social Responsibility? 7 Why Is Corporate Sustainability Important? 8 The Triple Bottom Line TBL/3BL 9 Analytics and Sustainability 10 Conclusion References Data Science and External Audit 1 Aspects of Using Big Data Analytics in External Audit 1.1 Assess Fraud Risk 1.2 Audit Evidence 2 Audit Data Analytics 3 Application of ADA 4 Audit Data Analytics Techniques 5 Audit Data Analytics Framework 6 Performing Audit Data Analytics (ADAs) 6.1 Plan the ADA 6.2 Access and Prepare the Data for Purposes of the ADA 6.3 Consider the Relevance and Reliability of the Data 6.4 Perform the ADA 7 BDA ́s Tools and Techniques 7.1 Artificial Intelligence 7.2 Data Analytics (Descriptive, Diagnostic, Predictive, and Prescriptive) 7.3 Machine Learning Application 7.4 Data Visualization 7.5 Data Mining 8 How to Use ADAs in Risk Assessment Procedures? 9 How to Use Audit Data Analytics (ADA) in Substantive Analytical Procedures? 10 How to Use ADAs in Tests of Detail? References Digitalization in Passengers ́ Air Travel 1 Introduction 2 Literature Review 2.1 Digitalization and Digital Technology 2.2 Current Aviation Industry 2.3 Digital and Aviation Industry 2.4 Digital and Passenger 2.5 Passenger and Door-to-Door (D2D) Model 3 Research Method 3.1 Survey Research 3.2 Data Analyses 4 Results and Discussion 4.1 General Journey 4.2 Stage 1-From Origin to Airport and Stage 5-From Airport to Destination 4.3 Stages 2 and 4-At Airports 4.4 Stage 3-Inflight 4.5 Sustainability 5 Conclusion and Implication 5.1 Conclusion 5.2 Research Limitation and Future Work References Application of Blockchain Innovative Technology in Logistics and Supply Chain Management: A New Paradigm for Future Logistics 1 Introduction 2 Literature Review 3 Methodology 4 Results 5 Practical Implication of Blockchain in Logistics and SCM 6 Conclusion References Industry 4.0: Drivers and Challenges in Developing the Smart Supply Chain Management 1 Introduction 2 Background 2.1 The Nine Technological Pillars of Industry 4.0 2.1.1 The Internet of Things 2.1.2 Cloud Computing 2.1.3 Big Data 2.1.4 Cyber Security 2.1.5 Systems Integration 2.1.6 Additive Manufacturing 2.1.7 Autonomous Systems 2.1.8 Augmented Reality 2.1.9 Simulation 3 Analysis 3.1 The Design Principles in Industry 4.0 3.1.1 Interoperability 3.1.2 Virtualization 3.1.3 Decentralization 3.1.4 Real-Time Capability 3.1.5 Service Orientation 3.1.6 Modularity 3.2 Industry 4.0 in Smart Supply Chain Management 3.2.1 Instrumented 3.2.2 Interconnected 3.2.3 Intelligent 3.2.4 Automated 3.2.5 Integrated 3.2.6 Innovative 3.3 Industry 4.0: Drivers and Challenges for Development of Smart Supply Chain Management 4 Conclusion References Reimaging Corporate Reporting in a Digital Economy Through Accounting Data Analytics 1 Introduction 2 What Is Corporate Reporting? 3 The Digital Economy 4 Accounting Data Analytics 5 What Is Big Data? 6 The Accountant and Big Data 6.1 Bookkeeping 6.2 Accounting 6.3 Calculating Machines 6.4 Computers 6.5 Internet 6.6 Cloud Computing 6.7 Internet of Things (IoT) 7 Categories and Characteristics of Data 8 Terminologies in Big Data Analytics 9 Types of Data Analytics 9.1 Descriptive Analytics 9.2 Diagnostic Analytics 9.3 Predictive Analytics 9.4 Prescriptive Analytics 10 Benefits of Big Data 11 Sources of Big Data 12 Conclusion References Responding to COVID-19 Emergency: The Packaging Machinery Sector in Italy 1 Introduction 2 Literature Review 3 Methodology (Surveys 1 and 2) 4 Context Analysis: The Automatic Machine Sector 5 Results 5.1 How the Sector Suffered from COVID-19? 5.1.1 Survey 1 Results 5.2 How the Sector Reacted to COVID-19? 5.2.1 Survey 2 Results 5.2.2 Adoption of Industry 4.0 Technologies 5.2.3 The Reaction to the COVID Emergency and Smart Working 6 Discussion and Conclusions References Achieving Sustainable Development Through Green HRM: The Role of HR Analytics 1 Introduction 1.1 The Environment Dimension of Sustainable Development 1.2 Circular Economy 2 Achieving Sustainable Development through Green HRM 2.1 Green HRM System Components 2.2 Green HRM and Circular Economy 2.3 Green HRM and Environmental Performance 3 Role of HR Analytics 3.1 HR Analytics: Concept Definition 3.2 HR Analytics Elements 3.3 Strategic Role of HR Analytics: Applying the Resource-Based View 3.4 Green HRM and HR Metrics 3.5 Linking HR Analytics with Green HRM and Circular Economy 4 Conclusion References Understanding Malaysian B40 Schoolchildren ́s Lifestyle and Educational Patterns Using Data Analytics 1 Introduction 2 Background 2.1 B40 Community 2.2 Education Monitoring System 2.3 Descriptive Data Mining and Related Works 2.4 Factors That Affect Academic Performance 2.4.1 Personality Traits 2.4.2 Financial Stability 2.4.3 Social-Emotional Development (at Home) 2.4.4 Social Emotional Development (at School) 2.4.5 Technology and Education 2.4.6 Health and Well-Being 2.4.7 Access to Physical and Material Resources 3 Methodology 3.1 B40 Schoolchildren Survey Data 3.2 Data Analytics Pattern Analysis Framework 4 Results and Discussion 4.1 Demographic Analysis 4.2 Relationship Patterns Using Correlation Analysis 5 Conclusion References