List of Contributors

Chapters in this book

1. Frontmatter I
2. Preface V
3. Contents IX
4. Part 1: Competition and Regulation
5. Chapter 1 The Rise of Artificial Intelligence: Towards a Modernisation of Competition Policy 1
6. Part 2: Production and Processes
7. Chapter 2 “KI-AGIL” – An Agile Process Model to Make AI Development Accessible to SMEs 19
8. Chapter 3 Automatic Classification of Files Based on the Classes of IEC 61355 31
9. Part 3: Finance and Accounting
10. Chapter 4 Auditing Algorithms in the (Non-)Financial Audit: Status Quo and Way Forward 45
11. Chapter 5 Barriers to the Use of Artificial Intelligence (AI) in Management Reporting 57
12. Chapter 6 Transforming Management Accounting with Robotic Process Automation – Requirements and Implications 71
13. Part 4: Organisation and Workflow
14. Chapter 7 Approach for the Identification of Requirements on the Design of AI-supported Work Systems (in Problem-based Projects 87
15. Chapter 8 Plug and Play AI – How Companies Can Benefit from AI as a Service 101
16. Part 5: HR and Employment
17. Chapter 9 Developing Personas of Ideal-type Candidates in AI-related Jobs 115
18. Chapter 10 Artificial Intelligence and Care Leaders: A Critical Perspective 131
19. Part 6: Artificial Intelligence and Humans
20. Chapter 11 Public Perception of Artificial Intelligence: A Systematic Evaluation of Newspaper Articles Using Sentiment Analysis 139
21. Chapter 12 Generational Differences in Framing for Social Robot Usage Intention from a Consumer Behaviour Point of View 155
22. Chapter 13 Towards a Structuralist Data Narratology 171
23. Chapter 14 Exploring the Adoption of AI for Customer Engagement Marketing by Small and Medium Enterprises in South Africa: A Literature Review of Challenges and Opportunities 185
24. Part 7: Forecasting
25. Chapter 15 Forecasting Brent Oil Volatility: DeepAR vs LSTM 203
26. Chapter 16 Energy Stock Price Forecast Based on Machine Learning and Sentiment Analysis – Which Approach Performs Best in Day Trading? 225
27. Chapter 17 Optimising Water Supply – Application of Probabilistic Deep Neural Networks to Forecast Water Demand in the Short Term 243
28. List of Contributors 257
29. About the Editors 261
30. List of Figures 263
31. List of Tables 265