Today, Revenue Management is a key practice in the air transport, tourism and hotel industries. Originally known as Yield Management, Revenue Management has gradually evolved into an integral revenue optimization strategy for businesses characterized by capacity constraints and fluctuating demand. Revenue Management in the Age of Artificial Intelligence explores, through numerous case studies and concrete examples, the principles, models and applications of Revenue Management, while addressing the ethical challenges and prospects offered by digital technology and artificial intelligence. This book is aimed at professionals, students, researchers and anyone wishing to understand the dynamics of price management in a constantly changing economic environment. It highlights the importance of transparency and fairness in maintaining consumer confidence, while demonstrating that Revenue Management is much more than a simple pricing technique: it is an essential strategic tool for many service companies.

Table of Contents:
Acknowledgements xiii Preface xv General Introduction xvii Chapter 1. What is Revenue Management? 1 1.1. Introduction 1 1.2. Origins and evolution of the discipline: from Yield Management to Revenue Management 2 1.3. Distinction between Revenue Management, Yield Management and pricing 4 1.3.1. Yield Management 5 1.3.2. Pricing 5 1.3.3. Revenue Management 7 1.4. General objectives of Revenue Management 9 1.4.1. Maximizing revenue 9 1.4.2. Demand management 9 1.4.3. Capacity optimization 10 1.4.4. Personalizing offers 10 1.5. Conditions of applying Revenue Management in service companies 10 1.5.1. Constrained company capacity 11 1.5.2. The perishability of supply 11 1.5.3. The level of fixed versus variable costs 11 1.5.4. The possibility of booking the service in advance 11 1.5.5. Temporal variability of demand 12 1.5.6. The possibility of segmenting demand 12 1.5.7. Price elasticity of demand from individual customers 13 1.5.8. Communication and distribution capacity 14 1.5.9. The absence of perfect competition 14 1.5.10. Demand forecasting capability 16 1.5.11. Low consumer sensitivity to pricing strategies 16 1.5.12. Organizational flexibility and operational flexibility 16 1.6. The fundamental components of Revenue Management 17 1.6.1. Price management 17 1.6.2. Capacity or inventory management 17 1.6.3. Analytics 18 1.7. The pillars of Revenue Management 18 1.7.1. Revenue Management forecasts 18 1.7.2. Capacity (inventory) and price optimization 19 1.7.3. Performance analysis and management control 19 1.8. Steps in the classic Revenue Management process 21 1.8.1. Analysis of the business environment and competition 21 1.8.2. Market and consumer behavior analysis 22 1.8.3. Demand analysis and segmentation 22 1.8.4. Qualifying and positioning the company’s offering 23 1.8.5. Setting up pricing grids and capacity management 28 1.8.6. Dynamic price and capacity management 30 1.8.7. Dynamic tariff class management rules 31 1.8.8. Bottom-up price management 32 1.8.9. Overbooking 32 1.9. Software and technological tools used in Revenue Management 34 1.10. The evolution and challenges of Revenue Management 35 1.11. Revenue Management’s disciplinary and scientific positioning 35 1.12. Toward a microeconomic approach to Revenue Management 37 1.13. Conclusion 38 Chapter 2. Revenue Management Models for the Tourism, Hotel and Transport Industries 41 2.1. Introduction 41 2.2. Revenue Management forecasting models 41 2.2.1. Traditional Revenue Management forecasting models 42 2.2.2. Advanced econometric models 46 2.2.3. Methods for forecasting seasonal variations 48 2.2.4. Method of unconstraining capacities 48 2.2.5. Comparative analysis of traditional forecasting methods 50 2.2.6. Modern techniques and the contribution of artificial intelligence 50 2.3. Capacity and price optimization models in Revenue Management 51 2.3.1. Probabilistic capacity and price optimization models 52 2.3.2. The bid price method 57 2.3.3. Threshold curve method 58 2.3.4. Empirical approaches to Revenue Management optimization 59 2.3.5. Overbooking models 60 2.3.6. Group management strategies 61 2.3.7. Optimizing distribution channels 62 2.4. Performance measurement and control models in Revenue Management 63 2.4.1. Occupancy rate 63 2.4.2. Revenue per available room. 63 2.4.3. Gross operating profit per available room 64 2.4.4. Average daily rate 64 2.5. Controversial Revenue Management models 64 2.5.1. Price models based on the lure effect 68 2.5.2. Models based on countdown techniques 71 2.6. Conclusion 72 Chapter 3. Revenue Management Perceptions and Consumer Behavior 75 3.1. Introduction 75 3.2. Concepts of fairness and unfairness in social relations 76 3.3. The contributions of Adams’ equity theory (1965) 76 3.4. Equity theory in Revenue Management 77 3.5. Impact of perceptions of inequity on behavior 79 3.5.1. Contributions of Deutsch’s model (1975) 80 3.5.2. Contributions of Oliver and Swan’s model (1989) 81 3.5.3. Contribution of behavioral economics theories 82 3.5.4. Organizational justice theories 82 3.6. The quest for fairness in Revenue Management 85 3.6.1. The instrumental model 85 3.6.2. The interpersonal model 85 3.6.3. The deontic model 86 3.7. Role of product value in price judgment 87 3.8. The importance of justifying pricing policies 87 3.9. Assigning responsibility in price judgments 88 3.10. Influence of perceived opacity on prices 89 3.11. The impact of normative deviance on the perception of Revenue Management 90 3.12. The influence of perceived risk on Revenue Management perception 92 3.13. Contingency factors of perceived unfairness toward Revenue Management 92 3.13.1. Consumer-internal contingency factors 93 3.13.2. External contingency factors 95 3.14. Consequences of perceptions of unfairness toward Revenue Management 95 3.14.1. Consequences for consumer attitudes 96 3.14.2. Consequences for consumer behavior 96 3.14.3. Impact on brand image and company performance 98 3.15. The integrative model 98 3.16. Limitations of models on perceived price unfairness 99 3.17. Conclusion 101 Chapter 4. Qualitative Study of Affective Reactions to Revenue Management 103 4.1. Introduction 103 4.2. State of the art on perceived price unfairness 104 4.3. Gaps in research into consumers’ affective reactions 106 4.4. Research methodology 107 4.4.1. Critical incident technique 107 4.4.2. Survey sample, transcription and preanalysis of data 107 4.4.3. Analysis techniques used 108 4.5. Research results 110 4.5.1. The multidimensionality of perceived unfairness in Revenue Management practices 110 4.5.2. Confirmation of the multidimensionality of perceived unfairness in Revenue Management 111 4.5.3. Characterization of the affective manifestations of perceived unfairness in Revenue Management 113 4.6. Clarifying indicators of perceived unfairness to Revenue Management 116 4.7. Conclusion: discussion, contributions and limitations of the study 118 Chapter 5. Measuring Perceived Unfairness in Revenue Management 121 5.1. Introduction 121 5.2. Models for measuring perceived unfairness in Revenue Management 122 5.3. Exploratory qualitative studies and identification of indices of perceived unfairness 123 5.4. Development of a scale to measure perceived unfairness in Revenue Management 124 5.4.1. Definition of the construct domain of perceived unfairness in Revenue Management 124 5.4.2. Specification of the measurement model and scale items 124 5.4.3. Exploratory factor analysis of perceived unfairness in Revenue Management 125 5.4.4. Results of the PCA of perceived unfairness in Revenue Management 126 5.4.5. Interpretation of the selected factorial axes 127 5.4.6. Confirmatory analysis of the Revenue Management perceived unfairness scale 128 5.4.7. Testing the reliability of the perceived unfairness Revenue Management scale 129 5.4.8. Measuring the validity of the Revenue Management perceived unfairness scale 129 5.5. Research discussions: contributions, limitations and avenues of research 134 5.5.1. Theoretical research contributions 134 5.5.2. The managerial contributions of research 135 5.5.3. Methodological contributions of the research 136 5.5.4. Limits of the proposed measurement model 136 5.5.5. Future research avenues 137 5.6. Conclusion 137 Chapter 6. Testing an Empirical Model of Responsible Revenue Management in the Hotel Sector 139 6.1. Introduction 139 6.2. The factors of responsible Revenue Management 140 6.2.1. Ethical issues in Revenue Management practices 140 6.2.2. Perceived price fairness 140 6.2.3. Transparent pricing information 141 6.3. Integrating ethics, fairness and transparency into Revenue Management practices 141 6.4. Testing the effects of fairness and transparency on unfairness reduction and WTP Revenue Management-based prices 143 6.4.1. Perceived injustice of Revenue Management 144 6.4.2. Willingness to pay prices resulting from Revenue Management 144 6.4.3. Direct effects of perceived fairness and transparency on reducing perceived injustice and WTP 145 6.4.4. Interaction effects of perceived justice and perceived transparency on perceived injustice and WTP 147 6.5. Research methodology 149 6.5.1. Quantitative data collection and preanalysis 149 6.5.2. Validation of measuring instruments 150 6.5.3. Justifying the choice of structural equations to test the explanatory model 151 6.6. Research results 151 6.6.1. Direct effects of perceived fairness and perceived transparency on the reduction of perceived injustice and on WTP 151 6.6.2. Interaction effects of perceived fairness and perceived transparency on reducing perceived injustice and WTP 153 6.7. Contributions, limits and avenues of research 155 6.7.1. Theoretical contributions 155 6.7.2. Managerial contributions 158 6.7.3. Research limits 159 6.7.4. Prospects and avenues for future research 160 6.8. Conclusion 161 Chapter 7. Towards Ethical and Responsible Revenue Management in the Tourism Sector 163 7.1. Introduction 163 7.2. Price fairness levers in the age of AI 164 7.2.1. Value-based pricing 164 7.2.2. Prices based on time and distance of use 165 7.3. The levers of Revenue Management transparency in the age of AI 166 7.3.1. The challenges of clear pricing information 167 7.3.2. Dynamic communication on the value of the offer 167 7.3.3. Reducing the opacity of Revenue Management-based prices 168 7.3.4. The challenges of information regarding price variation 168 7.3.5. Displaying reliable and transparent information 169 7.3.6. Displaying reference prices to guide consumers 170 7.3.7. Developing media communication on Revenue Management issues 171 7.3.8. The challenges of bottom-up pricing compared with fluctuating prices 172 7.4. The "Best Available Rate" method and its advantages 172 7.5. Price parity between distribution channels 172 7.6. Revenue Management in the mutual interest of both company and consumer 173 7.7. Bundling practices 174 7.8. Cross-selling 174 7.9. Data challenges for ethical personalization of the price offer 175 7.10. Developing a data-driven management culture 175 7.11. Compliance with European regulations on dynamic pricing 177 7.12. Conclusion 178 General Conclusion 181 References 185 Index 205