Revenue Management in the Age of Artificial Intelligence

Name: Revenue Management in the Age of Artificial Intelligence | Towards Ethical and Responsible Price Management in Air Transport, Tourism and Hospitality
Brand: Wiley
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Towards Ethical and Responsible Price Management in Air Transport, Tourism and Hospitality

Sourou Meatchi(Autor*in)

Wiley (Verlag)

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Erschienen am 8. August 2025

246 Seiten

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Inhalt

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