AI-driven dynamic pricing
Dynamic pricing (DP), the practice of adjusting prices in real time according to market conditions, is becoming an increasingly important tool for businesses seeking to enhance revenue and optimize resource allocation. The adoption of artificial intelligence (AI) technologies has further accelerated...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/440711 |
| Acceso en línea: | https://hdl.handle.net/2117/440711 |
| Access Level: | acceso abierto |
| Palabra clave: | Price fixing Price--Mathematical models Artificial intelligence--Financial applications Preus--Fixació Preus--Models matemàtics Intel·ligència artificial--Aplicacions a les finances Àrees temàtiques de la UPC::Economia i organització d'empreses::Competitivitat i innovació |
| Sumario: | Dynamic pricing (DP), the practice of adjusting prices in real time according to market conditions, is becoming an increasingly important tool for businesses seeking to enhance revenue and optimize resource allocation. The adoption of artificial intelligence (AI) technologies has further accelerated this transformation by enabling firms to analyse vast datasets and implement adaptive, predictive pricing models. This thesis presents a comprehensive analysis of AI-driven dynamic pricing across different sectors, with a specific focus on the food delivery market in Spain. The research explores the economic theory of DP, AI capabilities, ethical and regulatory implications, and sectoral differences in application. A practical case study of the Spanish food delivery market (including platforms such as Glovo, Uber Eats, and Just Eat) demonstrates how regulatory constraints, consumer expectations, and limited AI integration currently shape DP practices. The empirical results reveal that DP remains limited to delivery fee adjustments, with limited impact from external real-time variables, and that regulatory frameworks like the “Ley de los Riders” and EU AI transparency requirements have led some platforms to adopt more static pricing strategies. Furthermore, the thesis identifies key challenges at the intersection of DP and AI: distinguishing dynamic pricing from basic supply-demand adjustments, managing risks of market concentration, addressing fairness and transparency in consumer perception, and balancing stakeholder interests. The findings contribute to understanding how AI can support more effective, ethical, and accepted dynamic pricing systems. The thesis concludes by proposing four critical areas for future research: defining the boundaries of DP, analysing its effects on market structures, establishing fairness metrics, and leveraging AI to maximize efficiency and stakeholder value. |
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