Person: Sánchez Cartas, Juan Manuel
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First Name
Juan Manuel
Last Name
Sánchez Cartas
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Económicas y Empresariales
Department
Análisis Económico y economía cuantitativa
Area
Fundamentos del Análisis Económico
Identifiers
4 results
Search Results
Now showing 1 - 4 of 4
Item Platform acquisitions, product imitation and openness(Journal of Industrial and Business Economics, 2023) Sánchez Cartas, Juan ManuelPublic authorities have shown concern about the possible harmful effects of platforms acquiring or imitating complementary services sold on their platforms. The effect of these practices on the restrictions on participation, development, or use of platform services (platform openness) has started to attract the attention of policymakers and researchers alike, but the evidence is still limited. We build a model that considers the trade-off that a monopoly platform faces when deciding whether to acquire or imitate a complementor and how such a decision influences openness and welfare. We show that a platform always has an incentive to acquire or imitate complementors. Which one is preferred depends on whether the increase in platform value (acquisition) offsets the market expansion effect (imitation). We find that acquisitions reduce openness and welfare but may generate more valuable complements while imitation increases openness and welfare but may harm third-party developers.Item Responsible users and platform competition: a computational model(Heliyon, 2024) Katsamakas, Evangelos; Sánchez Cartas, Juan ManuelCorporate Social Responsibility (CSR) is an increasingly important topic in business, especially in the context of digital platforms where consumers and policymakers care about the social responsibility of platforms. This paper introduces the concept of responsible users, defined as users who make decisions considering their CSR preferences in platform settings. However, how responsible users may affect platform strategic behavior and competition is unclear. Therefore, we propose a computational model of platform price competition that considers the presence of responsible users. We find that CSR preferences have pro-competitive effects that reduce prices and profits in equilibrium. However, this effect depends on how large CSR preferences can be. We also explore several market asymmetries and clarify their implication for platform price structures and profits. Furthermore, we find that it only matters that users express their CSR preferences, regardless of how those preferences are generated. By integrating the responsible user concept into platform competition, our work contributes to both platform competition and CSR literature. We discuss practical implications for platform users and managers and future research opportunities.Item AI pricing algorithms under platform competition(Electronic Commerce Research, 2024) Sánchez Cartas, Juan Manuel; Katsamakas, EvangelosPlatforms play an essential role in the modern economy. At the same time, due to advances in artificial intelligence (AI), algorithms are becoming more widely used for pricing and other business functions. Previous literature examined algorithmic pricing, but not in the context of network effects and platforms. Moreover, platform competition literature has not considered how algorithms may affect competition. We study the performance of AI pricing algorithms (Q-learning and Particle Swarm Optimization) and naïve algorithms (price-matching) under platform competition. We find that algorithms set an optimal price structure that internalizes network effects. However, no algorithm is always the best because profitability depends on the type of competing algorithms and market characteristics, such as differentiation and network effects. Additionally, algorithms learn autonomously when an equilibrium is unstable and avoid it. When algorithm adoption is an endogenous strategic decision, several algorithms can be adopted in equilibrium; we characterize the conditions for the various outcomes and show that the equilibrium and platform profits are sensitive to algorithm design changes. Overall, our research suggests that AI algorithms can be effective in the presence of network effects, and platforms are likely to adopt a variety of algorithms. Lastly, we reflect on the business value of AI and identify opportunities for future research at the intersection of AI algorithms and platforms.Item A computational model of the effects of borrower default on the stability of P2P lending platforms(Eurasian Economic Review, 2024) Katsamakas, Evangelos; Sánchez Cartas, Juan ManuelPeer-to-peer (P2P) lending has attracted scholarly attention because of its economic significance and potential to democratize access to finance. However, P2P lending platforms face many challenges and failures that we need to understand more clearly. We build a computational model to study how borrower default affects P2P platform lending. We show that borrower default disrupts the P2P network formation process and undermines platform stability. Moreover, we find that defaults increase the inequality in accessing funding and provide a rationale for using curation rules, widely used in P2P platforms, in contrast to P2P insurance, which fosters cascading defaults. We also address a new trend in P2P lending platforms in which large companies (institutional investors) play an increasingly important role. We f ind that the presence of large companies creates a denser network (more loans) but generates a trade-off between making the platform more resilient to cascading defaults and more dependent on specific players. Overall, we explain how borrower defaults affect platform stability and what makes a platform vulnerable, threatening its survival. We discuss research and managerial insights into platform stability and the economic effect of P2P lending platforms.