Search

O presente estudo tem por objetivo analisar as práticas ilícitas denominadas geopricing e geoblocking por parte de determinadas empresas em face do mercado consumidor, através da ótica da defesa do consumidor e da defesa da concorrência, bem como de alguns aspectos da Lei Geral de Proteção de Dados. É notório que atualmente a organização dos mercados está a se redesenhar a partir da inovação tecnológica e das ferramentas que ela propicia. Com isso, é possível verificar uma redução severa nos custos de transação envolvendo operações de compra e venda de produtos, diante da progressiva desnecessidade de uma estrutura física e de todos os custos dela decorrentes. Entretanto, essa nova estrutura de oferecimento de produtos e serviços pode acarretar falhas de mercado, como assimetria informacional e externalidades negativas ao mercado consumidor, de modo a originar resultados econômicos ineficientes ou indesejáveis socialmente. Isso porque empresas podem abusar de seu poder de mercado e, valendo-se do uso discriminatório de dados pessoais dos consumidores e da assimetria informacional existente entre os agentes, praticam atos ilícitos, destacando-se o geopricing e o geoblocking, que consistem em práticas de diferenciação de preço e negativa de oferta de produtos ou serviços de acordo com a geolocalização do consumidor. Nesse sentido, a pesquisa se desenvolveu por meio do método lógico-dedutivo, lastreada na revisão bibliográfica, a partir do qual busca-se analisar a geodiscriminação com base na utilização de dados à luz da defesa do consumidor e da concorrência.

The purpose of this study is to analyse the General Data Protection Law for the Protection of Personal Data from the perspective of the protection of the consumer's personal data, with a view to ascertaining the main aspects of the legislation and verifying its impacts in relation to geopricing practices and geoblocking. To that effect, it begins by addressing the principles of the new legislation that inform the activity of processing personal data. Right after, the main axes of structuring the law are presented, focusing on aspects that concern the processing of consumer data. Finally, the practices of geodiscrimination will be examined, with the effect of assessing the legal treatment in relation to such techniques and how they may be affected after the entry into force of the General Data Protection Law. For that, the hypothetico-deductive methodology and the bibliographic research technique were adopted. Thus, it is observed that new data protection legislation added to the protection of consumers' rights in relation to the practices of geopricing and geoblocking, insofar as the standard was designed to prevent the disinformation of the personal data holder on the purpose of the treatment of your information and the illegitimate treatment of personal data, as well as covering the possibility of redressing the consumer who holds personal data if he experiences damage.

Purpose – This study aims to demonstrate that companies are not free to operate in the e-commerce field, notably with regard to geoblocking and geopricing practices, since they must duly respect constitutional economic order principles. Methodology/approach/design – The methodology of the paper is based on Mike Feintuck’s public interest theory, according to which there are values beyond those of market economics that should be preserved, often to the detriment of private interests. Furthermore, the Decolar.com case is used as an empirical case study. Findings – It has been identified that geoblocking and geopricing practices can effectively violate constitutional principles and that consumer and antitrust microsystems can suppress those conducts, shaping the performance of economic agents to the public interest. Practical implications – The results of this article indicate that consumer and competition agencies can act more actively to curb the harmful geoblocking and geopricing practices.

Copyright of Quaestio Iuris (QI) is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article introduces a new model which aims at spatially optimizing the price of a product or service by considering supply and demand features, including their geographical location. Introducing the concept of a geomarketing-mix, factorial analysis and fuzzy clustering can be employed to automatically detect business and strategic opportunities. The method is applied to the French secondhand car market. By so doing, it is possible, first, to identify geographic areas that are typical of a certain supply and, second, to specify the optimal prices and types of vehicles for sale in these areas in view of a given marketing strategy. [ABSTRACT FROM AUTHOR]

Cet article présente un nouveau modèle qui vise à optimiser spatialement le prix d'un produit ou service en prenant en compte les caractéristiques de l'offre et la demande, y compris leur localisation géographique. L'introduction du concept du géomarketing-mix, de l'analyse factorielle et d'une segmentation par algorithme flou peut servir à détecter automatiquement des opportunités commerciales et stratégiques. Cette méthode est appliquée au marché français des véhicules d'occasion. Ce faisant, il est possible, premièrement, d'identifier les zones géographiques qui sont représentatives d'une certaine offre et, deuxièmement, de préciser les prix optimaux et les types de véhicules à vendre dans ces zones afin d'établir une stratégie marketing donnée. [ABSTRACT FROM AUTHOR]

Copyright of Law, State & Telecommunications Review / Revista de Direito, Estado e Telecomunicações is the property of Grupo de Estudos em Direito das Telecomunicacoes (GETEL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a fully Bayesian approach to non-life risk premium rating, based on hierarchical models with latent variables for both claim frequency and claim size. Inference is based on the joint posterior distribution and is performed by Markov Chain Monte Carlo. Rather than plug-in point estimates of all unknown parameters, we take into account all sources of uncertainty simultaneously when the model is used to predict claims and estimate risk premiums. Several models are fitted to both a simulated dataset and a small portfolio regarding theft from cars. We show that interaction among latent variables can improve predictions significantly. We also investigate when interaction is not necessary. We compare our results with those obtained under a standard generalized linear model and show through numerical simulation that geographically located and spatially interacting latent variables can successfully compensate for missing covariates. However, when applied to the real portfolio data, the proposed models are not better than standard models due to the lack of spatial structure in the data. [ABSTRACT FROM AUTHOR]