Corporate social responsibility practices at brazilian firms

Corporate Social Responsibility practices have been on the rise in recent years in firms all over the world. Brazil, as one of the most important countries emerging on the international scene, is no exception to this, with more and more firms taking up these practices. The present study focuses on analyzing the corporate social responsibility practices that Brazilian companies engage into. The sample used is comprised of 500 firms grouped by geographical area; the theoretical framework is based on stakeholder and institutional theories; and the technique used for the analysis is the biplot, more specifically the HJ Biplot and cluster analysis. From the results obtained it is possible to infer that the CSR variables corresponding to environmental practices are more closely linked to companies located in the northern areas of Brazil. Social and community practices are related to companies primarily in the southern and northeastern regions of the country.

Contributo do enoturismo para o desenvolvimento de regiões do interior: o caso da rota dos vinhos do alentejo

The wine industry and its relationship with the tourism sector, as well as with the motivations, characteristics, and behaviors wine tourists are relevant for the valuable creation. Some scholars have identified distinct wine tourism market segments based on a supply-side analysis of certain wine-producing tourism destinations. Subsequent studies used cluster analysis to prove that specific wine tourism market segments can be identified in the Alentejo Wine Route. The significant dimension and economical importance of wine tourism in the Alentejo region point towards the need for more dynamic planning and branding efforts. Such efforts will no doubt place the Alentejo Wine Region on par with some of the foremost wine-producing regions in the world.

Return premiums of sustainable companies listed on the Spanish stock market

The purpose of this paper is to analyze whether companies with a greater commitment to corporate social responsibility (SRI companies) perform differently on the stock market compared to companies that disregard SRI. Over recent years, this relationship has been taken up at both a theoretical and practical level, and has led to extensive scientific research of an empirical nature involving the examination of the relationships existing between the financial and social, environmental and corporate governance performance of a company and the relationship between SRI and investment decisions in the financial market. More specifically, this work provides empirical evidence for the Spanish market as to whether or not belonging to a group of companies the market classes as sustainable results in return premiums that set them apart from companies classed as conventional, and finds no differences in the stock market performance of companies considered to be SRI or conventional.

Estratégias de oferta em leilões simétricos no âmbito dos mercados liberalizados de electricidade

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Os determinantes da capacidade regional de inovação nas Regiões Periféricas da União Europeia

Dissertação de Mestrado em Gestão de Empresas/MBA.

On clustering interval data with different scales of measures : experimental results

This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also acknowledge & be non-commercial.

Probabilistic approach for comparing partitions

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon, Portugal.

Clustering of variables with a three-way approach for health sciences

TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.

Diferenciais intermunicipais de condições de vida e saúde: construção de um indicador composto

OBJETIVO: Descrever um índice para reconhecimento das desigualdades de condições de vida e saúde e sua relação com o planejamento em saúde. MÉTODOS: Foram selecionadas variáveis e indicadores que refletissem os processos demográficos, econômicos, ambientais e de educação, bem como oferta e produção de serviços de saúde. Esses indicadores foram utilizados no escalonamento adimensional dos indicadores e agrupamento dos 5.507 municípios brasileiros. As fontes de dados foram o censo de 2000 e os sistemas de informações do Ministério da Saúde. Para análise dos dados foram aplicados os testes z-score e cluster analysis. Com base nesses testes foram definidos quatro grupos de municípios segundo condições de vida. RESULTADOS: Existe uma polarização entre o grupo de melhores condições de vida e saúde (grupo 1) e o de piores condições (grupo 4). O grupo 1 é caracterizado pelos municípios de maior porte populacional e no grupo 4 estão principalmente os menores municípios. Quanto à macrorregião do País, os municípios do grupo 1 concentram-se no Sul e Sudeste e no grupo 4 estão os municípios do Nordeste. CONCLUSÕES: Por incorporar dimensões da realidade como habitação, meio ambiente e saúde, o índice de condições de vida e saúde permitiu identificar municípios mais vulneráveis, embasando a definição de prioridades, critérios para financiamento e repasse de recursos de forma mais eqüitativa.

Turismo de nichos : uma análise ao papel dos seniores como participantes ativos

Dissertação de Mestrado, Gestão do Turismo Internacional, 3 de Dezembro de 2015, Universidade dos Açores.

Entrepreneurship Education : The role of the Higher Education Institutions in the Entrepreneurial Attitudes of the Students

8th International Conference of Education, Research and Innovation. 18-20 November, 2015, Seville, Spain.

Clustering an interval data set : are the main partitions similar to a priori partition?

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.