Uso da internet no setor de hotelaria de Recife-PE

A Internet é uma tecnologia que revolucionou o mundo, criando novas formas de interação entre pessoas, organizações e negócios. O setor hoteleiro é um segmento que muito tem se beneficiado dos serviços suportados pela Internet. O objetivo do estudo é identificar os diferentes fatores que influenciam ao uso da Internet sob três dimensões: individual, organizacional e ambiental. Um modelo conceitual foi postulado contendo nove variáveis independentes sobre duas variáveis dependentes, relativas ao padrão de uso da Internet. Os dados foram coletados junto a 52 hotéis localizados no litoral do Recife – PE. O resultado da análise inferencial dos dados mostrou um padrão diferenciado de uso da Internet nos hotéis de pequeno, médio e grande porte e como os fatores acima descritos podem ser mais bem explorados a fim de se atingir um eficiente padrão de uso, aumentando suas posições competitivas. Baseadas na análise e resultados obtidos do estudo, são esboçadas algumas recomendações e implicações para futuras pesquisas. ABSTRACT:The Internet technology has revolutionized the world, creating new forms of interaction among people, organizations and businesses. The hotel sector has reaped many benefits from services supported by the Internet. The object of this study is to explore different factors that influence the adoption of the Internet in three areas: individual, organizational and environment. A conceptual framework was advanced containing nine independent variables and two dependent variables related to the usage of the Internet. Data was collected from 52 hotels located along the coast of Recife, PE, Brazil. Analysis of the data has demonstrated the Internet use in small, medium and large size hotels. Some attributes of the Internet usage could be better utilized by owners and managers in order to achieve a more efficient pattern of use, improving their competitive position. Based on the findings obtained from the study, some recommendations and implications for future research are advanced

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model