Social Prosperity Perception in Cultural Tourism Destinations: the Case of Peña de Bernal, Huejotzingo and Yuanhuitlán, México

The present quantitative study with a descriptive design tries to determine the conditions of "social prosperity" of three Mexican cultural tourist destinations, Yanhuitlán, Huejotzingo and Peña de Bernal. These places face similar conditions in aspects like marginalization and poverty but with activities like tourism have tried to reverse these adverse conditions. The objective was to know the perception of their population about the improvement of their living conditions as a result of the tourist activity through the application of surveys carried out in the chosen destinations. The instrument used was a questionnaire with a Likert scale to facilitate the response of the informants and the processing of the information. For the validity and reliability of the measurement instrument a factor reduction analysis and a Cronbach's alpha were elaborated, after which a one-way ANOVA was elaborated to know the differences of means taking the Bonferroni and Scheffe tests. The results show a significant difference between the averages of destinations in how residents perceive prosperity in the selected tourist destinations

Spanish unemployment: normative versus analytical regionalisation procedures

In applied regional analysis, statistical information is usually published at different territorial levels with the aim of providing information of interest for different potential users. When using this information, there are two different choices: first, to use normative regions (towns, provinces, etc.), or, second, to design analytical regions directly related with the analysed phenomena. In this paper, provincial time series of unemployment rates in Spain are used in order to compare the results obtained by applying two analytical regionalisation models (a two stages procedure based on cluster analysis and a procedure based on mathematical programming) with the normative regions available at two different scales: NUTS II and NUTS I. The results have shown that more homogeneous regions were designed when applying both analytical regionalisation tools. Two other obtained interesting results are related with the fact that analytical regions were also more stable along time and with the effects of scale in the regionalisation process. Keywords: Unemployment, normative region, analytical region, regionalisation. JEL Codes: E24, R23, C61.

Design of homogenous territorial units: a methodological proposal

One of the main questions to solve when analysing geographically added information consists of the design of territorial units adjusted to the objectives of the study. In fact, in those cases where territorial information is aggregated, ad-hoc criteria are usually applied as there are not regionalization methods flexible enough. Moreover, and without taking into account the aggregation method applied, there is an implicit risk that is known in the literature as Modifiable Areal Unit Problem (MAUP) (Openshaw, 1984). This problem is related with the high sensitivity of statistical and econometric results to different aggregations of geographical data, which can negatively affect the robustness of the analysis. In this paper, an optimization model is proposed with the aim of identifying homogenous territorial units related with the analyzed phenomena. This model seeks to reduce some disadvantages found in previous works about automated regionalisation tools. In particular, the model not only considers the characteristics of each element to group but also, the relationships among them, trying to avoid the MAUP. An algoritm, known as RASS (Regionalization Algorithm with Selective Search) it also proposed in order to obtain faster results from the model. The obtained results permit to affirm that the proposed methodology is able to identify a great variety of territorial configurations, taking into account the contiguity constraint among the different elements to be grouped.

Fatores que afetam a adoção de análises de Big Data em empresas

With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.Con la cantidad total de datos duplicándose cada dos años, el bajo precio de la informática y del almacenamiento de datos, la adopción del análisis Big Data (BDA) es altamente deseable para las empresas, como un instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas empresas no han adoptado estas técnicas? Para responder a esta pregunta, ampliamos la teoría unificada de la adopción y uso de tecnología (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS). Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan las empresas para implementarla. Mientras que las compañías que planean usar BDA esperan muy buenos resultados, las usuarias actuales son más escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento de dados tornam a adoção de análises de Big Data (BDA) desejável para as empresas, como aquelas que obterão uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variáveis: resistência ao uso e risco percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários atuais são mais céticos em relação ao seu desempenho