Smoothed bootstrap Malmquist index based on DEA model to compute productivity of tax offices

This paper analyses the productivity growth of the SUMA tax offices located in Spain evolved between 2004 and 2006 by using Malmquist Index based on Data Envelopment Analysis (DEA) models. It goes a step forward by smoothed bootstrap procedure which improves the quality of the results by generalising the samples, so that the conclusions obtained from them can be applied in order to increase productivity levels. Additionally, the productivity effect is divided into two different components, efficiency and technological change, with the objective of helping to clarify the role played by either the managers or the level of technology in the final performance figures.

A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables

This study evaluates the technical efficiency of the learning-teaching process in higher education using a three-stage procedure that offers advances in comparison to previous studies and improves the quality of the results. First, it utilizes a multiple stage Data Envelopment Analysis (DEA) with contextual variables. Second, the levels of super efficiency are calculated in order to prioritize the efficiency units. And finally, through sensitivity analysis, the contribution of each key performance indicator (KPI) is established with respect to the efficiency levels without omission of variables. The analytical data was collected from a survey completed by 633 tourism students during the 2011/12, 2012/13 and 2013/14 academic course years. The results suggest that level of satisfaction with the course, diversity of materials and satisfaction with the teacher were the most important factors affecting teaching performance. Furthermore, the effect of the contextual variables was found to be significant.

Eficiencia en la distribución: una aplicación en el sector de agencias de viajes

El objetivo de este trabajo consiste en estimar la eficiencia productiva y de escala con la que operan los intermediarios del sector minorista español de distribución turístico. Adicionalmente, se pretende examinar los determinantes de la eficiencia en términos de la integración vertical, concentración horizontal, tamaño y resultados de las entidades. La metodología aplicada se apoya en diversas técnicas de medición de eficiencia (paramétrica de naturaleza estocástica y no paramétrica del Análisis Envolvente de Datos, DEA), así como en modelos tobit para conocer el impacto de los factores del mercado y de la empresa sobre los niveles de eficiencia. La aplicación empírica realizada en una muestra de 50 agencias de viaje de nuestro país evidencia, por un lado, unos elevados índices de ineficiencia técnica y de escala, destacando en el último caso los rendimientos decrecientes; y por otro, que el tamaño y el ROA son los factores determinante de la eficiencia de escala, mientras que la concentración del mercado explica la eficiencia técnica.

Valor de marca colectiva: aproximación desde el enfoque de la eficiencia

El objetivo del trabajo consiste en analizar la eficiencia de las empresas que integran una marca colectiva en una industria productora de bienes de experiencia. El supuesto básico es que la marca colectiva tiene un impacto positivo en la eficiencia de las empresas acogidas a la misma, el cual viene explicado porque la reputación colectiva fomenta una inversión eficiente en calidad. Sin embargo, la marca colectiva también puede tener un efecto opuesto sobre los incentivos de una empresa a una inversión en calidad ya que dicha marca puede crear un incentivo a “free ride”. Nuestra propuesta defiende que la interacción entre estos factores opuestos, reputación colectiva y “free ride”, viene moderada por las características de la marca colectiva y de la propia empresa. La metodología aplicada en el contraste de estas hipótesis se apoya en el Análisis Envolvente de Datos para estimar la eficiencia, así como en modelos econométricos para explicar la eficiencia empresarial mediante características de la marca colectiva y de la empresa. Los resultados obtenidos en el ámbito de las bodegas españolas evidencian que las marcas colectivas tienen un impacto positivo sobre la eficiencia, el cual viene moderado por el tamaño de la marca colectiva generando una relación curvilínea en forma de U invertida. Adicionalmente, el volumen de producción de la marca colectiva y el tamaño de las bodegas ejercen un efecto moderador en el impacto del tamaño de la marca colectiva sobre la eficiencia. En general, los resultados ponen de manifiesto la importancia de las marcas colectivas cuando se investigan industrias donde la calidad no es solamente señalizada por una marca típica individual.

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.

Economic efficiency of members of protected designations of origin: sharing reputation indicators in the experience goods of wine and cheese

The aim of this paper is to analyse the economic efficiency of members of protected designations of origin (PDO). For the first time we analyse the value of PDO labels from the point of view of economic efficiency. The central hypothesis is that a PDO has a positive impact on the economic efficiency of its member companies and that this is because a PDO label is a collective reputation indicator that foments efficient investment in quality in terms of member returns. The methodology applied to test this hypothesis is based on data envelopment analysis to estimate economic efficiency, and econometric models to explain company efficiency through both the PDO label, as an indicator of collective reputation, and the characteristics of the company. The results obtained in the experience goods of wine and cheese in Spain show that PDO labels have a positive impact on economic efficiency. Additionally, the age and size of the company have a positive effect while the wage level of the company has a different influence on efficiency depending on the sector considered. Overall, the results reveal the importance of PDOs in industries in which the signal of reputation is not only reliant on the individual brands.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.