Digital Divide Across the European Union

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Past/present and future worries: na fMRI study

For the past decade, numerous imaging techniques gave rise to remarka-ble progresses in the understanding of brain’s structure and function. Amongst the wide variety of studies onto the field of neuroscience, neuropsychiatric re-searches with resource to neuroimaging have attracted increasing attention. The present study will focus on the identification of brain areas recruited while normative subjects read sentences related to past/present or future wor-ries. Our main aim was to accurately characterize these brain areas while providing them with a time-stamp that would hopefully help us understand the implications of past/present memories and future envisioning in worrying episodes. With that purpose, functional magnetic resonance imaging data was collected from ten healthy individuals. The obtained data was processed and statistically treated using the General Linear Model and both Fixed and Ran-dom Effects Analysis for group-level results. Thereafter, a Multi-Voxel Pattern Analysis with Searchlight Mapping was performed in order to find patterns of activation that allow differentiation between conditions. The obtained results indicate higher brain activation while reading sen-tences related to past/present worries when compared to future worry or neu-tral sentences. The main areas include frontal cortex, posterior parietal, occipital and temporal areas. Worrying, per se, was characterized by activation of the medial posterior parietal cortex, left posterior occipital lobe and left central temporal lobe. With the searchlight mapping approach we were able to further identify patterns of distinction between conditions, which were located in the parietal, limbic and frontal lobes.

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

How can clusters help in the product type choice? The case of a specialized retail store

This work project (WP) is a study about a clustering strategy for Sport Zone. The general cluster study’s objective is to create groups such that within each group the individuals are similar to each other, but should be different among groups. The clusters creation is a mix of common sense, trial and error and some statistical supporting techniques. Our particular objective is to support category managers to better define the product type to be displayed in the stores’ shelves by doing store clusters. This research was carried out for Sport Zone, and comprises an objective definition, a literature review, the clustering activity itself, some factor analysis and a discriminant analysis to better frame our work. Together with this quantitative part, a survey addressed to category managers to better understand their key drivers, for choosing the type of product of each store, was carried out. Based in a non-random sample of 65 stores with data referring to 2013, the final result was the choice of 6 store clusters (Figure 1) which were individually characterized as the main outcome of this work. In what relates to our selected variables, all were important for the distinction between clusters, which proves the adequacy of their choice. The interpretation of the results gives category managers a tool to understand which products best fit the clustered stores. Furthermore, as a side finding thanks to the clusterization, a STP (Segmentation, Targeting and Positioning) was initiated, being this WP the first steps of a continuous process.

The late adopter scale: A measure of late adopters of technological innovations

Diffusion of Innovation is a topic of interest for researchers and practitioners. Although substantial research is conducted on user categories, researchers often focus on the first half of the curve, ignoring the late adopters. We conduct two studies to measure the attributes of late adopters. In our first study of mobile phone users, we develop the Late-Adopter Scale. We then test it on a sample of laptop users. This scale is multi-dimensional, presents nomological and discriminant validity and has three dimensions: 1) rate of adoption, 2) resistance to innovation, and 3) skepticism. Findings reveal that all three Late Adopter Scale dimensions are significantly associated with low price preference. Moreover, in both samples skepticism is associated with high preference for simple products, lower leading edge status, and lower product involvement. Discussion focuses on implications of this new scale to theory and practice of new product development and diffusion of innovation.