Satisfaction towards human resources practices and repatriates’ retention: an empirical examination in the portuguese companies context

This paper examines the relationship between the level of satisfaction towards Human Resources Management practices among repatriates and the decision to remain on the home company after expatriation. Data was collected through semi-structured interviews of 28 Portuguese repatriates who remain and 16 organisational representatives from eight companies located in Portugal. The results show that (1) compensation system during the international assignment; (2) permanent support during the international assignment and; (3) recognition upon the return of the work and effort of expatriates during the international assignment are the most important HRM practices for promoting satisfaction among repatriates. Moreover, it is at repatriation phase that repatriates show higher dissatisfaction with HRM support. These findings will be discussed in detail and implications and suggestions for future research will be proposed as well.

Empirical models for quantification of machining damage in composite materials

Dissertação para obtenção do Grau de Doutor em Engenharia Mecânica

Nominations across gender: An empirical investigation of Brazilian municipal elections

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

What makes people stay in or leave shrinking cities? An empirical study from Portugal

The relative attractiveness of cities as places to live determines population movements in or out of them. Understanding the appealing features of a city is fundamental to local governments, particularly for cities facing population decline. Pull and push attributes of cities can include economic aspects, the availability of amenities and psychological constructs, initiating a discussion around which factors are more relevant in explaining migration. However, a pull–push approach has been underexplored in studies of shrinking cities. In the present study, we contribute to the discussion by identifying pull and push factors in Portuguese shrinking cities. Data were collected using a face-to-face questionnaire survey of 701 residents in four shrinking cities: Oporto, Barreiro, Peso da Régua and Moura. Factor analysis and automatic linear modelling were used to analyse the data. Our results support previous findings that the economic activity of a city is the most relevant feature for retaining residents. However, other characteristics specific to each city, especially those related to heritage and natural beauty, are also shown to influence a city’s attractiveness as a place to live. The cause of population shrinkage is also found to influence residents’ assessments of the pull and push attributes of each city. Furthermore, the results show the relevance of social ties and of place attachment to inhabitants’ intention to continue living in their city of residence.

Empirical evidences on voluntary delisting phenomenon

The paper studies what drives firms to voluntary delist from capital markets and what differs in firms’ behavior and fundamentals between public-to-private transactions and M&A deals with listed corporations. Moreover, I study the relationship between ownership percentage in controlling shareholders’ hands and cumulative returns around the delisting public announcement. I perform my tests both for the Italian and the US markets and I compare the findings to better understand how the phenomenon works in these different institutional environments. Consistent with my expectations, I find that the likelihood of delisting is mainly related to size, underperformance and undervaluation, while shareholders are more rewarded when their companies are involved in PTP transactions than in M&As with public firms.

Human capital in export-based internationalisation. "Managerial determinants and their influence upon the export behaviour of the firm: four case-studies of catalan exporting SMEs"

Export activities are a major source of economic growth and are considered important both at the national level and for individual businesses. Moreover, in the case of SMEs, they gain particular relevance, exporting being the most common foreign market entry mode for these firms. The decision maker’s role in the export activity is crucial, particularly in the case of SMEs. However, the extant literature on internationalization is characterized by a lack of consensus among scholars as to what constitutes the managerial factor in determining exporting. Therefore, this study focuses on the following issue: Which are the decision maker’s characteristics and perceptions that may influence the export behaviour of Catalan SMEs? To address this question a multiple case study method is applied across four Catalan exporting SMEs. The methodology chosen for analysing the empirical data is relying on the proposition testing approach while the investigation is conducted including both within and cross-case analysis. The findings show that high educational level, language skills, high risk tolerance, innovativeness as well as strongly perceived export stimuli as compared to low and easy to overcome export barriers positively influence the export involvement and development of SMEs. The study provides further insights into the research topic by jointly studying managerial characteristics and perceptions. Additionally, the majority of research on exporting topics has been carried out in the USA, so there is a clear need of investigation in the field in other countries, moreover in Spain where the exporting activities have not been as widely studied.

Consumption Expenditures in Economic Impact Studies: An Application to University Students

This paper examines how appropriately to attribute economic impact to consumption expenditures. Consumption expenditures are often treated as either wholly endogenous or wholly exogenous, following a distinction from Input-Output analysis. For many applications, such as those focusing on the impacts of tourism or benefits systems, such binomial assumptions are not satisfactory. We argue that consumption is neither wholly endogenous nor wholly exogenous but that the degree of this distinction is rather an empirical matter. We set out a general model for the treatment of consumption expenditures and illustrate its application through the case of university students. We examine individual student groups and how the impacts of students at particular institutions. Furthermore we take into account the binding budget constraint of public expenditures (as is the case for devolved regions in the UK)and examine how this affects the impact attributed to students' consumption expenditures.

Expression of Recombinant Antigens in Escherichia coli: Application on Immunochemical Studies of Schistosoma mansoni Tegumental Antigens

Sm15 and Sm13 are recognized by antibodies from mice protectively vaccinated with tegumental membranes, suggesting a potential role in protective immunity. In order to raise antibodies for immunochemical investigations, the genes for these antigens were expressed in pGEX and pMal vectors so that comparisons could be made among different expression systems and different genes. The fusion proteins corresponding to several parts of the gene for the precursor of Sm15 failed in producing antibodies recognizing the parasite counterpart. On the other hand, antibodies raised against Sm13 MBP-fusion proteins recognized the 13 kDa tegumental protein. Thus the peculiarities of the gene of interest are important and the choice of the expression system must sometimes be decided on an empirical basis

An empirical analysis of a merger between a network and low-cost airlines

We use a difference-in-difference estimator to examine the effects of a merger involving three airlines. The novelty lies in the examination of this operation in two distinct scenarios: (1) on routes where two low-cost carriers and (2) on routes where a network and one of the low-cost airlines had previously been competing. We report a reduction in frequencies but no substantial effect on prices in the first scenario, while in the second we report an increase in prices but no substantial effect on frequencies. These results may be attributed to the differences in passenger types flying on these routes.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Impact of inadequate empirical therapy on the mortality of patients with bloodstream infections: a propensity score-based analysis.

The impact of the adequacy of empirical therapy on outcome for patients with bloodstream infections (BSI) is key for determining whether adequate empirical coverage should be prioritized over other, more conservative approaches. Recent systematic reviews outlined the need for new studies in the field, using improved methodologies. We assessed the impact of inadequate empirical treatment on the mortality of patients with BSI in the present-day context, incorporating recent methodological recommendations. A prospective multicenter cohort including all BSI episodes in adult patients was performed in 15 hospitals in Andalucía, Spain, over a 2-month period in 2006 to 2007. The main outcome variables were 14- and 30-day mortality. Adjusted analyses were performed by multivariate analysis and propensity score-based matching. Eight hundred one episodes were included. Inadequate empirical therapy was administered in 199 (24.8%) episodes; mortality at days 14 and 30 was 18.55% and 22.6%, respectively. After controlling for age, Charlson index, Pitt score, neutropenia, source, etiology, and presentation with severe sepsis or shock, inadequate empirical treatment was associated with increased mortality at days 14 and 30 (odds ratios [ORs], 2.12 and 1.56; 95% confidence intervals [95% CI], 1.34 to 3.34 and 1.01 to 2.40, respectively). The adjusted ORs after a propensity score-based matched analysis were 3.03 and 1.70 (95% CI, 1.60 to 5.74 and 0.98 to 2.98, respectively). In conclusion, inadequate empirical therapy is independently associated with increased mortality in patients with BSI. Programs to improve the quality of empirical therapy in patients with suspicion of BSI and optimization of definitive therapy should be implemented.

The Employment Precariousness Scale (EPRES): psychometric properties of a new tool for epidemiological studies among waged and salaried workers

Background: Despite the fact that labour market flexibility has resulted in an expansion of precarious employment in industrialized countries, to date there is limited empirical evidence about its health consequences. The Employment Precariousness Scale (EPRES) is a newly developed, theory-based, multidimensional questionnaire specifically devised for epidemiological studies among waged and salaried workers. Objective: To assess acceptability, reliability and construct validity of EPRES in a sample of waged and salaried workers in Spain. Methods: Cross-sectional study, using a sub-sample of 6.968 temporary and permanent workers from a population-based survey carried out in 2004-2005. The survey questionnaire was interviewer administered and included the six EPRES subscales, measures of the psychosocial work environment (COPSOQ ISTAS21), and perceived general and mental health (SF-36). Results: A high response rate to all EPRES items indicated good acceptability; Cronbach’s alpha coefficients, over 0.70 for all subscales and the global score, demonstrated good internal consistency reliability; exploratory factor analysis using principal axis analysis and varimax rotation confirmed the six-subscale structure and the theoretical allocation of all items. Patterns across known groups and correlation coefficients with psychosocial work environment measures and perceived health demonstrated the expected relations, providing evidence of construct validity. Conclusions: Our results provide evidence in support of the psychometric properties of EPRES, which appears to be a promising tool for the measurement of employment precariousness in public health research.

Spotting Translationese in two Corpora of Original and Translated Catalan Texts: an Empirical Approach

This research investigates the phenomenon of translationese in two monolingual comparable corpora of original and translated Catalan texts. Translationese has been defined as the dialect, sub-language or code of translated language. This study aims at giving empirical evidence of translation universals regardless the source language.Traditionally, research conducted on translation strategies has been mainly intuition-based. Computational Linguistics and Natural Language Processing techniques provide reliable information of lexical frequencies, morphological and syntactical distribution in corpora. Therefore, they have been applied to observe which translation strategies occur in these corpora.Results seem to prove the simplification, interference and explicitation hypotheses, whereas no sign of normalization has been detected with the methodology used.The data collected and the resources created for identifying lexical, morphological and syntactic patterns of translations can be useful for Translation Studies teachers, scholars and students: teachers will have more tools to help students avoid the reproduction of translationese patterns. Resources developed will help in detecting non-genuine or inadequate structures in the target language. This fact may imply an improvement in stylistic quality in translations. Translation professionals can also take advantage of these resources to improve their translation quality.

Measuring intangibles' productivity. Empirical evidence from Spanish firms

As companies and shareholders begin to note the potential repercussions of intangible assets uponbusiness results, the inability of the traditional financial statement model to reflect these new waysof creating business value has become evident. Companies have widely adopted newmanagement tools, covering in this way the inability of the traditional financial statement model toreflect these new ways of creating business value.However, there are few prior studies measuring on a quantifiable manner the level of productivityunexplained in the financial statements. In this study, we measure the effect of intangible assets onproductivity using data from Spanish firms selected randomly by size and sector over a ten-yearperiod, from 1995 to 2004. Through a sample of more than 10,000 Spanish firms we analyse towhat extent labour productivity can be explained by physical capital deepening, by quantifiedintangible capital deepening and by firm s economic efficiency (or total factor productivity PTF).Our results confirm the hypothesis that PTF weigh has increased during the period studied,especially on those firms that have experienced a significant raise in quantified intangible capital,evidencing that there are some important complementary effects between capital investment andintangible resources in the explanation of productivity growth. These results have significantdifferences considering economic sector and firm s dimension.