Value Traits Reinforcement and Perceived Importance: Does Context Matter?

This study examines whether business students' perception of value trait reinforcement and importance differ by their university context. Student perceptions in coeducational religious, coeducational public and all-female religious schools were compared. The results of this study indicate areas of similarity and differences among students in regard to the context of school type and that reinforcement differences seemed to contribute to the differences. This revised version was published online in August 2006 with corrections to the Cover Date.     

The mediating role of distributive justice perceptions in the relationship between emotion regulation and emotional exhaustion in healthcare workers

This research proposes that the use of emotion regulation strategies by employees in the service professions determines their perceptions of fairness in interactions with clients, which in turn influences their emotional exhaustion. Based on social exchange theory and models of self-control, the investigation tested whether: (1) the type of emotion regulation strategy that employees use to meet the emotional demands of their job role partially influences their perceptions of distributive justice (i.e. that clients respond to their efforts), and (2) these perceptions mediate the relationship between emotion regulation and emotional exhaustion. To test this, a longitudinal field survey study of a sample of primary care workers in Spain (general practitioners and nurses; N?=?233) was conducted. Findings showed that the relationship between emotion regulation and emotional exhaustion was mediated by perceptions of distributive justice. A bootstrapping mediational analysis showed a significant indirect effect of surface acting on emotional exhaustion through distributive justice when inter-individual differences at T1 and when intra-individual changes between T1 and T2 were considered. Deep acting indirect effects were not significant for intra-individual changes. The findings indicate that employees’ perception of distributive justice has implications for understanding the impact of emotion regulation on well-being.     

Spatial planning in small islands: the need to discuss the concept of ecological structure

The concepts of green infrastructure and ecological structure (ES) are current topics of discussion among the scientific community and spatial planners. ES is mandatory in Portugal in land-planning, such as municipal master plans, but no consensus has been reached on how to implement it. The concept has not yet been implemented in the Azores, a Portuguese autonomous region, even though the Region has the responsibility of adapting legislation or accomplishing the Portuguese legislature. This study presents a critical analysis of the available literature about ES and proposes a conceptual framework to define ES for small islands, which have unique characteristics, focusing on the Azorean archipelago. The conceptual framework meets the requirements of local legislation and uses an ecological function approach that is increasingly advocated in recent European recommendations. This framework is presented as a strategic and flexible way to identify both the important elements (recognized in current legislation) in a territory and the remaining potential of the territory. The framework also supports decision-making by allowing the identification of integrated solutions, decreasing trade-offs as much as possible and reconciling the needs of nature conservation and socioeconomic development. This framework could be applied to other small islands and other territories, with local adaptations.     

On the impact of collaborative strategies for goods delivery in city logistics

Abstract

City logistics refers to the process of total optimisation of the logistics and transport activities in urban areas while considering economic, environmental, social and safety aspects. This paper considers a collaborative scenario for the urban goods transport planning and management as a way to reduce transport costs, congestion and environmental impact of this activity. An approach from the Operations Management field, using mathematical modelling, for tactical and operational decision-making is proposed to discuss and compare both collaborative and non-collaborative scenarios. This approach is validated using real data taken from the city of Bogotá, Colombia. Results put in evidence the quantitative benefits that can be achieved when collaborative logistics operations are implemented, represented in both transportation costs and environmental impacts. Although these results might not be surprising (like in the field of supply chain management), the contribution of this paper is centred in the fact that the impacts of collaboration in city logistics have not yet been quantified until now. This allows an ‘ex ante’ evaluation of the benefits of collaborative goods transport in cities.     

Single‐ and Multiple‐Informant Research Designs to Examine the Human Resource Management−Performance Relationship

During the last decades, many empirical studies have analysed the relationship between human resource management and firm performance. Despite the call for multiple‐rater designs, a relatively large number of researchers still rely on survey responses provided by a single informant in each organization. Single‐informant designs suffer from a number of problems, especially when the responses provided by different types of raters across firms are pooled into a single dataset prior to assessing their equivalence across raters. Using an illustration of the relationship between high performance work systems and firm performance, in this paper we observe that responses provided by managers holding different positions (human resource managers and sales managers) differ significantly and therefore pooling their responses into a single dataset may result in confusing conclusions. Furthermore, we demonstrate that differences arise in the estimated parameters when a multiple‐key‐informant approach, compared to a single‐informant design, is adopted. For these reasons, data collection using multiple key informants is recommended, based on the assumption that some raters in the firm will be more knowledgeable about the variables of interest than others.     

Some stochastic properties of conditionally dependent frailty models

The frailty approach is commonly used in reliability theory and survival analysis to model the dependence between lifetimes of individuals or components subject to common risk factors; according to this model the frailty (an unobservable random vector that describes environmental conditions) acts simultaneously on the hazard functions of the lifetimes. Some interesting conditions for stochastic comparisons between random vectors defined in accordance with these models have been described in the literature; in particular, comparisons between frailty models have been studied by assuming independence for the baseline survival functions and the corresponding environmental parameters. In this paper, a generalization of these models is developed, which assumes conditional dependence between the components of the random vector, and some conditions for stochastic comparisons are provided. Some examples of frailty models satisfying these conditions are also described.     

A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection

Classification of high-dimensional data set is a big challenge for statistical learning and data mining algorithms. To effectively apply classification methods to high-dimensional data sets, feature selection is an indispensable pre-processing step of learning process. In this study, we consider the problem of constructing an effective feature selection and classification scheme for data set which has a small number of sample size with a large number of features. A novel feature selection approach, named four-Staged Feature Selection, has been proposed to overcome high-dimensional data classification problem by selecting informative features. The proposed method first selects candidate features with number of filtering methods which are based on different metrics, and then it applies semi-wrapper, union and voting stages, respectively, to obtain final feature subsets. Several statistical learning and data mining methods have been carried out to verify the efficiency of the selected features. In order to test the adequacy of the proposed method, 10 different microarray data sets are employed due to their high number of features and small sample size.