Altruistic Individualists: Motivations for International Volunteering Among Young Adults in Switzerland

Modernization theory posits a change from traditional or “collective” forms to modern or “reflective” forms of volunteering. In a research project using a combined qualitative–quantitative approach, the motivation of 118 young Swiss adults who showed an interest in international volunteering was investigated. Qualitative analysis revealed 12 different motives which could be categorized into three different groups: A first group called “Achieving something positive for others,” a second group named “Quest for the new,” and a third group of motives labeled “Quest for oneself.” Motivations of young Swiss adults for international volunteering clearly show the characteristics of “reflexive” volunteers. Most respondents displayed a combination of motives while for only 11% of them altruism (“Achieving something positive for others”) was the one and only driving force behind their interest in international volunteering. The inductively constructed typology of motives can be a useful planning device for organizations that run or intend to set up an international volunteering program for young adults.     

Nonprofit Development in Hong Kong: The Case of a Statist–Corporatist Regime

This paper adapts Salamon, Sokolowski, and Anheiers social origins theory to argue that the nonprofit regime in Hong Kong can be characterized as statist–corporatist. This statist–corporatist regime displays the hybrid character of both a statist and a corporatist regime: its statist character can be seen in the high degree of autonomy of the state, its tendency to limit freedom of association, and the low commitment to social provision. Its corporatist character is evident in the high level of participation by designated nonprofit organizations in selected areas of social provision under state funding. It is shown how the development of this nonprofit regime was historically shaped by four factors; namely, the interest of the colonial state in maintaining domination, economic and public financial policy, the historical formation of the welfare system, and political regime change. The findings illustrate the distinct historical forces and the path of development in an Asian state that might affect nonprofit development.     

Newcomers and oldtimers: Do classification methods matter in the study of amenity migration impacts in rural America?

Rural, high-amenity areas in the USA continue to attract significant numbers of migrants. A common approach to investigating the potential consequences of rural in-migration is to contrast the characteristics, attitudes, and/or actions of migrants and non-migrants (or “newcomers” and “oldtimers”). However, no consensus exists on the distinctions (or the lack thereof) between these two groups in the existing literature, in part because previous research used a variety of methods to classify residence status. Drawing on household survey data from nine communities in north-central Colorado, this study illustrates how different categorizations may yield different conclusions. Categorizing resident groups by the 1970/1980 cutoff or 5-, 10-, or 20-year length of residence substantially altered the differences and similarities between newcomers and oldtimers in selected sociodemographic, perceptual, and behavior indicators. It is recommended that researchers choose the most appropriate classification approach based on specific research questions and objectives as well as important transitions in the migration patterns of their study areas. These findings have direct implications for future research on rural demographic change and socioeconomic restructuring.     

Livelihood Assets and Vulnerability Context of Marine Park Community Development in Malaysia

This study assesses the community’s standard of living using the well-known Sustainable Livelihood Approach (SLA) as an analytical framework within the Marine Park Areas (MPAs) in Peninsular Malaysia. We focus on livelihood sustainability and environmental issues challenging MPAs. In order to protect and conserve vulnerable marine life, our efforts assess the residents’ wellbeing and identify major environmental issues associated with MPAs. The major findings of this study indicate that social and physical assets of the societies within the Marine Park Island improved with economic development, but they continue to lack in human capital, and financial and environmental assets. It is expected that this study would assist policy makers to formulate enhanced policies for conserving marine diversity as well as to improve the socio-economic status of the communities that reside therein.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

An exponentially weighted quantile regression via SVM with application to estimating multiperiod VaR

The square root of time rule under RiskMetrics has been used as an important tool to estimate multiperiod value at risk (VaR). However, the conditions for the rule are too restrictive to get empirical support in practice since multiperiod VaR is a complex nonlinear function of the holding period and the one-step ahead volatility forecast. In this paper, we propose a new model by considering an exponentially weighted quantile regression via SVM to provide greater accuracy for multiperiod VaR measure. In both numerical simulations and empirical studies on three stock indices, the proposed model outperforms several traditional methods including the volatility models, filtered historical simulation, and linear quantile regression approaches in terms of the value of the number of significant entries, the mean absolute error, and the p value of prediction test in Harvey et al. (Int J Forecast 13:281–291, 1997).     

Outlier detection and accommodation in general spatial models

This paper studies outlier detection and accommodation in general spatial models including spatial autoregressive models and spatial error model as special cases. Using mean-shift and variance-weight models respectively, test statistics for multiple outliers are derived and the detecting procedures are proposed. In addition, several key diagnostic measures such as standardized residuals and leverage measure are defined in general spatial models. Outlier modified models are proposed to accommodate outliers in the data set. The performance of test statistics, including size and power, are examined via simulation studies. Three real examples are analyzed and the results show that the proposed methodology is useful for identifying and accommodating outliers in general spatial models.     

Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of \(\hbox {NO}_{2}\) levels, by using geostatistical approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space–time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This methodology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.     

浅析竞争中的时间要素

21世纪,知识经济、信息化和全球化的发展使竞争态势更加激烈,时间因素在企业运营中作用日彰,成为信息社会中竞争优势的重要来源。传统企业大多忽视价值交付系统的时间消耗和系统全面反应能力,而现代企业以信息技术为基础,通过组织再造和流程重组,建立起快速反应能力,基于时间建立起强大的竞争优势。     

Approximation hardness of edge dominating set problems

We provide the first interesting explicit lower bounds on efficient approximability for two closely related optimization problems in graphs, MINIMUM EDGE DOMINATING SET and MINIMUM MAXIMAL MATCHING. We show that it is NP-hard to approximate the solution of both problems to within any constant factor smaller than . The result extends with negligible loss to bounded degree graphs and to everywhere dense graphs. An extended abstract of this paper was accepted at the 14th Annual International Symposium on Algorithms and Computation, ISAAC 2003.