Motivations for Environmental Alliances: Generating and Internalizing Environmental and Knowledge Value

Environmental alliances are a common response to societal sustainability demands. In environmental alliances, firms collaboratively exploit and explore environmental technologies to address market opportunities while simultaneously generating positive environmental impacts. A striking idiosyncrasy is that in addition to economic value, environmental alliances generate two types of external value: environmental value from positive effects on air, water, land and biodiversity, and knowledge value from innovations in environmental technologies. Research on motivations for environmental alliances is dispersed and underdeveloped compared to the well‐established literature on motivations for strategic alliances that emphasize economic value. This study therefore develops a classification of motivations for environmental alliances by combining the literature on strategic alliances and that on environmental and knowledge value. The resulting classification includes motivations for environmental alliances to generate environmental and knowledge value as well as motivations to create economic value by internalizing environmental and knowledge value. A systematic review of 123 articles on environmental inter‐firm alliances identifies specific motivations to populate the new classification. We show that alliance partners are motivated to share sustainable resources, reduce sustainability risk, respond to stakeholders or invest in specific sustainable assets to generate external value. They collaborate to reduce costs or enhance competitive advantage, reputation or legitimacy to internalize external value. The resource‐based view, resource‐dependence view, institutional theory and transaction cost economics have not previously distinguished between motivations to generate and internalize external value. We extend their area of application from strategic alliances to environmental alliances, and thus beyond the exclusive pursuit of economic value.     

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.     

Activation programmes for women with a partner in Germany: Challenge or replication of traditional gender roles

In 2005, Germany implemented major welfare benefit reforms that encourage an adult worker model of the family. In this study, we hypothesised that, despite these reforms, women's assignments to activation programmes would in practice still tend to replicate the degree of labour market attachment to which they had become accustomed relative to their partner in the past. We compared programme entries between women in former male breadwinner, dual earner, no-earner and female breadwinner households and applied event-history analysis to large-scale administrative data. Our findings showed that in western Germany – but not in eastern Germany – women's assignments to activation programmes indeed replicated their prior labour market attachment relative to their partner. Key Practitioner Message: • Among women receiving Unemployment Benefit II in Germany, women with partners participate in activation programmes less often; • This tendency applies especially to western German women with less employment experience and lower former earnings than their partners; • A framework should be devised to inquire about previously non-employed women's interests in ALMP participation and offer them such opportunities.     

Performance Management,Caseloads and the Frontline Provision of Social Services

Caseloads and performance management are important working conditions of workers delivering public social services in street‐level organizations. The literature on these working conditions argues that high caseloads and performance management have considerable consequences for workers' performance in terms of the quality of services they provide and the results they realize. This article empirically investigates and compares these consequences, drawing on the results of a quantitative study of frontline workers in 14 local welfare agencies in the Netherlands. These workers are responsible for the delivery of welfare‐to‐work policies to social assistance recipients. The findings show that high caseloads do, indeed, have a detrimental effect on workers' performance, whereas the impact of performance management is more modest, though confirming some of the findings reported in other studies on performance management. The results also show that by focusing service provision on a proportion of their caseload, workers are able to reduce the negative impact of high caseloads somewhat. Overall, the article finds that the negative impact of high caseloads is more pervasive than that of performance management. The article concludes that the recent focus in the literature on performance management and its consequences for public services should not turn scholars' attention away from the ‘traditional’ public administration problem of high caseload sizes.     

How Perceptions Matter: Organizational Vulnerability and Practices of Resilience in the Field of Migration

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article presents a qualitative analysis of the practices of civil society organizations (CSOs) to integrate migrants...