A platform-independent, interactive, multimedia learning platform with authentic clinical cases and multiple choice elements for the user is the ideal method for supporting and expanding medical education in radiology. The usefulness and the reasonable exertion of diagnostic modalities are conveyed in a practical context as teaching goals. The high acceptance among students is based on the interactivity and use of multimedia.

No abstract

Biogas from anaerobic digestion has become an important element in the renewable energy portfolio of many countries. In anaerobic digestion, digestate is produced as a byproduct. This could be used to produce fertilizers and potting soils for home gardeners substituting mineral fertilizers or peat-based products. However, this depends on consumer willingness to pay (WTP) for such products, which we investigate in this study. To this end, we conducted a discrete choice experiment (DCE) with 507 private consumers. From the 6084 decisions made, we derived Bayesian part-worth utilities using a preference share model and so calculated the WTP for different proenvironmental attributes of potting-soil products. We also assessed the influence of proenvironmental attitudes on the WTP. We discovered five distinct consumer groups in our respondents. Some show a significant WTP for proenvironmental attributes such as “organic”, “peat free”, and “without guano”. Three descriptions of digestate as a “renewable resource”, a “fermentation residue”, or a “biogas residue” elicited three markedly different WTP responses across all classes, with “renewable resource” garnering the highest WTP and “biogas residue” the lowest. Consumers with a stronger proenvironmental attitude exhibited a higher WTP for proenvironmental attributes. Our results can help marketers of digestate-based potting soils discover suitable price points for their products and design differentiated pricing strategies across consumer groups.

Abstract:In an effort to support the long-term viability of the bioenergy industry through an end market for digestate, we investigated purchasing preferences for fertilizer product features in the home gardening market. We conducted a discrete choice experiment (DCE), presenting 504 respondents with a total of 6048 product attribute choices in a simulated context that replicated the tradeoff decisions made in the real marketplace. We analyzed the choice data using a hierarchical Bayes estimate to generate part-worth utilities for fertilizer product attributes. We then conducted a latent class analysis to identify market segments that could be expected to respond to differentiated product design strategies. We were able to quantify both purchasing preferences for fertilizer product attributes as well as the importance of each attribute to the perceived utility of a product. We were further able to identify five distinct market segments that make clear the potential for differentiated strategies in the home gardening market. We found both negative and positive price sensitivities, with sociodemographically distinct subgroups that favored low-, mid-, and high-priced products. We also found purchasing preferences for brand status, product labeling and nutrient values. Our results provide insights that should help product managers in the biogas industry develop marketing strategies to integrate digestate into a sustainable energy production system.

Models of consumer heterogeneity play a pivotal role in marketing and economics, specifically in random coefficient or mixed logit models for aggregate or individual data and in hierarchical Bayesian models of heterogeneity. In applications, the inferential target often pertains to a population beyond the sample of consumers providing the data. For example, optimal prices inferred from the model are expected to be optimal in the population and not just optimal in the observed, finite sample. The population model, random coefficients distribution, or heterogeneity distribution is the natural and correct basis for generalizations from the observed sample to the market. However, in many if not most applications standard heterogeneity models such as the multivariate normal, or its finite mixture generalization lack economic rationality because they support regions of the parameter space that contradict basic economic arguments. For example, such population distributions support positive price coefficients or preferences against fuel-efficiency in cars. Likely as a consequence, it is common practice in applied research to rely on the collection of individual level mean estimates of consumers as a representation of population preferences that often substantially reduce the support for parameters in violation of economic expectations. To overcome the choice between relying on a mis-specified heterogeneity distribution and the collection of individual level means that fail to measure heterogeneity consistently, we develop an approach that facilitates the formulation of more economically faithful heterogeneity distributions based on prior constraints. In the common situation where the heterogeneity distribution comprises both constrained and unconstrained coefficients (e.g., brand and price coefficients), the choice of subjective prior parameters is an unresolved challenge. As a solution to this problem, we propose a marginal-conditional decomposition that avoids the conflict between wanting to be more informative about constrained parameters and only weakly informative about unconstrained parameters. We show how to efficiently sample from the implied posterior and illustrate the merits of our prior as well as the drawbacks of relying on

The paper explores transformations in agriculture during the period 1995-2015 and shows their impact on rural landscapes in the case of Austria. When Austria joined the European Union in 1995, this meant a minor gash in agricultural politics, from broad support of smallholder agriculture to a programme of modernisation and rationalisation. Austrian politicians defined this shift as a process of "ecological modernisation" (Fischler et al. 1994), incorporating agri-environmental schemes as instruments and modifying existing programmes of direct payments. The survey forms the groundwork for a discussion on landscape effects of the CAP as an "ecological" modernisation programme and possible impact of the CAP-reform 2020.

No abstract

Models of consumer heterogeneity play a pivotal role in marketing and economics, specifically in random coefficient or mixed logit models for aggregate or individual data and in hierarchical Bayesian models of heterogeneity. In applications, the inferential target often pertains to a population beyond the sample of consumers providing the data. For example, optimal prices inferred from the model are expected to be optimal in the population and not just optimal in the observed, finite sample. The population model, random coefficients distribution, or heterogeneity distribution is the natural and correct basis for generalizations from the observed sample to the market. However, in many if not most applications standard heterogeneity models such as the multivariate normal, or its finite mixture generalization lack economic rationality because they support regions of the parameter space that contradict basic economic arguments. For example, such population distributions support positive price coefficients or preferences against fuel-efficiency in cars. Likely as a consequence, it is common practice in applied research to rely on the collection of individual level mean estimates of consumers as a representation of population preferences that often substantially reduce the support for parameters in violation of economic expectations. To overcome the choice between relying on a mis-specified heterogeneity distribution and the collection of individual level means that fail to measure heterogeneity consistently, we develop an approach that facilitates the formulation of more economically faithful heterogeneity distributions based on prior constraints. In the common situation where the heterogeneity distribution comprises both constrained and unconstrained coefficients (e.g., brand and price coefficients), the choice of subjective prior parameters is an unresolved challenge. As a solution to this problem, we propose a marginal-conditional decomposition that avoids the conflict between wanting to be more informative about constrained parameters and only weakly informative about unconstrained parameters. We show how to efficiently sample from the implied posterior and illustrate the merits of our prior as well as the drawbacks of relying on means of individual level preferences for decision-making in two illustrative case studies.