Sozioökonomischer Status,Deprivation und die Affinität zur <Emphasis Type="Italic">AfD</Emphasis> – Eine Forschungsnotiz

Using three recent data sets (ALLBUS 2016, GLES 2016, ESS 2016), we show that the affinity for the AfD is a socially stratified phenomenon. Actors with minimal education and low income, the unemployed, workers, and actors who feel deprived in socioeconomic terms have a stronger tendency towards supporting the AfD than those actors with a higher socioeconomic status. This finding contrasts with previous research, which describes the rise of right-wing populism in Germany as the result of some kind of clash of idealistic cultures lacking any foundation in the economic basis of society.     

The Value of Environmental Information without Control of Subsequent Decisions

The standard value of information approach of decision analysis assumes that the individual or agency that collects the information is also in control of the subsequent decisions based on the information. We refer to this situation as the “value of information with control (VOI‐C).” This paradigm leads to powerful results, for example, that the value of information cannot be negative and that it is zero, when the information cannot change subsequent decisions. In many real world situations, however, the agency collecting the information is different from the one that makes the decision on the basis of that information. For example, an environmental research group may contemplate to fund a study that can affect an environmental policy decision that is made by a regulatory organization. In this two‐agency formulation, the information‐acquiring agency has to decide, whether an investment in research is worthwhile, while not being in control of the subsequent decision. We refer to this situation as “value of information without control (VOI‐NC).” In this article, we present a framework for the VOI‐NC and illustrate it with an example of a specific problem of determining the value of a research program on the health effects of power‐frequency electromagnetic fields. We first compare the VOI‐C approach with the VOI‐NC approach. We show that the VOI‐NC can be negative, but that with high‐quality research (low probabilities of errors of type I and II) it is positive. We also demonstrate, both in the example and in more general mathematical terms, that the VOI‐NC for environmental studies breaks down into a sum of the VOI‐NC due to the possible reduction of environmental impacts and the VOI‐NC due to the reduction of policy costs, with each component being positive for low environmental impacts and high‐quality research. Interesting results include that the environmental and cost components of the VOI‐NC move in opposite directions as a function of the probability of environmental impacts and that VOI‐NC can be positive, even though the probability of environmental impacts is zero or one.     

Leveraging global sources of knowledge for business model innovation

This article explores the concept of leveraging global knowledge for business model innovation, whereby knowledge is transferred across space and firm boundaries for the reconfiguration of an incumbent firm's business model. Considering the implications of an ever-increasing fragmentation of global value chains and the associated dispersion of global knowledge sources, we propose that supply chain partners at foreign locations can provide valuable knowledge that incumbents can leverage to change their business model. Integrating insights from global supply chain, business model, and organizational learning literature, we theorize and empirically test how different organizational capabilities enable firms to acquire knowledge from foreign partners, integrate external with internal knowledge, transform knowledge through experimentation, and finally apply global knowledge in the form of business model innovation. We conclude with suggestions for future research.     

Repeat and Non-returning Volunteers: The Promise of Episodic Events for Volunteer Recruitment and Retention

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In this article, we are concerned with the recruitment potential of one-off episodic events for attracting and retaining...     

HOW TO IMPUTE INTERACTIONS, SQUARES, AND OTHER TRANSFORMED VARIABLES

Researchers often carry out regression analysis using data that have missing values. Missing values can be filled in using multiple imputation, but imputation is tricky if the regression includes interactions, squares, or other transformations of the regressors. In this paper, we examine different approaches to imputing transformed variables; and we find one simple method that works well across a variety of circumstances. Our recommendation is to transform, then impute —i.e., calculate the interactions or squares in the incomplete data and then impute these transformations like any other variable. The transform-then-impute method yields good regression estimates, even though the imputed values are often inconsistent with one another. It is tempting to try and "fix" the inconsistencies in the imputed values, but methods that do so lead to biased regression estimates. Such biased methods include the passive imputation strategy implemented by the popular ice command for Stata.