Past research has revealed that knowledge integration is an important prerequisite for
the success of new product development. For this reason, companies deploy a number
of formal mechanisms to foster integration across multiple functions and hierarchical
levels. In many SMEs, however, such formal mechanisms are complemented or
even replaced by informal social networks among managers and employees. Despite
the relevance of intraorganizational networks, past research on SMEs, however, was
focused mainly on the interorganizational level of analysis. To cover this gap, we map
ego networks of senior managers in SMEs and explore their knowledge exchange relationships
both inside and across functional and hierarchical boundaries. Our study
shows that these social networks are an essential driver of knowledge integration and
Coleman formulated the thesis that social capital, which is rooted in social relations, provides certain options for action, such as, for example, the accumulation of human capital. Based on this theoretical assumption many authors in international research – mainly in the USA – investigated the effects of different forms of social capital on academic success. Therefore, the paper at hand focuses on the question whether the findings from abroad can be replicated for students in the secondary school system in Germany. Applying data of BiKS-8-14 and multi-level regressions, the effect of relations within and outside the family on school competences and grades is investigated. The results indicate on the one hand that for German students in secondary education their own social relations in school are important and on the other hand that effects vary in their meaning between the different school tracks.
Knowing household properties, such as number of persons per apartment, age of housing, type of water heating, etc. enables energy consultants and utilities to develop targeted energy conservation services. Load profiles captured by smart power meters, can—besides several other applications—be used to reveal energy efficiency relevant household characteristics. The goal of this work is to develop methods of supervised machine learning that deduce properties of private dwellings using consumption time series recorded in 30-min intervals. The contribution of this paper to the state of the art is threefold: we quadruplicate the number of features that describe power consumption curves to preserve classification relevant structures, indicate dimensionality reduction techniques to reduce the large-scale input data to a set of few significant features and finally, we redefine classes for some properties. As a result, the classification accuracy is elevated up to 82 %, while the runtime complexity is significantly reduced. The classification quality that can be achieved by our eCLASS methodology renders personalized efficiency measures in large-scale practical settings possible.
In a high altitude region such as the Silvretta Alps (Switzerland/Austria), past and extant settlement activities are known to have had large influences on the alpine flora and vegetation. The Silvretta Massif harbors more than 230 archaeological sites above 2000 m a.s.l. on a total area of 550 km2, from the Mesolithic period to Modern Times, but received little attention in these matters up to recently. The
Fimba Valley within the Silvretta area e with 47 known archaeological sites (6 prehistoric, 21 from the Medieval and/or Modern Times, 20 undated) located over an area of 62 km2 e provides evidence of a broad range of former human presence, as well as peat records allowing the reconstruction of Holocene
climatic change and anthropogenic impact on past vegetation. Here, we present a high resolution, multiproxy
study (including pollen, cryptogam spores, and non-pollen palynomorphs) on a 177-cm-long radiocarbon dated peat core from the Las Gondas Bog in the Fimba Valley (2363 m a.s.l.). Palynological
evidence adds and confirms previous dendrochronological results, revealing extensive high Pinus cembra (Arolla pine) stands around the bog at 10,400 cal BP and between ca. 8600e6700 cal. BP, more than 300 altitudinal meters above today’s timberline, and belonging therefore to the highest population known for
Central Europe. In addition, our palaeoecological results correlate well with the archaeologically known human impact during the Neolithic, Iron Age and Medieval periods. The exploitation of alpine landscape resources (cultivation of cereals in the valleys) and livestock grazing (in the subalpine and alpine areas) has therefore a long tradition going back at least for 6200 years in the Silvretta region.
Survey methodologists are searching for covariates to use in nonresponse adjustment models, ultimately hoping to find variables that are highly correlated with both the outcomes of interest and the propensity to respond. These covariates can come from auxiliary data that provide information on both respondents and nonrespondents. Two such types of auxiliary data are interviewer observations (a form of paradata) and commercially available data on small areas or households. Interviewer observations intended for use in nonresponse adjustment can be specifically designed to match the outcome variables of interest, while commercial data provide a broad set of small area descriptors that may be correlated with multiple outcomes. This analysis examines these two data sources to determine which is more predictive of the outcomes of interest for a particular survey, thereby fulfilling one of the criteria for a good adjustment variable. The outcomes of interest in this analysis are self-reports of household income and receipt of unemployment benefits from a survey of labor market participation. The findings suggest that at this point in time, compared to commercial data, interviewer observations are better at predicting these outcomes, particularly in the subpopulation that the survey targets. Therefore, the observations share more (accurate) information with the true value, making them better for adjustment on this dimension. The results will inform the work of both researchers wishing to improve their nonresponse adjustments and survey managers looking to make better use of their survey budget.
The use of personal names for screening is an increasingly popular sampling technique for migrant populations. Although this is often an effective sampling procedure, very little is known about the properties of this method. Based on a large German survey, this article compares characteristics of respondents whose names have correctly been classified as belonging to a migrant population with respondents, that are migrants and whose names have not been classified as belonging to a migrant population. Although significant differences with large effect sizes in some cases could be found, the overall bias introduced by name-based sampling seems to be small as long as procedures with a small false-negative rate are used.