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Für viele Fragstellungen, z. B. Unternehmensbewertung, Rating, strategische Entscheidungen etc., ist die Prognose der zukünftigen Erfolge von Unternehmen von entscheidender Bedeutung.
Das Buch stellt alle wichtigen Analysemöglichkeiten mit ihren Vor- und Nachteilen dar. Zahlreiche Fallbeispiele, Übersichten und Tabellen legen die Erfolgsquellen vieler Unternehmen offen. Besonders hilfreich ist die Auswertung einer umfassenden Datenbank mit den Jahresabschlüssen von ca. 2000 Unternehmen weltweit. Die Auswertungen liefern für viele Fragestellungen passende Betriebs- und Branchenvergleiche.
Wer einen Großteil seiner Arbeitszeit in die Analyse von Unternehmen steckt, sollte auf diese solide Arbeitsgrundlage nicht verzichten. Aber auch als Einstieg für Studierende ist das kompakte Buch bestens geeignet.
Insbesondere in wissensintensiven Unternehmen wie dem Krankenhaus birgt die Umstellung auf digitale Prozesse ein großes Innovations- und Produktivitätspotenzial. Der Beitrag erörtert und bewertet die Digitalisierungsfrage aus der betrieblichen Sicht des Krankenhausmanagements hinsichtlich der Voraussetzungen und der aktuell als realistisch einzuschätzenden Potenziale für das Unternehmen. Ausgehend von einer Begriffsklärung der Digitalisierung wird der Zusammenhang zwischen Digitalisierung und Krankenhausorganisation betrachtet. Daran knüpfen Ausführungen zu organisatorischen, personellen und finanziellen Voraussetzungen eines digitalen Krankenhauses an. Bei der Darstellung der Potenziale wird zwischen innerbetrieblichen und unternehmens- bzw. institutionenübergreifenden Aspekten eines digitalen Krankenhauses unterschieden. Ein besonderes Augenmerk wird auf die Elektronische Patientenakte (EPA) gelegt, da sie für den medizinisch-pflegerischen Kernprozess im Krankenhaus besonders wertvoll ist. Am Beispiel der Materialwirtschaft/Logistik werden die digitalen Anwendungsmöglichkeiten in den Unterstützungsprozessen des Krankenhauses erläutert.
Niche-based species distribution models (SDMs) have become an essential tool in conservation and restoration planning. Given the current threats to freshwater biodiversity, it is of fundamental importance to address scale effects on the performance of niche-based SDMs of freshwater species’ distributions. The scale effects are addressed here in the context of hierarchical catchment ordering, considered as counterpart to coarsening grain-size by increasing grid-cell size. We combine fish occurrence data from the Danube River Basin, the hierarchical catchment ordering and multiple environmental factors representing topographic, climatic and anthropogenic effects to model fish occurrence probability across multiple scales. We focus on 1st to 5th order catchments. The spatial scale (hierarchical catchment order) only marginally influences the mean performance of SDMs, however the uncertainty of the estimates increases with scale. Key predictors and their relative importance are scale and species dependent. Our findings have useful implications for choosing proper species dependent spatial scales for river rehabilitation measures, and for conservation planning in areas where fine grain species data are unavailable.
Land cover change is a dynamic phenomenon driven by synergetic biophysical and socioeconomic effects. It involves massive transitions from natural to less natural habitats and thereby threatens ecosystems and the services they provide. To retain intact ecosystems and reduce land cover change to a minimum of natural transition processes, a dense network of protected areas has been established across Europe. However, even protected areas and in particular the zones around protected areas have been shown to undergo land cover changes. The aim of our study was to compare land cover changes in protected areas, non-protected areas, and 1 km buffer zones around protected areas and analyse their relationship to climatic and socioeconomic factors across Europe between 2000 and 2012 based on earth observation data. We investigated land cover flows describing major change processes: urbanisation, afforestation, deforestation, intensification of agriculture, extensification of agriculture, and formation of water bodies. Based on boosted regression trees, we modelled correlations between land cover flows and climatic and socioeconomic factors. The results show that land cover changes were most frequent in 1 km buffer zones around protected areas (3.0% of all buffer areas affected). Overall, land cover changes within protected areas were less frequent than outside, although they still amounted to 18,800 km2 (1.5% of all protected areas) from 2000 to 2012. In some parts of Europe, urbanisation and intensification of agriculture still accounted for up to 25% of land cover changes within protected areas. Modelling revealed meaningful relationships between land cover changes and a combination of influencing factors. Demographic factors (accessibility to cities and population density) were most important for coarse-scale patterns of land cover changes, whereas fine-scale patterns were most related to longitude (representing the general east/west economic gradient) and latitude (representing the north/south climatic gradient).
Die Akzente im Stadtmarketing haben sich in den letzten Jahren in vielen Städten verschoben: Stand früher die Ausrichtung auf externe Stakeholder wie Touristen, Unternehmen oder potenzielle Arbeitskräfte im Zentrum, so richtet sich die Aufmerksamkeit heute in zunehmendem Maße auf interne Stakeholder, insbesondere auf die Bürgerinnen und Bürger. Diese werden nicht nur auf ihre Rolle als Kunden reduziert, sondern sie werden als Mitgestalter in Beteiligungsprozesse integriert. Der Beitrag beschreibt die Entwicklung einer Dachmarke in einem beteiligungsorientierten Stadtmarketing-Prozess unter besonderer Berücksichtigung der Methode Design Thinking.
Purpose
In a knowledge-based society, one of the main driving forces of prosperity at city and regional levels is the ability to attract students and recently qualified graduates. The purpose of this paper is to identify the urban factors that contribute to “attractiveness” from a student perspective and to subsequently analyse the influence of these factors on students’ location choices. In the corresponding case study, the criteria identified were used to evaluate student satisfaction with the German city of Osnabrück.
Design/methodology/approach
A multi-level empirical research concept was used to analyse the relevant criteria. Focus group discussions served as the basis to design and conduct a survey of almost 2,300 students in the university city of Osnabrück at the end of 2016. A follow-up study in November 2017 at two universities in Berlin provided data for comparison.
Findings
Accessible – safe – clean and with affordable housing: these terms sum up the core expectations that students have of an attractive city. In contrast to the findings of Richard Florida regarding the creative class, cultural opportunities, exciting nightlife and a multicultural population were not important factors among the survey participants.
Research limitations/implications
As the study was conducted in two cities only, the findings cannot be generalised. It should be replicated in at least five other cities to validate and compare the research findings presented here. Furthermore, this study focused on the factors influencing the attractiveness of the city as a whole. However, the attractiveness of a specific neighbourhood may be of greater relevance to the decision-making process. As housing plays a major role, students’ particular needs with regard to accommodation should also be examined at greater depth.
Practical implications
Insights generated by the empirical study provide relevant information that may assist city stakeholders in taking effective measures regarding place management to attract and retain students.
Originality/value
To date, little research has been undertaken to empirically examine the specific factors that German students look for when deciding where to study and live. The goal of this paper is to present new empirical insights concerning the quality-of-life factors that influence students’ decision-making processes.