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Um die Attraktivität eines Unternehmens für Bewerber zu steigern und die Zufriedenheit der Angestellten zu verbessern ist es heutzutage unumgänglich, eine Vielzahl an Work-Life-Balance Maßnahmen anzubieten. Doch die zeitlichen und finanziellen Kosten, welche deren Einführung verursacht, fordern eine Priorisierung der Maßnahmen. Zur Entwicklung einer solchen Empfehlung für Unternehmen untersucht diese Studie ob es Work-Life-Balance Maßnahmen gibt, welche einen höheren Einfluss auf die Arbeitszufriedenheit ausüben als andere, wie groß der relative Effekt von den Maßnahmen im Vergleich zu anderen arbeitsbezogenen und privaten Variablen auf die Veränderung der Arbeitszufriedenheit ist, ob es einen Zusammenhang zwischen der Effektivität einer Maßnahme und deren Nutzung gibt und ob es Unterschiede zwischen den Erwartungen der Angestellten und den Angeboten der Unternehmen gibt.
Diese Fragen sind in acht Forschungshypothesen formuliert, welche in einem quantitativen Design mit Daten von 289 Angestellten von fünfzehn verschiedenen deutschen Unternehmen aus einem Online-Fragebogen überprüft werden. Für die Bildung einer Hierarchie von Maßnahmen nach ihrem Einfluss auf die Arbeitszufriedenheit und die Untersuchung des relativen Effektes im Vergleich zu anderen Variablen wird eine multiple Regressionsanalyse verwendet, während für die Ermittlung der Unterschiede zwischen den Erwartungen der Angestellten und der Verfügbarkeit der Angebote T-Tests durchgeführt werden.
Unterstützung bei der Kindesbetreuung, Unterstützung bei ehrenamtlichen Tätigkeiten und Teambuilding-Events haben einen signifikant höheren Einfluss auf die Arbeitszufriedenheit als andere Maßnahmen, und die hypothetische Nutzung ist höher
als die tatsächliche Nutzung, was auf ein hohes Potenzial dieser Maßnahmen bezüglich der Verbesserung der Arbeitszufriedenheit durch deren Einführung schließen lässt. Darüber hinaus sind aus Sicht der Angestellten flexible Arbeitszeiten und Arbeitsorte sowie Freizeit- und Überstundenkonten die wichtigsten Maßnahmen, welche auch bereits flächendeckend in den befragten Unternehmen angeboten werden. Allgemein sind die Nutzung der verfügbaren Maßnahmen und die Anzahl der angebotenen Maßnahmen wichtiger im Hinblick auf die Arbeitszufriedenheit als die Art der Maßnahmen. Außerdem nehmen Work-Life-Balance Maßnahmen bei jüngeren Menschen einen höheren Stellenwert in Bezug auf die Arbeitszufriedenheit ein als bei älteren Menschen.
Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects.
The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia, that MTurk workers tend to exhibit more positive word-of mouth, perceived risk, customer orientation and commitment to the focal company. The authors discuss the study- results, point to limitations, and provide avenues for further research.
The aim of this paper is to identify and understand the risks and issues companies are experiencing from the business use of social media and to develop a framework for describing and categorising those social media risks. The goal is to contribute to the evolving theorisation of social media risk and to provide a foundation for the further development of social media risk management strategies and processes. The study findings identify thirty risk types organised into five categories (technical, human, content, compliance and reputational). A risk-chain is used to illustrate the complex interrelated, multi-stakeholder nature of these risks and directions for future work are identified.
The way information is presented to users in online community platforms has an influence on the way the users create new information. This is the case, for instance, in question-answering fora, crowdsourcing platforms or other social computation settings. To better understand the effects of presentation policies on user activity, we introduce a generative model of user behaviour in this paper. Running simulations based on this user behaviour we demonstrate the ability of the model to evoke macro phenomena comparable to the ones observed on real world data.
Modeling and publishing Linked Open Data (LOD) involves the choice of which vocabulary to use. This choice is far from trivial and poses a challenge to a Linked Data engineer. It covers the search for appropriate vocabulary terms, making decisions regarding the number of vocabularies to consider in the design process, as well as the way of selecting and combining vocabularies. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties arernused in the LOD cloud. Overall, the results of this survey help in understanding the strategies how data engineers reuse vocabularies, and theyrnmay also be used to develop future vocabulary engineering tools.
Iterative Signing of RDF(S) Graphs, Named Graphs, and OWL Graphs: Formalization and Application
(2013)
When publishing graph data on the web such as vocabulariesrnusing RDF(S) or OWL, one has only limited means to verify the authenticity and integrity of the graph data. Today's approaches require a high signature overhead and do not allow for an iterative signing of graph data. This paper presents a formally defined framework for signing arbitrary graph data provided in RDF(S), Named Graphs, or OWL. Our framework supports signing graph data at different levels of granularity: minimum self-contained graphs (MSG), sets of MSGs, and entire graphs. It supports for an iterative signing of graph data, e. g., when different parties provide different parts of a common graph, and allows for signing multiple graphs. Both can be done with a constant, low overhead for the signature graph, even when iteratively signing graph data.
This paper presents a method for the evolution of SHI ABoxes which is based on a compilation technique of the knowledge base. For this the ABox is regarded as an interpretation of the TBox which is close to a model. It is shown, that the ABox can be used for a semantically guided transformation resulting in an equisatisfiable knowledge base. We use the result of this transformation to effciently delete assertions from the ABox. Furthermore, insertion of assertions as well as repair of inconsistent ABoxes is addressed. For the computation of the necessary actions for deletion, insertion and repair, the E-KRHyper theorem prover is used.
Various best practices and principles guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users.
In this paper, we present the various types of support features for an ontology engineer to model a Linked Data dataset, discuss existing tools and services with respect to these support features, and propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We demonstrate that none of the existing tools and services incorporate all types of supporting features and illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. Hereby, the recommendations are made on the basis of an iterative multimodal search. LOVER uses different, orthogonal information sources for finding terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVER's recommendation mechanism in general and illustrate it alongrna real-life example from the social sciences domain.
E-KRHyper is a versatile theorem prover and model generator for firstorder logic that natively supports equality. Inequality of constants, however, has to be given by explicitly adding facts. As the amount of these facts grows quadratically in the number of these distinct constants, the knowledge base is blown up. This makes it harder for a human reader to focus on the actual problem, and impairs the reasoning process. We extend E-Hyper- underlying E-KRhyper tableau calculus to avoid this blow-up by implementing a native handling for inequality of constants. This is done by introducing the unique name assumption for a subset of the constants (the so called distinct object identifiers). The obtained calculus is shown to be sound and complete and is implemented into the E-KRHyper system. Synthetic benchmarks, situated in the theory of arrays, are used to back up the benefits of the new calculus.
The paper deals with a specific introduction into probability propagation nets. Starting from dependency nets (which in a way can be considered the maximum information which follows from the directed graph structure of Bayesian networks), the probability propagation nets are constructed by joining a dependency net and (a slightly adapted version of) its dual net. Probability propagation nets are the Petri net version of Bayesian networks. In contrast to Bayesian networks, Petri nets are transparent and easy to operate. The high degree of transparency is due to the fact that every state in a process is visible as a marking of the Petri net. The convenient operability consists in the fact that there is no algorithm apart from the firing rule of Petri net transitions. Besides the structural importance of the Petri net duality there is a semantic matter; common sense in the form of probabilities and evidencebased likelihoods are dual to each other.