Handeln in organisationalen Veränderungen : zur Bedeutung selbstregulativer Prozesse für ein ressourcenorientiertes Change-Management

Unternehmen sehen sich einem immer größer werdenden Veränderungs- und Innovationsdruck ausgesetzt. Ansätze zur ressourcenorientierten Gestaltung organisationaler Veränderungen finden in Praxis wie Wissenschaft zunehmende Verbreitung und Anerkennung. Die vorliegende Arbeit untersucht die Frage, wie ein selbstregulatorisches Anpassungs- und Veränderungsverhalten über Gestaltungsmaßnahmen hinaus zu einem effektvollen Umgang mit Veränderungen beiträgt. Auf theoretischer Ebene wird hierzu eine Verbindung der neueren Literatur zu organisationalem Wandel mit Ansätzen der Entwicklungspsychologie der Lebensspanne hergestellt, um die vor allem in den erstgenannten Ansätzen in der Regel fehlende zeitliche wie ressourcenorientierte Perspektive als Analysedimension für individuelle Veränderungsbereitschaft zu untersuchen. Als eine empirische Annäherung zu den auf theoretischer Ebene entwickelten Verbindungen werden drei Studien vorgestellt: Studie 1 (N = 1150) verdeutlicht die Bedeutung arbeitsstrukturaler Ressourcen in betrieblichen Veränderungen und zeigt, dass eine antizipierte Veränderung der Ressourcenlage unabhängig von deren initialer Ausprägung Auswirkungen auf Wohlbefinden und Veränderungsbereitschaft hat. Studie 2 (N = 100) untersucht die selbstregulatorische Steuerung des Kompetenzerlebens und des Befindens in betrieblichen Veränderungen. In Studie 3 (N = 194) wird der Zusammenhang von Merkmalen eines Veränderungsprozesses und dem individuellen Verhalten in Veränderungen beleuchtet. Die Bedeutung dieser Befunde für ein ressourcenorientiertes Veränderungsmanagement und eine nachhaltige Arbeitsgestaltung werden insbesondere mit Blick auf mögliche Diskrepanzen zwischen unternehmerischen wie individuellen Zielstrukturen diskutiert

Neue Beteiligung und alte Ungleichheit? Politische Partizipation marginalisierter Menschen

NEUE BETEILIGUNG UND ALTE UNGLEICHHEIT? POLITISCHE PARTIZIPATION MARGINALISIERTER MENSCHEN Neue Beteiligung und alte Ungleichheit? Politische Partizipation marginalisierter Menschen / Kaßner, Jan (Rights reserved) ( -

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots' joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots

Heterobimetallic conducting polymers based on salophen complexes via electrosynthesis

In this work, we report the first electrochemical synthesis of two copolymeric bimetallic conducting polymers by a simple anodic electropolymerization method. The adopted precursors are electroactive transition metal (M = Ni, Cu and Fe) salophen complexes, which can be easily obtained by direct chemical synthesis. The resulting films, labeled poly-NiCu and poly-CuFe, were characterized by cyclic voltammetry in both organic and aqueous media, attenuated total reflectance Fourier transform infrared spectroscopy, UV-vis spectroscopy, scanning electron microscopy, and coupled energy dispersive X-ray spectroscopy. The films are conductive and exhibit great electrochemical stability in both organic and aqueous media (resistant over 100 cycles without significant loss in current response or changes in electrochemical behavior), which makes them good candidates for an array of potential applications. Electrochemical detection of ascorbic acid was performed using both materials

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots