Refine
Year of publication
- 2018 (78) (remove)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (22)
- Fachbereich Energietechnik (13)
- Fachbereich Luft- und Raumfahrttechnik (13)
- IfB - Institut für Bioengineering (11)
- Fachbereich Maschinenbau und Mechatronik (9)
- Fachbereich Bauingenieurwesen (7)
- Fachbereich Medizintechnik und Technomathematik (7)
- Solar-Institut Jülich (4)
- ECSM European Center for Sustainable Mobility (2)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (2)
Document Type
- Conference Proceeding (78) (remove)
Keywords
- Serious Game (2)
- Agility (1)
- Antarctica (1)
- Awareness (1)
- CDG (1)
- CEO career variety (1)
- Competence Developing Game (1)
- Energy efficiency (1)
- Engineering optimization (1)
- Enterprise Architecture (1)
Explorer CEOs: The effect of CEO career variety on large firms’ relative exploration orientation
(2018)
Prior studies demonstrate that firms need to make smart trade-off decisions between exploration and exploitation activities in order to increase performance. Chief executive officers (CEOs) are principal decision makers of a firm’s strategic posture. In this study, we theorize and empirically examine how relative exploration orientation of large publicly listed firms varies based on the career variety of their CEOs – that is, how diverse the professional experiences of executives were prior to them becoming CEOs. We further argue that the heterogeneity and structure of the top management team moderates the impact of CEO career variety on firms’ relative exploration orientation. Based on multisource secondary data for 318 S&P 500 firms from 2005 to 2015, we find that CEO career variety is positively associated with relative exploration orientation.
Interestingly, CEOs with high career varieties appear to be less effective in pursuing exploration, when they work with highly heterogeneous and structurally interdependent top management teams.
Frequency Dependent Impedance Analysis of the Foundation-Soil-Systems of Onshore Wind Turbines
(2018)
As an interdisciplinary research network, the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” (CoE) comprises of around 150 researchers. Their scientific background ranges from mechanical engineering and computer science to social sciences such as sociology and psychology. In addition to content- and methodbased challenges, the CoE’s employees are faced with heterogenic organizational cultures, different hierarchical levels, an imbalanced gender distribution, and a high employee fluctuation. The sub-project Scientific Cooperation Engineering 1 (CSP1) addresses the challenge of interdisciplinary cooperation and organizational learning and aims at fostering interdisciplinarity and its synergies as a source of innovation. Therefore, the project examines means of reaching an organizational development, ranging from temporal structures to a sustainable network in production technology. To achieve this aim, a broad range of means has been developed during the last twelve years: In addition to physical measures such as regular network events and trainings, virtual measures such as the Terminology App were focused. The app is an algorithmic analysis method for uncovering latent topic structures of publications of the CoE to highlight thematic intersections and synergy potentials. The detection and promotion of has been a vital and long known element in knowledge management. Furthermore, CSP1 focusses on project management and thus developed evaluation tools to measure and control the success of interdisciplinary cooperation. In addition to the cooperation fostering measures, CSP1 conducted studies about interdisciplinarity and diversity and their relationship with innovation. The scientific background of these means and the research results of CSP1 are outlined in this paper to offer approaches for successful interdisciplinary cooperation management.
Motivation-based Learning: Teaching Fundamentals of Electrical Engineering with an LED Spinning Top
(2018)
This paper presents NLP Lean Programming
framework (NLPf), a new framework
for creating custom natural language processing
(NLP) models and pipelines by utilizing
common software development build systems.
This approach allows developers to train and
integrate domain-specific NLP pipelines into
their applications seamlessly. Additionally,
NLPf provides an annotation tool which improves
the annotation process significantly by
providing a well-designed GUI and sophisticated
way of using input devices. Due to
NLPf’s properties developers and domain experts
are able to build domain-specific NLP
applications more efficiently. NLPf is Opensource
software and available at https://
gitlab.com/schrieveslaach/NLPf.