Konferenzbeitrag: h5-Index < 30
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This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation.
Although entrepreneurship support, women entrepreneurship, and sustainable entrepreneurship are highly relevant for society, the environment, and politics, research on this topic is rare, the results are divergent and there is a lack of focus on the characteristics of support mechanisms. In light of this, we conduct a qualitative content analysis of an entrepreneurship support program for sustainable entrepreneurs in Sweden and its women entrepreneurs. The main objectives of this paper are to identify and investigate the factors that influence the matching of an entrepreneurship support program with women entrepreneurs, and to better understand these processes. This study aims to provide a comprehensive understanding of the establishment, recruitment, and decision to attend a support program from the different parties involved. By using data from semi-structured interviews, desk research and an additional dataset, we found ten prerequisites and factors that influence the matching process between an entrepreneurship support program and women entrepreneurs. The paper offers a matching model that highlights possible differences in the motivation of different stakeholders and the relationships between these differences. By focusing on women sustainable entrepreneurship, this paper contributes to the current discussion on the specific needs these entrepreneurs experience in the initial phases of entrepreneurship support and shows how policy makers and direct support providers can improve their support practices.
Tracing Career Trajectories of Corporate Entrepreneurs: Identifying Patterns & Future Research
(2024)
Corporate Entrepreneurs have become an important resource for established companies to drive entrepreneurial behavior for new types of innovation and cultural change. Identifying and hiring employees with tailored competences and behaviors of a Corporate Entrepreneur is critical given that most corporates struggle to find the knowledge and capabilities needed to develop new types of ventures. The knowledge of CVs is described as relevant for hiring and developing employees but has not yet been sufficiently considered in the context of CE. Therefore, this study investigates past career experiences by leveraging optimal matching analysis, we examine a dataset comprising 50 sequences extracted from LinkedIn profiles, representing various career transitions over a 10-year period. Our findings reveal a dominant trajectory within the corporate sector, with limited transitions to other categories such as startups or consulting roles. With this, our research contributes to a deeper understanding of the career experiences of corporate entrepreneurs and underscores the need for future studies to explore the holistic factors shaping their professional trajectories.
While Corporate Entrepreneurship (CE) units have become essential tools for creating new kinds of innovation within established companies, their performance measurement remains underexplored. With CE units, companies intend to contribute to new business and organizational transformation. Thereby, CE units are used to create outputs that are new for the core organization. Until now, scholars have neglected to investigate assessing CE unit performance, leading to a lack of understanding of appropriate metrics for CE units. Companies often use traditional metrics designed for relatively static contexts, but these metrics do not fit for CE units. This study explores the metrics used in CE units, analyzing 12 interviews with 11 German companies. The analysis reveals a list of different metrics, categories, and underlying dimensions for CE unit performance measurement. Finally, we suggest scientific and managerial implications and topics for future research.
Corporate Entrepreneurship (CE) has become an established tool to create discontinuous innovations for many established companies. Thus, they have started to implement multiple CE units in parallel. However, despite different positive effects potentially arising from the parallel use and purposeful coordination of CE units, managers and scholars alike have so far widely ignored such holistic perspectives. This study therefore wants to shed light on the effects the parallel use and coordination have on established companies' innovation performance. Following an explorative approach, it investigates quantitatively the relationships between the number of CE units as well as their heterogeneity (in terms of their forms) used by a company and companies' innovativeness. Further, it employs qualitative interview data to gain deeper insights into the effects. Interestingly, the results show that the mere number of CE units does not have a significant effect on the innovativeness, but that more heterogeneous sets of CE units do. This provides an argument for the strategic coordination and co-specialization of CE units in order to make use of positive effects associated with multiple CE units. The study thereby contributes both to Asset Orchestration theory and the CE literature and provides multiple managerial implications as well as different avenues for future research.
Corporate entrepreneurship (CE) is pivotal for innovation in established organizations. This study investigates the use of multiple Corporate Entrepreneurship Units (CEUs) within companies. Drawing on a multi-case study of three German companies, we explore the structural configurations, resource provisions, and sequential utilization patterns of co-specialized CEUs. Findings reveal diverse CEU configurations and types, highlighting their roles in providing resources and facilitating innovation flows. Our research provides actionable insights for organizations navigating the complex landscape of CE, offering a deeper understanding of CEU coordination. This study contributes to filling the gap in empirical research on multiple CEUs, setting a foundation for future investigations.
Stainless steels are used in many areas of industry not only because of their high resistance, but also because they are easy to clean. How well particles can adhere to surfaces depends on a variety of parameters.
The aim of this study is to show the influence of mechanical grinding processes on the surface produced. By specifically adapting the process parameters, it can be shown that a significant reduction in the adhesion of particles to the component surface can be achieved, even though the surface roughness is comparable.
The application of a less abrasive grinding process chosen here and the results obtained allow the discussion that a reduced negative influence on the metallic structure in the base material also has an influence on the cleanability and adhesion behavior of particles.
Shape memory alloys are functional materials with unique properties and are especially used in medical and aerospace applications for quite some time. The use of this group of materials in automotive systems was also researched but real series applications are still lacking, with a few exceptions. Although a large number of theoretical applications were demonstrated, there is still little acceptance among car manufacturers to use shape memory actuators in safety-relevant systems in the automotive sector, for example. As shape memory alloys are in the meantime available in excellent qualities and in larger quantities, new possibilities are opening up against the background of autonomous driving and the desired weight reduction of vehicles. The first series applications are therefore already available on the market for automotive comfort applications.
The target of this paper is to give a short introduction to the shape memory technology and a brief review of existing comfort and safety systems designed for modern and future cars.
Interacting multiple model filters are most commonly used in the context of maneuvering targets, as they can represent the different dynamics of a real system by combining the estimates of multiple models. However, the interacting multiple model approach generally requires more computational effort than a single Kalman filter. In this work, down-sampling is used to reduce the computational effort. We propose an adaptive scheme to maintain the accuracy of the estimator to a defined level. To this end, the trace of the innovation covariance matrix is evaluated, and if it lies above a certain threshold, out-of-sequence measurements are iteratively used to improve the estimate until the uncertainty threshold is met. The approach is evaluated by Monte Carlo analysis. The results show that with this approach, the number of measurements to be processed, and thus the computational effort can be dynamically reduced, while the accuracy remains at a desired level.
In extended object tracking, basic parametric shapes such as ellipses and rectangles or non-parametric shape representations such as Fourier series or Gaussian processes can be utilized as shape priors. However, flexible non-parametric shape representations can be disproportionately detailed and computationally intensive for many applications. Therefore, we propose to adopt deformable superellipses for a low-dimensional and flexible representation of basic parametric shapes in this paper. We present a measurement model in 2D space that can cope with boundary and interior measurements simultaneously by recursively estimating an artificial noise variance for interior measurements. We investigate and compare the model in a simulated and real-world maritime scenario with the result that the combination of deformable superellipses and artificial measurement noise estimation performs better than state-of-the-art methods.
