Innovation Effects of Science-Related Technological Opportunities - Theoretical Considerations and Empirical Findings for Firms in the German Manufacturing Industry -

This paper investigates the innovation effects of science-related technological opportunities. Against the background of theoretical considerations about the interrelation of innovation and the adaptation of external (knowledge) resources, the impacts of technological opportunities stemming from scientific institutions on firms' innovation input and output are empirically analyzed for the German manufacturing industry. The investigations focus on the question whether science-related technological opportunities are used as complements or substitutes in the innovation process. The estimations indicate complementary relationships between firms' innovation input and technological opportunities stemming from scientific institutions. The adaptation of science-related knowledge resources has stimulating effects on the intensity of inhouse R&D. The results for the innovation output effects are ambiguous. On the one hand, empirical evidence for complementary impacts on the realisation of improved products could be found. On the other hand, science-related technological opportunities have no enhancing effects on the probability of realizing new products. Obviously, knowledge from universities and research institutes stimulates the development of new products more indirectly by increasing inhouse capacities and enhancing R&D efficiency.innovation activities, technological opportunities, scientific institutions, manufacturing industry

Technological Opportunities, Absorptive Capacities, and Innovation

The aim of this paper is to analyze the effects of technological opportunities on the innovation activities of firms, depending on their absorptive capacities. The importance and impacts of the ability of firms to use external knowledge sources were inquired especially for external knowledge stemming from scientific research. Using a simple theoretic model, different innovation effects were empirically outlined for the German manufacturing industry for the first time. On the innovation input side, the effects of science-related technological opportunities in combination with absorptive capacities variables are stronger on the intensities as in the estimations without such proxies. Further, the innovation output of firms is positively influenced by the ability to adapt external knowledge efficiently. Firms in the German manufacturing industry with inhouse absorptive capacities and a high importance of scientific knowledge are characterized by higher sales shares of new and improved products and higher probabilities of patent registrations than other firms.

R&D Cooperation and Innovation Activities of Firms - Evidence for the German Manufacturing Industry -

The aim of the paper is to investigate in a simultaneous equation framework the role of R&D cooperation in the innovation process under two specific aspects. First, the analysis is concentrated on the impact of R&D cooperation - in line with other factors - on firm's innovation input and output. Second, it will be analyzed how the number of cooperation partners affects the development of new products. Starting with the discussion of theoretically expected effects of successfully R&D cooperation on the innovation activities of firms, the importance of inter-organizational arrangements in R&D is empirically investigated for firms in the German manufacturing industry. The estimation results can be summarized as follows: In the German manufacturing industry, R&D cooperations are used complementary in the innovation process, enhancing the innovation input and output of firms measured by the intensity of inhouse R&D respectively the realization of product innovations. On the input side, the intensity of inhouse R&D also stimulates the probability and the number of R&D cooperations with other firms and institutions.R&D cooperation, innovation behaviour, technological opportunities, manufacturing industry

Implikationen unterschiedlicher Ausgangs- bzw. Rahmenbedingungen auf das Innovationsverhalten von neugegruendeten und etablierten Unternehmen

For newly-founded and established firms different initial or prevailing conditions apply in the innovation process. Against this background, the question arises as to what implications this has for a firm's innovation behaviour and to what extent both groups of companies differ significantly from each other as regards internal and environment-related influences. The present contribution takes this as its starting point. With the aid of discrimination analysis it is established which variables can be drawn on to describe significant group differences. Empirical investigations show clear differences in innovation behaviour between start up firms and incumbent firms. Both groups of companies differ most greatly in resource input during the R&D and innovation phase. The variables, ‚personnel expenditure per employee', ‚investment expenditure per employee', ‚R&D employment intensity'and ‚innovation expenditure per employee in the launch phase' display the highest discrimination coefficients. But also the sales share of new products as an indicator of the firm's innovation output is characterized by a significant separatory power. Environmental factors of innovation behaviour, on the other hand, (technological level of the industry, R&D cooperation behaviour) possess minor discriminatory significance.Innovation Behaviour, Newly-founded Firms, Start up Firms, Incumbent Firms

Netzwerkmitgliedschaft und Innovationsverhalten von neugegruendeten und etablierten Unternehmen

The center piece of this analysis is to investigate the interdependence between newly founded firms, established firms and innovation networks. On grounds of the reflections on firms' innovation activities and technological opportunities, the influence of network-membership (among other aspects) on the innovation behavior of new firms and incumbent firms is investigated. The empirical analysis, based on the data of the "Kreditanstalt fuer Wiederaufbau (KfW)", suggests stimulating effects of network-membership on the innovation activities of start-up firms. A secondary target of start-ups is also to gain market share and thus increase their turnovers. In contrast to start-up firms, networks do not play a significant role with regard to innovation activities when we consider established firms. Network-membership as a determinant of the innovation behavior of established firms loses explanatory power according to the estimation results in this paper. The motivation of those firms, who join a network, rather is to benefit from cost-reducing and quality-improving innovations and finally, to expand their market share.Innovation network, Start-up firms, Incumbent firms, Innovation behavior, Technological opportunities

Stellenwert und Bedeutung von Innovationsnetzwerken fuer Unternehmensgruendungen

The main focus of this paper is on the examination of the role and importance of innovation networks in the decision to found a firm and after entry to the market has taken place. For newly-founded firms from business sectors with high innovation intensity, the implications resulting from the involvement of ‚start-ups‘ in a network of innovation are more closely analyzed. The examinations show, on the one hand, that contact to innovation networks has a positive effect on the decision-making behaviour of those wishing to found a firm. The empirical analysis, on the other hand, underline the fact that membership of a network plays an important role after entry to the market has taken place. By exploiting external resources, ‚start-ups‘ can expand internally available R&D capacities and reduce (financial) restrictions on resource provision. This makes it possible to a notable extent to realize synergy effects and learn strategies for the commercial exploitation of innovation efforts. Finally, multivariate estimations show that network membership has a statistically highly significant effect - measured by turnover or the development of the number of employees - on the growth and market success of newly-founded firms.Innovation Network, Start-ups, Newly-founded Firms, Innovation Behavior, Technological Opportunities

Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data

The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e.g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary similarity function can be used to find these observations. We introduce and evaluate different similarity functions. For the evaluation, we use five different classification tasks based on geo-spatial data. Each classification task consists of (tens of) thousands of items. We demonstrate, that the presented expected accuracy measures can be a good estimator for kNN performance, and the proposed adaptive kNN classifier outperforms common kNN and previously introduced adaptive kNN algorithms. Also, we show that the range of considered k can be significantly reduced to speed up the algorithm without negative influence on classification accuracy

Explainable Artificial Intelligence for Interpretable Data Minimization

Black box models such as deep neural networks are increasingly being deployed in high-stakes fields, including justice, health, and finance. Furthermore, they require a huge amount of data, and such data often contains personal information. However, the principle of data minimization in the European Union’s General Data Protection Regulation requires collecting only the data that is essential to fulfilling a particular purpose. Implementing data minimization for black box models can be difficult because it involves identifying the minimum set of variables that are relevant to the model’s prediction, which may not be apparent without access to the model’s inner workings. In addition, users are often reluctant to share all their personal information. We propose an interactive system to reduce the amount of personal data by determining the minimal set of features required for a correct prediction using explainable artificial intelligence techniques. Our proposed method can inform the user whether the provided variables contain enough information for the model to make accurate predictions or if additional variables are necessary. This humancentered approach can enable providers to minimize the amount of personal data collected for analysis and may increase the user’s trust and acceptance of the system

FLECSim-SoC: A Flexible End-to-End Co-Design Simulation Framework for System on Chips

Hardware accelerators for deep neural networks (DNNs) have established themselves over the past decade. Most developments have worked towards higher efficiency with an individual application in mind. This highlights the strong relationship between co-designing the accelerator together with the requirements of the application. Currently for a structured design flow, however, it lacks a tool to evaluate a DNN accelerator embedded in a System on Chip (SoC) platform.To address this gap in the state of the art, we introduce FLECSim, a tool framework that enables an end-to-end simulation of an SoC with dedicated accelerators, CPUs and memories. FLECSim offers flexible configuration of the system and straightforward integration of new accelerator models in both SystemC and RTL, which allows for early design verification. During the simulation, FLECSim provides metrics of the SoC, which can be used to explore the design space. Finally, we present the capabilities of FLECSim, perform an exemplary evaluation with a systolic array-based accelerator and explore the design parameters in terms of accelerator size, power and performance