Suchergebnisse

Moderne Innovationsprozesse sind durch eine zunehmende Komplexität gekennzeichnet, die dafür verantwortlich ist, daß kaum ein Unternehmen mehr allein in der Lage ist, alle relevanten Entwicklungen zu verfolgen, geschweige denn zu beherrschen. Aus diesem Grund wird der Rückgriff auf externe Wissensquellen immer notwendiger; man spricht in diesem Zusammenhang auch von einem kollektiven Innovationsprozeß. -- Für das Funktionieren dieses Prozesses müssen jedoch besondere Anforderungen erfüllt sein. Zunächst einmal muß externes Know-how in irgendeiner Form verfügbar werden, welches dann von den Unternehmen auch noch verstanden werden muß, um es für eigene Belange zu verwenden. In diesen beiden Anforderungen manifestieren sich der kooperative Aspekt - die Entstehung der Spillover-Effekte - und der kognitive Aspekt - die Verwendung externen Know-hows - kollektiver Innovationsprozesse, die den Untersuchungsgegenstand des Buches darstellen. In einem ersten Schritt werden die in entscheidenden Punkten konträren Innovationsvorstellungen der traditionellen neoklassischen Ökonomik und der neuen evolutorischen Innovationsökonomik einander gegenübergestellt. Nach einem Überblick über neoklassische Modellierungsversuche werden anhand der dafür herangezogenen Modelle die Schwächen dieser Vorgehensweise im Innovationskontext herausgearbeitet, um daraus Anforderungen an eine evolutorische Darstellung zu entwickeln. Darauf aufbauend wird schließlich eine evolutorische Modellierung kollektiver Innovationsprozesse vorgestellt. Für die Abbildung des kooperativen Aspekts wird ein Selbstorganisationsansatz gewählt, mit dem sich die Entstehung kooperativer Umgebungen oder informeller Netzwerke zum Know-how Austausch beschreiben läßt. Der kooperative Aspekt wird in der Tradition von Nelson und Winter in einer Mikrosimulation eines heterogenen Oligopols abgebildet

Innovation, Catch-up and Sustainable Development -- PART I INNOVATION -- The Influence of Science and 'Industrial Enlightenment' on Steelmaking, 1786-1856 -- Science and Technology Relatedness: The Case of DNA Nanoscience & DNA Nanotechnology -- Observable Factors of Innovation Strategy: Firm Activities and Industry Effects -- The Value of Industry Studies: Impact of Luigi Orsenigo's Legacy on the Field of Innovation and Industry Evolution -- PART II CATCHING-UP -- National Innovation Systems, Economic Complexity, and Economic Growth: Country Panel Analysis Using the US Patent Data -- Regulation and Product Innovation: The Intermediate Role of Resource Reallocation -- Catch-Up and Reverse Catch-Up Processes In the Market for Lithium-Ion Batteries -- Drug Patent Protection: A Determinant of the Location of Pharmaceutical FDI -- Total Factor Productivity, Catch-Up and Technological Congruence in Italy, 1861-20101 -- Acting as an Innovation Niche Seeder: How Taiwan Helps to Overcome the Reverse Salient in the Southeastern Asia -- Embeddedness and Local Patterns of Innovation: Evidence from Chinese Prefectural Cities -- Patent, Utility Model And Economic Growth -- PART III SUSTAINABILITY -- Schumpeterian Economic Dynamics of Greening: Propagation of Green Eco-Platforms -- Industrial Life Cycle: Relevance of National Markets in the Development of New Industries for Energy Technologies – The Case of Wind Energy -- On Application of the Precautionary Principle to Ban Gmvs: An Evolutionary Model of New Seed Technology Integration.

This book is at the cutting edge of the ongoing?neo-Schumpeterian? research program that investigates how economic growth and its fluctuation can be understood as the outcome of a historical process of economic evolution. Much of modern evolutionary economics has relied upon biological analogy, especially about natural selection. Although this is valid and useful, evolutionary economists have, increasingly, begun to build their analytical representations of economic evolution on understandings derived from complex systems science. In this book, the fact that economic systems are, necessarily, complex adaptive systems is explored, both theoretically and empirically, in a range of contexts. Throughout, there is a primary focus upon the interconnected processes of innovation and entrepreneurship, which are the ultimate sources of all economic growth. Twenty two chapters are provided by renowned experts in the related fields of evolutionary economics and the economics of innovation.

AbstractThe circular economy (CE) and bioeconomy (BE) are recognized as potential solutions for achieving sustainable development, yet little research has examined their potential contribution to the United Nations' Sustainable Development Goals (SDGs). In this study, we conducted a bibliometric analysis of 649 articles published between 2007 and 2022, as well as a systematic literature review of 81 articles, to assess the extent to which the CE and BE communities have addressed the SDGs. Our analysis identified 10 research gaps including the limited number of empirical quantitative papers, particularly in the context of BE, and the underrepresentation of developing regions such as Latin America and Africa in the literature. Our main finding reveals that the CE community primarily focuses on SDG 12, Responsible Consumption and Production, followed by SDG 9, Industry, Innovation, and Infrastructure; SDG 7, Affordable and Clean Energy; and SDG 6, Clean Water and Sanitation. The BE community, on the other hand, focuses primarily on SDG 7, followed by SDG 9 and SDG 12. However, both communities lack attention to social SDGs such as quality education, poverty, and gender equality. We propose that a combination of CE and BE, known as circular bioeconomy, could help countries achieve all SDGs. Further research is needed to develop and implement circular bioeconomy policies that address these gaps and promote sustainable development. In this sense, our study identified an important research gap that needs more attention in the future.