The Effect of the Dynamics of Knowledge Base Complexity on Schumpeterian patterns of Innovation: the upstream petroleum industry

This paper addresses important changes in innovation patterns in the upstream petroleum industry over the period from the 1970s to 2005. It argues that the shifts in patterns of innovation over that period can be explained by the dynamics of knowledge base complexity (KBC). We develop a quantitative method to explore KBC and show that increasing KBC has shifted innovation patterns, from a broadly Schumpeter Mark I to a 'modified' form of Schumpeter Mark II, led less by the established oil majors, but by a new class of integrated service providers

Managing Artificial Intelligence Technology for Added Value

Many industrial sectors are in the middle of a digital transformation that has emerged from the advancement of information and data technology, enhancing the use of computers and automation with smart and autonomous systems powered by data and machine learning. This revolution has been broadly adopted in industry by initiating the use of digital technologies, sensor systems, intelligent machines, and smart material in its processes.Some examples of industrial innovation are the invention of artificial intelligence (AI), the deployment of the Internet of Things (IoT)/Internet of Services (IoS), 3D printing/additive manufacturing, machine learning, and the use of Big Data. These have enables the digitization, automation, or integration of service and product value chains. Implementing digitization and automation is believed to help construction transform into a technology-driven industry and keep pace with other industries.&nbsp

A European research roadmap for optimizing societal impact of big data on environment and energy efficiency

We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to allow stakeholders and the big data community to identify and meet big data challenges, and to proceed with a shared understanding of the societal impact, positive and negative externalities, and concrete problems worth investigating. It builds upon a case study focused on the impact of big data practices in the context of Earth Observation that reveals both positive and negative effects in the areas of economy, society and ethics, legal frameworks and political issues. The roadmap identifies European technical and non-technical priorities in research and innovation to be addressed in the upcoming five years in order to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl

The Rise of Innovation Districts: A New Geography of Innovation in America

As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

State of Play and Sectoral Differentiation of Clusters in Visegrad Group Countries and in Germany in the Context of Increasing Competitiveness

In accordance with the definition by the European Commission regional competitiveness means the ability of companies, sectors and transnational groupings in the region exposed to international competition to generate sustainable and relatively high income and employment levels. Following this line of thinking, strengthening the potential of local economic operators and their environment should become the priority of economic policies of the governments. One among recognised mechanisms that back up enterprise potential is the organisation and fostering of the competitiveness of clusters. They are a specific case of economic networks based on cooperation and competitiveness which usually need targeted investment in order to be efficient in their operations. Cluster policy implemented by Western European countries is most often systemic, integrated between the central and the regional levels with the material scope of investment focusing on assisting innovation in clusters. From this perspective it is interesting to see the shape the policy takes in Central European countries after their economic transformation. We selected Visegrad Group countries as the subject of our analysis knowing that clusters have been known there since at least the end of 1990s. Although more than 10 years have passed the conclusions indicate that the policy is at its initial development stage and, differently from Western economies (Germany in our case), it hardly effects the innovation of national economies and regional systems of innovation.Zgodnie z definicją Komisji Europejskiej pod pojęciem konkurencyjności regionów należy rozumieć zdolność przedsiębiorstw, przemysłu, a także ponadnarodowych ugrupowań, zlokalizowanych w regionie, wystawionych na międzynarodową konkurencję, do osiągania trwałego i relatywnie wysokiego poziomu dochodu i zatrudnienia. Zgodnie z tym rozumieniem wzmacnianie potencjału rodzimych podmiotów gospodarczych i ich otoczenia, powinno być priorytetem polityk gospodarczych rządów. Jednym z uznanych mechanizmów wspierających potencjał środowisk przedsiębiorczości jest organizacja i wzmacnianie konkurencyjności klastrów. Stanowią one specyficzny rodzaj sieci gospodarczych opartych na logice współpracy i konkurencji, których sprawne funkcjonowanie najczęściej wymaga ukierunkowanych inwestycji. Polityka klastrowa realizowana przez kraje Europy Zachodniej ma dziś najczęściej charakter systemowy, zintegrowany między poziomem centralnym i regionalnym, natomiast rzeczowy zakres interwencji dotyczy przede wszystkim wspierania innowacyjności klastrów. Z tej perspektywy interesujące jest jaki kształt polityka ta przybiera w krajach Europy Środkowej po zmianach związanych z transformacją gospodarek. Jako przedmiot analizy wybrano kraje Grupy Wyszehradzkiej, wiedząc, że zjawiska klastrowe były tutaj znane już przynajmniej od końca lat 90-tych. Mimo, iż upłynęło już ponad 10 lat wnioski z analizy wskazują, że polityka ta jest dopiero w początkowym stadium rozwoju i w przeciwieństwie do gospodarek zachodnich (w analizowanym przypadku Niemiec) w znikomym zakresie oddziałuje na innowacyjność gospodarek krajowych i regionalnych systemów innowacyjnych

The transformative capacity of new technologies. How innovations affect sectoral change: Conceptual considerations

Following up on recent debates about sectoral systems of innovation and production, the paper introduces a heuristic framework for analyzing and explaining distinct patterns of technology-based sectoral change. The concept is based on two interrelated influencing factors. The first is the sectoral-specific transformative capacity of new technologies themselves, that is, their substantial or incremental impact on socioeconomic and institutional change in a given sectoral system. The second is the sectoral adaptability of socioeconomic structures, institutions, and actors confronted with the opportunities presented by new technologies. The first factor the sectoral transformative capacity of new technologies enables us to identify the technology-driven pressure to change and adjust the structural and institutional architectures of the sectoral system. The second, complementary factor sectoral adaptability helps us to discern the distinct social patterns of anticipating and absorbing this technology-based pressure. The specific interplay between the two influencing factors creates distinguishable modes of sectoral transformation, ranging from anticipative and smooth adjustments to reactive and crisis-ridden patterns of change. Even processes of radical sectoral change continue over longer periods of mismatch, characterized by a cumulation of numerous and mostly gradual organizational, structural, and institutional transformations. --

Learning from Trump and Xi? Globalization and innovation as drivers of a new industrial policy. Bertelsmann GED Focus 2020

Technological innovations are essential drivers of longterm and sustainable growth. Accordingly, there currently is a debate in Germany and the EU as to whether a new, strategic industrial policy can be an answer to the complex dynamics of digitization. Products of this discussion are, for example, the Industrial Strategy 2030 published by the Federal Ministry for Economic Affairs and Energy in November 2019 and the Franco-German Manifesto for a European Industrial Policy for the 21st Century. The focus here is on the question of how the EU and its member states can maintain their innovative and thus competitive ability in the face of diverse challenges. However, there is no standard recipe for building and expanding the innovative capacity of an economy. Different countries rely on different strategies that can be equally successful. An important distinguishing feature is the role of the state. A clear example of divergent innovation models are China and the USA. Although both countries have completely different approaches to an innovation-promoting industrial policy, both models are characterized by major technological successes. With an analysis of the Chinese and American innovation system, this study highlights the main features and success factors of both innovation models and discusses whether and to what extent these factors are transferable to the European and German case. Five fields of action for an innovation-promoting industrial policy in the EU and Germany emerge from this analysis • Implementation of a long-term innovation strategy • Expansion of venture capital • Expansion of cluster approaches at EU level • Thinking and strengthening of cybersecurity at EU level • Creation of uniform and fair conditions for competition In addition to these fields of action, which are relevant both for the EU and for individual member states, industrial policy measures in the following three areas could be useful for Germany. In particular: • Improvement of framework conditions for research and development • Gearing the education and research system more strongly towards entrepreneurship and innovation • State as a pioneer and trailblazer in new technologies In their implementation, however, strategic European and German industrial policies face a trade-off between the protection and promotion of legitimate self-interests on the one hand and the defense against economically damaging protectionism and ill-considered state interventionism on the other. The so-called “mission orientation” can make a significant contribution here: Accordingly, industrial policy should serve to address specific societal challenges (e. g. globalization, digitization, demographic change, climate change) and be coherently targeted towards these objectives. Furthermore, industrial policy is to be driven in parallel by different actors. Above all, it is a joint task of business and politics to enable a competitive business location where the state ensures good competition- promoting framework conditions and the private actors implement concrete actions