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In 2007 Finland changed ownership rights to inventions from its employees – “the professor's privilege” – to universities. We investigate how this change affected academic patenting using new data on inventors and patenting in Finland for the period 1995–2010. Matched sample panel data regressions using difference-in-differences show that patenting by individuals dropped by at least 29 percent after 2007. Unlike other countries studied, in Finland the reform was known before implementation. Adding the period after announcement to the reform period increases the drop in academic patenting to 46 percent. Our and others’ results call into question whether the European reform of the professor's privilege were good innovation policy.

The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-i-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

Patent data has been an obvious choice for analysis leading to strategic technology intelligence, yet, the recent proliferation of machine learning text analysis methods is changing the status of traditional patent data analysis methods and approaches. This article discusses the benefits and constraints of machine learning approaches in industry level patent analysis, and to this end offers a demonstration of unsupervised learning based analysis of the leading telecommunication firms between 2001 and 2014 based on about 160,000 USPTO full-text patents. Data were classified using full-text descriptions with Latent Dirichlet Allocation, and latent patterns emerging through the unsupervised learning process were modelled by company and year to create an overall view of patenting within the industry, and to forecast future trends. Our results demonstrate company-specific differences in their knowledge profiles, as well as show the evolution of the knowledge profiles of industry leaders from hardware to software focussed technology strategies. The results cast also light on the dynamics of emerging and declining knowledge areas in the telecommunication industry. Our results prompt a consideration of the current status of established approaches to patent landscaping, such as key-word or technology classifications and other approaches relying on semantic labelling, in the context of novel machine learning approaches. Finally, we discuss implications for policy makers, and, in particular, for strategic management in firms. proof

The integration of national research systems is one of the central objectives of European research policies. Yet the epistemic objectives of this project have been poorly defined, and scant attention has been paid to whether political, social and financial integration of the European Research Area (ERA) is accompanied by epistemic integration. We discuss the conceptual framework and methodological practices to monitor research integration, and conclude that most of them, such as research collaboration, are only partial indicators of it. To augment existing approaches with an analysis of epistemic integration, we analyse the geographical sources of knowledge of Finnish research in the period 1995-2010. We show a broad shift towards a European knowledge base, demonstrating epistemic integration into the ERA, and that Finnish researchers are, paradoxically, sourcing knowledge from an increasingly distributed system of European knowledge hubs. As policy implications, we recommend clarifying the ERA's epistemic objectives and redefining its strategy of 'reducing fragmentation'.

National systems of innovation have been serving as the conceptual framework and vehicle to address poverty and national competitiveness in developing countries. Zambia has been building its NSI since 1964, but the existing system suffers from serious shortcomings related to an insufficient conceptually inclusive framework and the absence of direct and concrete pro-poor innovation policy instruments. The system lacks strong interactive dynamics and suffers from weak governance. Zambia has also followed an export-driven industrial economy, but the expected knowledge spill-over gains from large mining operations have not been realised. The paper examines national innovation strategies by emphasising the importance of the informal sector and the potential for innovation at grass-roots level. It examines the evolution of science and technology policies by highlighting how the excluded areas can be included. The absence of pro-poor innovation and lack of inclusive policies are highlighted as factors contributing to low productivity and socioeconomic imbalances.

This article considers the role of domestic knowledge capabilities for developing countries and emerging economies, and in particular in the build-up of their national systems of innovation. Using bibliometric methods, we describe the geographic sources of knowledge and the users of Brazilian research in 2005–2009, and analyze the roles of domestic and foreign knowledge bases in it. Our results suggest that increasing reliance on domestic sources of knowledge is a feature of Brazil's improved science and technology capabilities. The ascendancy of Brazil's research informs us about the unfolding re-organization of global research, too, underlining nascent South-South knowledge flows, the prevailing relevance of EU research, and the relative decline of US research for Brazilian knowledge creation.

The complexity technologies require that companies have in-depth knowledge of the nature and effect of knowledge - its depth and breadth. Companies need to master expanding technological knowledge bases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big data to manage Competitive Technical Intelligence, companies can foster new forms of adaptive learning processes in MOT. This however requires that managers augment human judgment with machine-learning tools, prompting challenges to management traditions. We demonstrate how unsupervised learning creates insight into MOT by identifying topical knowledge foci and showing the dynamics of knowledge domains among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identification of opportunities in complex environments. We illustrate this using a case in globally operating telecommunication companies using a full-text copy of USPTO-database with approximately 6 million patents data. Our results show the landscape of the companies and the underlying knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing current needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discovery of latent patterns and identification of opportunities.

Applying distance-to-frontier analysis, we have used 2.9 million patents and population data to assess whether the relative capacity of world countries and major regions to create new knowledge and technology has become globally more equal or less equal between 1990 and 2010. We show with the Gini coefficient that the global distribution of inventors has become more equal between major countries and regions. However, this trend has been largely due to the improved performance of only two major countries, China and India. The worst performing regions, totalling a population of almost 2 billion, are actually falling behind. Our results suggest that substantial parts of the global population have fallen further behind countries at the global frontier in their ability to create new knowledge and inventions, and that the catch-up among the least developed and middle-income countries is highly uneven, prompting questions about the nature and future of the global knowledge economy.

Over the last decade, business, technology, and innovation have become globalized, leading to development of a global knowledge economy. This development is changing the roles traditionally played by advanced economies as well as by the emerging and developing economies. However, not all innovation comes from the advanced economies, nor are emerging and developing economies merely a source of resources. They are a true partner for collaboration and knowledge sharing. This chapter discusses the new globalization and explores how it has been addressed by the Finnish innovation system. It highlights the need for new forms of collaboration both nationally and internationally

Proliferation of emerging economies as significant sources of competitive knowledge is a key feature of globalisation, often driven by government policies fostering domestic research-based innovation capabilities. To cast light on this common emerging economy effort to movefrom catch-up towards the global innovation frontier, we analyse the evolution of the focus of Brazilian research frontiers 2005-2011 with bibliometric methods. Our results demonstrate a gradual and moderate shift from theoretical scientific knowledge to more innovation-centred knowledge in the most influential Brazilian research, indicating the increased prominence and quality of innovation focused efforts in country's knowledge system. Thus, the recent expansion of Brazil's innovation system is accompanied by qualitative transformations of the research system, in part driven by nationally emerging research fields and technologies. Conceptually, we consider the overall importance of scientific research for developing and emerging economy innovation system build-up.

The poster presents a mixed quantitative-qualitative FTA approach for identifying emerging technologies in complex systemic settings using "Arctic technology" as a case example. The "Arctic aspect" of emerging technologies is not to be understood as a function of one or two defining keywords, but it is actually an outcome of a network of functional conceptualisations across varied technological domains. The proposed approach has two steps: 1) the construction of a conceptual thematic network through qualitative expert workshops, and 2) quantitative patent analysis, which is based on the outcome of the qualitative phase. The approach enables to holistically show linkages and disconnections in a complex technology domain, and it can be used in emerging technology analysis in the context of various complex systemic domains, which are not easily conceptualised.

Technological innovation, big and small, has played a critical role in the evolution of the modern United States pulp and paper industry since its origins in the early nineteenth century. New technological knowledge and its implementation, more than anything else, gave entrepreneurs, firms, industries, and whole regions, the ability to create or capture new markets, or erect new production hubs, and to forge ahead competitors.

By examining the knowledge in- and outflows in Brazilian research 2005–2009, we undertake comparative bibliometric analysis of the dynamics of knowledge creation in development context. Specifically, we analyse knowledge creation dynamics to find out how knowledge flows between developing countries and the “North” shape research and its exploitation.

Understanding the nature and dynamics of Africa's collaborative research networks is critical for building and integrating the African innovation system. This paper investigates the collaborative structure of the African research systems, with focus on regions and integration. Drawing on a bibliometric analysis of co-authorship of African research publications in 2005---2009, we propose an empirically derived grouping of African research community into three distinct research regions: Southern---Eastern, Western, and Northern. The three regions are established and defined in terms of active co-authorship clusters within Africa, as well as through co-authorship links with non-African countries and regions. We examine co-authorship links both at the national and city levels in order to provide a robust and nuanced empirical basis for the three African research regions. The collaboration patterns uncovered cast light on the emerging innovation systems in Africa by pointing out the differing national, regional, and global roles of countries and cities within collaborative research networks. Lack of research capabilities is the primary factor arresting the development of African innovation systems, but our analysis also suggests that Africa's internal research collaboration suffers from structural weaknesses and uneven integration. We also identify that South Africa, and some emerging new research hubs, hold critical networking function for linking African researchers.

This paper considers the role of interactive learning for sectoral innovation systems and policies. The vehicle of this discussion is the emergence of Brazilian pulp and paper industry during the 20th century, and we explore its development as a network learning phenomenon, and highlight how dynamics of technological learning shifted as the organization of industry and its sectoral innovation system expanded and changed in tandem with the advance of strategic knowledge frontiers. Right balance of coordination and competition in the system fostered demand-led innovation system, allowed Brazilian firms to integrate efficiently in global learning networks, and nurtured entrepreneurial culture that eventually leveraged industrial change. The subsequent expansion and success of Brazilian pulp and paper industry can to a great extent be credited to the sectoral innovation system and its dual strategy, which has established a clear division of labor between fundamental innovation and knowledge transfer. Both private firms and the public sectoral innovation system have focused Brazilian cutting edge research, development, and innovation efforts on the improvement of eucalyptus, the primary source of superior competitive advantage for the Brazilian pulp and paper industry, and in other science and technology areas created efficient mechanisms to transfer the best available scientific and technological solutions from abroad to Brazil.

This book examines in detail the features and dynamics of sectoral systems of innovation and production in developing countries. Processes of rapid growth are usually associated with specific sectors such as automobiles, electronics or software, as well as with the transformation of traditional sectors such as agriculture and food. The book shows, however, that the variations across all these sectors in terms of structure and dynamics is so great that a full understanding of these differences is necessary if innovation is to be encouraged and growth sustained.