Time series of US patents per million inhabitants show cyclic structures which can be attributed to the different knowledge-generating paradigms that drive innovation systems. The changes in the slopes between the waves can be used to indicate efficiencies in the generation of knowledge. When knowledge-generating systems are associated with idem innovation systems, the efficiency of the latter can be modeled in terms of interactions among dimensions (for example, in terms of university–industry–government relations). The resulting model predicts an increase in efficiency with an increasing number of dimensions due to the effects of self-organization among them. The dynamics of the knowledge-generating cycles can be analyzed in terms of Fibonacci numbers; successive cycles are expected to exhibit shorter life cycles than previous ones. This perspective enables us to forecast the expected dates of future paradigm changes.
University-industry innovation networks (UIINs) are important agents of innovation, as they bring together the unique profiles of higher education and industry partners. Knowledge growth in these networks does not happen automatically. We analyze the impact of network density and heterogeneity on knowledge growth in UIINs. Knowledge grows through knowledge transfer, spillover, and knowledge innovation. Knowledge growth is a function of each agent's initial knowledge level, network density, and agent heterogeneity. To analyze these correlates of knowledge growth, we use a knowledge growth model based on multiple agents and simulate knowledge growth in a UIIN. Our results show that network density positively influences knowledge growth. Initially, this positive impact increases and then disappears with a further increase in network density. We also find that heterogeneity moderates the relationship between density and knowledge growth. Through the positive moderating effect of its impact on knowledge innovation, it promotes new knowledge generation in the entire innovation network, thus providing a basis for subsequent knowledge transfer. Our study supports and enriches the contingency view of knowledge growth in innovation networks.
In parallel with the increasing complexity and uncertainty of social, technological, economic, environmental, political and value systems (STEEPV), there is a need for a systemic approach in Foresight. Recognizing this need, the paper begins with the introduction of the Systemic Foresight Methodology (SFM) is introduced briefly as a conceptual framework to understand and appreciate the complexity of systems and interdependencies and interrelationships between their elements. Conducting Foresight systemically involves a set of ‘systemic’ thought experiments, which is about how systems (e.g. human and social systems, industrial/sectoral systems, and innovation systems) are understood, modelled and intervened for a successful change programme. A methodological approach is proposed with the use of network analysis to show an application of systemic thinking in Foresight through the visualisation of interrelationships and interdependencies between trends, issues and actors, and their interpretation to explain the evolution of systems. Network analysis is a powerful approach as it is able to analyse both the whole system of relations and parts of the system at the same time and hence it reveals the otherwise hidden structural properties of the systems. Our earlier work has attempted to incorporate network analysis in Foresight, which helped to reveal structural linkages of trends and identify emerging important trends in the future. Following from this work, in this paper we combine systemic Foresight, network analysis and scenario methods to propose an ‘Evolutionary Scenario Approach,’ which explains the ways in which the future may unfold based on the mapping of the gradual change and the dynamics of aspects or variables that characterise a series of circumstances in a period of time. Thus, not only are evolutionary scenarios capable of giving a snapshot of a particular future, but also explaining the emerging transformation pathways of events and situations from the present into the future as systemic narratives.
A key element of any government's Science, Technology and Innovation policy is stable analytical infrastructure to support strategic decision making. Experience from many countries shows that substantial policy decision making requires collecting and analysing a broad range of information to develop proactive and future-oriented policies. Accordingly, infrastructure providing this information as well as evidence for policy-making must possess the capabilities for collecting, assessing, and processing information. However, information in this context is highly specific and subject related information, which is frequently embodied within expert knowledge holders. Therefore, information management in this light imposes special challenges on infrastructure.
The present study discusses some methodological approaches and practical studies to set up a network of STI Foresight network in Russia, integrated into the national Foresight and planning system. We outline the principles for goal setting, network architecture, creating a network of experts, selecting key information products, and methodological support. Russia's STI Foresight network, built on principles presented here, has been fully operational since 2011 and provides expertise on a large scale for a variety of governmental and industry organizations.
Our review of some modern trends in the development of energy technologies suggests that the scenario of a significant reduction of the global oil demand can be regarded as quite probable. Such a scenario implies a rather significant decline of oil prices. The aim of this article is to estimate the sociopolitical destabilization risks that such a decline could produce with respect to oil exporting economies. Our analysis of the relationship between changes in oil prices and political crises in these economies shows a large destabilizing effect for price declines in the respective countries. The effect is highly non-linear, showing a power-law type relationship: oil price changes in the range higher than $60 per barrel only exert very slight influence on sociopolitical instability, but if prices fall below this level, each further decrease by $10 leads to a greater increase in the risks of crises. These risks grow particularly sharply at a prolonged oil price collapse below $40 per barrel, and become especially high at a prolonged oil price collapse below $35 per barrel. The analysis also reveals a fairly short-term lag structure: a strong steady drop in oil prices immediately leads to a marked increase in the risks of sociopolitical destabili- zation in oil-exporting countries, and this risk reaches critical highs within three years. Thus, the possible substantial decline of the global oil demand as a result of the development of the energy technologies reviewed in the first section of the present article could lead to a very substantial increase in the sociopolitical destabi- lization risks within the oil exporting economies. This suggests that the governments, civil societies, and business communities of the respective countries should amplify their effort aimed at the diversification of their economies and the reduction of their dependence on the oil exports.
There is a common agreement that innovation is driven by the people that form the heart of any company's innovation activity. Still, people perform innovation in a special institutional environment characterized by rules and regulations that might support or impede innovation. The open innovation paradigm expects companies to engage in external relationships for innovation; however companies often neglect the actual internal openness of employees, which is an absolute must before partnering with external partners. The article finds that company innovation culture comes in five main forms: closed innovation (driven by internal capabilities); doing, using, interacting (ad hoc processes, no link to knowledge providers); outsourcing innovation capabilities; extramural innovation, no matching internal culture/procedures and proactive innovation (match of internal and external openness). The empirical analysis shows that the closed innovation behavior is by far the most widespread among Russian companies whereas proactive innovation behavior remains an exception in the overall sample.
Drawing on the contemporary turn to discursive practices we examine how the organizing practices of industry, university and government facilitate (or impede) developing countries transition to a hybrid triple helix model of innovation. Placing emphasis on the everyday situated practices of institutional agents, their interactions, and collaborative relationships, we identified three domains of practices (advanced research capabilities and external partnerships, the quantification of scientific knowledge and outputs, and collective entrepreneurship) that constitutively facilitate (or impede) partnership and in turn the successful transition to a hybrid triple helix model. Our study also highlights the contextual influence of differential schemata of interpretations on how to organize innovation by the three institutional actors in developing countries.
The Great Divergence and, to a lesser extent, the Great Convergence phenomena have attracted considerable scholarly attention. However, the existing attempts at explaining these phenomena and their background share two significant drawbacks: first, no model (to the best of our knowledge) has managed to account for both the Great Divergence and the Great Convergence so as to explain the timing of the trend change (around 1970s). Second, most existing models concentrate heavily on the economic forces, frequently neglecting the demographic factor. We offer an approach to overcome these drawbacks, revealing a close coupling between phases of global demographic transition and phases of the Great Divergence and Great Convergence. As we account for the crucial role of the demographic component in these processes, we show that the timing of the trend change was not coincidental. Our findings suggest that the dynamics of global population growth and the Great Divergence and Great Convergence therefore may be considered so closely coupled as to be two sides of the same coin. On the other hand, they also suggest that the Great Divergence and Great Convergence should be treated as a single process, as two phases of the global modernization. © 2015 Elsevier Inc.
The FTA community relies on a set of disciplines and methods, which try to better understand and shape the future from different methodological perspectives. Whilst the community has grown since the first edition of the International Seville Conference on Future-oriented Technology Analysis (FTA), there is still little dialogue and exchange between those applying quantitative and those applying qualitative methods. The FTA events have, since the beginning, provided an avenue to debate methodological aspects and this paper summarises and furthers the discussion developed during the 2011 edition, building on the debates at the conference and between members of the conference Scientific Committee, to which the authors of this paper belong. In particular this paper describes the methodological state of the field through a tripartite taxonomy of increasing levels of qualitative and quantitative integration. It shows how significant progress has been made for simpler forms of combinations but not for more sophisticated (and perhaps more promising) ones. Following that, it suggests that an epistemological divide, common to the social sciences as a whole, combined with cultural differences and misconceptions within the FTA community are amongst the factors undermining further methodological integration. The paper concludes by suggesting some steps, combining research and practice, to overcome such barriers.
Roadmapping is a broadly applied management instrument for developing and implementing company technology and innovation strategies. During the last years this national science, technology and innovation (STI) policy makers have become aware of the potential roadmapping offers for strategic technology and innovation management and begun applying it in the context of STI policy and priority setting in this context especially. Still reality shows that roadmapping for STI policy purposes is by far more complex than company technology and innovation roadmapping.
The article therefore develops a structured, integrated and flexible approach to roadmapping for STI policy which we name “Smart Roadmapping for STI Policy”, taking into account the complexity of STI policy as well as the need for and implications of a Targeted Open Innovation approach to STI policy and the resulting requirements to roadmaps. The proposed approach is designed to allow integration in the broader policy decision making and different level STI strategy implementation.
Technologists and non-technologists have different perspectives that complicate their understanding of innovation. The taste and smell of Scotch Whisky is offered as a sensual experience (smell and/or taste) to assist people in gaining an understanding and appreciation of howprocess innovation leads to and is intertwined with product innovation for foods, chemical and engineered materials. The contribution of this paper is to demonstrate how to enhance learning and understanding about innovation through a straightforward exercise in experiential learning.
The Northern economies have been the main sources of technologies for the global garment manufacturing industry. Over the past decade, China has become an important alternative source of these technologies offering a range of technological choices for small scale and dispersed production of cheap consumer goods, particularly in the developing world. Preceding a national foresight exercise aimed at enhancing the capabilities of small-scale garment producers in Uganda, we examine the potential ‘inclusiveness’ of garment sewing machines imported from the Northern economies and China, and their individual potential to enhance the capabilities of poor garment producers, particularly, women and rural dwellers. Data for our study included a survey and semi-structured interviews with 147 garment firms and other key informants. Compared to the Chinese sewing machines, we found that the Northern machines have high acquisition cost, relies on scale and advanced infrastructure, and tend to exclude poor rural producers (often women). The transfer of Chinese technologies to Uganda, we also found is much easier, have larger spread effects, leading to smaller gaps in technological know-how between China and Uganda because of the context in which Chinese technological innovations are induced. We conclude with some implication of our study to theory and policy.
During the last years, new technologies have been developing at a rapid pace; however, new technologies carry risks and uncertainties. Technology forecasting by analogy has been used in the case of emerging technologies; nevertheless, the use of analogies is subject to several problems such as lack of inherent necessity, historical uniqueness, historically conditioned awareness, and casual analogies. Additionally, the natural process of selecting the analogy technology is based on subjective criteria for technological similarities or inductive inference. Since many analogies are taken qualitatively and rely on subjective assessments, this paper presents a quantitative comparison process based on the Social Network Analysis (SNA) and patent analysis for selecting analogous technologies. In this context, the paper presents an analysis of complex patent network structures using centrality and density metrics in order to reduce the lack of information or the presence of uncertainties. The case of Autonomous Vehicles (AVs) is explored in this paper, comparing three candidate technologies which have been chosen based on the similarities with the target technologies. The best candidate technology is selected based on the analysis of two main centrality metrics (average degree and density).
The paper aims to analyse the evolution of forward-looking activities in Russia vis-à-vis science, technology and innovation policy challenges and its development over the last century, with a particular focus on the period of transition to a market economy.With the development of more complex and elaborate policy instruments, demand for a better grounded long-termvision of social and economic trends has been growing both among policy makers and the S&T community. The study illustrates the emergence of technology foresight in Russia and its evolution along relevant stages of economic development, from an information source for S&T and innovation policy towards a fully-fledged anticipatory policy instrument.