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Of all publications in the section: 60
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Article
Vishnevskiy K., Karasev O., Meissner D. Technological Forecasting and Social Change. 2016. Vol. 110. P. 153-166.

Roadmapping is a complex long-term planning instrument that allows for setting strategic goals and estimating the potential of new technologies, products, and services. Until recently, roadmapping was used mainly for strategic planning, either from a technological or amarket research perspective. Roadmaps emphasized either technological development or satisfaction of market demands but rarely both. Consequently, roadmaps either excessively stress the technology side, which might lead to technically sophisticated solutions that lack applicability, or overstress customer needs, neglecting business competence-building. Therefore, this paper develops a newintegrated roadmapping approach that combines these two perspectives: it focuses on strategic planning by firms and public authorities for the long run goals of social and economic development, bringing together the market “pull” and technology “push” approach. This dual technique provides the potential for alternative means of choosing the most effective resource allocation. Integrated roadmaps include the various development stages of prospective innovations, e.g. stages of the existing innovation value chain, including R&D, manufacturing,market entry, services, andmarket expansion as well as prospective stages, including new technologies, products and services. The value of integrated roadmapping lies in its responsiveness to the challenges in innovation planning schemes for firms and sectors; it takes into consideration both future market requirements and the future resource basis for satisfying market needs, an approach not currently offered by traditional techniques. The paper develops a roadmapping methodology that can be used for planning firms' and public authorities' long-term innovation strategies.

Added: Feb 28, 2016
Article
Meissner D., Shmatko N. A. Technological Forecasting and Social Change. 2017. No. 123. P. 191-198.

Universities are increasingly seen as institutions which anticipate and address the challenges induced by interactions within the Knowledge Triangle (KT). The interactions between actors in the KT force individual agents to adjust and refine their models of operation and provide targeted output which supports the activities of other agents. Among companies, we saw the emergence of the open innovation concept which stresses the will of companies to cooperate in innovation. At the same time, the scientific community is increasingly challenged by open access to research findings and by online learning courses. These two recent developments are among the most important that were significantly initiated by gatekeepers, themselves especially important actors within the KT because they possess the power to orchestrate and direct the linkages between KT actors.

Until recently, the role of gatekeepers within the KT has been little analysed. The paper suggests that understanding the role and characteristics of gatekeepers is essential for substantial and sustainable interactions between KT agents and the fulfilment of the Third Mission of universities. Therefore, the linkages go beyond purely knowledge and technology transfer linkages but rather show how gatekeepers influence competency-building for delivering information and technologies to other organizations and enhancing institutions' absorptive capacity which is argued to be crucial for implementing effective, targeted, and productive interactions of universities. It is argued that universities need to be aware of gatekeepers' competences and powers well in advance to make use of knowledge exchange with other parties to shape society. In addition, it is argued that universities' skill base – as shown in researchers' competences – is a vital element of universities' intellectual capital which should be included in universities' performance evaluation frameworks. Finally, the paper argues that it is important for policy making in science, technology and innovation to possess knowledge of gatekeepers' position in the KT to enhance collaboration between KT agents and provide research institutions, namely universities, with the competences needed to vitalize the universities' ‘Third Mission’.

Added: Aug 17, 2016
Article
Meissner D., Carayannis E., Sokolov A. Technological Forecasting and Social Change. 2016. Vol. 110. P. 106-108.
Added: Sep 22, 2016
Article
Ivanova I., Leydesdorff L. Technological Forecasting and Social Change. 2015. No. 96 (2015) . P. 254-265.

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.

Added: Jan 25, 2016
Article
Mao C., Yu X., Zhou Q. et al. Technological Forecasting and Social Change. 2020. Vol. 151. No. article 119746. P. 1-9.

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.

Added: Jan 23, 2020
Article
Korotayev A., Zinkina J. V. Technological Forecasting and Social Change. 2011. Vol. 78. P. 1280-1284.
Added: Mar 7, 2013
Article
Saritas O., Nugroho Y. Technological Forecasting and Social Change. 2012. Vol. 79. No. 3. P. 509-529.

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.

Added: Dec 25, 2012
Article
Li S., Zhang X., Xu H. et al. Technological Forecasting and Social Change. 2020. Vol. 157. No. article 120119. P. 1-12.

As technological innovation plays an important role in today's knowledge economy, the most important output of technology development is intellectual property, which is highly valued for generating a monopoly position in providing payoffs to innovation. In this context, this paper considers Intellectual Property Management (IPM) efficiency based on the Patent Portfolio Model (PPM) to help organizations identify, enhance, and evaluate their technological strength. The Patent Portfolio Model (PPM) is built to assess the advantages and disadvantages of an organization, to identify the opportunities of development potentials and optimal distribution, to support the decision-making for optimizing resource allocation, and to develop a layout for the technical field. The case study of the Research Institute of China shows that this method is feasible and fulfills the needs of different institutions to provide suggestions for R&D technology management. The main finding of the paper is that PPM is an effective tool to be used in strategic planning because it identifies the technology advantages to define offensive and defensive strategies against competitors. The use of IPM and PPM helps decision-makers to visualize and simplify complex decision-making problems.

Added: May 28, 2020
Article
Oleg V. Ena, Chulok A., Sergey A. Shashnov. Technological Forecasting and Social Change. 2017. Vol. 119. P. 268-279.

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.

Added: Jun 2, 2016
Article
Korotayev A., Bilyuga S., Белалов И. Ш. et al. Technological Forecasting and Social Change. 2018. Vol. 128. P. 304-310.

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. 

 

Added: Oct 12, 2017
Article
Kratzer J., Meissner D., Roud V. Technological Forecasting and Social Change. 2017. Vol. 119. P. 128-138.

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.

Added: May 10, 2017
Article
Sarpong D., AbdRazak A., Alexander E. et al. Technological Forecasting and Social Change. 2017. Vol. 123. P. 142-152.

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.

Added: Sep 22, 2016
Article
Korotayev A., Goldstone J., Zinkina J. V. Technological Forecasting and Social Change. 2015. Vol. 95. No. June 01. P. 163-169.

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.

Added: Oct 7, 2015
Article
Sokolov A., Shashnov S. A., Kotsemir M. N. et al. Technological Forecasting and Social Change. 2019. Vol. 147. P. 221-242.

International cooperation in science, technology and innovation (STI) plays an increasingly significant role as it allows one to gain access to new knowledge, increase national competitiveness, jointly respond to Grand Challenges, and contribute to overall bilateral and multilateral political cooperation. International alliances aim to establish a win-win system of common STI priorities in order to coordinate their research efforts in a wider political context. Within such a system, individual countries have to use different policy instruments for achieving their own benefits via STI collaboration with foreign partners. The paper addresses the following research question: “How can quantitative analysis help better identify priorities for STI collaboration that provide additional benefits for a country participating in such work?”.

A set of common STI priorities for BRICS (Brazil, Russia, India, China, and South Africa) has been identified based on the analysis of strategic, Foresight, and STI policy documents and expert consultations. It includes a number of STI areas with a wide range of practical applications. Additional quantitative analysis shows how an individual member country can build its cooperation strategy by selecting particular thematic areas and relevant instruments for STI collaboration

Added: Aug 13, 2019
Article
Sokolov A., Haegeman K., Marinelli E. et al. Technological Forecasting and Social Change. 2013. No. 80. P. 386-397.

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.

Added: Mar 31, 2014
Article
Marinelli E., Scapolo F. Technological Forecasting and Social Change. 2013. Vol. 80. No. 3. P. 386-397.

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.

Added: Jan 2, 2013
Article
Shakina E., Parshakov P., Alsufiev A. Technological Forecasting and Social Change. 2021. Vol. 162. P. 120405.

In this paper, we rethink the corporate digital divide, a phenomenon not studied in detail in prior research. Motivated by innovation-diffusion, competence-based and skill-biased technical change theories, we hypothesize that all digital technologies’ innovations must be supported by demand for related skills and should be integrated into an innovation cycle. This research is conducted using a vast dataset of 1000 large Russian firms observed over ten years, with information collected from open internet-based sources and processed through content analysis. Among the key findings, the digital-innovation cycle has been explored and visualized, by identifying the most probable period of these innovations and their further diffusion. The digital-divide concept has been explicated by examining data on the relative dynamics of digital skills demanded by the same companies during the period of investigation. The empirical results deliver an interesting insight and encourage us to rethink the corporate digital divide through causality between competency accumulation and digital technological shifts. That, in turn, identifies the conditions necessary for the prediction of demand shocks in relation to digital competencies in labor markets.

Added: Oct 21, 2020
Article
Ivanova I., Leydesdorff L. Technological Forecasting and Social Change. 2014. Vol. 86. P. 143-156.

Using a mathematical model, we show that a Triple Helix (TH) system contains self-interaction, and therefore self-organization of innovations can be expected in waves, whereas a Double Helix (DH) remains determined by its linear constituents. (The mathematical model is fully elaborated in the Appendices.) The ensuing innovation systems can be expected to have a fractal structure: innovation systems at different scales can be considered as spanned in a Cartesian space with the dimensions of (S)cience, (B)usiness, and (G)overnment. A national system, for example, contains sectorial and regional systems, and is a constituent part in technological and supra-national systems of innovation. The mathematical modeling enables us to clarify the mechanisms, and provides new possibilities for the prediction. Emerging technologies can be expected to be more diversified and their life cycles will become shorter than before. In terms of policy implications, the model suggests a shift from the production of material objects to the production of innovative technologies.

Added: Jan 27, 2016
Article
Sokolov A., Veselitskaya N., Carabias V. et al. Technological Forecasting and Social Change. 2019. Vol. 148. No. November, article 119729. P. 1-16.

This article is devoted to an analysis of the key characteristics of smart cities. It provides insight into the key features of urban development that allow for distinguishing between smart cities and conventional ones as well as taking these features into consideration for improving existing policy instruments for smart cities. The authors used an approach based on the overview of the evolution of the concept of smart city as such and the identification of key factors/drivers of the development of smart cities. The influence of these factors was assessed with respect to their importance across 13 studies aimed at building scenarios for urban development. A set of factors peculiar to the scenarios related to smart cities was applied to an analysis of policy documents determining the development of three cities of differing scales: a megacity (Moscow), a large city (Kazan), and a small city (Winterthur).

Added: Sep 5, 2019
Article
Carayannis E., Grebeniuk A., Meissner D. Technological Forecasting and Social Change. 2016. Vol. 110. P. 109-116.

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.

Added: May 4, 2016
Article
Linton J. D. Technological Forecasting and Social Change. 2015. Vol. 100. P. 39-43.

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.

Added: Oct 21, 2015