Intelligence: Environmental and Horizon Scanning
Future-oriented technology analysis methods can play a significant role in enabling early warning signal detection and pro-active policy action which will help to better prepare policy- and decision-makers in today’s complex and inter-dependent environments. This paper analyses the use of different horizon scanning approaches and methods as applied in the Scanning for Emerging Science and Technology Issues project. A comparative analysis is provided as well as a brief evaluation the needs of policy-makers if they are to identify areas in which policy needs to be formulated. This paper suggests that the selection of the best scanning approaches and methods is subject to contextual and content issues. At the same time, there are certain issues which characterise horizon scanning processes, methods and results that should be kept in mind by both practitioners and policy-makers.
The present study aims to identify the relationship between intellectual abilities and the motives of occupational choice. Results of the study suggest what motives of occupational choice related to the level of certain intellectual abilities. So, for example, the negative connection between the level of mathematical abilities and the “career”, “confidence” and “authority” motives were found. The level of the “formallogic” ability is negatively related to the “joining”, “confidence” and “public benefit” motives. Most of the identified interrelations are negative. In particular, it was shown that respondents with the lower levels of intellectual abilities assessed the importance of majority motives much higher than respondents with the higher levels of various abilities in our sample. A new method intended to identify different motives of occupational choice was developed during this work. According to its results the factor structure of occupational choice motives has been obtained.
This book develops foresight techniques to turn future societal challenges into opportunities. The authors present foresight approaches for innovation policy and management. Future developments in fields such as education, energy, new materials, nanotechnologies are highlighted for different countries. Readers will discover tools and instruments to capture the potentials of the grand societal challenges as defined by the United Nations. This book is a valuable resource for researchers and scholars with an interest in foresight methods and gives practical hints for policy makers and managers to take account of the grand opportunities in their business and policy strategies.
Decision-makers at all levels are being confronted with novel complexities and uncertainties and face long-term challenges which require foresight about long-term future prospects, assumptions, and strategies. This book explores how foresight studies can be systematically undertaken and used in this context. It explicates why and how methods like horizon scanning, scenario planning, and roadmapping should be applied when dealing with high levels of uncertainty. The scope of the book moves beyond “narrow” technology foresight, towards addressing systemic interrelations between social, technological, economic, environmental, and political systems. Applications of foresight tools to such fields as energy, cities, health, transportation, education, and sustainability are considered as well as enabling technologies including nano-, bio-, and information technologies and cognitive sciences. The approaches will be illustrated with specific actual cases.
Science, technology and innovation (STI) involves numerous policy fields which are championed by different government ministries or agencies. A consistent and coherent anticipatory policy mix is understood to be one that ensures a timely development and implementation of various forward-looking policy instruments. Such timely implementation is crucial for the eventual impact of the policy measures. This also requires that foresight for STI policies looks beyond the potential development paths and challenges but includes the time dimension and the outline of necessary policy responses including a relevant implementation framework. In addition the institutions which are part of the National Innovation Systems (NIS) should to be considered thoroughly for a well-balanced and comprehensive policy mix. Not only national but also regional and local actors need to be involved—and they need to be involved not only in the implementation of policy but at much earlier stages in the foresight and subsequent design procedures of the policy mix. One practical approach for convincing and engaging NIS actors at different levels is to stress opportunities which offer advantages to each of them, instead of just focusing on challenges and problems.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.
Programmer's professional activity requires an amount of work with different artificial languages. Many studies report that effective programming is correlated with the high level of verbal intelligence. In this paper we study the dynamics of artificial language learning among programmers in comparison with psychologists and the group of non-professional users. We show that programmers learn artificial language in a different way, then the other groups, and this difference is based on their professional requirements.
Foresight has gained much attention as a tool for developing and informing science, technology and innovation policy and company strategies. It is frequently used for detecting not only potential development paths of technologies but also possible economic and societal changes; and for identifying challenges that nations, societies and companies might face in the future. Raising awareness within the respective communities of trends and challenges is critically important—and the biggest challenge is how we can develop measures to meet these anticipated challenges. Paradoxically, perhaps, it may be more helpful for creating and implementing successful measures if these are elaborated by thinking about grasping opportunities, rather than framing them in terms of threats that have to be responded to. Accordingly there is a need to change the mindsets in science, technology and innovation policy making—and to engender solution and opportunity orientation among scientists and engineers.