Foresight, Competitive Intelligence and Business Analytics for Developing and Running Better Programmes
Technology foresight has been increasingly undertaken by developing countries to identify technologies whose adoption might serve as a platform for future economic growth. However, foresight activities have not, by and large, resulted in well-developed policy initiatives. Three factors are relevant for improvement. First, foresight activities would benefit from being more informed by the convergence literature and global convergence experience over the past several decades, and should therefore incorporate organically the concepts of absorptive capacity and technology gap into foresight exercises. Second, certain preconditions – in particular the existence of a functional national innovation system – enhance the likelihood that foresight exercises will be successful. Third, in order to achieve wide buy-in and promote the sustainability of initiatives generated by the foresight activity, developing countries are advised to consult widely in the foresight process. Policies emanating from foresight activities should additionally address two core challenges: a) a clear definition of those technologies that should be developed internally vs. those that should be sourced from abroad and b) identification of the internal capabilities to be developed in conjunction with those technologies targeted for acquisition from abroad.
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.
Can competitive intelligence (CI) be used to assist in regional and sectoral economic development? This article looks at intelligence initiatives (largely around training) sponsored by various government departments and agencies in Canada and their link to regional and sectoral economic development. The article provides examples of the kind of intelligence initiatives that have been used in Canada to support regional and sectoral (industrial) economic development. The article proposes a method for categorizing these regional and sectoral intelligence programs and suggests methods for assessing the impact of these programs on regional and sectoral economic development. The Canadian programs are divided into three broad categories 1) Government programs aimed at enhancing their own ability to develop competitive intelligence 2) Programs that are sponsored by the government for industry and others to develop competitive intelligence and 3) Programs sponsored by the government to help communities develop competitive intelligence for local economic development. Positive economic impacts were identified using program review documents, government officer reports and anecdotal evidence from program participant surveys. However, while the evidence does support positive impact a more comprehensive approach to evaluating these impacts should be considered in the future.
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.
This paper aims to present a set of strategic options for Research and Innovation (R&I) stakeholders in the light of new and emerging ways of organising and performing research. Design/methodology/approach: The paper first reviews the evolution of the R&I landscape and identifies the most influential stakeholders engaged in R&I. In the light of the scenarios developed for the year 2030, a set of strategic options are identified and assessed for each stakeholder group. Findings: R&I systems are now more complex than 50 years ago and will be even more in the future. Radical changes are expected in terms of the ways research is funded, organised and carried out. Some of these transformations are captured by the scenarios developed. The analysis of scenarios indicated that their feasibility and desirability differ across different sectors of industry, and research areas within the research landscape. Research limitations/implications: Scenarios and strategies presented in the paper bring new considerations on the way research activities are practiced. Further research is considered to be useful on the new modes of research and implications for academia, industry, society and policy makers. Practical implications: The discussion around the responses of different stakeholders vis-à-vis specific scenarios about the future in R&I practices and organisation gives a practical view about how to deal with associated emerging trends and issues. Social implications: Society is a crucial stakeholder of all R&I activities. The transformative scenarios suggest that society will not only be playing a reactive role on the demand side but also more proactive role on the supply side in the decades to come. Originality/value: The paper is based on work undertaken within the Research and Innovation (RIF) 2030 project. As R&I activities will be important for the development and competitiveness of the EU and its member states, the work presented here is considered to be of value by highlighting how to create more resilient strategies in a fast-changing R&I landscape.
Tech 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.
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.