This paper provides an improved model, based on historical data, that describes the returns on assets that result from R&D efforts to assist managers of public and private R&D activities. Such a model may lead to better decision support tools to monetize the value that may be extracted from R&D, which is otherwise often undervalued. Real option pricing models are used to gauge appropriate funding levels for assets such as R&D projects that contain large time-dependent uncertainties. However, this study finds that assuming the Gaussian distribution describes fluctuations in value is not appropriate for assets whose value is derived from R&D activities. This conclusion is based on a study of 43 military R&D projects and 100 technology-intensive small firms. A power law, such as the Cauchy distribution, is shown to be more accurate in describing fluctuations in returns from R&D investments.
This article studies the specificities of Russian user innovators on a sample of 1,670 home interviews. The percentage of end users who innovate and their willingness to share ideas is much higher in comparison to western countries and rooted in community activities which spread during Soviet times. We identify two groups of user innovators: urban, male, well educated, and financially better-situated vs a much more diverse group of small town folks who innovate out of necessity. The first group confirms previous findings, the second group is unique to developing markets and to Russia in particular. As these user innovators are reluctant to commercialise their innovations and would rather keep them for themselves or share with their peers on a voluntary basis, a great source of ideas and commercial opportunity remains untouched.
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 Russian government plans to lift the country’s agriculture and food productions and aims to become the biggest global supplier of healthy, high-quality, and ecologically ‘clean’ foods. Although innovative activities in the field have been rising over the last decade, the intensity still remains far below both other Russian economic activities as well as other competitor nations. Policymakers focus on Russia’s Research and Technology Organizations (RTOs) as a channel to transfer new technologies to agricultural and food producers. As demand for new technologies is low, public funds are invested into RTOs to increase the quality of their basic research activities. Instead of converting these additional funds into better technology transfer, agricultural RTOs specialize in government-funded basic research and reduce further their role as applied research organizations. Thereby, RTOs do not seek to increase their competitive position but instead maximize their benefits from public support. This article questions the leverage effect that public support measures have for technology transfer activities in the present case, and suggests that a more holistic approach including both supply and demand is needed.
The evaluation of research performance increasingly relies on quantitative indicators determined by national science policies. We focus on two dimensions of research performance—productivity and excellence—as defined in the evaluation methodology of the Slovenian Research Agency. Our analysis focuses on the effects of two science policy factors—co-authorship collaboration and researcher funding—on the productivity and excellence of Slovenian researchers at the level of research disciplines. A multilevel analysis using a hierarchical linear model with regression analysis was applied to the data with several nested levels. As many variables have a semi-continuous distribution, a statistical model was used to address them. The results show a very strong positive effect of international co-authorship collaboration on productivity and excellence, while fragmentation of funding shows a negative impact only on excellence. We also include interviews with excellent Slovenian researchers regarding their views on scientific excellence and quantitative indicators.