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Regular version of the site

Tools for enhancing the efficiency of science, technology, and innovation foresight studies

Priority areas of development: economics
2019
The project has been carried out as part of the HSE Program of Fundamental Studies.

Over the last decades, basic and applied future studies of the science, technology and innovation have increasing incorporated evaluation exercises that are meant to increase their efficiency. This trend implies the use of a wide spectrum of information sources, as well as the use of intelligent big data analysis of this information. The present research project is aimed at the identification of special features, scope and approaches to undertaking foresight studies in the sphere of science, technology and innovation; the development of methods for increasing their performance. In order to attain this goal the following six research tasks were addressed.

1. The development of a forecasting methodology based on stochastic inter-sectoral models and the estimation of the parameters of this model by using available statistical data

The research methods included cluster analysis, Bayesian vector autoregression (BVAR), dynamic stochastic general equilibrium models (DSGE), long-term forecasting based on DSGE models.

The empirical base: in order to test the approaches to parameters’ estimation the following databases have been used. First, for mergers and acquisitions of financial technologies information on completed transactions was obtained from Zephyr database - Bureau van Dijk. The financial characteristics of the transactions were obtained from Bloomberg database. Second, for "green bonds" research two sources were used. Information on issuers of both green and climate bonds was obtained from the Climate Bonds Initiative (CBI). Information on the characteristics of green bonds and climate bonds was obtained from the Bloomberg database.

Results: the methodology for forecasting the impact of science, technology and innovation policies on economic development by using developed inter-sector dynamic stochastic general equilibrium model was developed. The preliminary results of the DSGE model allow evaluating the impact of technological shocks on the selected sector of economy. Parameters evaluation of the developed model are given with the example of mergers and acquisitions of financial technologies, as well as determining the effectiveness of green bonds.

Based on the results of this study, the impact of science, technology and innovation policy on selected sectors, as well as on the national economy can be analyzed. To improve the estimates of model parameters, additional studies can be done, including the empirical analysis of statistical data by sector of the economy. By using the developed DSGE model, it is possible to forecast the development of sectors and the economy to assist decision-making for managing science, technologies and innovations.

2. Identification of methods for increasing the efficiency of scenario analysis in foresight studies

Methods used inlcuded document analysis (applied policy analysis), analysis of publications, case study, scenario analysis.

The empirical base covers foresight studies containing information on the factors that influence the future of cities and statistical indicators of urban planning relevant to scenario analysis.

As a result of the study international foresight studies of urban development based on scenario analysis were analyzed. These studies differ in the degree of detailing the future scenarios, impact factors, as well as methods used for their identification (expert interviews, seminars, horizon scanning, etc.). Different approaches to scenario analysis have been classified, including the advantages and disadvantages that depend on targeting customers and scenario developers, as well as the amount of funds available. Approaches and tools for enhancing the effectiveness of scenario analysis in foresight were identified. If sufficient funds are available, it is advisable to use scenario analysis that combines advantages and minimizes disadvantages of the three approaches to scenario development described in this study. In this case, it is possible to investigate in sufficient detail the factors that affect the future developments, as well as organize large-scale expert discussions to design detailed future trajectories.

The results of this study can be used by relevant ministries and agencies to develop short-term, medium- and long-term urban development strategies and plans.

3. The development of methodology for semantic benchmarking of textual descriptions of science and technology results (outcomes) in different sources of information using word embeddings in the sphere of Science, Technology and Innovation

The following methods were applied: semantic analysis, word embeddings, deep neural networks with Transformer architecture and statistical analysis.

The empirical base included database of Web of Science’ research fronts, as well as more than 350 million texts of scientific articles, patents, media publications, conference materials, analytical reports and other documents.

The results include the comparative analysis of the state-of-the-art semantic analysis methods that allow identifying topics based on context. Through comparing the ability of word embeddings (a set of language modeling and feature learning techniques in natural language processing) to classify research fronts, SciBERT model was chosen as the optimal one, and its configuration was described. SciBERT was used to identify research fronts, technology trends and life cycle of technologies.

The research results have already been implemented in the intelligent big data analysis system iFORA (proprietary software developed at the HSE Institute for Statistical Studies and Economics of Knowledge).

4. Identification of the specific features of various information sources for addressing various foresight research tasks

The main methods used were theoretical analysis, analysis of scientific publications and comparative analysis. The empirical base – scientific publications and analytical reports in the field of planning and implementation of foresight studies.

As a result, key information sources used in foresight studies were outlined. Each information source was matched with a relevant research task it should address. Key methods for collecting information from these sources were identified and analyzed, including their advantages and disadvantages. In addition, the factors for efficient work with data sources were identified. It is recommended to take them into account when choosing information sources for particular foresight projects and develop strategies for their use.

The results of this study may be useful for expert groups at planning and implementation stages of foresight studies, as well as for relevant ministries and government agencies in the development of science and technology foresight exercises.

5. Development of methodological approaches to the use of foresight methods for increasing the efficiency of national and corporate digital transformation programs

The research methods include analysis of research publications, comparative analysis of national and corporate digital transformation programs, systematization method, and case study.

Empirical base of research covers digitalization programs and strategies at the national and corporate levels, reports on the implementation of digitalization strategies, and international organizations’ studies on the efficiency of various methods (including Foresight methods) for monitoring and evaluating the digitalization programs.

As a result of the study, methods for monitoring and evaluating the effectiveness of national and corporate digitalization programs using foresight tools, adapted for Russian actors, were proposed. This allowed formulating the main recommendations to increase the effectiveness of strategic planning related to digital technologies.

The suggested recommendations may be used in the development and evaluation of national and corporate digital strategies. Study outcomes may also open the way to take advantage of Foresight tools in digitalization strategic planning.

6. The study of approaches to identification of cross-sectoral effects of the nexus thematic areas in foresight research

The study’s methods include the analysis of methodology applied in foresight projects that focus on energy, agriculture and water supply and sanitation; analysis of methods for the study and assessment of the combined use of water, energy and food; content analysis.

Empirical base of research covers research publications containing the outcomes of foresight studies; research publications and analytical materials of expert and international organizations that describe methods and approaches to the study and assessment of sectoral, inter-sectoral, social and environmental effects of the water-food-energy nexus (combined use of water, food and energy resources).

The study’s results include tools for taking into account the nexus thematic areas in foresight studies with a final aim to increase the efficiency of such studies. Moreover, recommendations for increasing the efficiency of sectoral and national foresight studies through identification and synthesis of intersectoral /interdisciplinary priorities were developed.

The study outcomes may be used for increasing the efficiency of sectoral and national foresight studies and strategic planning documents that are based on foresight studies.

Publications:


De Moraes Silva D. R., Lucas L. O., Vonortas N. Internal barriers to innovation and university-industry cooperation among technology-based SMEs in Brazil // Industry and Innovation. 2020. Vol. 27. No. 3. P. 235-263. doi
Cheah S. L., Yang Y., Saritas O. Reinventing product-service systems: the case of Singapore // Foresight. 2019. Vol. 21. No. 3. P. 332-361. doi
Burmaoglu S., Saritas O. An evolutionary analysis of the innovation policy domain: Is there a paradigm shift? // Scientometrics. 2019. Vol. 118. No. 3. P. 823-847. doi
Proskuryakova L. N. Foresight for the 'energy' priority of the Russian Science and Technology Strategy // Energy Strategy Reviews. 2019. Vol. 26. P. 100378-1-100378-12. doi
Kuchin I., Baranovskii G., Dranev Y., Chulok A. Does green bonds placement create value for firms? / NRU Higher School of Economics. Series WP BRP "Science, Technology and Innovation". 2019. No. 101.
Sokolov A., Veselitskaya N., Carabias V., Yildirim O. Scenario-based identification of key factors for smart cities development policies // Technological Forecasting and Social Change. 2019. Vol. 148. No. November, article 119729. P. 1-16. doi
Mikova N., Sokolova A. Comparing data sources for identifying technology trends // Technology Analysis & Strategic Management. 2019. Vol. 31. No. 11. P. 1353-1367. doi
Lai Y., Vonortas N. Regional entrepreneurial ecosystems in China // Industrial and Corporate Change. 2019. Vol. 28. No. 4. P. 875-897. doi
Dranev Y., Frolova K., Ochirova E. The impact of fintech M&A on stock returns // Research in International Business and Finance. 2019. Vol. 48. P. 353-364. doi
Harms R., Hatak I., Chang M. Sensory processing sensitivity and entrepreneurial intention: The strength of a weak trait // Journal of Business Venturing Insights. 2019. Vol. 12. P. e00132-1-e00132-7. doi
Bosch A., Vonortas N. Smart Specialization as a Tool to Foster Innovation in Emerging Economies: Lessons from Brazil // Foresight and STI Governance. 2019. Vol. 13. No. 1. P. 35-47. doi
Aldieri L., Gennaro G., Kotsemir M. N., Vinci C. P. An investigation of impact of research collaboration on academic performance in Italy // Quality and Quantity. 2019. Vol. 53. No. 4. P. 2003-2040. doi