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Trend monitoring for linking science and strategy

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Abstract

Rapid changes in Science & Technology (S&T) along with breakthroughs in products and services concern a great deal of policy and strategy makers and lead to an ever increasing number of Foresight and other types of forward-looking work. At the outset, the purpose of these efforts is to investigate emerging S&T areas, set priorities and inform policies and strategies. However, there is still no clear evidence on the mutual linkage between science and strategy, which may be attributed to Foresight and S&T policy making activities. The present paper attempts to test the hypothesis that both science and strategy affect each other and this linkage can be investigated quantitatively. The evidence for the mutual attribution of science and strategy is built on a quantitative trend monitoring process drawing on semantic analysis of large amount of textual data and text mining tools. Based on the proposed methodology the similarities between science and strategy documents along with the overlaps between them across a certain period of time are calculated using the case of the Agriculture and Food sector, and thus the linkages between science and strategy are investigated.

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References

  • Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological Forecasting and Social Change, 96, 202–214.

    Article  Google Scholar 

  • Amanatidou, E., Butter, M., Carabias, V., Könnölä, T., Leis, M., & Saritas, O., et al. (2012). On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues. Science and Public Policy, 39(2), 208–221.

    Article  Google Scholar 

  • Angeli, G., Premkumar, M. J., & Manning, C. D. (2015). Leveraging linguistic structure for open domain information extraction. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian Federation of Natural Language Processing, ACL (pp. 26–31).

  • Anick, P. G., Verhagen, M., & Pustejovsky, J. (2014). Identification of technology terms in patents. In LREC (pp. 2008–2014).

  • Assfalg, J., Bernecker, T., Kriegel, H. P., Kröger, P., & Renz, M. (2009, April). Periodic pattern analysis in time series databases. In Database systems for advanced applications (pp. 354–368). Berlin: Springer.

  • Blei, D. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.

    Article  Google Scholar 

  • Burmaoglu, S., & Saritas, O. (2016). Changing characteristics of warfare and the future of Military R&D. Technological Forecasting and Social Change, Article in Press. doi:10.1016/j.techfore.2016.10.062.

    Google Scholar 

  • Carvalho, K. M., Winter, E., & de Souza Antunes, A. M. (2015). Analysis of technological Developments in the treatment of Alzheimer’s disease through patent documents. Intelligent Information Management, 7(05), 268.

    Article  Google Scholar 

  • Cassiman, B., Veugelers, R., & Zuniga, M. P. (2007). Science linkages and innovation performance: An analysis on CIS-3 firms in Belgium.

  • Chen, D., & Manning, C. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 740–750). Doha, Qatar: Association for Computational Linguistics.

  • Chidamber, S. R., & Kon, H. B. (1994). A research retrospective of innovation inception and success: The technology–push, demand–pull question. International Journal of Technology Management, 9(1), 94–112.

    Google Scholar 

  • Daim, T. U., Chiavetta, D., Porter, A. L., & Saritas, O. (Eds.). (2016). Anticipating future innovation pathways through large data analytics. Berlin: Springer.

    Google Scholar 

  • de Miranda Santo, M., Coelho, G. M., dos Santos, D. M., & Fellows Filho, L. (2006). Text mining as a valuable tool in foresight exercises: A study on nanotechnology. Technological Forecasting and Social Change, 73(8), 1013–1027.

    Article  Google Scholar 

  • Ena, O., Mikova, N., Saritas, O., & Sokolova, A. (2016). A technology trend monitoring methodology: The case of semantic technologies. Scientometrics, 108(3), 1013–1041.

    Article  Google Scholar 

  • Guan, J., & He, Y. (2007). Patent-bibliometric analysis on the Chinese science—technology linkages. Scientometrics, 72(3), 403–425.

    Article  Google Scholar 

  • Huang, Y., Schuehle, J., Porter, A. L., & Youtie, J. (2015). A systematic method to create search strategies for emerging technologies based on the Web of Science: Illustrated for ‘Big Data’. Scientometrics, 105(3), 2005–2022.

    Article  Google Scholar 

  • Jones, K. S. (1965). Experiments in semantic classification. Mech Translation, 8, 3–4.

    Google Scholar 

  • Judea, A., Schütze, H., & Brügmann, S. (2014). Unsupervised training set generation for automatic acquisition of technical terminology in patents. In COLING (pp. 290–300).

  • Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of medoids. In Y. Dodge (Ed.), Statistical data analysis based on L1-norm and related methods (pp. 405–416). Amsterdam: North-Holland.

  • Kerr, C. I., Mortara, L., Phaal, R., & Probert, D. R. (2006). A conceptual model for technology intelligence. International Journal of Technology Intelligence and Planning, 2(1), 73–93.

    Article  Google Scholar 

  • Kim, J., Hwang, M., Jeong, D. H., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems with Applications, 39(16), 12618–12625.

    Article  Google Scholar 

  • Lahoti, G., Porter, A., Zhang, C., Youtie, J., Wang, B., & Hicks, D. (2015). Tech mining to validate and refine a technology roadmap. In Proceedings of the 5th global TechMining conference. Atlanta, USA.

  • Li, H., Xu, F., & Uszkoreit, H. (2011). TechWatchTool: innovation and trend monitoring. In RANLP (pp. 660–665).

  • Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The Stanford CoreNLP natural language processing toolkit. In ACL (System Demonstrations) (pp. 55–60).

  • Martin, B. R. (1995). Foresight in science and technology. Technology Analysis and Strategic Management, 7(2), 139–168.

    Article  Google Scholar 

  • Mikova, N., & Sokolova, A. (2014). Global technology trends monitoring: Theoretical frameworks and best practices. Foresight-Russia, 8(4), 64–83.

    Google Scholar 

  • Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for Science, Technology and Innovation. Berlin: Springer.

    Book  Google Scholar 

  • Park, H., Ree, J. J., & Kim, K. (2013). Identification of promising patents for technology transfers using TRIZ evolution trends. Expert Systems with Applications, 40(2), 736–743.

    Article  Google Scholar 

  • Porter, A. L. (2009). Tech mining for future-oriented technology analyses. In J. C. Glenn & T. J. Gordon (Eds.), Futures research methodology.

  • Porter, A., & Cunningham, S. (2004). Tech mining: Exploiting new technologies for competitive advantage. Hoboken: Wiley.

    Book  Google Scholar 

  • Saritas, O. (2013). Systemic foresight methodology. In D. Meissner, L. Gokhberg, & A. Sokolov (Eds.), Science, technology and innovation policy for the future: Potentials and limits of foresight studies (pp. 83–117). Berlin: Springer.

    Chapter  Google Scholar 

  • Saritas, O., & Burmaoglu, S. (2015). Future of sustainable military operations under emerging energy and security considerations. Technological Forecasting and Social Change, 102(2015), 331–343.

    Google Scholar 

  • Saritas, O., & Smith, J. E. (2011). The big picture–trends, drivers, wild cards, discontinuities and weak signals. Futures, 43(3), 292–312.

    Article  Google Scholar 

  • Scherer, F. M. (1982). Demand-pull and technological invention: Schmookler revisted. The Journal of Industrial Economics, 30, 225–237.

  • Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55). Transaction Publishers.

  • Smith, J., & Saritas, O. (2011). Science and technology foresight baker’s dozen: a pocket primer of comparative and combined foresight methods. Foresight, 13(2), 79–96.

    Article  Google Scholar 

  • Sokolov, A., & Chulok, A. (2016). Priorities for future innovation: Russian S&T foresight 2030. Futures, 80, 17–32. doi:10.1016/j.futures.2015.12.005.

  • Sun, G., Guo, Y., & Yang, F. (2015). Technology early warning model: A new approach based on patent data. In Proceedings of the Second International Workshop on Patent Mining and its Applications (IPAMIN). May 27–28, 2015, Beijing, China. Accessed 14 Mar 2017. http://ceur-ws.org/Vol-1437/ipamin2015_paper4.pdf.

  • Szu-chia, S. L. (2010). Scientific linkage of science research and technology development: A case of genetic engineering research. Scientometrics, 82(1), 109–120.

    Article  Google Scholar 

  • Trumbach, C. C., Payne, D., & Kongthon, A. (2006). Technology mining for small firms: Knowledge prospecting for competitive advantage. Technological Forecasting and Social Change, 73(8), 937–949.

    Article  Google Scholar 

  • Verhaegen, P. A., D’hondt, J., Vertommen, J., Dewulf, S., & Duflou, J. R. (2009). Relating properties and functions from patents to TRIZ trends. CIRP Journal of Manufacturing Science and Technology, 1(3), 126–130.

    Article  Google Scholar 

  • Wang, X., Qiu, P., Zhu, D., Mitkova, L., Lei, M., & Porter, A. L. (2015). Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells. Technological Forecasting and Social Change, 98, 24–46.

    Article  Google Scholar 

  • Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications, 35, 124–135.

    Article  Google Scholar 

  • Yoon, J., & Kim, K. (2011). An automated method for identifying TRIZ evolution trends from patents. Expert Systems with Applications, 38, 15540–15548.

    Article  Google Scholar 

  • Yoon, J., & Kim, K. (2012a). An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells. Expert Systems with Applications, 39, 7709–7717.

    Article  Google Scholar 

  • Yoon, J., & Kim, K. (2012b). TrendPerceptor: A property–function based technology intelligence system for identifying technology trends from patents. Expert Systems with Applications, 39, 2927–2938.

    Article  Google Scholar 

  • Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179–191.

    Article  Google Scholar 

Download references

Acknowledgements

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’.

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Authors and Affiliations

  1. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, Moscow, Russian Federation

    Pavel Bakhtin, Ozcan Saritas, Alexander Chulok, Ilya Kuzminov & Anton Timofeev

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  1. Pavel Bakhtin
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  2. Ozcan Saritas
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  3. Alexander Chulok
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  4. Ilya Kuzminov
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  5. Anton Timofeev
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Correspondence to Ozcan Saritas.

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Bakhtin, P., Saritas, O., Chulok, A. et al. Trend monitoring for linking science and strategy. Scientometrics 111, 2059–2075 (2017). https://doi.org/10.1007/s11192-017-2347-5

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