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Science-technology-industry correlative indicators for policy targeting on emerging technologies: exploring the core competencies and promising industries of aspirant economies

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Abstract

This paper seeks to contemplate a sequence of steps in connecting the fields of science, technology and industrial products. A method for linking different classifications (WoS–IPC–ISIC concordance) is proposed. The ensuing concordance tables inherit the roots of Grupp’s perspective on science, technology, product and market. The study contextualized the linking process as it can be instrumental for policy planning and technology targeting. The presented method allows us to postulate the potential development of technology in science and industrial products. The proposed method and organized concordance tables are intended as a guiding tool for policy makers to study the prospects of a technology or industry of interest. Two perceived high potential technologies—traditional medicine and ICT—that were sought by two aspirant economies—Hong Kong and Malaysia—are considered as case studies for the proposed method. The selected cases provide us the context of what technological research is being pursued for both fundamental knowledge and new industries. They enable us to understand the context of policy planning and targeting for sectoral and regional innovation systems. While we note the constraints of using joint-publishing and joint-patenting data to study the core competencies of developing economies and their potential for development, we realize that the proposed method enables us to highlight the gaps between science and technology and the core competencies of the selected economies, as well as their prospects in terms of technology and product development. The findings provide useful policy implications for further development of the respective cases.

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Source: Adapted from Gittelman (2016, p. 1572)

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Notes

  1. Note that there are other non-bibliometric approaches (e.g. exploring the kind of university-industry joint research activities/investment, or understanding the type of exports from trade data) used to study the core competencies and capabilities of different kinds of scientific and technological systems (see Porter et al. 2005, Wong et al. 2013, 2015a).

  2. Routinized joint publishing and joint patenting activities between industrial players, university scientists and researchers from public research institutions is common in the context of industrialized countries (Tijssen et al. 2009; Wong and Singh 2013; Shiu et al. 2014).

  3. See CWTS Leiden ranking on impact and collaboration at www.leidenranking.com/ranking/2015.

  4. From specifying and selecting the data sources, search and retrieval, followed by basic and deep analyses, to interpretation and utilization of the indicators.

  5. This includes R&D program management, mergers and acquisitions, new product management, intellectual asset management, technical human resources management, foresight and forecasting, and strategic planning. While there are massive studies on S&T related matrices and indices, there are few attempts to utilize it to study the core competencies of innovation systems and pinpoint the prospects of innovation.

  6. Such level of development is evident in the case of ITRI’s R&D program for entrepreneurial activities in Hsinchu Science Park. ITRI managed to spin off two capable semiconductor firms—UMC and TSMC—in the 1980 s. UMC and TSMC excelled in semiconductor fabrication businesses and spun off new firms that were capable of defining new niches in the global technological value chain (such as MediaTek in system-on-chip for optical devices and chip solutions, and GUC in system-on-chip design foundry). The multiplier effect is explained in Wong et al. (2015b).

  7. MIMOS is active in patenting, though its agenda as a research institute means that most inventions are aimed at technology licensing by both local and foreign firms. Thus, it pursues an aggressive patent filing strategy in order to protect its inventions and encourage industrial adoption.

  8. Those who perform academic publishing would anticipate that their findings would lead to certain applications or solutions for specific problem.

References

  • Amsden, A. (2001). The rise of the rest: Challenges to the west from late-industrializing economies. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities for developed and developing countries. World Development, 32(4), 629–654.

    Article  Google Scholar 

  • Barre, R. (2004). S&T Indicators for Policy Making in a Changing Science-Society Relationship. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research: The use of publication and patent statistics in studies on S&T systems (pp. 115–132). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Braun, T. (2004). Keeping the Gates of Science Journals. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research: The use of publication and patent statistics in studies on S&T systems (pp. 95–114). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Brusoni, S., & Geuna, A. (2004). Specialization and Integration. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research: The use of publication and patent statistics in studies on S&T systems (pp. 733–758). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Dutrenit, G., Anyul, M. P., & Teubal, M. (2011). Building bridges between co-evolutionary approaches to science. Technology and Innovation and Development Economics: An Interpretive Model, Innovation and Development, 1(1), 51–74.

    Article  Google Scholar 

  • Fagerberg, J., Fosaas, M., & Sapprasert, K. (2012). Innovation: Exploring the knowledge base. Research Policy, 41, 1132–1153.

    Article  Google Scholar 

  • Fung, H. N., & Wong, C. Y. (2015). Exploring the modernisation process of traditional medicine: A Triple Helix perspective with insights from publication and trademark statistics. Social Science Information, 54(3), 327–353.

    Article  Google Scholar 

  • Gao, L., Porter, A. L., Wang, J., et al. (2013). Technological life cycle analysis method based on patent documents. Technological Forecasting and Social Change, 80, 398–407.

    Article  Google Scholar 

  • Gittelman, M. (2016). The revolution re-visited: Clinical and genetics research paradigms and the productivity paradox in drug discovery. Research Policy, 45, 1570–1585.

    Article  Google Scholar 

  • Grupp, H. (1998). Foundations of the economics of innovation, theory, measurement and practice. Cheltenham: Edward Elgar.

    Google Scholar 

  • Grupp, H., & Mogee, M. E. (2004). Indicators for National Science and Technology Policy. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research: The use of publication and patent statistics in studies on S&T systems (pp. 75–94). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Hafsi, T., & Hu, H. (2016). Sectoral innovation through competing logics: The case of antidepressants in traditional Chinese medicine. Technological Forecasting and Social Change, 107, 80-89.

    Article  Google Scholar 

  • Innovation and Technology Commission. (2015). Chinese medines. http://www.itc.gov.hk/en/area/chin.htm. Accessed April 27, 2016.

  • Innovation and Technology Fund HK. (2015). Distribution of approved projects among different technology areas. http://www.itf.gov.hk/l-eng/StatView107.asp. Accessed April 27, 2016.

  • Lall, S., & Albaladejo, M. (2004). China's competitive performance: A threat to east asian manufactured exports? World Development, 32, 1441–1466.

    Article  Google Scholar 

  • Lall, S., & Teubal, M. (1998). Market-stimulating technology policies in developing countries: A framework with examples from East Asia. World Development, 26(8), 1369–1385.

    Article  Google Scholar 

  • Leydesdorff, L., & Gauthier, E. (1996). The evaluation of national performance in selected priority areas using scientometric methods. Research Policy, 25, 431–450.

    Article  Google Scholar 

  • Leydesdorff, L., & Meyer, M. (2007). The scientometrics of a Triple Helix of university-industry-government relations (introduction to the topical issue). Scientometrics, 70, 207–222.

    Article  Google Scholar 

  • Lundvall, B. Å. (2010). National systems of innovation: Toward a theory of innovation and interactive learning (Vol. 2). London: Anthem Press.

    Google Scholar 

  • Lybbert, T. J., & Zolas, N. J. (2014). Getting patents and economic data to speak to each other: An ‘algorithmic links with probabilities’ approach for joint analyses of patenting and economic activity. Research Policy, 43(3), 530–542.

    Article  Google Scholar 

  • Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31, 247–264.

    Article  Google Scholar 

  • Newman, N. C., Porter, A. L., Roessner, J. D., Kongthon, A., & Jin, X.-Y. (2005). Differences over a decade: High tech capabilities and competitive performance of 28 nations. Research Evaluation, 14, 121–128.

    Article  Google Scholar 

  • OECD. (2011). ISIC Rev. 3 technology intensity definition. OECD Directorate for Science, Technology and Industry Economic Analysis and Statistics Division. https://www.oecd.org/sti/ind/48350231.pdf. Accessed April 29, 2016.

  • Paliokaitė, A., Martinaitis, Ž., & Reimeris, R. (2015). Foresight methods for smart specialisation strategy development in Lithuania. Technological Forecasting and Social Change, 101, 185–199.

    Article  Google Scholar 

  • Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy, 13, 343–373.

    Article  Google Scholar 

  • Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring research interdisciplinary. Scientometrics, 72, 117–147.

    Article  Google Scholar 

  • Porter, A. L., & Newman, N. C. (2004). Patent Profiling for Competitive Advantage. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research: The use of publication and patent statistics in studies on S&T systems (pp. 587–612). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719–745.

    Article  Google Scholar 

  • Porter, K., Whittington, K. B., & Powell, W. W. (2005). The Institutional Embeddedness of High-Tech Regions: Relational Foundations of the Boston Biotechnology Community. In S. Breschi & F. Malerba (Eds.), Clusters, networks and innovation. Oxford: Oxford University Press.

    Google Scholar 

  • Ramasamy, B., Chakrabarty, A., & Cheah, M. (2004). Malaysia’s leap into the future: An evaluation of the multimedia super corridor. Technovation, 24, 871–883.

    Article  Google Scholar 

  • Robinson, D. K. R., Huang, L., Guo, Y., & Porter, A. L. (2013). Forecasting innovation pathways (FIP) for new and emerging science and technologies. Technological Forecasting and Social Change, 80, 267–285.

    Article  Google Scholar 

  • Sauermann, H., & Stephan, P. (2013). Conflicting logics? A multidimensional view of industrial and academic science. Organization Science, 24(3), 889–909.

    Article  Google Scholar 

  • Schmoch, U. (2008). Concept of a technology classification for country comparisons. Final report to the World Intellectial Property Office (WIPO), Fraunhofer ISI, Karslruhe.

  • Shichijo, N., Sedita, S. R., & Baba, Y. (2015). How does the entrepreneurial orientation of scientists affect their scientific performance? Evidence from the Quadrant Model, Technology Analysis and Strategic Management, 27, 999–1013.

    Article  Google Scholar 

  • Shiu, J.-W., Wong, C.-Y., & Hu, M.-C. (2014). The dynamic effect of knowledge capitals in the public research institute: Insights from patenting analysis of ITRI (Taiwan) and ETRI (Korea). Scientometrics, 98, 2051–2068.

    Article  Google Scholar 

  • Stokes, D. E. (1997). Pasteur’s quadrant-basic science and technological innovation. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Tijssen, R. J. W., & Leeuwen, T. N. (2006). Measuring impacts of academic science on industrial research: A citation-based approach. Scientometrics, 66, 55–69.

    Article  Google Scholar 

  • Tijssen, R. J. W., Leeuwen, T. N., & Wijk, E. (2009). Benchmarking university-industry research cooperation worldwide: Performance measurements and indicators based on co-authorship data for the world’s largest universities. Research Evaluation, 18, 13–24.

    Article  Google Scholar 

  • United Nations Statistics Division. (2016). Available correspondences. https://unstats.un.org/unsd/cr/registry/regot.asp?Lg=1. Accessed April 27, 2016.

  • Van Looy, B., Vereyen, C., & Schmoch, U. (2014). Patent statistics: Concordance IPC v8—NACE rev 2. Eurostat. https://circabc.europa.eu/sd/a/d1475596-1568-408a-9191-426629047e31/2014-10-16-Final%20IPC_NACE2_2014.pdf. Accessed April 29, 2016.

  • Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organization and the growth of international collaboration in science. Research Policy, 34, 1608–1618.

    Article  Google Scholar 

  • Wong, C.-Y. (2016). Evolutionary targeting for inclusive development. Journal of Evolutionary Economics, 26, 291–316.

    Article  Google Scholar 

  • Wong, C.-Y. (2017). Convergence innovation in city innovation system: Railway technology case in Malaysia. In K. R. Lee (Ed.), Managing convergence in innovation (pp. 117–137). London: Routledge.

    Google Scholar 

  • Wong, P. K., Ho, Y. P., & Chan, C. K. (2007). Internationalization and evolution of application areas of an emerging technology: The case of nanotechnology. Scientometrics, 70, 715–737.

    Article  Google Scholar 

  • Wong, C.-Y., Hu, M.-C., & Shiu, J.-W. (2015a). Governing the economic transition: How Taiwan strategically transformed its industrial system to attain virtuous cycle development. Review of Policy Research, 32(3), 365–387.

    Article  Google Scholar 

  • Wong, C.-Y., Hu, M.-C., & Shiu, J.-W. (2015b). Collaboration between Public Research Institutes and Universities: A Study of Industrial Technology Research Institute, Taiwan. Science Technology & Society, 20, 161–181.

    Article  Google Scholar 

  • Wong, C.-Y., & Salmin, M. M. (2016). Attaining a productive structure for technology: The Bayh Dole effect on university industry government relations in developing economy. Science and Public Policy, 43, 29–45.

    Article  Google Scholar 

  • Wong, P. K., & Singh, A. (2013). Do co-publications with industry lead to higher levels of university technology commercialization activity? Scientometrics, 97, 245–265.

    Article  Google Scholar 

  • Wong, C.-Y., Siow, G., Li, R., & Kwek, K.-T. (2013). The impact of china on the emerging world: New growth patterns in chinese import-export activities. Engineering Economics, 24(4), 309–319.

    Article  Google Scholar 

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Acknowledgements

The authors is grateful to referees and editor for their help comments and suggestions that led to improvement of the paper. Funding from University of Malaya (RU003-2016) in supporting this project is gratefully acknowledged.

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Correspondence to Chan-Yuan Wong.

Appendix

Appendix

See Table 5.

Table 5 Coarse science-technology concordance

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Wong, CY., Fung, HN. Science-technology-industry correlative indicators for policy targeting on emerging technologies: exploring the core competencies and promising industries of aspirant economies. Scientometrics 111, 841–867 (2017). https://doi.org/10.1007/s11192-017-2319-9

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