The impact of convergence between science and technology on innovation

Article

Abstract

This study investigates the effects of convergence of science and technology on innovation impact, specifically how convergence helps R&D organizations to apply scientific knowledge to their R&D activities. In addition to direct effects of convergence, we address the moderating effects of scientific capacity, knowledge spillover, and knowledge maturity from the knowledge side. The empirical analysis, which employs a zero inflated negative binomial regression model uses data on 2074 patents granted to US organizations from the pharmaceutical industry. The results show that an increase in the proportion of scientific knowledge in convergence has a positive and curvilinear relationship with innovation impact. Also, we find that the organization’s scientific capacity, regional scientific knowledge spillover, and knowledge maturity positively moderate the relationship between convergence and innovation impact. Our findings underline the importance of convergence between science and technology as well as provide implications on how to improve the outcome of an organization’s research and development process.

Keywords

Convergence Knowledge Science Technology R&D Innovation 

JEL Classification

O31 O32 O38 O39 

References

  1. Abernathy, W. J., & Clark, K. B. (1985). Innovation: Mapping the winds of creative destruction. Research Policy, 14(1), 3–22.CrossRefGoogle Scholar
  2. Acs, Z. J., Audretsch, D. B., & Feldman, M. P. (1994). R&D spillovers and innovative activity. Managerial and Decision Economics, 15(2), 131–138.CrossRefGoogle Scholar
  3. Ahuja, G., & Katila, R. (2004). Where do resources come from? The role of idiosyncratic situations. Strategic Management Journal, 25(8–9), 887–907.CrossRefGoogle Scholar
  4. Ahuja, G., & Lampert, C. M. (2001). Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal, 22(6–7), 521–543.CrossRefGoogle Scholar
  5. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage.Google Scholar
  6. Almeida, P., Hohberger, J., & Parada, P. (2011). Individual scientific collaborations and firm-level innovation. Industrial and Corporate Change, dtr030. 1–29.Google Scholar
  7. Almeida, P., & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45(7), 905–917.CrossRefGoogle Scholar
  8. Angel, D. P. (1989). The labor market for engineers in the US semiconductor industry. Economic Geography, 65, 99–112.CrossRefGoogle Scholar
  9. Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42(3), 422–448.CrossRefGoogle Scholar
  10. Argote, L., McEvily, B., & Reagans, R. (2003). Managing knowledge in organizations: An integrative framework and review of emerging themes. Management Science, 49(4), 571–582.CrossRefGoogle Scholar
  11. Bottazzi, L., & Peri, G. (2003). Innovation and spillovers in regions: Evidence from European patent data. European Economic Review, 47(4), 687–710.CrossRefGoogle Scholar
  12. Brooks, H. (1994). The relationship between science and technology. Research Policy, 23(5), 477–486.CrossRefGoogle Scholar
  13. Callaert, J., Van Looy, B., Verbeek, A., Debackere, K., & Thijs, B. (2006). Traces of prior art: An analysis of non-patent references found in patent documents. Scientometrics, 69(1), 3–20.CrossRefGoogle Scholar
  14. Caloghirou, Y., Kastelli, I., & Tsakanikas, A. (2004). Internal capabilities and external knowledge sources: Complements or substitutes for innovative performance? Technovation, 24(1), 29–39.CrossRefGoogle Scholar
  15. Capaldo, A., Lavie, D., & Petruzzelli, A. M. (2014). Knowledge maturity and the scientific value of innovations the roles of knowledge distance and adoption. Journal of Management, 0149206314535442.Google Scholar
  16. Caraça, J., Lundvall, B. Å., & Mendonça, S. (2009). The changing role of science in the innovation process: From Queen to Cinderella? Technological Forecasting and Social Change, 76(6), 861–867.CrossRefGoogle Scholar
  17. Cardinal, L. B., Alessandri, T. M., & Turner, S. F. (2001). Knowledge codifiability, resources, and science-based innovation. Journal of Knowledge Management, 5(2), 195–204.CrossRefGoogle Scholar
  18. Cassiman, B., Veugelers, R., & Zuniga, P. (2008). In search of performance effects of (in) direct industry science links. Industrial and Corporate Change, 17(4), 611–646.CrossRefGoogle Scholar
  19. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.CrossRefGoogle Scholar
  20. Curran, C. S., Bröring, S., & Leker, J. (2010). Anticipating converging industries using publicly available data. Technological Forecasting and Social Change, 77(3), 385–395.CrossRefGoogle Scholar
  21. Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence–examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256–273.CrossRefGoogle Scholar
  22. Dalrymple, D. (2003). Scientific knowledge as a global public good: Contributions to innovation and the economy. In J. M. Esanu & P. F. Uhlir (Eds.), The role of scientific data and information in the public domain: proceedings of a symposium (pp. 35–51). Washington, DC: The National Academies Press.Google Scholar
  23. Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1–19.CrossRefGoogle Scholar
  24. DeBresson, C., & Amesse, F. (1991). Networks of innovators: A review and introduction to the issue. Research Policy, 20(5), 363–379.CrossRefGoogle Scholar
  25. DeCarolis, D. M., & Deeds, D. L. (1999). The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20(10), 953–968.CrossRefGoogle Scholar
  26. Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504–1511.CrossRefGoogle Scholar
  27. Fleming, L., & Sorenson, O. (2004). Science as a map in technological search. Strategic Management Journal, 25(8–9), 909–928.CrossRefGoogle Scholar
  28. Gambardella, A. (1992). Competitive advantages from in-house scientific research: The US pharmaceutical industry in the 1980s. Research Policy, 21(5), 391–407.CrossRefGoogle Scholar
  29. Gibbons, M., & Johnston, R. (1974). The roles of science in technological innovation. Research Policy, 3(3), 220–242.CrossRefGoogle Scholar
  30. Gittelman, M., & Kogut, B. (2003). Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patterns. Management Science, 49(4), 366–382.CrossRefGoogle Scholar
  31. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122.CrossRefGoogle Scholar
  32. Grupp, H. (1996). Spillover effects and the science base of innovations reconsidered: An empirical approach. Journal of Evolutionary Economics, 6(2), 175–197.CrossRefGoogle Scholar
  33. Hacklin, F. (2008). Management of convergence in innovation: strategies and capabilities for value creation beyond blurring industry boundaries. Springer Science & Business Media.Google Scholar
  34. Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and Statistics, 81(3), 511–515.CrossRefGoogle Scholar
  35. Hitt, M. A., Biermant, L., Shimizu, K., & Kochhar, R. (2001). Direct and moderating effects of human capital on strategy and performance in professional service firms: A resource-based perspective. Academy of Management Journal, 44(1), 13–28.CrossRefGoogle Scholar
  36. Jaffe, A. B. (1989). Real effects of academic research. The American Economic Review, 79(5), 957–970.Google Scholar
  37. Jeong, S., Kim, J. C., & Choi, J. Y. (2015). Technology convergence: What developmental stage are we in? Scientometrics, 104(3), 841–871.CrossRefGoogle Scholar
  38. Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094–1107.CrossRefGoogle Scholar
  39. Kim, E., Cho, Y., & Kim, W. (2014). Dynamic patterns of technological convergence in printed electronics technologies: patent citation network. Scientometrics, 98(2), 975-998.Google Scholar
  40. Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397.CrossRefGoogle Scholar
  41. Lawson, C., & Lorenz, E. (1999). Collective learning, tacit knowledge and regional innovative capacity. Regional Studies, 33(4), 305–317.CrossRefGoogle Scholar
  42. Lee, Y. G., Lee, J. D., Song, Y. I., & Lee, S. J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27–39.CrossRefGoogle Scholar
  43. Liebeskind, J. P., Oliver, A. L., Zucker, L., & Brewer, M. (1996). Social networks, learning, and flexibility: Sourcing scientific knowledge in new biotechnology firms. Organization Science, 7(4), 428–443.CrossRefGoogle Scholar
  44. lo Storto, C. (2006). A method based on patent analysis for the investigation of technological innovation strategies: The European medical prostheses industry. Technovation, 26(8), 932–942.CrossRefGoogle Scholar
  45. McMillan, G. S., Narin, F., & Deeds, D. L. (2000). An analysis of the critical role of public science in innovation: The case of biotechnology. Research Policy, 29(1), 1–8.CrossRefGoogle Scholar
  46. Mehta, A., Rysman, M., & Simcoe, T. (2010). Identifying the age profile of patent citations: New estimates of knowledge diffusion. Journal of Applied Econometrics, 25(7), 1179–1204.CrossRefGoogle Scholar
  47. Narin, F., & Noma, E. (1985). Is technology becoming science? Scientometrics, 7(3), 369–381.CrossRefGoogle Scholar
  48. Nightingale, P. (1998). A cognitive model of innovation. Research Policy, 27(7), 689–709.CrossRefGoogle Scholar
  49. No, H. J., & Park, Y. (2010). Trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology. Technological Forecasting and Social Change, 77(1), 63–75.CrossRefGoogle Scholar
  50. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37.CrossRefGoogle Scholar
  51. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press.Google Scholar
  52. Penner-Hahn, J., & Shaver, J. M. (2005). Does international research and development increase patent output? An analysis of Japanese pharmaceutical firms. Strategic Management Journal, 26(2), 121–140.CrossRefGoogle Scholar
  53. Pisano, G. P. (1994). Knowledge, integration, and the locus of learning: An empirical analysis of process development. Strategic Management Journal, 15(S1), 85–100.CrossRefGoogle Scholar
  54. Rosenberg, N. (1982). Inside the black box: technology and economics. Cambridge University Press.Google Scholar
  55. Rosenberg, N. (1990). Why do firms do basic research (with their own money)? Research Policy, 19(2), 165–174.CrossRefGoogle Scholar
  56. Schmoch, U. (1997). Indicators and the relations between science and technology. Scientometrics, 38(1), 103–116.CrossRefGoogle Scholar
  57. Shibata, N., Kajikawa, Y., & Sakata, I. (2010). Extracting the commercialization gap between science and technology-Case study of a solar cell. Technological Forecasting and Social Change, 77(7), 1147–1155.CrossRefGoogle Scholar
  58. Siegel, D. S., Waldman, D. A., Atwater, L. E., & Link, A. N. (2004). Toward a model of the effective transfer of scientific knowledge from academicians to practitioners: Qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21(1), 115–142.CrossRefGoogle Scholar
  59. Simeth, M., & Raffo, J. D. (2013). What makes companies pursue an open science strategy? Research Policy, 42(9), 1531–1543.CrossRefGoogle Scholar
  60. Skilton, P. F., & Dooley, K. (2002). Technological knowledge maturity, innovation and productivity. International Journal of Operations & Production Management, 22(8), 887–901.CrossRefGoogle Scholar
  61. Sorenson, O. (2003). Social networks and industrial geography. Journal of Evolutionary Economics, 13(5), 513–527.CrossRefGoogle Scholar
  62. Sorenson, O., & Fleming, L. (2004). Science and the diffusion of knowledge. Research Policy, 33(10), 1615–1634.CrossRefGoogle Scholar
  63. Subramanian, A. M., & Soh, P. H. (2010). An empirical examination of the science–technology relationship in the biotechnology industry. Journal of Engineering and Technology Management, 27(3), 160–171.CrossRefGoogle Scholar
  64. Tijssen, R. J. (2002). Science dependence of technologies: evidence from inventions and their inventors. Research Policy, 31(4), 509–526.CrossRefGoogle Scholar
  65. Tijssen, R. J. W., Buter, R. K., & Van Leeuwen, T. N. (2000). Technological relevance of science: An assessment of citation linkages between patents and research papers. Scientometrics, 47(2), 389–412.CrossRefGoogle Scholar
  66. Trajtenberg, M., Henderson, R., & Jaffe, A. (1997). University versus corporate patents: A window on the basicness of invention. Economics of Innovation and New Technology, 5(1), 19–50.CrossRefGoogle Scholar
  67. Van Geenhuizen, M., & Reyes-Gonzalez, L. (2007). Does a clustered location matter for high-technology companies’ performance? The case of biotechnology in the Netherlands. Technological Forecasting and Social Change, 74(9), 1681–1696.CrossRefGoogle Scholar
  68. Van Vianen, B. G., Moed, H. F., & Van Raan, A. F. J. (1990). An exploration of the science base of recent technology. Research Policy, 19(1), 61–81.CrossRefGoogle Scholar
  69. Vedovello, C. (1997). Science parks and university-industry interaction: Geographical proximity between the agents as a driving force. Technovation, 17(9), 491–531.CrossRefGoogle Scholar
  70. Verbeek, A., Debackere, K., Luwel, M., Andries, P., Zimmermann, E., & Deleus, F. (2002). Linking science to technology: Using bibliographic references in patents to build linkage schemes. Scientometrics, 54(3), 399–420.CrossRefGoogle Scholar
  71. Zander, U., & Kogut, B. (1995). Knowledge and the speed of the transfer and imitation of organizational capabilities: An empirical test. Organization Science, 6(1), 76–92.CrossRefGoogle Scholar
  72. Zucker, L. G., Darby, M. R., & Armstrong, J. S. (2002). Commercializing knowledge: University science, knowledge capture, and firm performance in biotechnology. Management Science, 48(1), 138–153.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Technology Management, Economics, and Policy Program (TEMEP)Seoul National UniversitySeoulSouth Korea
  2. 2.Samsung SDSSeoulSouth Korea
  3. 3.Department of Industrial EngineeringSeoul National UniversitySeoulSouth Korea

Personalised recommendations