The effect of technological relatedness on firm sales evolution through external knowledge sourcing

  1. Guerrero, Alex J.
  2. Huergo, Elena
  3. Heijs, Joost
Revista:
The Journal of Technology Transfer

ISSN: 0892-9912 1573-7047

Año de publicación: 2022

Tipo: Artículo

DOI: 10.1007/S10961-022-09931-3 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: The Journal of Technology Transfer

Resumen

This paper analyzes the impact of knowledge spillovers on firm performance measured through total sales, the percentage of innovative sales and a categorical variable that classifies firms into three different groups depending on the stage of their sales growth evolution: upturn, downturn, or transition. We specifically focus on whether there are asymmetric spillover effects depending on the intermediary role of firms’ technological relatedness, which we proxy by the use of external sources of knowledge. Using data on 5900 Spanish firms for the period 2004–2016, we find that spillover effects from intra-sector and upstream knowledge pools are—in general—positive, although with some differences depending on the measure of firm performance and on the moderating role of technological networking. Our results also suggest the presence of a “business stealing effect” in environments with a high proportion of knowledge-based gross added value. Furthermore, we find that spillover effects are asymmetric depending on the firm’s size and intensity of R&D employment. Knowledge spillovers seem to play a more significant role in the case of SMEs than in large companies, and firms with high intensities of R&D employment benefit more from upstream spillovers and less from horizontal spillovers than firms with low intensities.

Referencias bibliográficas

  • Agarwal, R., Audretsch, D., & Sarkar, M. B. (2010). Knowledge spillovers and strategic entrepreneurship. Strategic Entrepreneurship Journal, 4(4), 271–283.
  • Angue, K., Ayerbe, C., & Mitkova, L. (2014). A method using two dimensions of the patent classification for measuring the technological proximity: An application in identifying a potential R&D partner in biotechnology. Journal of Technology Transfer, 39, 716–747.
  • Arrow, K. J. (1962). Economic welfare and the allocation of resources for innovation. In R. Nelson (Ed.), The rate and direction of inventive activity. Princeton: NBER book, Princeton University Press.
  • Audretsch, D. B. (1998). Agglomeration and the location of innovative activity. Oxford Review of Economic Policy, 14(2), 18–29. Audretsch, D. B., & Belitski, M. (2020). The role of R&D and knowledge spillovers in innovation and productivity. European Economic Review, 123, 103391.
  • Audretsch, D., & Caiazza, R. (2016). Technology transfer and entrepreneurship: Cross-national analysis. The Journal of Technology Transfer, 41(6), 1247–1259.
  • Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86, 630–640. Audretsch, D. B., & Lehmann, E. (2006). Entrepreneurial access and absorption of knowledge spillovers: Strategic board and managerial composition for competitive advantage. Journal of Small Business Management, 44(2), 155–166.
  • Audretsch, D. B., & Lehmann, E. E. (2017). The knowledge spillover theory of entrepreneurship and the strategic management of places. The Wiley handbook of entrepreneurship. London: Wiley. Audretsch, D. B., Lehmann, E. E., Menter, M., & Seitz, N. (2019). Public cluster policy and firm performance: Evaluating spillover effects across industries. Entrepreneurship & Regional Development, 31(1–2), 150–165.
  • Balland, P. A., Boschma, R., Crespo, J., & Rigby, D. L. (2019). Smart specialization policy in the European Union: Relatedness, knowledge complexity and regional diversification. Regional Studies, 53(9), 1252–1268.
  • Barge-Gil, A. (2013). Open strategies and innovation performance. Industry and Innovation, 20(7), 585–610.
  • Bellemare, M. F., Masaki, T., & Pepinsky, T. B. (2017). Lagged explanatory variables and the estimation of causal effects. Journal of Politics, 79(3), 949–963.
  • Bloom, N., Schankerman, M., & Van Reenen, J. (2013). Identifying technology spillovers and product market rivalry. Econometrica, 81, 1347–1393.
  • Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39, 61–74.
  • Boschma, R., & Frenken, K. (2007). Applications of evolutionary economic geography. In K. Frenken (Ed.), Applied evolutionary economics and economic geography (pp. 1–24). Elgar. Boschma, R., & Frenken, K. (2009). Technological relatedness and regional branching. In H. Bathelt, M. P. Feldman, & D. F. Kogler (Eds.), Dynamic geographies of knowledge creation and innovation. Routledge, Taylor and Francis. Boschma, R., & Iammarino, S. (2015). Related variety, trade linkages, and regional growth in Italy. Economic Geography, 85(3), 289–311.
  • Boschma, R., & Martin, R. (2010). The aims and scope of evolutionary economic geography. In R. Boschma & R. Martin (Eds.), The Handbook of evolutionary economic geography (pp. 3–39). Edward Elgar.
  • Cappelli, R., Czarnitzki, D., & Kraft, K. (2014). Sources of spillovers for imitation and innovation. Research Policy, 43(1), 115–120.
  • Carreira, C., & Lopes, L. (2018). Regional knowledge spillovers: A firm-based analysis of non-linear effects. Regional Studies, 52(7), 948–958.
  • Castellacci, F. (2008). Technological paradigms, regimes and trajectories: Manufacturing and service industries in a new taxonomy of sectoral patterns of innovation. Research Policy, 37(6–7), 978–994. Chapman, G., Lucena, A., & Afcha, S. (2018). R&D subsidies & external collaborative breadth: Differential gains and the role of collaboration experience. Research Policy, 47(3), 623–636.
  • Chiang, Y. H., & Hung, K. P. (2010). Exploring open search strategies and perceived innovation performance from the perspective of inter-organizational knowledge flows. R&D Management, 40(3), 292–299.
  • Choi, S. B., & Williams, C. (2014). The impact of innovation intensity, scope, and spillovers on sales growth in Chinese firms. Asia Pacific Journal of Management, 31(1), 25–46.
  • Coad, A., & Hölzl, W. (2012). Firm growth: Empirical analysis. In M. Dietrich & J. Krafft (Eds.), Handbook on the economics and theory of the firm, chapter 24. Edward Elgar Publishing.
  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152. Dosi, G., & Nelson, R. R. (2010). Technical change and industrial dynamics as evolutionary processes. Handbook of the economics of innovation (pp. 51–127). Elsevier.
  • Dumont, M., & Meeusen, W. (2000). Knowledge spillovers through R&D cooperation. Workshop Paper. Retrieved from http://www.oecd.org/sti/innovationinsciencetechnologyandindustry/2093436.pdf
  • Enkel, E., Groemminger, A., & Heil, S. (2018). Managing technological distance in internal and external collaborations: Absorptive capacity routines and social integration for innovation. The Journal of Technology Transfer, 43(5), 1257–1290.
  • Ferreras-Méndez, J. L., Newell, S., Fernández-Mesa, A., & Alegre, J. (2015). Depth and breadth of external knowledge search and performance: The mediating role of absorptive capacity. Industrial Marketing Management, 47, 86–97.
  • Flor, M. L., Cooper, S. Y., & Oltra, M. J. (2018). External knowledge search, absorptive capacity and radical innovation in high-technology firms. European Management Journal, 36(2), 183–194. Frenken, K., Van Oort, F., & Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41, 685–697.
  • Goya, E., Vayá, E., & Suriñach, J. (2016). Innovation spillovers and firm performance: Micro evidence from Spain (2004–2009). Journal of Productivity Analysis, 45(1), 1–22.
  • Green, W. H. (1997). Econometric analysis. Prentice-Hall.
  • Griliches, Z. (1979). Issues in assessing the contribution of R&D to productivity growth. Bell Journal of Economics, 10, 92–116.
  • Grillitsch, M., & Nilsson, M. (2017). Firm performance in the periphery: On the relation between firm-internal knowledge and local knowledge spillovers. Regional Studies, 51, 1219–1231.
  • Hall, B. H., Mairesse, J., & Mohnen, P. (2009). Measuring the returns to R&D. NBER Working Papers 15622, National Bureau of Economic Research, Inc.
  • Hall, B. H., & Lerner, J. (2010). The financing of R&D and innovation. NBER Working Papers 15325, National Bureau of Economic Research, Inc.
  • Hauknes, J., & Knell, M. (2009). Embodied knowledge and sectoral linkages: An input-output approach to the interaction of high- and low-tech industries. Research Policy, 38, 459–469.
  • Heijs, J. (2004). Innovation capabilities and learning: A vicious circle. International Journal of Innovation and Learning, 5, 263–278.
  • Heijs, J. (2012). Innovation capabilities and learning. In M. D. Parrilli & B. T. Asheim (Eds.), Interactive learning for innovation: A key driver within clusters and innovation systems (pp. 206–233). Palgrave Macmillan.
  • Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317, 482–487.
  • Hipp, C., & Grupp, H. (2005). Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies. Research Policy, 34(4), 517–535. Howells, J. R. (2002). Tacit knowledge, innovation and economic geography. Urban Studies, 39(5–6), 871–884.
  • Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits, and market value. American Economic Review, 76(5), 984–1001.
  • Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598.
  • Kamien, M. I., & Schwartz, N. L. (1970). Market structure, elasticity of demand, and incentive to invent. Journal of Law and Economics, 13, 241–252.
  • Kamien, M. I., & Schwartz, N. L. (1982). Market Structure and Innovation. Cambridge University Press.
  • Lane, P. J., Koka, B. R., & Pathak, S. (2006). The reification of absorptive capacity: A critical review and rejuvenation of the construct. Academy of Management Review, 31(4), 833–863.
  • López-García, P., & Montero, J. M. (2011). Spillovers and absorptive capacity in the decision to innovate of Spanish firms: The role of human capital. Economics of Innovation and New Technology, 21(7), 589–612.
  • Los, B. (2000). The empirical performance of a new inter-industry technology spillover measure. In P. P. Saviotti & B. Nooteboom (Eds.), Technology and knowledge (pp. 118–151). Edward Elgar.
  • Malmberg, A., & Maskell, P. (2006). Localized learning revisited. Growth and Change, 37(1), 1–18.
  • MartínezArdila, H. E., Mora Moreno, J. E., & Camacho Pico, J. A. (2020). Networks of collaborative alliances: The second order interfirm technological distance and innovation performance. Journal of Technology Transfer, 45, 1255–1282.
  • Medda, G., & Piga, C. A. (2014). Technological spillovers and productivity in Italian manufacturing firms. Journal of Productivity Analysis, 41(3), 419–434.
  • Neffke, F., & Henning, M. (2013). Skill relatedness and firm diversification. Strategic Management Journal, 34(3), 297–316.
  • Neffke, F., Henning, M., & Boschma, R. (2011). How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Economic Geography, 87(3), 237–265.
  • Nieto, M. J., & Rodríguez, A. (2011). Offshoring of R&D: Looking abroad to improve innovation performance. Journal of International Business Studies, 42, 345–361.
  • Nieto, M. J., & Santamaría, L. (2010). Technological collaboration: Bridging the innovation gap between small and large firms. Journal of Small Business Management, 48(1), 44–69.
  • Nooteboom, B. (2000). Learning and innovation in organizations and economies. Oxford University Press.
  • Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & Van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36(7), 1016–1034. OECD. (2021). Main science and technology indicators, volume 2020 issue 2. OECD Publishing. https://doi.org/10.1787/0bd49050-en Book
  • Oughton, C., & Whittam, G. (1997). Competition and cooperation in the small firm sector. Scottish Journal of Political Economy, 44(1), 1–30.
  • Penrose, E. (1959). The theory of the growth of the firm (4th ed.). Oxford University Press.
  • Rodriguez, M., Doloreux, D., & Shearmur, R. (2017). Variety in external knowledge sourcing and innovation novelty: Evidence from the KIBS sector in Spain. Technovation, 68, 35–43. Spence, A. M. (1975). Monopoly, quality, and regulation. Bell Journal of Economics, 6, 417–429.
  • Spielkamp, A., & Rammer, C. (2009). Financing of innovation—Thresholds and options. Management & Marketing, 4(2), 3–18. Spithoven, A., & Teirlinck, P. (2015). Internal capabilities, network resources and appropriation mechanisms as determinants of R&D outsourcing. Research Policy, 44, 711–725.
  • Ter Wal, A. L. J., & Boschma, R. (2011). Co-evolution of firms, industries and networks in space. Regional Studies, 45(7), 919–933. Thompson, P., & Fox-Kean, M. (2005). Patent citations and the geography of knowledge spillovers: A reassessment. The American Economic Review, 95(1), 450–460. Tojeiro-Rivero, D., & Moreno, R. (2019). Technological cooperation, R&D outsourcing, and innovation performance at the firm level: The role of the regional context. Research Policy, 48(7), 1798–1808.
  • Tojeiro-Rivero, D., Moreno, R., & Badillo, E. R. (2019). Radical innovations: The role of knowledge acquisition from abroad. Review of Industrial Organization, 55(2), 173–207. Whittle, A., & Kogler, D. F. (2020). Related to what? Reviewing the literature on technological relatedness: Where we are now and where can we go? Papers in Regional Science, 99(1), 97–113.
  • Wieser, R. (2005). R&D, productivity and spillovers: Empirical evidence at firm level. Journal of Economic Surveys, 19(4), 587–621. Wooldridge, J. M. (1995). Selection corrections for panel data models under conditional mean independence assumptions. Journal of Econometrics, 68(1), 115–132.
  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). The MIT Press.