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Technological distance measures: new perspectives on nearby and far away

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Abstract

Understanding the competitive environment of one’s company is crucial for every manager. One tool to quantify the technological relationships between companies, evaluate industry landscapes and knowledge transfer potential in collaborations is the technological distance. There are different methods and many different factors that impact the results and thus the conclusions that are drawn from distance calculation. Therefore, the present study derives guidelines for calculating and evaluating technological distances for three common methods, i.e. the Euclidean distance, the cosine angle and the min-complement distance. For this purpose, we identify factors that influence the results of technological distance calculation using simulation. Subsequently, we analyze technological distances of cross-industry collaborations in the field of electric mobility. Our findings show that a high level of detail is necessary to achieve insightful results. If the topic in scope of the analysis does not represent the core business of the companies, we recommend filters to focus on the respective topic. Another key suggestion is to compare the calculated results to a peer group in order to evaluate if a distance can be evaluated as ‘near’ or ‘far’.

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References

  • Ahuja, G., & Katila, R. (2001). Technological acquisitions and the innovation performance of acquiring firms: A longitudinal study. Strategic Management Journal,. doi:10.1002/smj.157.

    Google Scholar 

  • Angue, K., Ayerbe, C., & Mitkova, L. (2013). 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. The Journal of Technology Transfer, 39(5), 716–747. doi:10.1007/s10961-013-9325-8.

    Article  Google Scholar 

  • Autant-Bernard, C., Billand, P., Frachisse, D., & Massard, N. (2007). Social distance versus spatial distance in R&D cooperation: Empirical evidence from European collaboration choices in micro and nanotechnologies. Papers in Regional Science, 86(3), 495–519. doi:10.1111/j.1435-5957.2007.00132.x.

    Article  Google Scholar 

  • Bar, T., & Leiponen, A. (2012). A measure of technological distance. Economics Letters, 116(3), 457–459. doi:10.1016/j.econlet.2012.04.030.

    Article  MathSciNet  Google Scholar 

  • Becker, W., & Dietz, J. (2004). R&D cooperation and innovation activities of firms—evidence for the German manufacturing industry. Research Policy, 33(2), 209–223. doi:10.1016/j.respol.2003.07.003.

    Article  Google Scholar 

  • Benner, M., & Waldfogel, J. (2008). Close to you? Bias and precision in patent-based measures of technological proximity. Research Policy, 37(9), 1556–1567. doi:10.1016/j.respol.2008.05.011.

    Article  Google Scholar 

  • Chang, S. Bin. (2012). Using patent analysis to establish technological position: Two different strategic approaches. Technological Forecasting and Social Change, 79(1), 3–15. doi:10.1016/j.techfore.2011.07.002.

    Article  Google Scholar 

  • Chang, P.-L., Wu, C.-C., & Leu, H.-J. (2010). Using patent analyses to monitor the technological trends in an emerging field of technology: A case of carbon nanotube field emission display. Scientometrics, 82(1), 5–19. doi:10.1007/s11192-009-0033-y.

    Article  Google Scholar 

  • Cloodt, M., Hagedoorn, J., & Van Kranenburg, H. (2006). Mergers and acquisitions: Their effect on the innovative performance of companies in high-tech industries. Research Policy, 35, 642–654. doi:10.1016/j.respol.2006.02.007.

    Article  Google Scholar 

  • Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. doi:10.2307/2393553.

    Article  Google Scholar 

  • Curran, C.-S., Bröring, S., & Leker, J. (2010). Anticipating converging industries using publicly available data. Technological Forecasting and Social Change, 77(3), 385–395. doi:10.1016/j.techfore.2009.10.002.

    Article  Google Scholar 

  • Duysters, G., & Man, A.-P. (2003). Transitory alliances: An instrument for surviving turbulent industries? R and D Management, 33(1), 49–58. doi:10.1111/1467-9310.00281.

    Article  Google Scholar 

  • Enkel, E., & Heil, S. (2014). Preparing for distant collaboration: Antecedents to potential absorptive capacity in cross-industry innovation. Technovation, 34(4), 242–260. doi:10.1016/j.technovation.2014.01.010.

    Article  Google Scholar 

  • Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25, 233–242. doi:10.1016/S0172-2190(03)00077-2.

    Article  Google Scholar 

  • Fung, M. K. (2003). Technological proximity and co-movements of stock returns. Economics Letters, 79, 131–136. doi:10.1016/S0165-1765(02)00297-5.

    Article  Google Scholar 

  • Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., et al. (2013). Technology life cycle analysis method based on patent documents. Technological Forecasting and Social Change, 80(3), 398–407. doi:10.1016/j.techfore.2012.10.003.

    Article  Google Scholar 

  • Gassmann, O., Zeschky, M., Wolff, T., & Stahl, M. (2010). Crossing the industry-line: Breakthrough innovation through cross-industry alliances with “Non-Suppliers”. Long Range Planning, 43(5–6), 639–654. doi:10.1016/j.lrp.2010.06.003.

    Article  Google Scholar 

  • Gauch, S., & Blind, K. (2015). Technological convergence and the absorptive capacity of standardisation. Technological Forecasting and Social Change, 91, 236–249.

    Article  Google Scholar 

  • Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10), 1717–1731. doi:10.1016/j.respol.2008.08.010.

    Article  Google Scholar 

  • Golembiewski, B., vom Stein, N., Sick, N., & Wiemhöfer, H. D. (2015). Identifying trends in battery technologies with regard to electric mobility: Evidence from patenting activities along and across the battery value chain. Journal of Cleaner Production, 87, 800–810. doi:10.1016/j.jclepro.2014.10.034.

    Article  Google Scholar 

  • Griliches, Z. (1990). Patent statistics indicators as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.

    Google Scholar 

  • Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits and market value. The American Economic Review, 76, 984–1001.

  • Jaffe, A. B. (1989). Characterizing the “technological position” of firms, with application to quantifying technological opportunity and research spillovers. Research Policy, 18, 87–97. doi:10.1016/0048-7333(89)90007-3.

    Article  Google Scholar 

  • Jones, W. P., & Furnas, G. W. (1987). Pictures of relevance: A geometric analysis of similarity measures. Journal of the American Society for Information Science, 38(6), 420–442. http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=16757477&site=ehost-live.

  • Joo, S. H., & Kim, Y. (2010). Measuring relatedness between technological fields. Scientometrics, 83(2), 435–454. doi:10.1007/s11192-009-0108-9.

    Article  MathSciNet  Google Scholar 

  • Kaiser, U. (2002). Measuring knowledge spillovers in manufacturing and services : An empirical assessment of alternative approaches. Research Policy, 31(1), 125–144.

    Article  Google Scholar 

  • Kaiser, H. M., Lo, W. F., Riahi, A. M., Spannenberg, A., Beller, M., & Tse, M. K. (2006). New synthetic protocols for the preparation of unsymmetrical bisindoles. Organic Letters, 8(25), 5761–5764. doi:10.1021/ol062338p.

    Article  Google Scholar 

  • Katz, J. S. (1994). Geographical proximity and scientific collaboration. Scientometrics, 31(1), 31–43. doi:10.1007/BF02018100.

    Article  Google Scholar 

  • Lee, C., Kang, B., & Shin, J. (2014). Novelty-focused patent mapping for technology opportunity analysis. Technological Forecasting and Social Change, 90, 355–365. doi:10.1016/j.techfore.2014.05.010.

    Article  Google Scholar 

  • Leydesdorff, L., Kushnir, D., & Rafols, I. (2014). Interactive overlay maps for US patent (USPTO) data based on international patent classification (IPC). Scientometrics, 98(3), 1583–1599. doi:10.1007/s11192-012-0923-2.

    Article  Google Scholar 

  • Luan, C., Liu, Z., & Wang, X. (2013). Divergence and convergence: Technology-relatedness evolution in solar energy industry. Scientometrics, 97(2), 461–475. doi:10.1007/s11192-013-1057-x.

    Article  Google Scholar 

  • McNamee, R. C. (2013). Can’t see the forest for the leaves: Similarity and distance measures for hierarchical taxonomies with a patent classification example. Research Policy, 42(4), 855–873. doi:10.1016/j.respol.2013.01.006.

    Article  Google Scholar 

  • Meyer, M., & Persson, O. (1998). Nanotechnology-Interdisciplinarity, patterns of collaboration and differences in application. Scientometrics, 42(2), 195–205.

    Article  Google Scholar 

  • Minesoft Ltd. (2015). www.patbase.com.

  • 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. doi:10.1016/j.respol.2007.04.003.

    Article  Google Scholar 

  • Peretto, P., & Smulders, S. (2002). Technological distance, growth and scale effects. Economic Journal, 112, 603–624. doi:10.1111/1468-0297.00732.

    Article  Google Scholar 

  • Raesfeld, A. Von, Geurts, P., & Jansen, M. (2012). When is a network a nexus for innovation? A study of public nanotechnology R&D projects in the Netherlands. Industrial Marketing Management, 41(5), 752–758. doi:10.1016/j.indmarman.2012.06.009.

    Article  Google Scholar 

  • Rothaermel, F. T., & Alexandre, M. T. (2009). Ambidexterity in technology sourcing: The moderating role of absorptive capacity. Organization Science, 20(4), 759–780. doi:10.1287/orsc.1080.0404.

    Article  Google Scholar 

  • Sahal, D. (1976). The generalized distance measures of technology. Technological Forecasting and Social Change, 9(3), 289–300. doi:10.1016/0040-1625(76)90013-5.

    Article  Google Scholar 

  • Schummer, J. (2004). Multidisciplinarity, interdisciplinarity, and research collaboration in nanoscience and nanotechnology. Scientometric, 59(3), 425–465. doi:10.1023/B_SCIE_0000018542_71314_38.

    Article  Google Scholar 

  • Sears, J., & Hoetker, G. (2014). Technological overlap, technological capabilities, and resource recombination in technological acquisitions. Strategic Management Journal, 35, 48–67. doi:10.1002/smj.

    Article  Google Scholar 

  • Sick, N., Preschitschek, N., Bröring, S., & Leker, J. (2015). Market convergence in the field of stationary energy storage systems. In PICMET ’15. Portland (Oregon).

  • Srivastava, M. K., Gnyawali, D. R., & Hatfield, D. E. (2015). Behavioral implications of absorptive capacity: The role of technological effort and technological capability in leveraging alliance network technological resources. Technological Forecasting and Social Change, 92, 346–358. doi:10.1016/j.techfore.2015.01.010.

    Article  Google Scholar 

  • Sternitzke, C., & Bergmann, I. (2009). Similarity measures for document mapping: A comparative study on the level of an individual scientist. Scientometrics, 78(1), 113–130. doi:10.1007/s11192-007-1961-z.

    Article  Google Scholar 

  • Stuart, T. E. (2000). Interorganizational alliances and the performance of firms: A study of growth and innovation rates in a high-technology industry. Strategic Management Journal, 811(21), 791–811.

    Article  Google Scholar 

  • Swan, K. S., & Kotabe, M. (1995). The role of strategic alliances in high-technology new product development. Strategic Management Journal, 16(January), 621–636.

    Google Scholar 

  • Vanhaverbeke, W., Gilsing, V., Beerkens, B., & Duysters, G. (2009). The role of alliance network redundancy in the creation of core and non-core technologies. Journal of Management Studies, 46(March), 215–244. doi:10.1111/j.1467-6486.2008.00801.x.

    Article  Google Scholar 

  • Vaughan, L. C., & You, J. (2006). Comparing business competition positions based on Web co-link data: The global market versus the Chinese market. Scientometrics, 68(3), 611–628. doi:10.1007/s11192-006-0133-x.

    Article  Google Scholar 

  • Von Delft, S., & Leker, J. (2011). Collaborative innovation in converging industries: The case of electromobility. In Proceedings of the 4th ISPIM innovation symposium.

  • vom Stein, N., Sick, N., & Leker, J. (2015). How to measure technological distance in collaborations—the case of electric mobility. Technological Forecasting and Social Change, 97, 154–168. doi:10.1016/j.techfore.2014.05.001.

    Article  Google Scholar 

  • Wagner, C. S. (2005). Six case studies of international collaboration in science. Scientometrics, 62(1), 3–26. doi:10.1007/s11192-005-0001-0.

    Article  Google Scholar 

  • Wang, B., & Hsieh, C.-H. (2015). Measuring the value of patents with fuzzy multiple criteria decision making: insight into the practices of the Industrial Technology Research Institute. Technological Forecasting and Social Change, 92, 263–275. doi:10.1016/j.techfore.2014.09.015.

    Article  Google Scholar 

  • Watts, R. J., & Porter, A. L. (1997). Innovation forecasting. Innovation in Technology Management. The Key to Global Leadership. PICMET ’97,. doi:10.1109/PICMET.1997.653329.

    Google Scholar 

  • Wernerfelt, B. (1984). A resource based view of the firm. Strategic Management Journal, 5(2), 171–180. doi:10.1002/smj.4250050207.

    Article  Google Scholar 

  • Yeh, H.-Y., Sung, Y.-S., Yang, H.-W., Tsai, W.-C., & Chen, D.-Z. (2013). The bibliographic coupling approach to filter the cited and uncited patent citations: A case of electric vehicle technology. Scientometrics, 94(1), 75–93. doi:10.1007/s11192-012-0820-8.

    Article  Google Scholar 

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Simon, H., Sick, N. Technological distance measures: new perspectives on nearby and far away. Scientometrics 107, 1299–1320 (2016). https://doi.org/10.1007/s11192-016-1888-3

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