, Volume 107, Issue 3, pp 1299–1320 | Cite as

Technological distance measures: new perspectives on nearby and far away

  • H. Simon
  • N. Sick


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’.


Collaboration Cross-industry innovation Patent analysis Technological distance 


  1. 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
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Bar, T., & Leiponen, A. (2012). A measure of technological distance. Economics Letters, 116(3), 457–459. doi: 10.1016/j.econlet.2012.04.030.MathSciNetCrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25, 233–242. doi: 10.1016/S0172-2190(03)00077-2.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Gauch, S., & Blind, K. (2015). Technological convergence and the absorptive capacity of standardisation. Technological Forecasting and Social Change, 91, 236–249.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Griliches, Z. (1990). Patent statistics indicators as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.Google Scholar
  22. 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.Google Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.
  25. Joo, S. H., & Kim, Y. (2010). Measuring relatedness between technological fields. Scientometrics, 83(2), 435–454. doi: 10.1007/s11192-009-0108-9.MathSciNetCrossRefGoogle Scholar
  26. Kaiser, U. (2002). Measuring knowledge spillovers in manufacturing and services : An empirical assessment of alternative approaches. Research Policy, 31(1), 125–144.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. Katz, J. S. (1994). Geographical proximity and scientific collaboration. Scientometrics, 31(1), 31–43. doi: 10.1007/BF02018100.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. Meyer, M., & Persson, O. (1998). Nanotechnology-Interdisciplinarity, patterns of collaboration and differences in application. Scientometrics, 42(2), 195–205.CrossRefGoogle Scholar
  34. Minesoft Ltd. (2015).
  35. 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.CrossRefGoogle Scholar
  36. Peretto, P., & Smulders, S. (2002). Technological distance, growth and scale effects. Economic Journal, 112, 603–624. doi: 10.1111/1468-0297.00732.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. Sick, N., Preschitschek, N., Bröring, S., & Leker, J. (2015). Market convergence in the field of stationary energy storage systems. In PICMET ’15. Portland (Oregon).Google Scholar
  43. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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
  47. 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.CrossRefGoogle Scholar
  48. 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.CrossRefGoogle Scholar
  49. Von Delft, S., & Leker, J. (2011). Collaborative innovation in converging industries: The case of electromobility. In Proceedings of the 4th ISPIM innovation symposium. Google Scholar
  50. 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.CrossRefGoogle Scholar
  51. Wagner, C. S. (2005). Six case studies of international collaboration in science. Scientometrics, 62(1), 3–26. doi: 10.1007/s11192-005-0001-0.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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
  54. Wernerfelt, B. (1984). A resource based view of the firm. Strategic Management Journal, 5(2), 171–180. doi: 10.1002/smj.4250050207.CrossRefGoogle Scholar
  55. 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.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Institute of Business Administration at the Department of Chemistry and PharmacyUniversity of MuensterMuensterGermany
  2. 2.Helmholtz-Institute MuensterMuensterGermany

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