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Scientometrics

, Volume 107, Issue 2, pp 819–837 | Cite as

Disentangling the automotive technology structure: a patent co-citation analysis

  • Manuel CastriottaEmail author
  • Maria Chiara Di Guardo
Article

Abstract

While most technological positioning studies were traditionally addressed by comparing firms technological patents classes and portfolios, only a few of them adopted science mapping patent co-citation techniques and none of these seeks to understand the impact of collective cognition on the technology structure of an entire industry. What is the firms technological positioning landscape within an high collective cognition sector? What is the groups technological positioning evolution? How do technology structures shift according to different economic scenarios? Through a strategic lens we contribute to technology strategy literatures by proposing an invention behavior map of automotive actors at a firm, groups and industry level. From Derwent Innovation Index, about 581,000 patents, 1,309,356 citations and 1,287,594 co-citations relationships between (a) the main 49 firms assignees of 1991–2013 and (b) the main 28 or 34 groups assignees by considering three timespan 1991–1997, 1998–2004, 2005–2013, were collected. Results: (1) most of the companies are located close together, depicting the sector technology structure as highly dense; (2) the market leaders do not coincide with technology production leaders and not necessarily occupy central technological positions; (3) the automotive groups considerably varies in the three timespan in terms of position and composition; (4) the market leaders groups occupy technological remoteness positions during economic growth timespan; (5) the sector technology structure is highly dense during growth, strongly scattered and lacking of technologically center positioned actors after economic decline. Finally, strategic implications supporting central locating or suburb R&D positioning planning and M&As recombinational partners decision making are discussed.

Keywords

Patent co-citation analysis Patent strategy Technology structure Technological positioning Collective cognition 

References

  1. Abraham, B. P., & Moitra, S. D. (2001). Innovation assessment through patent analysis. Technovation, 21(16757), 245–252.CrossRefGoogle Scholar
  2. Abrahamson, E., & Hambrick, D. C. (1997). Attentional homogeneity in industries: The effect of discretion. Journal of Organizational Behavior, 18(S1), 513–532.CrossRefGoogle Scholar
  3. Acedo, F. J., Barroso, C., & Galan, J. L. (2006). The resource-based theory: Dissemination and main trends. Strategic Management Journal, 27(7), 621–636.CrossRefGoogle Scholar
  4. Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550–560.CrossRefGoogle Scholar
  5. Archibugi, D., & Pianta, M. (1996). Measuring technological through patents and innovation surveys. Technovation, 16(9), 451–468.CrossRefGoogle Scholar
  6. Bensman, S. J. (2004). Pearson’s r and author cocitation analysis: A commentary on the controversy. Journal of the American Society for Information Science and Technology, 55(10), 935.CrossRefGoogle Scholar
  7. Brockhoff, K. (1991). Competitor technology intelligence in German companies. Industrial Marketing Management, 20(2), 91–98.CrossRefGoogle Scholar
  8. Brockhoff, K. K., Ernst, H., & Hundhausen, E. (1999). Gains and pains from licensing patent-portfolios as strategic weapons in the cardiac rhythm management industry. Technovation, 19(10), 605–614.CrossRefGoogle Scholar
  9. Brown, L. D., & Gardner, J. C. (1985). Using citation analysis to assess the impact of journals and articles on contemporary accounting research (CAR). Journal of Accounting Research, 23(1), 84–109.CrossRefGoogle Scholar
  10. Castriotta, M, & Di Guardo, M. C. (2015). A collective reasoning on the automotive industry: A patent co-citation analysis. In 15th ISSI conference, Istanbul (pp. 865–870).Google Scholar
  11. Cheung, K. Y., & Ping, L. (2004). Spillover effects of FDI on innovation in China: Evidence from the provincial data. China Economic Review, 15(1), 25–44.CrossRefGoogle Scholar
  12. Cho, J. (2014). Intellectual structure of the institutional repository field: A co-word analysis. Journal of Information Science, 40(3), 386–397.CrossRefGoogle Scholar
  13. Culnan, M. J. (1986). The intellectual development of management information systems, 1972–1982: A co-citation analysis. Management Science, 32(2), 156–172.CrossRefGoogle Scholar
  14. Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall. 2.Google Scholar
  15. Di Guardo, M. C., & Harrigan, K. R. (2012). Mapping research on strategic alliances and innovation: A co-citation analysis. The Journal of Technology Transfer, 37(6), 789–811.CrossRefGoogle Scholar
  16. Di Maggio, P., & Powell, W. W. (1983). The iron cage revisited: Collective rationality and institutional isomorphism in organizational fields. American Sociological Review, 48(2), 147–160.CrossRefGoogle Scholar
  17. Di Stefano, G., Gambardella, A., & Verona, G. (2012). Technology push and demand pull perspectives in innovation studies: Current findings and future research directions. Research Policy, 41(8), 1283–1295.CrossRefGoogle Scholar
  18. Eck, N. J. V., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651.CrossRefGoogle Scholar
  19. Egghe, L., & Leydesdorff, L. (2009). The relation between Pearson’s correlation coefficient r and Salton’s cosine measure. Journal of the American Society for Information Science and Technology, 60(5), 1027–1036.CrossRefGoogle Scholar
  20. Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233–242.CrossRefGoogle Scholar
  21. Gibson, C. B. (2001). From knowledge accumulation to accommodation: Cycles of collective cognition in work groups. Journal of Organizational Behavior, 22(2), 121–134.CrossRefGoogle Scholar
  22. Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929–964.CrossRefGoogle Scholar
  23. Hodgkinson, G. P., & Healey, M. P. (2008). Cognition in organizations. Annual Review of Psychology, 59, 387–417.CrossRefGoogle Scholar
  24. Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128–136.MathSciNetCrossRefGoogle Scholar
  25. Hu, C. P., Hu, J. M., Gao, Y., & Zhang, Y. K. (2010). A journal co-citation analysis of library and information science in China. Scientometrics, 86(3), 657–670.CrossRefGoogle Scholar
  26. Islam, G. (2015). Extending organizational cognition: A conceptual exploration of mental extension in organizations. Human Relations, 68(3), 463–487. CrossRefGoogle Scholar
  27. 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
  28. Jeong, Y. K., Song, M., & Ding, Y. (2014). Content-based author co-citation analysis. Journal of Informetrics, 8(1), 197–211.CrossRefGoogle Scholar
  29. Johnson, D. R., & Hoopes, D. G. (2003). Managerial cognition, sunk costs, and the evolution of industry structure. Strategic Management Journal, 24(10), 1057–1068.CrossRefGoogle Scholar
  30. Kaplan, S. (2011). Research in cognition and strategy: Reflections on two decades of progress and a look to the future. Journal of Management Studies, 48(3), 665–695.CrossRefGoogle Scholar
  31. Kaplan, D. M. (2012). How to demarcate the boundaries of cognition. Biology and Philosophy, 27(4), 545–570.CrossRefGoogle Scholar
  32. Kaplan, S., & Tripsas, M. (2003). Thinking about technology: Understanding the role of cognition and technical change. Harvard Business School Working Paper Series (No. 03-007).Google Scholar
  33. Kaplan, S., & Tripsas, M. (2008). Thinking about technology: Applying a cognitive lens to technical change. Research Policy, 37(5), 790–805.CrossRefGoogle Scholar
  34. Kay, L., Newman, N., Youtie, J., Porter, A. L., & Rafols, I. (2014). Patent overlay mapping: Visualizing technological distance. Journal of the Association for Information Science and Technology, 65(12), 2432–2443.CrossRefGoogle Scholar
  35. Kim, Y. G., Suh, J. H., & Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804–1812.CrossRefGoogle Scholar
  36. Kostoff, R. N., Stump, J. A., Johnson, D., Murday, J. S., Lau, C. G., & Tolles, W. M. (2006). The structure and infrastructure of the global nanotechnology literature. Journal of Nanoparticle Research, 8(3–4), 301–321.CrossRefGoogle Scholar
  37. Lai, K. K., & Wu, S. J. (2005). Using the patent co-citation approach to establish a new patent classification system. Information Processing and Management, 2, 313–330.CrossRefGoogle Scholar
  38. Leydesdorff, L., & Vaughan, L. (2006). Co-occurrence matrices and their applications in information science: Extending ACA to the web environment. Journal of the American Society for Information Science and Technology, 57(12), 1616–1628.CrossRefGoogle Scholar
  39. McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.CrossRefGoogle Scholar
  40. Mêgnigbêto, E. (2013). Controversies arising from which similarity measures can be used in co-citation analysis. Malaysian Journal of Library & Information Science, 18(2), 25–31.Google Scholar
  41. Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: Exploring tissue engineering. Research Policy, 31(8), 1389–1403.CrossRefGoogle Scholar
  42. Nadkarni, S., & Narayanan, V. K. (2007). Strategic schemas, strategic flexibility, and firm performance: The moderating role of industry clockspeed. Strategic Management Journal, 28(3), 243–270.CrossRefGoogle Scholar
  43. Narayanan, V. K., Zane, L. J., & Kemmerer, B. (2010). The cognitive perspective in strategy: An integrative review. Journal of Management, 37(1), 305–351.CrossRefGoogle Scholar
  44. Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155.CrossRefGoogle Scholar
  45. Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29(3), 319–336.CrossRefGoogle Scholar
  46. Nohria, N., & Garcia-Pont, C. (1991). Global strategic linkages and industry structure. Strategic Management Journal, 12(S1), 105–124.CrossRefGoogle Scholar
  47. Pierce, S. J. (1990). Disciplinary work and interdisciplinary areas: Sociology and bibliometrics. In C. L. Borgman (Ed.), Scholarly communication and bibliometrics (pp. 84–106). Newbury Park, CA: Sage.Google Scholar
  48. Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.CrossRefGoogle Scholar
  49. Porter, A. L., & Youtie, J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research, 11(5), 1023–1041.CrossRefGoogle Scholar
  50. Rafols, I., Porter, A. L., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871–1887.CrossRefGoogle Scholar
  51. Ravikumar, S., Agrahari, A., & Singh, S. N. (2015). Mapping the intellectual structure of scientometrics: A co-word analysis of the journal Scientometrics (2005–2010). Scientometrics, 102(1), 929–955.CrossRefGoogle Scholar
  52. Rowlands, I. (1999). Patterns of author cocitation in information policy: Evidence of social, collaborative and cognitive structure. Scientometrics, 44(3), 533–546.CrossRefGoogle Scholar
  53. Schulze, A., Brojerdi, G., & Krogh, G. (2014). Those who know, do. Those who understand, teach. Disseminative capability and knowledge transfer in the automotive industry. Journal of Product Innovation Management, 31(1), 79–97.CrossRefGoogle Scholar
  54. Seol, H., Lee, S., & Kim, C. (2011). Identifying new business areas using patent information: ADEA and text mining approach. Expert Systems with Applications, 38(4), 2933–2941.CrossRefGoogle Scholar
  55. Shiau, W. L., & Dwivedi, Y. K. (2013). Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research. Scientometrics, 94(3), 1317–1337.CrossRefGoogle Scholar
  56. Shiau, W. L., Dwivedi, Y. K., & Tsai, C. H. (2015). Supply chain management: Exploring the intellectual structure. Scientometrics, 105(1), 215–230.CrossRefGoogle Scholar
  57. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265–269.CrossRefGoogle Scholar
  58. Sugimoto, C. R., Pratt, J. A., & Hauser, K. (2008). Using field cocitation analysis to assess reciprocal and shared impact of LIS/MIS fields. Journal of the American Society for Information Science and Technology, 59(9), 1441–1453.CrossRefGoogle Scholar
  59. Van Eck, N. J., & Waltman, L. (2008). Appropriate similarity measures for author co-citation analysis. Journal of the American Society for Information Science and Technology, 59(10), 1653–1661.CrossRefGoogle Scholar
  60. Wallace, M. L., Gingras, Y., & Duhon, R. (2009). A new approach for detecting scientific specialties from raw cocitation networks. Journal of the American Society for Information Science and Technology, 60(2), 240–246.CrossRefGoogle Scholar
  61. Wang, X., Zhang, X., & Xu, S. (2011). Patent co-citation networks of Fortune 500 companies. Scientometrics, 88(3), 761–770.CrossRefGoogle Scholar
  62. Wells, P., & Nieuwenhuis, P. (2012). Transition failure: Understanding continuity in the automotive industry. Technological Forecasting and Social Change, 79(9), 1681–1692.CrossRefGoogle Scholar
  63. West, G. P. (2007). Collective cognition: When entrepreneurial teams, not individuals, make decisions. Entrepreneurship Theory and Practice, 31(1), 77–102.CrossRefGoogle Scholar
  64. White, H. D. (2003). Author cocitation analysis and Pearson’s r. Journal of the American Society for Information Science and Technology, 54(13), 1250–1259.CrossRefGoogle Scholar
  65. Yan, B. N., Lee, T. S., & Lee, T. P. (2015). Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis. Scientometrics, 105(2), 1285–1300.CrossRefGoogle Scholar
  66. Youtie, J., Shapira, P., & Porter, A. L. (2008). Nanotechnology publications and citations by leading countries and blocs. Journal of Nanoparticle Research, 10(6), 981–986.CrossRefGoogle Scholar
  67. Zapata, C., & Nieuwenhuis, P. (2010). Exploring innovation in the automotive industry: New technologies for cleaner cars. Journal of Cleaner Production, 18(1), 14–20.CrossRefGoogle Scholar
  68. Zhao, Q., & Guan, J. (2013). Love dynamics between science and technology: Some evidences in nanoscience and nanotechnology. Scientometrics, 94(1), 113–132.MathSciNetCrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.University of CagliariCagliariItaly

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