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

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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.

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References

  • Abraham, B. P., & Moitra, S. D. (2001). Innovation assessment through patent analysis. Technovation, 21(16757), 245–252.

    Article  Google Scholar 

  • Abrahamson, E., & Hambrick, D. C. (1997). Attentional homogeneity in industries: The effect of discretion. Journal of Organizational Behavior, 18(S1), 513–532.

    Article  Google Scholar 

  • Acedo, F. J., Barroso, C., & Galan, J. L. (2006). The resource-based theory: Dissemination and main trends. Strategic Management Journal, 27(7), 621–636.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Archibugi, D., & Pianta, M. (1996). Measuring technological through patents and innovation surveys. Technovation, 16(9), 451–468.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Brockhoff, K. (1991). Competitor technology intelligence in German companies. Industrial Marketing Management, 20(2), 91–98.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • 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.

    Article  Google Scholar 

  • Cho, J. (2014). Intellectual structure of the institutional repository field: A co-word analysis. Journal of Information Science, 40(3), 386–397.

    Article  Google Scholar 

  • Culnan, M. J. (1986). The intellectual development of management information systems, 1972–1982: A co-citation analysis. Management Science, 32(2), 156–172.

    Article  Google Scholar 

  • Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall. 2.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233–242.

    Article  Google Scholar 

  • Gibson, C. B. (2001). From knowledge accumulation to accommodation: Cycles of collective cognition in work groups. Journal of Organizational Behavior, 22(2), 121–134.

    Article  Google Scholar 

  • Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929–964.

    Article  Google Scholar 

  • Hodgkinson, G. P., & Healey, M. P. (2008). Cognition in organizations. Annual Review of Psychology, 59, 387–417.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Islam, G. (2015). Extending organizational cognition: A conceptual exploration of mental extension in organizations. Human Relations, 68(3), 463–487.

    Article  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.

    Google Scholar 

  • Jeong, Y. K., Song, M., & Ding, Y. (2014). Content-based author co-citation analysis. Journal of Informetrics, 8(1), 197–211.

    Article  Google Scholar 

  • Johnson, D. R., & Hoopes, D. G. (2003). Managerial cognition, sunk costs, and the evolution of industry structure. Strategic Management Journal, 24(10), 1057–1068.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kaplan, D. M. (2012). How to demarcate the boundaries of cognition. Biology and Philosophy, 27(4), 545–570.

    Article  Google Scholar 

  • 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).

  • Kaplan, S., & Tripsas, M. (2008). Thinking about technology: Applying a cognitive lens to technical change. Research Policy, 37(5), 790–805.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.

    Article  Google Scholar 

  • 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 

  • Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: Exploring tissue engineering. Research Policy, 31(8), 1389–1403.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Narayanan, V. K., Zane, L. J., & Kemmerer, B. (2010). The cognitive perspective in strategy: An integrative review. Journal of Management, 37(1), 305–351.

    Article  Google Scholar 

  • Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nohria, N., & Garcia-Pont, C. (1991). Global strategic linkages and industry structure. Strategic Management Journal, 12(S1), 105–124.

    Article  Google Scholar 

  • 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.

  • Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.

    Article  Google Scholar 

  • Porter, A. L., & Youtie, J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research, 11(5), 1023–1041.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rowlands, I. (1999). Patterns of author cocitation in information policy: Evidence of social, collaborative and cognitive structure. Scientometrics, 44(3), 533–546.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shiau, W. L., Dwivedi, Y. K., & Tsai, C. H. (2015). Supply chain management: Exploring the intellectual structure. Scientometrics, 105(1), 215–230.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, X., Zhang, X., & Xu, S. (2011). Patent co-citation networks of Fortune 500 companies. Scientometrics, 88(3), 761–770.

    Article  Google Scholar 

  • Wells, P., & Nieuwenhuis, P. (2012). Transition failure: Understanding continuity in the automotive industry. Technological Forecasting and Social Change, 79(9), 1681–1692.

    Article  Google Scholar 

  • West, G. P. (2007). Collective cognition: When entrepreneurial teams, not individuals, make decisions. Entrepreneurship Theory and Practice, 31(1), 77–102.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zapata, C., & Nieuwenhuis, P. (2010). Exploring innovation in the automotive industry: New technologies for cleaner cars. Journal of Cleaner Production, 18(1), 14–20.

    Article  Google Scholar 

  • Zhao, Q., & Guan, J. (2013). Love dynamics between science and technology: Some evidences in nanoscience and nanotechnology. Scientometrics, 94(1), 113–132.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. University of Cagliari, Viale Sant’Ignazio, 73, 09123, Cagliari, Italy

    Manuel Castriotta & Maria Chiara Di Guardo

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  1. Manuel Castriotta
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  2. Maria Chiara Di Guardo
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Correspondence to Manuel Castriotta.

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Castriotta, M., Di Guardo, M.C. Disentangling the automotive technology structure: a patent co-citation analysis. Scientometrics 107, 819–837 (2016). https://doi.org/10.1007/s11192-016-1862-0

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