Early Patterns of Commercialization in Graphene

  • Philip Shapira
  • Jan Youtie
  • Sanjay Arora


Graphene is a novel nanomaterial consisting of a single layer of carbon atoms. It has attracted significant attention due to its distinctive properties, which include great strength, electrical and thermal conductivity, and lightness. While many diverse and exciting potential applications are discussed, the commercialization of scientific discoveries such as graphene is inherently uncertain. There is often considerable time lag between the science, the early development of a new technology, and its adoption by corporate and other actors. In part this relates to the extent to which firms are able to absorb knowledge and engage in learning to implement applications of the new technology. In this chapter, we consider three different possible patterns of corporate learning and activity These are: (1) a linear process, where patenting follows scientific discovery; (2) a “double-boom” phenomenon, where corporate (patenting) activity is first concentrated in technological improvements and then followed by a period of technology productization; and (3) a concurrent model, where scientific discovery in publications occurs in parallel with patenting. We analyze corporate publication and patent activity across countries and lines of application. The results indicate that, while graphene as a whole is experiencing concurrent scientific development and patenting growth, country- and application-specific trends offer some evidence of both the linear and double-boom models. Thus the empirical path of development cannot be accounted for by just one of the models; nor is one model sufficient guidance for policy and strategy formation.


Patent Activity Corporate Activity National Innovation System Memory Area Graphene Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was undertaken with support from the Center for Nanotechnology in Society at Arizona State University (sponsored by the National Science Foundation under cooperative agreement #0937591). Additional support was provided through a UK-US Collaboration Development Award (Department for Business, Innovation & Skills, and the Foreign & Commonwealth Office’s Global Partnership Fund); and the UK-US Higher Education, New Partnership Fund (British Council). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the sponsors.

This paper was previously published as Philip Shapira, Jan Youtie and Sanjay Arora, “Early patterns of commercial activity in graphene,” Journal of Nanoparticle Research, 2012, 14:811, DOI:  10.1007/s11051-012-0811-y. It is reproduced here, with minor revisions. Permission from Springer is gratefully acknowledged.


  1. Abernathy W, Utterback J (1978) Patterns of industrial innovation. Technol Rev 80(7):40–47Google Scholar
  2. Brouwer E, Kleinknecht A (1999) Innovative output, and a firm’s propensity to patent: an exploration of CIS micro data. Res Policy 28(6):615–624CrossRefGoogle Scholar
  3. Cockburn I, Henderson R, Stern S (1999) The diffusion of science driven drug discovery: organizational change in pharmaceutical research. NBER Working Paper 7359. Cambridge, MAGoogle Scholar
  4. Cohen W, Levinthal D (1989) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35(1):128–152CrossRefGoogle Scholar
  5. Cohen W, Nelson R, Walsh J (2000) Protecting their intellectual assets: appropriability conditions and why U.S. manufacturing firms patent (or not). NBER Working Paper 7552, Cambridge, MAGoogle Scholar
  6. Cohen SS, Di Minin A, Motoyama Y, Palmberg C (2009) The persistence of home bias for important r&d in wireless telecom and automobiles. Rev Policy Res 26(1/2):55–76. doi: 10.1111/j.1541-1338.2008.00369.x CrossRefGoogle Scholar
  7. Edquist C (ed) (1997) Systems of innovation. Technologies, institutions and organizations. Pinter Publisher, London/WashingtonGoogle Scholar
  8. Grupp H (2000) Learning in a science-driven market: the case of lasers. Ind Corp Change 9(1):143–172CrossRefGoogle Scholar
  9. Helfat CE, Lieberman MB (2002) The birth of capabilities: market entry and the importance of pre-history. Ind Corp Change 11(4):725–760CrossRefGoogle Scholar
  10. Hobday M (2005) Firm-level innovation models: perspectives on research in developed and developing countries. TASM 17(2):121–146Google Scholar
  11. Hoppe H (2000) Second-mover advantages in the strategic adoption of new technology under uncertainty. Int J Ind Organ 18(2):315–338. doi: 10.1016/S0167-7187(98)00020-4 CrossRefGoogle Scholar
  12. Hoppe H (2002) The timing of new technology adoption: theoretical models and empirical evidence. Manch Sch 70(1):56–76. doi: 10.1111/1467-9957.00283 CrossRefGoogle Scholar
  13. ITRS (2010) International Technology Roadmap for Semiconductors 2010 Update Overview. Accessed 14 Apr 2011
  14. Katila R, Ahuja G (2002) Something old, something new: a longitudinal study of search behavior and new product introduction. Acad Manage J 456:1183–1194CrossRefGoogle Scholar
  15. Kogut B, Kulatilaka N (2001) Capabilities as real options. Organ Sci 12(6):744–758CrossRefGoogle Scholar
  16. Lavie D, Stettner U, Tushman ML (2010) Exploration and exploitation within and across organizations. Acad Manage Ann 4:109–155CrossRefGoogle Scholar
  17. Lieberman MB, Montgomery DB (1988) First-mover advantages. Strateg Manage J 9:41–58CrossRefGoogle Scholar
  18. Lieberman MB, Montgomery DB (1998) First-mover (dis)advantages: retrospective and link with the resource-based view. Strateg Manage J 19:1111–1125CrossRefGoogle Scholar
  19. Liebowitz SJ, Margolis SE (1995) Are network externalities a new source of market failure? Res Law Econ 7:1–22Google Scholar
  20. Lundvall BÁ (ed) (1992) National systems of innovation. Towards a theory of innovation and interactive learning. Pinter Publisher, LondonGoogle Scholar
  21. Malerba F (2005) Sectoral systems: how and why innovation differs across sectors. In: Fagerberg J, Mowery D, Nelson R (eds) Oxford handbook of innovation. Oxford University Press, OxfordGoogle Scholar
  22. March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2:71–87CrossRefGoogle Scholar
  23. Mowery D (2011) Nanotechnology and the U.S. national innovation system: continuity and change. J Technol Transfer. doi: 10.1007/s10961-011-9210-2
  24. Nelson RR, Winter S (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge, MAGoogle Scholar
  25. (2011) The Nobel Prize in Physics 2010. 2 Sep 2011
  26. Pavitt K (1984) Sectoral patterns of technical change: towards a taxonomy and a theory. Res Policy 13(6):343–373. doi: 10.1016/0048-7333(84)90018-0 CrossRefGoogle Scholar
  27. Porter ME (1990) The competitive advantage of nations. Free Press, New YorkGoogle Scholar
  28. Porter AL, Youtie J, Shapira P, Schoeneck D (2008) Refining search terms for nanotechnology. J Nanoparticle Res 10(5):715–728CrossRefGoogle Scholar
  29. Pries F, Guild P (2011) Commercializing inventions resulting from university research: analyzing the impact of technology characteristics on subsequent business models. Technovation 31(4):151–160. doi: 10.1016/j.technovation.2010.05.002 CrossRefGoogle Scholar
  30. Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New YorkGoogle Scholar
  31. Rogers D (2011) Graphene is beginning to revolutionise the market for plastic electronics. Plast Electron 3(6):57–61Google Scholar
  32. Rothaermel FT, Alexandre MT (2009) Ambidexterity in technology sourcing: the moderating role of absorptive capacity. Organ Sci 20:759–780CrossRefGoogle Scholar
  33. Schinwald A, Murphy FA, Jones A, MacNee W, Donaldson K (2012) Graphene-based nanoplatelets: a new risk to the respiratory system as a consequence of their unusual aerodynamic properties. ACS Nano 6(1):736–746. doi: 10.1021/nn204229f CrossRefGoogle Scholar
  34. Schmoch U (2007) Double-boom cycles and the comeback of science-push and market-pull. Res Policy 36(7):1000–1015CrossRefGoogle Scholar
  35. Segal M (2009) Selling graphene by the ton. Nat Nanotechnol 4:612–614CrossRefGoogle Scholar
  36. Shapira P, Youtie J, Mohapatra S (2003) Linking research production and development outcomes at the regional level. Res Evaluat 12(1):105–116CrossRefGoogle Scholar
  37. Shapira P, Youtie J, Kay L (2011) National innovation systems and the globalization of nanotechnology innovation. J Technol Transfer. doi: 10.1007/s10961-011-9212-0
  38. Takeuchi H, Nonaka I (1986) The new product development game. Harv Bus Rev 64:137–146Google Scholar
  39. Teece DJ (1986) Profiting from technological innovation: implications for integration, collaboration, licensing and public policy. Res Policy 15(6):285–305CrossRefGoogle Scholar
  40. Tuppura A, Hurmelinna-Laukkanen P, Puumalainen K, Jantunen A (2010) The influence of appropriability conditions on the firm’s entry timing orientation. J High Technol Manage Res 21:97–107CrossRefGoogle Scholar
  41. Van Noorden R (2011) Chemistry: the trials of new carbon. Nature 469(7328):14–6. Nature Publishing Group. doi: 10.1038/469014a Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Manchester Institute of Innovation ResearchManchester Business School, University of ManchesterManchesterUK
  2. 2.School of Public Policy, Georgia Institute of TechnologyAtlantaUSA
  3. 3.Enterprise Innovation InstituteGeorgia Institute of TechnologyAtlantaUSA
  4. 4.School of Public Policy, Georgia Tech’s School of Public PolicyAtlantaUSA
  5. 5.School of Public PolicyGeorgia Institute of TechnologyAtlantaUSA

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