DOI QR코드

DOI QR Code

A Technology Planning Approach Based on Network and Growth Curve Analyses : the Case of Augmented Reality Patents

네트워크분석과 기술성장모형을 이용한 기술기획 : 증강현실 기술의 특허를 활용하여

  • Kim, Jungwook (Department of Industrial Engineering, Konkuk University) ;
  • Jeong, Byeongki (Department of Industrial Engineering, Konkuk University) ;
  • Yoon, Janghyeok (Department of Industrial Engineering, Konkuk University)
  • Received : 2016.02.16
  • Accepted : 2016.06.11
  • Published : 2016.10.15

Abstract

As technologies' life-cycle shortens and their development directions are uncertain, firms' technology planning capability becomes increasingly important. Prior patent-based studies using technology growth curves identify developmental stages of technologies, thereby formulating technology development directions from an overall perspective. However, a technology generally consists of multiple sub-technologies and accordingly their development stages are likely various. In this regard, the prior studies failed to identify core sub-technologies and their specific development directions. Therefore, we suggest an approach consisting of 1) identifying core sub-technologies of a given technology using patent co-classifications and social network analysis, and 2) identifying each sub-technology's development stage and thereby determining its further development direction. We apply our approach to patents related to augmented reality to examine its applicability. It is expected that our approach will help identify evolving development stages for the core sub-technologies of a given technology, thereby effectively assisting technology experts in technology planning processes.

Keywords

References

  1. An, J., Kim, K., Noh, H., and Lee, S. (2016), Identifying Converging Technologies in the ICT Industry : Analysis of Patents Published by Incumbents and Entrants, Journal of the Korean Institute of Industrial Engineers, 42, 209-221. https://doi.org/10.7232/JKIIE.2016.42.3.209
  2. Azuma, R. T. (1997), A survey of augmented reality, Presence : Teleoperators and virtual environments, 6, 355-385. https://doi.org/10.1162/pres.1997.6.4.355
  3. Baek, D., Kim, E., and Kim, E. (2013), A Study on Improvement in Government R&D Support System for SMEs based on Technology Life Cycle, The Journal of Small Business Innovation, 35, 157-179.
  4. Bengisu, M. and Nekhili, R. (2006), Forecasting emerging technologies with the aid of science and technology databases, Technological Forecasting and Social Change, 73, 835-844. https://doi.org/10.1016/j.techfore.2005.09.001
  5. Carrillo, M. and Gonzalez, J. M. (2002) A new approach to modelling sigmoidal curves, Technological Forecasting and Social Change, 69, 233-241. https://doi.org/10.1016/S0040-1625(01)00150-0
  6. Chen, Y.-H., Chen, C.-Y., and Lee, S.-C. (2011), Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies, International Journal of Hydrogen Energy, 36, 6957-6969. https://doi.org/10.1016/j.ijhydene.2011.03.063
  7. Choi, J., Kim, H., and Im, N.-G. (2011), Keyword Network Analysis for Technology Forecasting, Journal of Intelligence and Information Systems, 17, 227-240.
  8. Choi, S., Yoon, J., Kim, K., Lee, J. Y., and Kim, C.-H. (2011), SAO network analysis of patents for technology trends identification : a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells, Scientometrics, 88, 863-883. https://doi.org/10.1007/s11192-011-0420-z
  9. Daim, T. U., Rueda, G., Martin, H., and Gerdsri, P. (2006), Forecasting emerging technologies : Use of bibliometrics and patent analysis, Technological Forecasting and Social Change, 73, 981-1012. https://doi.org/10.1016/j.techfore.2006.04.004
  10. Ernst, H. (1997), The use of patent data for technological forecasting : the diffusion of CNC-technology in the machine tool industry, Small Business Economics, 9, 361-381. https://doi.org/10.1023/A:1007921808138
  11. Feiner, S., Macintyre, B., and Seligmann, D. (1993), Knowledge-based augmented reality, Communications of the ACM, 36, 53-62.
  12. Freeman, L. C. (1979), Centrality in social networks conceptual clarification, Social Networks, 1, 215-239.
  13. Grupp, H. (1996), Spillover effects and the science base of innovations reconsidered : an empirical approach, Journal of Evolutionary Economics, 6, 175-197. https://doi.org/10.1007/BF01202593
  14. Han, M., Kim, B., Ryu, J., and Byeon, S. C. (2010), Technology Level Evaluation Based On Technology Growth Model and Its Implication, Journal of Korea Technology Innovation Society, 13, 252-281.
  15. Harell, G. and Daim, T. U. (2009), Forecasting energy storage technologies, foresight, 11, 74-85. https://doi.org/10.1108/14636680911004975
  16. Jang, S., Shin, Y., and Jeong, H. (2009), Relationship between R&D investment, technology, management capability and firm performance, Asia Pacific Journal of Information Systems, 38, 105-132.
  17. Jung, K.-H. (2009), Forecasting substitution behaviour of high corrosionresistant steel by fitting a technology growth curve, International Journal of Business and Systems Research, 3, 216-228. https://doi.org/10.1504/IJBSR.2009.024863
  18. Kauffman, R. J., Liu, J., and Ma, D. (2013), Technology Investment Decision-Making under Uncertainty : The Case of Mobile Payment Systems.
  19. Kim, C., Kim, S., Seol, H., and Park, Y. (2006), Identifying the linkage between technologies using co-classification analysis : TOPSIS-based approach, Journal of the Korean Institute of Industrial Engineers, 18, 711-717.
  20. Kim, D.-H., Park, S.-S., Young-Geun, S., and Dong-Sik, J. (2007), Patent Analysis of Information Security Technology for Network-Centric Warfare, Journal of The Korea Contents Association, 7, 355-364. https://doi.org/10.5392/JKCA.2007.7.12.355
  21. Kim, D., Park, S.-S., Shin, Y.-G., and Jang, D.-S. (2009), Forecasting the Diffusion of Technology using Patent Information : Focused on Information Security Technology for Network-Centric Warfare, Journal of Korean Contents, 9.
  22. Kim, H.-J. and Kwahk, K.-Y. (2011), Effects of Centrality on IT Usage Capability : A Perspective of Social Networks, The Journal of Information Systems, 20, 147-169. https://doi.org/10.5859/KAIS.2011.20.1.147
  23. Kim, J. and Lee, S. (2013), A Methodology to Evaluate Industry Convergence Using the Patent Information : Technology Relationship analysis, Journal of the Korean Institute of Industrial Engineers, 39, 212-221. https://doi.org/10.7232/JKIIE.2013.39.3.212
  24. Kim, M.-J., Park, J.-K., Lee, Y.-A., and Heo, E.-N. (2011), Co-classification analysis of inter-disciplinarity on solar cell research, Journal of the Korean society for New and Renewable Energy, 7, 36-44.
  25. Lee, H., Kim, C., Cho, H., and Park, Y. (2009), An ANP-based technology network for identification of core technologies : A case of telecommunication technologies, Expert Systems with Applications, 36, 894-908. https://doi.org/10.1016/j.eswa.2007.10.026
  26. Malerba, F., Breschi, S., and Lissoni, F. (1998), Knowledge proximity and technological diversification(Paper submitted to the Commission : March).
  27. Mann, D. (2002), Hands on systematic innovation(Creax).
  28. Modis, T. (2007), Strengths and weaknesses of S-curves, Technological Forecasting and Social Change, 74, 866-872. https://doi.org/10.1016/j.techfore.2007.04.005
  29. Noh, D. and Kim, J. (2013), Enlightening technology valuation considering product life cycle, Journal of Services Marketing, 6, 21-41.
  30. Park, H., Seo, W., and Yoon, J. (2012), Identifying Interdisciplinarity of Korean National R & D Using Patent CoIPC Network Analysis, Journal of the Korean Society for Library and Information Science, 46, 99-117.
  31. Park, H. and Yoon, J. (2014), Assessing coreness and intermediarity of technology sectors using patent co-classification analysis : the case of Korean national R&D, Scientometrics, 98, 853-890. https://doi.org/10.1007/s11192-013-1109-2
  32. Park, J. and Kwak, G. (2013), The Effect of Patent Citation Relationship on Business Performance : A Social Network Analysis Perspective, Journal of Intelligence and Information Systems, 19, 127-139. https://doi.org/10.13088/jiis.2013.19.3.127
  33. Sie, R. L., Ullmann, T. D., Rajagopal, K., Cela, K., Bitter-Rijpkema, M., and Sloep, P. B. (2012), Social network analysis for technology-enhanced learning : review and future directions, International Journal of Technology Enhanced Learning, 4, 172-190. https://doi.org/10.1504/IJTEL.2012.051582
  34. Tijssen, R. J. (1992), A quantitative assessment of interdisciplinary structures in science and technology : co-classification analysis of energy research, Research Policy, 21, 27-44. https://doi.org/10.1016/0048-7333(92)90025-Y
  35. Trappey, C. V., Wu, H.-Y., Taghaboni-Dutta, F., and Trappey, A. J. (2011), Using patent data for technology forecasting : China RFID patent analysis, Advanced Engineering Informatics, 25, 53-64. https://doi.org/10.1016/j.aei.2010.05.007
  36. Van Krevelen, D. and Poelman, R. (2010), A survey of augmented reality technologies, applications and limitations, International Journal of Virtual Reality, 9(1).
  37. Verspagen, B. (1997), Measuring intersectoral technology spillovers : estimates from the European and US patent office databases, Economic Systems Research, 9, 47-65. https://doi.org/10.1080/09535319700000004
  38. Wanki, K. (2014), Determination of Commercialization Potential Through Patent Attribute Assessment in Lithium Ion Battery Technology, Journal of the Korean Institute of Industrial Engineers, 40, 240-249. https://doi.org/10.7232/JKIIE.2014.40.2.240
  39. Wasserman, S. and Faust, K. (1994), Social network analysis : Methods and applications(Cambridge university press).
  40. Wong, C.-Y. and Goh, K.-L. (2010), Growth behavior of publications and patents : A comparative study on selected Asian economies, Journal of Informetrics, 4, 460-474. https://doi.org/10.1016/j.joi.2010.04.002
  41. Yoon, J. and Kim, G. (2011), A Study on Interdisciplinary Trends of Technological Convergence Using Patent Information : The Case of Air Pollutant Control Technology, Entrue Journal of Information Technology, 10, 21-31.
  42. Yoon, J. and Kim, K. (2011), Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks, Scientometrics, 88, 213-228. https://doi.org/10.1007/s11192-011-0383-0
  43. Yoon, J., Kim, M., Kim, D., and Kim, J. (2015), Monitoring the Change of Technological Impacts of Technology Sectors Using Patent Information, Industrial Engineeering and Management Systems, 14, 58-72. https://doi.org/10.7232/iems.2015.14.1.058
  44. Yoon, J., Park, Y., Kim, M., Lee, J., and Lee, D. (2014), Tracing evolving trends in printed electronics using patent information, Journal of nanoparticle research, 16, 1-15.
  45. Young, P. (1993), Technological growth curves : a competition of forecasting models, Technological Forecasting and Social Change, 44, 375-389. https://doi.org/10.1016/0040-1625(93)90042-6
  46. Zhou, F., Duh, H. B.-L., and Billinghurst, M. (2008), Trends in augmented reality tracking, interaction and display : A review of ten years of ISMAR (IEEE Computer Society).