International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 9, Issue 9 (September 2022), Pages: 1-8

----------------------------------------------

 Original Research Paper

 Research on technology contribution evaluation model for commercialization

 Author(s): Heung Su Kim *

 Affiliation(s):

 Division of Convergence Business, Korea University, Seoul, South Korea

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-7085-3269

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.09.001

 Abstract:

The purpose of this study is to calculate the quantitative and qualitative contribution of intellectual property rights owned by startups for successful commercialization. In the 4th industrial revolution economy, intellectual property rights, which play an important role in job creation and economic growth, play a very important role for startups. In particular, intellectual property rights are the most important asset for startups, and it is necessary to promote the sustainable growth of startups through efficient intellectual property management. This study evaluated the relative contribution of technology, human resources, and market assets, which are the sources of intangible assets for successful business start-ups through intellectual property transfer and technology trade. The contribution of the case companies to intangible assets was calculated by comprehensively judging four technologies related to each other. To this end, we find a strategy for the successful commercialization of intellectual property rights owned by startups by calculating the relative contribution of technical assets, human assets, and market assets, which are the sources of intangible assets. The contribution of the example company to intangible assets is calculated by comprehensively judging the four related intellectual property rights of the startup. In future research, we look forward to a follow-up study that can help companies make strategic decisions by comparing and analyzing various companies in consideration of industry and size.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Commercialization, Contribution, Degree, Start-ups, Technology asset

 Article History: Received 27 December 2021, Received in revised form 15 May 2022, Accepted 26 May 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Kim HS (2022). Research on technology contribution evaluation model for commercialization. International Journal of Advanced and Applied Sciences, 9(9): 1-8

 Permanent Link to this page

 Figures

 No Figure 

 Tables

 Table 1 Table 2 Table 3

----------------------------------------------    

 References (14)

  1. Baek DH, Sul W, Hong KP, and Kim H (2007). A technology valuation model to support technology transfer negotiations. R&D Management, 37(2): 123-138. https://doi.org/10.1111/j.1467-9310.2007.00462.x   [Google Scholar]
  2. Bodie Z, Merton RC, and Cleeton DL (2012). Financial economics. 2nd Edition, Pearson Learning Solutions, Boston, USA.   [Google Scholar]
  3. Chiu YJ and Chen YW (2007). Using AHP in patent valuation. Mathematical and Computer Modelling, 46(7-8): 1054-1062. https://doi.org/10.1016/j.mcm.2007.03.009   [Google Scholar]
  4. Cho D and Choi G (2011). Simple numeric model to compute technology-contribution factor for the technology valuation. In the Technology Management in the Energy Smart World, IEEE, Portland, USA: 1-5.   [Google Scholar]
  5. Contractor FJ (2001). Valuation of intangible assets in global operations. Greenwood Publishing Group, Westport, USA.   [Google Scholar]
  6. Fernandez P (2001). Valuation using multiples: How do analysts reach their conclusions. No. D/450, IESE Business School, Barcelona, Spain. https://doi.org/10.2139/ssrn.274972   [Google Scholar]
  7. Lee M and Khoe KI (2015). Development method of digital content finance-focused on by technical value evaluation. Journal of the Korea Convergence Society, 6(6): 111-117. https://doi.org/10.15207/JKCS.2015.6.6.111   [Google Scholar]
  8. Mohammed S (2019). Research on financial risk prevention and control methods based on big data. International Journal of Smart Business and Technology, 7(2): 1-14.   [Google Scholar]
  9. Oh HT (2015). A study on the effect of fair value hierarchy upon cost of capital through the convergence of market risk management and audit quality. Journal of the Korea Convergence Society, 6(5): 1-8. https://doi.org/10.15207/JKCS.2015.6.5.001   [Google Scholar]
  10. Park HW, Nah DB, and Park JK (2009). Proposition of a practical hybrid model for the valuation of technology. Management and Information Systems Review, 28(4): 27-44. https://doi.org/10.29214/damis.2009.28.4.002   [Google Scholar]
  11. Singh S (2019). Research on application of precision marketing based on big data. International Journal of Smart Business and Technology, 7(1): 17-26. https://doi.org/10.21742/IJSBT.2019.7.1.02   [Google Scholar]
  12. Xitiz U, Prashant B, and Ashish J (2017). Wine quality evaluation using machine learning algorithms. Asia-Pacific Journal of Convergent Research Interchange, 3(4): 1-9. https://doi.org/10.21742/apjcri.2017.12.07   [Google Scholar]
  13. Yang TS and Min KS (2007). A study on the improvement of the existing technology valuation solutions: Focused on high technology based start-up company. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 2(2): 93-120.   [Google Scholar]
  14. Yoon D and Kim J (2019). A study on the changes in the appraisal industry in the era of the 4th industrial revolution: Focus on the factors affecting intention to adopt big data in the appraisal field. International Journal of Smart Business and Technology, 7(1): 65-72. https://doi.org/10.21742/IJSBT.2019.7.1.07   [Google Scholar]