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Patent analysis for forecasting promising technology in high-rise building construction

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  • Cho, Han Pil
  • Lim, Hyunsu
  • Lee, Dongmin
  • Cho, Hunhee
  • Kang, Kyung-In

Abstract

For last 15years, the market size of high-rise building construction has rapidly increased by four times. Many global contractors are investing in the development of high-rise building construction technology, which needs more advanced construction technology than ordinary building construction to secure competiveness in the market. It is significant for contractors to prospect the promising field of technology for strategic investment. Therefore, this study analyzed patents to forecast promising technology fields in future high-rise building construction. 2875 patents related to high-rise building construction that were applied for in the US, Europe, Korea, China and Japan during 1995–2013 were analyzed for market prospect and promising technology. As a result of the market analysis, Korea and China are in the developing phase of the technology market; and in particular, the Chinese market is showing the most drastic growth. On the other hand, the analysis of technology suggests the following technologies offer promising technology: 1) monitoring technology to enhance the efficiency of high-rise building construction; 2) information modeling technology and energy reduction technology in the construction phase based on information modeling technology; and 3) safety management technology based on information modeling technology. This study is intended to provide directions for high-rise building construction technology investment, and objective data for decision making for future global contractors.

Suggested Citation

  • Cho, Han Pil & Lim, Hyunsu & Lee, Dongmin & Cho, Hunhee & Kang, Kyung-In, 2018. "Patent analysis for forecasting promising technology in high-rise building construction," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 144-153.
    • Handle: RePEc:eee:tefoso:v:128:y:2018:i:c:p:144-153
    DOI: 10.1016/j.techfore.2017.11.012
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    References listed on IDEAS

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    1. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
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    Cited by:

    1. Lijie Feng & Kehui Liu & Jinfeng Wang & Kuo-Yi Lin & Ke Zhang & Luyao Zhang, 2022. "Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes," Energies, MDPI, vol. 15(20), pages 1-22, October.
    2. WANG, La-yin & ZHAO, Dong, 2021. "Cross-domain function analysis and trend study in Chinese construction industry based on patent semantic analysis," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    3. Koopo Kwon & Jaeryong So, 2023. "Future Smart Logistics Technology Based on Patent Analysis Using Temporal Network," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    4. Xi Yang & Xiang Yu & Xin Liu, 2018. "Obtaining a Sustainable Competitive Advantage from Patent Information: A Patent Analysis of the Graphene Industry," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
    5. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    6. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    7. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    8. Noh, Heeyong & Lee, Sungjoo, 2020. "What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives," Technological Forecasting and Social Change, Elsevier, vol. 157(C).

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