IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i14p5606-d383562.html
   My bibliography  Save this article

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Author

Listed:
  • Jinho Choi

    (School of Business, Sejong University, Seoul 05006, Korea)

  • Yong Sik Chang

    (Department of IT Management, Hanshin University, Gyeonggi-do 18001, Korea)

Abstract

In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, we identify the promising technology areas with longitudinal analyses of M&As over the entire period. Third, cross-sectional analysis is proposed to determine which technology areas are more promising through a relative comparison among technology areas within the IT sector for a specific period. The main significance of our research is that it is a prior data-based analytic method based on M&A transaction information to identify the growth of industry and technology. We hope this study will provide insights for R&D (Research&Development) policymakers and investment firms as a new approach that complements previous methods in exploring promising industry or technology areas.

Suggested Citation

  • Jinho Choi & Yong Sik Chang, 2020. "Development of a New Methodology to Identity Promising Technology Areas Using M&A Information," Sustainability, MDPI, vol. 12(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5606-:d:383562
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/14/5606/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/14/5606/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonio Salvi & Felice Petruzzella & Anastasia Giakoumelou, 2018. "Green M&A Deals and Bidders’ Value Creation: The Role of Sustainability in Post-Acquisition Performance," International Business Research, Canadian Center of Science and Education, vol. 11(7), pages 96-105, July.
    2. Hicks, Diana, 2011. "Structural change and industrial classification," Structural Change and Economic Dynamics, Elsevier, vol. 22(2), pages 93-105, June.
    3. Ricardo Hausmann & Lant Pritchett & Dani Rodrik, 2005. "Growth Accelerations," Journal of Economic Growth, Springer, vol. 10(4), pages 303-329, December.
    4. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    5. Cefis, Elena & Marsili, Orietta, 2015. "Crossing the innovation threshold through mergers and acquisitions," Research Policy, Elsevier, vol. 44(3), pages 698-710.
    6. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    7. Burmaoglu, Serhat & Sartenaer, Olivier & Porter, Alan, 2019. "Conceptual definition of technology emergence: A long journey from philosophy of science to science policy," Technology in Society, Elsevier, vol. 59(C).
    8. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    9. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    10. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    11. Klevorick, Alvin K. & Levin, Richard C. & Nelson, Richard R. & Winter, Sidney G., 1995. "On the sources and significance of interindustry differences in technological opportunities," Research Policy, Elsevier, vol. 24(2), pages 185-205, March.
    12. Hong, Jae-pyo, 2017. "Causal relationship between ICT R&D investment and economic growth in Korea," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 70-75.
    13. Elena Cefis, 2010. "The impact of M&A on technology sourcing strategies," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 19(1), pages 27-51.
    14. Bruno Cassiman & Reinhilde Veugelers, 2006. "In Search of Complementarity in Innovation Strategy: Internal R& D and External Knowledge Acquisition," Management Science, INFORMS, vol. 52(1), pages 68-82, January.
    15. Inchae Park & Byungun Yoon, 2018. "Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis," Sustainability, MDPI, vol. 10(11), pages 1-32, November.
    16. Jrisy Motis, 2007. "Mergers and Acquisitions Motives," Working Papers 0730, University of Crete, Department of Economics.
    17. Lindman, Åsa & Söderholm, Patrik, 2016. "Wind energy and green economy in Europe: Measuring policy-induced innovation using patent data," Applied Energy, Elsevier, vol. 179(C), pages 1351-1359.
    18. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    19. Ola Olsson, 2005. "Technological Opportunity and Growth," Journal of Economic Growth, Springer, vol. 10(1), pages 31-53, January.
    20. Thais González-Torres & José-Luis Rodríguez-Sánchez & Eva Pelechano-Barahona & Fernando E. García-Muiña, 2020. "A Systematic Review of Research on Sustainability in Mergers and Acquisitions," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    21. Rakesh Mohan, 2008. "The Growth Record of the Indian Economy, 1950-2008: A Story of Sustained Savings and Investment," Working Papers id:1388, eSocialSciences.
    22. Niebel, Thomas, 2018. "ICT and economic growth – Comparing developing, emerging and developed countries," World Development, Elsevier, vol. 104(C), pages 197-211.
    23. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    24. Aaldering, Lukas Jan & Leker, Jens & Song, Chie Hoon, 2019. "Uncovering the dynamics of market convergence through M&A," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 95-114.
    25. Jinho Choi & Nina Shin & Hee Soo Lee, 2020. "Exploring the Dynamics between M&A Activities and Industry-Level Performance," Sustainability, MDPI, vol. 12(11), pages 1-24, May.
    26. Coenen, R., 1972. "The use of technological forecasts in government planning," Research Policy, Elsevier, vol. 1(2), pages 156-172, April.
    27. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinho Choi & Nina Shin & Yong Sik Chang, 2021. "Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis," Sustainability, MDPI, vol. 13(7), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jinho Choi & Nina Shin & Yong Sik Chang, 2021. "Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis," Sustainability, MDPI, vol. 13(7), pages 1-20, March.
    2. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    3. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    4. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    5. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    6. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    7. Teng, Fei & Sun, Yuling & Chen, Fang & Qin, Aning & Zhang, Qi, 2021. "Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    8. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    9. Seol, Youngjin & Lee, Seunghyun & Kim, Cheolhan & Yoon, Janghyeok & Choi, Jaewoong, 2023. "Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis," Journal of Informetrics, Elsevier, vol. 17(4).
    10. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    11. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    12. Jumi Hwang & Kyung Hee Kim & Jong Gyu Hwang & Sungchan Jun & Jiwon Yu & Chulung Lee, 2020. "Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    13. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    14. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    15. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    16. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.
    17. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    18. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
    19. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    20. Jinho Choi & Sunghun Chung & Yong Sik Chang, 2019. "Is M&A Information Useful for Exploring Promising Industries and Technologies?," Sustainability, MDPI, vol. 12(1), pages 1-19, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5606-:d:383562. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.