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Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology
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Hochang Choi (Graduate School of Business IT, Kookmin University)
Kee-Young Kwahk (School of MIS, Kookmin University)
Namgyu Kim (School of MIS, Kookmin University)
Vol. 24, No. 1, Page: 101 ~ 124
10.13088/jiis.2018.24.1.101
Keywords
Technology Convergence, Social Network Analysis, Promising Technology Forecast, Potential Growth Centrality, Topic Modeling
Abstract
Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis.
Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon.
Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology.
Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study.
To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period.
In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.
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기술 성숙도 및 의존도의 네트워크 분석을 통한유망 융합 기술 발굴 방법론
최호창 (국민대학교 비즈니스 IT전문대학원)
곽기영 (국민대학교 경영대학 경영정보학부)
김남규 (국민대학교 경영대학 경영정보학부)
Keywords
기술 융합, 소셜 네트워크 분석, 유망 기술 예측, 잠재 성장 중심성, 토픽 모델링
Abstract
최근 다양한 분야에서 새로운 기술이 출현하고 있으며, 이들 대부분은 기존 기술들의 융합(Convergence)을통해 형성되고 있다. 또한 가까운 미래에 출현하게 될 유망한 융합 기술을 예측함으로써 변화하는 기술 지형에선제적으로 대응하기 위한 수요가 꾸준히 증가하고 있으며, 이러한 수요에 부응하여 많은 기관과 연구자들은미래 유망 융합 기술 예측을 위한 분석을 수행하고 있다. 하지만 이와 관련한 기존의 많은 연구들은 (i) 고정된기술 분류 기준을 분석에 사용함으로써 기술 분야의 동적 변화를 반영하지 못했다는 점, (ii) 예측 모형 수립 과정에서 주로 범용성 네트워크 지표를 사용함으로써 기술의 융합이라는 목적에 부합하는 고유 특성을 활용하지못했다는 점, 그리고 (iii) 유망 분야 예측 모형의 정확성 평가를 위한 객관적 방법을 제시하지 못했다는 점 등에서 한계를 갖고 있다. 이에 본 연구에서는 (i) 토픽 모델링을 통해 기존의 고정된 분류 기준이 아닌 실제 기술시장의 동적 변화에 따른 새로운 기술군을 도출하고, (ii) 기술 성숙도 및 기술군 간 의존 관계에 따라 각 기술군의 융합적 특성을 반영하는 잠재 성장 중심성(Potential Growth Centrality) 지표를 산출하였으며, (iii) 잠재 성장 중심성에 근거하여 예측한 유망 기술의 성숙도 증가량을 시기별로 측정하여 예측 모형의 정확도를 평가하는방안을 제시한다. 이와 더불어 제안 방법론의 성능 및 실무 적용 가능성의 평가를 위해 특허 문서 13, 477건에대한 실험을 수행하였으며, 실험 결과 제안한 잠재 성장 중심성에 따른 예측 모형이 단순히 현재 활용되는 영향도 기반의 예측 모형에 비해 최대 약 2.88배 높은 예측 정확도를 보임을 확인하였다.
Cite this article
JIIS Style
Choi, H., K.-Y. Kwahk, and N. Kim, "Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology", Journal of Intelligence and Information Systems, Vol. 24, No. 1 (2018), 101~124.

IEEE Style
Hochang Choi, Kee-Young Kwahk, and Namgyu Kim, "Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology", Journal of Intelligence and Information Systems, vol. 24, no. 1, pp. 101~124, 2018.

ACM Style
Choi, H., Kwahk, K.-Y., and Kim, N., 2018. Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology. Journal of Intelligence and Information Systems. 24, 1, 101--124.
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@article{Choi:JIIS:2018:715,
author = {Choi, Hochang and Kwahk, Kee-Young and Kim, Namgyu},
title = {Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2018},
volume = {24},
number = {1},
month = Mar,
year = {2018},
issn = {2288-4866},
pages = {101--124},
url = {http://dx.doi.org/10.13088/jiis.2018.24.1.101 },
doi = {10.13088/jiis.2018.24.1.101},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Technology Convergence, Social Network Analysis, Promising Technology Forecast, Potential Growth Centrality and Topic Modeling },
}
%0 Journal Article
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%A Hochang Choi
%A Kee-Young Kwahk
%A Namgyu Kim
%T Discovering Promising Convergence Technologies Using Network Analysis of Maturity and Dependency of Technology
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 24
%N 1
%P 101-124
%D 2018
%R 10.13088/jiis.2018.24.1.101
%I Korea Intelligent Information System Society