• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.11a
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• pp.927-945
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• 2015

본 연구는 과학 커뮤니케이션 분야에서 널리 쓰이고 있는 뉴스 내용분석 방법론이 하이프 사이클 모델에 부합하는 지를 탐색적으로 살펴보고자 한다. 즉 과학기술 뉴스 내용분석이 하이프 사이클 모델에서 설명하는 사회적 가시성의 실체적 파악을 위한 기술기획의 유용한 보완적 방법론으로 쓰일 수 있음을 밝히는데 본 연구의 의의가 있다. 이를 위해 대표적인 유망기술로 클라우드 컴퓨팅을 대상으로 뉴스 내용분석을 수행하였다. 분석의 초점은 클라우드 컴퓨팅 기술 관련 뉴스의 빈도, 보도태도(긍정, 중립, 부정), 5가지 뉴스 프레임 관점에서 분석이 이루어졌고, 뉴스 보도경향이 하이프 사이클 흐름을 따라가는 지를 살펴보았다. 종합지 경제지와 IT전문지를 대상으로 한 뉴스 내용분석 결과는 뉴스 빈도, 보도 태도, 뉴스 프레임 모두 하이프 사이클의 흐름을 따르고 있었으며, 특히 2014년 이후의 흐름은 하이프 사이클 상에서 기대붕괴 지점을 지나 현실인식의 지점으로 진화되는 시점임을 추론할 수 있었다. 본 연구결과는 최근 확산되고 있는 텍스트 마이닝, 감성어 자동식별 분석 기술 등과 접목하여 사회적 맥락 파악을 위한 기술기획 분석의 보완적 방법론으로 기여할 수 있을 것으로 판단된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.337-339
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• 2021

본 연구는 최근 펜데믹 위기에서 교육의 변화하는 모습을 점검하고 미래의 학습에 대한 모습들을 예측하기 위해 이러닝과 학습분석에 대한 신흥기술의 동향을 살펴보고자 한다. 연구방법으로 신흥기술의 '하이프 사이클'과 '이러닝 예측 하이프 커버'를 기반으로 하여 각 단계별 기술들을 점검하고 펜데믹 위기에서 더 공고히 된 이러닝과 학습 관련 기술들이 무엇인지 살펴본다. 또한 하이프 사이클의 5단계인 기술촉발 단계, 부풀려진 기대의 정점 단계, 환멸 단계, 계몽 단계, 생산성 안정 단계인 각 단계별 학습과 관련된 기술들은 어떤 것이 있으며, 그 기술들이 이러닝과 학습분석에 어떠한 영향을 미칠 것인지 예측해 본다. 향후 연구로는 본 연구를 기반으로 인공지능이 이러닝과 학습분석에서의 역할을 알아보고자 한다.

• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.80-104
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• 2016

Existing methodologies of technology planning about promising new technology focused on target technology itself, so it is true that socio-environmental context which the relevant technology has influence on is not well understood. In this respect, this study is aimed to questingly examine that news content analysis methodologies widely available in the field of science communication can be applied as a complementary methodology for contextual understanding of socio-environment in terms of technology planning about promising new technology. In the co-evolutionary environment of technology-society, promising new technology shows hype phenomenon regarding the relation with the society. Based on this, this study performed news content analysis and examined if the consequences of analysis would match hype cycle. It tried to explore substantive content understanding by socio-environment factors according to specific news frame content. To do this, new content analysis was performed targeting cloud computing as a representative promising new technology. The result of news content analysis targeting general newspapers, business news, IT special newspapers revealed that the tendency of news reporting matched the trend of hype cycle. Particularly, it was verified that reporting attitude and news frame analysis provided useful information to understand contextual content depending on social, economic, and cultural environment factors about promising new technology. The results of this study implied that news content analysis could overcome the limitation of technology information analysis focusing on academic journal patent usually applied for technology planning and could be used as a complementary methodology for understanding the context depending on macro-environment factors. In conclusion, application of news content analysis on the phase of macro-environment analysis of technology planning could contribute to the securement of mutually balanced view in the co-evolutionary perspective of technology-society.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.67-68
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• 2016

본 연구는 매해 가트너에서 발표하는 'Emerging Technology'들 중 최근 5년간의 하이프 기술들의 포지셔닝을 기준으로 주요 국가의 연구 문헌과 특허 정보를 이용하여 국가별 기술 경쟁력 평가 지표에 대한 다면적 분석 방법을 제안한다. 급변하는 IT 기술 환경에서 미래 산업을 선도하기 위해서는 국가 R&D 기획에 있어 더욱 면밀하고 창의적인 방안들의 연구가 필요하며, 특히 다양한 산업 분야 중 급속한 변화를 보여주는 ICT 분야에 있어서는 이를 뒷받침할 고도의 R&D 투자방향을 예측할 수 있어야 한다. 이를 위해 본 연구는 많은 방법들 중 세계 기술 성숙도를 다루는 가트너 하이프 사이클과 연구 개발 투자가 집중되는 특허 정보 다면적 요소들을 통합 분석한 후, 국가별 기술 경쟁력의 평가 지표를 선정하였고, 이를 판단할 수 있는 기준으로 시장성, 잠재성, 확장성, 감소성을 제시하였다. 그 결과 가트너 하이프 사이클 기술들의 포지셔닝의 움직임과 다면적 분석 결과 변화가 유사하게 나타났다. 이에 따라 본 연구를 통해 ICT 기술 변화와 경쟁력 등을 직관적으로 파악할 수 있었고, 국가 R&D 투자방향을 설정하는데 도움을 줄 수 있을 것으로 예상된다

• Proceedings of the Korea Technology Innovation Society Conference
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• 2013.10a
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• pp.570-583
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• 2013

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.185-196
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• 2011

In order to draw some strategic implications for the sustainable growth of emerging technologies this paper attempts to dynamics underlying the 'hype cycle' ever occurring in course of coevolution of technology and society. Particularly, a series of basic questions in the context of sustainability are explored to answer by simulating the hype system structure: What makes hype cycle occur? how to enhance the tapering level at the final stage of coevolution? what are the key policy leverages and when is the right time for the policy intervention? This study perhaps give some insights not necessarily to the academics but also to the practitioners and policy makers.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Electronics and Telecommunications Trends
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• v.30 no.2
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• pp.104-114
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• 2015

최근 무선통신서비스의 수요 급증에 따른 주파수 부족 심화 현상에 대해 제한된 주파수 자원의 재분배와 같은 해결책으로는 근본적인 극복이 어렵다는 공감대를 바탕으로 주파수 공유에 대한 연구가 다양한 분야에서 진행 중이다. 특히 인지무선기술은 무선통신에 관련된 모든 기술분야와 밀접하게 연관되어 다양한 기술분야의 협업을 통한 현 문제점을 해결할 수 있는 도전 기술임은 자명하다. 인지무선기술을 적용한 다양한 기술 표준들은 마무리 되었거나 마무리 단계에 있지만, 광의적 개념의 CR(Cognitive Radio)기술 측면에서 보면 아직도 해결해야 할 기술 이슈들이 산재해 있으며, 이러한 이유로 2014년 가트너 하이프 사이클상 태동기(innovation trigger)에 위치해 있다. 따라서, 본고에서는 최근 진행되고 있는 CR 관련 국내외 표준화 동향 및 기술개발에 대해 정리하고, 해결해야 할 주요 기술별 도전 이슈들에 대해 제시하고자 한다.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1232-1255
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• 2011

This paper proposes a model for demand forecasting that will require less effort in the process of utilizing the new product diffusion model while also allowing for more objective and timely application. Drawing upon the theoretical foundation provided by the hype cycle model and the consumer adoption model, this proposed model makes it possible to estimate the maximum market potential based solely on bibliometrics and the scale of the early market, thereby presenting a method for supplying the major parameters required for the Bass model. Upon analyzing the forecasting ability of this model by applying it to the case of the hybrid car market, the model was confirmed to be capable of successfully forecasting results similar in scale to the market potential deduced through various other objective sources of information, thus underscoring the potentials of utilizing this model. Moreover, even the hype cycle or the life cycle can be estimated through direct linkage with bibliometrics and the Bass model. In cases where the hype cycles of other models have been observed, the forecasting ability of this model was demonstrated through simple case studies. Since this proposed model yields a maximum market potential that can also be applied directly to other growth curve models, the model presented in the following paper provides new directions in the endeavor to forecast technology diffusion and identify promising technologies through bibliometrics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1041-1048
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• 2018

Gartner's Hype Cycle model is widely used to describe technology maturity, acceptability, and commercialization. In the Hype Cycle model, the techniques go through five stages, those are Innovation Trigger(first stage), stage Peak of Inflated Expectations(second stage), Trough of Disillusionment(third stage), Slope of Enlightenment(fourth stage) and Plateau of Productivity(fifth stage). In many studies, Hype Cycle is widely used as a basis for future prediction of technology, but the verification is somewhat lacking. In this paper, we analyzed the technologies that appeared in the Hype Cycle for the emerging technologies from 1995 to 2017. Through this, we found technologies that appeared as non first stage when first appearing, and techniques that showed a reversal of the maturity stage. In addition, we found that none of the technologies from 1995 to 2017 had gone through stages 1-5.