• International Journal of Contents
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• v.15 no.2
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• pp.1-12
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• 2019

Despite their various similarities, Seoul's' Gangnam and Seocho districts showed different patterns in the adoption of the RFID household-based waste charging system. Gangnam, one of the 25 wealthiest districts in Seoul, first adopted the RFID system in 2012, but decided abandon it a year later due to inconvenience, sanitation, budget limitations, and management related issues. Unlike Gangnam, Seocho, a largely similar district to Gangnam, started to implement the RFID system in 2015 and successfully adopted this innovation. In this paper, we explain the adoption behaviors of these two districts using a Technology Hype Curve Model with 5 stages. Unlike traditional technology adoption theory, the Hype Curve Model concentrates on the big chasm between early majorities and late majorities, which is a core reason for discontinuity in innovation diffusion. Based on our case study result, the early majority easily gave up adoption due to immature technological and institutional infrastructure. However, Seocho district, who waited until the deficiencies had been sufficiently fixed since late majorities, succeeded at incremental diffusion. Since its invention by Gartner cooperation, the Hype Curve Model has not received enough attention in academia. This paper demonstrates its explanatory power for innovation diffusion. Similarly, this paper focuses on the importance of institutional framework in the diffusion of innovation. Lastly, we compare the behavior of two local governments in supporting and diffusing RFID systems to draw relevant policy implications for innovation diffusion.

• Korean System Dynamics Review
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• v.7 no.2
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• pp.5-20
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• 2006

In the advent of ubiquitous information technology (u-IT) as a new emerging horizon of information society, inflated expectations regarding u-IT are growing very fast and higher than those made in the past, which would perhaps result in serious bust after boom and incur tremendous amount of social costs. This paper thus investigates a dynamic mechanism underlying the coevolution between information technology and society by applying systems thinking, particularly, with a focus on the typical phenomenon, 'hype curve' which shows how new technologies initially grow too fast for their own good, crashing from a peak of inflated expectations into a trough of disillusionment before stabilizing on a plateau of productivity. Three basic questions are explored to answer by investigating the mechanisms underlying the 'boom-bust' phenomenon: First, why hype curve appears in the process of technology and society coevolution. Second, how to enhance the stabilization level. Third, when is the right time for the policy intervention.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.171-197
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• 2008

By applying Systems Simulation technique, this paper aims to investigates the dynamics underlying the coevolution of IT(information technology) and the society. Particularly, a series of basic questions are explored to answer by developing a simulation model for the mechanisms underlying the 'hype curve' ever occurring in the course of technology diffusion into society: First, why hype curve appears in the process of technology and society coevolution. Second, how to enhance the tapering level at the final stage of coevolution. Third, what are the key policy leverages and when is the right time for the policy intervention. As now, inflated expectations regarding ubiquitous information technology (u-IT) are growing very fast and higher than those for the previous technologies, which would result in overshoot followed by collapse of visibility and thus incur tremendous amount of social costs. In this regard implications drawn from this study perhaps give some insights not necessarily to the academics but also to the practitioners and policy makers facing the advent of u-IT as a new emerging horizon of information society.

• 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.

• 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.

• 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 the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.628-641
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• 2012

This research investigates a dynamic mechanism underlying the co-evolution between network and space by applying hype-curve model, typical phenomenon which shows how new technologies and ideas initially adapted in the society. This study analysis the knowledge intensive industry of digital contents using social network analysis (SNA) in terms of structural, spatial, and temporal aspects, year of 2000, 2005, and 2010 focused on Seoul area. First of all, network and space establish 'inter-feedback' as a result of evolution and differentiation process. Second, it happen temporal 'delay' through the learning process stage of 'peak of inflated expectation' and 'trough of disillusionment.' As a result, Seoul develops with the technology commercialized-orient strategy affect government policy. This trend changes to technology-oriented development in Seoul area in the late of 2000 established 'self-organization' with geographical proximity organizations through learning process.