• 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.

• 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 Korea Technology Innovation Society
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• v.16 no.4
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• pp.1109-1133
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• 2013

In an effort to discover new technologies and to forecast social changes of technologies, a number of technology life-cycle models have been developed and employed. The hype cycle, a graphical tool developed by a consulting firm, Gartner, is one of the most widely used models for the purpose and it is recognised as a practical one. However, more research is needed on theoretical frames, relations and empirical practices of the model. In this study, hype cycle comparisons in Korean and global search websites were performed by means of web-search traffic which is proposed as an empirical measurement of public expectation, analysed in a specific product or country in previous researches. First, search traffic and market share for new cars were compared in Korea and the U.S. with a view to identifying differences between the hype cycles in the two countries about the same product. The results show the similarity between the two countries with the statistical significance. Next, comparative analysis between search traffic and supply rate for several products in Korea was conducted to check out their patterns. According to the analysis, all the products seem to be at the "Peak of inflated expectations" in the hype cycles and they are similar to one another in the hype cycle. This study is of significance in aspects of expanding the scope of hype cycle analysis with web-search traffic because it introduced domestic web-search traffic analysis from Naver to analyse consumers' expectations in Korea by comparison with that from Google in other countries. In addition, this research can help to explain social phenomina more persuasively with search traffic and to give scientific objectivity to the hype cycle model. Furthermore, it can contribute to developing strategies of companies, such as marketing strategy.

• 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.

• 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.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2013.10a
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• pp.518-531
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• 2013

• 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.

• 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.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.72-80
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• 2021

Metaverse is an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other. If we do not manage the hype for the metaverse, which has recently been receiving a surge in interest, the metaverse will fail to cross the chasm. In this study, to provide stakeholders with insights for the successful introduction and growth of the 3D immersive next-generation virtual world, metaverse, we analyzed user-side interest, media-side interest, and research-side interest. For this purpose, in this study, search traffic, news frequency and topic, and research article frequency and topic were analyzed. The methodology and results of this study are expected to provide insight for the stable success of metaverse transformation and the coexistence of the real world and the virtual world through hyper-connection and hyper-convergence.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.728-744
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• 2024

The presence of hydrogen absorbed by zirconium-based cladding materials during reactor operation can trigger degradation mechanisms and endanger the rod integrity. Ensuring the durability of the rods in extended time-frames like dry storage requires anticipating hydrogen behavior using numerical modeling. In this context, the present paper describes a hydrogen post-processing tool for Falcon - HYPE, a PSI's in-house tool able to calculate hydrogen uptake, transport, thermochemistry, reorientation of hydrides and hydrogen-related failure criteria. The tool extracts all necessary data from a Falcon output file; therefore, it can be considered loosely coupled to Falcon. HYPE has been successfully validated against experimental data and applied to reactor operation and interim storage scenarios to present its capabilities.