• 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 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 Information Technology Services
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• v.12 no.3
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• pp.345-377
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• 2013

Government agencies have many difficulties for the information system development and operation. One of the difficulties is a budget estimation. Each government agency suggests individually different estimation for the personnel expenses and IT infrastructure adoption costs in the same field of informatization promotions. The other one is the operation costs are increased exponentially in every year[42, 51]. Those difficulties make government agencies can not help adopting SaaS. In fact, most of IT consulting company and government agencies already recognized a variety of SaaS advantages. The most typical SaaS's advantages are cost reduction, Software rapid development and deployment. However, once government agencies decide to adopt SaaS, they can not avoid many problems and difficulties. There is no information in a detailed item in a budget. In those kinds of situation, there is no choice whether government agencies accept SaaS provider's suggesting adoption costs or not. Thus, we provide a sheet of SaaS adoption cost estimation to government agencies. To know the cost factors, this study uses TCO(Total Cost of Ownership)'s criteria. To give a management point, this study uses Gartner's Application development Life Cycle. In this study, the integration check framework which is SaaS adoption cost estimation makes government agencies possible to establish a adequate budget.

• 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.