Abstract

Many forms of technology cycle models have been developed and utilized to identify emergent technologies and forecast social changes, and among these, the technology hype cycle introduced by Gartner has become established as an effective method widely utilized in the field. However, if the hype cycle indeed exists in the various dimensions that constitute the socio-technical system, those who seek to analyze innovative activities using bibliometrics will be confronted with the new problem of actors' choices and the need to analyze their hype cycles. In seeking to overcome such limitations of conventional studies, this paper analyzes the hype cycles of three actors that constitute the core of the socio-technical system through the case study of the successful market entry of hybrid cars. The hype cycle of the user, the first actor, is analyzed based on the search traffic generated by their web searches, and the hype cycle of the producer or researcher, the second actor, is measured based on the data regarding patent applications. Lastly, the hype cycle of the information distributor, namely individuals constituting the market network, is analyzed by examining the exposure in news reports. The outcomes of this research showed that among the three actors, the consumers and the information distributors exhibited hype cycle patterns (bell curves) that were distinct from the market trend, and that there was a difference in time interval of around five quarters. By contrast, it was found that the hype cycle of the producers reflected a logical response, exhibiting a pattern similar to the S-curve during the market's growth period unlike the pattern found in other actors. In conclusion, this study of the particular case of hybrid cars confirmed that the two components of the hype cycle can be respectively verified using consumer search traffic and the patent applications made by the producers. If in the future, such analyses of the hype cycles of producers and consumers are expanded in application to various other industries, it will be possible to obtain more generalizable research outcomes. This is expected to contribute to determining technological life cycles or hype cycles with greater objectivity and efficacy, and furthermore to facilitate the systematic identification of promising technologies.

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