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Identifying Idiosyncratic Characteristics of Technological Hype from the Data Perspective of Producer, Consumer and Distributer : 기술 하이프의 속성 비교: 데이터 소비자, 생산자, 유통자

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Authors
서민호
Advisor
박용태
Major
공과대학 산업공학과
Issue Date
2018-08
Publisher
서울대학교 대학원
Description
학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 박용태.
Abstract
The goal of this study is to add analytical value to the hype cycle theory through

empirical evidence drawn from 70 technologies by emphasizing the perspectives of

the components influencing the hype cycle: producer, consumer and distributer,

and to ultimately propose a novel procedure capable to predict technology trends

in the future. Conventional hype cycle researches tended to draw conclusions

based on data from one or two emerging technologies. If their objective was to

further develop the hype cycle theory, their study would severely lack in sample

size. Also, they focused mainly on comprehending and forecasting specific

technology trends by use of bibliometric analysis.

Alternately, this study targets to expound on the idiosyncrasies of the







ii

hype cycle theory based on empirical evidence obtained from a large sample size

with bibliometric methods and analyzing its content with structural topic

modeling (STM), text mining algorithms to summarize documents into a number

of topics and significant keywords associated with the topics. In addition, this

study will utilize three social actors that are responsible for understanding the

different facets of science and technology. The results of this analysis will be able

to confirm or deny the observations made by previous studies and consequently

enhance the forecasting capabilities of the hype cycle model.

The proposed technological hype analysis consists of the following five

steps: (1) construct a database by collecting and preprocessing web documents of

patent data, search traffic data and article data of 70 technologies from selected

websites, (2) plot the three data metrics from step 1 on the y-axis and time on the

x-axis in order to see if these metrics produce any patterns useful for analysis, (3)

run STM on the content of the articles and patents to analyze the pattern of

technological hype. (4) record any generalized patterns
and (5) propose various

potential technological forecasting methods.

Based on combined quantitative and qualitative analysis of three

indicators, the analysis stage of this study can be summarized by the following

three broad observations: (1) The distributer (article data) graphs peak first, the

consumer graphs (search traffic data) peak second and the producer (patent data)

graphs peak last. (2) The article data, search traffic data and patent data all

depict distinct characteristics and patterns. (3) Comparing old and new

technologies, the time lapse of an innovative technology disseminating from article

to search traffic becomes shorter.







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Once the recorded observations of a hype cycles components and its

corresponding indicators are verified by data from numerous technologies and

industries, it will become possible to obtain general conclusions and develop a

potential technology forecasting method. For example, R&D managers will be able

to use this studys data on hype indicators to measure the current visibility of a

technology and also to estimate the future visibility. With this study, managers

and investors will be able to make systematic decisions regarding emerging

technologies much more effectively than they did in the past, with reduced

amount of time, labor, and thus the total costs.
Language
English
URI
https://hdl.handle.net/10371/143903
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Theses (Master's Degree_산업공학과)
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