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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
- 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
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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
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