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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
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박용태 | - |
dc.contributor.author | 서민호 | - |
dc.date.accessioned | 2018-12-03T01:44:44Z | - |
dc.date.available | 2018-12-03T01:44:44Z | - |
dc.date.issued | 2018-08 | - |
dc.identifier.other | 000000153536 | - |
dc.identifier.uri | https://hdl.handle.net/10371/143903 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 박용태. | - |
dc.description.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 | - |
dc.description.abstract | 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. iii 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. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Theoretical background 4 2.1 Hype cycle model 4 2.2 Social actors of STS, producer, consumer and distributer 7 2.3 Bibliometric analysis of hype cycle 10 2.4 Text-mining 15 Chapter 3 Proposed procedure 17 3.1 Proposed procedure 17 3.2 Constructing database 18 3.2.1 Selection of technology 19 3.2.2 Web crawling 22 3.3 Bibliometric analyis 24 3.4 Structural topic modeling 25 Chapter 4 Analysis and its intrepretation 28 4.1 Analysis 28 4.1.1 observation 1 28 4.1.2 observation 2 30 4.1.3 observation 3 34 4.2 Potential methods of analyzing technolgoy trend 37 Chapter 5 Conclusion and future research 41 5.1 Conclusion 41 5.2 Limitation and Future work 42 Appendix 44 Bibliography 50 국문초록 55 감사의 글 58 | - |
dc.format | application/pdf | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject.ddc | 670.42 | - |
dc.title | Identifying Idiosyncratic Characteristics of Technological Hype from the Data Perspective of Producer, Consumer and Distributer | - |
dc.title.alternative | 기술 하이프의 속성 비교: 데이터 소비자, 생산자, 유통자 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 공과대학 산업공학과 | - |
dc.date.awarded | 2018-08 | - |
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