DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Soung-Hie | - |
dc.contributor.advisor | 김성희 | - |
dc.contributor.author | Kang, Dong-Woo | - |
dc.contributor.author | 강동우 | - |
dc.date.accessioned | 2015-04-23T07:07:30Z | - |
dc.date.available | 2015-04-23T07:07:30Z | - |
dc.date.issued | 2014 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=569770&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/197136 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 경영공학부, 2014.2, [ 53 p. ] | - |
dc.description.abstract | This paper defines big data analysis a type3 innovation and extends our previous studies on the adop-tion/assimilation of innovation technologies. The paper develops an three-stage adoption integrative model based on the past diffusion context literatures. The model utilizes TOE(Technology-Organization-Environment) framework as antecedents of this adoption process. Big data analysis technology has distinctive characteristic, which is amorphous. That is, big data analysis involves many goals and technique so that it could be perceived differently according to settings. So based on the perception model, we hypothesize how perceived di-rect/indirect benefit, financial readiness, IS competence, industrial pressure affects big data analysis adoption at the organizational level. These five factors are tested using SEM(Structural Equation Modeling) and our analysis leads to following key findings. (1)Financial readiness, IS competence, and industrial pressure are found to af-fect adoption stages significantly but we could not find such relationship between perceived direct/indirect bene-fit and the following stages. (2)IS competition had expansive influence on the overall adoption process. (3)Adoption stage is influenced by external factors, which is industrial pressure for our case. (3) Pre and post stages of adoption are affected by internal resources of organization rather than environments. After the detailed examination of the findings, implications of the findings and future research areas are discussed. | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | big data analysis | - |
dc.subject | IS혁신 | - |
dc.subject | 도입 프로세스 | - |
dc.subject | 확산 이론 | - |
dc.subject | TOE 모델 | - |
dc.subject | 빅데이터 분석 | - |
dc.subject | TOE model | - |
dc.subject | diffusion theory | - |
dc.subject | adoption process | - |
dc.subject | IS innovation | - |
dc.title | Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process | - |
dc.title.alternative | 빅데이터 분석의 도입 프로세스: 새로운 IS혁신으로서의 빅데이터 정의와 그 프로세스에 영향 미치는 요인들에 관한 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 569770/325007 | - |
dc.description.department | 한국과학기술원 : 경영공학부, | - |
dc.identifier.uid | 020123003 | - |
dc.contributor.localauthor | Kim, Soung-Hie | - |
dc.contributor.localauthor | 김성희 | - |
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