• Asia pacific journal of information systems
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• 제23권3호
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• 해양안보
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• 제5권1호
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• pp.1-53
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• 2022

중러 관계 강화는 강대국 경쟁이 재개되고 있음을 보여주는 한 가지 징후라고 볼 수 있다. 공식적인 방위동맹을 체결하지 않았음에도 불구하고 양국의 군사관계가 강화되고 있다는 사실을 눈여겨 볼 필요가 있다. 특히, 중국과 러시아가 세계 최강의 해군력을 보유하고 있다는 점에 비추어 본다면, 양국간 해양안보협력 강화는 최근 수년 간 나타난 국제안보 전개상황 중 가장 중요한 양상으로 꼽을 수 있다. 여러 플랫폼과 장소에서 펼쳐진 중러 해상합동훈련은 고위급 인사교류와 중국의 대규모 러시아 무기 구매, 중러 우호조약 체결 및 다양한 협력형태로 수년간 지속되었다. 양국간 해상합동훈련은 냉전기의 대치국면이 종식된 직후 시작되었으나, 그 중요성은 최근 십년의 기간 동안 더욱 부각되고 있다고 볼 수 있다. 해상합동훈련이 양국 국방동맹의 핵심으로 부상하고 있기 때문이다. 양국은 그 어느 때보다도 다양한 장소에서 다양한 무기체계를 활용해 해상훈련에 임하고 있다. 앞으로 양국의 합동군사훈련은 북극, 초음속 운반수단, 아프리카, 아시아, 중동의 신규 파트너를 비롯해 새로운 위치와 전력을 동원해 펼쳐질 가능성이 크다. 또한, 경비함정 및 제병 연합부대를 동원한 해상합동 훈련을 수행하는 등 최근에 보여준 획기적인 전개를 지속할 것으로 보인다. 중국과 러시아는 양자간 해군협력을 토대로 일련의 목표를 추구하고 있다. 중화인민공화국과 러시아 연방 사이에 체결된 선린우호협력조약 (Treaty of Good-Neighborliness and Friendly Cooperation)은 공동방어 조항을 포함하고 있지는 않지만, 공동의 위협에 대해 상호 논의하도록 언급하고 있다. 전통적/비전통적 군사작전 (예: 대해적 작전, 인도적 구호 및 최고수준의 전투수행)을 모의하는 해상훈련은 합동군사활동을 통해 공동의 도전과제에 대한 양국의 대응력을 강화하는 수단이 된다. 이러한 합동훈련이 전투력 측면에서 높은 수준의 상호운용성을 구현하지 못하더라도, 이를 통해 중러 양국이 단합된 해군력을 동원할 수 있는 역량을 갖추고 있다는 사실을 국제사회에 알릴 수 있다. 양국의 해상무역의존도나 영해를 둘러싼 국가간 갈등을 감안하면 이는 중요한 메시지라고 할 수 있다. 한편으로는 해상합동훈련을 통해 자국의 전투력을 향상시키고, 동시에 서로의 전략, 전술, 전투기술 및 절차에 대한 이해를 강화할 수 있다. 점차 부상하고 있는 중국 해군은 특히 러시아군으로 부터 많은 혜택을 얻을 수 있다. 러시아군은 복수의 제병협동작전을 중심으로 중국인민해방군 (People's Liberation Army, PLA) 보다 훨씬 많은 해상임무수행 경험을 보유하고 있기 때문이다. 그러나 한편으로는 전투력 강화를 통해 양국 정치지도자들이 군사력을 동원하거나 다른 국가와 대치할 경우, 긴장을 더 고조시키는 방향을 선택할 가능성이 더욱 커졌다는 부정적인 측면이 지적된다. 이러한 모든 영향은 양국 해군이 대부분의 해상합동훈련을 수행하는 동북아시아 지역에 더욱 큰 파급력을 미친다. 동북아시아 지역은 중국과 러시아가 미국 및 일본과 벌이는 그리고 불편한 상태로 한국을 사이에 둔 해상에서의 대치상황이 펼쳐지는 격전지가 되고 있다. 중러 해군 협력 강화가 공고해지면서 한미 군사계획이 더욱 복잡해지고, 북한에 집중되어야 할 자원이 전환되어 결국 지역 안보환경을 악화시키는 결과로 이어지고 있다. 한미일 해군 실무자의 입장에서는 중러 해군이 모두 포함된 시나리오를 수립해야 할 필요성이 더욱 커지고 있다. 가령, 한미 정책 결정가들은 중러 군사력의 공동 무력대응에 대비하기 위해 미 국방부가 과도한 지출을 하게 만들고, 한반도에서 한미안보 부재가 발생하지 않도록 대한민국 해군을 신속하게 보충해야 하는 상황이 발생하게 되었다. 북한이 한국 및 동맹국과 해상에서 대치할 경우 이를 중러 해군이 지원할 수 있다는 가능성은 또다른 심각한 도전을 제기한다. 이 같은 긴급사태 발생 가능성을 고려해 안보결속을 강화하겠다는 한일 간의 약속을 토대로, 한미일 3국 공동군사훈련을 더욱 확대할 필요가 있다.