초록
오늘날 불확실성이 증가하면서 조직구성원의 전문역량, 즉 혁신역량에 대한 중요성이 커지고 있다. 이러한 실정에도 불구하고, 조직원 개인의 혁신역량에 대해 혁신산출물을 만들기 위한 투입요소, 혁신 활동(혁신역량보유, 발휘, 증진노력 등) 그리고 혁신산출물 간의 관계에 관한 연구는 아직까지 많지 않은 편이다. 이런 원인은 기존 혁신연구단위가 국가단위, 산업단위, 기업단위에서 이루어지는 것이 일반적이기 때문이며, 상대적으로 특정기업의 투입, 산출 및 혁신활동 자체에 대한 분석이 다소 부족한 편이다. 그러므로 이 연구는 기존 혁신연구의 틀과 시각에서 벗어나, 특정조직중심의 개인별 혁신성과와 그 성과와 관련성이 높을 것으로 추정되는 혁신활동들에 대한 네 가지 가설, 즉 첫째는 역량의 차이에 따라 혁신성과는 차이가 있을 것이다. 둘째, 학습시간의 차이에 따라 역량은 차이가 있을 것이다. 셋째, 학습시간의 차이에 따라 혁신성과는 차이가 있을 것이다. 넷째, 학습시간과 혁신성과 간의 관계에는 혁신역량의 매개효과가 있을 것이다(구조모형 검정)는 문제에 대해 정량분석을 시도하여, 조직 경쟁력을 향상시키기 위한 정책적 시사점을 도출하는 것을 연구목적으로 한다.
We increasingly see the importance of employees acquiring enough expert capability or innovation capability to prepare for ever growing uncertainties in their operation domains. However, despite the above circumstances, there have not been an enough number of researches on how operational input components for employees' innovation outcome, innovation activities such as acquisition, exercise and promotion effort of employee's innovation capability, and their resulting innovation outcome interact with each other. This trend is believed to have been resulted because most of the current researches on innovation focus on the units of country, industry and corporate entity levels but not on an individual corporation's innovation input components, innovation outcome and innovation activities themselves. Therefore, this study intends to avoid the currently prevalent study frames and views on innovation and focus more on the strategic policies required for the enhancement of an organization's innovation capabilities by quantitatively analyzing employees' innovation outcomes and their most suggested relevant innovation activities. The research model that this study deploys offers both linear and structural model on the trio of learning, innovation capability and innovation outcome, and then suggests the 4 relevant hypotheses which are quantitatively tested and analyzed as follows: Hypothesis 1] The different levels of innovation capability produce different innovation outcomes (accepted, p-value = 0.000<0.05). Hypothesis 2] The different amounts of learning time produce different innovation capabilities (rejected, p-value = 0.199, 0.220>0.05). Hypothesis 3] The different amounts of learning time produce different innovation outcomes. (accepted, p-value = 0.000<0.05). Hypothesis 4] the innovation capability acts as a significant parameter in the relationship of the amount of learning time and innovation outcome (structural modeling test). This structural model after the t-tests on Hypotheses 1 through 4 proves that irregular on-the-job training and e-learning directly affects the learning time factor while job experience level, employment period and capability level measurement also directly impacts on the innovation capability factor. Also this hypothesis gets further supported by the fact that the patent time absolutely and directly affects the innovation capability factor rather than the learning time factor. Through the 4 hypotheses, this study proposes as measures to maximize an organization's innovation outcome. firstly, frequent irregular on-the-job training that is based on an e-learning system, secondly, efficient innovation management of employment period, job skill levels, etc through active sponsorship and energization community of practice (CoP) as a form of irregular learning, and thirdly a model of Yί=f(e, i, s, t, w)+${\varepsilon}$ as an innovation outcome function that is soundly based on a smart system of capability level measurement. The innovation outcome function is what this study considers the most appropriate and important reference model.