This research introduces a novel probabilistic approach to consider the effects of uncertainty parameters during the design and construction process, providing a fresh perspective on the evaluation of the structural performance of reinforced concrete structures. The study, which categorized various random design and construction process variables into three groups, selected a two-story reinforced concrete frame as a prototype and evaluated it using a nonlinear analytical model. The effects of the uncertainty propagations to seismic responses of the prototype RC frame were probabilistically evaluated using non-linear dynamic analyses based on the Monte-Carlo simulation sampling with the Latin hypercube method. The derivation of seismic fragility curves of the RC frame from the probabilistic distributions as the results of uncertainty-propagation and the verification of whether the RC frame can meet the seismic performance objective from a probabilistic point of view represent a novel and significant contribution to the field of structural engineering.

How to manage these marketing and R&D functions is very important in the new product development (NPD) process. Which function should have more power to make more decisions? Previous study seldom touched this question. Further, according to strategic contingent theory, perceived uncertainty is very important determinant for power structure in the NPD process (Hickson, Hinings, Schneck, & Pennings, 1971). However, Pfeffer and Salancik (1978) argued that there is indeterminacy between environment and power structure. Thus, is external environmental uncertainty related to power structure in the NPD process? Resource dependence theory gives us a hint to solve this puzzle, that is, the concept of institutionalization (Pfeffer, 1981; Pfeffer and Salancik, 1978). The current study tends to adopt NPD duration reflected institutionalization (Pfeffer, 1981) to examine the moderating effect of NPD duration on the relationship between environmental uncertainty and marketing-R&D power structure in the NPD process. In general, power is defined as that the relation among social actors in which a specific social actor can potentially influence the decision to achieve his or her desire outcomes (Dahl, 1957; Emerson, 1962; Pfeffer, 1981; Salancik & Pfeffer, 1977). This definition also suggested that power is the structure in human aggregates like complex organization (Pfeffer, 1981). Thus, the power structure in the NPD process is defined as the proportion of decision making by marketing and R&D functions in the NPD process. When a NPD team faces the high market uncertainty, marketing function can gather more resources because of its special ability. A new product team has the limited resources, so another important function like R&D will have fewer resources than marketing function. Thus, our first hypothesis is that the higher market uncertainty, the more power marketing function has. R&D members have background knowledge to overcome the difficult of processing technological language and decide the main resolutions. The team will tend to allot more resources to deal with the problems of technological change as such the R&D members can buy the license of new technologies to apply it on their new products and to create the disruptive innovation like smartphones or tablets successfully. Therefore, the second hypothesis is that the higher technology uncertainty, the more power R&D function has. According to resource dependence theory, however, the relationship between environmental uncertainty and power structure does not always exist (Pfeffer, 1981; Pfeffer & Salancik, 1978). Pfeffer and Salancik (1978) indicated that the perceived environmental uncertainty of a subunit is weakly related to subunit’s power structure when an organization is highly institutionalized. When one subunit has more power than others, it tends to maintain the current power structure. So, the subunit makes rules or norm to formalize its power legally. This process is so called institutionalization (Pfeffer, 1981; Pfeffer & Salancik, 1978). In general, as time goes by, organizations will form their own social norms, and some of these norms will become the principles or rules in organizations (Pfeffer, 1981). As a result, when NPD time is long, marketing and R&D functions form norms or official rules. Then, the relationship between their perceived environmental uncertainty and power structure in the NPD process is weaker than the relation in the shorter duration of NPD. Therefore, our hypothesis is that the relationship between environmental uncertainty and power structure in the long-run project time is weaker than the relationship in the short-run project time. The current study used questionnaire survey and purposive sampling method to collect data. In order to eliminate the bias of common method variance (CMV), this study conducted multiple sources including project managers, the member charging marketing, and the member charging R&D to administrate questionnaires differently. In order to avoid selection bias, this study, moreover, asked the informants select the most recent new products developed and launched for minimum of twelve months. We sent three types of questionnaires to project managers, the member charging marketing, and the member charging R&D respectively. The current study sent questionnaires to 112 firms, and 69 firms are returned. The response rate is 61.61%. At new product level, there are 207 new product projects, and 100 firms are returned. The response rate is 48.31%. We also do tests of bias due to nonresponse which were conducted by using a comparison of early to late respondents’ all variable means (Armstrong & Overton, 1977). No evidence of a bias was found. Our variables are included market and technology uncertainty, and power structure which the left side is totally decided by marketing and the right side is totally decided by R&D. Moreover, NPD time is from star-up projects to launch it. In order to rule out other effects, we controlled industrial category, firm age, the number of marketing and R&D members involved in the NPD process, environmental hostility, and NPD process formalization. Every overall fit index in our measurement model is shown that χ(55)^2=71.5259,p-vaule=.066，χ^2/df=1.30<2, goodness of fit index=.90，adjusted goodness of fit index=.84，comparative fit index=.97，normed fit index=.87，non-normed fit index=.95, and root mean square error of approximation=.06. In general, all fit indexes in our measurement model are acceptable, and the average variance extracted (AVE), composite reliability (CR), and Cronbach’s α of all constructs are acceptable. Their ranges are .45-.70, .70-.93, and .75-.93 respectively. The overall model showed that the higher market uncertainty, the more power marketing has (β=-.279, t-value=-3.11, p-value<.050) but technology uncertainty is not significantly related to power structure. We used the mean of the NPD time as the cutting point to split short-run and long-run project time, and the mean is about one year and half in our sample. The result showed that in the short-run group the higher market and technology uncertainty, the more power marketing and R&D function have (β=-.355, t-value=-2.53, p-value<.050; β=.296, t-value=2.23, p-value<.050) . However, in the long-run group the relationship between environmental uncertainty and power structure is statistically insignificant. Additionally, in the long-run group the more NPD process formalization, the more power R&D function has (β=.277, t-value=2.33, p-value<.050). Back to the original puzzle, that is, does external environmental uncertainty determine power structure in the new product development process? The empirical evidence is shown that it is dependent on how long an organization develops new products to the market. Because the dominant subunit involved in the NPD process tends to maintain it power, it institutionalizes rules or norms to have legitimacy in the organization, and this argument is consistent with resource dependence theory (Pfeffer & Salancik, 1978). We also found that in short-run perceived environmental uncertainty are positively related to power structure in the NPD process. Consistent with strategic contingent theory’s proposition, the one subunit enable to reduce or respond external environment pressure, and it can have more power in the organization (Hickson, et al., 1971; Hinings, et al., 1974). We additionally found that in long-run group process formalization is positively related to R&D power. R&D function plays a main role in the NPD process as especially in the manufacturing industry; therefore, R&D function has much motive to maintain its power (Workamn, 1993). So R&D function can use formal rule to maintain its power when R&D function formalize the NPD process. As a consequence, formalizing the NPD process helps R&D function to gain more power in the long run. The contribution of our study is that we tested the proposition in strategic contingent theory, and the empirical evidences supported our hypotheses. Furthermore, our study also is the first study to test and find the support evidence with the institutionalization proposition in resource dependence theory. We not only explored the relationship between environmental uncertainty and power structure in the NPD process, but also extended strategic contingent theory and resource dependence theory to the NPD research. The further study can follow our definition of power structure to find what strategy marketing and R&D function will used to take back or maintain their power (Eisenhardt & Bourgeois, 1988; Li & Atuahene-Gima, 2001; Pfeffer, 1981).

현지투자환경이 불확실한 경우 기업들은 어떠한 과정과 요인에 의해 해외직접투자를 확대하는가? 미얀마와 베트남에 진출한 3개 한국기업의 해외직접투자 확대과정을 분석한 결과 사례기업들은 초기투자를 통해 성장기반을 확보한 다음 현지사업역량을 구축하면서 투자의 불확실성이 줄거나 유리한 투자기회가 왔을 때 후속투자를 실행하는 것으로 나타났다. 또한 불확실한 투자환경 하에서 투자의 철회불가능성에 대처하고 투자가치를 극대화하기 위해 소규모 합작을 바탕으로 해외직접투자를 확대하는 것으로 분석되었다.

수문 ․ 기상레이더는 강우량을 바로 추정하지 못하고 여러 단계의 정량적 강우량 추정과정을 거치게 되므로 많은 불확실성 발생요소가 존재한다. 불확실성 관련한 기존 연구들은 정량적 레이더기반 강우량 추정과정에서 보정방법을 이용하여 각 단계별 불확실성을 줄이는 연구들을 수행하였다. 하지만 기존 연구들은 전체 과정에 대한 포괄적인 불확실성을 나타내지 못하고 각 단계별 불확실성의 상대적인 비율도 제시하지 못하는 단점이 있다. 본 연구에서는 정량적 레이더강우량 추정과정의 각 단계별 불확실성을 정량화하고 불확실성 전파를 나타낼 수 있는 적합한 방법을 제시하였다. 첫 번째로 초기와 최종 불확실성, 각 단계별 불확실성의 변동과 상대적인 비율을 나타낼 수 있는 새로운 개념을 제안하였다. 두 번째로 레이더기반 추정과정의 불확실성 정량화와 전파과정을 분석하기 위해 Maximum Entropy Method (MEM)와 Uncertainty Delta Method (UMD)를 적용하였다. 세 번째로 레이더기반 강우량 추정과정의 불확실성 정량화를 위해 2개 품질관리 알고리즘, 2개 강우량 추정방법, 2개 후처리 강우량 보정방법을 2012년 여름철 18개 사례에 대하여 사용하였다. 적용결과, MEM에서 최종 불확실성(후처리 강우량 보정 불확실성: ME = 3.81)이 초기 불확실성(품질관리 불확실성: ME = 4.28)보다 작게 나타났으며, UMD에서도 최종 불확실성(UMD = 4.75)이 초기 불확실성(UMD = 5.33)보다 작게 나타나 불확실성이 감소하는 것으로 나타났다. 하지만 레이더강우량 추정단계의 불확실성은 증가하는 것으로 나타났다. 또한 레이더강우량 추정과정에서 각 단계별로 적합한 방법을 선정하는 것이 각 단계별로 불확실성이 감소시킬 수 있음을 확인하였다. 따라서 본 연구는 새로운 방법이 명확히 불확실성을 정량화할 수 있으며 정확한 정량적 레이더 강우추정에 기여할 것으로 판단한다.