The external R&D, which includes the adoption of the external technology and knowledge in addition to the internal R&D, is one of important factors for the innovation. Especially for small and medium-sized enterprises (SMEs), the external R&D has been considered as a key factor to carry out the innovation more efficiently due to the limitations of their resources and capacities. However, most of extant studies related to external R&D have focused on analyzing the influence of external R&D on innovation outputs or outcomes. Only a few studies have explored the impact of external R&D on the innovation efficiency. This study therefore investigates whether the external R&D effects the industry’s innovation efficiency and productivity. On this study, we used Korean manufacturing industry data of SMEs from 2012 to 2014 and employed a global Malmquist productivity analysis technique, which is based on the Data Envelopment Analysis (DEA), to assess the innovation efficiency and productivity. Innovation performances of external R&D group and internal R&D group are compared. Then, the sectoral patterns of both innovation efficiency and productivity are analyzed with respect to the technological intensity, which is introduced by OECD. The results show that the gap of innovation efficiency between external and internal R&D groups has gradually decreased because of the continuous improvement of the external R&D group’s performance, while the external R&D group lag behind the internal R&D group. In addition, patterns of the innovation efficiency and productivity change were different depending on the technological intensity, which means that the higher the technological intensity, the greater the effect of external R&D.

The external R&D, which includes the adoption of the external technology and knowledge in addition to the internal R&D, is one of important factors for the innovation. Especially for small and medium-sized enterprises(SMEs), the external R&D has been considered as a key factor to carry out the innovation more efficiently due to the limitations of their resources. However, most studies related to external R&D have focused on innovation outputs or outcomes. Only a few studies have explored the impact of external R&D on the innovation efficiency. This study therefore investigates whether the external R&D effects the industry’s innovation efficiency and productivity. On this study, we used Korean manufacturing industry data of SMEs from 2012 to 2014 and employed a global Malmquist productivity analysis technique, which is based on the Data Envelopment Analysis(DEA), to assess the innovation efficiency and productivity. Then, the sectoral patterns of both innovation efficiency and productivity are analyzed with respect to the technological intensity, which is introduced by OECD. We show that the gap of innovation efficiency between external and internal R&D groups has gradually decreased because of the continuous improvement of the external R&D group’s performance, while the external R&D group lag behind the internal R&D. In addition, patterns of the innovation efficiency and productivity change were different depending on the technological intensity, which means that the higher the technological intensity, the greater the effect of external R&D.

본 연구에서는 IPO시점을 전후하여 기업의 혁신 활동과 성과의 변화에 대한 분석을 목적으로 연구개발 투자 비중이 높은 의료 및 의약 분야의 코스닥 IPO 상장 기업을 대상으로 7개의 투입, 산출 모형에 대한 효율성 분석과 비교를 실시하였다. 의약분야 기업의 IPO 전후 3년간의 혁신활동 효율성을 측정하고 차이를 분석하기 위해 DEA모형을 적용하였 다. 본 논문의 주요한 결과는 첫째, 의약분야의 IPO 기업의 창업에서 IPO까지 평균 12.86년 이 소요되었고, 혁신활동은 평균적으로 IPO 이전보다 더욱 활발해져 연구개발 투자액이 증 가되었다. 출원특허의 수는 IPO 이전 3년 동안 8.43개에서 IPO 이후 3년 동안에는 상장기업 평균 16.67로 급증하였으며 기업의 기술 영역도 전후 3년 동안 상장기업 평균 11개에서 22개 기술분야로 크게 확대되는 모습을 보였으나, 재무적 측면에서 성장 추이와 수익성은 IPO 이 전보다 낮아졌다. 둘째, 연구개발 투자와 특허출원 활동에 대한 재무적인 성과는 IPO 이후 효율성이 모두 약화되었으며, 최종적인 성과에 이르기까지 투자와 활동을 분리하여 연구개발 투자에 따른 특허 출원 성과, 특허 출원 활동에 따른 재무적인 성과에 대한 효율성 역시 IPO 이전보다 모두 낮아진 것으로 분석되었다. 셋째, 특허활동에 따른 재무성과에 대한 효율성은 연구개발 투자에 따른 특허 출원 성과, 연구개발투자와 특허활동에 따른 재무성과에 대한 효 율성에 비해 낮았고, 특히, IPO 이후 특허활동에 따른 재무성과의 비효율성은 규모의 비효율 성으로 인해 발생되었음이 분석되었다. 마지막으로, 본 연구에서 IPO 이후 전체적인 효율성 하락은 IPO를 퉁한 연구개발투자 확대가 시장의 재무적 성과로 이어지지 못하였고, IPO 이 후 전체적인 비효율성은 연구개발투자를 통한 혁신의 결과 도출 단계보다는 혁신활동의 결 과물이 시장의 성과를 이끌어 내는 데 나타나는 비효율성으로 인한 것으로 분석되었다.

A well-known dilemma in strategic marketing is whether a firm can be simultaneously both efficient in its existing business and innovative in creating new business (Atuahene-Gima 2005; Christensen 1997). Beleaguered companies such as AOL, Kmart, Motorola, Nokia, Polaroid, and Sears are examples that were once highly efficient in serving customers, but partly due to that efficiency in their existing business, paradoxically failed to introduce innovations. The potential tension “between innovation and efficiency—is one that’s bedeviling CEOs everywhere” (Hindo 2007). Two questions regarding the efficiency–innovation tradeoffs are especially intriguing to researchers and managers alike. First, to what extent are such tradeoffs driven by efficient firms’ lack of eagerness or willingness to innovate in the first place, or lack of ability to innovate and promote innovations? Second, can certain strategic marketing factors mitigate the tension of such tradeoffs? Indeed, anecdotal evidence indicates that not all firms that are efficient in their current business (e.g., Charles Schwab, Capital One) lack innovative thrust. In fact, efficient firms may actually be eager to innovate: Nokia, for instance, originally innovated an online “app store” service as well as touchscreen smartphones and Internet tablets in the 1990s and 2000s, much earlier than Apple (Ben-Aaron 2009; MobileGazzette 2008). Similarly, Polaroid was originally a pioneer in developing digital cameras and imaging services in the 1980s (Tripsas and Gavetti 2000). The eventual failures of Nokia’s and Polaroid’s innovation efforts, thus, do not seem to be due to their lack of eagerness to innovate, but perhaps the inability to manage the efficiency–innovation tension. In contrast, other companies seem to be able to manage this tension. For instance, in financial services, Charles Schwab is often commended both for its efficiency and its innovativeness, and the firm itself feels the “need to invest in innovation to maintain a competitive edge” (Gilson 2012). Against this backdrop, we focus on two questions: (1) What exactly are the tradeoffs and tensions between a firm’s existing efficiency, innovativeness in its new offerings, and new offering performance? And (2) how can strategic marketing assets such as customer base and advertising intensity mitigate the tradeoffs? Should such assets help to alleviate the inherent tension, they would give executives tools to pursue both efficiency and innovation at the same time and succeed with their new innovative offerings. Empirically, we focus on the service sector, whereby the actual technical development of innovations is not very costly in tangible financial terms (Crawford and di Benedetto 2008; Droege et al. 2009; Thomke 2003)―making the intangible firm capabilities most likely determinants of (innovation) performance rather than tangible resources (cf. Vorhies, Morgan, and Autry 2009). Therewith, we examine our research questions with a comprehensive census dataset of all new service introductions (n≈500) in one national market: The Finnish mutual funds industry (1997–2010). The sector of financial services is especially relevant for the efficiency–innovation tradeoffs because in this sector, many firms are compelled to engage in both efficient operations and effective (financial) innovations. Our empirical focus on all firms in one market precisely identifies and measures the efficiency levels of all competing firms, relative to the best-performing competitors, as well as innovativeness (earliness) in introducing new services compared to all rivals. For a marketing perspective, we focus on firms’ existing customer-perceived service efficiency (over the entire portfolio of existing services, i.e., funds)—defined through the ratio of output value that customers obtain from the firms’ current services to the (customer) cost inputs. We also carefully delineate between (a) innovativeness of a new service introduction and (b) its performance. Doing so can reveal the potentially contradictory effects of existing efficiency on new service innovativeness (willingness to innovate) vis-à-vis new service performance (ability to make innovations succeed). As our key results, we firstly identify and explicate the baseline efficiency–innovation tradeoffs. Specifically, our results suggest that while existing service efficiency increases the innovativeness of new services introduced by the firm, it simultaneously (1) leads to decreased business performance for the new services introduced and (2) diminishes the positive influence of innovativeness on performance. In sum, these findings imply that on the baseline, highly efficient service firms may be too eager to innovate, considering the sub-par performance they are likely to receive for those innovations. Secondly, our results reveal two strategic marketing factors, which have the potential to mitigate the tradeoffs. We find that the firm’s (a) focused customer base and (b) high advertising intensity can nullify the negative effect of existing service efficiency on innovativeness and the negative moderating effect of efficiency on the innovativeness–performance link.

The current environment of technological and competitive changes influences not only the business R&D environment but also government driven national R&D strategies. Open innovation has now become an important paradigm that is replacing the outdated paradigm of closed innovation. Many companies and nations have been increasing R&D investment because R&D has been considered a driving force for national and corporate competitive advantage. The purpose of this paper is to evaluate and compare the performance of R&D focused on open innovation according to scientific and technological outputs which is based on paper publications, patents and etc. Comparisons should not be only based on the quantity but also on the quality of the output. This paper shows that it is possible to develop DEA models that utilize the Analytical Hierarchical Process in order to transform the qualitative index into a quantitative index. Hence, the relative efficiency for R&D organizations is obtained based on both quantity and quality outputs and subsequently provides comprehensive and realistic methods for decision makers to identify levels of project efficiency.

The average BOD concentration was found to be about 269mg/L before a process innovation, but after the innovation, it became 30mg/L, which satisfied the effluent standard of 120mg/L. The removal effluent standard of 120mg/L. The removal efficiency was about 60～80%, and the concentration of the treated water was found th be low. after the process innovation, the average COD concentration was 29mg/L, and the CODmn removal efficiency became low to the level of about 65～76%, which was found lower than the effluent standard of 130mg/L. After the process innovation the SS average concentration of the treated water was 13mg/L, which was lower than that before the innovation (32mg/L). By the activated sludge process innovation, the SS removal efficiency was improved to be 30～70%. The average concentration of total coliform before the process innovation was 6100 count/mL because an Activated sludge process only occasionally pass over the allowed standard(The average number of the total coliform of Activated sludge process treated water was about 8100 count/mL), UV disinfection process was introduced. after the introduction, the average number of the total coliform was 1800 count/mL, which satisfied the allowed effluent standard of 3000 count/mL.

In this paper, firstly, from the input-output perspective of technological innovation, technological innovation process is divided into technological development process and technological achievements transformation process. On this basis, technological innovation efficiency can be decomposed into technological development efficiency and technological transformation efficiency. Secondly, on the basis of the two-stage technological innovation, by analyzing each stage in the process of technological innovation input and output elements, we design the input-output index system in each stage respectively, and ultimately get the high-tech industrial technology innovation efficiency evaluation index system. Thirdly, we build a chain network DEA model to evaluate the two-stage process of technological innovation of high-tech industry, include the overall efficiency of technological innovation and the efficiency of the two sub-stages. Finally, use chain network DEA model to calculate the technology innovation efficiency, technological development efficiency and technological transformation efficiency of high-tech industry of China's 25 regions in 2000-2010. According to the calculation results of h the empirical analysis of the efficiency of technological innovation of China's high-tech industry, and provide a reference for the provision of policy advice.