• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.104-130
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• 1997

This study empirically examined different patterns of collaborative R&D project with their key success factors(KSFs), using data from 80 projects in the Korean electronic parts industry The patterns in this study were categorized into 4 types by two criteria : product types(off-the-shelf/unique) and project initiator (focal/partner). The bivariate relationships revealed that project characteristics (technological complexity, demand certainty), partner characteristics(the number of partners, precious experience), process characteristics (participation in the project formulation, specificity of the collaboration process and outcomes) appear to be different among four types of collaboration. Furthermore, this study found that each type of collaborative R&D projects has different KSFs for their commercial success. The KSFs of type 1 (off-the-shelf product and focal organization initiation), for instance, include the strategic importance of the project, the problem solving performance of the focal organization while those of type 4(unique product and partner initiation) are technological complexity, demand certainty, reliability of partner relationship, specificity of the goals, specificity of the process and outcomes, information sharing. Finally, based on this empirical results, managerial, policy, and theoretical implications of the study were discussed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.14 no.4
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• pp.48-59
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• 2006

This Study focused on the process of technology transfer for the aircraft development program by the strategic alliance. Especially, this study showed how the learning firms absorb new technology from the foreign leading company. This case study concludes that teachability, asset specificity, relation capital with partner, information sharing system in organization and knowledge base are key factors for absorbing the new technology from the technology leading partner.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.122-158
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• 1998

This study empirically examined different patterns of collaborative R&D project with their key success factors(KSFs), using data from 82 projects in the Korean electronic parts industry. The patterns of R&D collaboration were categorized into 4 types by two criteria development motive(technology Push/market pull) and Project initiator (focal firm/partner). The bivariate relationships revealed that project characteristics (technological complexity, market uncertainty), management characteristics (participation in project formulation), problem solving characteristics(problem solving performance of the focal firm, users active role in problem solving, active role of university or research institute in problem solving) and success rates appear to be different among four types of collaboration. Each type of collaborative R&D projects also had different KSFs. The KSFs of type 1 (technology Push and focal firm initiation), for instance, include the strategic importance of the project, focal firms share of cost, active role of university or research institute in problem solving, while those of type 4(market pull and customer initiation) cover reliability of partner relationship, a time at partners involvement, information sharing. The findings suggest that the different contingencies brought different patterns and KSFs of collaborative R&D project, since different information, resources, and partners roles were needed to successfully implement the projects according to development motive and project initiator Finally, managerial, policy, and theoretical implications for the collaborative R&D activities in the Korean electronics parts industry were discussed, based on empirical results of this study.

• Proceedings of the Technology Innovation Conference
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• 2005.02a
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• pp.42-56
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• 2005

Firms cooperating in R&D face a moral hazard problem, because with R&D effort not being observable each partner will focus on its own profit when choosing its effort level. This paper aims to explain the use of optimal license contract for R&D cooperation such as cross-licensing agreement. We argue that in the situations of asymmetric information, the optimal incentive scheme that can solve moral hazard problem is . a linear function of the likelihood ratio. Especially in the case of parallel research, each firm has an extra incentive for cooperative R&D effort, given by the license fee that considers the profit of the cooperating firm, which solely depends on his R&D success if the cooperating firm fails.

• The Journal of Small Business Innovation
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• v.19 no.4
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• pp.1-17
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• 2016

The purpose of this study is to suggest an efficient way for ventures to achieve innovation performance through R&D cooperative arrangements. Achieving innovation is one of the critical factors for the survival of ventures. Unlike established firms, ventures often do not have the specialized assets necessary to take technological developments to the product and market stages. Young and resource-constrained firms can achieve innovation by finding and accessing to the complementary resources from R&D cooperation. In the current business environment, many firms are likely to engage in multiple simultaneous R&D cooperations with different partners. Recent research stream addresses the importance of efficient cooperation management from the holistic portfolio perspective. Since maintaining the multiple cooperative relations require substantial amount of time and effort, managing cooperative relationships play a more important role to resource-constrained firms. In order to find an efficient composition of R&D cooperative partners, we mainly focus on the diversity of partner type and dependence level in partnership. We analyze the data on Korean manufacturing ventures collected in the Korean Innovation Survey (KIS) which was conducted by the Science and Technology Policy Institute (STEPI). The KIS questionnaire assesses the existence of cooperative relationships with different types of partners respectively. The types of cooperating partners are affiliated companies, suppliers, clients & customers, competitors or other firms in the same industry, consulting firms, universities, and research institutes. We confirm that ventures obtain relatively higher benefits from R&D cooperation compared with established firms in terms of innovation performance. The results show that a moderate level of diversity in cooperative partner type composition increases innovation. Moreover, diversity of cooperation dependency among the partners enhances innovation performance. Likewise, concentrating on the quality aspects of cooperative composition, such as diversity of partners and degree of dependencies, this study offers some implications for ventures in managing partners from an integrative perspective.

• Asia pacific journal of information systems
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• v.23 no.3
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.79-86
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• 2006

Especially, MTO(Make-To-Order) companies take collaborative approaches with their partner companies to make low-price products and/or technologically low intensive products. The collaborative approach to manufacturing requires collaboration with partner companies for inventory review, production plan, and manufacturing to fulfill customer's orders. However, frequent changes of partnerships binder partner companies from sharing production information in effective ways since their information systems have different data architectures and platforms. Therefore, it is required flexible and standardized system integration approach fir effective information sharing. This research studies current status and problems of collaborative production system, proposes an architecture for collaborative production systems based on Web Services which is a standard information technology, and discusses expected effects and the vision of Web Services.

• Journal of Technology Innovation
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• v.19 no.2
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• pp.153-184
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• 2011

This paper investigates what kind of technological partner firms want to cooperate with in terms of partner location. Two geographical factors are considered. One is geographical proximity, given the tradeoff between the effectiveness of knowledge spillovers in proximity and diverse knowledge absorption from geographically distant partners. The other is how many other firms are co-located with potential partners because it is known that clustering regions can create more technological outputs. Analysis on 2008 Korea Innovation Survey data finds that partner proximity is the single most important factor in choosing a cooperation partner. While firms that are located in a region crowded with related industries prefer proximate partners, others that are surrounded by unrelated industries are more likely to cooperate with distant partners. The findings suggest that geographical proximity matters in partner selection because it not only stimulates knowledge spillovers but also reduces costs involving R&D cooperation such as monitoring costs and information costs. Moreover, firms take into consideration both the benefits and risks of clustering regions. If there are so many unrelated firms that they create agglomeration diseconomies such as congestion costs and unintentional knowledge leakages, firms are more likely to try to find their cooperation partners in other regions.

• Journal of Information Technology Services
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• v.4 no.2
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• pp.135-144
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• 2005

The plenty of successful cases of Multi-national companies has been realized benefits of offshore outsourcing in particular "cost savings" from offshore IT outsourcing services, such as call center, software development, IT support and maintenance etc. A few Korean companies recently started to make the feasibility study of offshore IT outsourcing to catch up with the global trend. The objective of this study is to present the feasibility of offshore IT outsourcing of Korean companies through the analysis of pilot projects results between Indian and Chinese companies. The analysis include key elementsof cost, productivity, quality, practical issues as well as Gartner's framework, "AD Sourcing Cost Model", composed of 7 model factors. The findings of this study are not limited to understand offshore IT outsourcing but also provide useful guidelines covering wide range from the theoretical framework of selecting suitable offshore partner.

• Proceedings of the Technology Innovation Conference
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• 2005.08a
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• pp.5-28
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• 2005

This paper estimates the effect of networks on innovative performance at the firm level , using Korean Innovation Survey (KIS) dataset Product innovation, product improvement , and process innovation are used as proxies for innovative activity. The explanatory variables such as firm size, market concentration ratio, lagged profitability, foreign ownership, export ratio, firm's age, formal R&D activity, and industrial R&D intensity are yet other considerations. With two year-long (2000 and 2001) data from 1,124 Korean manufacturing firms, we estimated the logistic regression model. The research finding indicates that the external networks have a strong positive effect on innovative output regardless of type of innovation. However, the network effects by partner (other firms or research institutions) vary across the type of innovation. Especially, we found that the user-supplier linkage plays an important role in product ion innovation, product improvement, and process innovation.