A Study on Relationship between Dynamic Capability and Technology Transfer Performance of Public Research Institutes

Technology transfer is one of important strategies in sustainable economic growth. There are supply-push and demand-pull directions of technology transfer, and recently Korean research institutes have paid increasing attention to demand-pull technology transfer in an attempt to improve public research institutes’ technology transfer performance (TTP). However, our view is that simply adopting a demand-pull or a supply-push model does not always guarantee improved TTP. We argue that technology marketing strategies, such as mass marketing and target marketing, should also be considered. This study aims to investigate the relationship between technology transfer directions and TTP, and the role of technology marketing strategies. We collected a Korean research institute’s technology transfer data from 2014 to 2015, and then employed a two-way ANOVA to analyze the data. The result of the analysis shows that TTPs differ by technology transfer directions and technology marketing strategies. More importantly, we found that the demand-pull model yields higher TTP, especially when the model is associated with target marketing strategies rather than mass-marketing strategies. This result implies that marketing strategies, such as market segmentation and customer targeting, are needed if an organization wants to improve TTP by implementing the demand-pull technology transfer model.

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Exploring the Factors Affecting Technology Transfer in Government-Funded Research Institutes: The Korean Case

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7040228 ◽

2021 ◽

Vol 7 (4) ◽

pp. 228

Author(s):

Sehwan Ko ◽

Woojoong Kim ◽

Kangwon Lee

Keyword(s):

Technology Transfer ◽

Resource Dependence ◽

Resource Dependence Theory ◽

Policy Implications ◽

Transfer Performance ◽

Public Infrastructure ◽

Factors Affecting ◽

Funded Research ◽

Research Institutes ◽

The Impact

Based on the resource dependence theory and the resource-based view, this study examined the impact of the resources and capabilities of government-funded research institutes (GRIs) on technology transfer. Panel analysis was performed on 21 GRIs in South Korea representing three mission types—basic future leading, public infrastructure, and industrialization—for the 2015–2019 period. The analysis confirmed that the factors affecting technology transfer performance differed among GRIs depending on their mission type. For basic future leading GRIs, the number of technology transfer cases was strongly associated with the number of research personnel, while there was a negative relationship between technology transfer and the total budget, the number of research publications, and the number of patent registrations. None of the variables affected the revenue from technology fees. Researchers at these GRIs appear to have a strong motivation for technology transfer, but the priority for resource allocation at the institutional level is the production of papers and patents rather than technology transfer. For public infrastructure GRIs, the number of patents held and the number of technology licensing office (TLO) personnel had a positive impact on the number of technology transfer cases, while none of the variables affected the revenue from technology fees. Thus, the number of patents is more favorable for technology transfer at this type of GRI compared to those that pursue a mission of basic future leading, possibly because their research focus is more related to engineering than to basic science. For industrialization GRIs, the number of TLO personnel affected the number of cases of technology transfer, and the number of patent registrations and TLO personnel affected the revenue from technology fees. The speed of technology development and industrial application is thus much faster in industrialization GRIs than in the other GRI types. The results of this analysis show that mission attributes are important drivers of technology transfer performance. This study thus offers policy implications by illustrating those different resources should be provided to different types of GRI to optimize their technology transfer performance.

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