Analyzing Technological Spillover Effects Between Technology Classes: the Case of Korea Technology Finance Corporation

• Published in: IEEE access 2018-01, Vol.6, p.3573-3584
• Main Authors: Choi, Sungchul, Noh, Maeng Seok, Yoon, Janghyeok, Park, Hyunseok, Seo, Wonchul
• Format: Article
• Language: English
• Subjects: Association rule mining; Data mining; DEMATEL; Empirical analysis; Feature extraction; Finance; Indicators; Knowledge engineering; Quantitative analysis; Reliability; Reliability analysis; Support systems; Technological innovation; technological spillover effect; Technology assessment; technology evaluation; technology financing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2788918

A technology evaluation system is mandatory to successfully implement a technology-based financial support system. Technology evaluation has generally been relied on the experts' manual work. Various quantitative indicators have been presented to improve the efficiency of this manual work. Among these indicators, the spillover effect is perceived as useful for the disposal of patents of a firm, which received credit guarantee but lost its ability to service its debt. A model for measuring the spillover effects has already been proposed, but it has low reliability. Therefore, this paper presents a systematic approach for measuring technological spillover effects between technology classes. The approach mainly relies on patent data due to its features of the latest reliable sources of technological intelligence. We first extract co-classification information from patent data and generate association rules between technology classes. The relationships represented by the rules, however, can only depict the direct effects. Therefore, we first derive the indirect effects from the direct ones and then integrate both the effects to measure the technological spillover effects. We conduct an empirical study to show the applicability of the presented approach using patents granted in the Korean Intellectual Property Office. We expect that this paper can contribute to establish a quantitative evaluation model to help assess technologies for successful technology-based credit guarantee system. It will improve the reliability of the technology assessment by reducing the variance of the qualitative evaluation results due to the individual differences of the evaluator. Furthermore, it will also enhance the efficiency of evaluation work.