• Clinical and Experimental Vaccine Research
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• v.12 no.1
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• pp.13-24
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• 2023

This systematic and meta-analysis aims to evaluate humoral and cellular responses to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine among kidney transplant recipients (KTRs). We conducted a systematic literature search across databases to evaluate seroconversion and cellular response rates in KTRs receiving SARS-CoV-2 vaccines. We extracted studies that assessed seroconversion rates described as the presence of antibody de novo positivity in KTRs following SARS-CoV-2 vaccination published up to January 23rd, 2022. We also performed meta-regression based on immunosuppression therapy used. A total of 44 studies involving 5,892 KTRs were included in this meta-analysis. The overall seroconversion rate following complete dose of vaccines was 39.2% (95% confidence interval [CI], 33.3%-45.3%) and cellular response rate was 41.6% (95% CI, 30.0%-53.6%). Meta-regression revealed that low antibody response rate was significantly associated with the high prevalence of mycophenolate mofetil/mycophenolic acid (p=0.04), belatacept (p=0.02), and antiCD25 induction therapy uses (p=0.04). Conversely, tacrolimus use was associated with higher antibody response (p=0.01). This meta-analysis suggests that postvaccination seroconversion and cellular response rates in KTRs are still low. And seroconversion rate was correlated with the type of immunosuppressive agent and induction therapy used. Additional doses of the SARS-CoV-2 vaccine for this population using a different type of vaccine are considered.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.373-396
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• 2015

This study analyzed the determinants of high-growth firms using the technology appraisal data of the Korea Technology Finance Corporation. This study is differentiated from previous studies for three reasons. First, it analyzed the determinants of firms that will grow into high-growth firms in the future, not the characteristics of current high-growth firms. Second, it analyzed high-growth firms by dividing them in two aspects: sales and employment. In other words, they were divided into three types: the case in which a firm achieves high growth in both sales increase and creation of jobs, the case in which a firm achieves high growth in creation of jobs but low growth in sales increase, and the case in which a firm achieves high growth in only sales increase but low growth in creation of jobs. Third, this study applied the technology appraisal indicators of Kibo Technology Rating System(KTRS) by the Korea Technology Finance Corporation as the explanatory variable. As a result of analysis, it was found that a firm achieved high growth in both sales and employment if the position in the technology life cycle was appropriate and the technology readiness level was high. However, it turned out that the management system of technical manpower had conflicting effects on high growth of employment and sales. In other words, a firm that had well managed its technical manpower achieved high growth in terms of employment, but rather showed low growth in terms of sales. This result suggests the inference that firms showing high growth in employment may appear mainly in the high-tech industry where management of technical manpower is important. Accordingly, as a result of adding dummy variables that represent whether or not firms are in the high-tech industry, it was found that the result supported the inference, as firms in the high-tech industry were highly likely to achieve high growth in employment.

• Management & Information Systems Review
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• v.36 no.2
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• pp.93-111
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• 2017

This study aims to examine economic effects and success factors of ICT based growth engine industry for forth generation industrial revolution. KTRS(kibo Technology Rating System) provided by KIBO is used for an analysis of this study. Specifically, the economic effects of growth engine industy are classified with financial(productivity, growth rate, etc) and non-financial(R&D investment and employment) effects. Moreover, the impacts of KTRS factors including CEO capability, technology ability, commercialization and marketability on the economic effects are investigated. The result of this study would raise management issues on technology innovation and provide implications on industrial policies for ICT based growth engine industry.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2993-3002
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• 2018

This case study introduces the process of developing the technology rating evaluation model for investment. The technology evaluation rating model for investment is a project that the Financial Services Commission and the Ministry of Commerce, Industry and Energy collaborated to expand the scope of technology finance from loan to investment. The technology evaluation model for investment was developed with the aim of predicting high growth companies. The model consists of a statistical model and an expert model. Here, statistical models were modeled by using logistic regression analysis. Expert models gathered opinions of experts and identified the weight of each evaluation item and set the model. The rating system of the model is composed of 10 grades. The distribution of the model was consistent with KTRS grade distribution. Interestingly, the emphasis is on technology and marketability. In the technology valuation grade model for the goddess, there is a considerable difference from the emphasis on managerial competence or business performance.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.