The financial valuation of a drug that is still under development is required for various purposes. The risk-adjusted net present value (r-NPV) method, which recently emerged in the biotech industry, uses the development attrition rate as a discount factor to reflect risk during each development phase. The r-NPV method was developed to overcome the disadvantages of the prevailing discounted cash flow and real options methods and considers drug type, as well as the stage of development in its approach. Using this method, the current study examines technology values in the biopharmaceutical industry and matches the clinical development periods and success rates of these new drugs by analyzing datasets from ClinicalTrials.gov and MedTrack DB. It thus provides support for an empirical valuation model for experts in the field. Notably, there is limited research on the attrition rate and development period of new substance drugs and the research results are not consistently presented. In addition to new substance drugs, further research is necessary to deepen understanding of the attrition rate and development period of biologically-based drugs because of their inherent physical and developmental differences. Similarly, research on performance specifics within drug class models would enable refinement of the model.

The essence of the experience of East Asia has been on technological capability building and dynamic industrial transitions from one stage to the next. While many studies for understanding the catch-up process and post catch-up agendas exist, empirical and comparative studies that consider the transition from catch-up to post catch-up are still rare. The significance of this research can be summarized in two ways. First, this study verifies the conformity of existing major catch-up to post catch-up transition studies with quantitative evidence. Second, by comparing commonalities and patterns from South Korea and China, this study examines the generality of the discourses and arguments about the transition from catch-up to post catch-up. The reflexive study of understanding transition from catch-up to post catch-up was conducted with a technology cycle time (TCT), self-citation ratio at a country level, and the basic research expenditure of Korea and China by using the US Patents and Trademark Office’s (USPTO) patent citation (for technology cycle time & self-citation ratio) and OECD’s database (basic research expenditure and gross domestic expenditure on basic research) for time period from 1998 to 2012. Empirical evidence of technology cycle time, self-citation ratio and gross domestic expenditure on basic research matched well with the prior transition arguments. First, Korea’s case shows a post catch-up trend with an increasing technology cycle time while China’s case presents catch-up trend (short-cycle period) and shifts to post catch-up trend (post catch-up trend). Self-citation ratios for both countries show increasing and converging pattern. In terms of basic research activities, both countries show increasing pattern. Korea exceeded the gross domestic expenditure on basic research of Japan and the US. Even though, China’s gross domestic expenditure on basic research has been tripled from 1998 to 2012, China’s gross domestic expenditure on basic research has still a long way to go to close the gap and to show a converging pattern. Many developing countries that once experienced a certain level of successful catch-up did not overcome the middle income trap have fallen behind. Understanding transition process of catch-up to post catch-up discussed in this paper may present a better understanding of long-term sequential development and economic sustainability of developing countries.

Technology valuation methods are classified into income-based, cost-based, and market-based approaches depending on the perspective of valuing technology. The market approach evaluates the value of technology by referring to cases in which similar technologies have been traded before. In this study, we use prior technology transaction data to estimate the technology value based on the market approach and to identify influential factors to the estimated value. To this end, we adopt a multivariate k-nearest neighbor (MKNN) regression model to accommodate mixed-type input variables aiming at estimating multivariate technology values, selecting influencing factors, and the relative importance of the selected factors. In addition, we can optimize the number of transaction cases k in k-NN regression. Our proposed regression model outperforms an embedding model with cosine similarity in predicting multivariate response variables. In addition, we illustrate how to select and assess the influential factors based on the real-life dataset.