The smart media (SM) industry has demonstrated that it has the characteristics to increase user innovative activities, enhance open innovativeness, and increase the segmentation of innovation value. This study introduces and evaluates an innovation system that reflects the characteristics of the SM industry. We categorize the SM industry into hardware, network, platform, and content industries and perform an AHP analysis (based on a survey of 96 experts) to evaluate the relative importance of the factors/factor groups affecting the creation of innovation. The results show that “collaboration activity” is a more important factor than other innovation factor groups (financial support, R&D, policy environment, human resources) in the SM industry. The results also show that the important factors/factor groups differ by industry.

This research seeks to answer the basic question, "What would be the most determining factors if I perform regression analysis using several independent variables?" This paper suggests the way to estimate the proper royalty rate and up-front payment using multiple data I can get simply as input. Design/methodology/approach: This research analyzes the dataset, including the royalty-related data like running royalty rate (back-end payments) and up-front payment (up-front fee + milestones), regarding drug candidates for specific drug class of anticancer by regression analysis. Then, the formula to predict royalty-related data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal, TCT (Technology Cycle Time) median value for the IPC code (IP) of the IP, Market size of the technology, CAGR (Compound Annual Growth Rate) of the corresponding market and the revenue data of the license buyer (licensee). Findings: For the anticancer (antineoplastics) drug classes, the formula to predict the royalty rate and up-front payment is as follows. Royalty Rate ¼ 9:997 þ 0:063 Ã Attrition Rate þ 1:655 Ã Licensee Revenue-0:410 Ã TCT Median-1:090 Ã Market Size-0:230 Ã CAGR Formula 1 ð Þ Up-Front Payment Up-front þ Milestones ð Þ ¼ 2:909-0:006 Ã Attrition Rate þ 0:306 Ã Licensee Revenue-0:74 Ã TCT Median-0:113 Ã Market Size-0:009 Ã CAGR Formula 2 ð Þ In the case of Equations Equation 1 to estimate the royalty rate, it is statistically meaningful at the significance level of 1 % (P-Value: 0.001); however, in the case of Equations Equation 2 to estimate the up-front payment it is statistically not meaningful (P-Value: 0.288), thus requiring further study.

No abstract

No abstract