Sentence examples for licensing propensity from inspiring English sources

At the same time, the licensing propensity of large firms is also high due to the effects of cross-licensing.

This paper is a first attempt to analyze IP strategy of Japanese firms, by using large datasets from the Japan Patent Office's (JPO), called "Survey of Intellectual Property Activities (SIPA)." Descriptive regressions of IP strategy indicators suggest a non-linear relationship between firm size and licensing propensity.

In this paper, we use a two-step model to estimate a firm's licensing propensities; the first step estimates the determinants of potential licensors (willingness to license) and the second step identifies the factors of the actual licensing out of technology (licensing propensity).

Given that these public institutions are non-pecuniary sources as they have socio-economic rather than pure financial objectives, the determinants of in-licensing propensity in firm-public institution engagement are likely to be different from that in firm-firm transaction.

Building on the TCE theory and earlier works that established the positive relationship among licensing experience, benefits and propensity, we postulated in this study that firms' benefits from in-licensing of university/PRIs' IPs, represented by RICV, would be positively associated with their propensity to repeat in-licensing transactions with PRIs.

The dependent variable is the firm's propensity to repeat licensing, which is used as a proxy indicator of the impact that prior PRIs' IP licensing and knowledge transfer has on the firm's innovation output.

Third, we established empirically the relative significance of RICV as a better indicator than IP licensing fees in predicting firms' propensity to repeat licensing from PRIs, thereby indicating some form of academic innovation impact on firms.

Our finding of RICV as a predictor of firms' propensity to repeat licensing engagement with universities/PRI has contributed to the TCE theory.

In H2, the same binary-dependent variable used in H1, firm's propensity to repeat licensing, is regressed using binary logistic regression on the independent variable, RICV.

With a positive coefficient (b = 1.67, p < 0.05), RICV is established to be a significant and positive predictor of the firm's propensity to repeat licensing, thereby validating H2 that firms that attain higher levels of RICV with universities/PRIs have higher propensity to repeat their license transaction.

Operating under the same agency, the PRIs are assumed to have the same propensity to out-license their technologies to enterprises.