Financial constraints to investing in intangibles: Do innovative and non-innovative firms differ?

Identifying the implementation effect of technology transfer policy using system dynamics: a case study in Liaoning, China

Given that technology transfer provides an important boost for promoting national economic development, technology transfer policy (TTP) has attracted more and more attention from academia and industry. The government issued many policies. However, the implementation effect of TTP still needs to be clarified. This study is carried out from the progressive level of "text content-influence path-implementation effect.'' It aims to adopt a systematic analysis method to analyze policy tools and policy implementation stages, then builds a conceptual framework of the influence path of TTP. Then the relationship between variables in the qualitative model was clarified, and the system dynamics (SD) model was used to build a quantitative model with four feedback loops. Finally, taking Liaoning, China as an example, the system simulation and sensitivity analysis of the main parameters are implemented in Vensim PLE. Different policy tools have different roles in the TTP impact stages of research, transfer, and industrialization. Based on the data of 2013-1019, the SD model constructed in this paper can be used to predict the implementation effect of TTP during 2020-2015. Simulation and sensitivity analysis results provide practical enlightenment for government departments to improve the implementation effect of the existing TTP. This study also provides other researchers with a systematic understanding for improving the implementation effect of TTP with a "text content-influence path-implementation effect" conduction chain and provides new insights for further research on TTP.

The effects of the limited exhaustibility of knowledge on firm size and the direction of technological change

The new knowledge intensive direction of technological change is magnified at the firm level by the limited exhaustibility of knowledge. This limited exhaustibility triggers cumulability and extensibility for which the larger the firm, the lower the knowledge generation costs from using a larger stock of existing knowledge, and the lower the knowledge exploitation costs related to a larger output based on use of the same piece of knowledge. The consequences for the direction of technological change are twofold. First, the larger the firm size, the larger the share of intangible capital in total capital. Second, the output elasticity of intangible capital increases with the size of the firm. We test our hypotheses on data on US listed companies over the period 1977–2016. The results of ordinary least squares, two-stage least squares and production function estimations confirm our theoretical expectations.