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As the carrier of knowledge, intellectual capital plays a crucial role in technology capability. However, most of the previous studies focus on technological capability from a static perspective, rather than take dynamic technology capability into consideration. Based on this research gap, the purpose of this paper is to investigate the effects of intellectual capital and its sub-dimensions on dynamic technology capability, measuring by the factor scores of five technological input and output variables.

The authors combine the system dynamic method and empirical study to guarantee the internal and external validity. Specifically, the authors design the system dynamic model and simulation to analyze the system mechanism of intellectual capital and its sub-dimensions on dynamic technology capabilities from four cause and effect feedback loops. Then, the authors propose eight hypotheses based on this system dynamic model. In the empirical test phase, the authors employed a panel data set pertaining to Chinese manufacturing firms from 2007 to 2017, and adopted the fixed effect panel model according to Hausman test.

The authors find that intellectual capital efficiency (ICE) and its sub-dimensions (i.e. human capital efficiency, organizational capital efficiency and capital employed efficiency (CEE) have significantly positive impacts on dynamic technology capability. The results also show that the positive effects of ICE and OC on dynamic technology capability would be strengthened in state-owned enterprises compared with non-state-owned enterprises, while this moderation effect is weakened on the relationship between CEE and dynamic technology capability.

In this study, the authors first introduce the system dynamic method to explore the relationship of intellectual capital and dynamic technology capability, which is a valuable trial on combining system science and empirical study. Additionally, the authors continue to expand the dynamic technology capability from the intellectual capital perspective, and also find the moderating effect from the ownership aspect. It is beneficial to the theoretical development of intellectual capital and dynamic technology capability. Furthermore, the authors provide significant inspirations and implications for enterprise’s managers.

This paper investigates the impact of credit risk shocks on the evolution of banking efficiency in China.

This paper introduces credit risk as a bad output into a bootstrap data envelopment analysis (bootstrap-DEA) model.

During a credit risk shock, the efficiency levels of both state-owned commercial banks and joint-stock commercial banks are significantly higher than those of urban/rural commercial banks, and the efficiency differences between these banks further increase during a period of economic slowdown. This paper also finds that the efficiencies of joint-stock commercial banks are the most sensitive to credit risk shocks; these banks are the first to be affected and the first to completely adjust. However, urban/rural commercial banks adjust very slowly.

Most scholars still use the traditional DEA method to estimate China's banking efficiency. The bootstrap-DEA method is clearly able to obtain a more exact estimated efficiency score. In fact, in comparison with the bootstrap-DEA model, we found that the traditional DEA method overestimates China's banking efficiency, and this is an especially serious problem for those banks that have a high efficiency score.