R & D Attention and Profit Performance—An Empirical Study on Listed Companies in China’s Electric Power and Electrical Industries

Abstract

The electric power and electric industries are basic and strategic emerging industries in China’s national economy. Based on the data of listed companies in China’s electric power and electric industries from 2015 to 2019, this paper empirically studies the impact of research and development (R & D) attention on corporate profitability. The results show that attention to R & D by listed companies in electric power and electric industries will significantly improve enterprise’s profit performance, and this kind of effect has yearly heterogeneity. From 2015 to 2017, R & D attention gradually improved the profit performance of enterprises, but the profit spillover effect of R & D investment in recent years needs to be further strengthened. Further study found that R & D attention has a significant effect on corporate profitability in the private enterprise group, the R & D attention of state-owned enterprises and R & D structure adjustment should be paid more attention; R & D investment can significantly improve the profitability of the electrical industry, but the promotion effect was relatively slow in recent years, and R & D investment has a significant effect on the profitability of listed companies in the electric power industry. Although the promotion effect is not obvious, it shows a steady improvement trend. After variable and model replacement, the empirical regression conclusion of this paper is still robust. The results of this paper help to deepen the understanding of the stage characteristics of the impact of R & D attention on profit performance, and it is of great significance to optimize the efficiency of R & D investment and pay attention to the adjustment of R & D structure in the electric power and electrical industries.

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“…Before factor analysis, KMO value test and Bartlett sphere test shall be carried out [7], When KMO value exceeds 0.6, factor analysis is applicable; The closer the KMO value is to 1, the stronger the correlation between variables. If the original assumption is denied, it indicates that the variable is not independent, and factor analysis can be carried out [8].…”