This article investigates the impact of political association and managerial power heterogeneity on corporate risk-taking using data of listed companies in China from 2006 to 2015. Politically associated companies demonstrate higher corporate risk-taking, and the impact of managerial power thereon depends on the source thereof. Structurally speaking, board of directors' supervision, and shareholders' supervision power are positively associated with corporate risk-taking, but ownership, expert, and prestige power are negatively associated. Political association weakens the influence of structural and prestige power on corporate risk-taking and strengthens the impact of ownership and expert power thereon. The article adds to the literature on political association, managerial power, and corporate risk-taking.
ARTICLE HISTORY
The text information of enterprise technical requirements is miscellaneous, which leads to the feature extraction is not prominent enough, and cannot be further accurately and effectively matched to the scientific research team of colleges and universities. In this paper, attention mechanism is added to the two way LSTM network to calculate the contribution score of the category to which the output vector belongs, and the word vector combined with attention matrix is connected to the maximum pooling layer, then RCNN_ATT model for enterprise technical requirement text is proposed, so that the technical requirements text can be automatically classified according to the industry. The experimental results show that, compared with other neural network models, this model performs better in technical requirement text classification, which can narrow the scope of supply and demand matching and improve the efficiency of matching calculation.
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