From the perspective of innovation mechanism,capital,personnel,achievements and conversion,this study analyzes the current situation of technological innovation in Beijing agricultural science and technology enterpris...From the perspective of innovation mechanism,capital,personnel,achievements and conversion,this study analyzes the current situation of technological innovation in Beijing agricultural science and technology enterprises,and summarizes the characteristics including single financing channel of R&D funds,low conversion rate of innovative products,unbalanced distribution of technology and innovative talents,the underestimated position of enterprises as the main body of technological innovation,the large gap of innovation achievements between developed provinces and Beijing.At last,this study puts forward the ways to improve technological innovation ability in Beijing agricultural science and technology enterprises as follows:developing technological innovation strategies;improving the R&D expenditure and expanding the financing channels;perfecting the mechanism of professional personnel training in agricultural technology innovation;enhancing the level of innovation performance management;establishing and improving the corporate culture of innovation and cultivating technology innovation spirit.展开更多
Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial ...Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.展开更多
基金Supported by Beijing Natural Science Foundation(9122006)Beijing Social Science Foundation(13JGB041)
文摘From the perspective of innovation mechanism,capital,personnel,achievements and conversion,this study analyzes the current situation of technological innovation in Beijing agricultural science and technology enterprises,and summarizes the characteristics including single financing channel of R&D funds,low conversion rate of innovative products,unbalanced distribution of technology and innovative talents,the underestimated position of enterprises as the main body of technological innovation,the large gap of innovation achievements between developed provinces and Beijing.At last,this study puts forward the ways to improve technological innovation ability in Beijing agricultural science and technology enterprises as follows:developing technological innovation strategies;improving the R&D expenditure and expanding the financing channels;perfecting the mechanism of professional personnel training in agricultural technology innovation;enhancing the level of innovation performance management;establishing and improving the corporate culture of innovation and cultivating technology innovation spirit.
文摘Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.
