Innovation has attracted attention of researches in last 20 years, while networks and clusters are relatively new research subjects. In our paper we made an attempt to find the relationship between network centrality indexes and innovation performance. Each index represents different features of being in the network. To find the network indexes we have constructed adjacency matrixes based on alliance data. For our research we have chosen China's automobile industry network as an example, for the reason that Chinese automobile industry showed tremendous growth in recent decade and is fit to research scope which we are conducting. We have collected the data on innovation performance for 59 firms in China's automobile industry. We used UCINET software program to get the data regarding network properties. After we ran the negative binomial regression model on Gretl software program and constructed 5 models, with total of 7 variables. We have analyzed the relationship between innovation performance and three network centrality measures. According to our new findings firms in the network with more total number of connections and firms with more connections with well-connected firms have better innovation performance. We found

Social capital and innovation became one of the most significant topics for researchers in last decade. Our research also makes its contribution to develop the theoretical basis and tries to find out relationship between social capital and innovation performance. In our research social capital has been measured in forms of four networks: external personal networks, internal personal networks, membership in national trade associations and membership in regional trade associations. Innovation has been measured by two parameters such as the number of patents and the number of new products of the firm. We tested individual and collective effect of these four networks on innovation performance. We used e-mailed survey among 195 CEO's of China's chemical industry participants to collect the data and obtained 105 (53.8 %) full responses, which were suitable for our research. Then we collected the rest of necessary data of innovation on the Official web site of State Intellectual Property Office of The People's Republic of China. After using negative binomial regression we made 12 models, where we sought the support for five hypotheses, which we had proposed. We found that three of four studied networks are significantly and positively associated with innovation performance. Only internal personal network had less effect on innovation in two models. Our findings and conclusions can be useful and beneficial for top managers operating in all kind of industries, particularly to CEOs of large firms such as China chemical industry firms.

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