Innovation has attracted attention of researches in last 20 years, while networks and clusters\nare relatively new research subjects. In our paper we made an attempt to find the relationship\nbetween network centrality indexes and innovation performance. Each index represents\ndifferent features of being in the network. To find the network indexes we have constructed\nadjacency matrixes based on alliance data. For our research we have chosen China�s\nautomobile industry network as an example, for the reason that Chinese automobile industry\nshowed tremendous growth in recent decade and is fit to research scope which we are\nconducting. We have collected the data on innovation performance for 59 firms in China�s\nautomobile industry. We used UCINET software program to get the data regarding network\nproperties. After we ran the negative binomial regression model on Gretl software program\nand constructed 5 models, with total of 7 variables. We have analyzed the relationship\nbetween innovation performance and three network centrality measures. According to our\nnew findings firms in the network with more total number of connections and firms with\nmore connections with well-connected firms have better innovation performance. We found that there is no effect on innovation performance when firms have capability to pass\ninformation fast.
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