The Research Of Effect Of Core Enterprise Leadership On Alliance Performance In Technology Innovation Strategy Alliance

With the rapid development of economy of countries all over the world, in order to keep their competitive advantage, and get more information and resources complementary resources, in order to adapt to the new manufacturing mode, enterprises combined with other enterprise institution, which have the common interest, to form a new organization model. Technology innovation strategy alliance becomes important for enterprise networks optimization, improvement of the efficiency of innovation, and expand the market scale and participate in the international competition more effectively, it is also an effective way for the national, regional and industry response competition and challenge in the era of knowledge economy, and improve the ability of independent innovation. The influence of the core enterprise will drive and guide the strategic alliance to achieve common strategic target, we named the core enterprise’s leadership.This paper defined the technology innovation coalition and leadership of core enterprise based on the relevant papers of both home and abroad. Then we researched the influence of leadership of core enterprise to relationship network and knowledge sharing in coalition, concluded that leadership of core enterprise can harmonize the relationship network and improve the extent of knowledge sharing. On this basis, this paper further researched the influence of leadership of core enterprise to the whole technology innovation coalition. Coalition performance is evaluated from two aspects, relational performance and innovative performance. This paper created model and raised hypothesis based on relevant papers. Data are collected from High-tech companies in Xi’an. We carry out correlation analysis by using SPSS and the model’s goodness of fit analysis by AMOS. Results showed the data didn’t fit the model well and some hypothesis cannot hold. In order to improve the research, we modified the model and deleted the invalid hypothesis. After this, the data fit the model well, and all the hypothesis are testified right.