Research Of The Resource Synergy In M&A Base On Time-Spatial

Many M&A Cases are founded on the forecast of synergy effect. However, there are the cases demonstrate that some enterprises rapidly developed after five huge M&A waves; but there are the challenges to synergies the based on truth that almost50%M&A cases failed in real life. A collaboration smooth avenue or a collaboration trap, it is a question. Then, the research for Synergies is very necessary and urgent. Synergies can simply be explained as1+1>2. The "greater than" means the superposition of elements in double enterprise that will not lead to win. The existing of some uncertain factors, and these uncertain factors can convert into positive certainty,--it is the Synergies. For the uncertainty research, resources are the core content of synergies analysis as the basic elements and competitive advantages of an enterprise. Time and spatial is the material existence situation, the economic activity dimension. Therefore, resources configuration must fulfill the characteristic of time and spatial. The basis of enterprise resources, the vision of time and spatial are elaborated in this paper, which also take various analysis to research the synergies of M&A."Theory-Model-Category-Case" is the logical route, totally6parts in this paper. Except the routine M&A collaboration research, it is also review the related research of time, space and time&spatial aspects, as well as the worth of time and special perspective. The main jobs of this paper are constructing an analysis model for "time&spatial-resources collaboration" and also, combining enterprise resources, time and spatial, synergies together for analysis. For better practical using, the models are demonstrated by three types of M&A, and cases are included. The core conclusion of this paper: resources collaboration needs to respect the rules of time an spatial; uncertain factors need to be improved into positive certainty, which will obtain synergies. Meanwhile, there still exist some inadequate aspects in this paper research. The research of data analysis in complex time and spatial factors need to go further.