Research On Fit Between Strategic M&A Partners

Mergers & Acquisitions (M & A), as a reasonable economic phenomenon, performs distinct function in the world economy, following its own disciplinarian. In strategic M & A decision-making, the principal mission, that the acquiring firm's management has to face with, is to hunt a satisfying target. The fit between M & A partners is the premise and basis of determining whether the transaction can obtain synergy and create value. It is imperative for researchers to identify the internal drivers of M & A fit and modeling the analysis framework. This dissertation used System theory, Resource-based theory, Knowledge Capital theory and Value chain theory as theory basis, as well as Fuzzy mathematics and Neural Network as analysis tools, and adopted the research paradigm of integrating qualitative analysis and quantitative analysis, normative study and empirical study. This dissertation followed the consecution, theory basis—mechanism analysis—measurement of resources fit degree—M & A outcome prediction, to analyze fit between M & A partners. This dissertation is sponsored by the National Natural Science Foundation of China 'Research on the Potential Synergy of Strategic M&A' (No.70472003).This dissertation comprises seven chapters.In chapter one, firstly, the relevant background and significance are introduced; secondly, some important conceptions are defined; thirdly, the investigative method and framework are confirmed; finally, the main conclusions and innovations are summarized.In chapter two, the paper reviews previous literature about M & A fit, and points out the insufficiencies, then, puts forward the fields need to be researchedIn chapter three, from the views of System Theory, the paper demonstrates Resource-Based Theory of the Firm, and categorizes resources into tangible asset, human capital, organizational capital and relationship capital, which constitutes the theory basis of this dissertation.In chapter four, the internal mechanism of the resources fit is demonstrated. First, proposing the analysis framework of M & A fit; second, building the resources value chain system to identify the resources of M & A partners; and then, analyzing the inherent mechanism of resources fit, according to the interrelation of resources, and activity-based process.In chapter five, model system of evaluating resources fit is designed. Firstly, we build the conception model of evaluating resources fit; secondly, set up the index system measuring the four kinds of resources; thirdly, modeling fuzzy synthesis evaluation of resourcesIn chapter six, we put forward the BP Neural Network model of predicting M&A fit. Taking resources fit including human capital fit, organizational capital fit and relationship capital fit as input node, and the outcome M&A (success or failure) as output node, the MATLAB software is applied to modeling and train BP Neural Network.In chapter seven, we conclude the main views and the innovation points of this dissertation, and indicate the insufficiencies and further research field.The innovations of this dissertation are listed as below.Firstly, M&A fit between partners is discussed from philosophical perspective, constituting the methodology foundation for the whole theoretical system. We analyzed the interrelationships among firm, M&A and M&A fit, that is, the firm means a system comprising resources and relationship, and M&A will bring about new resources and relationships, then resources fit between merger partners is drived by the resources and relationships, which laying out a clear line of reasoning for the construction of M&A fit analysis framework;Secondly, proposing a fundamental conceptual framework for analyzing M&A fit degree, with creating value as the final objective, and resources fit as the core. First of all, based on the different resources nature, we construct the six power model of firm resources. Then, according to the degree of impacting value, the resources are further classified into tangible asset and intellectual capital. The process of creating value is tightly related with the drivers, which can help to realize the inherent causes of the fit between M&A partners, and act as the core.Thirdly, illustrating the mechanism of M&A fit. By analyzing the resource factors that drive M&A fit, this paper puts forward a value chain model in order to identify and evaluate resources, and analyzes resource fit mechanism according to the activity-based process of value chain. Furthmore, we will analyze the resources fit in term of six processes of value chain and five sorts of interrelationships between the resources of acquiring firm and target.Fourthly, establishing a systematic measurement of M&A fit degree. First, Build the conceptual model of evaluating resources fit; second, set up the index system evaluating resource advantage; finally, modeling fuzzy synthesis evaluation of resources. The author takes value chain as the tool for identifying and comparing resources of M&A partners, and on this basis, builds measurement matrix on resource fit, finding an appropriate measurement of M&A fit degree.Finally, putting forward the BP Neural Network model of predicting M&A fit. Through normative analysis, this dissertation proposed that resources fit and integration experience affect the M & A performance. To test the relationships between the outcome and independent variables, we build the BP Neural Network model. After sample training and simulating, we got a stable and convergent network structure. Which showed that the model can be applied to predict the M & A outcome, at the same time, proved the hypothesis of resources fit driving value creation is reasonable.