Competitive Strategic Identification Methods And Its Applications

Competitive strategy identification is an important issue in the competitive strategy research.However, because of the ambiguity of the concept and its lack of operability, subjectivity of strategicdimension and strategic variable selection, availability of the data, as well as the flaw of statisticalclustering methods, there exist difficult empirical problems which seriously limit the development ofthe competitive strategy theory.In accordance with the existing problems in competitive strategy identification research, we putforward two identification paths, construction of the competitive strategy’s structural dimensionframework and competitive strategy recognition from process and dynamic perspective by collectingand refining the relevant research literature. By introducing the latest theories and methods in themultivariate statistical analysis and data envelopment analysis, we put forward new competitivestrategy identification methods based on panel data clustering and DEA theory respectively. Theempirical research is made on competitive strategy identification of China’s manufacturing enterprises.To expand the research perspective from external characteristics to internal process and static view todynamic view provides not only new means for competitive strategy identification but also enterprisemanagement operation and practice for competitive strategy theory. The main contents of the thesisinclude the following aspects:Firstly, the structure dimension research framework of competition strategy is constructed. Theexisting competitive strategy identification literature is lack of research framework. While this thesisput forward two identification paths of typology and taxonomy through deep excavation of thedomestic and foreign related literature. Based on proper treatment of the contradiction between singledimension and multi dimension, quantitative analysis and concept decomposition, as well as staticdescription and dynamic analysis in study of competitive strategy identification, we construct thecompetitive strategy structure framework and give the integration path and the way of its realization.We construct identification framework of competitive strategy based on analysis of the logicalstructure from different perspectives and internal logic structure among various identification methods.It provides a structured and concept operation solution for method innovation and empirical researchfinally.Secondly, a cross efficiency DEA model is constructed and applied in competitive strategyidentification from process perspective. The existing study is limited to external strategic characteristics identification and subjective defect. While this thesis first use DEA theory to expandthe perspective from external characteristics to internal process and put forward identificationproblem from process perspective. Based on the DMUS’ similarity and dissimilarity information ofinput and output weights, we put forward an input-output weights clustering method of competitivestrategy identification by introducing interval number and constructing the multivariable intervalcross efficiency DEA model. By introducing several DMUS’ non cooperative game, we construct thegame efficiency DEA model and design the virtual worst DMU efficiency as the initial values for anapproximate search algorithm. The algorithm is presented to be with convergence and Nashequilibrium solution is obtained. We construct two-stage DEA game cross-efficiency model. Thegame between each DMU is realized in the self-evaluation and peer evaluation system and game crossefficiency is improved and optimized. By fusing inner and outer evaluation results, a new clusteringmethod based on game efficiency weight ratio and game cross-efficiency matrix is proposed to beapplied to the empirical study for competitive strategy identification of China’s listed machinerymanufacturing companies. The empirical results show that the former method can distinguish efficientDMUS with Unity and rationality in comprehensive evaluation, and the latter method can realize theNash equilibrium solution in the self-evaluation and peer evaluation mode with higher distinguishdegree. The two methods are of better explanatory power and objectivity compared with traditionalclustering methods. The problem of the multiplicity of DMUS’ weights and competition amongDMUS is resolved. We fully consider the resource allocation and operation range as the strategic inputand output dimensions and present the relative importance of index and their causal relations.Thirdly, a multivariate panel data clustering method is put forward and applied in competitivestrategy identification from dynamic perspective. The perspective of existing study based on crosssection data clustering is static that is lack of information extraction in time series dimension. Whilethis thesis first use panel data multivariate statistics theory to expand the perspective from static todynamic and put forward identification problem from dynamic perspective. We describe threedimensions and format of panel data in time, space and index. A factor analysis of panel data is putforward and the ward clustering method for multivariable panel data is improved and applied incompetitive strategy identification. By combining the panel data’s local changes characteristics in timeseries dimension with global distance, we comprehensively extract the panel data’s time seriescharacteristics in “positional index”,“incremental index” and “incremental rate index”, then putforward a multivariate panel data series clustering method to identify competitive strategy and avoidthe problem of non synchronization. We propose an adaptive sliding window segmentation methodand realize the shape feature extraction of local changes of time series. Then a multivariate panel data clustering method based on shape characteristics is put forward and applied in competitive strategyidentification. The empirical study for dynamic competitive strategy identification of China’shousehold electrical appliance enterprises show that there exist four kinds of competitive strategiesincluding cost-leadership strategy, differentiation strategy, hybrid strategies and stuck in the middle.The empirical results show that the first method can meet the requirement of systematic uniformityand ensure the indicators are not related. It is able to overcome the errors caused by mean of timeseries dimension and less information loss. The second and third method can comprehensively extracttime and space information which effectively reduce the influence of noise and get betterclassification results. The distortion of identification results caused by correlation of indices and datamutation is solved and the result accuracy is improved.Fourth, a multivariate panel data fusion clustering method is put forward and applied incompetitive strategy turning point identification. The existing relative study based on strategic stabletime period (SSTP) is lack of information extraction of strategic evolution. The thesis first introducedpanel data clustering method into studying competitive strategy evolution and put forwardevolutionary problems. We put forward a multivariate panel data ordinal clustering method by using Fnorm to construct square deviation function. A multivariate panel data fusion clustering method basedon fusion theory is proposed to identify strategic turning point. Empirical study of China’s householdelectrical appliance enterprises is made to identify the strategic types and turning point of strategicevolution. The empirical results show that the new method can ensure no correlation of indices,overcome the errors caused by mean of time series dimension and less information loss. It can solvethe panel ordered data clustering problem and make up the one-sidedness and limitation of singleanalysis. The problem of evolutionary information loss caused by mean of SSTP is solved and theresult accuracy of strategic turning point is improved.