Structural Equation Model And Dynamic Analysis On Absorptive Capacity For Big Data Technology

With the popularity of the internet and information technology, the big data era is coming. The new transformational trend leads to some novel features in data correlation, cross-domain fusion and depth-application, which set new demands on enterprises management. The development of big data technology is lag behind in China, especially in the adoption, learning and applying ability, which are particularly weak in non-internet enterprise. Therefore, the research of influential factors in big data technology absorptive capacity by both qualitative and quantitative analysis is a subject demanding prompt solution nowadays. In this thesis both static analysis and dynamic evolution analysis are used to establish the evaluating system of big data technology absorptive capacity. The main contributions are as follows:(1) This study presents an index system from four dimensions like technical maturity, policy support, enterprise innovation and market response to study the absorptive capacity of big data technology based on both literature reading and questionnaire survey. Due to the complex combination of several factors like the inner resource factors of the company and the exterior environment factors, there is uncertainty in the identification, diagnosis, estimation and forecast of enterprises management need to solve. The final index system includes four dimensions and 13 indexes which has a fine guiding value for the recognition of technical maturity, policy support, improving enterprise innovation and handling market response.(2) The correlation mechanism of how the external and internal factors influences the absorptive capacity in big data technology is analyzed based on Structure Equation Modeling and effective test, especially the quantitative analysis of the complex multiple factors. At present, the existing research is limited in qualitative analysis and discussion, particularly lack quantitative research. This thesis adopts structure equation model to solve the difficult and make an active exploration in quantitative analysis of the casual relationship between all the key variables in the system. Both the direct effects and indirect effects between the multiple variables are identified, which provide valuable knowledge and laws for the decision- makers to acquire the correlative mechanism.(3) Dynamic simulation analysis of the influential factors which affect the absorptive capacity of big data technology is made based on the Fuzzy cognitive map, which is very conducive to forecasts the tendency of the complex multiple factors as well as to optimal management schemes. Due to the fact that the concept nodes are dynamic associated and their contributions are difference for the absorptive capacity coefficient of system. The path coefficient obtained in structural equation model is adopted to determine the weight of the concept nodes in Fuzzy cognitive map, and the dynamic evolution analysis for the system can provide a good reference for big data innovation in tradition enterprises.