The Impact of Big Data Analytics Capabilities on the Diversification of E-Commerce Firms

Abstract

Abstract With the arrival of the big data era, e-commerce enterprises are facing more and more competition and market changes. A fuzzy clustering model is created by combining fuzzy set theory and fuzzy logic with a clustering algorithm in this paper. Then, for the shortcomings of the fuzzy C-mean clustering algorithm, the genetic simulated annealing algorithm is used to weight the data points, the fuzzy C-mean clustering model is improved, and a fuzzy clustering algorithm based on the genetic simulated annealing optimization is proposed. By using this algorithm, e-commerce enterprises’ diversification is being analyzed. The results show that the mean and standard deviation of each supply chain inventory, production orders, supplier ordering, and platform ordering quantity after optimization are reduced to different degrees, some of which reach more than 60%. Stabilizing the development of e-commerce enterprises can be achieved by reducing supply chain inventory and order quantity. E-commerce enterprises that excel in their enterprise production quality (0.741), credibility (0.748), and product sales competitiveness (0.726) are more competitive. E-commerce enterprises can utilize the ability of big data analysis to achieve diversification.