E-commerce utilization analysis and growth strategy for smes using an artificial intelligence

Article type: Research Article

Authors: Zhong, Yijie^{; *}

Affiliations: Department of Economic Management, Shanghai Xingjian College, Shanghai, China

Correspondence: [*] Corresponding author. Yijie Zhong, Department of Economic Management, Shanghai Xingjian College, Shanghai, China. E-mail: [email protected].

Abstract: E-commerce is becoming a robust catalyst to enlarge the business actions and construct an active consumer based on emergence of a global economy. E-commerce is offering the opportunities for Small and Medium-sized Enterprises (SMEs) with limited resources to decrease the operating costs and improve the profitability by overcoming the operational problems. In addition, SMEs use e-commerce websitesas sales channels between the businesses, their competitor, and consumers. Between the success of e-commerce and manufacturing SMEs, however, the moderating influence of entrepreneurial competencies does not seem to be as significant. Hence, in this paper, Deep Convolutional Neural Network based onSales Prediction Model (DCNN-SPM) has been suggested for analyzing SME enterprises’ e-commerce utilization and development. Consistent with the user decision-making requirements of online product sales, united with the impelling factors of online product sales in different SME industries and the benefits of Artificial Intelligence (AI), this study builds a sales prediction model appropriate for online products. Furthermore, it evaluates the model’s adaptability to different types of online products. Our model can automatically extract the useful features from raw log data and predict the sales utilizing those extracted features by DCNN. The experimental outcomes show that our suggested DCNN-SPM has achieved a high customer satisfaction ratio of 98.7% and a customer is buying behaviour analysis of 97.6%.

Keywords: E-commerce utilization analysis, growth strategy for SMEs, artificial intelligence

DOI: 10.3233/JIFS-232406

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7619-7629, 2023