LONGITUDINAL AND PREDICTIVE ANALYSIS OF CLOUD-BASED E COMMERCE IMPACT ON INDIAN SMBS USING SEM AND MACHINE LEARNING TECHNIQUES

Abstract

The impact of cloud-based e-commerce on small and medium-sized enterprises (SMBs) in 
India is examined in this research, with an emphasis on the operational effectiveness, 
profitability, and competitiveness of the market. A mixed-methods approach is used in the study 
to examine how cloud adoption affects SMB performance. Structural Equation Modeling 
(SEM) and machine learning approaches, such as Support Vector Machines (SVM), Random 
Forest (RF), and Gradient Boosting (GB), are integrated. Comparing the research to individual 
models, the hybrid strategy of SVM + RF + GB shows better performance in important 
measures, such as accuracy (90%), data integrity (99.5%), and scalability improvements. The 
study also emphasizes the benefits of cloud adoption for operational effectiveness, customer 
engagement, and revenue growth. Regulatory compliance, worker skill shortages, and 
cybersecurity threats, among other issues, continue to be major obstacles to wider use. The 
study uses sentiment analysis to measure customer opinions, and the positive sentiments found 
on cloud solutions confirm their long-term advantages for small and medium-sized businesses. 
This study highlights how important cloud computing is to helping SMBs stay competitive in 
a world that is becoming more digital, but it also points to the necessity of further advancements 
in deep learning for better predictive analytics and blockchain for data security. Cloud-based 
e-commerce in Industry 4.0 will continue to grow and be sustainable thanks to these 
developments.