Longitudinal And Predictive Analysis Of Cloud-Based ECommerce 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.