Effect of Machine Learning on Human Resources, Marketing, and Financial Aspects of E-Commerce

Abstract

The rapid growth of e-commerce platforms has intensified the need for intelligent, data-driven decision-making across organizational functions. Machine Learning (ML) has emerged as a transformative technology enabling e-commerce firms to optimize human resource management, enhance marketing effectiveness, and improve financial performance. Despite growing adoption, empirical studies examining the integrated impact of machine learning across HR, marketing, and financial dimensions of e-commerce organizations remain limited.

The present study conducts a comprehensive empirical analysis of how varying levels of machine learning adoption influence human resource effectiveness, marketing performance, and financial outcomes in e-commerce firms. Using a quantitative research design, data were collected from 200 e-commerce organizations, categorized into high ML adoption and low ML adoption groups. Statistical techniques such as independent samples t-tests, chi-square tests, and discriminant analysis were employed to evaluate differences across functional performance dimensions.

The findings reveal statistically significant differences between high and low ML-adopting e-commerce firms. Organizations with advanced machine learning capabilities demonstrate superior HR efficiency, enhanced marketing outcomes, and stronger financial performance. The results highlight machine learning as a strategic enabler of organizational competitiveness and sustainability in the e-commerce sector.