Reconfiguring Global E-Commerce Ecosystems in the Post-Pandemic Era: Consumer Behavior, Big Data Intelligence, Platform Governance, and Cross-Domain Analytical Convergences

Abstract

The COVID-19 pandemic constituted one of the most disruptive global shocks to economic systems in modern history, accelerating structural transformations that would otherwise have unfolded over decades. Among the most profoundly affected domains was e-commerce, which rapidly evolved from a supplementary retail channel into a central pillar of consumption, production, and service delivery worldwide. This research article provides an extensive, theoretically grounded, and integrative analysis of global e-commerce transformation during and after the COVID-19 crisis, drawing strictly upon the provided multidisciplinary references. The study situates e-commerce not merely as a technological phenomenon but as a socio-economic system shaped by consumer psychology, digital infrastructure, governance mechanisms, data intelligence, and crisis-driven behavioral shifts.

Building on global policy reviews and institutional analyses, particularly those offered by UNCTAD and the OECD, this article examines how lockdowns, supply chain disruptions, and changes in mobility fundamentally altered demand structures and digital adoption trajectories across developed and developing economies. It further explores micro-level behavioral dynamics by synthesizing research on impulsive buying, live-stream shopping, gender moderation effects, and personality-driven channel choice, demonstrating how psychological and social variables became magnified under pandemic conditions. The analysis extends into the technological core of e-commerce systems, emphasizing the role of big data analytics, business intelligence architectures, user-engagement metrics, recommendation systems, and service-level indicators in enabling platform resilience and scalability during periods of unprecedented demand volatility.

A distinctive contribution of this article lies in its cross-domain analytical perspective, which draws conceptual parallels between big data applications in e-commerce and in healthcare analytics, particularly stroke prediction and outcome modeling. By comparing methodological logics, data governance challenges, and predictive objectives across these domains, the study highlights convergent patterns in how large-scale data ecosystems support decision-making under uncertainty. The article also incorporates emerging managerial metrics such as customer acquisition cost optimization and cohort-based performance evaluation, positioning them within a broader strategic governance framework.

Methodologically, the study adopts a qualitative-integrative research design grounded in systematic literature synthesis and theoretical elaboration. The findings underscore that post-pandemic e-commerce ecosystems are characterized by heightened data dependency, intensified consumer heterogeneity, and increased ethical and sustainability considerations, particularly in cybersecurity and digital inclusion. The discussion critically evaluates limitations within current research and outlines future directions for interdisciplinary inquiry, emphasizing the need for more holistic models that integrate behavioral science, data engineering, and public policy. The article concludes that the pandemic has irreversibly transformed e-commerce into a core socio-technical infrastructure, demanding new theoretical lenses and governance paradigms to ensure inclusive, resilient, and sustainable digital economies.