Artificial Intelligence-Driven Commerce: Consumer Trust, Adoption Behaviour and Firm Performance a Mixed-Methods Investigation Across Emerging and Developed Markets

Abstract

This study examines how AI-driven commerce shapes consumer trust, technology adoption and firm performance across six countries spanning emerging economies (India, Nigeria, Brazil) and developed economies (the United States, the United Kingdom, Germany). Three theoretical frameworks the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Trust-Risk Theory were combined within a single hierarchical structural equation model (SEM), with each framework contributing distinct constructs: TAM provided the adoption antecedents; UTAUT2 introduced contextual moderators; and Trust-Risk Theory supplied the mediating constructs of consumer trust and perceived risk. Survey data from 847 consumers and 214 firm managers, stratified by gender, age, income quartile and urban-rural status, were analysed alongside 32 semi-structured interviews with industry practitioners and regulators. Perceived usefulness, algorithmic transparency and data privacy assurance emerged as the strongest trust-building predictors across all markets, though their relative weight varied by context. Consumers in emerging markets placed comparatively greater emphasis on performance expectancy and peer endorsement, while those in developed markets prioritised algorithmic explainability and privacy controls differences traceable to cultural profiles and divergent regulatory regimes including the EU’s GDPR, Brazil’s LGPD and Nigeria’s NDPR. At the organisational level, AI adoption improved efficiency, customer retention and revenue, but only where implementation was sufficiently mature and complementary capabilities were in place.