AI Adoption in Retail: Social Influences, Trust, and Technological Readiness Across Emerging and Developed Economies A Literature Review and the STAR-AI Framework

artificial intelligence, retail adoption, social influence, trust calibration, technological readiness, UTAUT2, Technology Readiness Index, cultural dimensions, perceived risk, algorithmic transparency, omnichannel, Generation Z, emerging economies, developed economies, STAR-AI Framework

Artificial intelligence (AI) is fundamentally transforming the retail sector through personalised recommendations, conversational agents, and autonomous decision-making systems. Adoption patterns, however, differ markedly between emerging and developed economies due to variations in cultural values, trust dynamics, and technological infrastructure. This literature review synthesises empirical evidence from 31 peer-reviewed studies, selected from 630 records across five major databases in accordance with PRISMA guidelines (2020–2024), to examine how social influence, trust, and technological readiness shape consumer AI adoption. Through a comparative analysis of India, representing an emerging economy, and Australia, representing a developed economy, the study introduces the STAR-AI (Social, Trust, Adoption, and Readiness for AI) Framework as an integrative theoretical contribution. The findings indicate that social influence operates through informational, normative, and identification mechanisms, exerting a stronger impact on consumer attitudes in collectivist cultures, such as India, than in individualistic cultures, such as Australia. Trust emerges as a dynamic construct, ranging from initial transparency to performance-based calibration. Technological readiness, which includes digital infrastructure, AI literacy, and generational disposition, significantly moderates adoption intentions. High uncertainty avoidance is found to reduce the impact of performance and effort expectancies, while Generation Z exhibits distinct adoption behaviours driven by digital fluency and heightened personalisation expectations. The STAR-AI Framework integrates macro-level contexts, meso-level antecedents, individual-level constructs, and specific AI touchpoints to predict adoption outcomes. The study offers actionable managerial and policy implications, recommending culturally tailored strategies such as leveraging community endorsements in collectivist markets and prioritising algorithmic transparency in high-uncertainty-avoidance cultures, alongside robust data privacy and AI literacy initiatives