Factors influencing big data analytics adoption in micro and small enterprises: the retail sector in Brazil

Purpose This study examines why micro and small enterprises (MSMEs) continue to lag in adopting big data analytics (BDA) despite its widely recognized strategic value. Drawing on the unified theory of acceptance and use of technology (UTAUT), we analyze how performance expectancy, effort expectancy and facilitating conditions shape adoption intentions among 500 Brazilian business owners and managers in the retail sector, while accounting for the moderating roles of experience, task usefulness, and market competition. Design/methodology/approach A structured questionnaire was used in a telephone survey via an automated telephone center. The analysis, based on PLS-SEM, identifies the most relevant factors affecting the adoption of business data analytics tools. Similarly, a stepwise estimation strategy was implemented for assessing the direct effects of core UTAUT constructs and for evaluating the antecedents and interaction terms that significantly increase explained variance in IABDA. Findings Using PLS-SEM, the findings reveal a paradox: although performance expectancy strongly drives adoption intention, prior experience with data analytics reduces willingness to adopt, suggesting that familiarity may expose hidden implementation challenges rather than alleviate them. Contrary to dominant assumptions, effort expectancy does not significantly influence adoption decisions, indicating that perceived complexity is not a primary deterrent in this context. This suggests that resource constraints, such as budget constraints and the lack of specialized personnel, create barriers that hinder adoption despite prior familiarity with technology. Originality/value The study contributes to the literature by shifting the focus from structural barriers to cognitive and experiential mechanisms underlying technology adoption in MSEs. It highlights that adoption decisions are not solely driven by perceived benefits but are shaped by nuanced interpretations of feasibility, usefulness and prior experience. These insights offer important implications for policymakers and practitioners seeking to accelerate data-driven transformation in resource-constrained firms.