The Paradox of AI Adoption in Emerging Economies: A Structural Equation Analysis of Usage Intensity and SMEs Performance

Purpose: The Jakarta Provincial Government aims for 80% of MSMEs to be digitalized by 2025; however,empirical evidence on whether the intensity of Artificial Intelligence (AI) use truly enhances business performance in developing economies remains inconclusive. This study examines the relationship between AI usage intensity and business performance among MSMEs in Jakarta, while accounting for firm size variations. Method: A cross-sectional survey was conducted among 300 MSME owners/managers in Jakarta’s five administrative regions who had used at least one AI tool in the past 3 months. The Technology Acceptance Model (TAM) was extended with a Resource-Based View (RBV) perspective and firm size variables (micro, small, medium). Data were analyzed using CB-SEM with Maximum Likelihood estimation and FIML to handle missing data. Results: The model demonstrated an excellent fit (χ2/df = 1.13; CFI = 0.993; RMSEA= 0.021; SRMR = 0.018). However, AI usage intensity did not have a significant direct effect on business performance (β = 0.085; p = 0.113). Firm size had a substantial direct effect on performance (small: β = 0.446, p < 0.001; medium: β = 0.548, p < 0.001). Small firms tended to have higher AI usage intensity (β = 0.269, p < 0.001). Nevertheless, mediation analysis confirmed that AI usage did not function as a significant mechanism for improving performance among small or medium firms. Implications: The findings indicate the presence of adoption without impact—access to and intensity of AI use alone are insufficient; business value emerges only when complementary resources (dynamic capabilities, data governance, and human resource skills) are available. Policy programs should therefore integrate managerial training and infrastructure financing rather than merely providing technology license subsidies. Originality/Value: This study is among the earliest quantitative examinations in the ASEAN context exploring the relationship between AI usage intensity and performance among MSMEs, using a SEM–TAM approach that incorporates firm size as a contingency variable.