<![CDATA[This study examines the predictive roles of digital financial literacy (DFL), financial stress (FS), financial self-efficacy (FSE), and peer influence (PI) in predicting predatory-lending vulnerability (PLV) among female industrial workers, a structurally exposed yet under-studied demographic within Indonesia's digital-credit ecosystem, where 6,991 illegal lending platforms were blocked between 2022 and 2024. A cross-sectional explanatory survey was administered to 378 female workers in three Batam integrated industrial estates using validated instruments (OECD/INFE DFL; Prawitz IFDFW; Lown FSES). Data were analyzed through PLS-SEM (SmartPLS 4) with bootstrapping (5,000 resamples), following STROBE reporting guidelines. All four hypotheses were supported (p < .001). The model explained 52.4% of the variance in PLV (Q² = .342). Financial stress was the strongest predictor (β = +.342), followed by peer influence (β = +.276), digital financial literacy (β = –.218), and financial self-efficacy (β = –.187), confirming a push-pull risk architecture. The single-region cross-sectional design limits causal inference and external validity, while self-reported use of illegal lenders likely understates the actual prevalence due to social desirability bias. The findings apply primarily to feminized manufacturing labor in Southeast Asian export zones. This study presents the first integrated PLS-SEM model testing cognitive, affective, psychological, and social predictors of predatory-lending vulnerability among Indonesian female industrial workers. It advances gender-sensitive financial-vulnerability research and provides rank-ordered evidence for demand-side interventions under OJK Regulation No. 11/2024, aligned with SDG 5 and SDG 8.]]>
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