Analyzing the impact of data privacy awareness and digital financial literacy in enhancing quick response payment adoption in Indonesia

Purpose This study aims to examine the role of data privacy awareness (DPA) and digital financial literacy (DFL) in influencing quick response (QR) code payment adoption by integrating protection motivation theory (PMT) and the technology acceptance model (TAM) into a unified framework. It addresses a critical gap in understanding how privacy perceptions and financial competencies jointly shape digital payment adoption in emerging financial ecosystems. Design/methodology/approach Data were collected from 312 Indonesian respondents through a structured survey. The research model was tested using partial least squares-structural equation modeling to assess measurement validity and structural relationships, including mediation and moderation effects. Findings Results confirm that DPA significantly and positively influences QR payment adoption. PMT constructs (perceived severity, vulnerability, response efficacy, self-efficacy and response cost) and TAM constructs (perceived usefulness and ease of use) predict DPA, whereas perceived security has no effect. Mediation analysis demonstrates that DPA partially mediates the relationship between PMT, TAM and adoption, while DFL strengthens the DPA–adoption link, highlighting its moderating role. Research limitations/implications The cross-sectional design and Indonesian sample may limit generalizability. Practical implications The findings underscore the need for privacy management tools and literacy programs to enhance trust and adoption. Strengthening privacy protection and digital literacy can foster secure, inclusive and sustainable financial ecosystems, advancing consumer empowerment and financial inclusion. Originality/value This study contributes to theory by positioning DPA as a central mediator and introducing DFL as a contextual moderator within an integrated PMT–TAM framework, offering a more holistic explanation of adoption behavior in data-sensitive contexts.