An ISS-grounded structural model of QRIS usage: quality, trust, and intention-behavior translation: Evidence from Indonesia
DOI: https://doi.org/10.3846/bmee.2026.25324Abstract
Purpose – This study examines how Indonesia’s QRIS translates willingness into actual use via the Information Systems Success (ISS) model, focusing on quality dimensions, trust, regulatory support, and the intention-behavior link.
Research methodology – A survey of 493 QRIS users (March–August 2025) was analyzed using PLS-SEM with 5,000 bootstrap resamples to test hypothesized relationships and the moderating role of perceived regulatory support.
Findings – Information quality was the strongest driver of trust (β = 0.539, t = 13.793), followed by system quality (β = 0.305, t = 8.449) and service quality (β = 0.086, t = 2.784). Trust significantly increased intention to use (β = 0.393, t = 7.908), while perceived regulatory support did not moderate the trust–intention link (β = 0.033, t = 0.655). Intention strongly predicted actual use (β = 0.762, t = 32.762), with substantial explained variance (R²trust = 0.678; R²intention = 0.595; R²use = 0.581) and acceptable model fit (SRMR = 0.078).
Research limitations – The information-quality measure emphasizes presentation/usability over content veracity, which may inflate its effect on trust; future studies should disaggregate these facets (e.g., hierarchical/bifactor models). The model omits factors such as habit, social influence, incentives, merchant density, and perceived risk-multi-level data are advised.
Practical implications – To convert intention into real payment behavior, providers should prioritize interface clarity and transaction-confirmation cues (readable amounts, fast receipts, clear failure explanations), system responsiveness and task completion reliability, and service recovery mechanisms (guided dispute/refund flows with predictable resolution).
Originality/Value – This study offers early empirical evidence applying ISS to a national interoperable QR ecosystem and quantifies the trust-based mechanism linking quality perceptions to actual fintech usage, providing actionable guidance for QRIS providers and policymakers in developing-country contexts.
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information quality, service quality, system quality, trust, intention to use, actual usageHow to Cite
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