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Implementation of the institutional level financial application (sakti) in financial management at pusdatin, ministry of atr/bpn : the user acceptance analysis using the technology acceptance model Salsabila, Cahaya Nisrina; Gandhy, Abel; Herdiyana, Herdiyana
Jurnal Mantik Vol. 9 No. 4 (2026): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v9i4.7072

Abstract

The process of digital transformation in public financial management has sped up the use of integrated financial information systems, nevertheless empirical evidence on user acceptance in mandatory institutional Financial Application System (SAKTI) at Pusat Data dan Informasi (PUSDATIN), Kementerian ATR/BPN, Indonesia, using the Technology Acceptance Model (TAM). A descriptive quantitative method was employed to collect data from five SAKTI operators and finance department staff through likert scale questionnaires, direct observations, and semi structured interviews. The four TAM constructs : Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention to Use (BI), and Actual System Use (AU) all archieved very high mean scores (over 4.25 on 5 point scale), indicating strong user acceptance. However, technical challenges such as system lag during peak periods, module glitches, and OTP delivery failures were identified as barriers impacting PEOU and overall system reliability. This study provides an empirical application of TAM in a mandatory government financial system context, demonstrating that while perceptions of usefulness drive acceptance, infrastructure quality significantly moderates system performance. The findings suggest that policymakers and system administrators should prioritize infrastructure investments and optimized maintenance schedules to sustain user adoption. This study is limited by its small sample size and single agency focus, future research should incorporate larger samples, structural equation modeling (SEM), and multi agency comparisons to enhance generalizability.