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KOMPARASI TINGKAT KESEHATAN BANK ANTARA BANK KONVENSIONAL DAN BANK DIGITAL BERDASARKAN METODE RGEC Saputra, Septian Rahul Dika; Tarigan, Thia Margaretha; Prasetyo, Christianus Yudi; Setiabudi, Andang Wirawan
Jurnal Akuntansi Vol 18 No 1 (2024): Jurnal Akuntansi
Publisher : Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jak.v18i1.5160

Abstract

The research aims to compare the soundness level of banks between conventional banks and digital banks that have been listed on the Indonesia Stock Exchange in 2020 and 2021 using the Risk Profile, Good Corporate Governance, Earning, Capital and RGEC methods. The research was conducted using a descriptive research type with the use of secondary data originating from the website www.idx.com and annual reports issued by banking companies officially to support the research. The sample was done using the purposive sampling technique. The data analysis technique used is the Risk-based Bank Rating method. The results of the study show that conventional banks will be healthier than digital banks in 2020 and 2021 based on 4 assessment aspects, namely: Good Corporate Governance, Earning, Capital, and RGEC methods. Meanwhile, based on the Risk Profile factor, in 2020 and 2021, conventional banks and digital banks will have the same level of soundness.
Large Language Models in Accounting Tasks: Driving Factors and Ethical Dilemmas Among Accounting Students Josephine, Katherine Olivia; Tarigan, Thia Margaretha; Weli, Weli
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2531

Abstract

This research aims to identify the key factors that affect accounting students’ intention to adopt and the actual usage of Large Langauge Models (LLMs), including ChatGPT, in academic contexts. It also addresses ethical concerns that may arise from their use. Using a quantitative design, data were collected through an online survey involving 302 students from various universities in the Greater Jakarta area who had prior experience using LLMs. This research aims to address the gap in literature on AI-based technology acceptance within the accounting field by extending the Technology Acceptance Model (TAM) with trust and academic ethics. The study offers a theoretical contribution by deepening insights into technology acceptance within accounting education and a practical contribution by emphasizing the integration of ethical considerations in the use of LLMs in higher education. The study focuses on key constructs including perceived ease of use, perceived usefulness, trust, academic ethics, behavioral intention, and actual usage behavior. Data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique via SmartPLS 4 software. The results show that all examined factors positively influence students’ intention to use LLMs, with perceived usefulness stands out as the most significant driver. Furthermore, behavioral intention significantly predicts actual use, suggesting that students who see practical value in these tools are more likely to adopt them in their learning routines. What sets this research apart is its integration of motivational and ethical dimensions in examining technology acceptance within accounting education.