International Journal of Finance & Banking Studies

This study analyzes the effect of sustainable investment awareness and risk perception on students' interest in green finance instruments, with financial literacy as a mediating variable. A quantitative approach was used, involving 180 student respondents in the Special Region of Yogyakarta, who were selected through a purposive sampling technique using an online questionnaire via Google Forms. Data analysis was conducted using the SEM-PLS method through SmartPLS version 4.1.1.2. The results showed that sustainable investment awareness, risk perception, and financial literacy positively and significantly affect student investment interest in green finance. In addition, financial literacy acts as a significant mediator in the relationship between sustainable investment awareness and risk perception on interest in investing in green finance. This study contributes to developing literature on sustainable investment behavior among the younger generation, especially university students, by integrating financial literacy variables as a mediating mechanism. The findings expand the understanding of how psychological factors and financial knowledge influence investment decisions in sustainability.  

This study looks at the effect of accounting flexibility on earnings quality in public companies listed on the Indonesia Stock Exchange during the period 2016 to 2019. This study uses two models to measure earnings quality, namely ERC and earnings persistence. The results showed that accounting flexibility, namely the limitations of companies to carry out accrual earnings management, can reduce earnings quality, as shown by the abnormal accrual model which shows a significant positive coefficient direction, and for the ERC model and earnings persistence which shows a negative and significant coefficient direction. The higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018). Theoretical contributions show that the higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018; Purba, Surya, Joshi & Tiagy, 2023). The Implications of this research show that accounting flexibility is carried out when abnormal accruals reach the highest limit point reflected in accounting choices in net operating assets in the balance sheet, so in this case it results in a low level of earnings informativeness and earnings persistence. 

This study investigates the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for predicting Bitcoin prices, with the objective of enhancing investment approaches. Given Bitcoin's significant volatility and inherent risk, accurate price forecasting is vital for making strategic decisions. The research evaluates model performance by analyzing Bitcoin price data spanning May 25, 2020, to May 25, 2024, employing quantitative methodologies like experimental design and predictive analysis. Results indicate the LSTM model achieves superior predictive accuracy over the RNN model, demonstrated by reduced Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) figures. Additionally, the research delves into how these models affect the Dollar Cost Averaging (DCA) investment strategy, offering perspectives on the advantages and drawbacks of using machine learning to optimize cryptocurrency investments. 

This research explores conservation practices at Borobudur, one of Indonesia's prominent heritage sites, and comprehends collaborative conservation strategies that preserve cultural heritage amidst rising tourist pressures. We employ a qualitative approach that incorporates narrative methods, in-depth interviews, triangulation, and observation. Furthermore, we used a thematic examination to achieve a deeper understanding of the data. The findings emphasize the necessity for mutual understanding and collaboration among stakeholders. The study highlights the challenges faced by disadvantaged groups, tour operator firms, pilgrim tourist services, and Taman Wisata Candi (TWC), which have the authority to provide service.   So, BKB, the responsible institution, has to encourage parties to collaborate on actions respectively. This study contributes to tourism service theory by offering strategic guidance to mitigate the adverse effects of conservation policies on stakeholders. It underscores the importance of collaborative efforts and tailored strategies.

Capital financing plays a crucial role in sustaining the growth potential of pharmaceutical companies, enabling them to expand operations and compete effectively in the market. In the face of a slowing global economy, maintaining solid financial performance across existing business lines becomes increasingly important. This study aims to examine the relationship between capital structure and the financial performance of pharmaceutical companies in Indonesia. The research sample consists of nine publicly listed pharmaceutical firms over the period 2013–2023. The empirical analysis employs three panel data estimation approaches—Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM)—to account for different assumptions about unobserved firm-specific characteristics. Based on the Hausman test, FEM is determined as the most suitable model for the dataset. The estimation results reveal that the Long-term Debt to Total Assets (LDTA) ratio exerts a statistically significant negative effect on Return on Assets (ROA), indicating that a higher proportion of long-term debt tends to reduce profitability. Conversely, the Short-term Debt to Total Assets (SDTA) and Total Debt to Total Equity (TDTE) ratios have positive but statistically insignificant impacts on ROA, suggesting that neither short-term liabilities nor overall leverage significantly influence profitability in the observed period. These results imply that excessive reliance on long-term borrowing can impose interest burdens and financial constraints, thereby weakening performance. Consequently, financial managers in the pharmaceutical sector are encouraged to optimize their financing structure, prioritizing internal funds or short-term financing sources that align with operational needs and market conditions to sustain profitability and ensure long-term competitiveness.

Capital financing plays a crucial role in sustaining the growth potential of pharmaceutical companies, enabling them to expand operations and compete effectively in the market. In the face of a slowing global economy, maintaining solid financial performance across existing business lines becomes increasingly important. This study aims to examine the relationship between capital structure and the financial performance of pharmaceutical companies in Indonesia. The research sample consists of nine publicly listed pharmaceutical firms over the period 2013–2023. The empirical analysis employs three panel data estimation approaches—Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM)—to account for different assumptions about unobserved firm-specific characteristics. Based on the Hausman test, FEM is determined as the most suitable model for the dataset. The estimation results reveal that the Long-term Debt to Total Assets (LDTA) ratio exerts a statistically significant negative effect on Return on Assets (ROA), indicating that a higher proportion of long-term debt tends to reduce profitability. Conversely, the Short-term Debt to Total Assets (SDTA) and Total Debt to Total Equity (TDTE) ratios have positive but statistically insignificant impacts on ROA, suggesting that neither short-term liabilities nor overall leverage significantly influence profitability in the observed period. These results imply that excessive reliance on long-term borrowing can impose interest burdens and financial constraints, thereby weakening performance. Consequently, financial managers in the pharmaceutical sector are encouraged to optimize their financing structure, prioritizing internal funds or short-term financing sources that align with operational needs and market conditions to sustain profitability and ensure long-term competitiveness.

This study looks at the effect of accounting flexibility on earnings quality in public companies listed on the Indonesia Stock Exchange during the period 2016 to 2019. This study uses two models to measure earnings quality, namely ERC and earnings persistence. The results showed that accounting flexibility, namely the limitations of companies to carry out accrual earnings management, can reduce earnings quality, as shown by the abnormal accrual model which shows a significant positive coefficient direction, and for the ERC model and earnings persistence which shows a negative and significant coefficient direction. The higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018). Theoretical contributions show that the higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018; Purba, Surya, Joshi & Tiagy, 2023). The Implications of this research show that accounting flexibility is carried out when abnormal accruals reach the highest limit point reflected in accounting choices in net operating assets in the balance sheet, so in this case it results in a low level of earnings informativeness and earnings persistence. 

This study analyzes the effect of sustainable investment awareness and risk perception on students' interest in green finance instruments, with financial literacy as a mediating variable. A quantitative approach was used, involving 180 student respondents in the Special Region of Yogyakarta, who were selected through a purposive sampling technique using an online questionnaire via Google Forms. Data analysis was conducted using the SEM-PLS method through SmartPLS version 4.1.1.2. The results showed that sustainable investment awareness, risk perception, and financial literacy positively and significantly affect student investment interest in green finance. In addition, financial literacy acts as a significant mediator in the relationship between sustainable investment awareness and risk perception on interest in investing in green finance. This study contributes to developing literature on sustainable investment behavior among the younger generation, especially university students, by integrating financial literacy variables as a mediating mechanism. The findings expand the understanding of how psychological factors and financial knowledge influence investment decisions in sustainability.  

This study examines the implementation of the vMedis ERP system at Greencare Pharmacy using Balanced Scorecard (BSC) and SWOT analysis. The research evaluates financial, customer, internal process, and learning perspectives, revealing significant improvements post-implementation. Key findings include a 15% profit margin increase, enhanced customer satisfaction, streamlined inventory management, and improved staff competencies. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed that these improvements across the BSC perspectives were significant (p < 0.05). SWOT analysis highlights internal strengths, such as financial stability and advanced technology adoption, and external opportunities like market growth potential. Challenges include regulatory complexity and high competition. Overall, vMedis demonstrates its effectiveness in enhancing operational efficiency and competitiveness in the healthcare sector.

This study investigates the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for predicting Bitcoin prices, with the objective of enhancing investment approaches. Given Bitcoin's significant volatility and inherent risk, accurate price forecasting is vital for making strategic decisions. The research evaluates model performance by analyzing Bitcoin price data spanning May 25, 2020, to May 25, 2024, employing quantitative methodologies like experimental design and predictive analysis. Results indicate the LSTM model achieves superior predictive accuracy over the RNN model, demonstrated by reduced Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) figures. Additionally, the research delves into how these models affect the Dollar Cost Averaging (DCA) investment strategy, offering perspectives on the advantages and drawbacks of using machine learning to optimize cryptocurrency investments.