Does Intellectual Capital Efficiency Translate into Financial Performance for Islamic Banks in Indonesia? Evidence from the Pre-Pandemic Period

This study investigates the determinants of financial performance in Indonesian Islamic banks, focusing on the Financing to Deposit Ratio (FDR) and Capital Adequacy Ratio (CAR) as key indicators. Utilizing data from Bank Syariah Indonesia (BSI) and Bank Muamalat Indonesia (BMI) between 2016 and 2020, the research employs Ordinary Least Squares (OLS) regression analysis to examine the influence of Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE) on FDR and CAR. The findings reveal that while CEE has a statistically significant negative relationship with FDR, the model for CAR lacks a statistically significant fit. This research contributes to understanding the role of intellectual capital in Islamic banking performance and offers insights for stakeholders in the Indonesian Islamic finance sector, with implications for the post-pandemic era.


Introduction
Islamic banking in Indonesia plays a crucial role in the nation's economic landscape, adhering to unique financial Intellectual Capital (IC) encompasses a company's intangible assets, including knowledge, information, intellectual property, and experience, which contribute to competitive advantage and wealth creation (Stewart, 2010).This aligns with the resource-based theory, positing that unique resources like IC can create sustainable profits for companies that effectively leverage them (Mavridis, 2004).Scholars have developed frameworks for measuring IC, focusing on three core components: Separate OLS regression models were estimated for each dependent variable.The general model specification can be represented as follows: Where: Y represents the dependent variable (either FDR or CAR) α represents the constant term β 1 , β 2 , β 3 represent the regression coefficients for the independent variables HCE, SCE, and CEE, respectively X 1 , X 2 , X 3 represent the independent variables (HCE, SCE, and CEE) ε represents the error term The following linear regression models were estimated for FDR and CAR:

Sample Size and Limitations
The study utilizes panel data, with two cross-sections (BSI and BMI) and five time periods (2016)(2017)(2018)(2019)(2020).This results in a total of 10 observations (2 x 5).While this falls short of the recommended 40 observations for panel data regression (4 variables x 10), the limited sample size is acknowledged as a limitation of the study.The limited number of observations may affect the reliability and robustness of the results, leading to high variability in estimates and low statistical power.To address this limitation, the study employs panel data analysis techniques, which allow for controlling both individualspecific and time-specific effects, thereby improving the efficiency and accuracy of estimates.However, the effectiveness of these techniques may be limited by the small sample size.
The study also recognizes the potential for omitted variable bias or model misspecification issues.To mitigate these concerns, robustness checks are performed to assess the sensitivity of the results to different model specifications, sample selections, or estimation techniques.Despite these limitations, the study aims to provide valuable insights into the relationship between intellectual capital components and the financial performance of Indonesian Islamic banks.The findings should be interpreted cautiously, considering the specific context and timeframe of the analysis.

Classical Assumption Tests
The study conducts classical assumption tests to ensure the validity of the OLS estimates.These tests include multicollinearity, normality, heteroscedasticity, and autocorrelation.The results of these tests are compared between BSI and BMI to determine the feasibility of proceeding with hypothesis testing.

Data Analysis
The Ordinary Least Squares (OLS) method was employed to estimate the coefficients ( ) of the regression models.OLS minimizes the sum of squared residuals between the predicted and actual values of the dependent variable.The statistical significance of the estimated coefficients was assessed using t-tests.The goodness-of-fit of the models was evaluated using various metrics, including the coefficient of determination (R-squared), adjusted R-squared, F-statistic, and Durbin-Watson statistic.R-squared and adjusted R-squared measure the proportion of variance in the dependent variable explained by the independent variables.The F-statistic tests the overall significance of the model, while the Durbin-Watson statistic assesses the presence of autocorrelation in the residuals.The data analysis was conducted using E-Views.

Descriptive Statistics
The descriptive statistics for both banks indicate minimal variability in the independent variables (HCE, SCE, and CEE) and the dependent variables (FDR and CAR).This is evidenced by the standard deviation value, which is smaller than its average.

Classical Assumption Tests
Prior to interpreting the Ordinary Least Squares (OLS) regression results, a series of classical assumption tests were conducted to ensure the validity of the estimates.These tests aim to verify that the model adheres to the assumptions necessary for BLUE (Best Linear Unbiased Estimator) properties.It is crucial to compare the results of these tests between BSI and BMI to determine the feasibility of proceeding with hypothesis testing.The results of the classical assumption tests suggest no major violations for either BSI or BMI, allowing for further analysis using OLS regression.

Goodness-of-Fit Test
The goodness-of-fit test, also known as the model evaluation test, assesses how well the estimated regression model explains the variation in the dependent variable.This evaluation is achieved through a combination of measures: the coefficient of determination (R-squared), the F-statistic test, and the t-test for individual parameters.CEE has a statistically significant negative relationship with FDR (β₃ = -5.307860,p = 0.0003), while HCE and SCE have no significant effects.From the regression results, it can be inferred that the constant term (C) has a value of 90.46087.This implies that if the values of HCE, SCE, and CEE remain unchanged, the FDR for each study period (quarterly) will be 90.46087.The coefficient for HCE (-0.729193) indicates that a 1 rupiah increase in HCE will lead to a decrease in FDR by 0.729193 percent, assuming that the other variables remain fixed.This estimation result has no effect.Similarly, the coefficient for SCE (3.844742) suggests that a 1 rupiah increase in SCE will result in an increase in FDR by 3.844742, assuming that the other variables remain constant.This estimation result has no effect.On the other hand, the coefficient for CEE (-5.307860) indicates that a 1 rupiah increase in CEE will lead to a decrease in FDR by 5.307860 percent, assuming that the other variables remain constant.In this case, the estimation result has an effect.Table 3 presents the regression results for CAR.The model lacks a statistically significant fit (F-statistic p = 0.084949 > 0.05), and none of the independent variables have significant effects on CAR.Thus, it can be inferred that the constant C value of 14.28088 implies that if the variables HCE, SCE, and CEE remain unchanged, they will increase by 14.28088.
The coefficient value for HCE is 0.116441, indicating that an increase of 1 rupiah in HCE will result in an increase of 0.116441 percent in CAR, assuming other variables are held constant (the estimation results are not affected).The coefficient value for SCE is 0.005539, suggesting that an increase of 1 rupiah in SCE will lead to an increase of 0.005539 percent in CAR, assuming other variables remain constant (the estimation results are not affected).The coefficient value for CEE is -0.905131, signifying that an increase of 1 rupiah in CEE will result in a decrease of 0.905131 percent in CAR, assuming other variables remain constant (the estimation results are affected).

Coefficient of Determination (Adj. R 2 )
The coefficient of determination signifies the proportion of the variance in the dependent variable explained by the

F-test (Simultaneous Significance)
The F-statistic test evaluates the joint significance of all independent variables in explaining the dependent variable.In the FDR model, the F-statistic p-value (0.003) is less than 0.05, indicating that the model is jointly significant.In the CAR model, the F-statistic p-value (0.084949) is greater than 0.05, implying that the model is not jointly significant at the 5% level.
These findings suggest that the chosen model explains a significant portion of the variation in FDR, with CEE being the only independent variable with a statistically significant individual effect.The CAR model does not jointly explain a significant portion of the variation in the data, and none of the individual independent variables have statistically significant effects.

Discussion
The presented regression analysis examines the factors influencing the Financing to Deposit Ratio (FDR) and Capital Adequacy Ratio (CAR) of two Islamic banks (BSI and BMI) over a specific period (2016-2020).The results offer insights into the relationship between human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE) and these key financial ratios.The analysis offers valuable insights applicable to the post-pandemic era.
The pre-pandemic period is considered a normal period for both banks, similar to the post-pandemic reality referred to as the "new normal" era.This study focuses on the pre-pandemic period due to insufficient observations for conducting robust research in the post-pandemic period, which was officially started on June 21, 2023.Therefore, the pre-pandemic data, representing a normal period, is considered representative and can provide suggestions for the post-pandemic "new normal" period.By using pre-pandemic data, the study aims to draw conclusions and offer recommendations that can be applied to the post-pandemic era, as both periods represent relatively stable economic conditions for the banks.This approach allows for a more comprehensive understanding of the relationships between HCE, SCE, CEE, and the financial ratios (FDR and CAR) during the normal pre-pandemic period, which can provide guidance and suggestions for managing these factors in the "new normal" post-pandemic environment.The details of the discussion are as follows: Pre-Pandemic Context The model for the financing-to-deposit ratio (FDR) demonstrates a good fit, explaining nearly half (48.99%) of the variation in the data.This indicates that Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE) collectively play a significant role in determining the ability of Islamic banks to distribute funds to the public relative to their deposits.Among the independent variables, only CEE has a statistically significant negative impact on FDR.This suggests that during the observed period, CEE played a key role in determining the FDR of Islamic banks.
Increased efficiency in capital usage may have led to two main outcomes.Firstly, it may have resulted in conservative lending practices, with banks holding onto more deposits as a buffer.This reflects a cautious approach to lending even in a relatively stable economic environment.Secondly, efficient capital utilization could indicate that banks were prioritizing specific investment opportunities, potentially impacting the allocation of funds for financing activities.
It is noteworthy that HCE and SCE do not have significant effects on FDR in this model.This suggests that within the observed timeframe (pre-pandemic period), human capital and structural capital efficiency may not be primary drivers of variations in the banks' ability to distribute funds.
Moving on to the model for the Capital Adequacy Ratio (CAR), it does not achieve a statistically significant fit.This indicates that the chosen independent variables (HCE, SCE, and CEE) collectively do not explain a significant portion of the variation in the banks' CAR.Additionally, none of the individual independent variables have statistically significant effects on CAR.This suggests that factors beyond HCE, SCE, and CEE likely play a more prominent role in determining the capital adequacy of banks.These factors could include external regulatory requirements, risk management strategies, and overall economic conditions.The lack of significant influence from HCE, SCE, and CEE on CAR suggests that factors beyond these internal efficiency measures were more crucial for maintaining capital adequacy during the observed prepandemic period.These factors may include regulatory requirements, risk management strategies, and the stability of the economic environment.
The findings suggest that Capital Employed Efficiency (CEE) has a significant negative impact on FDR in the prepandemic era.This implies that as Islamic banks became more efficient in utilizing their capital, they may have held onto a larger portion of deposits or become more selective in financing activities, leading to a lower FDR.

Relevance to the Post-Pandemic Era
The COVID-19 pandemic has had a profound impact on the economic landscape, presenting both challenges and opportunities for Islamic banks.In the post-pandemic era, the focus on capital efficiency (CEE) becomes even more crucial.Banks may need to exercise greater caution with their capital due to two main factors.Firstly, there is an increased risk of loan defaults.The pandemic has led to a rise in non-performing loans, which requires banks to hold onto more capital as a buffer.This is necessary to mitigate the potential losses resulting from these defaults.Secondly, economic uncertainty persists in the post-pandemic recovery.Banks may prioritize efficient capital allocation to navigate this uncertainty.By ensuring that capital is allocated effectively, banks can better manage risks and adapt to the changing economic landscape.
While the factors influencing capital adequacy ratio (CAR) pre-pandemic, such as regulations, risk management, and economic conditions, remain important, adjustments may be necessary in the post-pandemic era.Regulatory bodies may need to modify capital adequacy requirements in response to the pandemic's impact on the banking sector.Additionally, banks may need to refine their risk management strategies to address new risks arising from the economic fallout of the pandemic.The overall pace of post-pandemic economic recovery will also influence the level of capital adequacy that banks need to maintain.
The pandemic has significantly affected economic activity and financial institutions globally.Islamic banks may have adopted stricter capital management practices post-pandemic due to increased uncertainty.This aligns with the prepandemic finding of a negative relationship between CEE and loan defaults, suggesting a potential continuation of this trend.However, the post-pandemic era may introduce new dynamics.Governments and central banks may encourage lending to stimulate economic recovery, potentially leading to a rise in loan defaults.Islamic banks may also need to adapt their financing strategies to cater to the evolving needs of businesses and individuals in the post-pandemic period.
The results indicate that the average financing-to-deposit ratio (FDR) of two banking units is reasonable in maintaining liquidity.The capital adequacy ratio (CAR) for these units is also relatively good, with average values of 13.83% and 14.00% respectively.This suggests that these banks have the ability to cover the decline in assets resulting from bank losses caused by risky assets.These findings support previous studies conducted by Wahyudin (2023), Fadila and Pangestuti (2022), Ghifar et al. (2022), and(Kocaoğlu, 2010).
The COVID-19 pandemic has brought about significant changes to the economic landscape, posing challenges and opportunities for Islamic banks.The emphasis on capital efficiency becomes even more crucial in the post-pandemic era.
Banks may need to exercise caution with their capital due to increased loan defaults and economic uncertainty.While prepandemic factors influencing capital adequacy remain important, adjustments may be necessary in response to regulatory changes and the need for enhanced risk management.Islamic banks may also need to adapt their financing strategies to cater to the evolving needs of businesses and individuals in the post-pandemic period.
The regression model for FDR demonstrated a good fit, explaining approximately 49% of the variance.This suggests that HCE, SCE, and CEE collectively play a significant role in determining the FDR of Indonesian Islamic banks.Among these variables, only CEE had a statistically significant negative impact on FDR.This indicates that increased efficiency in capital usage led to more conservative lending practices, with banks retaining a larger portion of deposits as a buffer.This cautious approach likely reflects a strategy to mitigate potential risks and maintain liquidity, even during a stable economic This suggests that increased efficiency in capital utilization by the banks might be associated with a lower ratio of financing to deposits, potentially due to more conservative lending practices or selective financing activities.
The model for CAR did not achieve a statistically significant fit, indicating that the chosen variables (HCE, SCE, and CEE) do not collectively explain a significant portion of the variation in this ratio.Additionally, none of these variables had a statistically significant individual effect on CAR.This implies that factors beyond these internal efficiency measures, such as regulatory requirements, risk management strategies, and economic conditions, likely play a more prominent role in determining the capital adequacy of Islamic banks.
The study acknowledges several limitations, including the specific time period, limited sample size, and potential omission of other relevant variables.Future research can build upon these findings by: 1. Expanding the sample size and timeframe to enhance generalizability.

2.
Investigating the influence of additional variables on FDR and CAR, such as market competition, regulatory changes, and economic fluctuations.By addressing these limitations and incorporating a more comprehensive set of variables, future research can provide deeper insights into the complex dynamics influencing the financial performance of Islamic banks, particularly in the evolving post-pandemic economic landscape.This will help stakeholders better understand how intellectual capital components, regulatory factors, risk management strategies, and the broader economic environment interact to shape the Qeios, CC-BY 4.0 • Article, June 5, 2024 Qeios ID: CHBTCM.2 • https://doi.org/10.32388/CHBTCM.2 1/15

3 .
Exploring the impact of the COVID-19 pandemic on the relationships between HCE, SCE, CEE, FDR, and CAR in Islamic banks.

Table 2 .
FDR Regression Test

Table 2
presents the regression results for FDR.The model explains 48.99% of the variation in FDR (Adj.R² = 0.4899).

Table 3 .
CAR Regression Test 99% of the variation in FDR.The remaining 51.01% of the variation is attributed to other factors not included in the model or error terms.Similarly, the Adjusted R 2 value of 0.2056 in the CAR model suggests that the model explains 20.56% of the variation in CAR, with the remaining 79.44% explained by other factors.The t-test assesses the statistical significance of the estimated coefficients for each independent variable.In the FDR model, the p-value for the CEE coefficient is 0.0003, indicating that CEE's effect on FDR is statistically significant at the 5% level.The p-values for HCE and SCE are greater than 0.05, signifying their effects on FDR are not statistically significant.Similarly, in the CAR model, the p-value for the CEE coefficient is 0.0208, indicating statistical significance, while HCE and SCE are not significant.
The insignificance of HCE and SCE in influencing FDR suggests that human capital and structural capital efficiency were not primary drivers of financing activities during the pre-pandemic period.This finding underscores the need for further investigation into other potential factors influencing FDR, such as market demand, competitive dynamics, and the specific nature of financing products offered.Conversely, the regression model for CAR did not achieve a statistically significant fit, indicating that HCE, SCE, and CEE did not collectively explain a substantial portion of the variance in CAR for the studied banks.This suggests that other factors, such as regulatory requirements, risk management strategies, and macroeconomic conditions, played a more dominant role in determining capital adequacy during the pre-pandemic period.ConclusionThis study investigated the determinants of the Financing to Deposit Ratio (FDR) andCapital Adequacy Ratio (CAR)in two Indonesian Islamic banks, Bank Syariah Indonesia (BSI) and Bank Muamalat Indonesia (BMI), for the pre-pandemic period of 2016-2020.The analysis employed Ordinary Least Squares (OLS) regression to examine the influence of Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE) on these key financial ratios.The model for FDR achieved a good fit, explaining nearly half (48.99%) of the variation in the data.CapitalEmployed Efficiency (CEE) emerged as the only statistically significant factor, exhibiting a negative relationship with FDR.