Based on the table above, it shows that all government commercial banks have published financial reports from 2019-2023 and show that none of them experienced losses. These results illustrate that the research data used as part of the background can be used to support this research.

According to data released by the Financial Services Authority (OJK), there was a decline in the ROA of commercial banks from 2.69% to 2.56% on an annual basis (YoY) as of May 2024. This decline shows the challenges faced by the banking sector in maintaining profitability amid economic dynamics and changes in interest rates. One of them is at Bank BTN in 2023, BTN's ROA fell from 0.9% to 0.8% due to pressure on the net interest margin (NIM), especially the increase in the cost of funds. BTN's improvement efforts seek to reduce the cost of funds through various digital banking initiatives and increase business in the consumer funding area. The target is to reduce the cost of funds to 3.8% by the end of 2023. BTN is also trying to keep ROA below 1.0%. This data is also used as part of the Background that can be used to support this research.

As per Rena Vidia Hodijah et al. (2023), "Value Added Capital Employed (VACA) and Structural Capital Value Added (STVA) have an effect on Return on Assets (ROA)." This means that the financial performance of an Islamic bank will be impacted if a bank manages its capital employed, human capital, and structural capital effectively. In contrast, Ghadah, Mufidah, et al. (2024) state that "the structural capital coefficient and value added intellectual coefficient do not have a significant effect on price book value in the same period and object, while the variables of value added capital employed and return on assets affect the price book value partially."

The researcher is interested in carrying out a study with the same title and variables, but at a different location and time, based on the background information and conclusions from earlier research. Thus, "The Effect of Value Added Capital Employed (VACA) and Structural Capital Value Added (STVA) on Return on Assets (ROA) in Indonesian State-Owned Banks" is the title that the researcher has chosen.

The capital element evaluates how well a bank's capital supports its many needs. Since the bank's capital evaluation is predicated on its duty to meet the set minimum capital requirements, this factor is related to the level of solvency (Wufron2, 2019). The solvency level is crucial for a number of reasons, including figuring out how well the bank can withstand unforeseen losses, figuring out how much money is needed to finance its operations, figuring out how much of the bank's wealth is owned by shareholders, and figuring out how much capital the bank has. Management can effectively run operations in line with shareholder expectations thanks to this knowledge (Mieke Yustia Ayu Ratna Sari, 2020).

Informational and knowledge-based resources make up intellectual capital, an intangible asset that helps businesses become more competitive and operate better (Segoro, 2019). Financial statements do not now formally contain intellectual capital. After the recognition of intangible assets was governed by paragraph 9 of the Statement of Financial Accounting Standards (PSAK) No. 20 of 2000, the idea of intellectual capital started to take shape. However, it has been acknowledged that intellectual expertise is a crucial component in propelling growth and giving businesses a sustained competitive edge (Wulan Dwi Astuty, 2024).

Intellectual Capital is an intangible asset consisting of informational and knowledge-based resources that contribute to enhancing competitiveness and company performance (Segoro, 2019). To date, intellectual capital has not been formally included in financial statements. The concept of intellectual capital began to develop after the recognition of intangible assets was regulated in paragraph 9 of the Statement of Financial Accounting Standards (PSAK) No. 20 of 2000. Nevertheless, intellectual knowledge has been recognized as a key factor in driving development and achieving sustainable competitive advantage for companies (Wulan Dwi Astuty, 2024).

VAIC™ is an instrument used to measure the performance of a company's Intellectual Capital, consisting of:

Value Added Capital Employed (VACA): This component reflects the company’s ability to manage its physical capital; when handled properly, financial performance can improve. VACA is determined by how well the company uses its capital for each unit of value added it can generate (Azahra & Gustyana, 2020).

Structural Capital Value Added (STVA): The final component, STVA, reflects the ability of structural capital to contribute to value creation. According to Azahra and Gustyana (2020), structural capital represents the infrastructure that supports employee productivity—such as databases, software, patents, and trademarks—and includes organizational infrastructure, routines, procedures, and culture. Afriyani and Suzan (2021) stated that strong structural capital helps individuals to explore new ideas and continuously learn and grow (Badri, 2024). Resource-Based Theory

According to Wernefelt (1984) as cited in Wijayani (2017), the resource- based theory explains that the resources owned by a company can become competitive advantages that enable the company to achieve good long-term performance. This theory emphasizes the importance of effective and efficient management and utilization of resources so that the company can create added value and advantages over its competitors (Sujana Romayudi, 2020).

VACA and STVA are measurements of intellectual capital, which is part of intangible assets regulated under PSAK No. 19. The standard states that to be recognized as intangible assets, an item must fulfill three criteria: identifiability, control over the resource, and the expectation of future economic benefits (Indonesian Financial Accounting Standards Board, 2015).

A measure of the amount of Value Added that may be produced from a single unit of human capital is called VACA. According to Putri et al. (2023), this ratio illustrates how each unit of capital used contributes to the rise or fall in the company's VA.

Using the Pulic (1997) formula, this VA is determined as the difference between output (OUT) and input (IN) as follows:

= –

OUT = Output (total sales and other income)

IN = Input (operational and non-operational expenses except employee expenses)

Here is the formula for finding the VACA value of the public model:

= VA/VE

VACA = Ratio of Value Added to Capital Employed VA = Value Added

CE = Capital Employed (total equity)

STVA is an indicator to determine how much structural capital can be generated from the creation of value added. This ratio shows the contribution generated from structural capital in the success of the value creation process.

= SC/VA

STVA = ratio of Structural Capital to Value Added VA = Value added

SC = Structural Capital (Value Added – Human Capital)

VAIC is the sum of the three previous components, namely VACA, VAHU, and STVA with the following formula:

= + +

Hery (2018) states that the general goals and advantages of profitability ratios are as follows: determining the amount of net income generated from each rupiah of funds invested in total assets; determining the amount of net income generated from each rupiah of funds invested in total equity; determining the gross profit margin on net sales; determining the operating profit margin on net sales; determining the net profit margin on net sales; and evaluating the company's profit position by comparing prior years with the current year.

Return on Assets (ROA) is one of the profitability ratios. Profitability is a ratio that can be used to describe the extent to which an entity succeeds in generating profit from the management of its total assets, derived from sales activities, asset utilization, and capital usage (Firdaus et al., 2019). ROA serves as an indicator used to measure an entity’s ability to generate profit from its total assets.

Therefore, ROA is often chosen to measure the performance of banks. The following is the mathematical formula for calculating ROA according to Kasmir (2014):

Return On Asset (ROA) = / − %

Profit after tax: refers to the profit earned before interest and tax. Total assets: refers to the total wealth or assets owned by the company.

The following are findings from previous studies related to the topic of this research:

The Relationship Between Value Added Intellectual Coefficient (VAIC) Measurement and Return on Assets (ROA):

H₀: Structural Capital Value Added (STVA) has no effect on Return on Assets (ROA) in Indonesian State-Owned Banks.

Hₐ: In Indonesian State-Owned Banks, Return on Assets (ROA) is impacted by Structural Capital Value Added (STVA).

Hypothesis 3

H₀: In Indonesian state-owned banks, Return on Assets (ROA) is unaffected by both Value Added Capital Employed (VACA) and Structural Capital Value Added (STVA) at the same time.

Hₐ: In Indonesian state-owned banks, Return on Assets (ROA) is impacted by both Value Added Capital Employed (VACA) and Structural Capital Value Added (STVA) at the same time.

The t statistical test is also called the individual significance test. This test shows how far the independent variable partially influences the dependent variable. In the end, one conclusion will be drawn, H0 is rejected or Ha is accepted (Sudaryana B, Agusiady H.R.R, 2022).

The F test is to find out all independent variables and explain the dependent variable, so a simultaneous hypothesis test is carried out using the F statistical test (Sudaryana B, Agusiady H.R.R, 2022).

We can characterize the researcher's data distribution as follows based on the findings of the aforementioned descriptive test:

Based on the results of the normality test, the significance value is 0.200 > 0.05, so it can be concluded that the residual value is normally distributed.

The tolerance value of VACA (X1) is 0.995, indicating that 0.995 > 0.10, while the VIF value is 1.005, indicating that 1.005 < 10. Therefore, it may be said that multicollinearity does not occur in the VACA variable. Given that the VIF value for the STVA variable is 1.005 < 10 and the tolerance value obtained is 0.995, indicating that 0.995 > 0.10, it can be said that the STVA variable likewise does not exhibit multicollinearity.

In the table above, the VACA (X1) value is 0.731 (>0.05) and STVA 0.117 (>0.05) which means that all variables do not experience heteroscedasticity.

K (dependent variable) = 2, N (sample) = 40, Durbin-Watson (d) = 1.593, dL = 1.391, dU = 1.600, 4-dL = 2.609, 4-dU = 2.400. Thus it can be concluded from the results of the data obtained, namely the value of dU < d < (4 - dL) which means the value of dU < d < (4 - dL) or 1.600 < 1.593 < 2.400 and the results indicate that there is no autocorrelation.

From the results of the table above, the secondary data results obtained are as follows:

= + + + ⋯. + + e ROA = (-0.016) + 0.024 + 0.046 + 0.010

Based on the analysis, the interpretation of the equation is as follows: the constant value of -0.016 indicates that when the variables VACA (X1) and STVA (X2) are equal to zero (0), the model assumes a constant. The constant value of -

0.016 implies a negative influence, meaning that the dependent variable (ROA) decreases.

Furthermore, the regression coefficient for VACA is 0.024, which is positive. This means that if VACA increases, ROA will also increase. Next, the regression coefficient for STVA is 0.046, also positive, indicating that if STVA increases, ROA will increase as well.

The hypotheses put forward in the study can be tested using the t-test. Forty samples (N) and two independent variables (K) are present. The following calculation can be used to find the crucial value for the t-table based on the above table:

VACA as X1 had a significance level (α) of 0.076 and a t-count significance value of 1.825. Given that (0.076 > 0.05), it can be said that ROA (Y) is not significantly impacted by VACA (X1).

STVA as X2 had a significance level (α) of 0.006 and a t-count significance value of 2.944. Given that (0.006 < 0.05), it can be said that ROA (Y) is significantly positively impacted by STVA (X2).

According to the above table's data, the three independent variables VACA (X1) and STVA (X2) have a simultaneous impact on ROA, as indicated by the F-count significance value of 4.691 and the significant value (α) of 0.015, which indicates that (0.015 <0.05).

a. Determination Test (R2)

Based on the table above, the R² value is 0.207 or 20.7%, which indicates that VACA (X1) and STVA (X2) contribute 20.7% to ROA (Y), while the remaining 79.3% is influenced by other factors not examined in this study.

The study shows that VACA does not have a significant partial effect on ROA (significance value 0.076 > 0.05). This indicates that physical capital has not been optimally managed to provide added value to the bank’s profitability.

It also suggests that asset utilization may not be efficient, or perhaps the bank’s investment strategy is not productive enough in generating profit from its assets. STVA Has a Significant Effect on ROA

STVA has a positive and significant effect on ROA (significance value 0.006 < 0.05), indicating that structural capital such as systems, procedures, and information technology plays a crucial role in enhancing bank profitability.

This supports the idea that internal infrastructure and knowledge management are key determinants of operational and financial effectiveness.

Simultaneously, VACA and STVA have a significant effect on ROA (significance value 0.015 < 0.05). This proves that the combination of physical and structural capital management can boost financial performance, even though VACA alone does not show strong partial significance.

This finding aligns with the Resource-Based Theory, which states that unique and hard-to-replicate resources such as capital employed and structural capital can create sustainable competitive advantage.

The coefficient of determination (R² = 20.7%) shows that only a small portion of ROA variability is explained by VACA and STVA. This indicates that other variables beyond intellectual capital—such as credit risk, operational costs, asset quality, and managerial efficiency—are also important to consider in explaining the financial performance of banks.