The Role of Firm Size in Mediating The Effects of Liquidity, Leverage, And Efficiency on The Financial Performance of Mining Companies on The Indonesian Stock Exchange

1. Introduction

The mining sector is known to be one of several industrial pillars officially listed on the Indonesia Stock Exchange (IDX) and is considered resilient to fluctuations in the domestic macroeconomic environment. Based on data from the Ministry of Energy and Mineral Resources (ESDM) and the Ministry of Finance, this sector’s contribution to state revenue is partly derived from Non-Tax State Revenue (PNBP). In 2020, revenue realization was recorded at only Rp 34.6 trillion as a result of the COVID-19 pandemic, which constrained mining operational activities and global commodity prices. Entering 2021, there was a substantial recovery to Rp.75.2 trillion in line with the escalation of energy demand and the rise in coal and base metal prices post-pandemic. Revenue peaked in 2022 at Rp.152.3 trillion, driven by a surge in commodity prices and the implementation of mineral downstreaming policies. Although revenue contracted in 2023 due to weakening global prices, revenue levels remained high thanks to strategic contributions from the nickel and copper downstream industries. In 2024, revenue is projected to accelerate again to Rp 145 trillion, driven by strengthened downstream processing policies, improved compliance with non-tax state revenue (PNBP) and royalty payments, and more comprehensive oversight of production and exports (ESDM, 2024; Ministry of Finance, 2024).

This oscillating growth is inextricably tied to the evolvements of governance and financial performance that lie at the heart of our dealings with mining companies operating in Indonesia. Internal determinants such as liquidity, leverage, and efficiency are never hidden from the mining companies' financial performance. Liquidity represents the ability of a company to meet its short-term obligations, which is based on the use of current assets (Sartono, 2012). On the one hand, (Brigham & Houston, 2021) further state that leverage is the degree to which a company employs debt instruments to obtain funding for its investments and corporate operations. On the contrary, according to (Kasmir, 2019), efficiency is interpreted as a picture of a company that utilizes its resources optimally in producing profits with minimum additional costs. All three variables are interdependent to the extent of determining a firm's ability to achieve optimal and sustainable financial performance, which in turn helps with the protection of national economic stability, especially among high-risk-profile capital-intensive businesses such as mining.

According to (Saputra & Syarfan, 2017), financial performance is the ability of the company to generate profits within a certain period, or reach a level that has been determined by making financial ratio analysis. It is a technique to measure the extent of a company's ability to make a profit, utilize its assets in an efficient manner, and meet its financial obligations. Financial ratio analysis is a technique to assess the financial condition of a company in both the past and present (Harahap, 2018). This assessment can roughly be done by using Return on Assets (ROA) is a financial ratio that indicates how profitable a company is relative to its total assets. The ROA measures a company's profitability and how efficient it is in using its assets during a specific period. Liquidity is considered one of the primary indicators to assess whether a company can meet its current liabilities in the near term using more liquid assets. (Kasmir, 2019) stated that an adequate level of liquidity indicates that the company has enough room for cash flow in a business to meet the day-to-day needs and liabilities, which can further increase creditors and investors confidence.

Meanwhile, leverage shows us how much of a company is funded with borrowed money used to finance operational activities and investments. According to (Brigham & Houston, 2021)), leverage can enhance returns to shareholders when lenders employ loan capital prudently. The flip side, though, is that it increases the financial risk of a company by not providing good interest servicing and principal repayment on debt. Leverage creates distortion in the mining sector as it is used to finance fixed-asset and exploration activities, which entail significant capital allocation (Rangga & Agnes, 2025). Leverage is not just a tool for financing, but also an indication of managerial prowess in assuring risk and shareholder wealth maximization. An optimal leverage ratio has the potential for increasing corporate value and sound financial performance when combined with high capital use efficiency and credible governance. Efficiency gives an idea of how well the company is using its resources on hand. This includes everything: assets, labor, and capital to produce revenue and profit appropriately. (Kasmir, 2019) states that efficiency is a type of measure as an important ratio in managerial performance since it shows the extent to which each company is able to minimize costs while carrying out operational activities without decreasing productivity. Efficiency is the vital survival strategy for long-run success in the capital-intensive mining industry, where production/ exploration activities are costly and heavily impacted by fluctuations of global commodity prices (Humphreys, 2019).

The above relationships between the variables are not direct in their effect on financial performance, which is why firm size is needed as a mediating variable. According to (Handayani & Darma, 2018) firm size could be an important variable as it reflects the economic power, asset capacity, and risk management ability of an entity facing both financial and operational risks. Firm size is defined as a scale measured in several proxies such as total assets, the logarithm of sales volume, and market capitalization (Agustia & Suryani, 2018). Semantically, firm size does not strictly refer to operational scale but indeed refers to the fundamental strength of a firm as a structure that allows for some measure of resilience from aggressive industry competition. This is especially true for mining, where high-risk profiles and large capital requirements are commonplace. Earlier empirical investigations affirmed that shocks in macro financial variables have a robust effect on the financial performance. According to Lely & Maria (2020), the current ratio affects corporate finance performance. On the other side, (Astutik et al., 2019) states that the current ratio partially does not affect financial performance. In contrast, total asset turnover significantly positively affects financial performance. (Kusumawati et al., 2022) stated that financial performance is influenced by the debt ratio. (Rahmawati et al., 2022) stated that the total asset turnover does not affect financial performance significantly.

Moreover, past research on macroeconomic factors has predominantly employed datasets prior to 2020 and did not fully consider the dynamics of disruptive changes due to the COVID-19 pandemic. This process, however, led to increased volatility across the economy and could ultimately serve to weaken mining firm project financial performance. This study is required because of the urgency to create a milder new room based on relevant information until 2024 regarding the financial performance of Indonesian mining companies as reflected in liquidity, leveraging, and efficiency. Thus, this study is very important to analyze the effect of the third variable on financial performance through firm size as a mediating variable in the mining company that is officially listed on the IDX. We hope that this study can provide a meaningful contribution to the academic literature, as well as basic enabling for practical recommendations for a commercial landscape where company management and investors are managing financial risks in an increasingly volatile global economic environment. This research uses the size of the firm as an intervening variable and intends to further examine the effect of liquidity, leverage, and efficiency on the financial performance of mining issuers officially listed on IDX. The aim of this study is to make a theoretical and practical contribution to the existing financial literature and as input for future thought processes from management, investors, and policymakers in anticipation of financial conditions.

2. Literature

2.1. Agency Theory

(Jensen & Meckling, 1976) explained the agency theory, which discusses the relationship of agents (or managers) and shareholders. Shareholders are the parties who provide resources to management. Meanwhile, management is understood as the party with the authority to make decisions aimed at achieving the company’s objectives. This theory specifically addresses the potential for conflicts of interest between agents and principals. As (Sartono, 2012) stated, when a company has excess cash flow, managers make excessive investment efforts and incur expenses outside the core business to increase their power. While an acquisition or over-exploration does not likely provide a good return on assets (ROA) to shareholders, mining company managers do have a real motive for growing their company size.

The theory of agency states that information asymmetry can create conflicts of interest between management and shareholders. According to (Faisal et al., 2020), in the context of Indonesia, this conflict frequently develops into a Type 2 agency dispute, which is a conflict that happens between controlling (majority) shareholders and minority shareholders. Concentrated ownership creates space for high-earning shareholders to partake in wealth appropriation for personal economic benefit, thereby potentially reducing the overall value of the firm (Faisal et al., 2024). In this system, the debt (leverage) is presented as a device of discipline (Faisal et al., 2020), which constrains management’s discretion regarding the uses of free cash flow; consequently, forcing management to be expansive in order to meet its obligations and maintain investor confidence. (Ermad et al., 2026) add that, besides its function as a source of funding, debt in agency theory is also perceived as an external control mechanism that limits the opportunism of managers. Reduces the free cash flow available to equity holders and thus decreases management discretion over investing in unproductive activities. While this is consistent in theory with the control hypothesis (i.e., debt transfers some of the monitoring function away from stockholders and onto creditors and should lead to managerial interests that are better aligned with other stakeholders),

2.2. Signaling Theory

An introduction to signaling theory was first proposed by (Spence, 1973). The longtime investor also elaborated that the owner or sender of information is able to create a signal, which gives insight into how the company operator has been doing and whether it would be economically useful for investors or recipients. This theory also aims to provide a better understanding of how the management perspective in terms of growth potential and future prospectiveness is going to impact investors' attitude towards that entity (Brigham & Houston, 2021). These may include information about managerial attempts to bring it around to meet shareholders' expectations. This information helps investors make an appropriate judgment on their investment. (Hartono, 2022) states that the information that has been published by the company and received by investors is collected, interpreted, and analyzed earlier to find out whether it includes a positive signal or, conversely, a negative signal. According to (Faisal et al., 2024), signaling theory dictates that (a) financial decisions and (b) firm characteristics must convey information to the market about issuer quality. The large firm size is perceived as an indicator of economic stability because large firms usually find it easier to access capital and are able to withstand liquidity shocks better than small firms. Even information about financial resilience grows important when extraordinary events like the COVID-19 pandemic take hold. Strong governance frameworks give a reassuring signal in crises, as do normal market investors who help explain why uncertainty today does not harm firm value. The above theory has a similar focus on calls for the information released by companies that can influence investors making decisions in the market from outside of these companies. As an investor, this is essential data to understand because it exposes the details of a company’s state and analysis for creating investment options.

2.3 Liquidity on Financial Performance

Liquidity is a basic indicator used to represent the entity's ability to use current assets to pay short-term liabilities. A high liquidity ratio indicates that cash flow management is both operational and financial, which in turn will increase the credibility of the company in the eyes of investors and creditors (Kasmir, 2019). (Laksmita et al., 2020) concur with this view. Substantial liquidity indicates optimal financial performance as it demonstrates a company’s ability to settle short-term debt while optimizing the use of its assets. Furthermore, empirical findings by (Jumantari et al., 2022) and (Diana & Osesoga, 2020) confirm that an increase in liquidity aligns with improved corporate financial performance. This is based on the company’s ability to fulfill its obligations on time and optimize current assets as instruments supporting operational activities. Adequate accumulated current assets can be utilized as a crucial source of short-term funding to accelerate sales volume in order to maximize profit and serve as a reserve for debt repayment.

H_(1):Liquidity significantly influences financial performance.

2.4. Leverage on Financial Performance

Leverage is defined by (Sartono, 2012) as a company’s ability to utilize borrowed funds to increase the rate of return to shareholders, assuming that the rate of return on debt-financed investments exceeds the interest costs incurred. (Kasmir, 2019) also explains that leverage is used to assess the extent to which a company’s assets are financed through debt compared to equity. In mining companies, leverage is typically measured using the Debt-to-Equity Ratio (DER), which has been shown to have a direct, negative, and significant impact on financial performance according to research conducted by (Sari & Mahardika, 2023). This indicates that every increase in debt within a company’s capital structure leads to a decline in profitability, as measured using Return on Assets (ROA). This occurs because high leverage levels indicate the company’s reliance on external financing, which triggers a drastic increase in interest expenses.

H_(2):Leverage significantly influences financial performance.

2.5. Efficiency on Financial Performance

(Kasmir, 2019) explains efficiency as a company’s ability to utilize its assets productively to yield the best possible results. An efficient company can minimize operational costs without compromising product or service quality while maximizing profits from the same assets. Efficiency is known to have a strong positive impact on financial performance because it reflects how well a company utilizes its resources to generate profits. Companies with high levels of efficiency are known to be able to minimize input costs while maximizing output, resulting in higher profitability and improved financial ratios such as return on assets and return on equity. (Margaritis & Psillaki, 2010) conducted a study demonstrating that increased efficiency directly impacts higher profitability and increased firm value, particularly in competitive industries where cost management is critical. Efficiency also drives financial performance by boosting productivity while reducing inefficiencies that erode profitability.

H₃: Efficiency significantly influences financial performance.

2.6.Liquidity on Firm Size

Liquidity is fundamentally the most important metric derived from the cash flows that give an indication of how stable a company will be in fulfilling its obligations over a short amount of time. Sufficient liquidity means a company has the ability to fund everyday business operations and strategic projects without taking on too much external funding. Liquidity in the sense of growth and investment allows companies to keep confidence in internal funding resources when it comes to strategic expansion choices. Well-managed liquid assets allow companies to afford the investment in fixed assets and technological renewal, as well as the development of market penetration. This is consistent with the results of (Pham et al., 2018) that firms with stronger liquidity positions show steadier business growth and higher returns, and long-term profitable business expansion underlie larger firm scales. (Almeida et al., 2011) also stated that higher liquidity levels can reduce investment constraints, thereby enabling companies to expand their asset base and workforce, which directly contributes to an increase in firm size.

H₄: Liquidity significantly influences firm size.

2.7. Leverage on Firm Size

Leverage is well understood as a capital structure metric that indicates how much debt is used to help fund a company's assets and business operations. The leverage ratio indicates a firm’s willingness to take risks in order to grow its capacity. This is in line with the study done by (Li et al., 2020), which proves that leverage also helps in obtaining external funds rather than being obtained internally and helps to increase operational capability through financial risk. According to a study conducted by (Vo & Ellis, 2017), firms that are moderately leveraged, in such a way, are larger than other firms that finance their investment with internal funds only. This is because they have greater expansion ability. Bigger firms typically have better reputations, more consistent cash flow, and easier access to financial markets. The two studies concur that using optimal leverage can help a company grow and expand, while overly leveraging the business creates increased financial risk, which can similarly stifle growth. Thus, an appropriate capitalization policy is one of the main determinants of whether a company will be successful in scaling its business.

H₅:Leverage significantly influences firm size.

2.8. Efficiency on Firm Size

According to (Halim, 2024), efficiency is a measurement of how well a company converts its resources into income. Efficiency, subject to its level of efficiency, is known to affect firm size. A high efficiency means that a company is able to manage its operational models, fixed assets, and investments in an ideal manner, which further increases profitability and corporate value. On the other hand, extremely low efficiency levels indicate excess bad or non-use of resources in an economy, which eventually impacts negatively on the firm’s bottom line. Findings by (Kapelko et al., 2017), firms that have higher levels of efficiency are generally associated with a larger potential growth and end up increasing the size of the firm. In line with this, similarly, (Beccalli et al., 2015) also cite that efficiency is a driving contribution to long-term firm growth. It is concluded that the relationship between efficiency and firm size is in turn bidirectional. Bigger firms are usually more efficient (economies of scale). When firms operate at a large scale, they will be able to reduce the cost per unit and become very efficient. This is in line with (Kapelko et al., 2017), who highlight that the reason for larger firms being more efficient is their ability to exploit economies of scale in production.

H₆ : Efficiency significantly influences firm size.

2.9. Firm Size on Financial Performance

(Weston et al., 1996) consider firm size as a physical dimension of an entity that reflects operating scale, sales volume, total assets, and market capitalization. The most important of these parameters not only indicate the economic strength of a company and its bargaining power in the market, but are also a key to external sources of financing. In the mining industry, especially for issuers listed on IDX, who are expected to enhance income by increasing production capacity, but still be resilient against fluctuations in commodity prices over a long period, firm size will become an essential element because it drives financial performance based upon factors that are generally referred to interchangeably as direct determinants of investment extent, which will lead to firm value. The result from (Diantimala et al., 2021) suggests that, certainly, the size of a company increased financial performance since large businesses are able to achieve economies of scale and competitive advantage. Large-scale firms can usually be expected to have more capital, human resources, and better market penetration, enabling sales increases to be considered a good proxy of performance. It has been existing information that small-scale companies are more susceptible to market changes, and also large-scale high-performance companies tend to have better financial stability. These findings are consistent with the work of (Sembiring et al., 2024), who stated that firm size has a moderate impact on financial performance. Growth in total assets indicates that firm scale is growing, making it easier to tap both banking institutions and capital markets. Having easy access to funds allows large, publicly listed companies to incorporate adjustments in financial capital for multiple strategic investment opportunities. With enough capital backing, as a company, you can still invest in profitable ventures and maximize profit potential.

H₇ : Firm size significantly influences financial performance.

2.10. Firm Size Mediates the Liquidity Effect on Financial Performance

Firm size as a mediator of the effect of liquidity on financial performance is an established fact. The reason for this is that large firms tend to hold higher liquidity levels than smaller companies. According to (Lan et al., 2021), large firms with more financial flexibility can use their liquidity in a more efficient manner, allowing them to seize the market opportunities and finance growth without risking their financial health. However, smaller firms differ in their productivity-invoking balance between liquidity and investment (growth) to a degree that profitability and finance performance are more pronouncedly detached. Consistent with this, (Zeitun & Tian, 2007) argue that firm size is a critical determinant of the impact of liquidity on financial performance. They concluded that liquidity has a more positive impact on the firm's financial performance via investment and operational flexibility. On the other hand, high liquidity may stem from inefficiency in small companies as they have limited productive investment opportunities to soak up those assets. This opinion is also held by (Ahsan, 2016), who state that the capacity of a company to absorb liquidity shocks and be sustainably stable in operation is largely based on size. More incorporated organizations with better market positions can endure economic downturns and developing height limitations because their liquidity positions protect them over time. On the contrary, smaller businesses are more affected by liquidity constraints and variability in many cases, leading to worse performance.

H₈ : Firm size mediates the liquidity effect on financial performance

2.11. Firm Size Mediates the Leverage Effect on Financial Performance

According to (Fajar et al., 2025), the leverage effect on financial performance is not always a direct influence but rather depends upon the size of these assets held by the company. The very large mining companies have more collateral to offer. Those companies are observed as more prone to draw interest and acquire external finance with less effort in order to cope with their enormous operational costs. (Fajar et al., 2025) also clarify that the relationship between leverage and financial performance is not necessarily direct, but is rather contingent upon a company's size. Large mining companies have a greater capacity to use their assets as collateral, giving easier access to external capital via attracting investors in exchange for the right chunk of mine activities, which might generate returns. High leverage in large firms does not affect financial performance because large firms have economies of scale that make the risks and interest costs manageable compared with a small firm. On the contrary, for small firms, an increase in leverage without sufficient asset backing is a sign of high risk that could lead to relatively declining profit margins due to degrees of leverage outgrowing fundamental limits on productive capabilities via heavy debt loads.

H₉ : Firm size mediates the leverage effect on financial performance in mining companies listed on the IDX.

2.12. Firm Size Mediates the Efficiency Effect on Financial Performance

(Dang et al., 2014) state that efficient resource allocation presents an opportunity for organizations to respond better to changes in the economy and also maintain good financial performance across different business cycles. Efficient companies usually take better managerial and investment decisions and become more productive as well as financially sound over time. On the other hand, firm sizes serve as a common mediator of efficiency and firm performance, where large companies capture higher efficiencies since their economies of scale magnify efficiency gains into profits. (Wang et al., 2024) examined this relationship in a particular context of manufacturing firms and confirmed that firm size positively mediates this association because larger firms can play resource allocation tactics better and also possess stronger bargaining power with suppliers, thereby converting cost efficiencies into superior ROA when streamlined processes are in place.

H₁₀ : Firm size mediates the efficiency effect on financial performance

3. Method

3.1. Sample

The population used was all mining companies officially listed on the IDX during the observation period of 2020-2024. This study only focuses on the energy sector (oil, gas, and coal subsectors) based on the IDX Fact Book (2024) and IDX Industrial Classification (IDX- IC). Companies involved in distribution, drilling, equipment services, and production all fall under this classification, with a total of 89 companies identified among them. Purposive sampling (also known as non-probability sampling) technique based on critical factors was employed in the selection of the sample to achieve relevant data. The sample characteristics of this study are delineated as follows.

1. Sample companies operating during the observation period from 2020 to 2024.

2. Sample companies that have published financial reports

3. Companies that did not have negative equity during the 2020–2024 observation period.

4. Companies that did not report a net loss during the observation period from 2020 to 2024.

Based on the sampling criteria, 19 companies that met the administrative and financial characteristics to be part of the sample for this research were selected. Since the data collection period (2020-2024) is equal to five years, we derived a total of 95 observations. With this amount of data, it is believed that it can be used as the basis for subsequent analysis to give a complete picture of the phenomenon under study in the mining sector in Indonesia.

3.2. Data Collection

This study employed a documentation method based on the financial statements of mining companies from 2020 to 2024 that were obtained officially through the IDX or directly from these companies.

3.3. Data Analysis

In this study, the main analysis used is panel data regression with Eviews 12 software. This selection is determined based on the integration of time series data for 2020–2024 with cross-sectional data related to mining sector companies officially listed on IDX. The first step was to use descriptive statistics. Furthermore, the three methods were used to decide the best estimation models: Common Effects Model (CEM), Fixed Effects Model (FEM), and Random Effects Model (REM). The model selection process is a sequence of formal tests: Chow test to compare CEM and FEM vs. the reference model, Hausman test, and LM (Lagrange Multiplier) to rank CEM and REM. We tested different models, and the results of these tests are used to pick a model that has performed well at inference.

The panel data regression method is used to test independent variables against dependent variables, involving mediation mechanisms. The estimation of the model is performed in two stages, where Path I examines direct effects between the independent and the dependent. In this context, Path II is aimed at assessing the effect of liquidity, leverage, and efficiency indirectly on financial performance through firm size as a mediating variable. In the last section of the analysis, hypotheses were tested by means of the coefficient of determination in order to assess how well the model can explain the variance of the dependent variable. The F-test is also used to check the significance of the effect of independent variables together, and the t-test is used to determine if individual variables are significant. In this study, we used the Sobel test as a verification tool for the mediation effect to prove that firm size is the mediating variable that mediates the relationships among variables.

4. Result and Discussion

4.1. Descriptive Analysis

Table 1 Descriptive Test

In Table 1, the Financial Performance (Y) variable shows a mean of 0.1332 with a standard deviation of 0.1412. The significance of this value exceeds its mean. This indicates a considerable degree of variability or dispersion in financial performance among the sample companies. This phenomenon is reflected in a fairly contrasting range of values, where the minimum value is recorded at 0.0000, and the maximum value reaches 0.6200, representing dynamic fluctuations in financial conditions in the sector under study.

Analysis of the independent variables reveals that the mean value of liquidity is 2.2679, with a standard deviation of 1.5244, and that it ranges from 0.2000 to 10.0700. Meanwhile, the mean of the leverage variable is 0.7239, with a deviation of 0.5225. It is distributed within an interval ranging from a minimum of 0.000 to a maximum of 2.4600. Furthermore, the efficiency variable presents a mean value of 0.8117 with a standard deviation of 0.5929, and its values are distributed within the range from 0.07 to 2.77. Firm size (Z) shows a mean different from the other variables, namely 20.0439, with a relatively low deviation of 1.1967. This standard deviation is relatively low compared to the mean, indicating that the sample companies have operational scale characteristics that tend to be homogeneous within the range of 18.2647 to 23.1012.

Based on firm size, the mean value differs from that of the other variables, standing at 20.04309, with its standard deviation also known to be relatively low at 1.967. This standard deviation value falls into the low category when compared to the mean, indicating that the sample companies exhibit operational scale characteristics that tend to be homogeneous within the range of 18.647 to 23.1012.

From the perspective of data distribution, the majority of variables show Jarque-Bera probability values below the 0.05 threshold, suggesting that the data is not normally distributed, except for the Firm Size (Z) variable, which yields a probability value of 0.0619, and thus can be statistically considered normally distributed.

4.2. Analysis of Panel Data Regression Model Selection

4.2.1. Path Equation I

The estimation in Path Equation 1 is designed to analyze the direct effects of the independent variables on the dependent variable within the study’s model framework. The structure of this model integrates Liquidity (X1), Leverage (X2), and Efficiency (X3) as primary predictors, along with firm size (Z) as a mediating variable predicted to play an intermediary role in this structural relationship. This approach allows for the identification of the partial contribution of each variable to the dynamics of the dependent variable under study.

To ensure the accuracy and validity of the estimation results, a comparison of three potential models was conducted through a series of formal statistical tests. The selection of the best model among the CEM, FEM, and REM was carried out through a structured series of tests. This process begins with the Chow Test to determine the superiority between CEM and FEM. Subsequently, the Hausman Test is applied to validate whether FEM or REM is more appropriate to use. Finally, if necessary, the Lagrange Multiplier (LM) Test serves as a selection criterion between CEM and REM. Through these hierarchical steps, researchers can ensure that the selected econometric model accurately represents the characteristics of the analyzed panel data.

4.2.1.1 Chow Test

The table below presents the Chow test for selecting between CEM and FEM.

Table 2 shows the application of the Chow test yielded a cross-sectional chi-square probability of 0.0000. Given that this value falls < 0.05, the alternative hypothesis (Ha) is accepted. This finding statistically confirms that FEM yields a higher level of accuracy than CEM in estimating the data in this study.

4.2.2.2 Hausman Test

The Table 3 below presents the Hausman test to determine the choice between FEM and REM.

In Table 3, the test yielded a value of 0.9700 >0.05, indicating that the alternative hypothesis (Ha) is rejected. This finding statistically confirms that REM has a higher level of accuracy compared to FEM in estimating the data in this study.

4.2.2.3 Lagrange Multiplier

The following table presents the LM test to determine which model is better between REM or the CEM.

Table 4 shows the LM test yielded a p 0.000. Given that this significance level is < 0.05 threshold, the alternative hypothesis is accepted. This finding statistically confirms that REM yields a higher level of accuracy compared to CEM.

4.2.2. Path Equation II

Path Equation II is designed to examine the effects of independent variables and mediating variables on the dependent variable. The independent variables used in this study include three variables: liquidity, leverage, and efficiency. The mediating variable is firm size, denoted by z, and the dependent variable is financial performance, denoted by y.

Chow Test

Table 5 below presents the test to choose between CEM and FEM.

Table 5 shows the Chow test yielded a cross-section chi-square probability of 0.0019. Given that this significance value is far from the threshold, or well < 0.05 threshold, it concludes that the null hypothesis is rejected and the alternative hypothesis is accepted.

1. Hausman Test

Table 6 below presents the Hausman test to determine whether to choose FEM or REM.

In Table 6, the Hausman test yielded a cross-sectional chi-square probability value of 0.1188. Since this significance > 0.05, this indicates that the H1 is rejected.

1. Lagrange Multiplier Test

The following table presents the LM test to determine which model is better between the fixed-effects model and the random-effects model.

In Table 7, the LM test yielded a cross-sectional chi-square probability 0.001. Since the associated significance value does not reach 0.05, the H1 is accepted. This finding statistically confirms that REM achieves a higher level of accuracy compared to CEM.

4.3. Hypothesis Testing

4.3.1 Path Equation I

4.3.1.1 Partial Test (t-Test)

Table 8 below presents the t-test for Path I.

Table 8 describes that :

1. Liquidity (X1) on Firm Size (Z)

The Liquidity variable (X1) affects Firm Size (Z) significantly negatively, based on a regression coefficient of -0.0515 and p 0.0226 (p < 0.05). This finding indicates that every increase in the liquidity ratio statistically contributes to a decrease in the scale of firm size in the observed entities.

1. Leverage (X2) on Firm Size (Z)

The Leverage variable (X2) does not have an impact significantly on firm size. This finding is based on a p 0.106 > 0.05. Thus, an increase in a company’s debt structure is found not to produce a statistically significant implication on the expansion or change in firm size during the observation period.

1. Efficiency (X3) on Firm Size (Z)

The Efficiency variable (X3) affects firm size significantly positively. The P 0.013 (p < 0.05) confirms that the optimization of operational efficiency directly contributes to an increase in the operational scale or size of the business entity. This indicates that the efficient use of assets is a key determinant in the growth of firm size.

4.3.1.2 F-Test

The table 9 below shows the F-test for Path I.

In Table 9, it is observed that all independent variables collectively exert a significant influence on the mediating variable, as the p of the F-statistic is 0.000543< 0.05.

4.3.1.3 Determination Coefficient (R²) Test

Table 10 shows the R test for Path I.

In Table 10 above, it is observed that the R-squared is 0.14, indicating that the three independent variables can explain 14% of the variation in the mediating variable related to firm size, while the remaining 86% is explained by external variables not included in this study’s model.

4.3.2 Path Equation II

4.3.2.1 Partial Test (t-Test)

Table 11 below shows the t-test for Path II.

Based on Table 11 presented, the following insights can be drawn:

1. Liquidity (X1) on Financial Performance (Y)

The liquidity (X1) does not affect financial performance significantly. This is evidenced by a significance 0.715, with p> 0.05. Thus, fluctuations in liquidity levels do not increase or decrease the financial performance of the sample companies.

1. Leverage (X2) on Financial Performance (Y).

The Leverage variable (X2) affects financial performance negatively significantly. This finding is based on a significance 0.000, with p< 0.05. Empirically, these results indicate that an increase in debt structure contributes significantly to a decline in financial performance within the company.

1. Efficiency (X3) on Financial Performance (Y)

The efficiency variable (X3) significantly positively affects financial performance. The recorded significance value is 0.0000, with p < 0.05, indicating that optimizing operational efficiency is a strong determinant of the acceleration of a business entity’s financial performance.

1. Firm Size (Z) on Financial Performance (Y)

The firm size variable (Z) shows that this variable significantly positively affects financial performance. This is confirmed by p 0.004, which does not reach the 0.05 threshold.

4.3.2.2 F-Test

Table 12 below shows the t-test on Path II.

From the explanation in Table 12 above, it is observed that p 0.00 < 0.05; therefore, it is concluded that the three independent variables simultaneously affect the mediating variable significantly.

4.3.2.3 Determination Coefficient (R²) Test

Table 13 below shows the R test for Path II.

Referring to Table 13 above, it is observed that the R-squared of 0.61 indicates the independent and mediating variables explain 61% of the dependent (financial performance), while the remaining 39% is explained by external variables not included in the model.

1. Sobel Test

The Sobel test was applied to determine whether the intervening variable significantly mediates the independent variable on the dependent variable and whether the independent variable has a direct, indirect, or both effect. The test results based on the Sobel calculator are as follows.

1. Liquidity (X1) on Financial Performance (Y) mediated by Firm Size (Z).

For the path of influence of liquidity (X1) through firm size (Z) on financial performance (Y), the results of the Sobel test show p 0.0687. This p is > 0.05. It proves firm size does not mediate the liquidity impact on the financial performance.

1. Leverage (X2) on Financial Performance (Y) Mediated by Firm Size (Z)

The second path test indicates that the leverage (X2) impact through firm size (Z) on financial performance (Y) yields p 0.0154. Since this p< 0, the hypothesis regarding the mediating effect of firm size on the influence of leverage on financial performance is accepted. These results indicate that firm size exerts an indirect influence on financial performance.

1. Efficiency (X3) on Financial Performance (Y) Mediated by Firm Size (Z)

The Sobel test for the efficiency (X3) effect through firm size (Z) on financial performance (Y) resulted in 0.0455, indicating that firm size significantly mediates the efficiency on financial performance. This is because the significance level < 0.05, specifically, 0.0455 < 0.05. This indicates that a company’s size, when combined with efficient operations, will indeed lead to an improvement in the financial performance of mining companies.

4.5. Discussion

4.5.1 . Liquidity Affecting Financial Performance

The result reveals that liquidity does not affect financial performance significantly. Given that the significance is 0.7157, which is > 0.05, the first hypothesis is rejected. This finding suggests that an entity’s ability to meet short-term liabilities is not the primary determinant directly influencing a company’s profitability in this industry. For a capital intensive mining business, fixed assets and exploration are the predominant drivers of resource allocation. As a result, the main source of profit growth within current assets is limited compared to the role of long-lived productive assets. This phenomenon is in line with the opinion of (Zeitun & Tian, 2007), who argue that an excessively high liquidity ratio shows idle assets. This is where such assets do not productively generate additional value, and therefore contribute only marginally to the profitability of the entity hosting them. Yet, these results contradict signaling theory, which indicates that persistent liquidity can act as a positive signal for investors about a company's ability to honor near-term commitments. Additionally, the results also contradicted those of (Brigham & Houston, 2021), who stated that liquidity plays an important role in a corporation's performance. From that perspective, sufficient liquidity would be a buffer against default risk that might jeopardize profitability. Nonetheless, for companies that have been successfully mining in Indonesia for several years now, operational efficiency with fixed assets appears to have more impact on firm financial performance than short-run liquidity positions.

4.5.2. Leverage Affecting Financial Performance

Based on the analysis, it is concluded that leverage significantly negatively affects financial performance. Also, the second hypothesis obtained a significance 0.000 (< 0.05), and was thus accepted. The results show that there is an inverse relation of debt structure and the profitability of an entity. Hence, the direct opposite impact on the key financial performance is the fact that an increased debt ratio is an absolute risk-taking endeavor. This inverse relationship is a result of the burden on the company due to additional interest expense costs and payment obligations from debt amortization. These fixed financial costs eat heavily into the net profit margin created from operational activities. Given the highly operational risk in mining and my subjective perception of widespread low sentiment in pricing volatility of international commodity prices, more than optimal debt financing could lead to the risk of financial distress. These findings are consistent with research by (Suryani, 2020), which states that high leverage can reduce a company’s profitability because the cash inflows from operations will be mostly used for long-term liabilities. In addition, these findings support the view of (Zeitun & Tian, 2007) about the harmful effect of dependence on external. External financing above the needed level is not only a cost that elongates the risk profile but mathematically inflates the company's weighted average of profit through interest, fundamentally detracting from ROA.

4.5.3. Efficiency Affecting Financial Performance

The findings from the data reanalysis show that efficiency is proven to affect the financial performance. We accepted the third hypothesis, obtaining a significance equal to 0.000, where p < 0.05. Developments show that enhanced economic performance displays a linear reciprocal correlation with asset use optimization. With huge operational costs, mining is an industry where asset utilization efficiency quickly becomes a key driver of profitability. A higher wedge signifies the success of management in transforming that company's asset capacity into revenue. In the backdrop of such a convoluted cost structure, the successful elimination of operational inefficiencies allows companies to preserve as much of their profit margins as possible. Results from the present study agree with (Wang et al., 2024), who also highlight operational efficiency as a primary determinant variable of corporate profitability. Consistent with this theory, (Weston et al., 1996) contend that those who possess the ability to boost assets will also have more stable cash flows and higher returns. The competitive advantage gained from efficient operations not only improves the bottom line of any company but also increases the value of an entity in front of its stakeholders (shareholders).

4.5.4. Liquidity Affecting Firm Size

The analysis using multiple linear regression data indicates that liquidity negatively significantly affects firm size. This supports the fourth hypothesis (H4 ), its significance is 0.026, which is <0.05. The output of evidence suggests a negative association where if liquidity risk rises, firm size (total assets proxy) will decrease. This condition describes managerial policies on corporate resource allocation: under ultra-liquid states, there is a growing tendency to accumulate assets in cash or other current instruments. This focus on current assets, by definition, restricts cash available for constructive long-term fixed asset investments. Within the mining sector, firm size is fundamentally determined by ownership of physical assets (i.e., land concessions for mineral extraction, infrastructure for operation, industrial machines/capital) and large-scale production capacity. Those companies that follow liquidity over capital expansion show a lack of growth in respect to total assets. That is because a few profits are reinvested in productive assets of longer useful life. This observation also supports the notion of (Weston et al., 1996) that maintaining liquidity in excess is always at the expense of investment funds while sacrificing budgets for growth. This reduced allocation to the fixed asset base ultimately stunts firms' ability to create more scale and grow their operations in the market.

4.5.5. Leverage Affecting Firm Size

In detail, the results of this data regression analysis cannot provide leverage on how large companies that are officially listed on IDX are only concentrated on mining. The fifth hypothesis was rejected as the obtained p> 0.05. The results hence demonstrate that the tendency of companies to turn towards debt as a source of outside funding somehow does not have a direct impact on total business size. Leverage is, in turn, frequently framed within the finance literature as a tool for management to access additional liquidity to grow more and/or be able to produce at a higher capacity. Yet in the broader context of mining, decisions about expansion and asset accumulation are made with little thought into whether debt capital is even available. Some basic strategic factors, such as mineral reserve potential (proven reserves), global commodity price fluctuations, and long-term investment strategies, are the main drivers for a company to allocate its assets. This finding is consistent with the results of (Zeitun & Tian, 2007), who argue that the relationship between leverage and firm size does not always produce statistically significant results, especially for industries characterized by capital-intensive and long-term investments. Based on this view, large firms typically have access to several alternative funding sources other than debt, such as using retained earnings or equity issuance (Weston et al., 1996). So the use of debt does not entirely determine asset growth; therefore, leverage cannot be seen as a deterrent factor for mining in Indonesia's market expansion as the leverage effect on firm size increases.

4.5.6. Efficiency Affecting Firm Size

The result shows that efficiency significantly positively affects the size of companies listed on the IDX in the mining sector. So, Hypothesis 6 is accepted at 0.0132 (point through which we reject). The findings reveal a direct relationship of the firm size and the degree of efficient utilization of assets. That translates into the resources consumed by an entity, directly reflecting on growth in the amount of assets to that entity. In the capital-intensive mining business, operational performance efficiency relative to fixed asset investments or manufacturing companies that invest in plant or factories is a key driver of corporate growth. The ability of management to operate this production equipment, mining facilities, and supporting resources in a way that it generates the most cash flow possible for the entity. This normalization of cash flow gives the business some vital financial flexibility to recapitalize and grow scale when new productive assets or operations become available. These results are consistent with the findings of (Wang et al., 2024), which validate that one of the main pillars driving business growth is operational efficiency. Entities that are highly efficient use less to make more, and this is finally visible in an increase in total assets. Following this view, (Weston et al., 1996) discuss that operational efficiency is directly proportional to return on investment ROI. This efficiency creates profitability, which a firm can reinvest into sustainable business growth that fortifies the company in the industry.

4.5.7. Firm Size Affecting Financial Performance

The data regression analysis results show that firm size positively significantly affects financial performance in companies listed on the IDX mining sector. The analysis also accepted hypothesis 7 with p 0.0042< 0.05. This indicates a positive relationship of firm size and financial performance; smaller companies display poorer performance than firms with larger operational scales. The impressively large size of a corporation indicates significant operating capacity, huge all-time-high assets and access to multiple funding resources. Few corporations are International corporations that have room for extensive mineral reserves, prominent production sites, and up-to-date extraction procedures in the mining sector. With those structural advantages come the ability to ramp up production capacity and generate peak revenues. On the other hand, substantial company size offers an increased resilience towards risk from price fluctuation of commodities on a global scale. The outcome of the study correlates with economies of scale theory by (Weston et al., 1996), where they argue that bigger firms are believed to have an advantage in other contexts, e.g., delivering cheaper per-unit production costs, because fixed costs must be spread over a large output volume. Such a mechanism allows various organizations to identify their areas of operational ineffectiveness and convert those into highly profitable streams. Moreover, bigger companies typically create an upper hand in the capital market, thus obtaining external financing at a low cost of capital. This condition gives the strategic flexibility to pursue a large-scale investment scale-up that systematically improves overall financial performance and the sustainability of the business.

4.5.8. Firm Size Mediates the Liquidity Affecting Financial Performance

Based on the Baron and Kenny triangle plot, a p-value of 0.7157 was obtained, > 0.05. Since this p-value > 0.05, this indicates that firm size does not mediate the liquidity and financial performance. It proves the eighth hypothesis (H8) is rejected. These results show that although significantly liquidity affects firm size and firm size affects financial performance, the indirect effect of liquidity on financial performance through firm size is not significant. Furthermore, the direct effect of liquidity on financial performance is also not significant after including the mediating variable. In the mining industry, growth in firm size is fundamentally driven by the intensity of investment in fixed assets, such as production equipment, mining infrastructure development, and mineral reserve exploration activities. Therefore, short-term liquidity does not serve as a direct determinant driving the growth of total assets, which is a primary indicator of firm size. Since there is a need for sustained capital allocation to scale over time in a capital-intensive industry, an influx of current assets does not necessarily equate to operational scale that would drive significant increases in production. This study finding is consistent with the proposition by (Weston et al., 1996) that long-term investment opportunities, rather than periodic liquidity conditions, are more likely to govern expansion decisions within capital-intensive industries. As a result, firm size does not function as an intermediary variable that measures liquidity with financial performance. These findings highlight that liquidity can indicate a firm's operating logistics, and this effect is isolated from the scaling or firm size effects on profitability, seeking to promote profits.

4.5.9. Firm Size Mediates the Leverage Affecting Financial Performance

The Sobel test yielded p 0.0154 with a statistic of 1.425254. This p < 0.05 shows that firm size mediates the leverage effect on financial performance. It shows acceptance of the ninth hypothesis (H9). Firm size has a partial mediator (partial mediation) between the leverage and financial performance. The reason is that the indirect effect of leverage on financial performance does not disappear once you take that through a mediating variable into account. The findings are that leveraged debt is a natural response in the financial performance of the firm (else we would shy away from it) through its ability to increase the scale or size of the operations of a firm. Externally-sourced financing can be effectively managed to allow for productive investment in expansion, acquisition of modern production technologies to generate a broader range of output at increased efficiency, and full operational infrastructure to ensure sustainable economic viability. Such investments lead to an incremental growth in the total assets of the company and thus increase its dimensions over time.

These results are in line with the research by (Suryani, 2020), which showed that companies that use leverage for asset expansion to a greater strategic ratio have better temporary capabilities. As the company grows, these entities have accumulated a greater competitive advantage and access to larger resources. In this case, as mentioned by (Wang et al., 2024), the enlargement of scale brings about an increase in ROA through economies of scale mechanisms (cost efficiency) and production volume. These results coincide with the claim of (Zeitun & Tian, 2007) that in capital-intensive industries, firm size is an important channel through which new external financing translates into more efficient asset utilization. That is, this study reinforces the establishment that firm size can act as a mediating factor linking capital structure (leverage) with the target of optimal financial performance of mining companies on IDX. The effect of leverage on profits does not move in a vacuum and needs debt usage to be routed through growth for it to have meaningful financial.

4.5.10. Firm Size Mediates the Efficiency Affecting Financial Performance

The Sobel test yielded p 0.0455 with a statistic of 1.915050. This p < 0.05, indicating that firm size mediates efficiency impact on financial performance. It confirms that the tenth hypothesis (H10) is accepted. Firm size partially mediates the relationship between efficiency and financial performance (partial mediation). The results indicate that operational efficiency gives a direct profit benefit and also scales up the firm. It is clear that management can identify potential revenue-generating productive assets. This helps the organization to grow its business and gain assets in total. The growth in firm size resulting from internal efficiency allows the firm to pursue economies of scale. This mechanism is significant in minimizing per-unit production cost, thus increasing profit margin too. On the other hand, major players have leveraged competitive benefits thanks to cutting-edge technology adoption, labor productivity enhancement and increasing strategic welfare networks in the global market.

This result supports (Wang et al., 2024) in that the growth of corporations is mainly driven by operational efficiency, as effective firms were more likely to evolve into larger operational entities, and this information has an effect on their future performance. Moreover, when internal efficiency stimulates growth in the firm size, ROA remains stable as entities have some level of bargaining power that is generally high, given the mining industry supply chain. In summary, firm size acts as an important mediator through which operational efficiency advantages translate into better financial performance. For mining firms that are publicly listed in the IDX market, leveraging both high levels of operational efficiency and scale to ensure sustained financial performance during times of instability for commodity markets is critical.

5. Conclusion

The result concludes that:

1. Liquidity does not affect financial performance significantly. This indicates that an entity’s capacity to meet short-term liabilities is not a primary determinant of profitability fluctuations in mining companies.

2. Leverage negatively significantly affects financial performance. Consistent optimization of asset utilization can stimulate an increase in operating profit.

3. Efficiency significantly positively affects financial performance. Consistent optimization of asset utilization can stimulate an increase in operating profit.

4. Liquidity negatively significantly affects firm size. A high allocation to current assets tends to hinder the expansion of fixed assets, thereby slowing the company’s growth in scale.

5. Leverage does not affect firm size significantly. Financing decisions based on debt do not directly transform the entity’s asset scale in that sector.

6. Efficiency significantly positively affects firm size. Managerial capability in efficiently managing assets provides financial support for expanding operational scale and total asset accumulation.

7. Firm size significantly positively affects financial performance. Larger companies benefit from economies of scale, which lead to improved financial performance.

8. Firm size does not mediate the liquidity impact on financial performance; It proves no mediation occurs. This implies that changes in a company’s liquidity will not affect financial performance either directly or through firm size.

9. Firm size partially mediates the leverage impact on financial performance (partial mediation). This is because the direct effect of leverage on financial performance remains significant even when the mediating variable is included in the model.

10. Firm size partially mediates the efficiency impact on financial performance (partial mediation). Operational efficiency drives asset growth and business expansion, serving as a crucial bridge in achieving superior financial performance.

Thus, the findings described that the financial performance model is a function of adjusting the predictors, namely liquidity, leverage, efficiency, and the mediator, namely firm size. It strengthens the theory regarding financial performance and can be a scientific reference in the future. This study also produces several practical recommendations for both practitioners and academics, namely :

1. Management of mining entities is advised to consistently evaluate and maintain an optimal capital structure. Given that the use of leverage has been shown to stimulate firm size growth, management must ensure that such external financing is prudently allocated to exploration projects and productive assets with a positive Net Present Value (NPV). This strategy is crucial to ensure that the company’s scale expansion contributes significantly to strengthening financial performance, particularly the ROA ratio.

2. Investors and financial analysts need to consider firm size as one of the primary parameters in assessing risk and investment security. Companies with a larger asset scale have thus proven to offer broader access to various funding sources and possess greater resilience through more robust sources in coping with global commodity price volatility and macroeconomic fluctuations. The economic scale of large companies serves as an indicator of stability in maintaining long-term profitability.

3. Future researchers are advised to expand the scope of the study by integrating new independent variables relevant to current macroeconomic dynamics, such as interest rates, inflation, or specific commodity prices. Future researchers are also expected to expand the sample to other industrial sectors or extend the observation period (longitudinal study) to test the consistency and stability of the research results across different economic cycles.