IJAR Indonesian Journal of Advanced Research 2986-0768 Formosa Publisher 10.55927/ijar.v4i7.14945 Factors Affecting Profitability in Telecommunication Sector Companies Listed on the Indonesia Stock Exchange for the Period 2016–2023 Rizka Auliah University of Swadaya Gunung Jati, Cirebon, Indonesia auliahrizka10@gmail.com Aryaningsih Nabilah Risna University of Swadaya Gunung Jati, Cirebon, Indonesia Budianto Erwin University of Swadaya Gunung Jati, Cirebon, Indonesia 27 07 2025 11 05 2025 25 06 2025 27 07 2025 4 7 1507 1522

The sustainability and growth of a company are strongly determined by its ability to generate profits. One key metric used to evaluate profitability is Return on Assets (ROA). To remain competitive and resilient, companies must regularly monitor and analyze their financial performance, including identifying the factors that influence ROA. This study examines the relationship between ROA and several financial indicators, namely the current ratio (CR), debt-to-asset ratio (DAR), total asset turnover (TATO), and sales growth (SG). The research utilized regression analysis on a sample of five companies within the Indonesian telecommunications subsector that met specific criteria during the 2016–2023 period. The findings reveal that sales growth does not significantly impact ROA, whereas current ratio, debt-to-asset ratio, and total asset turnover have a significant effect. The model explains 75% of the variance in ROA, with the remaining 25% attributed to other unexamined variables. These results suggest that asset efficiency, liquidity, and solvency are critical factors influencing the profitability of telecommunications companies, while sales growth alone does not directly enhance the ability to generate net profit from assets.

 Current Ratio Debt to Asset Ratio Total Asset Turn Over Sales Growth http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License. INTRODUCTION

Information and communication technology plays a crucial role in various dimensions of life, including in supporting the pace of economic growth. In addition, the continuous increase in the population has also encouraged the increasing need for effective means of communication. Reporting from Katadata.co.id (March 23, 2022) Indonesia occupies a position as one of the countries with the largest number of internet users in the world, namely 204.7 million people in January 2022, with a penetration of 73.7% of the total population of 277.7 million people. In the last five years, there has been a significant growth in internet users of 54.25% compared to 2018, when the penetration rate was still at 50%. This shows a rapid increase in internet adoption nationwide. This growth encourages the telecommunications industry to adapt amid increasingly high competition intensity. Companies in this sector are required to continue to innovate in order to gain a competitive advantage in order to be able to compete sustainably. Therefore, companies need to evaluate and analyze their performance carefully, one of which is through financial analysis based on financial statements. The telecommunications sub-sector itself is part of the service sector that focuses on providing information technology, communication, and telecommunication network infrastructure services.

Reporting from Bisnis.com (June 21, 2022), PT First Media Tbk's (KBLV) revenue recorded the most significant decline, namely by 22.09% on an annual basis (yoy), from the previous IDR 34.74 billion to IDR 27.07 billion. Based on (Liputan6.com, 2024) PT Centratama Telekomunikasi Indonesia Tbk (CENT) recorded an increase in revenue to IDR 2.52 trillion, growing 8.74% compared to 2022 which reached IDR 2.32 trillion. In addition, the company also managed to reduce losses throughout 2023, with net losses falling by 60.6% from IDR 2.14 trillion in 2022 to IDR 844.39 billion. Meanwhile, PT Bali Towerindo Sentra Tbk (BALI) showed a decline in both revenue and profit in 2023. Revenue was recorded at IDR 955.26 billion, down 2.36% compared to the same period the previous year of IDR 978.37 billion, while net profit decreased by 29.03% to IDR

150.49 billion from the previous IDR 212.08 billion in 2022. Meanwhile, reporting from (Bisnis.com, 2024), PT Telkom Indonesia Tbk (TLKM) recorded an increase in revenue to IDR 112.21 trillion, but net profit decreased due to an increase in operating expenses. The decline in profit reflects profitability instability that has an impact on the achievement of Return On Assets (ROA) of telecommunications companies. This condition indicates the existence of financial problems within the company, which then becomes the basis for further studies in this study. Overall, telecommunications sub-sector companies have increased revenue while net profit has decreased, this shows the need for an in-depth analysis of the financial performance of telecommunications companies in order to adapt to industry dynamics and maintain profitability.

Financial performance generally indicates how well a company operates and attempts to create value with its assets (Apriani et al., 2023). This information is reflected in the financial statements and serves as a basis for evaluating the effectiveness and efficiency of the company in generating profits and creating value for stakeholders. As a key indicator of a company's financial health, good

financial performance indicates a company's ability to manage finances, generate profits, and maintain a sustainable business. On the other hand, poor performance can indicate problems such as losses, unmanaged debt, or low liquidity. Companies can strengthen their financial position, increase shareholder value, and achieve their long-term goals by appropriately integrating and managing financial performance. One way to assess a company's financial performance is to look at the Return On Assets (ROA) calculation, which calculates a telecommunications company's profit based on the number of assets it owns. In other words, the rate of return is higher if the value of the ROA is proportional to the number of assets.

Several important factors drive the value of return on assets. First, the current ratio has a significant role in determining how much profit or profitability can be achieved (Čavlin et al., 2021) The ideal current ratio reflects the stability of the company's liquidity, so that the company can explain its operational activities without any disruption due to short-term debt repayment pressures. According to the findings of the study by Tripuspitorini et al. (2022), the current ratio significantly and favorably affects return on assets (ROA) for businesses in the food and beverage subsector. However, the results from (Mulyana, Elis Badariah, Imat Hikmat, 2023) show that ROA in telecommunications sub-sector enterprises is not significantly impacted by the current ratio.

The efficiency of the business's capital structure, as evidenced by the Debt to Asset Ratio (DAR), is a crucial component in assessing the level of profitability, especially the Return on Asset (ROA). Pusung et al. (2024) found that the debt to asset ratio has a direct impact on the return on assets in companies in the telecommunications subsector, but Eka and Nafisah (2024) looked into the potential that it does not directly affect the return on assets at PT Mayora Tbk.

Asset utilization efficiency, measured by total asset turnover ratio (TATO), is a critical factor in increasing a company's profitability. Total Asset Turnover (TATO) reflects the level of effectiveness of a company in utilizing all assets owned to generate income. The higher the value of TATO, the more efficient the company will be in managing its assets, which ultimately contributes positively to the increase in Return On Assets (ROA). but if it is not balanced with operational cost control or other costs, so that even though the asset generates sales, the net profit obtained remains low, as research conducted by (Oktaviani et al., 2022) shows that the total asset turnover ratio does not have a significant effect on the return on assets at PT Pyridam Farma.

Sales growth performance shows a business's ability to increase its revenue gradually. This sales growth theoretically increases the company's profitability level, especially Return On Asset (ROA), because increased sales indicate increased operational effectiveness and asset optimization in generating profits. Research conducted by (Sari & Aulia, 2021) shows that Sales Growth has a significant effect on the return on assets at PT Milkjaya Industri CO Tbk.

 LITERATURE REVIEW Current Ratio

The Current Ratio (CR) can be used to gauge a company's capacity to fulfill its short-term commitments (Mufalichah & Nurhayati, 2022). In contrast, Return On Assets (ROA) is a profitability metric that assesses how well a business can produce a profit from the operations that its owners carry out (Ramli & Yusnaini, 2022). To pay off short-term debt, businesses must raise current assets and decrease current obligations. While a low current ratio denotes weak financial conditions, a high current ratio shows strong liquidity and a company's capacity to settle its debts. According to research by Tripuspitorini et al. (2022), return on assets is positively and significantly impacted by the current ratio.

So the hypothesis in this study is:

H1 : Current Ratio Affects Return On Asset

 Debt to Asset Ratio

The Debt to Asset Ratio (DAR) is a metric that quantifies the ratio of a company's total obligations to its total assets (Kasmir, 2019). The high DAR value suggests that the majority of the company's assets are financed by debt from outside sources. If the business is unable to properly handle its financial responsibilities, this condition may raise the default risk. A low DAR, on the other hand, denotes a less risky capital structure and a more steady potential for profitability because of lower interest costs and financial risk. According to research by Anggraeni and Nasution (2022), return on assets is impacted by the debt to asset ratio. Thus, the following is the study's hypothesis:

H2 : Debt to Asset Ratio Affects Return On Asset

 Total Asset Turnover Ratio

The Total Assets Turnover Ratio (TATO), according to Kasmir (2019), is a ratio that assesses a company's capacity to turn a profit on each rupiah of assets. This ratio shows how well a business optimizes all of its assets to support operational operations that generate revenue. The more successfully a company leverages assets to create sales, the greater its TATO value, which will help to increase Return On Assets (ROA). According to research by Mulyana, Elis Badariah, and Imat Hikmat (2023), Return On Asset (ROA) is significantly impacted by the Total Asset Turnover Ratio (TATO). Thus, the following is the study's hypothesis:

H3 : Total Asset Turnover Ratio Affects Return On Asset

 Sales Growth

(Fahmi, 2020) defines Growth ratio as an indicator used to show the extent to which a company is able to maintain and improve its position in terms of asset ownership and overall economic performance High sales growth indicates the company's success in increasing revenue, for example through marketing strategies, market expansion, or new product launches. If this growth is accompanied by efficient cost management, it will increase net profit, which in turn has an impact on increasing Return On Asset (ROA). The research conducted (Pusung et al., 2024) stated that there was no significant effect between sales growth on return on assets. So the hypothesis in this study is:

H4 : Sales Growth Affects Return On Asset

Current Ratio, Debt To Asset Ratio, Total Asset Turnover Ratio, and Sales Growth Against Return On Assets

Among the financial elements that affect Return On Asset (ROA) are liquidity, capital structure, operational effectiveness, and business expansion. The current ratio shows how well-positioned the company is to satisfy its short- term obligations. The debt-to-assets ratio shows the amount of debt used to finance a company's assets in proportion to financial risk. The efficiency with which a business generates revenue from all of its assets is demonstrated by the total asset turnover ratio. Furthermore, the rise in sales shows that the business may raise revenue over time. The asset return rate of the business may be impacted by each of these variables. Thus, the following is the study's hypothesis:

H5: Current Ratio, Debt to Asset Ratio, Total Asset Turnover Ratio, and Sales Growth have an effect on Return on Assets

Figure 1. Frame of Mind

 METHODOLOGY

This study examines a certain population or sample using a quantitative research methodology. Structured research tools were used to gather data, and statistical analysis was performed (Sugiyono, 2021). The main objective is to investigate if Return on Assets (ROA) in telecommunications sub-sector businesses listed on the Indonesia Stock Exchange (IDX) from 2016 to 2023 is influenced by the Current Ratio, Debt to Asset Ratio, Total Asset Turnover Ratio, and Sales Growth. The sample selection is focused on companies within the telecommunications sub-sector during this period, chosen due to the sector’s noticeable profit decline despite rapid technological advancements. From a total population of 21 companies, 5 were selected as the sample using purposive sampling—a method that selects samples based on specific criteria relevant to the research objectives. The study covers 8 years of data, resulting in 40 observation points. Data was obtained through a documentation method by reviewing the companies' financial statements. For data analysis, SPSS software version 25 was used, with data sourced from the official website of the Indonesia Stock Exchange (www.idx.co.id).

 RESEARCH RESULTS Descriptive Analysis

The variables used in this study are Current Ratio (X1), Debt to Asset Ratio (X2), Total Asset Turnover Ratio (X3), and Sales Growth (X4) as independent variables and Return on Asset (Y) as dependent variables. Descriptive statistical testing is a data completion process, so that the data used in this study is normally distributed.

Source: Output Data SPSS 25 (2025)
Descriptive Statistics N Minimum Maximum Mean Std. Deviation
ROA (Y) 40 .0039 .1648 .057670 .0469476
CR (X1) 40 .1822 2.8086 .852613 .6043906
DAR (X2) 40 .3103 .9312 .573870 .1762739
TATO (X3) 40 .1021 .6477 .245128 .1778112
Sales Growth (X4) 40 -.1310 .4939 .126175 .1287762
Valid N (listwise) 40

There are 40 observation data points in all, according to Table 1. With a mean of 0.057670 and a standard deviation of 0.0469476, the Return on Assets (ROA) variable shows minimal variability across the sample, with a minimum value of 0.0039 (IBST in 2023) and a high value of 0.1648 (TLKM in 2017). The Current Ratio (CR), which reflects discernible variations in liquidity among corporations, spans from a low of 0.1822 (TOWR in 2023) to a maximum of 2.8086 (IBST in 2021), with an average of 0.852613 and a standard deviation of 0.6043906. The Debt to Asset Ratio (DAR) indicates substantial variance, ranging from a low of 0.3103 (IBST in 2021) to a maximum of 0.9312 (TBIG in 2016). The average is 0.573870, with a standard deviation of 0.1762739. Again showing very little variance, the Total Asset Turnover Ratio (TATO) shows values ranging from 0.1021 (IBST in 2021) to 0.6477 (TLKM in 2016), with a mean of 0.245128 and a standard deviation of 0.1778112. With a minimum of -0.1310 (IBST in 2021) and a maximum of 0.4939 (BALI in 2016), as well as an average of 0.126175 and a standard deviation of 0.1287762, the Sales Growth (SG) variable, on the other hand, exhibits a wider dispersion, indicating a comparatively high degree of variation in sales performance across the companies under observation.

 Classic Assumption Test

Autocorrelation Test

The Durbin-Watson test is specifically used to identify first-order autocorrelations, assuming that the regression model contains an intercept and that there are no mediators among the independent variables. (Ghozali, 2021). The hypotheses to be tested are:

H0 : no autocorrelation (r = 0)

Ha : there is an autocorrelation (r ≠ 0)

Table 2. Autocorrelation Test Results (Durbin Watson's)

 Source: Output SPSS 25

Dependent Variable: ROA (Y)

Predictors: (Constant), Sales Growth (X4), DAR (X2), TATO (X3), CR (X1)

Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .933a .871 .856 .0177567 1.095

Referring to the table above, the results of the autocorrelation test using the Durbin-Watson (DW) test yielded a value of 1.095. This figure is compared against the critical values at a 0.05 significance level, with a sample size (n) of 40 and four independent variables (k). According to regression analysis guidelines, the decision rule is as follows: if the DW value falls between 0 < d < dl, autocorrelation is present; if it lies between du < d < 4 – du, there is no autocorrelation; and if d falls between dl < d < du or between 4 – du < d < 4 – dl, no definitive conclusion can be drawn. In this case, the lower limit (dl) is 1.2848 and the upper limit (du) is 1.7209, making 4 – du equal to 2.2791. Since the DW value of 1.095 falls within the range of 0 to 1.2848, it indicates the presence of positive autocorrelation in the regression model. To address this issue, a data transformation was carried out using the lag method. The transformed data is then utilized for subsequent analysis.

Source: Output SPSS 25 (2025)

Predictors: (Constant), Lag_SalesGrowth, Lag_DAR, Lag_TATO, Lag_CR

Dependent Variable: Lag_ROA

Model Summaryb
Model R R Square Adjusted Square R Std. Error of the Estimate Durbin-Watson
1 .881a .777 .750 .0159204 1.921

Referring to Table 3, the autocorrelation test conducted using the Durbin- Watson (DW) method resulted in a value of 1.921. This value is compared to the critical values at a 5% significance level, with a sample size (n) of 39 and four independent variables (k). Based on the decision rule, if the DW value lies within the range of du < d < 4 – du, it indicates the absence of both positive and negative autocorrelation. In this case, the lower limit (dl) is 1.2734 and the upper limit (du) is 1.7215, while 4 – du equals 2.2785. Because the DW value of 1.921 falls within the range of 1.7215 < 1.921 < 2.2785, it can be concluded that the regression model does not exhibit autocorrelation.

 Normality Test

Table 4. Kolmogorov-Smirnov One-Sample Normality Test

 Source: Output SPSS 25 (2025)

Test distribution is Normal.

Calculated from data.

Lilliefors Significance Correction.

One-Sample Kolmogorov-Smirnov Test Unstandardized Residual
N 39
Normal Parametersa,b Mean .0000000
Std. Deviation .01505915
Most Extreme Differences Absolute .134
Positive .134
Negative -.129
Test Statistic .134
Asymp. Sig. (2-tailed) .076c

According to Table 4, the normality test findings indicate that 39 (N = 39) data points were employed in this investigation. The Kolmogorov-Smirnov One- Sample technique was used to perform the normalcy test, and an Asymp value was produced. Two-tailed sig. is 0.076. The regression model's residual can be said to be regularly distributed since the significance value (0.076 > 0.05) is higher than the significance level of 0.05. As a result, the regression model can be utilized in the study and the assumption of normalcy is met.

 Multicollinearity Test

Table 5. Multicollinearity Test

 Source: Output SPSS 25 (2025)

Dependent Variable: Lag_ROA

Model Unstandardized Coefficients Standardized Coefficients Beta t Sig. Collinearity Statistics
B Std. Error Tolerance VIF
1 (Constant) -.041 .013 -3.224 .003
Lag_CR .015 .006 .243 2.310 .027 .595 1.680
Lag_DAR .099 .027 .401 3.663 .001 .547 1.827
Lag_TATO .257 .024 .953 10.817 .000 .847 1.180
Lag_SalesGrowth -.003 .025 -.011 -.135 .894 .956 1.046

Based on Table 5, the Variance Inflation Factor (VIF) values for each independent variable are as follows: Current Ratio (CR) is 1.680, Debt to Asset Ratio (DAR) is 1.827, Total Asset Turnover (TATO) is 1.180, and Sales Growth is

1.046. All VIF values are well below the standard threshold of 10. Additionally, the tolerance values for each variable are CR at 0.595, DAR at 0.547, TATO at 0.847, and Sales Growth at 0.956, all of which exceed the minimum acceptable value of 0.10. These results indicate that there is no indication of multicollinearity among the independent variables in the regression model.

 Heteroscedasticity Test

Source: Output SPSS 25 (2025)

Dependent Variable: ABRES

Model Unstandardized Coefficients Standardized Coefficients Beta t Sig.
B Std. Error
1 (Constant) .004 .008 .422 .676
Lag_CR .004 .004 .211 .976 .336
Lag_DAR .017 .018 .219 .971 .339
Lag_TATO .007 .016 .076 .418 .679
Lag_SalesGrowth -.018 .017 -.180 -1.059 .297

Based on Table 6, the heteroscedasticity test using the Glejser method produced the following significance values for each independent variable: Current Ratio (CR) at 0.336, Debt to Asset Ratio (DAR) at 0.339, Total Asset Turnover (TATO) at 0.679, and Sales Growth at 0.297. Since all significance values exceed the 0.05 threshold, it can be concluded that there are no signs of heteroscedasticity in the regression model. Therefore, the assumption of homoscedasticity—meaning consistent residual variance across observations— has been satisfied.

 Multiple Regression Analysis

Table 7. Multiple Regression Analysis Results

 Source: SPSS Output 25 (2025)

Dependent Variable: Lag_ROA

Model Unstandardized Coefficients Standardized Coefficients Beta t Sig.
B Std. Error
1 (Constant) -.041 .013 -3.224 .003
Lag_CR .015 .006 .243 2.310 .027
Lag_DAR .099 .027 .401 3.663 .001
Lag_TATO .257 .024 .953 10.817 .000
Lag_SalesGrowth -.003 .025 -.011 -.135 .894

Based on table 7, it can be seen that regression analysis produces the following regression models:

Return On Asset = -0.041 + 0.015 CR + 0.099 DAR + 0.257 TATO - 0.003 SG + e... (1)

Means:

The intercept value of -0.041 indicates that if all independent variables— Current Ratio (CR), Debt to Asset Ratio (DAR), Total Asset Turnover (TATO), and Sales Growth—are at zero, the estimated Return on Assets (ROA) would be -4.1%. This suggests that in the absence of these factors, the company would experience a negative return on its assets.

The regression coefficient for the Current Ratio (CR) is 0.015, implying a positive correlation between CR and ROA. A 1% increase in CR is associated with a 1.5% increase in ROA, assuming other variables remain constant. This highlights the role of liquidity in enhancing company profitability.

The coefficient for the Debt to Asset Ratio (DAR) is 0.099, showing a positive relationship with ROA. This means that for every 1% rise in DAR, ROA increases by 9.9%, indicating that effective debt utilization can significantly boost returns on assets.

The Total Asset Turnover (TATO) variable has a coefficient of 0.257, revealing a strong positive link with ROA. A 1% increase in TATO leads to a 25.7% increase in ROA, suggesting that higher asset efficiency in generating sales results in greater profitability.

The Sales Growth variable has a negative coefficient of -0.003, indicating an inverse relationship with ROA. Every 1% increase in sales growth corresponds to a 0.3% decline in ROA. This may imply that sales increases are not accompanied by proportional gains in efficiency or profit, thus lowering asset returns.

Table 8. Coefficient of Determination

 Source: Output SPSS 25 (2025)

Predictors: (Constant), Lag_SalesGrowth, Lag_DAR, Lag_TATO, Lag_CR

Dependent Variable: Lag_ROA

Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .881a .777 .750 .0159204

The four independent variables—current ratio, debt to asset ratio, total asset turnover ratio, and sales growth—can account for 75% of the variation in the dependent variable, return on assets (ROA), according to Table 8's Adjusted R2 value of 0.750. Other factors not covered by this research model are responsible for the remaining 25% of the variation.

 Hypothesis Test

Partial Test (T Test)

(Ghozali, 2021). The t-test's objective is to determine how much each independent variable contributes to the explanation of the variances observed in the dependent variables. In this test, the hypothesis is accepted if the significance

value (p-value) is less than 0.05 (α < 0.05) and the estimated t-value is bigger than the t-table.

Table 9. Partial Test (T Test)

 Source: Output SPSS 25 (2025)

Dependent Variable: Lag_ROA

Model Unstandardized Coefficients Standardized Coefficients Beta t Sig.
B Std. Error
1 (Constant) -.041 .013 -3.224 .003
Lag_CR .015 .006 .243 2.310 .027
Lag_DAR .099 .027 .401 3.663 .001
Lag_TATO .257 .024 .953 10.817 .000
Lag_SalesGrowth -.003 .025 -.011 -.135 .894

Based on table 9, the results of the partial test (t-test), the conclusions obtained from each variable are as follows:

The Effect of Current Ratio (CR) on Return on Assets (ROA):

According to partial test results from the SPSS output, throughout the 2016–2023 timeframe, the Current Ratio (CR) significantly and favorably affects Return on Assets (ROA) in telecoms sub-sector enterprises. A significant level of 0.027, which is below the 0.05 cutoff, and a computed t-value of 2.310, which is higher than the necessary t-table value of 2.032, corroborate this. As a result, it is confirmed that CR has a considerable impact on ROA, rejecting the null hypothesis (H₀) and accepting the alternative hypothesis (Hₐ).

The Effect of Debt to Asset Ratio (DAR) on Return on Assets (ROA): Additionally, the partial test demonstrates that ROA is favorably and strongly impacted by the Debt to Asset Ratio (DAR). With a significance value of 0.001—far below 0.05—the t-calculated value is 3.663, which is higher than the t-table value of 2.032. Thus, Ha is accepted and H₀ is rejected, suggesting that DAR significantly affects ROA.

The Effect of Total Asset Turnover (TATO) on Return on Assets (ROA): The Total Asset Turnover Ratio (TATO) demonstrates a very strong and significant positive effect on ROA. The calculated t-value is 10.817, far exceeding the t-table value of 2.032, with a significance level of 0.000. Accordingly, H₀ is rejected and Hₐ is accepted, meaning that TATO significantly influences ROA.

The Effect of Sales Growth on Return on Assets (ROA):

On the other hand, there is no discernible impact of the Sales Growth variable on ROA. This is supported by a significance value of 0.894, which is significantly higher than 0.05, and a t-value of -0.135, which is lower than the t-table value of 2.032. As a result, the alternative hypothesis (Hₐ)

is rejected and the null hypothesis (H₀) is accepted, suggesting that ROA is not greatly impacted by sales growth.

 Simultaneous Test (F Test)

To ascertain if all independent factors taken together significantly affect the dependent variable, the F test is utilized. Ghozali (2021) states that the alternative hypothesis (Hₐ) is accepted and the regression model as a whole is statistically significant if the significance value is less than 0.05 and the computed F value is more than the F table value. The null hypothesis (H₀), on the other hand, is accepted if the significance value is more than 0.05 or the computed F value is less than the F table value, indicating that the model does not meaningfully explain the variance in the dependent variable.

Table 10. Simultaneous Test (F Test)

 Source: Output SPSS 25 (2025)

Dependent Variable: Lag_ROA

Predictors: (Constant), Lag_SalesGrowth, Lag_DAR, Lag_TATO, Lag_CR

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .030 4 .007 29.536 .000b
Residual .009 34 .000
Total .039 38

Assessing the Impact of Sales Growth, Debt To Asset Ratio (DAR), Current Ratio (CR), and Total Asset Turnover Ratio (TATO) on Return On Assets in Telecommunications Sub-Sector Businesses for the 2016–2023 Timeframe.

Table 10 is used to determine the F-value, which comes out to be 29.536 with a significance value of 0.000—smaller than 0.05. The F table is 2.88 when the degrees of freedom are df₁ = 3 and df₂ = 34. The factors Current Ratio (CR), Debt to Asset Ratio (DAR), Total Asset Turnover Ratio (TATO), and Sales Growth all have a substantial impact on Return On Asset (ROA) at the same time since F is computed (29.536) > F table (2.88), and the significance is less than 0.05. As a result, Hₐ is approved and H₀ is refused.

 DISCUSSION

The study's conclusions provide some crucial new information about the connections between sales growth, the debt-to-asset ratio, the current ratio, and the total asset turnover ratio. First, the study's findings show that return on assets (ROA) is significantly impacted by the current ratio. These results demonstrate how the company's capacity to fulfill its immediate obligations is a reflection of its liquidity stability, which in turn supports the availability of assets for operational operations and helps to generate profit from their utilization. The study's findings are consistent with those of another study (Anisa & Febyansyah, 2024), which

found that the current ratio, a measure of a company's liquidity, significantly and favorably affects profitability.

Furthermore, the debt to asset ratio (DAR) shows a significant influence on return on assets (ROA). This is allegedly due to the lack of optimal company in managing its funding structure, so that it has an impact on the efficiency of asset use in generating profits, so that this makes profits not in line with the funds in the company because the larger the debt, the more profitability obtained by the company. The results of this study are in line with previous research conducted by (Luckieta et al., 2021) and (Maiyaliza & Parlina, 2024) which stated that the Debt to Asset Ratio has a significant effect on Return On Asset.

Return on assets is significantly impacted by total asset turnover. This implies that the rate of return on assets (measured by ROA) will be impacted by changes in the efficiency of the business's asset utilization (measured by TATO). One of the key factors influencing a company's asset profitability is asset utilization efficiency. The results of this study are in line with those of a prior study by Pangestika et al. (2021), which found that Return on Asset is significantly impacted by the Total Asset Turnover Ratio.

Return on assets (ROA) is not significantly impacted by sales growth. This suggests that the company's ability to efficiently generate net profit from its assets is not directly or significantly impacted by changes in sales growth rates. Companies in the telecommunications subsector have a high operating cost structure, including infrastructure, marketing, and technology costs, despite a growth in sales. Sales growth has little effect on return on assets because of these wasteful expenses, which might lower net profit. This result is consistent with earlier study that found no relationship between sales growth and profitability (Sang Ayu Made Riska Vidyasari, Ni Putu Yuria Mendra, 2021).

 CONCLUSIONS AND RECOMMENDATIONS

According to study conducted on Telecommunications Sub-Sector Companies listed on the Indonesia Stock Exchange (IDX) for the years 2016–2023, the Current Ratio (CR) has a substantial impact on Return On Asset (ROA). Similarly, ROA has a strong correlation with both Total Asset Turnover (TATO) and the Debt to Asset Ratio (DAR). However, unlike other factors, sales growth has no discernible effect on return on assets (ROA). When examined together, the four factors—current ratio (CR), debt to asset ratio (DAR), total asset turnover (TATO), and sales growth—were found to have an effect on these firms' return on assets (ROA).

qThe results of the study on Telecommunications Sub-Sector Companies listed on the IDX for the 2016-2023 period have several important implications for company management. The significant influence of the Current Ratio on ROA signifies the importance of optimal liquidity management, while the significance of the Debt to Asset Ratio emphasizes the need for a balanced capital structure to maximize profitability. The significant influence of Total Asset Turnover underlines the importance of efficient use of assets in improving the company's financial performance. Meanwhile, the absence of a significant influence of Sales Growth on ROA shows that revenue growth alone does not guarantee increased

profitability if it is not balanced with operational efficiency. The simultaneous influence of these four variables indicates the need for a holistic approach in the financial management of telcos companies, where decisions related to liquidity, capital structure, and asset utilization must be integrated to optimize overall asset returns.

 ADVANCED RESEARCH

There are still a lot of shortcomings in this study that need to be fixed. The use of research objects that are restricted to the Telecommunications Sub-Sector listed on the Indonesia Stock Exchange, the use of only five companies as samples, and the use of research variables that only include the current ratio (CR), debt to asset ratio (DAR), total asset turnover (TATO), sales growth, and return on assets (ROA) are some examples of these limitations.

Based on these limitations, it is hoped that the next researcher can expand research objects such as Telecommunications Sub-Sector companies in other countries to produce better populations and selection results. In addition, it is recommended for future researchers to expand the number of company samples to improve the generalization ability of research findings. The addition of other variables that have the potential to affect Return On Assets (ROA), such as Inventory Turnover, is also expected to provide more comprehensive results.

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