Globalization and digital economic transformation are prevalent in today’s world, managing capital structure has become a vital element of a company’s financial strategy Afiezan et al. (2020) Among key components of capital structure, debt policy holds a significant role in shaping how companies finance their growth and innovation initiatives. Globally, the trend of funding through debt has shown a significant shift triggered by market conditions, monetary policy, and competitive pressures. In Indonesia, this phenomenon is no exception, where various industry sectors have begun to change their funding patterns in response to economic uncertainty and growing investment needs.

The healthcare sector in Indonesia has a very strategic role, not only as a support for the welfare of the community, but also as one of the main contributors to the Gross Domestic Product (GDP). The industry includes hospitals, clinics, pharmaceutical companies, and medical device providers, all of which rely on large investments in infrastructure, medical technology, and service improvements.

Healthcare sector companies in Indonesia show fluctuations in earnings. In 2020, this sector experienced profit growth of 11.6%, which then slightly increased to 12.16% in 2021 Rezy (2022). Nevertheless, this sector remains the highest contributor to GDP growth compared to other sectors. In 2022, growth dropped dramatically to 2.47% (Victoria, 2023). This change in percentage growth indicates that despite a consistent increase in demand, cost pressures, regulatory changes, and other external factors, for example COVID-19 pandemic has impacted financial performance of companies in healthcare sector. This data not only demonstrates the sector's strength in the face of challenges, but also highlights the need to understand how companies manage funding strategies, particularly debt policies, to cope with such fluctuations.

In the midst of complex growth dynamics and revenue fluctuations, healthcare companies are required to carry out careful financial management, especially in determining debt policy. Profitability, liquidity, solvency, and firm size are key variables that are believed to impact choice to obtain external funding through debt.

Because it indicates company’s capacity to turn a profit from its resources, profitability is essential to both business continuity and expansion (Feryyanshah & Sunarto, 2022). Debt policy has a direct impact on company’s profitability, because a business’s capacity to turn a profit demonstrates efficient operations and encourages investors to make capital investments, given its good prospects (Adnin & Triyonowati, 2021). Prior studies by Aw et al. (2021), Wardana (2021) andTarigan et al. (2022) claimed impact of profitability on debt policy is substantial, where more profitable companies prefer to use their own profits to finance their operations, not debt. This indicates that company’s choice of debt policy is influenced by level of profitability.

Ability of a business to pay short-term debts, including staff salaries, operating costs, and other pressing financial requirements, is referred to as liquidity (Ilyas Lamuda et al., 2023). Current ratio, which contrasts a company’s current assets and liabilities, is frequently used to measure it. Previous studies have shown mixed results regarding relationship between liquidity and debt policy. For example, research by Tarigan et al. (2022) found a significant negative relationship, supporting idea that businesses with high liquidity levels typically employ less debt, as they prefer to fund their operations internally. In contrast, a study by Sunardi et al. (2020), which looked at how firm size and liquidity affected debt policy in Indonesia’s retail trading industry, discovered that liquidity had a slight but favorable impact on debt policy.

Meanwhile, solvency provides an overview of long-term financial stability. Solvency is an important factor that companies need to consider in determining their debt policy. Solvable companies have greater flexibility in managing their debt, and they also have easier and cheaper access to funding sources. Signaling Theory states that businesses with high levels of solvency may use debt to demonstrate their financial strength and send out positive signals to investors and other external parties. This study demonstrates that solvency considerably enhances debt policy based on partial test results. Prior studies by Susanti & Windratno (2020) and Shelinzky et al. (2022) demonstrated that solvency had a substantial influence on debt policy.

The size of company, whichh is frequently determined by its market capitalization or total assets, is also a significant factor in assessing degree of confidence that creditors and investors have in it. Larger businesses typically have easier access to the capital market because creditors view their default risk as lower. This relates to the Signaling Theory, which holds that big businesses can use debt to send positive signals because their income and assets are more stable, making them better equipped to handle high debt risks (Ghozali, 2020). Therefore, an in-depth analysis of these four variables is essential to understand how healthcare companies optimize their capital structure in the face of external and internal pressures.

Previous research by Parsi et al. (2024) emphasized the importance of considering internal factors such as firm size and profitability in determining debt policy. The study’s conclusions imply that a company’s debt policy is significantly unaffected by its size. This shows that companies with greater profitability and size tend to have smaller debt policies. Meanwhile, other research by Andrianti et al. (2021) demonstrates how a company’s size significantly affects its debt policy. Because larger businesses have easier access to capital market and can easily obtain loans due to their substantial assets that can be pledged as collateral, firm size is frequently linked to a higher debt policy.

With phenomenon that occurs in the Indonesian healthcare sector as well as in light of earlier findings, this study attempts to give a more comprehensive understanding of how liquidity, profitability, solvency, and firm size impact debt policy in healthcare firms that are listed on the Indonesia Stock Exchange (IDX).

This study applies quantitative research method with causal-comparative design, aiming to examine the relationship between internal financial factors namely, profitability, liquidity, solvency, and firm size on debt policy. A quantitative method is considered appropriate for testing hypotheses and identifying statistical relationships using numerical data derived from financial reports.

The data used are secondary data, collected from the audited annual reports of healthcare companies listed on the Indonesia Stock Exchange (IDX) for the period 2019–2023. These reports were accessed through the official websites of the respective companies as well as the IDX’s official platform ( www.idx.co.id ). All data were processed and analyzed using SPSS Statistics 25. This statistical method was used to determine the extent to which the selected financial factors influence corporate debt policy, both individually and collectively.

The population of the study includes all healthcare companies listed on the IDX during the specified period, totaling 34 companies. The sample was selected using a purposive sampling technique, which involves selecting companies based on specific, predefined criteria to ensure the relevance and quality of the data. The sampling criteria were as follows:

Based on these criteria, 9 companies met the requirements and were included in the final sample. After the necessary screening and refinement, the dataset comprised 43 firm-year observations, which served as the basis for statistical analysis.

The methodological approach employed in this study is designed to ensure analytical rigor and empirical validity. By using a focused sampling strategy and standardized statistical procedures, the research aims to contribute to a more comprehensive understanding of capital structure decisions in Indonesia’s healthcare sector.

Descriptive Statistic

Table 1. Descriptive Statistic

(Source: Data Processed, 2025)

Table 1 displays the descriptive statistics for all variables used in the study after applying lag transformation and outlier removal, based on 43 valid observations. These statistics provide an overview of the data distribution and variability.

The dependent variable, LAG_Y (debt policy), has a mean of 6.8169 and a relatively large standard deviation of 10.10661, indicating substantial variation in debt policy decisions among the sampled companies. This suggests differences in how firms manage or utilize debt within the healthcare sector.

LAG_X1 (profitability) shows a mean of 3.5021, with a wide range from - 9.49 to 13.69. This implies that while some firms experience negative profitability, others report relatively high earnings, reflecting financial disparity within the sample.

LAG_X2 (liquidity) and LAG_X3 (solvency) have lower mean values and narrower ranges, indicating more consistency in short-term financial capacity and debt-paying ability. Meanwhile, LAG_X4 (firm size) appears the most stable variable, with low variability and a mean of 7.6529.

Classical Assumption Test

Normality Test

Table 2. Normality Test

(Source: Data Processed, 2025)

The 2-tailed Asymp. Sig. Of 0.200 > 0.05 clearly indicates that data is normal, as can be seen from table.

Multicollinearity Test

Table 3. Multicollinearity Test

(Source: Data Processed, 2025)

Multicollinearity is not present in regression model, as indicated by output above, where tolerance value is near to 1 and VIF value for all variables is < 10.00.

Autocorelation Test

Table 4. Autocorelation Test

(Source: Data Processed, 2025)

The Durbin-Watson (DW) value obtained is 1.967. Based on the number of independent variables (k = 4) and observations (n = 38), the Durbin-Watson decision rule states that if DU < DW < 4 – DU, there is no indication of autocorrelation. From the Durbin-Watson table, DU is approximately 1.628, so the condition becomes: 1.628 < 1.967 < 2.372 (since 4 – 1.628 = 2.372). Because the DW value falls within this range, the test confirms that the regression model is free from both positive and negative autocorrelation.

Heteroscedasticity Test

Figure2. Heteroscedasticity Test

(Source: Data Processed, 2025)

As can be seen from scatterplot output above, points are scattered and do not obviously form a pattern. Therefore, it is possible conclude heteroscedasticity issue is not present.

Multiple Linear Regression Test

Table 5. Multiple Linear Regression

(Source: Data Processed, 2025)

Regression coefficient analysis is done in following manner to find multiple regression equation:

Y= β0 + β1X1 + β2X2 + β3X3 + β4X4 + e

Y = 9.008 + 0.028 X1 - 1.213 X2 + 38.772 X3 - 0.669 X4 + e

This is how regression equation above can be understood:

T Test (Partial)

Table 6. T Test (Partial)

(Source: Data Processed, 2025)

Additionally, table shows t test results with a significance level of 5%, yielding following findings:

> the t table, namely -10.697 > 2.024 and significance value (Sig.) 0.000 <0.05, it is possible to conclude Liquidity significant negative impact on Debt Policy.

F Test (Simultaneous)

Table 7. F Test (Simultaneous)

(Source: Data Processed, 2025)

Based on SPSS output, Fcount value 952,289. To determine Ftable value, we refer to F-distribution table at a significance level of α = 0.05 with degrees of freedom calculated as N–k–1, which is 43–4–1 = 38. This gives an Ftable value 2.61. Since Fcount (952,289) is much > Ftable (2.61) and significance level is 0.000 (which is < 0.05), it is possible conclude profitability, liquidity, solvency, and firm size significant impact on debt policy.

Coefficient of Determination

Table 8. Coefficient of Determination

(Source: Data Processed, 2025)

Adjusted R-squared value, as determined by SPSS, is 0.989. Dependent variable’s variation is able to be described by independent variables in 98.9% of cases. The remaining 1.1% is impacted by additional variables that are not covered in this research.

From research results, it is possible to conclude profitability no significant impact on debt policy, it indicate size of company's profitability does not impact company's decision to rely on external financing such as debt and rely more on internal funds for operations and expansion. Liquidity also significant negative impact on debt policy, suggesting companies with high liquidity prefer to use their current assets rather than debt.

On other hand, solvency significant positive impact on debt policy, supporting theory companies with high solvency levels can signal to creditors that they are able to manage debt well and have capacity to pay long-term obligations. this can increase lender confidence, making it easier for companies to obtain loans.

Firm size significant negative impact on debt policy, it indicates large companies actually reduce the use of debt because they have stronger internal funding sources or want to avoid dependence on debt. Based on signaling theory, this indicates that large companies have the confidence to finance expansion without having to take on additional debt.

Simultaneously, four variables of profitability, liquidity, solvency, and firm size significant impact on debt policy in healthcare firm listed on IDX for 2019-2023 period. This indicates that funding strategy in this sector is influenced by various interacting financial factors.

Healthcare companies listed on IDX are encouraged to carefully consider internal financial factors such as liquidity, solvency, and firm size when making debt policy decisions. High liquidity firms should assess the potential benefits of using debt strategically. Firms with strong solvency can signal good financial health to creditors, making it easier to access external funding. Larger firms are advised to balance internal funds and external debt to maintain an optimal capital structure.

This study is limited to 9 healthcare companies over the 2019–2023 period with four main variables. Future research could expand the sample size, include more recent data, or compare with other sectors. Researchers are also encouraged to examine additional variables such as interest rates, macroeconomic factors, or corporate governance. Exploring moderating or mediating variables could provide deeper insight into debt policy behavior

The author sincerely thanks the academic supervisors and peers for their constructive feedback. Gratitude is also extended to the Indonesia Stock Exchange (IDX) for data access and to all those who supported the completion of this study.