Financial Risk, Debt, and Efficiency in Indonesia’s Construction Industry: A Comparative Study of SOEs and Private Companies

Abstract

This study aims to evaluate the financial risk, debt, and efficiency of state-owned enterprises (SOEs) in Indonesia’s construction industry and compare these aspects with those of private companies through financial ratio analysis and efficiency analysis approaches. Four SOEs from the construction sector were evaluated and compared to five private companies with financial data ranging from 2015 to 2022. Financial ratio analysis was applied to assess debt and financial risk, while efficiency analysis utilized data envelopment analysis (DEA) and paired t-tests to validate differences between the two groups of companies. This study reveals that the financial ratio performance of state-owned companies is relatively poor, with low profitability, critical liquidity, and a high debt ratio. Debt, as a source of capital in financing construction projects, causes companies to face a greater debt risk. This study also validates that SOEs have lower efficiency compared to private companies. In response to current challenges, SOEs should prioritize enhancing liquidity through faster receivable collections, debt restructuring, capital infusions, and divestment, reducing non-essential investments, focusing on asset recycling, and improving project efficiency.

1. Introduction

As a developing country, Indonesia is accelerating infrastructure development to boost the national economy. This infrastructure growth is supported by the state-owned and private-owned construction sectors. The Indonesian government, through its substantial infrastructure projects, has directed significant funding toward state-owned construction enterprises (SOEs), providing them with a competitive advantage over private construction companies. Typically, the government has proactively addressed challenges related to SOEs’ financial positions, ensuring efficiency, stabilizing employment structures, and maintaining company debt at manageable, low-risk levels (Anggraini and Febrianty 2022). But recently, SOEs in Indonesia have experienced financial difficulties. The large number of government projects has increased the operational burden of SOEs, resulting in increased debt levels.

In their operations, SOEs face challenges that negatively impact their financial performance, such as negative net income growth, high debt-to-equity ratios, and suboptimal financial ratios. This study aims to evaluate the financial condition of SOEs in Indonesia and compare them with private companies using the financial ratio analysis approach as conducted by Vibhakar et al. (2023), as well as efficiency analysis as researched by Jung and Kim (2023). Poor financial health is a crucial aspect affecting these companies’ sustainability and their role as a critical foundation in Indonesia’s infrastructure development.

SOEs consist of four significant issuers, namely PT Adhi Karya (Persero) Tbk (ADHI), PT Housing Development (Persero) Tbk (PTPP), PT Waskita Karya (Persero) Tbk (WSKT), and PT Wijaya Karya (Persero) Tbk (WIKA). The deterioration in the condition of SOEs began with the realization of investment and income, which tended to decline significantly. The realization of SOEs’ investment has declined due to the transfer of investment assets, coupled with the condition of SOEs’ net income, which experienced a drastic decline since 2018 and grew slowly from 2020 to 2022 (Figure 1). The decline in SOEs’ net income has also been exacerbated by the demands of government projects, which have increased the company’s operating expenses (Nur and Woestho 2022).

The next obstacle is the problem of debt settlement. The high level of debt in SOEs has caused the debt-to-equity ratio (DER) to increase sharply. Compared to other construction companies, SOEs have the highest DER level. Negative short-term sentiment and SOE debt restructuring plans are the main drivers of the problem (BEI 2023). Furthermore, regarding return on assets (ROA), SOEs show lower performance than their competitors. This illustrates that the returns generated have not been effective in achieving good profitability.

In addition, the above indicates that a high level of short-term debt is the cause of the decline in the company’s financial performance. Jayiddin et al. (2017) stated that the short-term debt ratio has a significant negative relationship with the performance of construction companies in Malaysia. As is known, construction companies in Malaysia in 2010–2014 had a high debt ratio, making them more vulnerable to bankruptcy risk due to fluctuations in interest rates. In addition, Hamid et al. (2015) also stated that the debt ratio has a significant negative relationship with company profitability. Companies that use more debt cause erosion of company profits (Baharuddin et al. 2011). Especially at this time, most construction companies are too dependent on bank debt, which contains greater risk. Excessive dependence on the banking system can increase the instability of the entire financial system (Wang 2023).

The management of corporate debt must be adjusted to the nominal debt and the income generated or the assets owned by the company, ensuring that the company can repay the debt and interest at maturity. SOEs, in the face of debt pressure, often restructure debt (Putri and Putri 2024). It is essential for management to always maintain an optimal level of debt and prevent financial distress (Adhi and Alfarisi 2019).

The impact of the COVID-19 pandemic, coupled with massive government projects, has caused SOEs to be increasingly burdened in financing their operations. Competition with private companies is also a significant challenge for SOEs. The market share of government-owned construction companies has decreased. Increasing price competition with competing companies and a lack of technical experience in some sectors have contributed to the erosion of SOEs’ market share.

The condition of SOEs that are experiencing fundamental problems must be corrected immediately, given the critical role of SOEs in achieving the government’s vision for infrastructure development to improve real sector efficiency, provide transportation facilities, support economic growth, and achieve equitable national development (Asian Development Bank 2020; Utomo et al. 2022). In addition, competition with other companies now competing in digitalization and technological innovation also puts pressure on SOEs. Therefore, improving operational efficiency and strengthening technical capacity are crucial for the sustainability and competitiveness of SOEs in the construction sector.

The financial performance of SOEs in Indonesia has exhibited a declining trend over recent periods, necessitating an evaluation of the principal causes behind this downturn. This study aims to assess whether private companies in the same sector are experiencing similar issues, and to determine whether the observed trends are attributable to broader weaknesses within the Indonesian construction industry or to specific performance deficiencies in SOEs compared to private companies. Utilizing financial ratio analysis, based on the Peterson (2013) method, to assess debt and financial risk, along with efficiency analysis conducted through data envelopment analysis (DEA), this research will compare the financial performances of SOEs and private companies. Additionally, a paired t-test will be conducted to confirm any differences in efficiency between the two groups, utilizing publicly available financial data from the period of 2015 to 2022. The outcomes of this evaluation are expected to serve as a preliminary step in developing improvement strategies for relevant stakeholders.

The subsequent sections of this paper will explore the literature review, data sources, and methods used in this study. This is followed by an analysis of the research findings and a discussion on their implications. The paper concludes with a summary of the main findings in Section 6.

2. Literature Review

Financial ratio analysis is used to evaluate a company’s position in the industry and other companies to assess the company’s performance over some time. This analysis is also considered helpful for measuring the performance of managers and departments and projecting future performance trends of the company for relevant stakeholders (El-Kholy and Akal 2021). Studies on financial ratio analysis, such as those conducted by Vibhakar et al. (2023), analyzed the financial ratios of 100 construction companies in India over ten years (2008–2017) using factor analysis to classify financial ratios as important sub-factors for construction companies. Five critical financial ratio factors were identified: investor returns, business efficiency, operations management, activity efficiency and risk coverage, and asset management. In a global context, similar research is often conducted, such as in the study of Emin et al. (2007) and Chong et al. (2013).

In Indonesia, research on financial ratio analysis of construction companies was conducted by Daryanto et al. (2021) by measuring financial ratios before and during the COVID-19 pandemic using the Altman Z-Score approach. The results showed that PT PP had lower liquidity, solvency, profitability, and activity ratios during the pandemic. Research by Afriza and Daryanto (2019) examined the financial ratios of construction SOEs in Indonesia based on the reference of the Decree of the Minister of SOEs Number KEP-100/MBU/2002 and found that construction SOEs in Indonesia were in a stable and good financial condition in 2011–2015. Meanwhile, research by Muhammad and Rahadi (2023) analyzed financial ratios in construction-sector SOEs and concluded that all SOEs experienced a decline in financial performance during the 2019–2021 period.

This study uses a different analytical point of view by utilizing Peterson’s financial ratio approach to measure construction companies’ good and bad financial ratios. Peterson’s (2013) financial ratios focus on several unique aspects explicitly designed for the construction industry, which differ significantly from typical accounting practices due to the unique nature of construction projects. Accounting practices in the construction industry must adapt to the specific requirements of long-term contracts, where revenue recognition is often linked to completion percentage. This is particularly important as it affects the financial reporting and health of the construction company throughout the project (Zadorozhnyi and Ometsinska 2020).

Managing construction company finances requires a project-based approach, considering each project’s unique costs and schedules. This involves detailed cost tracking and forecasting, critical to maintaining profitability and managing cash flow effectively (Sowmya and Malisetty 2023). The construction industry requires a specialized approach to cost accounting to handle the direct and indirect costs associated with construction projects. This is difficult because costs must be allocated to specific projects and can vary significantly between projects (Bozgulova and Adambekova 2023). These aspects underscore the need for specialized accounting and financial management techniques in construction, comprehensively discussed in Peterson (2013), to meet the sector’s unique challenges.

In terms of efficiency analysis, this study follows Jung and Kim (2023), who used DEA to analyze the efficiency of 520 construction firms from 2017 to 2021. Their findings showed significant scale-related inefficiencies, suggesting the need for these firms to reduce production factors. The concrete construction sector was much more efficient than other sectors. The COVID-19 pandemic contributed to a temporary drop in efficiency in 2020, followed by a partial recovery in 2021. The study also reported a notable decline in operating profit efficiency in 2021. The study emphasizes that specialized construction companies must improve efficiency by managing input factors more effectively, especially in light of rising raw material prices.

In the Indonesian context, recent research by Sukandar et al. (2018) on the efficiency of the construction industry in Indonesia also used DEA for the period of 2010–2016. The results showed that state-owned companies were more efficient than private companies. This is due to the large number and value of projects from the government in the infrastructure sector. This study shows that companies with many projects, significant revenues (sales), and low costs are more efficient. Therefore, private firms should receive more projects and larger project values, including from government projects.

In this study, financial ratio analysis and efficiency analysis were carried out on state-owned companies in the construction sector and private companies as a relative comparison; so this study is comparative to private companies in the research period. In addition, this study is also comparative with previous studies that used different research periods. In analyzing efficiency using DEA, this study continued with a different test between the efficiency of SOEs and the private sector using a paired t-test for statistically more robust validation.

3. Data Sources and Methods

3.1. Data and Research Variables

This study uses two main variables: financial ratio variables for financial ratio analysis and input–output variables for efficiency analysis (Appendix A). There are 68 observations in the financial ratio analysis from 2015 to 2022, which are obtained from the financial statements of each construction company. The population of this study is construction companies in Indonesia, with a sample consisting of nine construction companies with the largest capitalization, which includes four state-owned construction companies and five private construction companies (Appendix B). The data are taken from the annual financial statements published by each company.

3.2. Financial Ratio Analysis

The financial ratio analysis in this study uses the Peterson (2013) approach. Peterson’s approach was chosen because it provides comprehensive coverage for analyzing the company’s financial performance through various relevant financial ratios. Peterson’s financial ratios in this study include the following:

• Liquidity ratio: Measures a company’s ability to meet its short-term obligations. This ratio is essential to assess the health of the company’s liquidity and ensure it has enough current assets to cover its current liabilities. The liquidity ratio proxies in this study are the quick and current ratios;
• Profitability ratios: Assess a company’s ability to generate profits relative to its sales, assets, and equity. This ratio measures operational efficiency and the company’s ability to provide profits to shareholders. The profitability ratio proxies in this study are gross profit margin (GPM), asset turnover (ATPM), return on assets, and return on equity (ROE);
• Debt or leverage ratio: This ratio measures a company’s capital structure, specifically how much it relies on debt to fund its assets. Understanding this ratio is essential to understanding financial risk and the company’s ability to manage its long-term obligations. The debt ratio proxies in this study are current-liabilities-to-net-worth ratio (CLNWR), debt-to-equity ratio (DER), and accounts-payable-to-receivables ratio (APRR).

This analysis uses benchmark financial ratios for the construction industry as a reference (Appendix C).

3.3. Efficiency Analysis

This study uses the data envelopment analysis (DEA) approach and paired t-tests for efficiency analysis. DEA is a non-parametric linear programming-based method used to calculate the efficiency of a decision-making unit (DMU) by modeling the complex relationship between inputs and outputs. DEA constructs efficient bounds on the data and calculates the efficiency of each DMU relative to those bounds. DEA efficiency indicates the extent to which a DMU is ahead of its peers and represents its ability to convert many inputs into many outputs (Saini et al. 2023).

DEA uses two main models for efficiency analysis (Saini et al. 2023):

• CCR (Charnes–Cooper–Rhodes) Model: The basic DEA model that assumes Constant Return to Scale (CRS) (Charnes et al. 1978). This model is suitable when the scale of DMU operations does not affect efficiency, i.e., proportional input changes will result in proportional output changes. Assuming CRS, the CCR model calculates overall technical efficiency;
• BCC (Banker–Charnes–Cooper) Model: A model that assumes Variable Return to Scale (VRS) (Banker et al. 1984). This model is used when the scale of DMU operations affects efficiency, i.e., proportional input changes do not necessarily result in proportional output changes. The BCC model makes it possible to measure pure technical efficiency by considering different scales of operation.

This study uses the CCR model with CRS assumptions and an input-oriented approach. This model was chosen for several reasons. First, the CCR model provides a more straightforward approach to efficiency analysis, assuming that the scale of DMU operations does not affect efficiency. This aligns with the research objective of assessing the overall technical efficiency between SOEs and the private sector without considering variations in the scale of operations. Finally, the ease of implementation of the CCR model makes it easier to implement and understand, especially in the context of research involving many input and output variables, allowing for a more direct calculation of efficiency.

The input-oriented CCR model is formulated as follows:

$$\mathrm{M}\mathrm{i}\mathrm{n} \theta$$

With the following constraints:

$$\begin{array}{c}{\displaystyle \sum _{j=1}^n} {\lambda }_j{x}_{ij}\le \theta x_{io}\forall i\\ {\displaystyle \sum _{j=1}^n} {\lambda }_j{y}_{rj}\ge y_{ro}\forall r\\ {\lambda }_j\ge 0\forall j\end{array}$$

where the variables represent the following:

• $\theta$ is the efficiency score sought;
• $x_{ij}$ is the sum of the i-th input of DMU j;
• $y_{rj}$ is the sum of the rth output of DMU j;
• $x_{io}$ and $y_{ro}$ is the sum of the i-th input and r-th output of the evaluated DMU;
• ${\lambda }_j$ is a decision variable that shows the relative weight of DMU j.

Using this formulation, DEA compares the DMU under evaluation with a linear combination of other DMUs to determine its efficiency at converting inputs into outputs. If $\theta =1$, the DMU is considered efficient. If $\theta <1$, the DMU is considered inefficient.

Seo and Choi (2011) used DEA to analyze the efficiency of construction companies in Korea, with inputs in the form of total assets, selling and administrative expenses, and total debt, as well as outputs in the form of total revenue and operating profit. Then, Sukandar et al. (2018) used inputs in the form of construction costs per m² of building, capital, total operating costs, and debt and outputs in the form of sales (revenue), EBIT, and net profit in analyzing the efficiency of construction companies in Indonesia. Debt is an input for construction companies because the total capital structure of each construction company can generally be financed through equity capital or debt capital, each of which has its advantages and disadvantages. Debt capital can be considered the cheapest source of funding, but on the other hand, debt capital that is too excessive can significantly increase the financial risk of the company (Mohammed 2007). This study does not use debt as an input. However, debt can be reflected in the company’s total assets, where the construction company’s assets are financed by debt (Utamaningsih and Muharis 2020).

To validate the real difference in efficiency results between state-owned and private construction companies, this study utilized a two-sample paired t-test. This statistical method is particularly parsimonious and robust for our analysis. The paired t-test is suitable because it primarily assesses the difference in means between two related groups, relying on the difference within pairs rather than the overall data distribution. This characteristic makes it especially applicable when the sample size is small and the assumption of normality may be violated (Manfei et al. 2017).

The steps of the two-sample paired t-test are as follows:

• Hypothesis:
  • Null Hypothesis (H0): There is no difference in average efficiency between state-owned and private construction companies;
  • Alternative Hypothesis (H1): There is a difference in average efficiency between state-owned and private construction companies;
• The t-test formula:

  $$t=\frac{\overline{d}}{s_d/\sqrt{n}}$$

  where the variables represent the following:
  • $\overline{d}$ is the average difference of the sample pairs;
  • $s_d$ is the standard deviation of the difference of the sample pairs;
  • n is the number of sample pairs;
• Test Decision:
  • If the calculated t value exceeds the critical t value, reject H0;
  • If the calculated t value is less than or equal to the critical t value, fail to reject H0.

Using a two-sample paired t-test, this study was able to determine whether there is a significant difference in efficiency between state-owned and private construction companies.

4. Research Results

In this section, results of the financial ratio analysis based on Peterson’s approach are presented, including the liquidity ratios, profitability ratios, and leverage ratios. Additionally, efficiency measure outcomes using the DEA approach are discussed, along with the validation of efficiency differences between SOEs and private companies using the t-test. All analyses are dynamically presented across the entire study period from 2015 to 2022, while the averages are displayed in tabular form in the Appendix D, Appendix E, and Appendix F.

4.1. Liquidity Ratio

Table A4. Average liquidity ratio of state-owned and private construction companies.

Liquidity Ratio
Company	Quick Ratio	Benchmark	Current Ratio	Benchmark
ACST	26%	Average: 120% Range: 60%–190%	123%	Average: 150% Range: 120%–280%
ADHI(s)	36%		127%
BDKR	98%		124%
BUKK	50%		122%
JKON	90%		168%
PTPP(s)	56%		132%
TOTL	82%		139%
WIKA	51%		129%
WSKT	33%		116%

Description: (s): State-owned company.

(Appendix D) shows that all SOEs have quick ratio and current ratio values below the industry average. Compared to private companies, most SOEs have lower quick ratio values. Meanwhile, the current ratio value of SOE companies is similar to that of private companies, except for JKON.

Based on Figure 2, the majority of state-owned companies from 2015 to 2021 have a quick ratio and current ratio trend that tends to be negative, where the lowest quick ratio and current ratio values occur in 2020–2021. Meanwhile, in 2020–2021, the quick and current ratios in most private companies have shown a positive trend. In addition, the figure also shows that the current ratio of SOEs tends to be more stable than the private sector.

4.2. Profitability Ratio

Table A5. Average profitability ratio of state-owned and private construction companies.

Profitability Ratio
Company	GPM	Benhcmark	ATPM	Benhcmark	ROA	Benchmark	ROE	Benchmark
ACST	−0.1%	Average: 16%	−27%	Average: 1.9% Range: 1.5%–8.1%	−8.28%	Average: 5.6% Range: 1.5%–21%	−99.44%	Average: 15.1% Range: 4.2%–53%
ADHI(s)	13.73%		2.62%		1.48%		6.5%
BDKR	46.18%		14.05%		5.62%		10.61%
BUKK	16.95%		8.79%		8.59%		16.74%
JKON	15.96%		4.1%		4.61%		8.66%
PTPP(s)	14.16%		3.96%		2.31%		9.63%
TOTL	14.3%		6.92%		5.68%		17.08%
WIKA(s)	11.31%		3.92%		2.28%		8.42%
WSKT(s)	14.84%		−4.07%		1.02%		−3.64%

Description: (s): State-owned company.

(Appendix E) shows that almost all SOEs have GPM and ROA values below the industry average. The majority of the sample of SOE construction companies have ATPM values above the industry average. Private companies’ GPM, ATPM, ROA, and ROE values (except ACST) are higher than those of state-owned companies.

Based on Figure 3, all state-owned and private companies experienced a decline in ROA and ROE in 2020. However, private companies experienced a more significant decline in ROA than SOEs. The state-owned company WSKT and the private company ACST experienced the most significant decline in ROE.

4.3. Leverage Ratio

Table A6. Average leverage ratio of state-owned and private construction companies.

Leverage Ratio
Company	CLNWR	Benchmark	DER	Benchmark	APRR	Benchmark
ACST	709%	Average: 123% Range: 38%–259%	265.98%	Average: 140% Range: 5%–280%	53%	Average: 8.2% Range: 3.1%–13.3%
ADHI(s)	337%		130.2%		73%
BDKR	59%		50.62%		5%
BUKK	76%		34.83%		10%
JKON	63%		23.13%		7%
PTPP(s)	184%		97.7%		75%
TOTL	161%		1.15%		7%
WIKA(s)	187%		106.04%		50%
WSKT(s)	212%		283.22%		45%

Description: (s): State-owned company.

(Appendix F) shows that SOEs have CLNWR values above the industry average and APRR values below the industry average. Compared to the private sector, SOEs tend to have higher CLWR, DER, and APRR values than most private companies.

Based on Figure 4, from 2015 to 2021, state-owned companies tended to experience increased CLNWR values. On the other hand, all private companies experienced a decrease in CLNWR values from 2020 to 2021, and some continued to decline until 2022. The DER value of private companies (except ACST) tends to be more stable than SOEs. SOEs tended to experience an increase in DER value, with the highest DER value occurring for WSKT, especially in 2019. Meanwhile, the DER value of private companies tended to decrease from 2020 to 2021.

When looking at the four state-owned companies, PTPP and WIKA are mostly superior in all financial ratios during 2015–2022. WSKT excels in GPM, but with high GPM; WSKT cannot obtain high ATPM, ROA, and ROE. WSKT’s DER level is the highest among other companies. This indicates that WSKT’s debt burden erodes the GPM obtained.

4.4. Efficiency Analysis with DEA

The input value of SOEs, which consists of revenue cost, operating expenses, and total assets, has a higher average than the input of private companies, likewise with the output in the form of revenue generated. Input distribution data with high standard deviation and max–min values show that construction companies in Indonesia exhibit a relatively high level of diversity (

Table 1. Description of inputs and outputs of state-owned and private construction companies.

	Input						Output
	Cost of Revenue (Billion IDR)		Operating Expenses (Billion IDR)		Total Assets (Billion IDR)		Revenue (Billion IDR)
	SOE	Private	SOE	Private	SOE	Private	SOE	Private
Mean	16,946	2423	1638	265	55,624	3681	19,737	2833
Median	14,295	2227	1125	193	51,805	3236	16,325	2629
Stand dev	7367	1318	1332	169	29,781	2006	9160	1555
Minimum	8415	226	395	93	16,761	819	9390	413
Maximum	39,926	5231	5433	786	124,392	10,447	48,789	6040

).

Figure 5 shows the average efficiency of state-owned and private construction companies in Indonesia under Constant Return to Scale (CRS) conditions during 2015–2022. Both state-owned and private construction companies have relatively high-efficiency values. However, this efficiency value is relative to the company with the highest efficiency value, BUKK.

Based on Figure 6, it can be seen that private companies (except ACST) have higher efficiency than state-owned companies. State-owned companies such as WIKA and WSKT experienced a significant decline in efficiency in 2020. The same thing happened to all private companies. However, the efficiency scores of SOEs and private companies started improving in 2021. The lower efficiency of SOEs compared to the private sector is validated through the t-test with a value of t = −2.701 and p-value = 0.008916, meaning there is enough evidence to reject the null hypothesis or to state that there is a significant difference in the efficiency of the two groups.

5. Discussion

The performance of financial ratios of state-owned construction companies through the measurement of profitability, liquidity, and solvency (leverage) ratios based on Peterson’s (2013) construction sector financial ratio goodness measure shows that the four state-owned construction companies have poor performance. This is in line with Utamaningsih and Muharis (2020), where the evaluation of solvency and liquidity shows that SOEs are less able to use their debt to finance all their assets, as well as their low ability to pay their debts. State-owned construction companies have experienced a decline in financial performance, especially during the COVID-19 pandemic in Indonesia (Muhammad and Rahadi 2023). Continuous analysis of a firm’s liquidity, profitability, capital structure, activity efficiency, profit margin, growth, and asset structure factors, as identified by Emin et al. (2007), provides sufficient information related to the relative state of the industry and each construction company concerning time and economic changes.

Low profitability in state-owned construction companies is caused by the large gap between gross profit margin and net profit margin, which is influenced by cost overruns for materials, equipment, subcontractors, and financial and administrative expenses (Utamaningsih and Muharis 2020). High solvency indicates a significant dependence of SOEs on debt to carry out their business activities, in contrast to private companies with lower debt levels (Sukandar et al. 2018). Having more debt than necessary can seriously increase the company’s interest costs. It can also lead to a sharp increase in the company’s financial risk. Therefore, the company’s debt burden greatly influences its financial stability (Mohammed 2007). The more significant the portion of debt capital, the more likely the company is to experience the risk of bankruptcy (Van Horne 1983).

Short-term loans are the leading indicator of operational funding for state-owned construction companies, leading to high loan interest expenses and eroding profitability. Short-term loans pose a greater risk to the company than long-term funding. This happens because the interest costs will fluctuate more, so that the risk of inability to repay the debt is greater (Brigham and Gapenski 1994). The proportion of bank debt to the total liabilities of state-owned construction companies is quite significant, such as WSKT at 56.5%, PTPP at 30.6%, and ADHI at 33.5%. In addition to loans, the companies also have trade payables to suppliers and subcontractors. Liquidity ratios close to 0 or negative indicate that state-owned construction companies are in critical cash condition. In contrast, private companies with lower DER indicate a more conservative and sustainable financial strategy in the long term (Hariandja et al. 2022; Septian et al. 2021).

This study also shows that state-owned construction companies have a relatively low efficiency level compared to private construction companies. The performance gap between SOEs and private companies is often attributed to inefficiencies in using inputs to produce outputs (Soetanto and Fun 2014). Significant assets and revenue do not necessarily increase efficiency, mainly if financed by high debt (Sufian and Shah Habibullah 2010). Nevertheless, research by Sukandar et al. (2018) revealed that state-owned construction companies are more efficient than private companies. This is due to the government’s availability of many projects of value. State-owned companies with high debt are still efficient because they have an excellent income value from government projects.

The five business segments of state-owned construction companies (infrastructure, building, energy, industry, and investment) show that efficient input allocation is essential to produce profitable outputs. However, poor performance in some business segments suggests inefficiencies in resource allocation (Utamaningsih and Muharis 2020). For example, WIKA experienced negative profits in the investment and property segments, while ADHI experienced significant losses in the rail and infrastructure construction segments.

Overall, state-owned construction companies need more capital to finance their projects and business operations, as evidenced by high debt levels and low revenues. The government needs to find new sources of capital or financing schemes that can ease the burden on companies in managing government projects. In addition, companies should implement a focused strategy to optimize profitable business segments and carry out business transformation to avoid further losses.

Limited cash liquidity affects the selection of market segments and project investments. Factors such as technical capabilities, experience in each value chain stage, and the ability to act as a leader or supporter are also vital considerations. In the current situation, the business focus should be limited to each phase with the following strategies: (1) accelerate the collection of receivables and strengthen liquidity through debt restructuring, accelerated collectability, additional capital deposits, and divestment; (2) avoid unnecessary investments; (3) focus on asset recycling; and (4) improve project efficiency.

This study reveals the financial performance of state-owned construction companies and compares them with private construction companies using only secondary data, so the details of the analysis are only superficial. To understand the problem of SOE construction performance in more depth, it is necessary to conduct further analysis through strategic studies on all SOE construction companies. A strategic management study with primary data collection could address the shortcomings of this research and provide more comprehensive insights.

6. Conclusions

The financial performance of Indonesian SOEs has been declining, prompting a need to identify the underlying causes. This study evaluates whether similar issues affect private companies and determines if the trends stem from broader industry weaknesses or specific inefficiencies in SOEs compared to private counterparts. Using financial ratio analysis and data envelopment analysis (DEA), we quantitatively assessed the efficiency and financial performance of these companies over the selected period. The study results show that the financial ratio performance of state-owned companies is relatively poor, with low profitability, critical liquidity, and a high debt ratio. Debt, as a source of capital in financing construction projects, makes companies face a greater debt risk. This study validates that SOEs have lower efficiency compared to private companies. In response to current challenges, SOEs should prioritize the following: (1) enhancing liquidity through faster receivable collections, debt restructuring, capital infusions, and divestment; (2) curtailing non-essential investments; (3) focusing on asset recycling; and (4) improving project efficiency.