Working environmental quality and financial distress: evidence from Indonesia

Abstract

This study investigates the impact of the quality of the working environment on a company’s financial distress, focusing on non-financial public companies listed on the Indonesia Stock Exchange from 2019 to 2021. The observation utilizes 1,103 samples derived from annual reports and employs logistic regression, Coarsened Exact Matching (CEM), and Heckman Two-Stage analysis to ensure robust results. The findings reveal a significant inverse relationship between the quality of the working environment and financial distress, which remains consistent across different proxies of financial distress. These results offer valuable insights for company management in Indonesia, suggesting that enhancing the work environment can be a strategic approach to mitigating financial distress risks. Additionally, the study’s outcomes can aid regulators in formulating policies that foster a high-quality working environment, thereby contributing to the stability of the business sector in a region characterized by unique social, cultural, and economic dynamics. The research underscores the importance of the working environment in influencing companies’ financial performance in Indonesia, providing practical guidance for both corporate management and policymakers.

IMPACT STATEMENT

This study highlights the relationship between the quality of the work environment and the financial performance of companies, as well as the application of human capital theory in the context of corporate finance. These findings provide practical guidance for decision-makers and managers in managing corporate risks, while also offering implications for regulators in assessing the adaptability of regulations to accommodate non-financial factors. Thus, this research provides valuable insights for both the public and policymakers to understand the importance of a conducive work environment in managing financial risks and enhancing overall company performance.

Keywords:

• Working environment quality
• financial distress
• governance
• Indonesia

JEL CLASSIFICATION:

• J01; G32; G41

REVIEWING EDITOR:

• Collins Ntim, University of Southampton, United Kingdom of Great Britain and Northern Ireland

1. Introduction

The company’s condition reflects the manner of work, culture, and work environment built so far. The company’s internal states, such as the work environment, will be directly related to human resources. The working environment positively influences employee performance (Saidi et al., 2019). Spector (1997) explains that a business that ig006Eores the working environment in its management will harm employee performance. The decision of the company’s leadership stakeholders regarding bonuses to employees aims to stimulate improvements in the company’s operational activities (Sarto, 2020).

Previous studies have indicated that a high-quality work environment influences employee job satisfaction (Kebede & Fikire, 2022; Kurniawaty et al., 2019). As employee productivity rises, there is a positive correlation with the company’s financial performance, particularly an increase in the company’s cash balance (Flugum et al., 2021). This is possible because employees work more efficiently and effectively when their work environment supports such outcomes. However, the question arises: can a favorable work environment reduce a company’s financial distress risk? Therefore, this study seeks to examine this relationship. Several previous studies have extensively examined how environmental disclosure can influence the risk of corporate bankruptcy (Shahab et al., 2018). However, there has been no direct research investigating the relationship between the quality of the work environment and the potential risk of corporate bankruptcy. Therefore, this paper aims to fill this gap.

In line with the concept reflected in human capital theory, Investment in Human Capital can benefit workers individually while building the company’s business output and revenue (Becker, 1962). This theory also states that humans increase their productivity and efficiency through a greater focus on education and training. So, the company needs to pay attention to a good work environment so that employee productivity is attainable, ultimately contributing to improving the company’s performance.

This research contributes to the existing body of literature by offering fresh insights into the connection between the quality of the work environment and the likelihood of financial distress. Additionally, the study introduces a novel perspective by applying human capital theory within corporate finance. This expands our understanding of how investments in employees and a positive work environment can impact financial outcomes.

Moreover, the research provides practical guidance for managers and decision-makers in effectively overseeing corporate risk. It emphasizes the advantages of investing in a high-quality work environment as a preventative measure against financial distress. Simultaneously, it encourages decision-makers to consider non-financial variables in their choices. This holistic approach acknowledges the diverse factors influencing business operations.

Furthermore, the findings of this research have implications for regulators. They can utilize the study results to assess the adaptability of existing regulations and policies to accommodate non-financial factors in risk management. This, in turn, facilitates regulatory enhancements and adjustments that better align with the intricate nature of contemporary business conditions.

The Employment Population Ratio (EPR) illustrates the ratio of the working population in Indonesia to the working-age population. The National Labor Force Survey results by the Indonesian Statistics Center in February 2022 stated that the national EPR was 65.03, with a total of 135.61 million workers recorded in February 2022, with 40.03% being formal workers of 54.28 million workers. Workers in Indonesia holding standard positions such as professionals, technicians, administrative roles, service business personnel, and others typically receive a stable monthly salary, as indicated by Spisakova (2019).

By utilizing a sample of 1,103 non-financial public companies in Indonesia over the period 2019–2021, the research reveals that the quality of the work environment contributes to reducing the risk of financial distress for companies. The results of this study are robust, as demonstrated by testing using CEM and Heckman’s two-stage regression analysis. Additional tests employing alternative proxies for financial distress further strengthen the main findings of this research by consistently demonstrating similar outcomes.

This study will be organized into sections: section 2 contains past research, relevant theory, and hypothesis development. Furthermore, in section 3, this article discusses the research methodology, which includes sample selection and research design. Meanwhile, the research results, which consist of primary regression, robustness analysis, and additional analysis, are presented in section 4. Finally, section 5 contains conclusions.

2. Literature review & hypothesis development

2.1. Human capital theory

Aligned with the principles reflected in human capital theory, investing in human capital can yield individual benefits for workers while enhancing the company’s business output and revenue (Becker, 1962). This theory asserts that individuals can amplify their productivity and efficiency by emphasizing education and training. Consequently, companies need to prioritize a conducive work environment to ensure employee productivity, ultimately contributing to the enhancement of overall company performance.

In addition, Employees who are happy and comfortable with their work environment are more likely to stay with the company (Chi & Gursoy, 2009). Good relations between employees and companies are represented by happy and comfortable conditions in the work environment, which can increase employee productivity and support the company’s goals. Employees are one of the stakeholders whose effect is directly on company performance (Taliento et al., 2019). Some previous studies also found that the higher performance of the firm employee will increase the subsequent balance of the cash in the company (Flugum et al., 2021). This study has explained that good employee performance can affect various aspects of the company’s operations, which leads to increased revenue and cost savings so that subsequent cash balances will also increase.

With the increased productivity of individuals, the outcomes are not only experienced by the employees but are also reflected in the company’s overall performance. Well-skilled and trained employees can substantially contribute to achieving the company’s business objectives. Therefore, investing in human capital is not just an investment in individuals but also an investment in the company’s long-term success.

To attain this success, companies can design appropriate education and training programs, create a positive work environment, and provide the necessary support for employee career development. Thus, human capital becomes a theoretical foundation and a practical basis for sustainable human resource development strategies.

2.2. Working environment quality and financial distress

In this study, we still argue that the better quality of the working environment harms corporate financial distress. This argument aligns with human capital theory, which states that by creating a good quality work environment, the company can reduce the risk of financial distress by increasing productivity, efficiency, employee retention, and innovation. As human capital theory explains, human capital is an intangible asset within the company that can give potential economic value to the company (Becker, 1962). This theory also states that individuals can increase their productive capacity through higher education and skills training. Therefore, this theory has explained that investing in employee development and empowerment can result in higher human capital, contributing to better company performance and lower risk of financial distress.

Financial distress is a risk that most companies will face (Habib et al., 2020). The risk of financial distress in the company can be caused by several factors, including a decrease in the company’s financial performance (Blaylock et al., 2012; Hanlon, 2005; Lev & Nissim, 2004; Zhang, 2015), the ESG disclosure (Almubarak et al., 2023; Jia & Li, 2022; Shi et al., 2023), the company’s inability to carry out good financial management (Sayidah et al., 2020; Tron et al., 2023; Younas et al., 2021), board size and the presence of women directors in the board of directors (Kalbuana et al., 2022), and the existence of macroeconomic uncertainty (Altman et al., 2017; Bonsall et al., 2013; Ceylan, 2021; Chordia & Shivakumar, 2005; Liou, 2007; Tinoco & Wilson, 2013).

This matter has a dire impact on the company because it gives a bad image to stakeholders. Previous findings have found that if a company experiences financial distress, this will harm the company’s investment returns (Gupta & Mahakud, 2022). This is due to restrictions on external financing, so the company’s cash flow decreases, thus lowering the company’s financial performance.

Furthermore, previous research has found that the institution’s quality of anticorruption efforts reduces financial distress (Stef, 2021). It indicates that corruption control has the strongest association with the likelihood of monetary recovery, as measured by the Z-Score. Firms operating in less corrupt environments were more likely to overcome financial difficulties, particularly in the post-crisis period of 2010–2017. This research highlights the importance of institutional quality in the economic recovery of firms and emphasizes the need for improved institutional efficiency.

A work environment that is comfortable, safe, and provides space for employees to continue to grow can trigger employee productivity (Abdul Basit et al., 2018; Pandey, 2023; Schilleci, 2022; Voordt & Jensen, 2023). Employees who feel valued and given adequate facilities are more likely to work more efficiently and effectively. In addition, a work environment that supports employees’ mental and physical well-being can reduce absenteeism, stress, and work-related health problems (Aruldoss et al., 2021; Kurniawaty et al., 2019). When employees can be more productive, work more efficiently, and have less stress, it will be easier for them to contribute to better company financial performance.

H1: A higher quality working environment is associated with reducing the company’s financial distress risk.

3. Methodology

3.1. Sample selection

This study utilizes secondary panel data from a non-financial public company’s annual reports on the Indonesian stock exchange spanning 2019 to 2021. The primary variable, the quality index of the working environment, is obtained explicitly through a hand-collected process, while other financial variables are sourced from the OSIRIS database. The study’s sample comprises 630 public companies, excluding 1,063 entities from the financial industry (SIC-6) due to their distinct characteristics and policies. Additionally, 426 observations with missing data were excluded. Consequently, the final sample for this study consists of 1,103 observations. Detailed information regarding the sample selection is presented in Table 1.

Table 1. Sample selection and data distribution.

Panel A. Sample selection
Description		Number of observations
Total listed company from IDX for 2019–2021		2,592
(-) Financial Industry (SIC-6)		(1,063)
(-) Missing data		(426)
Total sample observation		1,103
Panel B. Sample distribution based on SIC
SIC	Frequency	Percent	Cumulative
0 (Agriculture, Forestry, and Fishing)	43	3.90%	3.90%
1 (Mining)	157	14.23%	18.13%
2 (Construction)	310	28.11%	46.24%
3 (Manufacturing)	190	17.23%	63.46%
4 (Transportation, Communication, and Utilities)	146	13.24%	76.70%
5 (Wholesale and Retail Trade)	114	10.34%	87.04%
7 (Service Industries)	111	10.06%	97.10%
8 (Health, Legal & Educational services, and Consulting)	32	2.90%	100%
Total	1,103	100%	100%

Moreover, the industry segmentation detailed in panel B of Table 1 provides a breakdown of our sample based on various industry segments. The construction industry holds the most substantial share at 28.11% of the distribution, emphasizing its predominant presence. Following closely, the manufacturing sector secures the second-highest position, representing 17.23%. This data underscores the influential roles played by the construction and manufacturing sectors in the Indonesian business landscape. In contrast, industries such as health, legal services, education, and consulting exhibit the lowest representation within our sample. This discrepancy can be attributed to their comparatively slower development compared to other more rapidly expanding industries in Indonesia.

3.2. Research design

Following the previous research, we employed a quantitative methodology, specifically opting for the logistic regression technique (Choi, 2021; Hasan et al., 2018). The rationale behind the selection of this method is multifaceted. Logistic regression is an apt choice for analyzing binary outcomes, given its ability to efficiently capture the connection between the dependent variable and explanatory variables (Chen, 2005; Hosmer et al., 2000). This model applies in prospective and retrospective studies, with the former typically favored in research that encompasses numerous quantitative risk factors (Breslow & Powers, 1978). Firstly, our study aligns with the post-positivism paradigm, a philosophical perspective that allows us to recognize and systematically address potential biases originating from prior theoretical underpinnings. This approach underscores our commitment to thoroughness and objectivity in evaluating the research problem.

Secondly, the choice of logistic regression harmonizes seamlessly with our overarching research objective, which revolves around delving into the intricate connection between the quality of the working environment and the phenomenon of financial distress. Lastly, using numerical data extracted from publicly available financial statements of listed companies underscores the appropriateness of the quantitative approach. This type of data, inherently quantitative, aligns seamlessly with the tenets of quantitative research. Moreover, it’s worth noting that this data source has been a cornerstone of prior quantitative studies, adding to the methodological continuity and facilitating comparisons with existing literature.

Moreover, the logistic regression is suitable for this study because the dependent variable of our analysis is the value of 1 or 0. Our DISTRESS variable is one if the Z-Score is below 1.8 and 0 otherwise (Islam et al., 2023). The Z-Score is calculated by following the Altman model below: (1) $$Z-\mathit{\text{Score}}=1.2\hspace{0.17em}\hspace{0.17em}\mathit{\text{WC}}/\mathit{\text{TA}}+1.4\hspace{0.17em}\hspace{0.17em}\mathit{\text{RE}}/\mathit{\text{TA}}+3.3\hspace{0.17em}\hspace{0.17em}\mathit{\text{EBIT}}/\mathit{\text{TA}}+0.6\hspace{0.17em}\hspace{0.17em}\mathit{\text{MV}}/\mathit{\text{TL}}+1.0\hspace{0.17em}\hspace{0.17em}\mathit{\text{CS}}/\mathit{\text{TA}}$$(1) where WC is working capital, RE is retained earnings, EBIT is earnings before interest and tax, MV is the market value of equity, CS is current sales, TA is total assets, and TL is total liabilities.

Then, the empirical model of our study is developed to achieve our primary objective. The equation below is our practical model to answer our proposed hypothesis about the relationship between the quality of working environment and financial distress: (2) $$\begin{array}{l}\mathit{\text{DISTRESS}}\hspace{0.17em}i.t\hspace{0.17em}=\hspace{0.17em}\beta 0\hspace{0.17em}+\hspace{0.17em}\beta 1\hspace{0.17em}\hspace{0.17em}\mathit{\text{WORKQUALITY}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 2\hspace{0.17em}\hspace{0.17em}\mathit{\text{BIG}}4\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 3\hspace{0.17em}\hspace{0.17em}\mathit{\text{BOARDSIZE}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 4\hspace{0.17em}\hspace{0.17em}\mathit{\text{INDCOM}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 5\hspace{0.17em}\hspace{0.17em}\mathit{\text{FIRMSIZE}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 6\hspace{0.17em}\hspace{0.17em}\mathit{\text{ROE}}\hspace{0.17em}i.t\hspace{0.17em}\\ +\hspace{0.17em}\beta 7\hspace{0.17em}\hspace{0.17em}\mathit{\text{PPE}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 8\hspace{0.17em}\hspace{0.17em}\mathit{\text{CATA}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 9\hspace{0.17em}\hspace{0.17em}\mathit{\text{RECT}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\beta 10\hspace{0.17em}\hspace{0.17em}\mathit{\text{INVREC}}\hspace{0.17em}i.t\hspace{0.17em}+\hspace{0.17em}\mathit{\text{Year Fixed}}\hspace{0.17em}\mathit{\text{Effect}}\hspace{0.17em}+\hspace{0.17em}\mathit{\text{Industry Fixed}}\hspace{0.17em}\mathit{\text{Effect}}\hspace{0.17em}+\hspace{0.17em}\epsilon \_\left(i.t\right)\end{array}$$(2)

The independent variable of our study is the environment working quality. This variable is measured by averaging the total 11 index of the working environment quality. This index encompasses economic rewards, work flexibility, and the quality of career path development. Furthermore, we also include control variables based on previous research (Aruldoss et al., 2021; Flugum et al., 2021; Harymawan et al., 2021; Stef, 2021; Younas et al., 2021), divided into two categories: Governance and financial variables. The governance control variable consists of BIG4, the size of the auditor that audited the company; BOARDSIZE, the size of the company’s total board; and INDCOM, the size of the independent commissioner. Then, for the financial control variable, we included the FIRMSIZE, capturing the size of the company’s assets; ROE, return on equity; PPE, the size of the company’s plant property and equipment; CATA, current assets to total assets value; REC, the size of firm’s account receivable; INVREC, the total of inventory and receivable of the company. Moreover, we include the year and industry fixed effect to prevent the potential of biased results because of the different characteristics of different years and industries. All explanations regarding the description and operationalization of each variable are further elaborated in Appendix A.

4. Result & discussion

4.1. Univariate and descriptive analysis

The univariate analysis of this study started by doing a descriptive analysis. The descriptive analysis of this research is available in Table 2. From this table, we can infer that the mean and the median value of financial distress are 0.711 and 1, respectively. It means that, on average, 71.1% of Indonesian companies have experienced a financial crisis. This value is higher than in another country (Islam et al., 2023). Moreover, the working quality variables have a mean and median value of 0.855 and 0.636, respectively. The governance control variable, such as BIG4, has a mean value of 0.302. It can be concluded that, on average, 30.2% of companies in Indonesia are audited by BIG 4 audit firms. However, other governance control variable such as BOARDSIZE and INDCOM has a mean value of 2.027 and 0.413, respectively, while financial control such as FIRMSIZE, ROA, PPE, LEV, CATA, CURRENT, and RECT has a mean value of 28.368, 0.047, 27.003, 1.385, 0.434, 2.776 and 0.120 respectively.

Table 2. Descriptive analysis.

	Mean	Minimum	P25	Median	P75	Maximum
DISTRESS	0.711	0.000	0.000	1.000	1.000	1.000
WORKQUALITY	0.855	0.636	0.818	0.818	0.909	1.000
BIG4	0.302	0.000	0.000	0.000	1.000	1.000
BOARDSIZE	2.027	1.386	1.792	1.946	2.303	2.890
INDCOM	0.413	0.167	0.333	0.400	0.500	0.750
FIRMSIZE	28.368	24.428	27.154	28.240	29.586	32.355
ROA	0.047	−0.354	−0.002	0.044	0.095	0.478
PPE	27.003	19.868	25.807	27.123	28.521	31.460
LEV	1.385	−6.553	0.338	0.738	1.534	21.741
CATA	0.434	0.028	0.236	0.416	0.620	0.960
CURRENT	2.776	0.036	0.909	1.489	2.701	48.115
RECT	0.120	0.000	0.034	0.092	0.173	0.498

Furthermore, we also do a Pearson analysis for another univariate analysis, presented in Table 3. It shows that univariately, the quality of the working environment has a negative significant correlation with financial distress at 5%. It gives an interpretation that if the quality of the working environment is improved univariately, it will motivate employees to be more productive and contribute to the company’s performance. Other variables such as BIG4, BOARDSIZE, FIRMSIZE, ROA, CATA, CURRENT, and RECT also significantly lower financial distress. It means that when the company implements good corporate governance and has high assets, it will lower the company’s economic crisis. Moreover, the correlation between leverage and financial distress is significantly positive. It makes sense that if the company has a high leverage value, it will urge them to have a more considerable potential for financial distress.

Table 3. Correlation matrix.

	DISTRESS	WORKQUALITY	BIG4	BOARDSIZE	INDCOM
DISTRESS	1.000
WORKQUALITY	−0.058**	1.000
	(0.048)
BIG4	−0.101***	0.072**	1.000
	(0.000)	(0.013)
BOARDSIZE	−0.130***	0.204***	0.253***	1.000
	(0.000)	(0.000)	(0.000)
INDCOM	0.029	0.011	−0.029	−0.146***	1.000
	(0.311)	(0.705)	(0.322)	(0.000)
FIRMSIZE	−0.052*	0.246***	0.284***	0.648***	−0.010
	(0.076)	(0.000)	(0.000)	(0.000)	(0.731)
ROA	−0.472***	0.058**	0.116***	0.160***	0.066**
	(0.000)	(0.045)	(0.000)	(0.000)	(0.024)
PPE	0.038	0.184***	0.264***	0.562***	−0.038
	(0.195)	(0.000)	(0.000)	(0.000)	(0.195)
LEV	0.101***	0.068**	0.018	0.052*	0.003
	(0.001)	(0.020)	(0.544)	(0.073)	(0.911)
CATA	−0.481***	0.004	−0.029	−0.036	0.002
	(0.000)	(0.900)	(0.321)	(0.221)	(0.938)
CURRENT	−0.097***	−0.038	−0.057*	−0.150***	−0.022
	(0.001)	(0.187)	(0.050)	(0.000)	(0.443)
RECT	−0.349***	0.018	−0.023	−0.032	−0.021
	(0.000)	(0.535)	(0.425)	(0.264)	(0.467)

Table

	FIRMSIZE	ROA	PPE	LEV	CATA	CURRENT	RECT
FIRMSIZE	1.000
ROA	0.185***	1.000
	(0.000)
PPE	0.849***	0.138***	1.000
	(0.000)	(0.000)
LEV	0.145***	−0.052*	0.119***	1.000
	(0.000)	(0.076)	(0.000)
CATA	−0.196***	0.190***	−0.406***	−0.039	1.000
	(0.000)	(0.000)	(0.000)	(0.180)
CURRENT	−0.191***	−0.017	−0.274***	−0.114***	0.225***	1.000
	(0.000)	(0.561)	(0.000)	(0.000)	(0.000)
RECT	−0.215***	0.163***	−0.262***	−0.044	0.556***	−0.029	1.000
	(0.000)	(0.000)	(0.000)	(0.130)	(0.000)	(0.315)

p-values in parentheses.

* p < 0.1, **p < 0.05, ***p < 0.01.

4.2. Main regression

Table 4 explains the result of the primary analysis of this study by using the Ordinary-Least-Square (OLS) approach. It is confirmed from the result of the regression that there is a significant negative relationship between working environment quality and financial distress at 10% with a coefficient value of –1.091 (t-value: –1.767). These results confirm the proposed hypothesis that when a company provides opportunities for employees to attain better economic rewards, work flexibility, and enhanced career development, the company experiences the benefit of increased productivity from its employees. These findings are also consistent with human capital theory (Becker, 1962), stating that employees’ needs to be competent and highly competitive can be fulfilled by creating a quality work environment. Therefore, the company has a better chance of avoiding potential financial difficulties. This discovery also aligns with previous studies (Abdul Basit et al., 2018; Aruldoss et al., 2021; Pandey, 2023; Voordt & Jensen, 2023), indicating that a quality work environment can make employees more productive, thus influencing the overall performance of the company.

Table 4. Main regression quality of working environment on financial distress.

	(1)
	DISTRESS
WORKQUALITY	−1.091*
	(–1.767)
BIG4	−0.292***
	(–2.681)
BOARDSIZE	−0.398**
	(–2.139)
INDCOM	0.328
	(0.664)
FIRMSIZE	0.099
	(1.128)
ROE	−1.346***
	(–6.147)
PPE	−0.160**
	(–2.107)
CATA	−4.227***
	(–10.191)
RECT	−1.820***
	(–3.037)
INVREC	0.407
	(0.956)
_cons	5.892***
	(5.366)
Year FE	Yes
Industry FE	Yes
r2_p	0.378
N	1103

t statistics in parentheses.

* p < 0.1, **p < 0.05, ***p < 0.01.

4.3. Robustness analysis

In our primary analysis, we assume that the quality of the working environment and the company’s financial distress are exogenous variables. We observe a correlation between a higher-quality working environment and a reduced risk of financial distress. However, financial distress and a favorable working environment could be determined endogenously. If companies with a lower risk of financial distress are more inclined to maintain a high-quality working environment, a self-selection bias may be affecting our results. We conducted two robustness analyses to validate our research findings and address potential endogeneity issues: coarsened exact matching (CEM) and Heckman two-stage regression.

4.3.1. Coarsened Exact Matching (CEM)

Selection bias occurs when a sample is not randomly generated and, thus, does not represent the population. This study uses Coarsened Exact Matching (CEM) to comply with bias issues because of the observed variable. Matching is a preprocessing data nonparametric method to control for some or all of the potentially confounding influence of pretreatment control variables by reducing the imbalance between the treated and control groups. However, the Heckman two-stage regression supports the selection bias and endogeneity issue because of the unobservable variable (Heckman, 1979).

CEM analysis matches the same variables’ characteristics based on three strata. The result is shown in Table 5. Based on 882 matched sample observations, the result is consistent with the primary regression with a coefficient (t-value) of –2.345 (t-value: –1.765). It confirms the result from the preliminary analysis. It strengthens the argument that the negative relationship between the quality of the working environment and financial distress is free from observed endogeneity bias issues. It confirms the result from the primary analysis. It strengthens the argument that the negative relationship between the quality of the working environment and financial distress is free from observed endogeneity bias issues.

Table 5. Coarsened Exact Matching (CEM) analysis.

	WORKING QUALITY = 0	WORKING QUALITY = 1
All	210	974
Matched	200	682
Unmatched	10	292
	(1)
	DISTRESS
WORKQUALITY	−2.345*
	(–1.765)
BIG4	−0.333
	(–1.267)
BOARDSIZE	−0.704*
	(–1.666)
INDCOM	1.212
	(0.906)
FIRMSIZE	0.049
	(0.264)
ROA	−16.809***
	(–5.445)
PPE	−0.215
	(–1.198)
LEV	0.130
	(1.253)
CATA	−7.362***
	(–7.890)
CURRENT	−0.058***
	(–2.808)
RECT	−1.681
	(–1.067)
_cons	12.839***
	(4.767)
Year FE	Yes
Industry FE	Yes
r2_P	0.513
N	882

t statistics in parentheses.

* p < 0.1, ** p < 0.05, *** p < 0.01.

4.3.2. Heckman two-stage regression

Then, we presented Heckman’s stage regression in Table 6. It is obtained by estimating the model on two-stage regression. The instrument variable used in the first stage is the industry average value of the environment working quality index. This variable is used for our instrument variable on the first stage of the Heckman two-stage regression because it explains how companies will follow how most other companies act. It is shown from the result of the first state that there is a positive significant relationship between the industry average value of working quality and the industry average value of working quality itself at a 1% significant level. It is analyzed that if, on average, a company applies an excellent working environment quality, then the other company will follow this culture.

Table 6. Heckman two-stage regression.

	First Stage	Second Stage
	D_WORKQUALITY	DISTRESS
MEAN_QUAL	37.406***
	(14.198)
WORKQUALITY		−1.192*
		(–1.864)
BIG4	−0.280	0.189*
	(–1.014)	(1.876)
BOARDSIZE	1.063**	−0.452***
	(2.458)	(–2.799)
INDCOM	0.858	−0.239
	(0.865)	(–0.532)
FIRMSIZE	0.030	0.175***
	(0.209)	(3.225)
ROE	−0.035	−0.598***
	(–1.080)	(–4.336)
PPE	0.027	−0.056
	(0.272)	(–1.351)
CATA	1.875**	−1.346***
	(2.493)	(–3.975)
RECT	1.733	1.876***
	(1.129)	(2.842)
INVREC	−1.369	0.620
	(–1.242)	(1.280)
MILLS		−0.035
		(–0.581)
_cons	−33.100***	−2.486**
	(–10.241)	(–2.328)
Year FE	Yes	Yes
Industry FE	Yes	Yes
r2_p	0.465	0.502
N	1103	1103

t statistics in parentheses.

* p < 0.1, **p < 0.05, ***p < 0.01.

Furthermore, in the second stage, we are testing the robustness of our primary model with an additional MILLS ratio. It is shown from the result that there is still a negative significant relationship between the quality of the working environment and financial distress with the coefficient value and t-value of –1.192 (t-value: –1.864) at the 10% significant level. The MILLS ratio from the second stage also shows an insignificant result. Therefore, it is proven that our primary regression is consistent and free from unobservable bias. It also strengthens our central argument that a better quality of the working environment will reduce the possibility of the company experiencing financial distress.

4.4. Additional analysis

To get further results, we will do an additional analysis. This additional analysis is performed by using another proxy of financial distress. The first proxy of the financial distress in this supplementary analysis is established on the corporation’s leverage position. Following the previous research, they state that the bigger leverage of the firm is related to the higher probability of a firm’s financial distress (Lee et al., 2011). Then, we measure our first proxy by creating five quartiles of the leverage. DISTRESS2 is one of the companies leveraged in the fifth quartile; otherwise, it is 0.

Moreover, we also used another proxy to measure financial distress. The second proxy of financial distress (DISTRESS3) in this additional analysis is measured by the ZM score (Zmijewski, 1984). A high-value ZM score means a higher value of financial distress. Specifically, the equation to calculi the ZM score is presented on the equation below: (2) $$\mathit{\text{ZM score}}\hspace{0.17em}=\hspace{0.17em}-4.336\hspace{0.17em}-\hspace{0.17em}4.513\hspace{0.17em}\mathit{\text{NI}}/\mathit{\text{TA}}\hspace{0.17em}+\hspace{0.17em}5.679\hspace{0.17em}\mathit{\text{TL}}/\mathit{\text{TA}}\hspace{0.17em}+\hspace{0.17em}0.004\hspace{0.17em}\mathit{\text{CA}}/\mathit{\text{CL}}$$(2)

NI is total Net income, TL is total liabilities, CA is total current assets, CL is current liabilities, and TA is the firm’s total assets.

Considering the results from the first alternative proxy (DISTRESS2) with a coefficient of –0.997 and a t-value of –1.857, as well as the results from the second alternative proxy (DISTRESS3) with a coefficient of –0.998 and a t-value of –2.169, it can be interpreted that the quality of the working environment continues to have a significant impact in reducing the risk of financial distress for the company. The result of this additional analysis is presented in Table 7.

Table 7. Additional analysis: quality of working environment on financial distress using another proxy of financial distress.

	(1)	(2)
	DISTRESS2	DISTRESS3
WORKQUALITY	−0.997*	−0.998**
	(–1.857)	(–2.169)
BIG4	0.187*	−0.006
	(1.864)	(–0.071)
BOARDSIZE	−0.449***	−0.445***
	(–2.782)	(–3.191)
INDCOM	−0.240	0.191
	(–0.533)	(0.544)
FIRMSIZE	0.174***	0.173***
	(3.212)	(3.379)
ROE	−0.602***	−2.442***
	(–4.355)	(–24.988)
PPE	−0.054	0.094**
	(–1.313)	(2.277)
CATA	−1.329***	−1.250***
	(–3.910)	(–4.788)
RECT	1.882***	2.059***
	(2.853)	(4.072)
INVREC	0.602	1.546***
	(1.242)	(4.236)
_cons	−2.695***	−7.725***
	(–2.679)	(–9.568)
Year FE	Yes	Yes
Industry FE	Yes	Yes
r2_p	0.090	0.205
N	1103	1103

t statistics in parentheses.

* p < 0.1, ** p < 0.05, *** p < 0.01.

In other words, the improvement in the quality of the working environment remains associated with a decrease in the risk of financial distress, even when using different alternative proxies. This finding adds additional reliability to the relationship, affirming that the analysis results remain consistent with our main argument. Therefore, it can be considered that the quality of the working environment positively influences reducing the risk of financial distress for the company, aligning with the proposed central hypothesis.

5. Conclusion

This study investigates the correlation between the quality of the working environment and a firm’s financial distress using logistic regression analysis on secondary data from the annual reports of non-financial public companies listed on the Indonesian Stock Exchange. The findings indicate that a higher quality working environment significantly reduces the likelihood of a company facing financial crises in Indonesia. This underscores the potential for companies to achieve better economic outcomes when prioritizing a positive work culture.

The study makes theoretical and practical contributions by strengthening and enriching previous research on the link between the working environment and corporate financial distress. Theoretically, it enhances our understanding of this relationship. Practically, it implies that fostering a positive work culture is crucial for financial success in the Indonesian business landscape. However, it is essential to acknowledge the complexity of the work environment concept, suggesting that future research should employ a more comprehensive measurement approach.

The implications of the study extend to managerial and regulatory domains. The findings stress the importance of cultivating a positive work culture for managers. Regulators should consider developing policies that incentivize practices promoting a high-quality work environment to bolster the stability of the business sector.

Despite the valuable insights gained, this study has limitations. The complexity of the work environment necessitates a more nuanced measurement approach. Additionally, the study’s focus on a single country limits the generalizability of the findings. Future research should consider cross-cultural factors to enhance the external validity of the results.

Future research could adopt a more comprehensive measure of the work environment to address the limitations, considering its multidimensional nature. Furthermore, expanding the scope to encompass diverse cultural contexts will contribute to a more robust understanding of the relationship between the working environment and financial outcomes. Comparative analyses across countries could provide valuable insights into these relationships’ universality or cultural specificity.

In conclusion, this study demonstrates a significant negative relationship between a high-quality working environment and financial distress for companies listed on the Indonesian Stock Exchange. The theoretical and practical contributions emphasize fostering positive work cultures for financial success. However, the study’s limitations, including the complex nature of the work environment and a single-country focus, underscore the need for further research that employs more comprehensive measures and considers diverse cultural contexts.

Author contributions statement

The contributions of the authors to this research are divided as follows:

Amalia Rizki: The first author was responsible for the research design, data collection and analysis, and initial manuscript writing.

Mohammad Nasih: The second author contributed to developing the theoretical framework formulation of research questions and significantly contributed to manuscript revisions.

Fiona Vista Putri: The third and fourth authors jointly provided crucial insights into data analysis and contributed to writing specific sections of the manuscript.

Each author has read and approved the final manuscript before submission.

Disclosure statement

We, the authors of this manuscript, declare that we have no financial or non-financial conflicts of interest that could be perceived as potentially influencing the research, analysis, or interpretation of the findings presented in this work.

This research was conducted with complete transparency and impartiality, and no external entities or interests have influenced this study’s content, methodology, or outcomes. We confirm that this manuscript is solely the result of our research efforts and intellectual contributions.

Each author has reviewed and endorsed this disclosure of interest statement.

Additional information

Funding

This work was supported by the Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi [1220/UN3.LPPM/PT.01.03/2023]. This research has been funded by the Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research, and Technology.

Notes on contributors

Amalia Rizki

Amalia Rizki, Lecturer at Universitas Airlangga, Indonesia. With research emphasis on Financial Accounting, Information Systems, and Cost Accounting. Holds a B.A. in accounting from Universitas Airlangga.

Mohammad Nasih

Mohammad Nasih, Professor at Universitas Airlangga, Indonesia, holds a Ph.D. in accounting from the same university. With research emphasis on corporate governance, accounting implications of political-military ties, and financial reporting quality. Holds a Master’s in Industrial and Management Engineering from Institut Teknologi Bandung and a B.A. in accounting from Universitas Airlangga.

Fiona Vista Putri

Fiona Vista Putri, Student at Universitas Airlangga, Indonesia, holds a post graduate major in accounting from the same university. With research emphasis on corporate governance and management accounting. Holds a B.A. in accounting from Universitas Airlangga.

Appendix A.

Variable definition

	Definition	Sources
Dependent variable
DISTRESS	Dummy 1 if Z-Score < 1.8, 0 otherwise	Osiris
Independent variable
WORKQUALITY	Quality of Working Environment Index, By measure the average value of the total disclosure quality index, which consists of Generous Fringe Benefits; Management Departures; Bonus Plan for Employees/Managers; Diversity Opportunity Policy/Diversity and Opportunity; Diversity Opportunity Policy/Work Life Balance; Diversity and Opportunity Processes/Diversity and Opportunity Policy; Diversity and Opportunity Processes/Work Life Balance; Flexible Working Schemes; Training and Career Development Policy/Skills Training; Training and Career Development Policy/Career Development; and Management Training divided by the total quality index that should be disclosed.	Annual report
Control variable
BIG4	Dummy 1 if the company is audited by BIG4 auditor, 0 otherwise	Annual report
BOARDSIZE	Natural logarithm of the total value of commissioner and director	Annual report
INDCOM	Independent commissioner divided by total commisioner	Annual report
FIRMSIZE	Natural Logarithm of total assets	Osiris
ROE	Return on Equity, Net profit divided by total equity	Osiris
PPE	Natural Logarithm value of Plant Property and Equipment	Osiris
CATA	Total current assets divided by total assets	Osiris
RECT	Total account receivables divided by total assets	Osiris
INVREC	Total inventory plus receivables divided by total assets	Osiris