A cultural aspect, firms’ financial health and earnings management: evidence from the Asia-Pacific region

Abstract

This research utilizes a fixed effect model to examine how uncertainty avoidance, as a cultural value of a nation, and the financial health of the firms impact earnings management practices in nine Asia-Pacific countries. This study reveals that companies operating in countries with higher uncertainty avoidance and those with better financial performance are less likely to manage earnings figures. Additionally, the study highlights that firms that are more prone to financial failure tend to exhibit a greater degree of earnings management. Our findings assist stakeholders in identifying a firm’s specific characteristic of financial performance as early warning signals for managing earnings figures and understanding the influence of national culture on international differences in financial reporting. This study contributes to the existing literature on agency theory and earnings management by highlighting the significance of national culture and a firm’s financial performance in explaining corporate managers’ discretionary practices in Asia Pacific countries. By examining the Asia Pacific region, which encompasses diverse cultures, this study provides a comprehensive understanding of how cultural factors shape agency relationships and influence earnings management practices. Furthermore, considering the financial performance of firms in the Asia Pacific provides insights into how managers use earnings management techniques to achieve financial goals, manipulate performance indicators, or align their interests with those of shareholders.

Keywords:

• earnings management
• culture value
• uncertainty avoidance
• financial health
• Asia Pacific

Reviewing Editor:

• Md. Mahmudul Alam, Universiti Utara Malaysia, Malaysia

Subjects:

• Asian Studies
• Business, Management and Accounting
• Cultural Studies

1. Introduction

Accounting policies include a set of standards that govern how a company prepares its financial statements. All accounting policies must conform to generally accepted accounting principles (GAAP) and or international financial reporting standards (IFRS). However, as GAAP has been designed to expand management discretion, the choices of accounting practices may differ from company to company. The management may manipulate earnings legally by selecting a set of accounting policies. Based on agency theory, corporate managers may practice earnings management by using accounting techniques to deliberately produce financial statements to show that the reported earnings match an intended or specific target (Atik, 2009).

The empirical literature on earnings management is pervasive, and the evidence of earnings management has been examined in the context of various events. Generally, previous studies have suggested that firm managers engage in earnings management opportunistically for their benefit. For example, Healy and Wahlen (1999) document that managers exercise discretion in financial reporting and structuring transactions to manipulate financial statements to deceive stakeholders or influence contractual outcomes. Broome (2004) and Zhao (2017) highlight some specific cases where management deliberately misclassifies financials to present a better picture of the company’s operating performance. Wang and Hagigi (2019) note that firm managers can strategically manipulate financial figures to optimize their earnings. While earnings management has been criticized for being unethical and misleading, they argue these practices can provide valuable information to investors and analysts.

Furthermore, some other studies (e.g., Lin et al., 2016; Lazzem & Jilani, 2018; Li et al., 2020; Viana et al., 2022) investigate the earnings management practice of financially distressed firms. Likewise, Astami et al. (2017), Han et al. (2010), Guan and Pourjalali (2010), and Viana et al. (2022) examine the influence of national culture on earnings management behavior. However, there is a lack of consistent empirical evidence regarding the association of such rules with earnings management. In addition, most prior empirical studies have focused on firms’ performance or cultural values in isolation rather than in combination. This study investigates the influence of the uncertainty avoidance dimension as a nation’s cultural value and the firms’ financial health on the practice of earnings management by managers in publicly listed companies across national cultural differences.

Our study contributes to the current level of knowledge in many areas. First, it contributes to the literature by providing empirical evidence of how uncertainty avoidance, a national level of a cultural aspect (Hofstede, 1983), assists in explaining the magnitude of earnings management. Second, it provides empirical evidence of how financially healthy and distressed firms in vibrant Asia-Pacific countries manage their reported earnings, as these companies have been associated with an agency problem. Third, in Asia-Pacific Economic Cooperation (APEC) economies, there is a growing focus on barriers to cross-border trade and investment, particularly in financial reporting practices. Regulations and structures within each country create these barriers. By examining evidence from the Asia-Pacific region, we can benefit from a unique cultural difference and consider other factors that may impact these barriers. Fourth, the Asia-Pacific region is of great geopolitical importance and plays a vital role in the global economy. It contributes to a quarter of the global GDP and is projected to make up over 35% of the world economy by 2020 (Schumacher, 2016). Therefore, the findings of this study are valuable for decision-makers such as investors, managers, and regulators.

The paper is organized as follows. Section 2 provides the theoretical framework and proposed hypotheses. Section 3 presents the research design encompassing the sample selection process and the variables used. Section 4 reports the results and discussions of our study. Finally, section 5 offers the study’s conclusion, implications, and limitations.

2. Theoretical framework and hypotheses

2.1. The agency theory and earnings management

Agency theory has been widely used to explain the incentives of corporate management to manage earnings figures. Earnings management arises due to a conflict of interest in the agency relationships between shareholders (principals) and managers (agents). This conflict of interest is commonly manifested through the manipulation of earnings information. Corporate managers may have incentives to manipulate earnings data to serve their interests, which may not align with the interests of shareholders. In his study, Wang (2006) explores the behavior of professional managers concerning the provision of financial information. He highlights the tendency among these managers to present financial information that deviates from the actual economic transactions. This behavior can be attributed to the agency problem, where managers act in their self-interest rather than the shareholders’ best interests.

Numerous past studies commonly cited reasons for managing reported earnings, including meeting analysts’ forecasts, avoiding violating debt covenants, maximizing executive remuneration, conveying private information, and maximizing corporate value (Saleh & Ahmed, 2005; Walker, 2013; Grougiou et al., 2014; Choi et al., 2018). Beyond those reasons, Ehsan et al. (2021) and Wang and Hagigi (2019) observe that the firms’ long-term financing requirements also influence the managers’ incentive for earnings management. However, several high-profile accounting scandals renewed the interest in managing earnings and the various factors that may influence corporate managers to manipulate the firm’s reported earnings (Filip & Raffournier, 2014; Astami et al., 2017; Ater & Hansen, 2020; Santos-Jaén et al., 2021; Jaggi et al., 2022).

Earnings management refers to the deliberate actions taken by corporate managers to present a company’s distorted or misleading image to attract stakeholders’ attention. The types of earnings management cover a broad spectrum. Generally, it occurs when managers take deliberate steps within the constraints of Generally Accepted Accounting Principles (GAAP) to achieve the desired level of reported earnings (Davidson et al., 1987; Evans et al., 2015). This study posits that selecting accounting methods is the most appropriate characterization of opportunistic earnings management.

2.2. Nation’s culture and managers’ behavior on earnings management

Previous research has indicated consistent differences in earnings management practices among countries internationally. These studies propose that the diversity in national cultures significantly impacts these variations (Doupnik, 2008; Gray et al., 2015). Ansah and Louw (2019) report that national culture strongly influences the organizational culture of multinational companies. In addition, Seleim and Bontis (2009) and DiRienzo (2019) note that culture plays a crucial role in shaping social norms and influencing an individual’s decision to engage in ethical behavior¹. In a similar vein, Rodriguez et al. (2015) suggest that variations in national culture across different countries may potentially affect the agency relationship and, thus, earnings management practices.

Hofstede’s (1983) classifies four underlying differences in nations’ cultural values: individualism, power distance, uncertainty avoidance, and masculinity. Amongst cultural dimensions of Hofstede’s, uncertainty avoidance is directly and strongly linked to earnings management practices (Doupnik, 2008; Guan & Pourjalali, 2010; Gray et al., 2015). A nation with high uncertainty avoidance attempts to avoid risk and create security by emphasizing technology and other infrastructure, such as buildings, laws and rules, and religion. On the other hand, a weak uncertainty avoidance society will adopt a more flexible approach, prioritizing practical implementation over rigid principles, thus allowing for greater tolerance (Zhang & Wu, 2014). Prior works by Han et al. (2010), Guan and Pourjalali (2010), and Astami et al. (2017) confirm that the cultural dimension of uncertainty avoidance negatively affects the level of earnings management. Those studies infer that firms operating in countries with higher uncertainty avoidance are less inclined to manage their earnings figures. In a recent study using a sample of firms from 11 emerging markets and 22 developed countries, Viana et al. (2022) confirm that firms with higher uncertainty avoidance and individualism are less likely to engage in earnings management. Furthermore, they suggest that the influence of uncertainty avoidance is more pronounced in developing countries. Based on the above argument and applying the cultural values as proposed by Hofstede’s (1983), the following hypothesis is proposed:

H₁: The higher the degree of uncertainty avoidance in a nation, the less likely the firms are to practice earnings management.

2.3. Firms’ financial health and earnings management

Many empirical studies generally document that firms facing more significant financial difficulties have incentives to inflate their reported earnings (Filip & Raffournier, 2014; Franz et al., 2014; Campa & Camacho-Miñano, 2015; Viana et al., 2022). In other words, Jaggi et al. (2022) propose that companies with weaker financial conditions tend to present favorable information in their financial statements. Specifically, managers of troubled firms systematically take income-increasing action to keep their jobs and reduce potential intervention by the firms’ boards (Moyer, 1990; Dichev & Skinner, 2002; Habib et al., 2013). A survey by Graham et al. (2005) supports this perspective, indicating that CFOs acknowledge the importance of prioritizing survival strategies in the face of a company’s decline, thus overshadowing their reporting responsibilities.

An illustration of this phenomenon can be found in the research conducted by Lazzem and Jilani (2018), which demonstrates that companies that experience an increase in financial leverage tend to participate in earnings management activities. Concerning breaches of debt covenants, Iatridis and Kadorinis (2009) and Fung and Goodwin (2013) document that managers of companies close to debt covenant violations are highly motivated to engage in income-increasing earnings management to avoid the costs of a breach. Chen et al. (2010) reveal that financially troubled companies in China increased their reported income to avoid a delisting threat and special monitoring by the government. Furthermore, Bisogno and De Luca (2015) find that non-publicly listed Italian small and medium entities (SMEs) practice earnings management to secure bank financing. Finally, using a sample of Chinese listed firms for 2007–2015, Li et al. (2020) reveal empirical findings that financially distressed firms engage in earnings management. Interestingly, these firms prefer accrual earnings management instead of real earnings management. Given the above empirical evidence on the association between distressed firms and earnings management strategies, our second hypothesis is stated as follows:

H₂: The better (worse) the firm’s financial health, the less (more) likely it is to practice earnings management.

3. Research approach

3.1. Sample selection

This study focuses on large capitalized companies in the Asia-Pacific region listed on the Hong Kong, Singapore, Indonesia, Malaysia, New Zealand, Australia, Taiwan, Shenzhen, and Thailand Stock Exchanges. The data are collected from the ORBIS database for 2013–2015. Similar to past earnings management research, all firms from the financial sector are eliminated from the study as firms in this sector are subject to different regulatory requirements (Zmijewski, 1984; Bamber et al., 1993). Our initial population comprised 6,020 firms, equating to 18,027 firm-year observations. Because of some cases where complete financial information could not be collected, the final sample was reduced to 4,612 firms or 11,999 firm-year observations.

3.2. Proxy for dependent and independent variables

Earnings management. We use a standard methodology by employing discretionary accruals (DAC) as a proxy for earnings management. Consistent with existing literature in earnings management, our study focuses on the absolute value rather than the actual sign of discretionary accruals. The amount of unsigned discretionary accruals is the best measure to indicate the opportunistic behavior of management and appears insensitive to whether firms manage earnings upwards or downwards (Kim & Sohn, 2013; Francis et al., 2016). To construct the value of DAC from the modified model of Jones (1991), we first compute total accruals (TAC) calculated as: $${\mathit{\text{TAC}}}_{\mathit{\text{jt}}}=\left(\Delta {\mathit{\text{CA}}}_{\mathit{\text{jt}}}-\Delta {\mathit{\text{Cash}}}_{\mathit{\text{jt}}}\right)-\left(\Delta {\mathit{\text{CL}}}_{\mathit{\text{jt}}}-\Delta {\mathit{\text{LTD}}}_{\mathit{\text{jt}}}-\Delta {\mathit{\text{ITP}}}_{\mathit{\text{jt}}}\right)-{\mathit{\text{DPA}}}_{\mathit{\text{it}}}$$

Where:

TAC_jt is total accruals for firm j in year t; ΔCA_jt is the change in current assets for firm j from year t-1 to t;

ΔCash_jt is the change in cash balances for firm j from year t-1 to t; ΔCL_jt is the change in current liabilities for firm j from year t-1 to t; ΔLTD_jt is the change in long-term debt included in current liabilities for firm j from year t-1 to t; ΔITP_jt is the change in income tax payable for firm j from year t-1 to t; and DPA_jt is depreciation and amortization expense for firm j in year t.

TAC is then decomposed into non-discretionary accruals or normal accruals (NAC) and DAC using the cross-sectional modified Jones (1991) model estimated as: $${\mathit{\text{TAC}}}_{\mathit{\text{jk}},t}/{\mathit{\text{TA}}}_{\mathit{\text{jk}},t-1}={\alpha }_{\mathit{\text{jt}}}[1/{\mathit{\text{TA}}}_{\mathit{\text{jk}},t-1}]+{\beta }_{\mathit{\text{jt}}}\left[\right({\Delta \mathit{\text{REV}}}_{\mathit{\text{jk}},t}-{\Delta \mathit{\text{REC}}}_{\mathit{\text{jk}},t})/{\mathit{\text{TA}}}_{\mathit{\text{jk}},t-1}]+{\gamma }_{j,t}[{\mathit{\text{PPE}}}_{\mathit{\text{jk}},t}/{\mathit{\text{TA}}}_{\mathit{\text{jk}},t-1}]+{\epsilon }_{\mathit{\text{jk}},t}$$

Where:

TAC _jk,t is total accruals for firm j in industry k in year t; TA_jk,t-1 is total assets for firm j in industry k at the end of year t-1; ΔREV_jk,t is the change in net sales for firm j in industry k between years t-1 and t; ΔREC_jk,t is the change in accounts receivable for firm j in industry k between years t-1 and t; PPE_jk,t is gross property, plant and equipment for firm j in industry k in year t; α_j, β_j, γ_j is industry specific estimated coefficients, and ε_j is the error term.

NAC is the fitted value from Equation 2, and DAC is defined as the residual value (ε_jk,t).

Uncertainty avoidance as a cultural aspect. Our study employs a cultural variable of Hofstede’s (1983) to scrutinize earnings management behavior in different countries. As referenced in the literature, among the four factors underlying differences in nations’ cultural values of Hofstede’s (1983), the uncertainty avoidance dimension has a clear relationship with earnings management (Doupnik, 2008; Guan & Pourjalali, 2010; Gray et al., 2015). Country cultural scores for each country are acquired from Hofstede and Bond (1988).

Firms’ financial health. The assessment of the financial health of firms is of particular importance in this study. Numerous bankruptcy models have been previously developed in the literature (e.g., Ohlson, 1980; Zmijewski, 1984; McKeown et al., 1991; Altman, 1993; Mutchler et al., 1997; Carcello & Neal, 2000; Fich & Slezak, 2008; Tinoco & Wilson, 2013; Mousavi et al., 2015). This study employs the financial default prediction modelling technique developed by Altman (1993). The Altman Z-Score model utilizes multiple discriminant analysis (MDA) methods to classify and identify firms with a high probability of failure (Lin et al., 2016; Mselmi et al., 2017). Altman initially employed 22 potentially useful financial ratios to assist in default prediction in the study sample of manufacturing companies. From these 22 ratios, Altman selects five financial ratios, namely, working capital, retained earnings, earnings before interest and tax expenses, leverage, and assets turnover, that provide the best overall power in predicting corporate bankruptcy. These five financial ratios represent the firm’s liquidity, profitability, leverage, solvency, and activity and are used to compute the risk score of a company. This model suggests that the greater the score, the lower the probability of a firm likely to go bankrupt. The Altman score is estimated from the following formula: $$\begin{array}{c}Z=1.2\left(\mathit{\text{working capital}}\hspace{0.17em}\mathit{\text{to total}}\hspace{0.17em}\mathit{\text{assets}}\right)\hspace{0.17em}+\hspace{0.17em}1.4\left(\mathit{\text{retained earnings}}\hspace{0.17em}\mathit{\text{to total}}\hspace{0.17em}\mathit{\text{assets}}\right)\\ +\hspace{0.17em}3.3\left(\mathit{\text{earnings before}}\hspace{0.17em}\mathit{\text{taxes and}}\hspace{0.17em}\mathit{\text{interest to}}\hspace{0.17em}\mathit{\text{total assets}}\right)\\ +\hspace{0.17em}0.6\left(\mathit{\text{market value}}\hspace{0.17em}\mathit{\text{of equity}}\hspace{0.17em}\mathit{\text{to total}}\hspace{0.17em}\mathit{\text{liabilities}}\right)\hspace{0.17em}+\hspace{0.17em}1.0\left(\mathit{\text{net sales}}\hspace{0.17em}\mathit{\text{to total}}\hspace{0.17em}\mathit{\text{assets}}\right)\end{array}$$

Z represents an estimated risk index of the financial condition and indicates the propensity of the firm to be bankrupt or in financial distress. Firms with high Z scores are less likely to face financial distress. Therefore, per our proposed hypothesis (H₂) and the Altman score, this study would suggest the larger (smaller) the Altman index of a firm, the less (more) likely the firm is to practice earnings management.

Table 1 presents an overview of the firm’s financial health of the sample characteristics. It shows the results of our assessment of firms’ financial health for each of the nine countries represented in the study. The study uses an Altman Z-score above 2.99 as the threshold for splitting the sample firms into distressed and healthy firms sub-samples, as well-established in the literature (Altman, 1993; Casillas et al., 2019). A score of 2.99 or higher indicates that a company is financially stable and has a low risk of bankruptcy.

Table 1. Sample firms classified into distressed and healthy firms based on the Altman prediction models.

Country	Sample		Distressed Firms		Healthy Firms
	n	%	n	%	n	%
China	4,612	38.44	746	16.18	3,866	83.82
Australia	1,084	9.03	331	30.54	753	69.46
Hong Kong	312	2.60	78	25.00	234	75.00
Indonesia	630	5.25	171	27.14	459	72.86
Malaysia	1,312	10.93	251	19.13	1,061	80.87
Singapore	813	6.78	232	28.54	581	71.46
New Zealand	145	1.21	25	17.24	120	82.76
Taiwan	2,316	19.30	338	14.59	1,978	85.41
Thailand	775	6.46	240	30.97	535	69.03
Total	11,999	100.00	2,412	20.10	9,587	79.90

Table 1 shows that most companies (4,612 firms or 38.44%) in the sample are listed on the Shenzhen Stock Exchange, while the smallest percentage is listed on the New Zealand Stock Exchange (145 firms or 1.21%). The most significant proportion of companies classified as distressed using the Altman model has publicly listed firms in Thailand, with 240 companies or 30.97% of the total sample. Also, 14.59% of firms listed in Taiwan indicated financial hardship.

3.3. Proxy for control variables

This study includes control variables (firm size, firm’s leverage, financial performance, level of investment, cash flow from operations, and audit quality) to evaluate further the impact of the cultural aspect and firms’ financial health on earnings management behavior. Several prior studies (Lilien & Pastena, 1982; Sutton, 1988; Chen et al. 2021) have shown that large firms are more inclined to engage in income-decreasing accounting practices to avoid the political actions of regulators. Leverage is included as a control variable given the work that shows firms have a propensity to smooth reported earnings to avoid violating debt covenants (Franz et al., 2014; Campa & Camacho-Miñano, 2015; Chen et al. 2021). Loss is another control variable given in earlier works (Kothari et al., 2005; Chen et al. 2021), indicating that firms’ reported earnings depend on a firm’s financial performance. We include a Loss variable to control the possible compounding effects of a company’s financial performance. The study also includes a variable of Invest measured as the increase or decrease of investment in tangible fixed assets in the current year deflated by lagged total assets. Following Dhaliwal et al. (1999) and Reynolds and Francis (2001), we predict a negative association between Invest and earnings management measures. Premised on the works of Becker et al. (1998) and Reynolds and Francis (2001), we include CFO given the findings of CFO influence on corporate management actions in managing earnings. Consistent with the previous study (Habib et al., 2013), the coefficient of this variable is predicted to be negative. Audit quality is included since this may impact the magnitude of earnings management (Frankel et al., 2002; Gul et al., 2003; Astami et al., 2017). Prior works (Mayhew & Wilkins, 2003) distinguish between non-Big4 and Big4 audit firms and find the latter to be of higher quality. Each proxy measure for the dependent, independent, and control variables is defined in Table 2.

Table 2. Variable definition and description.

Variable description	Prediction	Variable title
Dependent variable:
The firm’s absolute DACs (measured by the Modified Jones, 1991 model).		AbsDAC
Independent variables:
A country’s cultural scores for uncertainty avoidance were obtained from Hofstede and Bond (1988).	-	UnAvoid
A firm’s financial health takes a value of one if a company’s Altman Z-score is higher than 2.99 and zero otherwise.	-	Altman
Control variables:
The firm’s total assets are expressed as a natural logarithm.	-	Size
The cash flow from operations to total assets ratio	-	CFO
The indicator variable scored one if a firm’s auditor is a Big-4 accounting firm; otherwise, it scored zero.	-	Big 4
The total debt to total assets ratio	+	Leverage
The indicator variable scored one if a firm has experienced a financial loss in the current fiscal year; otherwise, it scored zero.	+	Loss
The amount of the increase or decrease in tangible fixed assets from the previous year to the current year, scaled by the last year’s total assets	-	Invest

3.4. Empirical model equation

The Hausman test is employed as a guide in using fixed or random effect estimation. The result suggests the use of fixed effect estimation. Dummies for the year, industry, and country account for the unobserved country, year, and industry-specific characteristics. The following modelling estimation can be constructed to test the above hypotheses: $$\begin{array}{c}\text{DAC}={\alpha }_0+{\alpha }_1\text{UnAvoid}+{\alpha }_2\text{Altman}+{\alpha }_3\text{Size}+{\alpha }_4\text{CFO}+{\alpha }_5\text{Big}4+{\alpha }_6\text{Leverage}+{\alpha }_7\text{Loss}+{\alpha }_8\text{Invest}\\ +\text{Year\hspace{0.17em}Fixed Effects}+\text{Industry Fixed Effects\hspace{0.17em}}+\text{Country Fixed Effects}+{\epsilon }_i\end{array}$$

Where α₀₎ is the regression intercept; α₁ …α₈ is the regression coefficients; ε_i is the error term

4. Results and discussions

4.1. Descriptive statistics

Table 3 depicts the descriptive statistics for the study’s dependent, independent, and control variables.

Table 3. Descriptive statistics.

	Mean	Median	Std Dev	Min	Max
Panel A Continuous variables
Dependent variable:
Earnings management:
a. DAC	0.0006	−0.0025	0.1224	−0.9678	0.9714
b. AbcDAC	0.0768	0.0472	0.0953	0.0001	0.9714
Independent variables:
Cultural aspect (UnAvoid)	41.9349	36.0000	17.9944	8.0000	69.0000
Control variables:
Size (Total Assets in thousand USD)	1,847,668	271,928	14,055,529	680	877,789,728
CFO	0.0460	0.0454	0.1058	−1.0851	1.1852
Leverage	0.4281	0.4215	0.2031	0.0883	0.8835
Invest	0.0061	−0.0027	0.0966	−0.9624	0.5368
Panel B – Categorical Variables				Frequency	Percentage
Independent variable:
Healthy firms				9,587	79.87
Distressed firms				2,412	20.10
Control variables:
Non-Big4				4,883	40.70
Big4				7,116	59.30
Loss				2,497	20.81
Profit				9,502	79.19

See Table 2 for complete definitions and descriptions of the dependent, independent, and control variables.

The average DAC is 0.06% of total assets at the beginning of the year. This value is slightly lower compared to the earnings management scores of Asia-Pacific countries in the years 2005–2010 (Astami et al., 2017). However, the distribution of discretionary accruals indicates a wide range of variation. In addition, the number of firms with positive and negative discretionary accruals is relatively balanced, with 5,845 firms displaying positive discretionary accruals and 6,154 firms exhibiting negative discretionary accruals. The approximate equal percentage of positive and negative discretionary accrual firms is consistent with prior research in this field (Klein, 2002; Rusmin, 2010). The average index score of cultural dimension (UnAvoid) is 41.93, ranging from 8 in Singapore to 69 in Taiwan. Han et al. (2010) argue that countries with high uncertainty avoidance will be highly regulated and incorporate numerous rules and structures to support uniformity in financial reporting, thus providing fewer opportunities for companies to practice earnings management. Using both Taiwan and Singapore as an example in interpreting Unvoid, Taiwan, with a high uncertainty avoidance score of 69, is a highly regulated, rules-based economy that supports uniformity in financial reporting, and as such there has less flexibility for companies to practice earnings management. On the other hand, Singapore is low uncertainty avoidance and affords companies in Singapore a greater opportunity to undertake earnings management.

The average firm size measured by total assets in thousands of USD is 1,848 million, with a median value of USD 272 million. The median value is significantly lower than the mean figure and indicates a small number of large capitalized companies in the sample firms. The total assets have a wide range of minimum and maximum values, and the data show that the sample’s total assets are skewed to the left. Consistent with the methodology applied in other studies, this study transforms the data of total assets into the natural logarithm when measuring a firm’s size. Table 3 reports that our sample firms generate small amounts (4.60% of the beginning total assets) of cash flow from operations. The sample firms’ average total debt to total assets ratio is 42.81%, with a median of 42.15%. The average growth rate of tangible fixed asset investment is 0.61%. In addition, healthy firms represent 79.87% of the sample. Big4 accounting firms audit a large percentage (59.30%) of the sample observations. Finally, most companies (79.19%) in the sample firms report a profit in the sample fiscal year.

4.2. Correlations

Table 4 exhibits the Spearman correlation matrix that displays the correlation coefficients between the dependent, independent, and control variables. Correlation results do not fully support the study hypotheses. A statistically significant (p < 0.01) correlation exists between AbsDAC and UnAvoid. However, the correlation coefficient is positive. As predicted, AbsDAC is negative and significantly correlated at p < 0.01 with the Altman score. The magnitudes of the correlations between the independent variables are small; thus, the problem of multicollinearity does not occur in the regression models. With regards to the correlations between independent and control variables, as well as among control variables, many significant correlations are reported in the correlation matrix. The highest correlation is between Big4 and UnAvoid, with a coefficient of -0.395. However, those are below the critical limit of 0.80 (Cooper & Schindler, 2003).

Table 4. Spearman correlation matrix.

	AbsDAC	UnAvoid	Altman	Size	CFO	Big4	Leverage	Loss
AbsDAC
UnAvoid	0.043*
Altman	−0.341*	−0.024
Size	−0.168*	−0.324*	−0.001
CFO	−0.046*	0.100*	0.149*	0.057*
Big4	0.009	−0.395*	0.013	0.007	−0.131*
Leverage	0.066*	−0.035*	−0.284*	0.341*	−0.126*	−0.005
Loss	0.132*	0.083*	−0.211*	−0.259*	−0.309*	−0.007	0.096*
Invest	0.074*	−0.166*	−0.044*	−0.196*	−0.025*	0.120*	0.032*	−0.218*

* and ** indicate significance at P < 0.01 and P < 0.05 (based on two-tailed tests). See Table 2 for definitions and descriptions of the dependent, independent, and control variables.

4.3. Univariate test

We conduct univariate tests to examine whether the magnitude of earnings management between financially healthy and distressed firms is statistically and significantly different.

Table 5 shows that the mean AbsDAC of healthy firms is significantly smaller than that of distressed firms (5.82% versus 15.05%). The difference in the means is statistically significant at P < 0.01. This result suggests that managers of healthy firms practice less earnings management than those of financially distressed firms. Moreover, we conduct a multivariate analysis to scrutinize whether the firms’ financial health explains the managers’ earnings management practice.

Table 5. Univariate test on differences in AbsDAC between sub-samples.

	n	Mean AbsDAC	Std Dev	t-value
Distressed firms	2,412	0.1505	0.1524	29.134*
Healthy firms	9,587	0.0582	0.0617

* indicates significance at P < 0.01.

4.4. Multivariate results

Table 6 reports the relationship between cultural aspects, firms’ financial health, and earnings management using the fixed effect estimation.

Table 6. Cultural aspects, firms’ financial health and earnings management.

Variables	Prediction	Panel A-Country fixed effect		Panel B-Year fixed effect		Panel C-Industry fixed effect		Panel D-Country, year, industry fixed effect
		Beta	t-stat	Beta	t-stat	Beta	t-stat	Beta	t-stat
(Constant)			6.218*		37.109*		27.024*		5.037*
UnAvoid	–	−0.225	−4.774*	−0.004	−2.399**	−0.004	−2.358**	−0.251	−3.876*
Altman	–	−0.091	−42.816*	−0.102	−48.070*	−0.091	−43.133*	−0.100	−47.407*
Size	–	−0.012	−20.896*	−0.012	−24.449*	−0.012	−22.962*	−0.012	−21.495*
CFO	–	−0.006	−0.771	−0.010	−1.339	−0.005	−0.698	−0.009	−1.171
Big4	–	−0.001	−0.695	−0.001	−0.841	0.001	−0.297	−0.002	−0.744
Leverage	+	0.017	3.797*	0.011	2.563*	0.014	3.103*	0.012	2.770*
Loss	+	−0.001	−0.346	−0.003	−1.220	0.001	0.619	0.004	2.040**
Invest	–	−0.073	−8.998*	−0.050	−6.225*	−0.075	−9.168*	−0.050	−6.213*
Country dummies		Yes		No		No		Yes
Year dummies		No		Yes		No		Yes
Industry dummies		No		No		Yes		Yes
Model Summary
Adj. R-Squared		0.216		0.240		0.214		0.251
F-Statistic		221.639*		222.358*		143.241*		168.331*
Sample Size		11.999		11.999		11.999		11,999

* and ** indicate significance at P < 0.01 and P < 0.05, respectively (based on two-tailed tests). See Table 2 for full definitions and descriptions of the dependent, independent, and control variables.

As presented in Table 6, Panels A to D, the regression model estimates exhibit statistical significance with an F-statistic p-value less than 0.01. The explanatory power ranges from a high of 25.1% (Panel D) to a low of 21.4% (Panel C). A consistent finding across all regressions is that UnAvoid exhibits a negative and statistically significant association with AbsDAC. The results of our study indicate that companies operating in countries with higher uncertainty avoidance scores prefer to adopt more conservative accounting practices, leading to a reduced level of earnings management compared to firms in countries with a low uncertainty avoidance cultural dimension score. In other words, our study provides further evidence that firms operating in countries with higher uncertainty avoidance scores engage in lower earnings management levels. The result supports H₁: the higher the degrees of uncertainty avoidance in a nation, the less likely the firm is to practice earnings management. Our finding aligns with prior studies (for example, Guan & Pourjalali, 2010; Astami et al., 2017; Viana et al., 2022), which document that corporate managers in countries with high uncertainty avoidance are less inclined to manage their reported earnings. One possible reason for this decreased tendency is that companies operating in these societies prioritize integrity and accountability. They value their reputation and the trust of stakeholders. Additionally, countries with high uncertainty avoidance have strong regulations and strict enforcement of accounting standards, ensuring reliable financial information (Zhang & Wu, 2014; Gray et al., 2015). This study also highlights the relevance of agency theory. Rodriguez et al. (2015) argue that societies with high uncertainty avoidance strongly prefer stability, predictability, and clear regulations. This preference affects how principals and agents behave. Principals are keen on protecting their interests by closely monitoring and controlling agents’ actions, exhibiting less tolerance for earnings management.

Coefficients on Altman Z-Score are negative and highly significant (at P < 0.01) in all models (see Panels A–D). These results suggest that the companies with larger Altman Z scores are less likely to conduct earnings management, specifically when their Altman Z scores are more than 2.99. These findings imply that firms with stronger financial positions might have less incentive to manipulate their earnings figures as they face a lower bankruptcy risk. They may have sufficient cash flows, profitability, and liquidity to meet their financial obligations, reducing the need for earnings management (Jaggi et al., 2022). Additionally, as presented in Table 5, this study also finds that the magnitude of earnings management is higher amongst companies likely to encounter financial difficulties. Thus, the results support our hypothesis H₂ and several previous studies (Lazzem & Jilani, 2018; Li et al., 2020; Jaggi et al., 2022; Viana et al., 2022). Our study emphasizes the importance of aligning the interests of shareholders and managers and the need for effective corporate governance mechanisms to mitigate agency conflicts and reduce the likelihood of earnings management (Mishra et al., 2021).

Regarding control variables, we find that Size, leverage, Loss, and Invest help explain earnings management behavior. Coefficients on Size and Invest are negative, while Leverage and Loss are positive and significantly associated with the measure of earnings management. Our findings confirm that large firms exhibit less aggression in earnings management. One possible interpretation is that our result strongly supports the political costs hypothesis, which argues that in comparison to smaller firms, larger firms are subject to more public scrutiny and political actions (Watts & Zimmerman, 1986; Moses, 1987). In particular, larger firms have incentives to choose accounting procedures that result in reduced reported earnings. Several previous studies (Lilien & Pastena, 1982; Sutton, 1988) have provided affirmative evidence that large firms are more likely to engage in income-decreasing accounting practices to avoid the political actions of regulators. Han and Wang (1998) show similar behavior by US oil companies, probably due to high political costs. In line with previous studies (Dhaliwal et al., 1999; Reynolds & Francis, 2001), our study shows that firms with a more significant investment in fixed assets are less likely to manage their income figures. The coefficients on Leverage are positive and significant (at p < 0.01) in all models (Panels A to D). This finding is consistent with previous studies, such as Franz et al. (2014) and Chen et al. (2021). They find a significant positive association between Leverage and AbsDAC. The directional sign on the coefficients for Loss is positive but only significant at p < 0.05 (see Panel D). This result supports the argument that companies manage reported earnings to avoid losses and earnings declines (Kothari et al., 2005; Chen et al. 2021).

4.5. Additional sensitivity and robustness checks

We undertake several additional sensitivity and robustness tests to increase the reliability of the main findings. In the first check, we replace the use of a dichotomous measure to assess the financial health of sample firms with a continuous measurement. The results of this first additional sensitivity test are summarized in Table 7.

Table 7. Robustness checks: continuous measure.

Variables	Prediction	Earnings management
		Beta	t-stat
(Constant)			27.818*
UnAvoid	–	−0.003	−1.964**
Altman	–	−0.001	−19.380*
Size	–	−0.015	−25.941*
CFO	–	−0.016	−1.996**
Big4	–	−0.007	−3.640*
Leverage	+	0.026	5.050*
Loss	+	0.011	5.059*
Invest	–	−0.064	−7.448*
Country dummies		Yes
Year dummies		Yes
Industry dummies		Yes
Model summary
Adj. R-Squared		0.123
F-Statistic		169.857*
Sample size		11.999

* and ** indicate significance at P < 0.01 and P < 0.05, respectively (based on two-tailed tests). See Table 2 for full definitions and descriptions of dependent, independent, and control variables.

Comparing the statistical findings in Tables 6 and 7, we can see a consistent association between uncertainty avoidance (UnAvoid) and firms’ financial health (Altman) with earnings management. Regarding control variables, we also find that Size and Invest have a highly significant negative relation to the measure of earnings management. Additionally, Table 7 shows that Leverage and Loss are positive and significant control variables in the model. Table 7 reports that the coefficients on CFO and Big4 are negative and statistically significant at p < 0.005 and p < 0.001, respectively. Thus, when using a continuous measurement to assess the financial health of sample firms, our study reports that Size, CFO, Big4, Leverage, Loss, and Invest assist in explaining management incentives to manage reported earnings.

In the second additional test, we repeat our regression analysis by following Kothari et al. (2005) and estimate earnings management by using the modified Jones (1991), which includes the return on assets (ROA) as a control for firms’ operating performance. Third, we exclude firms from a large country cluster, China, which accounts for approximately 38.44% of the sample size, to ensure an individual country does not drive the main reported results. The regression results of the second and third additional tests are presented in Table 8.

Table 8. Sensitivity and robustness checks: modified Jones + ROA and excluding large sample size.

Variables	Prediction	Panel A- Modified Jones + ROA		Panel B- Excluding Large Sample
		Beta	t-stat	Beta	t-stat
(Constant)			5.232*		5.333*
UnAvoid	–	−0.374	−4.339*	−0.391	−4.496*
Altman	–	−0.089	−31.760*	−0.097	−27.206*
Size	–	−0.011	−15.300*	−0.010	−10.911*
CFO	–	−0.019	−1.901	−0.001	−0.020
Big4	–	0.003	1.082	0.001	0.302
Leverage	+	0.047	8.243*	0.036	4.852*
Loss	+	−0.002	−0.606	−0.004	−1.227
Invest	–	−0.020	−4.040*	−0.063	−2.500*
Country dummies		Yes		Yes
Year dummies		Yes		Yes
Industry dummies		Yes		Yes
Model Summary
Adj. R-Squared		0.135		0.156
F-Statistic		79.109*		63.018*
Sample Size		11.999		7.387

Legend:.

*, **, and *** indicate significance at p < 0.01 and p < 0.05, respectively (based on two-tailed tests). See Table 2 for full definitions and descriptions of the dependent, independent, and control variables.

All regression model estimates reported in Table 8 are highly significant (F-statistic, p < 0.01), with the explanatory power (adjusted R²) at 13.5% (Panel A) and 15.6% (Panel B). The results of the multiple regression analysis from the sensitivity and robustness tests are qualitatively similar to that of the primary regression analysis (Table 6). The coefficients of the UnAvoid and firms’ financial health (Altman) are all highly significant (at p < 0.01) and in the predicted direction. Thus, these findings support the results of the primary regression presented in Table 6. Additionally, except for the variable of Loss, the results for control variables in Table 8 generally align with the main finding reported in Table 6. In conclusion, the results of additional sensitivity and robustness tests (1) do not change when we proxy for financially distressed firms using a dichotomous measure, (2) are robust when using alternative techniques to detect earnings management practices, and (3) are not driven by an individual country.

5. Conclusion

This article aims to link a nation’s cultural value and a firm’s financial health to managers’ responses to earnings management practices. This study reports that companies in countries with a strong cultural emphasis on uncertainty avoidance tend to engage in a lower magnitude of earnings management. Moreover, in line with previous findings, our study reveals compelling evidence suggesting that firms with solid financial stability exhibit a lower tendency to manage their earnings figures. Finally, our results document that earnings management practices are higher amongst companies more likely to encounter financial difficulties. Several additional sensitivity and robustness checks provide further support for the drawn inferences.

The findings of this study have implications for policymakers or regulators. Policymakers should consider societies’ cultural values and norms when designing regulations and enforcement mechanisms. By recognizing the impact of uncertainty avoidance on earnings management, policymakers can develop rules and policies that promote transparency and discourage unethical practices. Furthermore, policymakers can establish a conducive atmosphere by urging firms to prioritize financial stability and long-term sustainability, reducing the incentive for managing earnings.

Finally, our study adds to the existing literature on agency theory and earnings management by documenting that both national culture and the firm’s financial performance are important factors that explain corporate managers’ earnings discretion practices across the Asia-Pacific region. Thus, the results of this study offer valuable insights to interested parties concerning the practices of earnings management and present a pathway for future research in the field of international business.

This study focuses on predicting earnings management practices by examining the financial characteristics of firms at the firm’s level and the uncertainty avoidance cultural aspect on a national level. Future research can scrutinize other accounting behaviors in each country that may explain earnings management practices, such as investor protection, which may also be a critical aspect to investigate. Further study can also explore the severity of agency costs and the extent of earnings management practices across national boundaries.

Disclosure statement

No potential conflict of interest was reported by the author(s)

Additional information

Notes on contributors

Rusmin Rusmin

Rusmin is a faculty member at Universitas Teknologi Yogyakarta. Rusmin completed his auditing and financial accounting doctorate at Curtin University, Australia. He has published research papers in journals such as the Managerial Auditing Journal, the International Journal of Public Administration, the International Journal of Accounting and Information Management, the International Journal of Accounting, Auditing, and Performance Evaluation, the Asia Review of Accounting, and Cogent Economics and Finance. His primary interests are auditing, corporate governance, earnings management, and environmental management.

Zulhawati Zulhawati

Zulhawati Zulhawati (co-author) is a faculty member at Universitas Teknologi Yogyakarta. She has held of several senior academic roles, and her research interests include corporate governance, organizational studies, and numerous issues in the entrepreneurial enterprise.

Emita W. Astami

Emita Astami (corresponding author) holds a PhD in Accounting from Curtin University, Australia, and has been on the faculty at Universitas Teknologi Yogyakarta. She has conducted research in the areas of corporate governance, financial reporting, and financial and environmental disclosures. She has published research papers in journals such as the International Journal of Accounting, Asian Review of Accounting, International Journal of Accounting and Information Management, Australasian Accounting Business and Finance Journal, and Cogent Economics and Finance.

John Evans

John Evans (co-author), before joining VinUniversity as the Dean of the College of Business and Management, was the Pro Vice-Chancellor and President of Curtin University Dubai. John Evans holds a PhD from the University of Illinois at Urbana Champaign and is a fellow of CPA Australia. He has held several senior academic roles, served on editorial boards, and is an active researcher.

Notes

1 The practices of earnings management may be perceived as an unethical behavior.