Business Strategies and Financial Distress Risk in Seasoned Listed Companies: Extending Earnings Quality Role

Authors	Key findings
Bryan, Dinesh & Tripathy (2013), Chen & Keung (2019), Agustia et al. (2020) and Thu (2023)	Adopting an effective business strategy (cost leadership or differentiation) improves financial performance and lowers the risk of FD/bankruptcy.
Diab, Eissa & Hamdy (2021)	Firms with either cost leadership or differentiation face lower bankruptcy risk. However, FF increases risk for cost leadership firms (agency costs, overinvestment) while mitigating risk for differentiation firms (innovation support, adaptability).
Josephson, Johnson & Mariadoss (2016), Rostami & Rezaei (2021)	Less financially flexible firms adopt defensive cost leadership to survive, while financially flexible firms adopt proactive differentiation strategies that lower FD risk.
Lopo & Ferreira (2019)	Cost leadership firms in Brazil are more prone to irregular corporate practices, which increases long-term bankruptcy risk.
Wu et al. (2015), Purba, Fransisca & Joshi (2022) and Fedora et al. (2025)	Differentiation firms face less pressure, have higher PMs, rely less on external financing and are less inclined to use EM → lower FD risk. Cost leadership firms have lower margins, depend more on external finance and engage more in earnings manipulation → higher FD risk.
Ahmed et al. (2025)	Asymmetric cost behaviour (slower cost reduction during sales declines) may increase vulnerability to FD, especially when managers are overly optimistic.
Hassanein & Younis (2020), Hartlieb & Loy (2022)	Managerial decisions to retain costs are influenced by non-monetary factors such as morale and reputation, affecting financial risk.
Lefebvre (2025)	Cost stickiness can mitigate effects of sales fluctuations in some firms.
Costa & Habib (2023)	Depending on expected sales trends, cost stickiness may have positive or negative effects on financial standing.
Misztal & Comporek (2025)	In the Polish market, financially weak firms use anti-stickiness strategies, while financially secure firms LEV cost stickiness to enhance operational efficiency.

Post hoc pairwise comparisons using Dunn test (Bonferroni-corrected) following Kruskal–Wallis analysis of business strategies and EQ across Z-score zones

Business strategies
Proxy	Sample 1 – Sample 2	Test stat.	Std. error	Std. test stat.	Sig.	Adj. sig.
STICKY_COGS	Uncertain zone – safe zone	−5.05	34.43	−0.15	0.883	1.000
	Uncertain zone – distress zone	79.30	41.71	1.90	0.057	0.172
	Safe zone – distress zone	74.24	34.29	2.17	0.030	0.091
STICKY_SG&A	Safe zone – uncertain zone	6.14	34.43	0.18	0.858	1.000
	Safe zone – distress zone	23.08	34.29	0.67	0.501	1.000
	Uncertain zone – distress zone	16.94	41.71	0.41	0.685	1.000
ATO	Distress zone – safe zone	−96.44	34.29	−2.81	0.005	0.015
	Distress zone – uncertain zone	−294.97	41.71	−7.07	<0.001	0.000
	Safe zone – uncertain zone	198.52	34.43	5.77	<0.001	0.000
PM	Distress zone – uncertain zone	−283.76	41.71	−6.80	<0.001	0.000
	Distress zone – safe zone	−546.70	34.29	−15.94	<0.001	0.000
	Uncertain zone – safe zone	−262.94	34.43	−7.64	<0.001	0.000
EQ
Proxy	Sample 1 – Sample 2	Test stat.	Std. error	Std. test stat.	Sig.	Adj. sig.
SMOOTH	Uncertain zone – safe zone	−89.11	34.43	−2.59	0.010	0.029
	Uncertain zone – distress zone	413.20	41.71	9.91	<0.001	0.000
	Safe zone – distress zone	324.09	34.29	9.45	<0.001	0.000
CONS	Distress zone – safe zone	−55.60	33.89	−1.64	0.101	0.303
	Distress zone – uncertain zone	−95.07	41.22	−2.31	0.021	0.063
	Safe zone – uncertain zone	39.47	34.03	1.16	0.246	0.738
DACC	Distress zone – safe zone	−78.97	34.29	−2.30	0.021	0.064
	Distress zone – uncertain zone	−87.23	41.71	−2.09	0.036	0.109
	Safe zone – uncertain zone	8.26	34.43	0.24	0.810	1.000
\|DACC\|	Distress zone – safe zone	104.99	34.308	3.06	0.002	0.007
	Distress zone – uncertain zone	66.481	34.447	1.93	0.054	0.161
	Safe zone – uncertain zone	38.513	41.718	0.923	0.356	1.000

Independent-sample Kruskal–Wallis test results: business strategies vs_ EQ across Z-score zones

Business strategies			EQ
No.	Null hypothesis	Sig.	No.	Null hypothesis	Sig.
1	The distribution of STICKY_COGS is the same across different Z-score zones	0.073	1	The distribution of SMOOTH is the same across different Z-score zones	<0.001
2	The distribution of STICKY_SG&A is the same across different Z-score zones	0.797	2	The distribution of CONS is the same across different Z-score zones	<0.001
3	The distribution of ATO is the same across different Z-score zones	<0.001	3	The distribution of DACC is the same across different Z-score zones	0.048
4	The distribution of PM is the same across different Z-score zones	<0.001	4	The distribution of \|DACC\| is the same across different Z-score zones	0.005

PLUM regression results with Cauchit links: Effects of business-related variables and EQ proxies on FD risk (model with DACC)

Variable/threshold		Estimate	Stand. error	Wald	df	p-value	VIF
Threshold	Z-score – distress zone	0.998	0.203	24.107	1	<0.001	-
Threshold	Z-score – uncertain zone	2.531	0.212	144.981	1	<0.001	-
Predictors	COVID	−0.026	0.122	0.046	1	0.831	1.005
	TANG	−1.596	0.257	38.708	1	<0.001	1.050
	STL	−2.291	0.108	449.221	1	<0.001	1.098
	LEV	−0.144	0.021	47.911	1	<0.001	1.009
	ATO	0.719	0.071	103.275	1	<0.001	1.801
	PM	0.069	0.019	13.451	1	<0.001	1.009
	STICKY_COGS	−0.119	0.033	13.168	1	<0.001	1.027
	SMOOTH	−0.141	0.014	102.466	1	<0.001	1.134
	CONS	−0.001	0.001	4.695	1	0.032	1.110
	DACC	−0.066	0.061	1.157	1	0.282	1.752
Goodness-of-fit – deviance		Model fitting information		Test of parallel lines		Cox and Snell	0.425
c²	Sig.	c²	Sig.	c²	Sig.	Nagelkerke	0.496
3,153.633	1.000	1,132.934	<0.001	7.772	0.651	McFadden	0.286

Key findings from previous studies on EQ and FD

Authors	Key findings
Panigrahi (2019), Debdas, Chanchal & Ananya (2021), Hsiao, Szu & Ai (2010), Habib, Uddin & Islam (2013), Bisogno & De Luca (2015), Muljono & Suk (2018), Viana et al. (2022) and Li et al. (2020)	Firms engaging in accrual-based EM increase their risk of FD; distressed firms rely more on EM, lowering EQ.
Agrawal & Chatterjee (2015), Ghazali, Shafie & Sanusi (2015)	Healthier firms engage more in accrual-based EM, while distressed firms show less manipulation, suggesting relatively higher EQ under distress.
Franz, HassabElnaby & Lobo (2014), Campa & Camacho-Miñano (2015) and Haga, Höglund & Sundvik (2018)	Distressed firms often substitute real activities manipulation for accrual-based EM, indicating shifts in how EQ is affected depending on the type of EM (Spain, US, UK).
Thu (2023)	Firms engaging in earnings manipulation via accounting choices face higher FD risk, as such practices delay problem detection and reduce competitive capacity.
Sholikhah & Suryani (2020)	Conditional conservatism improves EQ and significantly reduces the likelihood of FD; conservative accounting accelerates loss recognition and enhances early warning signals.
Hsu, O’Hanlon & Peasnell (2011)	Conditional conservatism, via asymmetric recognition of losses vs. gains, improves EQ and provides early warning of FD.
Kuang (2021), Allayannis & Simko (2022), Shabani & Sofian (2018) and Martina & Wadi (2025)	Income smoothing and discretionary EM influence FD risk through delayed loss recognition, lower credit quality or constrained managerial discretion; effects vary by context.
Lizińska & Czapiewski (2023)	Firms not threatened by FD manipulate earnings through accruals and real activities, especially under defensive working capital policies, showing active shaping of financial results.
Comporek & Misztal (2025)	Real EM strongly increases FD risk in Polish listed firms, particularly among firms already in the distress zone; managers use EM to avoid debt covenant breaches, maintain capital access or pursue private benefits.

PLUM regression results with logit links: Effects of business-related variables and EQ proxies on FD risk (model with DACC)

Variable/threshold		Estimate	Stand. error	Wald	df	p-value	VIF
Threshold	Z-score – distress zone	0.627	0.213	8.664	1	0.003	-
Threshold	Z-score – uncertain zone	2.391	0.22	117.919	1	<0.001	-
Predictors	COVID	−0.079	0.129	0.375	1	0.540	1.005
	TANG	−0.53	0.28	3.584	1	0.058	1.050
	STL	−2.919	0.13	504.511	1	<0.001	1.098
	LEV	−0.145	0.021	48.567	1	<0.001	1.009
	ATO	0.534	0.074	52.52	1	<0.001	1.801
	PM	0.116	0.018	41.337	1	<0.001	1.009
	STICKY_COGS	−0.127	0.034	13.847	1	<0.001	1.027
	SMOOTH	−0.14	0.015	84.04	1	<0.001	1.134
	CONS	0.174	0.022	64.311	1	<0.001	1.11
	DACC	0.005	0.105	0.002	1	0.963	1.752
Goodness-of-fit – deviance		Model fitting information		Test of parallel lines		Cox and Snell	0.472
c²	Sig.	c²	Sig.	0.552	Sig.	Nagelkerke	0.552
2,826.732	1.000	4,219.15	<0.001	15.387	0.119	McFadden	0.330

Robustness check of PLUM regression results with Cauchit links: Effects of business-related variables and EQ proxies on FD risk

Variable/threshold		Estimate	Stand. error	Wald	df	p-value	VIF
Threshold	Z-score – distress zone	0.998	0.203	24.107	1	<0.001	-
Predictors	COVID	−0.495	0.193	6.587	1	0.010	1.006
	TANG	0.576	0.390	2.185	1	0.139	1.045
	STL	−1.365	0.111	150.336	1	<0.001	1.081
	LEV	−0.040	0.012	11.880	1	<0.001	1.009
	ATO	0.700	0.117	35.994	1	<0.001	1.798
	PM	0.316	0.052	36.508	1	<0.001	1.009
	STICKY_COGS	−0.071	0.040	3.183	1	0.074	1.025
	SMOOTH	−0.094	0.016	34.935	1	<0.001	1.132
	CONS	0.003	0.001	4.221	1	0.040	1.096
	DACC	−0.037	0.059	0.400	1	0.527	1.751
Goodness-of-fit – deviance		Model fitting information		Test of parallel lines		Cox and Snell	0.194
c²	Sig.	c²	Sig.	-		Nagelkerke	0.340
1,368.703	1.000	469.214	<0.001	-		McFadden	0.255

Mann–Whitney U test results: Business strategies and EQ across ‘G’ INE PAN zones

Business strategies			EQ
No.	Null hypothesis	Sig.	No.	Null hypothesis	Sig.
1	The distribution of STICKY_COGS is the same across different ‘G’ INE PAN zones	0.181	1	The distribution of SMOOTH is the same across different ‘G’ INE PAN zones	<0.001
2	The distribution of STICKY_SG&A is the same across different ‘G’ INE PAN zones	0.411	2	The distribution of CONS is the same across different ‘G’ INE PAN zones	<0.001
3	The distribution of ATO is the same across different ‘G’ INE PAN zones	<0.001	3	The distribution of DACC is the same across different ‘G’ INE PAN zones	<0.001
4	The distribution of PM is the same across different ‘G’ INE PAN zones	<0.001	4	The distribution of \|DACC\| is the same across different ‘G’ INE PAN zones	<0.001

Descriptive statistics of business strategies and EQ proxies across ‘G’ INE PAN zones

Business strategies and proxies	Zones	Statistical measure	Value	EQ proxies	Zones	Statistical measure	Value
STICKY_COGS	Distress	Mean	0.425	SMOOTH	Distress	Mean	4.464
		Median	0.000			Median	1.819
		St. dev.	2.427			St. dev.	6.163
	Safe	Mean	−0.071		Safe	Mean	2.035
		Median	0.000			Median	0.839
		St. dev.	1.817			St. dev.	4.393
STICKY_SG&A	Distress	Mean	−0.014	CONS	Distress	Mean	−7.323
		Median	0.054			Median	0.000
		St. dev.	1.354			St. dev.	64.953
	Safe	Mean	−0.576		Safe	Mean	7.382
		Median	0.040			Median	0.000
		St. dev.	5.998			St. dev.	86.785
ATO	Distress	Mean	0.781	DACC	Distress	Mean	−0.383
		Median	0.520			Median	−0.011
		St. dev.	0.945			St. dev.	10.776
	Safe	Mean	1.068		Safe	Mean	0.096
		Median	0.810			Median	0.003
		St. dev.	4.092			St. dev.	3.960
PM	Distress	Mean	−3.228	\|DACC\|	Distress	Mean	1.151
		Median	−0.093			Median	0.057
		St. dev.	4.739			St. dev.	10.741
	Safe	Mean	0.036		Safe	Mean	0.167
		Median	0.054			Median	0.045
		St. dev.	1.763			St. dev.	3.995

Descriptive statistics of business strategies and EQ proxies across Z-score zones

Business strategies and proxies	Zones	Statistical measure	Value	EQ proxies	Zones	Statistical measure	Value
STICKY_COGS	Distress	Mean	0.349	SMOOTH	Distress	Mean	3.990
		Median	0.000			Median	1.345
		St. dev.	2.422			St. dev.	5.924
	Uncertain	Mean	−0.055		Uncertain	Mean	1.842
		Median	0.000			Median	0.744
		St. dev.	1.400			St. dev.	4.267
	Safe	Mean	−0.101		Safe	Mean	2.025
		Median	0.000			Median	0.856
		St. dev.	1.877			St. dev.	4.354
STICKY_SG&A	Distress	Mean	0.109	CONS	Distress	Mean	3.386
		Median	0.040			Median	0.000
		St. dev.	1.886			St. dev.	119.341
	Uncertain	Mean	−0.027		Uncertain	Mean	15.637
		Median	0.000			Median	0.000
		St. dev.	2.233			St. dev.	126.983
	Safe	Mean	−0.873		Safe	Mean	2.102
		Median	0.063			Median	0.000
		St. dev.	7.044			St. dev.	34.982
ATO	Distress	Mean	0.846	DACC	Distress	Mean	−0.271
		Median	0.617			Median	−0.009
		St. dev.	0.938			St. dev.	9.110
	Uncertain	Mean	1.147		Uncertain	Mean	0.007
		Median	1.002			Median	0.004
		St. dev.	1.016			St. dev.	0.119
	Safe	Mean	1.047		Safe	Mean	0.137
		Median	0.719			Median	0.002
		St. dev.	4.896			St. dev.	4.781
PM	Distress	Mean	−2.561	\|DACC\|	Distress	Mean	0.852
		Median	−0.012			Median	0.0542
		St. dev.	4.388			St. dev.	9.061
	Uncertain	Mean	0.008		Uncertain	Mean	0.076
		Median	0.033			Median	0.051
		St. dev.	0.331			St. dev.	0.091
	Safe	Mean	−0.123		Safe	Mean	0.206
		Median	0.065			Median	0.043
		St. dev.	3.350			St. dev.	4.771

PLUM regression results with Cauchit links: Effects of business-related variables and EQ proxies on FD risk (model without DACC)

Variable/threshold		Estimate	Stand. error	Wald	df	p-value	VIF
Threshold	Z-score – distress zone	0.993	0.203	23.891	1	<0.001	-
Threshold	Z-score – uncertain zone	2.525	0.210	144.582	1	<0.001	-
Predictors	COVID	−0.026	0.122	0.044	1	0.835	1.006
	TANG	−1.595	0.257	38.637	1	<0.001	1.050
	STL	−2.290	0.108	448.290	1	<0.001	1.102
	LEV	−0.145	0.021	48.029	1	<0.001	1.009
	ATO	0.716	0.071	102.874	1	<0.001	1.800
	PM	0.069	0.019	13.353	1	<0.001	1.009
	STICKY_COGS	−0.118	0.033	13.046	1	<0.001	1.031
	SMOOTH	−0.143	0.014	106.758	1	<0.001	1.134
	CONS	−0.001	0.001	4.336	1	0.037	1.112
Goodness-of-fit – deviance		Model fitting information		Test of parallel lines		Cox and Snell	0.406
c²	Sig.	c²	Sig.	c²	Sig.	Nagelkerke	0.474
3,157.575	1.000	1,202.878	<0.001	6.992	0.638	McFadden	0.269

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DOI: https://doi.org/10.2478/ceej-2026-0005 | Journal eISSN: 2543-6821 | Journal ISSN: 2544-9001

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Language: English

Page range: 76 - 99

Submitted on: Oct 6, 2025

Accepted on: Mar 6, 2026

Published on: Apr 30, 2026

Published by: Faculty of Economic Sciences, University of Warsaw

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

seasoned public companies

Related subjects:

Business and economics,

Political economics,

Economic theory, systems and structures,

Microeconomics,

Macroeconomics,

Economic policy

© 2026 Michał Comporek, published by Faculty of Economic Sciences, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 13 (2026): Issue 60 (January 2026)