Overcoming the liability of newness: The interplay of debt, equity, and profitability in nascent SMEs

ABSTRACT

Small and medium-sized enterprises (SMEs) in their early stages face the liability of newness that can undermine their survival. This study investigates the impact of strategic financing decisions and profitability on the solvency of 1,106 Ghanaian SMEs during their first 5 years. Leveraging the pecking order theory, the study examines how internal equity and debt, in conjunction with net profit after tax, as well as returns on equity and assets, influences the liability of newness. Regression analysis highlights a positive association between equity financing and return on equity with solvency, while return on assets shows no significant relationship. The study emphasizes the importance of internal equity in mitigating the liability of newness. Considering this study’s findings regarding the significance of equity ratio, debt-to-equity ratio, return on equity, and net profit after tax in mitigating the liability of newness, SME owners are advised to take specific measures to boost these metrics.

KEYWORDS:

• Liability of newness
• strategic financing
• profitability
• pecking order theory
• nascent SMEs

Introduction

Since Arthur Stinchcombe’s (1965) influential study on the liability of newness, subsequent research has endeavored to explore strategies for overcoming this concept, thereby leaving the subject open for further investigation. The term “liability of newness” posits the fragile nature of emerging businesses, suggesting that a significant number may not endure beyond their initial stages (Yang & Aldrich, 2017, p. 1). New businesses face heightened failure rates due to challenges such as creating new roles, fostering interpersonal trust, and difficulty in establishing legitimacy and associations with external stakeholders. Task assignments in nascent businesses involve ambiguity and negotiation, leading to temporary inefficiencies (Schoonhoven, 2015). Furthermore, new businesses grapple with the challenge of swiftly converting assets into cash and attaining profitability (Wiklund et al., 2010), leading to a substantial number succumbing to the liabilities of newness and failing within five years (Amaglo, 2019). Therefore, the essential question is: How can small and medium-sized enterprises (SMEs) overcome the liability of newness and progress from nascency to maturity?

Examining the liability of newness is crucial, given the significance of SMEs in advancing the United Nations Sustainable Development Goals, particularly in the fight against poverty (Gaspar, 2017). Thus, this study addresses a call from studies like O’Toole and Ciuchta (2020), urging an exploration of innovative strategies to create safeguards against the liability of newness and facilitate the transition of nascent SMEs from a prelegitimate to a legitimate status. Prior research has approached the challenge of overcoming the liability of newness from various perspectives. For instance, Zuo et al. (2022) suggested that engaging in corporate social responsibility activities can be a cost-effective and balanced solution to address both internal and external challenges associated with newness, ultimately improving the legitimacy and performance of new ventures. Maintaining creditworthiness has also been demonstrated to mitigate the liability of newness (Wiklund et al., 2010). Additionally, scholars propose the existence of financial legitimacy threshold inflection points that must be attained to counteract the challenges associated with newness (Rutherford et al., 2016).

Drawing inspiration from the idea that an optimal capital mix leads to increased net profits and liquidity (Dhankar, 2019), we assert that mitigating liability of newness for nascent SMEs involves prioritizing capital structure decisions to minimize insolvency risk. This study further asserts that SMEs should prioritize sustained profitability. Thus, the objective of this study is to examine the interplay between debt, equity, and profitability, which constitutes a delicate balancing act for the survival of a nascent SME. In alignment with the approach of Wiklund et al. (2010), which utilized indicators of firm failure as a proxy for the liability of newness, our study assesses the concept by examining the solvency of SMEs below the age of five years. Solvency is interpreted here as akin to financial distress, drawing from insights by Amoa-Gyarteng (2019). Leveraging the framework outlined by Amoa-Gyarteng and Dhliwayo (2022), this study employs metrics like total debt to assets ratio, total equity to assets ratio, and total debt to total equity ratio (ER) to gauge capital structure. Similarly, profitability is evaluated using metrics such as return on equity (ROE), net profit after tax (NPAT), and return on assets (ROA). Solvency is measured via solvency ratio (SR), following Lamberg and Valming (2009).

This article contributes significantly by merging pecking order theory with the liability of newness concept, illustrating how appropriate financing decisions foster growth and survival in nascent SMEs. The study reinterprets the liability of newness beyond mere survival threats, emphasizing actionable levers for SMEs to survive. In the realm of academic literature concerning SMEs and capital structure in developing economies, a notable blind spot exists (Kumar et al., 2020). The study addresses the identified gap by specifically examining nascent SMEs in Ghana. While the majority of capital structure studies focus on large, publicly traded companies, as demonstrated by Yazdanfar and Öhman (2015), this study establishes the applicability of the pecking order theory to nascent SMEs.

The remaining sections of the article follow this order: the second section outlines the theoretical framework and broad hypotheses, the third section discusses the research methodology, the fourth section presents the main empirical findings, and the fifth section encompasses the article’s discussion, limitations, and concluding observations.

Theory and hypotheses

Pecking order theory

Although capital structure theories mostly focus on large firms (Baker et al., 2020), they offer useful insights on small business financing decisions (Daskalakis & Psillaki, 2008). A framework for understanding how firms navigate financing decisions is crucial for any enterprise to ensure sustainability. We propose nascent firms can mitigate liability of newness by adhering to the pecking order theory. This theory states that firms prefer internal over external funding to minimize information costs (Aktas et al., 2015). With only external options, debt is favored over external equity to avoid adverse selection and maintain control (Aktas et al., 2015). The pecking order perspective directly relates to how vulnerable, capital-constrained new ventures can make strategic financing choices to enhance viability.

Debt and solvency of nascent SMEs

Increased access to debt finance is essential for the performance of SMEs, as it enables them to boost investment and carry out new projects (Rusu & Roman, 2022). However, the potential for over-leveraging may lead to rising solvency risks (Diez et al., 2021). Therefore, while debt finance is crucial for SME growth, the quality and management of debt are critical factors that can influence the solvency of SMEs (World Bank, 2019). Organizational decline is occasionally linked to high leverage, while new ventures exhibit a greater likelihood of survival when characterized by lower leverage, as suggested by Wiklund et al. (2010). Building on the existing literature and considering that this study measures debt using debt ratio (DR) and debt-to-equity ratio (DER), we hypothesize that

H_1a: An increase in the DR will have a negative impact on the solvency of nascent SMEs.

H_1b: An increase in the DER will have a negative impact on the solvency of nascent SMEs.

Equity and solvency of nascent SMEs

Equity has the potential to mitigate the liability of newness, as evidenced by Cultera and Bredart (2016). SMEs with low equity-to-asset ratios, as identified in their study, experience reduced short-term solvency, thereby facing heightened risks of failure. SMEs predominantly financed through equity possess assets with fewer encumbrances, providing potential collateral or a buffer against insolvency when needed. Consequently, a high equity-to-asset ratio, as indicated by Wiklund et al. (2010), diminishes the likelihood of failure for emerging SMEs. Drawing from the literature, we propose:

H₂: A rise in the ER will positively influence the solvency of nascent SMEs.

Profitability and solvency of nascent SMEs

The foremost harbinger of insolvency is a downturn in profitability, as asserted by Boata and Gerdes (2019). Wiklund et al. (2010) underscored that robust profitability, among various financial indicators, acts as a safeguard against the liability of newness. Importantly, profitable firms have the financial capacity to endure higher debt levels, thereby fortifying themselves against the threat of insolvency (Abor, 2008). Based on the current body of literature and acknowledging the use of ROE, ROA, and NPAT as measures of profitability in this study, we propose that

H_3a: High ROE positively influences the solvency of nascent SMEs.

H_3b: High ROA positively influences the solvency of nascent SMEs.

H_3c: High NPAT positively influences the solvency of nascent SMEs.

Methodology

Research design

This study employs a cross-sectional survey and quantitative methodology to examine the interplay between debt, equity, profitability, and solvency in nascent SMEs. The study focuses on SMEs operating in Ghana’s Accra, Kumasi, and Tema metropolitan areas, specifically those registered with the Ghana Enterprises Agency, a government body fostering the development of micro-, small, and medium-sized enterprises. The target population consists of SMEs registered in these areas, amounting to 10,368 businesses as of 2021. Emulating the approach of Crawford et al. (2015) and Günzel-Jensen and Holm (2015), our study focuses on SMEs with less than five years of operation. With a sample of 1,106 SMEs meeting the selection criteria, this study focused on examining their solvency and the impact of various financial factors.

Data collection and analysis

The study utilized financial statement data of nascent SMEs from the Ghana Enterprises Agency. Financial ratios for debt, equity, profitability, and solvency were computed using the provided formulas in Table 1. Following Amoa-Gyarteng and Dhliwayo (2023), we control for firm size and sales growth. Data analysis was conducted using Statistical Package for Social Sciences version 27, involving descriptive statistics (skewness and kurtosis), Pearson correlation analysis, and multiple regression. This statistical approach aligns with the methodologies employed in studies by Amoa-Gyarteng and Dhliwayo (2023) and Ayepa et al. (2019). Multiple regression was chosen due to its suitability when examining relationships between a dependent variable and two or more independent variables (Hair et al., 2011).

Table 1. Variables and their formulas.

Variables	Abbreviation	Formula	Source
Return on Equity	ROE	Net Income/Equity	Ahsan (2012, p. 132)
Return on Assets	ROA	Net Income/Total Assets	Brigham and Houston (2021, p. 119)
Net Profit After Tax	NPAT	Revenue – (Expenses + Taxes)	Jayathilaka (2020)
Debt Ratio	DR	Total Liabilities/Total Assets	Quesada-Pineda (2019, p. 9)
Debt-to-Equity Ratio	DER	Total Liabilities/Shareholder Equity	Gibson (2012, p. 260)
Equity Ratio	ER	Total Equity/Total Assets	Situm (2014, p. 30)
Solvency Ratio	SR	(Net Profit After Tax + Depreciation) Total Liabilities	Ucal and Oksay (2011, p. 167).
Size	SIZE	Log of Total Assets	Abor (2005, p. 442)
Sales Growth	SG	(Current Period Sales – Prior Period Sales)	Abor (2005, p. 442)

Descriptive results

In deciding to use multiple regression to analyze the data, a critical step was to ensure that the data could be analyzed using this test. Assumptions of normality, multicollinearity, linearity, and homoscedasticity were checked.

Testing for normality

The results of this test, as shown in Table 2, indicate that the data are normal because the values of skewness and kurtosis were <−2 or >+2 for each measure, indicating that it is suitable for multiple regression analysis. Moreover, the standard errors for skewness and kurtosis were 0.076 and 0.152, respectively, which are very small values.

Table 2. Normality assessment.

Construct	Skewness	Std. error	Kurtosis	Std. error
Return on Assets	1.092	0.076	1.514	0.152
Return on Equity	0.878	0.076	0.756	0.152
Net Profit After Tax	1.042	0.076	0.683	0.152
Debt Ratio	−0.219	0.076	−0.534	0.152
Debt-to-Equity Ratio	1.044	0.076	1.091	0.152
Equity Ratio	0.007	0.076	−0.475	0.152
Solvency Ratio	0.932	0.076	0.425	0.152

To further ensure that the data were normal, it was plotted against a theoretical normal distribution with the points forming an approximate straight line. Deviations from normality will be indicated by large deviations from this straight line. The scatters of the residuals almost fall straight on the normal distribution line, indicating a normal distribution of residuals, as can be seen in the P-P Plot in Figure 1. The random points are not perfectly aligned along the diagonal line, but they are close enough to normal for the analysis to continue.

Figure 1. P-P plot of regression standardized residual dependent variable solvency ratio.

Multicollinearity assessment

A correlation matrix of the variables is shown in Table 3 to examine the degree of collinearity. Generally, the correlation coefficients are not sufficiently large to cause collinearity problems in the regressions. Field’s (2009) ballpark value was met because none of the predictor values correlated above 0.80.

Table 3. Correlation matrix between predictor variables.

Variables	ROA	ROE	NPAT	DER	DR	ER
Return on Assets (ROA)	1.00
Return on Equity (ROE)	0.105** (0.000)	1.00
Net Profit After Tax (NPAT)	0.267** (0.000)	0.231** (0.000)	1.00
Debt-to-Equity Ratio (DER)	−0.040 (0.098)	0.318** (0.000)	−.187** (0.000)	1.00
Debt Ratio (DR)	0.043 (0.084)	0.325** (0.000)	−.168** (0.000)	0.309** (0.000)	1.00
Equity Ratio (ER)	0.199** (0.000)	0.186** (0.000)	0.095** (0.001)	−.037 (0.119)	0.349** (0.000)	1.00

Note. p-values are in brackets. Sig. (1-tailed). **p < .01.

Multicollinearity between the independent variables was further investigated in this study using variance inflation factor (VIF). As seen in Table 4, the VIF values ranged from 1.813 to 3.078. Tolerance values ranged from 0.13 to 0.56. Each value met the cutoff points as suggested by Field (2009), Hair et al. (2011), and Pallant (2013).

Table 4. Tolerance and VIF.

	Collinearity Statistics
Variable	Tolerance	VIF
ROA	0.492	2.032
ROE	0.353	2.830
Debt Ratio	0.399	3.078
Debt to Equity Ratio	0.154	2.502
Equity Ratio	0.546	1.829
Net Profit After Tax	0.551	1.813

Testing linearity and homoscedasticity

Linearity was assessed via a scatterplot of residuals, revealing a direct linear relationship, as depicted in Figure 2. Homoscedasticity was also verified by visually inspecting the scatterplot, which confirms a consistent distribution of points without discernible patterns, as seen in Figure 2, aligning with Tabachnick and Fidell’s (2007) criteria.

Figure 2. Scatter plot showing linearity and homoscedasticity.

Inferential results

A multiple regression analysis was performed to model the SR as a function of various financial indicators, including NPAT, ROA, ROE, DR, ER, and DER . The outcomes of the analysis are succinctly presented in Table 5.

Table 5. Multiple regression results.

								Model Summary
								F-statistic p-value	R	R^2
Variable								0.000	0.784	0.615
SR	B	95 Percent CI for B		SEB	β	T	Sig.
		LL	UL
NPAT	1.748E-6*	4.4E-7	0.3E-6	0.000	0.070*	2.671	0.008
ROA	0.128	−.001	0.257	0.066	0.066	1.940	0.053
ROE	0.482*	0.413	0.551	0.035	0.520*	13.660	0.000
DR	−0.247*	−.308	−.186	0.031	−.429*	−7.974	0.000
ER	0.398*	0.354	0.443	0.023	0.573*	17.534	0.000
DER	0.038*	0.023	0.053	0.008	0.239*	4.823	0.000

Note. B = unstandardized regression coefficient; CI = confidence interval; LL = lower limit; UL = upper limit; SEB = standard error of the coefficient estimate; β = standardized regression coefficient; T = T-statistic; Sig. = significance level or p-value. *p < .05.

The results demonstrate a significant negative relationship between the DR and SR, indicated by a regression coefficient of β = −0.429, t = −7.974, p < .05. Therefore, H1a, positing a negative impact of increased DR on solvency, is substantiated. H1b posited a negative impact of increased DER on the solvency of nascent SMEs. The findings do not support this, with DER significantly and positively predicting SR, β = 0.239, t = 4.823, p < .05. H2 predicted a positive relationship between the ER and solvency in nascent SMEs. Findings support this, indicating a significant effect (β = 0.573, t = 17.534, p < .05). Notably, the ER has the highest beta coefficient, suggesting it makes the strongest unique contribution to explaining solvency when controlling for other variables.

H3a proposed a positive influence of return on equity on the solvency of nascent SMEs. The results in Table 5 affirm this, with return on equity significantly predicting SR (β = 0.520, t = 13.660, p < .05). Thus, H3a is supported. H3b suggested a positive impact of ROA on the solvency of nascent SMEs. However, the results, as indicated in the table, reveal that the relationship is not statistically significant (p > .05). H3c posited a positive influence of NPAT on the solvency of nascent SMEs. The results in the table confirm this, with NPAT significantly predicting SR (β = 0.070, t = 2.671, p < .05).

Discussion, contributions, and concluding remarks

Discussion

In light of the results, our findings underscore the critical role that capital structure and profitability metrics play in shaping the liability of newness landscape for nascent SMEs. DR emerged as a significant and unique contributor to explaining liability of newness. The observed negative relationship with SR aligns with prior studies, such as Wiklund et al. (2010), emphasizing that an excessive reliance on debt heightens the liability of newness as it escalates the risk of insolvency. The findings further indicate that a debt–equity mix modestly enhances the SR. This aligns with the perspective presented by Nukala and Prasada Rao (2021), underscoring the significance of striking the right balance between debt and equity to maximize overall benefits. This study shows that ER significantly contributes to explaining the SR, emerging as the primary factor.

Given the absence of external equity markets in the sample, equity in this context is internal, aligning with the notion proposed by Nguyen and Rugman (2015) that internal equity offers advantages, contributing to enhanced performance. Based on the findings, equity exhibits a positive relationship with solvency for nascent SMEs, while excessive reliance on debt without adequate equity buffers heightens insolvency risks during the early stages. In developing countries such as Ghana, where financial markets are typically underdeveloped and access to external equity is limited, internal equity often becomes a crucial source of financing for SMEs, even though it may not be sufficient (Agbozo & Yeboah, 2012). Ghana’s venture capital sector, for example, exhibits a relatively low level of activity when compared to its counterparts in other countries (Gatsi & Osie Nsenkyire, 2010). As this study has shown that the right balance between debt and equity also maximizes nascent SME survival, there is a need for the development of a robust financial system.

Our study establishes that ROE significantly drives solvency for nascent SMEs, and NPAT marginally contributes to increased solvency. This underscores the importance of profitability for the survival of companies, as highlighted by Amoa-Gyarteng (2019). In contrast, our study finds that ROA does not exhibit statistical significance in predicting solvency. Although ROA is a valuable metric for evaluating a company’s ability to cover asset costs with net income (Gadoiu, 2014), its lack of significant association with the SR can be attributed to its failure to consider debt obligations, as emphasized by Stancu (2007). As indicated by Fonseca et al. (2022), ROA is less sensitive to variations in leverage.

Contributions

The current study holds significant implications for both theory and practice. It contributes to the literature on nascent SMEs by highlighting the importance of financial decision making of nascent SMEs in emerging economies. This contribution is particularly noteworthy as the majority of existing studies on nascent SMEs primarily focus on developed economies. Although nascent SMEs may lack an extensive financial history, this study shows that even for newly established firms, financial indicators significantly impact the likelihood of survival in the early years. The significant contribution of the ER to explaining the SR reinforces the pecking order theory’s emphasis on the importance of internal equity, as there is no access to external equity markets in the study sample. Furthermore, the observation that excessive debt dependence heightens insolvency risk reflects how reliance on external financing should be calibrated to repayment capacity to avoid jeopardizing sustainability.

Thus, this study shows that applying the pecking order theory to nascent SMEs may help offset the effects of the liability of newness. By so doing, the study contributes significantly to theory. In light of this study’s findings regarding the significance of ER, DER, ROA, and NPAT in mitigating the liability of newness, SME owners are advised to take specific measures to boost these metrics.

SME owners are advised to follow the pecking order theory, utilizing debt only when internal equity is depleted. Additionally, optimizing resource utilization by minimizing assets for sales and focusing on price, variable costs, and fixed costs is crucial for enhancing net profits and, consequently, solvency. Recognizing the impact of debt on survivability, owners are urged to use it sparingly, aligning with corresponding internal equity.

Concluding remarks

The findings of this study underscore the pivotal role of financial metrics and capital structure in shaping the trajectory of nascent SMEs, providing valuable insights for both practitioners and researchers. However, it has limitations. One limitation of our study is that using a single proxy (solvency) to measure the liability of newness may not fully capture the multifaceted nature of this concept. Examining only solvency does not take into account other important factors, such as lack of networks, knowledge gaps, and unclear organizational processes. Future studies could consider additional proxies. Another limitation of this study is its cross-sectional design, which limits the establishment of a causal relationship. Additionally, reliance on financial statement data from SMEs in Ghana raises concerns about generalizability of the results. Future research should employ longitudinal designs and an expanded dataset across geographical regions to address these methodological limitations.

Disclosure statement

No potential conflict of interest was reported by the authors.