Abstract
Orientation: The coronavirus disease 2019 (COVID-19) pandemic presented a severe liquidity shock that forced small and medium enterprises (SMEs) to adapt their working capital management (WCM) strategies. Understanding these responses is vital to assessing SME resilience in financial crises.
Research purpose: The purpose of this study was to evaluate how SMEs, listed on the Johannesburg Stock Exchange’s Alternative Exchange (AltX), adjusted their net working capital (NWC) policies in response to the financial crises caused by the COVID-19 pandemic.
Motivation for the study: While global literature has examined liquidity management during crises, limited evidence exists on how listed SMEs in emerging markets, particularly in South Africa, adapted their NWC in response to systemic shocks. The study integrates contingency theory, the resource-based view (RBV) and liquidity preference theory to explain company-level heterogeneity in financial adaptation.
Research approach/design and method: A quantitative archival design was used. Secondary data from SMEs listed on the AltX covering 2017 to 2022 were analysed using the linear mixed model (LMM), the Wilcoxon signed-rank test, and descriptive statistics to evaluate variations in NWC across time and companies.
Main findings: The results show no statistically significant difference in NWC before, during and after the financial crisis. However, descriptive analyses revealed a temporary liquidity build-up in 2021, indicating precautionary behaviour consistent with liquidity preference motives. The findings demonstrate context-driven, specific and resource-driven adjustments rather than structural policy changes.
Practical/managerial implications: The study highlights the importance of integrating contingency planning and internal liquidity capabilities in SME financial management. Companies with stronger internal resources and access to capital exhibited greater working capital stability during the crisis.
Contribution/value-add: This study contributes to the emerging literature of SME financial resilience by empirically linking liquidity preference, contingency adaptation and resource heterogeneity. It offers insights for policymakers and SME managers seeking to strengthen liquidity management and crisis preparedness in volatile environments.
Keywords: AltX; SMEs; net working capital; contingency theory; resource-based view; liquidity preference theory; COVID-19; financial resilience.
Introduction
Small and medium enterprises (SMEs) have been identified as key in growing the economy and being a major source of job creation in many countries, accounting for approximately 90% of businesses and more than 50% of employment worldwide (World Bank 2023). This is also evident in South Africa, where SMEs account for approximately 91%–92% of formal businesses and employ about 56%–61% of employees in the formal sector (Van Wyk 2023). The National Development Plan also highlights SMEs as an important part of creating jobs and growing the economy (South African Government 2014). As great as SMEs are, they often face challenges and fail as a result, with a failure rate estimated to be between 40% and 90% (Bushe 2019). One of the contributors to the challenges faced by SMEs is a financial crisis, which often leaves SMEs more vulnerable because of reasons such as dependence on credit, lower capitalisation, and having fewer financing options (Lacina & Vavřina 2013). A financial crisis can be described as a rare economic disaster or an event that occurs infrequently but has extremely harmful consequences when it does, and, according to the National Bureau of Economic Research (2020), such rare disasters eventually do occur. According to Hodorogel (2009), a financial crisis harms SMEs and slows down their rate of development.
During such crises, working capital management (WCM) becomes critical for business continuity and survival (Aktas, Croci & Petmezas 2015; Baños-Caballero, García-Teruel & Martínez-Solano 2014; Garcia–Turuel & Martinez-Solano 2007). These events often cause a recession and, according to Campello, Graham and Harvey (2010), companies’ liquidity management policies will be affected during a financial crisis. In times of crisis, companies may shift from an aggressive net working capital (NWC) policy to a more conservative NWC policy to manage or reduce risk and ensure the company remains liquid (Chiou, Cheng & Wu 2006; Pais & Gama 2015; Tarighi et al. 2024).
One such disaster that has caused a financial crisis is the coronavirus disease 2019 (COVID-19) pandemic. On 11 March 2020, the World Health Organization (WHO 2020) declared COVID-19 a pandemic. The South African government placed the country under alert level 5 on 27 March 2020, which represented one of the strictest lockdowns in the world, even though at the time, there were no deaths reported and only 1170 cases were confirmed globally (Olivier, Botha & Craig 2020). This effectively halted most economic activity (Carlitz & Makhura 2021; Cele & Tshikovhi 2023), which severely disrupted most business activities except for those deemed essential services.
For many companies, the lockdown meant that they could not sell their inventory, which, in turn, meant that holding costs associated with inventory would increase (Zimon et al. 2021). It also made it hard for businesses to collect their debt from their customers, thereby increasing receivables as customers began to focus on their cash flow. Companies that also struggled with selling their inventory and collecting their debt would, in turn, also struggle to make payments to their creditors. Net working capital, defined as the sum of current assets less current liabilities (Correia 2024; Oladimeji & Aladejebi 2020), became a crucial indicator of liquidity. Management of an entity has to make a decision on the optimal level of current assets and current liabilities, or NWC, as this has been shown in the literature to have an impact on liquidity, cash flow and profitability (Louw et al. 2017). The decision on the best amount of current assets to hold and how they should be financed is known as the working capital policy (Vishnani & Shah 2007). Companies can adopt conservative, aggressive or moderate NWC policies, depending on their risk appetite and operating environment (Tauringana & Adjapong Afrifa 2013; Weinraub & Visscher 1998).
The COVID-19 pandemic caused significant strain on SMEs, limiting their cash flows, liquidity and access to capital. This access is especially important in times of crisis. The Johannesburg Stock Exchange (JSE) established AltX precisely to enable SMEs to access capital and grow into larger companies (JSE 2025). A recent study found that listing positively influences the performance of SMEs (Egu & Chiloane-Tsoka 2023). However, research shows that SMEs are not resilient to shocks such as COVID-19, with secondary and tertiary sector companies more vulnerable, while those in agriculture, forestry and utilities are comparatively more resilient (Anakpo & Mishi 2021). Evaluating the NWC responses during the COVID-19 pandemic, therefore, provides insights with both academic and policy relevance.
Internationally, studies show mixed results. Zimon and Tarighi (2021) investigated the effects of COVID-19 on the WCM policies among Polish SMEs, and the results of their study showed that firms employed a moderate-conservative WCM policy, and the pandemic did not significantly change the WCM policy. Grofčíková, Musa and Streimikis (2023) investigated how the COVID-19 pandemic affected the NWC in industrial production companies by using the Wilcoxon signed-rank test in order to examine whether there was a significant interannual change between the period from 2017 to 2021 in the NWC. The findings of that study indicated that there was an increase in NWC during the time of COVID-19. These contradictions highlight the need for further research.
In South Africa, however, there has been limited empirical work examining how SMEs, particularly companies listed on the AltX, adjusted their NWC policies during COVID-19. This study, therefore, aims to fill this gap by analysing whether the pandemic had a significant impact on the NWC policies of SMEs listed on the AltX, by focusing on the interannual changes in NWC before, during, and after the crisis. By doing so, the study contributes both theoretically and practically by testing crisis-related applications of contingency and resource-based theories as well as offering insights into SME resilience and liquidity management for policymakers and practitioners.
Literature review
The theory that provides a foundational framework for understanding WCM is the contingency theory. The contingency theory emerged in the 1960s mainly through the work of Burns and Stalker (1961) and was expanded by Lawrence and Lorch (1967), who emphasise that a universal optimal way to manage an organisation does not exist. Instead, the contingency theory states that there are multiple ways in which a company can manage its operations and that a company must adapt their strategies to internal conditions and external environments (Otley 2016). When applied to WCM, the theory states that companies will differ in how they adjust their NWC policies by choosing moderate or aggressive policies to respond to external financial crises. During a financial crisis, for example, some companies may prioritise liquidity over profitability and shift towards a more conservative working capital policy.
The theory that complements this contingency theory is the resource-based view (RBV) theory, which highlights the role of internal resources in shaping competitive advantage and resilience (Barney 1991). For SMEs, financial resources such as cash, accounts receivable and access to credit markets become critical strategic assets. Companies that are listed on the AltX are particularly relevant, since the listing provides SMEs with improved access to capital markets (Egu & Chiloane-Tsoka 2023; JSE 2025). The RBV theory, therefore, assists in understanding how SMEs listed on the AltX leverage or adapt their financial resources to survive a systemic financial crisis caused by COVID-19.
Other theoretical contributions include the liquidity preference theory of Keynes (1937), which states that companies will prioritise holding liquid assets in times of uncertainty. The liquidity preference theory aligns with evidence that, during COVID-19, companies increased their current asset holdings and liquidity buffers at the expense of efficiency (Tarighi et al. 2024). The three theories discussed provide a multilayered lens to interpret NWC policy responses to a financial crisis.
Net working capital is generally defined as the difference between current assets and current liabilities, capturing both liquidity and short-term solvency (Correia 2024; Oladimeji & Aladejebi 2020). Working capital strategies (policies) can be classified into conservative, moderate and aggressive (Tauringana & Adjapong Afrifa 2013; Weinraub & Visscher 1998). A conservative working capital policy means that an entity prioritises liquidity by maintaining higher levels of current assets and by holding large inventories, having relaxed credit terms for its customers, and paying its liabilities a bit later than usual, while an aggressive policy minimises current assets and seeks efficiency gains, often at the cost of higher risk (Talonpoika et al. 2016). The balance between these policies reflects managerial trade-offs between profitability and risk, which necessitates that a company finds an optimal balance, as empirical evidence shows that shifts in policy occur during periods of macro-economic instability (Chang, Chou & Dandapani 2025).
Prior research highlights that financial crises have a direct influence on WCM policies. Ahmad, Bashir and Waqas (2022) compared the global financial crisis of 2008 with the COVID-19 pandemic and found that WCM had a more pronounced impact on company performance during the COVID-19 pandemic. Specifically, the authors found differences in the influence of NWC and related metrics on profitability, with some indicators, such as current ratios, negatively affecting returns on assets; on the other hand, it positively influences market valuations. Their findings suggest that periods of crises alter the balance between liquidity and profitability, reinforcing the contingency theory perspective.
Similarly, Tarighi et al. (2024) examined Iranian firms and found evidence of shifts towards conservative policies during the COVID-19 pandemic as companies increased current assets, liquidity ratios and NWC while reducing operational cycles and receivables. Their findings supported the liquidity preference theory, as managers prioritised financial stability and resilience over efficiency.
Zimon and Tarighi (2021), by contrast, reported that Polish SMEs maintained a moderate to conservative policy stance, with the COVID-19 pandemic not significantly changing their WCM policies. Meanwhile, Grofčíková et al. (2023) observed significant increases in NWC among companies in the industrial production sector in Slovakia, pointing to sectoral variations. These conflicting findings highlight the importance of context, economic structure, industry and company size in shaping responses in times of financial crisis.
Studies in South Africa on WCM during financial crises remain limited. Struwig and Watson (2022) found that companies in South Africa faced both liquidity problems and system disruptions, which create a need to stabilise cash positions, respond to demand volatility and pursue digital transformation. Enow and Brijlal (2014), using AltX-listed companies, confirmed that debtor and inventory management have a significant impact on profitability, with creditors’ terms negatively influencing cash conversion cycles. At a broader level, Anakpo and Mishi (2021) reported that SMEs in the secondary and tertiary sectors were particularly vulnerable to the COVID-19 pandemic, while companies in the primary sectors, such as agriculture, were more resilient. These findings demonstrate the uneven impact of financial crises across industries and highlight the value of examining sectoral effects within SMEs listed on the AltX.
Research gaps and conceptual model
Although prior studies have documented how firms adjust WCM policies in responding to financial crises, two key gaps emerge:
- Limited evidence from South Africa’s AltX SMEs. Most international studies (Ahmad et al. 2022; Tarighi et al. 2024; Zimon & Tarighi 2021) focus on the global or European context, with little empirical evidence on South African SMEs, despite their importance in job creation and economic growth. AltX companies provide a unique lens for examining working capital policy adjustments during a financial crisis, given their hybrid positions as both SMEs and publicly listed companies.
- Mixed and contradictory findings. While some studies report significant increases in NWC during a financial crisis such as the COVID-19 pandemic (Grofčíková et al. 2023; Tarighi et al. 2024), other studies suggest no major changes (Zimon & Tarighi 2021). Similarly, the impact of WCM on profitability has been found to differ across measures (Ahmad et al. 2022).
This study addresses these gaps by evaluating year-on-year changes in the NWC of SMEs listed on the AltX before, during and after the COVID-19 pandemic, offering both theoretical and practical insights into the resilience of SMEs under crisis conditions.
Research design
Building on the identified gap in prior research regarding how SMEs, particularly those listed on the AltX, adjust their NWC policies during a financial crisis, the objective of this study was to evaluate the NWC responses of SMEs listed on the AltX during a financial crisis. The study employed quantitative statistical research methods to objectively investigate how SMEs listed on the AltX adjusted their NWC policies in response to the financial crisis caused by the COVID-19 pandemic. The study used secondary numerical data from financial statements of companies listed on the AltX, obtained from IRESS. Numerical data for the financial period from 2017 to 2022 was analysed. This range was chosen as it covers periods before, during and after the COVID-19 pandemic, and will be used to evaluate whether there was a significant shift in working policy as a result of the recent financial crisis.
The following two-tailed hypotheses were formulated at the 5% level of significance:
H0: The COVID-19 pandemic had no significant effect on the value of net working capital of companies listed on the AltX over two consecutive periods (H0: µ0 = µ1), and
H1: The COVID-19 pandemic had a significant effect on the value of net working capital of companies listed on the AltX between two consecutive periods (H1: µ0 > µ1 resp. H1: µ0 < µ1). The hypotheses will be verified at the significant level a = 0.05.
The study analysed data for the period 2017 to 2022, providing a longitudinal of the NWC behaviour of SMEs across different phases of the COVID-19 pandemic. Previous studies on the 2008 global financial crisis similarly adopted multi-year windows: Akgün and Memiş Karataş (2020) investigated WCM and business performance, looking at the period from 2003 to 2012, while Haron and Nomran (2016) investigated the determinants of WCM before, during and after the global financial crisis of 2008 by looking at the period from 2002 to 2012. The precedent from these studies supports the structure of this study, which l evaluates the changes in the NWC before, during and after the COVID-19 pandemic.
For the sample period, 22 companies were listed on the AltX. This study included all SMEs listed on the AltX for which complete financial statements were available for the years 2017 to 2022, rather than applying a sampling method. Two companies were excluded from the study: one because of not having current assets for the period under investigation, and the other for only having financial statements for the period after the lockdown, namely 2022 and 2023. The final dataset of 20 companies was analysed. Financial services, investment holding and property-related entities were retained in the sample because the study employed a census approach of SMEs listed on the AltX with complete financial data for the period under review, rather than applying an industry-specific sampling strategy. Although working capital studies often exclude financial services firms because of differences in operating and regulatory structures, the objective of this study was to evaluate the NWC responses of the AltX SME population as a whole. To account for company-level heterogeneity arising from differences in business models, the company was included as a random effect in the linear mixed model. Some companies have their financial statements reported in foreign currency; their figures were translated at the current spot rate to represent the years on the basis of current prices.
Data collection and analysis
In order to account for variation in firm characteristics across industries and over time, the study used the LMM (Field 2018; Gelman & Hill 2007). Linear mixed model is a model that can be used to assess the effect of a random variable across a series of data, particularly where observations are not independent (Gelman & Hill 2027) This model is suitable when analysing grouped or hierarchical data, as it allows for both fixed effects (like a year) and random effects (like company or sectoral level variation) (Hox 2010; Snijders & Bosker 2012). Within the context of this study, the company was treated as a random effect. That is, the model evaluates the impact of the NWC for various companies across consecutive years and tests whether changes are associated with a stressed economic period. While sectoral differences may contribute to variation, the AltX sample is relatively homogeneous, and thus sectoral effects were not expected to be the primary driver of observed differences. If the results indicate that a substantial proportion of the total variance is attributable to the company-level random effect, this suggests that differences between companies contribute meaningfully to variation in NWC, consistent with the interpretation of variance components in multilevel models (Snijders & Bosker 2012). By treating the company as a random effect, the model helped quantify how much of the variability in the NWC can be assigned to company-specific factors.
The LMM results analysis was conducted in R (R Core Team 2025) using the lme4 and lmerTest packages (Bates et al. 2015; Kuznetsova, Brockhoff & Christensen 2017), and the results were integrated with the Wilcoxon signed-rank test and descriptive statistics to offer a more robust understanding of the potential working capital policy responses among SMEs listed on the AltX. The Wilcoxon signed-rank test, a non- parametric statistical test used to compare paired observations (Field 2018), was performed using Microsoft Excel. The calculation was performed as follows. For each variable, the difference between the two periods was calculated. Where the difference equalled zero, it was excluded from the ranking. All differences that are not equal to zero are assigned ranks in ascending order. In the case of a tie, the average rank was used. Then the sums of the ranks corresponding to positive and negative differences were calculated separately. The test statistic (T) is the smaller of the two values (Equation 1):
where T + equals the sum of the ranks of the positive differences and T – equals the sum of the ranks of the negative differences. The rejection rule was defined as: reject H0 if T ≤ T0, where T0 is the critical value for the two-sided test at a 5% level of significance.
As part of the analytical procedure, the Wilcoxon signed-rank test was used to identify whether changes in NWC between consecutive periods were statistically significant. The interpretation of the Wilcoxon results was as follows:
- A Z-score based on positive differences indicated that most of the companies recorded a year-on-year decrease in NWC, consistent with a shift toward a more aggressive policy.
- A Z-score based on negative differences indicated that most of the companies recorded an increase in NWC, consistent with a shift toward a more conservative policy.
Complimenting this inferential test, descriptive statistics were used to assess year-on-year mean changes in NWC. An increase in average NWC suggests a shift toward a conservative policy, while a decrease suggests a shift towards a more aggressive policy. In addition, the coefficient of variation was used to assess the relative dispersion of NWC across companies in each year. This measure was considered appropriate because it standardises variability relative to the mean, allowing comparison of working capital volatility across years despite differences in the scale of company balances.
Ethical considerations
Ethical clearance to conduct this study was obtained from the North-West University Economic and Management Sciences Research Ethics Committee (Ref. No. NWU-00845-25-A4).
Results
For the LMM, the model was fitted with company as a random effect. Company represents the sector variable and aids in assessing whether there is influence. The model was used to support the use of the Wilcoxon signed-rank test to answer the objective of the study, which is to evaluate the responses in the NWC of the companies listed on the AltX. The results of the LMM model are presented in Table 1. The results show that the random effect, company, has a moderate influence on the variation explained. As shown in Table 1, year did not have a statistically significant fixed effect on NWC (F [5,105] = 1.25, p = 0.2912). The random effect of company accounted for approximately 42% of total variance, indicating a moderate between-company influence. This moderate company-level influence validates that using companies from different sectors does not significantly change the result of the study, even if companies in the same sector were used.
| TABLE 1: Fixed effect of year in the linear mixed-effects model. |
The coefficient estimates of each year, using 2017 as the reference category, are presented in Table 2. None of the changes from year to year was statistically significant at the 5% level. The LMM model additionally assessed whether there was a significant difference in NWC across consecutive years. Based on the results of the model at a 5% significance level, there are no statistically significant differences in NWC across the years; however, 2021 and 2022 show upward trends, although not statistically significant. The largest positive estimate was for 2021 (β = 0.3471, p = 0.1358), followed by 2022 (β = 0.2898, p = 0.2129), although these effects were not statistically significant at the 5% level.
| TABLE 2: Coefficient estimates from linear mixed-effect model. |
Wilcoxon and descriptive statistics
The Wilcoxon signed-rank test was used to examine changes in NWC year-by-year to address the objective of the study. Descriptive statistics were considered alongside the Wilcoxon results to examine the direction and magnitude of these changes to assess responses in NWC policy of companies listed on the AltX. Table 3 summarises the Wilcoxon results. In all comparisons, the test statistic exceeded the critical value, meaning the null hypothesis of no difference was not rejected at the 5% significance level.
| TABLE 3: Wilcoxon signed ranked test results for consecutive years. |
T+ and T- represent the sum of positive and negative ranks, respectively, from the Wilcoxon signed-rank test. Wstat is the test statistic, which is the smaller of T+ and T-, Wcrit is the critical value from the Wilcoxon signed-rank test table for the given sample and significance level (α). For the Wilcoxon signed-rank test, wstat>wcrit, the null hypothesis is not rejected.
From 2017 to 2018, the Z-score was based on negative differences, which meant that there was an increase in NWC. This was also confirmed by the descriptive statistic, which showed that the average NWC increased by 5%. The difference, however, was not statistically significant. The move showed companies on average shifted towards a conservative NWC strategy.
From 2018 to 2019, the Z-score was based on positive values, meaning there was a decrease in NWC, confirmed by the average NWC decreasing by 14%. This demonstrated a shift towards a more aggressive policy. The result was not statistically significant.
From 2019 to 2020, the Z-score was based on positive values, again showing that the companies were moving towards an aggressive NWC strategy. This was confirmed by a decrease in the average NWC of 18% during the period. The difference was still not statistically significant.
From 2020 to 2021, the difference was based on negative values, showing that there was a shift in the NWC strategy where companies adopted a more conservative approach to managing their NWC. The descriptive statistic showed a 166% increase in the average NWC (R94 340 000.00 to R250 634 000.00) of companies listed on the AltX. The difference was, however, still not statistically significant. These results support observations made from the LMM results in 2021.
From 2021 to 2022, the Z-score was based on negative differences and showed no statistical difference. Although the ranked-based result suggested that some companies increased their NWC, the descriptive statistic showed a decrease of 8% in the average NWC balance. This suggests that the 2021 liquidity build-up was not sustained uniformly across companies and that some companies began normalising their working capital positions after the peak crisis period. The difference was still not statistically significant.
Table 4 presents descriptive statistics for NWC over the period under examination. Variability, which is measured by the coefficient of variation (CV), remained high, but declined from 61% in 2018 to 2019 to 44% in 2022. Correlations between the consecutive years were generally very strong, with the highest between 2021 and 2022 (91%), indicating persistence in company-level NWC behaviour. Figure 1 illustrates year-by-year correlation in NWC behaviour, confirming strong performance in company-level behaviour despite the external shock. Even at the height of the crisis, companies that entered with higher working capital tended to maintain relatively stronger positions.
| TABLE 4: Descriptive statistics and correlation for net working capital. |
 |
FIGURE 1: Correlation analysis of net working capital between consecutive years. |
|
Figure 2 shows the coefficient of variation for each year, highlighting that volatility remained elevated across the period, and trended downward after 2019 from 61% to 44% in 2022. This suggests that by 2022, companies listed on the AltX displayed more stable working capital patterns compared to the earlier years and gradually adjusted and stabilised their policies after the crisis period.
 |
FIGURE 2: Coefficient of variation of net working capital from 2017 to 2022. |
|
Discussion
The objective of the study was to evaluate whether SMEs listed on the AltX adjusted their NWC policies because of the financial crisis caused by the COVID-19 pandemic. Both the LMM and the Wilcoxon signed-rank test results confirmed that year-by-year changes in NWC before, during and after the crisis were not statistically significant, supporting the null hypothesis (Ho). Nonetheless, the descriptive results and the graphical analyses provide valuable insights into underlying dynamics.
The correlation analysis (Figure 1) reveals a largely consistent pattern in NWC behaviour, with correlations rising from 60% in 2018 to 88% in 2020, followed by a temporary decline to 60% in 2021 and a strong recovery to 91% in 2022. This pattern indicates that while SMEs experienced short-term disruptions during the peak of the COVID-19 pandemic, they tended to maintain their pre-crisis policies rather than engage in wholesale shifts. These findings align with the findings of Zimon and Tarighi (2021), who found that Polish SMEs did not significantly change their WCM policies during COVID-19, adopting a moderate to conservative policy throughout.
Our findings also resonate with Grofčíková et al. (2023), who investigated the changes in the volume of NWC of companies in the industrial production sector operating in Slovakia. They observed decreases in NWC from 2019 to 2020, followed by increases in 2021 as companies rebuilt their liquidity positions. While some of these changes were statistically significant, some of them were small, suggesting modest shifts in practices rather than systematic transformations. In our study, the same directional pattern was observed – decreases from 2019 to 2020, followed by a sharp increase in 2021 (+166%), though not statistically significant, but nonetheless material. This contrast demonstrates the importance of context; our SMEs listed on the AltX, with a smaller sample size and different industries, showed similar behavioural trends, but without measurable significance found in more homogeneous industrial sectors.
At the same time, our results diverge from Tarighi et al. (2024), who reported that Iranian firms adopted conservative working capital policies, significantly increasing liquidity ratios during the COVID-19 crisis. In the same way, Ahmad et al. (2022) showed that the relationship between working capital and company performance shifted more strongly during COVID-19 compared to the 2008 financial crisis. A possible explanation for these differences can be contextual factors: Companies listed on the AltX have access to capital markets, which may have cushioned liquidity pressures relative to SMEs in emerging markets without such access.
The sharp but temporary increase in NWC observed in 2021 (+166%) is notable. While it may not be statistically significant, it suggests precautionary liquidity accumulation, which is consistent with the liquidity preference theory and with the findings of Struwig and Watson (2022), who emphasised the importance of having cash buffers and system resilience during the pandemic. However, by 2022, this sharp increase was followed by a modest decline (−8%), suggesting that companies drew down reserves once restrictions eased.
These findings highlight the contingency theory perspective: A company’s NWC policy responses were not only shaped by the crisis itself, but also by company-specific resource endowment and strategic choices. The lack of statistical significance across tests highlights the methodological contribution of using both non-parametric testing and descriptive analyses to uncover the directional but heterogeneous shifts in behaviour.
Conclusion
Implications
The findings have several implications for theory, practice, policy and methodology. From a theoretical perspective, the study results extend the application of the contingency theory by demonstrating that NWC responses are highly dependent on the context shaped by crisis conditions, but also mediated by company-specific characteristics. At the same time, the study enriches the RBV theory by showing that heterogeneity in a company’s internal resources and capabilities had an influence on the extent to which companies listed on the AltX could accumulate or maintain liquidity buffers during the COVID-19 pandemic. In this way, the study highlights the interplay between external shocks and internal resources in shaping financial resilience. The results also support the liquidity preference theory, as the temporary spike in NWC observed in 2021 reflects a precautionary liquidity accumulation, although it was short-lived.
In practice, the findings of the study suggest that managers of SMEs should not only adjust their NWC policies dynamically in responding to a financial crisis but should also build company-specific capabilities that enhance resilience. Companies with stronger internal resources were better positioned to adopt conservative liquidity policies during periods of uncertainty, while resource-constrained companies had more volatile responses. For policymakers, the evidence highlights the importance of supporting SMEs through targeted interventions, such as guarantees, extended credit and deferred tax obligations. Payment relief measures and programmes that improve access to working capital finance for SMEs to ease liquidity constraints during periods of financial crisis.
Finally, this study applied both the LMM and the Wilcoxon signed-rank test to evaluate crisis responses in NWC by SMEs listed on the AltX. The combined use of multiple methods supported a more nuanced interpretation of the findings: While the statistical significance was not reached, the directional patterns and variance decomposition provided a richer understanding of how SMEs listed on the AltX responded to the financial crisis caused by the COVID-19 pandemic.
Limitations and recommendations
This study is not without limitations. Although Wilcoxon is suited to small sample sizes, the sample was restricted to 20 AltX companies, which limits generalisability beyond the South African context. The reliance on secondary financial data may obscure intra-year adjustments or company-level strategic decisions. The study further focused on NWC as an aggregate measure, without decomposing the relative roles of receivables, inventory and payables.
Future research should expand the sample size, examine sectoral differences more closely and incorporate qualitative evidence from management interviews to provide a deeper insight into decision-making during a financial crisis. Comparative studies between SMEs listed on the AltX and much larger companies listed on the JSE would also help clarify the role of company size and resources in shaping responses in times of financial crises.
Acknowledgements
This article is based on research originally conducted as part of Molefe J. Seshabela’s master’s thesis titled ‘Evaluating net working capital responses of SMEs listed on the AltX’, submitted to the Faculty of Economic and Management Sciences, North-West University in 2025. The thesis is currently unpublished and not publicly available. The thesis was supervised by Daniel P. Schutte and Heleen H. Janse Van Vuuren. The thesis was reworked, revised and adapted into a journal article for publication. The author confirms that the content has not been previously published or disseminated and complies with ethical standards for original publication.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Molefe J. Seshabela: Conceptualisation, Formal analysis, Investigation, Methodology, Writing – original draft. Daniel P. Schutte: Conceptualisation, Supervision, Writing – review & editing. Heleen H. Janse Van Vuuren: Supervision, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The data that support the findings of this study are not openly available because of it being obtained from the proprietary IRESS database and are not publicly available. The corresponding author, Molefe J. Seshabela, can be contacted for data availability relevant to this study upon reasonable request, as access is restricted to licensed users.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
References
Ahmad, M., Bashir, R. & Waqas, H., 2022, ‘Working capital management and firm performance: Are their effects same in covid 19 compared to financial crisis 2008?’, Cogent Economics & Finance 10(1), 2101224–2101210. https://doi.org/10.1080/23322039.2022.2101224
Akgün, A. & Memiş Karataş, A., 2020, ‘Investigating the relationship between working capital management and business performance: Evidence from the 2008 financial crisis of EU-28’, International Journal of Managerial Finance. ahead-of-print. https://doi.org/10.1108/IJMF-08-2019-0294
Aktas, N., Croci, E. & Petmezas, D., 2015, ‘Is working capital management value-enhancing? Evidence from firm performance and investments’, Journal of Corporate Finance 30, 98–113. https://doi.org/10.1016/j.jcorpfin.2014.12.008
Anakpo, G. & Mishi, S., 2021, ‘Business response to COVID-19 impact: Effectiveness analysis in South Africa’, Southern African Journal of Entrepreneurship and Small Business Management 13(1), 7. https://doi.org/10.4102/sajesbm.v13i1.397
Baños-Caballero, S., García-Teruel, P.J. & Martínez-Solano, P., 2014, ‘Working capital management, corporate performance, and financial constraints’, Journal of Business Research 67(3), 332–338. https://doi.org/10.1016/j.jbusres.2013.01.016
Barney, J., 1991, ‘Firm resources and sustained competitive advantage’, Journal of Management 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bates, D., Mächler, M., Bolker, B. & Walker, S., 2015, ‘Fitting linear mixed-effects models using lme4’, Journal of Statistical Software 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Burns, T. & Stalker, G., 1961, ‘Mechanistic and organic systems of management’, Management of Innovation 21, 96–125. https://doi.org/10.1093/acprof:oso/9780198288787.003.0006
Bushe, B., 2019, ‘The causes and impact of business failure among small to micro and medium enterprises in South Africa’, Africa’s Public Service Delivery and Performance Review 7(1), 1–26. https://doi.org/10.4102/apsdpr.v7i1.210
Campello, M., Graham, J.R. & Harvey, C.R., 2010, ‘The real effects of financial constraints: Evidence from a financial crisis’, Journal of Financial Economics 97(3), 470–487. https://doi.org/10.1016/j.jfineco.2010.02.009
Carlitz, R.D. & Makhura, M.N., 2021, ‘Life under lockdown: Illustrating tradeoffs in South Africa’s response to COVID-19’, World Development 137, 105168. https://doi.org/10.1016/j.worlddev.2020.105168
Cele, Z.P. & Tshikovhi, N., 2023, ‘Review of the lockdown regulations’ impact on informal businesses during the COVID-19 pandemic’, African Journal of Inter/Multidisciplinary Studies 5(1), 1–10. https://doi.org/10.51415/ajims.v5i1.1180
Chang, C.-H., Chou, J. & Dandapani, K., 2025, ‘Navigating the trade-offs: The impact of aggressive working capital policies on stock return volatility’, Journal of Risk and Financial Management 18(3), 137. https://doi.org/10.3390/jrfm18030137
Chiou, J.-R., Cheng, L. & Wu, H.-W., 2006, ‘The determinants of working capital management’, Journal of American Academy of Business 10(1), 149–155.
Correia, C., 2024, Financial management, 10th edn., Juta and Company (Pty) Ltd, Claremont.
Egu, M.E. & Chiloane-Tsoka, E.G., 2023, ‘Does listing on the JSE’S Altx improve the performance of small and medium-sized enterprises?’, Cogent Business & Management 10(3), 2282750. https://doi.org/10.1080/23311975.2023.2282750
Enow, S.T. & Brijlal, P., 2014, ‘The effect of working capital management on profitability: The case of small medium and micro enterprises in South Africa’, Journal of Accounting and Management 2, 7–15.
Field, A., 2018, Discovering statistics using IBM SPSS statistics, 5th edn., Sage, London.
Garcia–Turuel, P. & Martinez-Solano, P., 2007, ‘Effects of WCM on SME profitability’, International Journal of Management Finance 3, 164–177. https://doi.org/10.1108/17439130710738718
Gelman, A. & Hill, J., 2007, Data analysis using regression and multilevel/hierarchical models, Cambridge University Press, Cambridge.
Grofčíková, J., Musa, H. & Streimikis, J., 2023, ‘How did the COVID-19 pandemic affect net working capital in industrial production companies?’, Economics & Sociology 16, 207–229. https://doi.org/10.14254/2071-789X.2023/16-4/10
Haron, R. & Nomran, N., 2016, ‘Determinants of working capital management before, during, and after the global financial crisis of 2008: Evidence from Malaysia’, Journal of Developing Areas 50, 461–468. https://doi.org/10.1353/jda.2016.0029
Hodorogel, R.G., 2009, ‘The economic crisis and its effects on SMEs’, Theoretical & Applied Economics 16(5), 79–88.
Hox, J.J., 2010, Multilevel analysis: Techniques and applications, 2nd edn., Routledge, New York, NY.
Johannesburg Stock Exchange (JSE), 2025, AltX, viewed 03 September 2025, from https://www.jse.co.za/raise-capital/equities-market/altx.
Keynes, J.M., 1937, ‘The general theory of employment’, Quarterly Journal of Economics 51(2), 209–223. https://doi.org/10.2307/1882087
Kuznetsova, A., Brockhoff, P.B. & Christensen, R.H.B., 2017, ‘lmerTest package: Tests in linear mixed effects models’, Journal of Statistical Software 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13
Lacina, L. & Vavřina, J., 2013, ‘The impact of financial and economic crisis on SME’s in Greece and Ireland’, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61(4), 1005–1016. https://doi.org/10.11118/actaun201361041005
Lawrence, P. & Lorch, J., 1967, Organization and environment, Graduate School of Business Administration, Harvard University, Boston, MA.
Louw, E., Hall, J. & Brümmer, L., 2017, ‘Working capital management of South African retail firms’, Journal of Economic and Financial Sciences 9(2), 545–560. https://doi.org/10.4102/jef.v9i2.58
National Bureau of Economic Research, 2020, Rare events and financial markets, viewed 21 May 2024, from https://www.nber.org/reporter/2020number1/rare-events-and-financial-markets.
Oladimeji, J.A. & Aladejebi, O., 2020, ‘The impact of working capital management on profitability: Evidence from selected small businesses in Nigeria’, Journal of Small Business and Entrepreneurship Development 8(1), 27–40. https://doi.org/10.15640/jsbed.v8n1a3
Olivier, L.E., Botha, S. & Craig, I.K., 2020, ‘Optimized lockdown strategies for curbing the spread of COVID-19: A South African case study’, IEEE Access 8, 205755–205765. https://doi.org/10.1109/ACCESS.2020.3037415
Otley, D., 2016, ‘The contingency theory of management accounting and control: 1980–2014’, Management Accounting Research 31, 45–62. https://doi.org/10.1016/j.mar.2016.02.001
Pais, M.A. & Gama, P.M., 2015, ‘Working capital management and SMEs profitability: Portuguese evidence’, International Journal of Managerial Finance 11(3), 341–358. https://doi.org/10.1108/IJMF-11-2014-0170
R Core Team, 2025, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, viewed 18 May 2026, from https://www.R-project.org.
Van Wyk, I., 2023, ‘Strategic decision-making in small and medium enterprises in South Africa’, Southern African Journal of Entrepreneurship and Small Business Management 15(1), a684. https://doi.org/10.4102/sajesbm.v15i1.684
Snijders, T.A.B. & Bosker, R.J., 2012, Multilevel analysis: An introduction to basic and advanced multilevel modeling, 2nd edn., Sage, London.
South African Government, 2014, National development plan 2030, viewed 18 April 2024, from https://www.gov.za/sites/default/files/gcis_document/201409/ndp-2030-our-future-make-it-workr.pdf.
Struwig, M. & Watson, S., 2022, ‘Working capital management and systems disruption during the COVID-19 pandemic: Evidence from South Africa’, Athens Journal of Business & Economics 9(1), 45–58. https://doi.org/10.30958/ajbe.9-1-3
Talonpoika, A.-M., Kärri, T., Pirttilä, M. & Monto, S., 2016, ‘Defined strategies for financial working capital management’, International Journal of Managerial Finance 12(3), 277–294. https://doi.org/10.1108/IJMF-11-2014-0178
Tarighi, H., Zimon, G., Sheikh, M.J. & Sayrani, M., 2024, ‘The impact of firm risk and the COVID-19 crisis on working capital management strategies: Evidence from a market affected by economic uncertainty’, Risks 12(4), 72. https://doi.org/10.3390/risks12040072
Tauringana, V. & Adjapong Afrifa, G., 2013, ‘The relative importance of working capital management and its components to SMEs’ profitability’, Journal of Small Business and Enterprise Development 20(3), 453–469. https://doi.org/10.1108/JSBED-12-2011-0029
Vishnani, S. & Shah, B.K., 2007, ‘Impact of working capital management policies on corporate performance – An empirical study’, Global Business Review 8(2), 267–281. https://doi.org/10.1177/097215090700800206
Weinraub, H.J. & Visscher, S., 1998, ‘Industry practice relating to aggressive conservative working capital policies’, Journal of Financial and Strategic Decisions 11(2), 11–18.
World Bank, 2023, Small and Medium Enterprises (SMEs) finance, viewed 18 May 2026, from https://www.worldbank.org/ext/en/topic/competitiveness/small-and-medium-enterprises-smes-finance.
World Health Organization (WHO), 2020, WHO timeline-COVID-19, viewed 19 April 2024, form https://www.who.int/news/item/27-04-2020-who-timeline---covid-19.
Zimon, G. & Tarighi, H., 2021, ‘Effects of the COVID-19 global crisis on the working capital management policy: Evidence from Poland’, Journal of Risk and Financial Management 14(4), 169. https://doi.org/10.3390/jrfm14040169
Zimon, G., Babenko, V., Sadowska, B., Chudy-Laskowska, K. & Gosik, B., 2021, ‘Inventory management in SMEs operating in Polish group purchasing organizations during the COVID-19 pandemic’, Risks 9(4), 63. https://doi.org/10.3390/risks9040063
|
Hide
Show all
Email this article (Login required)
