The Impact of Corruption on SMEs’ Trade Credit Management Effectiveness

Abstract

The continued rise in SMEs’ corruption-related activities results in uncertainty around their ability to sustainably contribute to economic growth, leaving SMEs financially fragile and exposed to problems associated with trade credit management resulting in business exits. Given that little research has been conducted on how corruption affects smaller businesses while corruption’s impact on SMEs’ trade credit management effectiveness remains largely unexamined, the study aims to determine the impact of corruption on SMEs’ trade credit management effectiveness. By addressing this unanswered research gap, SMEs could be better equipped to understand how corruption affects their trade credit management in support of their overall finances. The study employed a quantitative research design with purposive sampling using a survey by administrating 10450 online questionnaires tested by a sample of 450 SMEs across South Africa. The result aligns with expectations around corruption being detrimental to SMEs’ trade credit management effectiveness while also indicating, unexpectedly, SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit. The study adds to the existing literature on corruption and SMEs’ trade credit management while also providing anti-corruption recommendations to SMEs that are dependent on trade credit. In so doing, SMEs could be better equipped to understand how corruption affects their trade credit management to support their overall finances contributing to improved SME creation rates and fostering entrepreneurship as a pivotal mechanism for improving South Africa’s sustainable development goals.

1. Introduction

Entrepreneurship stimulates economic growth by bolstering human development and primary economic development goals, including lowering unemployment, decreasing income inequality, and lessening systemic poverty (Gu and Wang 2022; Meyer and Meyer 2020). Additionally, entrepreneurship is a noteworthy solution to innovation and technological advancement, wealth creation, economic diversification, foreign investment, public sector revenue accumulation, and counteracting the loss of skilled individuals emigrating to developed countries (Bowmaker-Falconer et al. 2023). Small and medium-sized enterprises (SMEs) are widely regarded as transformers of economic growth, thereby contributing to South Africa’s attempts to attain its sustainable development goals (SDGs) as set out in the National Development Plan (NDP) by 2030. As outlined in the 2023 SDG Country Report, South Africa has reached the halfway mark in achieving its targets based on its 2030 global development agenda. However, ample developmental challenges constrain the country’s attempts to reach these targets. These challenges include unemployment (increasing from 32.9% in the 1st quarter to 33.5% in the 2nd quarter of 2024), poverty1 (53.2% in 2010 increasing to 55.5% by 2014), and income inequality2 (63.4% in 2010 decreasing slightly to 63.0% by 2014) (Statistics South Africa 2023, 2024a; World Bank 2024a, 2024c). In addition, South Africa has observed a gradual decline in gross domestic product (GDP) growth (4.7% in 2021, decreasing to 1.9 in 2022 and 0.6 in 2023), proving to be significant impairments to the country’s attempts to attain its SDGs (World Bank 2024b). The South African economy has underperformed for a decade with real GDP per capita growth plummeting since 2011 (Statistics South Africa 2024b). It is observed in the 2023/2024 Global Entrepreneurship Monitor (GEM) South African Report that the country remains one of the most unequal societies globally, despite the creation of its new democracy nearly three decades ago (Bowmaker-Falconer et al. 2023). South Africa’s hopes for a prosperous entrepreneurial sector that cultivates high economic growth seem far attached from reality (Hill et al. 2023).

Despite these underperforming macroeconomic indicators, SMEs’ importance in stimulating economic growth and contributing to economic development across all economies is amplified. These enterprises contribute to around 80% of global economic growth, ranging between 50% and 60% for the Organisation for Economic Cooperation and Development (OECD) countries and close to 33% for developing countries (Nowakowska-Grunt et al. 2018). On local soil, SMEs’ contribution to GDP totalled 36% in 2015 (Herrington and Kew 2018). However, considering SMEs’ propensity to fail these noteworthy contributions can seem temporary. South African SMEs failure rates remain alarmingly high at 59.4% in 2022 and 59.5% in 2023, obtaining the third highest fear of failure ranking for all Level C countries (category average of 44.3% in 2023). South Africa is positioned as the 4th lowest out of the 46 participating countries surveyed in the 2023/2024 GEM Report (Hill et al. 2023). In terms of business discontinuance rate3, South Africa increased from 4.9% (2019) to 13.9% (2021), which was 3.3% higher (negative) than the African region’s average in 2021. In 2022, the country’s business discontinuance rate improved to 4.9% although it remains higher compared to other developing countries like China (3.40%) and Morocco (4.25%), as well as the overall Level C country average of 3.62% in 2022 (Bowmaker-Falconer et al. 2023). Such current negative macroeconomic estimates coupled with the country’s inability to attain its SDGs holistically question the belief that entrepreneurship can sustainably contribute to economic development, employment creation, and poverty reduction. In addition, in considering widespread acts of corruption being prevalent amongst SMEs, the notion of SME propensity to failure seems understandable.

Bowmaker-Falconer et al. (2023) observed that to remedy South Africa’s ongoing pessimistic economic realities, the country must orient toward a more employment-intensive and innovation-driven trajectory. However, remnants from the COVID-19 pandemic, electricity disruptions, prevailing crime and corruption, and crippling infrastructure coupled with poor public service delivery hamper entrepreneurial efforts. Although many challenges impair the country’s attempts to transform the economy from destitute to prosperous, corruption stands out as a major obstacle to economic growth. Corruption costs the national economy billions with its repercussions lingering well beyond the abuse of public sector monies into the private sector (Bowmaker-Falconer and Meyer 2022). South Africa’s SME corruption-related activities, namely irregular payments and bribes, have increased, rising from 53/91 to 137/138 (Herrington and Kew 2018). The 2023 SDG Country Report identified six challenges and priorities that urgently need to be addressed for the country to stay on course in achieving its SDG targets. Some of these challenges include fighting corruption, which encompasses the perceived high levels of corruption and bribery, weakened institutions, and the continued need to increase access to justice in a resource-constrained environment. To eliminate this challenge, two SDG targets were formulated, which include (1) substantially reducing corruption and bribery in all its forms, and (2) developing effective, accountable, and transparent institutions at all levels (Statistics South Africa 2023). Despite these idealistic targets, South Africa’s Corruption Perception Index (CPI) decreased to its lowest score ever, from 43/100 (2022) to 41/100 (2023), ranking the country 83/180 in 2023 (Transparency International 2023). These negative realities are also observed in the 2023 SDG Country Report as the former mentioned SDG target scored a no progress tracking status4 indicating that the data are showing a negative trend (increasing from a baseline value of 0.09 in 2016 to 0.31 in 2018), while the latter mentioned SDG target obtained an insufficient score due to no data (Statistics South Africa 2023). Unfortunately, Changwony and Kyiu (2024) note that, for SMEs in developing countries to remain competitive, acts of corruption such as bribery that entails making illegal payments to government officials to obtain public services are accumulative. According to Piper (2019), many global business leaders (64%) associate acts of bribery and corruption as constraints to SMEs, with estimates increasing to 83% and 84% for Sub-Saharan Africa and Central America, respectively. Several studies observed the devastating effects of corruption on SMEs’ operational viability, especially in developing countries, by reporting on the harmful consequences of corruption that impair SMEs’ financial viability and investments resulting in severe cash flow shortages and a decline in short-term debt servicing capabilities that could lead to bankruptcy (Amin and Motta 2023; Le and Doan 2020; Wellalage et al. 2019a; Barkemeyer et al. 2018; Bryant and Javalgi 2016). The study by Changwony and Kyiu (2024) notes that corruption increases in countries with low business freedom, especially those with weak financial reporting standards, which often applies to developing nations. Ngomi (2017) points out that payment delays proliferate SMEs’ willingness to partake in corruption, as payments can be delayed forcefully only to be completed once a bribe, such as contract payments, is obtained (Bowen et al. 2012; Méon and Weill 2010). Ample studies in South Africa suggest that corruption is a considerable factor contributing to their high failure rate, observing that corruption hinders SMEs from applying acceptable business processes. According to Leboea (2017), “in South Africa, there is a culture of corruption where financial incentives are given out in order to fast track businesses success and bypass regulatory red tape. This negatively impacts SMEs as their chances of success are diminished based on this corrupt culture”. The study by Soni and Smallwood (2024), which also focuses on South African SMEs, supports this notion stating that corruption amongst SMEs undermines operational deliverables and accumulates unethical behaviour. Soni and Smallwood (2024) further added that bribery poses a major challenge to local SMEs as it undermines the quality standards of manufacturing SMEs. Therefore, corruption is widely regarded as a stumbling block, especially for developing nations such as South Africa, yet remains relevant to SMEs’ business environment, from which their trade credit management cannot be separated.

The available stream of literature focuses on corruption as an informal institution and its relatedness to the allocation of resources and social norms (Giannetti et al. 2021). All the while, corruption’s impact on SMEs’ trade credit management remains largely unexamined while little research has been conducted on how corruption affects smaller businesses (Le and Doan 2020). Therefore, within the context of the referenced research, it is necessary to ask the following question: what is the impact of corruption on SMEs’ trade credit management effectiveness? The study intends to fill this unanswered research gap, and in so doing, SMEs could be better equipped to understand how corruption affects their trade credit management practises in support of their overall finances. In this way, the paper can contribute to supporting entrepreneurial activity by broadening SMEs’ understanding of how the tested variables apply to current business operations generating improved trade credit management. This can then contribute to improved SME creation rates and economic prosperity. As far as can be established, no study has set out to relate knowledge concerning the stated research question. Therefore, the article’s primary objective was to determine the impact of corruption on SMEs’ trade credit management effectiveness. The secondary research objectives include the following: (1) empirically testing the relationship between corruption and SME trade credit management effectiveness and, thereafter, (2) providing recommendations to relevant stakeholders.

The study contributes to the literature in the following ways. Firstly, where previous studies have focused on corruption from a macroeconomics perspective (Cai et al. 2023; Gründler and Potrafke 2019), this study aims to investigate how corruption affects SMEs’ trade credit management effectiveness. In so doing, the study supports the findings obtained with theories that align with the multidimensional characteristic of the results, altogether expanding the corruption and trade credit management literature. Secondly, because the study enriches the literature on SME trade credit management, it adds to various authors’ bodies of literature concerning trade credit determinants (Fisman and Love 2003; Petersen and Rajan 1997). Thirdly, by contributing to the ongoing debate on the effectiveness of anti-corruption campaigns (Giannetti et al. 2021; Zhang et al. 2019), the provided recommendations should be useful to other developing countries burdened by corruption and distorted credit environments, especially for African countries, given their extensive corruption realities (Transparency International 2023).

The paper is structured as follows. Section 2 reviews the existing literature on trade credit management, corruption, and SMEs, while expanding on various theories that inform these variables, ending with a section devoted to the definition of SMEs. Section 3 describes the research methodology employed. Section 4 reports on the empirical results that were conducted to investigate the impact of corruption on SMEs’ trade credit management effectiveness and outlines the discussion of these findings. Section 5 provides recommendations. Lastly, Section 6, provides concluding remarks that summarise the main findings and recommendations of the study while also describing the study limitations and areas for future research.

2. Literature Review

Before empirically determining the impact of corruption on SMEs’ trade credit management effectiveness, the sections to follow will review the literature on trade credit and the use thereof, late payment and default risks in SMEs as a result of trade credit funding, followed by a review of asymmetric information theory as the main theory that informs trade credit, and the credit rationing theory that extends from the former theory. The literature will further review corruption and SMEs, then deduce the opposing theory viewpoint related to the relationship between corruption and entrepreneurship. The last section explains what defines businesses classified as SMEs.

2.1. Trade Credit and SMEs

Trade credit can be defined as a form of credit, granted by the creditor to the debtor, allowing the debtor the purchasing opportunity without making an immediate repayment to the creditor (Petersen and Rajan 1997). Therefore, SMEs act as financial intermediaries, by providing finance to others comprising both the time differential between the delivery of goods and services and payment, including the proportional discounts allowed for payment in bulk or before the payment due date (Andrieu et al. 2018). Trade credit has unique features for both creditor and debtor that include the knowledge obtained concerning the debtor’s creditworthiness (McGuinness and Hogan 2016). Creditors can obtain this unique trade credit feature through effective trade credit management that entails monitoring repayment schedules and sale orders, including the competencies to enforce the repayment of outstanding debtor accounts or to stop future supplies (McGuinness and Hogan 2016). In addition, trade credit allows the debtor the feature of trade discounts from prompt repayment. This can result in more favourable credit terms and ultimately reduce the overall credit financing costs (McGuinness and Hogan 2016). Trade credit can be divided into two basic forms, namely the simpler form is characterised as net terms, and the more complex form termed as two-part terms. Firstly, net terms specify that full payment is due within a certain period after product delivery or after monthly statements. Secondly, two-part terms consist of three basic elements, namely the discount percentage, the discount period, and the final payment time. Given their interrelatedness, SMEs act as both debtors and creditors in the completion of the trade credit agreement, as trade credit is widely recognised as a ‘two-way transaction’ requiring SMEs to concurrently manage both components as, due to the nature of working capital management, these two credit components influence each other (Petersen and Rajan 1997). Therefore, the management of trade credit is crucial in alleviating performance (Ferrando and Mulier 2013). SMEs are poised to manage their net trade-credit position effectively to expand business growth (Silva 2024).

The previous literature indicates that trade credit represents a substantial investment in current assets and liabilities (Petersen and Rajan 1997). Concerning business-to-business (B2B) sales funded by trade credit purchases, the Atradius Reports observe that 52% of the USA, 50% of Eastern Europe, 55% of Western Europe, and 54% of Asia B2B sales were funded by trade credit despite the risk associated with this funding source (Atradius 2020a, 2020b, 2020c, 2021). More recently, in terms of B2B sales funded by trade credit purchases for 2024, these values totalled 46% (USA), 47% (Central and Eastern Europe), 50% (Western Europe), and 49% (China) (Atradius 2023, 2024a, 2024b, 2024c). South Africa’s trade credit usage accords with the excessive use observed by the previous countries mentioned as observed next. From an investment perspective, approximately 50% of current and 32% of total assets are funded by trade credit. From a financing perspective, trade credit contributes to approximately 67% of current and 53% of total liabilities (Kwenda and Holden 2014). A study by Machokoto et al. (2020) documented an 89% increase in South African corporate debt for the period between 1991 and 2015, which could raise SMEs’ exposure to financial risks such as late payments or default risks transcending into irrecoverable debts associated with trade credit.

2.1.1. Late Payment and Default Risks in SMEs

SMEs are motivated to use trade credit funding when making purchases as it can improve profits should trade debtors agree to pay inflated prices for the same products in the future, while benefitting from delayed payment to trade creditors (Yazdanfar and Öhman 2017). Prior research observes a proportional relationship between granting trade credit and increased profitability, which includes decreasing transaction costs and increasing customer loyalty (Petersen and Rajan 1997; Smith 1987). More recently, the impact of the previous global financial crises on corporate profitability was softened for those firms who invested in trade debtors (Kestens et al. 2012). Contrary to this, high investment in trade debtors resulted in operational vulnerabilities, such as the trade-off between increased profitability and risk as profit maximisation can impair operational liquidity (Silva 2024). Evidence of this trade-off is confirmed by Lefebvre (2023), who observed that SMEs deliberately accept longer payment durations to support growth. This mostly occurs in countries characterised by firms exhibiting strong repayment qualities, including European Union (EU) SMEs.

Overdue debtor payments have remained a critical constraint to SMEs, as late payments affect approximately 80% of EU firms while close to 2% of EU firms forgone annual sales due to bad debts (Intrum 2019). The European Payment Report of 2022 adds to concerns around the recurring problem of late payments within SMEs, indicating that most European businesses report financial difficulties among debtors as the primary reason for late payments received (Intrum 2022). The European businesses surveyed revealed that 62% (2018) of EU SMEs concur with the previous reasons as the primary contributor to late payments, 48% of EU SMEs report intentional late payments as a primary reason, followed by 45% reporting debtor administrative inadequacies as a primary reason (Intrum 2022). Therefore, late payments remain a major constraint for businesses attributable to ineffective trade credit management. In addition, approximately 50% of all USA B2B invoices are paid late, 45% are paid late in Eastern Europe, followed by 94% in Western Europe, while 53% of all Asian businesses reported a lengthening in days sales outstanding (DSO) (Atradius 2020a, 2020b, 2020c, 2021). More recently, late payments affect approximately 50% (USA), 66% (Central and Eastern Europe), 50% (Western Europe), and 44% (Asia) of all B2B invoices, while bad debts relative to B2B invoices totalled as follows: 8% (USA), 8% (Central and Eastern Europe), 8% (Western Europe), and 5% (Asia) (Atradius 2023, 2024a, 2024b, 2024c). Added to this, the COVID-19 pandemic exuberated SMEs’ late payments given that in 2020, a 5% growth in days beyond term5 (DBT) was observed, resembling an increase in credit (default) risk from a creditor’s perspective (Lui 2020). The study by Zimon and Dankiewicz (2020) observed that, during the COVID-19 pandemic, SMEs were forced to adapt their trade credit management strategies from moderately to highly conservative to remain financially liquid. SMEs opted for safer trade credit strategies to enhance management effectiveness that included, strictly controlling all trade debtors and restricting credit limits on long-term credit supplies.

Furthermore, most SMEs, unlike their larger counterparts, cannot assess credit risk, which raises the possibility of accepting high risk customers prone to default, resulting in liquidity constraints that often impair operational viability. SMEs are exposed to vulnerabilities synonymous with bad debts primarily due to their dependability on internally generated funding, such as trade credit. Trade credit often generates a high degree of information asymmetry contributing to financial problems and eventual business exits (Cassar 2004). Therefore, SMEs are constrained by financial challenges that include trade credit mismanagement, with various authors describing it as a substantial limitation to enterprise success (Braimah et al. 2021). Otto (2018) observes that SME trade credit mismanagement largely contributes to negative net working capital. Limited working capital results in cash flow pressures that could spiral into a continuation of cash flow shortages. These liquidity shortages affect not only the individual enterprise but also other SMEs along the supply line, given the interrelatedness of SME sectors. Should SMEs be forced through financial stringency to keep their working capital constant, increased payment delays from debtors must be counteracted with late payments to creditors, articulating the destructive ‘spill-over’ effect between SMEs as the outgoing delay must exceed the incoming delay for SMEs to balance cash flows (Bams et al. 2020; Otto 2018). These negative spillovers testify to the importance of effective trade credit management because of its associated financial problems that may lead to insolvency (Zimon and Dankiewicz 2020). Mian and Smith (1992) support the importance of an effective credit management process in managing outstanding debts, reflected by assessing customers’ default risk and enforcing credit terms. More recently, a study by (Peter et al. 2022) reports that the application of effective trade credit management practises can improve SMEs’ financial performance. Numerous other authors concur that effective credit management is essential to business success as the choice to neglect trade credit management could materialise the risk of bad debts, impairing profitability and business growth (Zimon and Dankiewicz 2020). Therefore, understanding the business environment surrounding SMEs and how it affects their trade credit management is of high importance (Otto et al. 2022).

Given SMEs’ credit rationed realities, SMEs’ primary funding source, apart from net cash flows, largely remains trade credit extended by suppliers, testifying to the importance of effective trade credit management especially in the presence of financial problems such as adverse selection attributable to asymmetric information (Herrington and Kew 2018; Yazdanfar and Öhman 2017). The section below will review the asymmetric information and credit rationing theories of trade credit.

2.1.2. Asymmetric Information Theory

Smith (1987) defined asymmetric information as a culmination of financial uncertainties for the creditor concerning the debtor’s creditworthiness. The presence of information asymmetries is evident particularly for unestablished SMEs due to their unknown business reputation and limited credit history (Andrieu et al. 2018). The presence of information asymmetries becomes unavoidable for creditors when screening a potential debtor, especially for SMEs, as the creditor is less informed about the debtor’s repayment qualities than the debtor self (Jensen and Meckling 1976). To reduce information, asymmetries creditors should resort to credit management processes such as credit screening before credit extension, thereby collecting information about the debtor’s liquidity and leverage ratios (Andrieu et al. 2018). The study by Vander Bauwhede et al. (2015) observed that the improved effectiveness of SMEs’ financial reporting, measured by credit management quality, would result in lower asymmetric information between SMEs and suppliers. Creditors who do not follow a rigid credit management process may run the risk of adverse selection due to information asymmetries, stifling SMEs’ trade credit management effectiveness (Nguyen and Ramachandran 2006). Adverse selection resonates in the debtor’s actions to accept an inferior product due to unobservable private information regarding the product supplied by the creditor (Berndt and Gupta 2009). However, trade credit management can deal with non-payment concerns or the potential risk thereof, due to asymmetric information, as creditors can provide guarantees that reduce the debtors’ concern by offering a time duration for approving product quality and making payment to creditors after accessing the product (Ng et al. 1999). Also, the choice of trade credit terms will be influenced by asymmetric information by offering specified credit terms, a debtor can reveal product quality satisfaction through their payment practises. This holds true, because a debtor’s response to credit terms helps identify businesses that have trouble in repayment of accounts, trade credit terms can be designed to signal information on creditworthiness (Ng et al. 1999). Petersen and Rajan (1997) concur that trade credit management empowers SMEs to be in a better position to evaluate their customers’ ability to pay, solve incentive problems, and enable the reselling of products in the event of default. Creditors offering trade credit benefit from informational advantages in lending to less creditworthy firms compared to other credit lenders (Berger and Udell 2006). However, their management effectiveness is of paramount importance given trade credit’s associated financial risks.

In addition, corruption can resonate in SMEs because of asymmetric information creating credit lending uncertainty and risk as the information misalignment between creditor and debtor may entice debtors to engage in corrupt activities without the creditor’s knowledge adversely affecting transaction costs, thereby making creditors less willing to grant and debtors less acceptable to receive trade credit leading to credit rationing (Bardhan 2017). Cai et al. (2023) added that local corruption may impair the contract environment, making creditors more cautious when formulating their trade credit contracts, thus reducing the supply of trade credit by creditors. Local corruption also distorts the allocation of non-trade credit financing such as short-term bank loans and government grants, thus increasing the demand for non-trade credit sources by debtors, resulting in credit rationing as a serious external business environment constraint to SMEs given their limited access to external capital attributable to asymmetric information because of corruption. SMEs, especially those in developing economies, remain under resource pressures, therefore credit rationing remains a burden on SMEs’ operational efficiency (Wellalage et al. 2019b; Terziovski 2010). Because trade credit acts as a contractual mechanism aiding financial problems attributable to asymmetric information, the credit rationing theory will be discussed next as part of the asymmetric information theory of trade credit.

2.1.3. Credit Rationing Theory

Stiglitz and Weiss (1981) articulated the credit rationing theory explaining that creditors and financiers can decide to offer a variety of interest rates that would ration the availability of credit to debtors. Importantly, corruption results in externalities in the form of financial problems associated with information asymmetry that includes increased transaction and monitoring costs, thereby restricting credit and resulting in SMEs becoming credit rationed. The study by Cai et al. (2023) proved that local corruption, through corporate misconduct that intensifies information asymmetries between SMEs, proliferates SMEs’ credit rationing realities as it negatively affects the supply and demand of trade credit.

Ultimately, several studies support that corruption undoubtedly expands the divide between creditor and debtor, because of asymmetric information, creating stifled cooperation and mutual distrust in the credit agreement worsening accounting quality because of depreciating transparency and accountability of accounting information (Chen et al. 2020; Lourenço et al. 2016). The result of this could be detrimental to SMEs’ trade credit management effectiveness.

2.2. Corruption and SMEs

Corruption, although regarded as a multidimensional phenomenon contributing to complexities prevalent across worldwide economies, can be broadly defined as knowingly misusing public resources for private benefit, which includes fraud, bribery, nepotism, and embezzlement (Al Qudah et al. 2024; Rose-Ackerman 1999). SMEs are surrounded by business environmental constraints, such as challenges in adapting to institutional environmental fluctuations and being exposed to adaptations to economic policy because of corruption (Rashid and Saeed 2017). The effect of corruption is more pervasive in SMEs compared to their larger counterparts as explained next. Firstly, SME business structure allows for a closer relationship between staff members and the relatively greater degree of informality can result in a culture where corruption is more easily tolerated. Secondly, most SME staff focus only on the present or short-term future of the business by narrow-mindedly viewing the short-term benefits of corruption without considering the long-term shortfalls associated with corruption. Thirdly, due to external funding constraints and smaller profit margins, SMEs cannot always refuse to pay for bribes or other unofficial payments. Lastly, SMEs lack the bargaining power and influence to oppose bribery requests or similar solicitations due to weak network linkages with higher bureaucrats and politicians. Altogether, this increases SMEs’ willingness to partake in corruption as corrupt officials hardly fear any resistance from SMEs due to costs associated with marginalisation payable by those unwilling to partake (Le and Doan 2020). In addition, several authors note the disparity in corruption behaviour between small and larger SMEs as smaller SMEs become compelled to partake in acts of corruption, like bribery, due to their vulnerability as opposed to larger SMEs’ willingness to execute acts of corruption to advance in the market strategically by initiating high value bribes (Nguyen 2020) as cited by Andrade-Rojas and Erskine 2024). The section below will distinguish between two popular perspectives, one resembling a positive implication because of business corruption and the other a negative one, apprising the relationship between corruption and entrepreneurship.

The Relationship Between Corruption and Entrepreneurship

The previous literature distinguishes between the ‘greasing the wheels’ and ‘sanding the wheels’ perspectives in describing the relationship between corruption and entrepreneurship in cultivating economic growth (Fradanbeh et al. 2024). The first perspective, known as the efficiency hypothesis of corruption or ‘greasing the wheels’, explains that corruption can proliferate economic growth; therefore, SMEs knowingly decide to engage in corruption due to problems combining regulatory compliance and bureaucracy (Gaviria 2002; Kaufmann and Wei 1999). Corruption is often regarded as a favourable practice for SMEs operating in developing countries constrained by problematic government policy and ill-functioning institutions (Weill 2011). This holds true, as SMEs that refuse to provide officials with additional payments, while most other SMEs do, will eventually have to face marginalisation. As observed in a study by Wellalage and Thrikawala (2021), 18% of the study sample’s total sales were devoted to the payment of bribers and informal payments to public officials. Not partaking in corruption regularly results in SMEs not obtaining the necessary permits and licences, not receiving public contracts, failing to comply with the inspectors, and ultimately aggravating the company’s competitive position in the market (Della Porta and Vannucci 1999). The costs imposed on SMEs in this respect are quite substantial and might even encourage smaller enterprise units to engage in corruption if they perceive it to be prevalent, as reluctance to do so can significantly impair business operations (Getz and Volkema 2001). A similar trend in the literature, aligning with the ‘greasing the wheels’ view, was observed from the study by (Trinh et al. 2024), investigating the relationship between local corruption and SME investment in a developing economy as part of the Global South. Contrary to the conventional literature about corruption being harmful to business operations, their study announced that local corruption is positively related to SMEs’ investment expenditure and efficiency.

The second perspective, known as the inefficiency hypothesis of corruption, or ‘sanding the wheels’, describes corruption as harmful to entrepreneurship for several reasons. Corruption affects the operational capacity of the business because of financing constraints, that could impair SMEs’ trade credit management, undermining the practice of good corporate governance, and hindering the possibility of securing funding for high-yielding business projects (Le and Doan 2020; Reinikka and Svensson 2005). The study by Wellalage et al. (2019a) observed that SMEs could operate in business cycles, whereby they are unable to avoid paying bribes or other unofficial payments, as the refusal to partake in corruption endangers the livelihood of the enterprise. However, the decision to engage in corrupt business practises remains costly for SMEs, as 68.2% of their study sample was more likely to be credit-constrained compared to their equivalents who do not partake in bribery or related acts of corruption. Wellalage et al. (2019a) further observed that corruption yields an increase in SMEs’ credit constraint by 7.63%. Le and Doan (2020) state that, especially in developing countries, an increase in corruption correlates positively with corporate financial fragility. More recent studies investigating the effect of corruption on SME operations in developing countries observed a similar result. The study by Andrade-Rojas and Erskine (2024) supports the conventional, ‘sanding the wheels’, view around corruption adversely affecting SME operations by observing that bribery negatively affects SMEs’ ability to digitalize operations. On local soil, the study by Soni and Smallwood (2024) investigated the perception of corruption by SMEs operating in the South African construction industry, observing corruption as a major issue that requires immediate interventions to counteract the devastation caused. Their study alluded to several constraints experienced by SMEs because of corruption, namely impairing industry economic growth, discouraging whistleblowing, underdelivering on project milestones due to substandard materials and low-quality workmanship attributable to below-average procurement processes, and unethical behaviour.

South African SMEs are thus both cursed and blessed because deciding not to partake in corruption exposes the enterprise to financial limitations that could impair their trade credit management while SMEs continue to operate in a business environment susceptible to corruption that proliferates their choice to partake in corruption.

2.3. Definition of SMEs

An extensive review of the literature informs that no universally approved definition of the phrase ‘small and medium-sized enterprises’ (SMEs) exists (Beck et al. 2005). This is attributable to most definitions, including the South African definition, applying a compilation of qualitative and quantitative elements to formulate a single structured definition. However, given that this study set out to determine how corruption within South African businesses impacts their trade credit management effectiveness, the definition of SMEs from a South African perspective is accentuated. To this end, the National Small Enterprise Act of South Africa of 1996, as amended in 2003 and 2004 by Government Gazette Statutes of South Africa (2019a, 2019b) and updated to Government Notice no. 399 of Government Gazette 42304, dated 15 March 2019, provides a schedule of size standards for the definition of SMEs. The schedule includes all the sectors of the South African economy for small enterprises or SMEs. A schedule of size standards for the definition of SMEs is provided (refer to Appendix B).

3. Materials and Methods

The study applied the quantitative research method using the survey method by employing an online questionnaire as the primary data-collection instrument (refer to Appendix A).

3.1. Population and Sampling

The study focused on formal SMEs within South Africa that operate in various industries, all of which were representative of high trade credit volumes, especially for those SMEs operating in manufacturing and retail (Selima 2007). These industries included retail and distribution, professional service, mining, manufacturing, information and communication technology, government, financial services, and engineering targeting individuals working as trade credit managers/accountants appointed by the SME and/or the SME owner themselves. Therefore, the population frame of SMEs included a wide range of SMEs operating in different capacities, and, in so doing, the researcher ensured that the sample frame of SMEs represents differentiation in their business characteristics making the sample heterogeneous. In addition, the sample frame of SMEs varied in employee count and establishment duration further ensuring heterogeneity. For a more detailed review of respondent demographics, refer to Otto (2022). SMEs operating in these industries were deemed suitable, as they formed a good representation of industries dealing with high volumes of trade credit in South Africa and thus, for ease of sampling, these were suitable for data collection. In addition, all respondents, positioned as either employees or SME owners, worked as trade-credit managers (involved in the management of both debtors and creditors).

The population frame of SMEs consisted of formal SMEs that were obtained from Interactive Direct. The researcher made use of the services offered by iFeedback Consulting (Pty) Ltd., a private company specialising in data collection, and shared expert advice and insights drawn from past similar studies. All SMEs listed on this database fit the criteria for businesses to be defined as SMEs, as stipulated in Government Gazette Statutes of South Africa (2019a, 2019b). In so doing, iFeedback Consulting (Pty) Ltd. was able to email the online questionnaire to a population frame of SMEs (n = 45,313) obtained from the Interactive Direct Business Database. To obtain a reasonable response rate the researcher purposively targeted a suitable number of SMEs (n = 10,450) as the population frame, while all SME names were crosschecked to eliminate double counting. The margin of error for this study, using the Zikmund sample size calculator, ranges from 3% to 7%, with a 5% margin of error and 95% confidence interval as the most accepted range (Zikmund et al. 2010). In applying the Zikmund sample size calculation at a 5% margin of error and 95% confidence level, when the parameter in population is assumed to be over 85% or under 15%, the required sample size, representing the minimum recommended sample size when targeting as population frame of 10,450 SMEs as a representative sample, totalled n = 297 (Zikmund et al. 2010). However, for this study, the actual sample size totalled n = 450, with n = 434 questionnaires returned after 10,450 questionnaires were initially distributed, making the response rate 4.15% of the total population. The number of completed and accepted questionnaires (actual sample size for statistical analysis) totalled n = 422, which presents a questionnaire completion rate between 70.41% and 100%, while the remaining 12 questionnaires were rejected, as these presented a questionnaire completion rate between 0% and 69.39%. It should further be noted that the missing values presented a minor challenge before the commencement of data analysis, given the low number of item non-response cases.

3.2. Data Collection and Analysis

Concerning questionnaire development, scale items were obtained from a combination of studying the literature for theoretical constructs and empirical conclusions, re-working the questionnaire based on a questionnaire used in a previous study (Otto 2022) and help from experienced statisticians who set out to peer debrief the questionnaire statements. The measuring instrument was designed to measure the impact of the business environment (internal and external) on SMEs’ trade credit management. Three sections were included the following: a 49-item questionnaire testing SMEs’ business environment, 35-item section testing SMEs’ trade credit management, and a demographical section. Across all questionnaire sections, five and six-point Likert scale questions were asked apart from the demographic section.

The data collection procedure was conducted via e-mail, which contained a letter detailing the title of the study, a short introduction of the researcher, the time to complete the questionnaire, and the assurance that the completion of the questionnaire is voluntary, along with the necessary ethical practices’ disclosure, such as assurance that any data obtained from the questionnaire will be used to complete research for the University of Johannesburg. The electronic link to the completion of the online questionnaire was also available in the letter sent by e-mail, as distributed to the sample. Repeated reminder e-mails, restricted to a maximum of three per respondent, were mailed to the sample of respondents to ensure that they completed the questionnaire. The software used in administering the questionnaire was Typeform.com, as administered by the services of iFeedback Consulting (Pty) Ltd. (Barcelona, Spain).

Excel was used for data capturing which was then uploaded to SPSS (Statistical Package for Social Sciences) 29 for analysis using statistical tests such as frequency distribution, mean, standard deviation, internal consistency, factor analysis, as well as correlation and regression analyses.

3.3. Ethical Considerations

All required ethical clearance processes as stipulated by the University of Johannesburg were adhered to. The School of Accountancy Research Ethics Committee awarded ethical clearance in 2018 (SAREC20180502-02). All participating respondents gave their consent to complete the questionnaire while being properly informed as to the completion of the questionnaire being completely voluntary, while all information supplied by respondents alongside the identity of each respondent was treated as strictly confidential. All respondents were well informed concerning the research process, purpose, and rights so that all participating respondents could make an informed decision.

4. Results and Discussion

The empirical results provided set out to determine the impact of corruption on SMEs’ trade credit management effectiveness.

4.1. Demographical Data

The questionnaire collected demographic data, revealing that the average age of the respondent was 52 years, predominantly classified as white male, and nearly half of the total respondent group had a post-graduate qualification, while their average trade credit management experience amounted to 18 years. Most SMEs were based in Gauteng, with nearly half of the respondents operating within the manufacturing industry and three-quarters of the total group operating independently. In addition, over half of the total group of respondents employ up to 50 staff members, while both SMEs and individuals equally represent the largest percentage allocation as a description of the respondents’ clientele.

4.2. Validity and Reliability Statistics for Questionnaire Sections

Content validity was attained through a comprehensive literature study to select valid theoretical constructs while empirical conclusions enhanced content validity. To further ensure the measurement instrument’s content validity, all questionnaire questions were peer-debriefed by experienced academics and statisticians who related valuable feedback used to improve the questionnaire. In ensuring the face validity of the questionnaire, appropriate coverage of the concepts under study to ensure conceptual clarity was attained through insights drawn from the researcher including experienced statisticians during the development of the measurement instrument.

Table 1. Validity statistics of questionnaire sections before EFA.

Questionnaire Section Before EFA	Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy6	Barlett’s Test for Item Validity (df)7	Barlett’s Test for Item Validity (Sig)
SMEs business environment	0.860	1176	0.000
SMEs trade credit management	0.934	595	0.000

Source: SPSS calculations.

indicates the relevant validity statistics for the two main sections of the online questionnaire namely SMEs business environment (Section B), which tested for SME corruption, and SMEs trade credit management (Section C), which tested for SME trade credit management effectiveness, before exploratory factor analysis (EFA).

Table 2. Reliability statistics for SMEs corruption and trade credit management factors after EFA.

SME corruption factors after EFA	Cronbach’s alpha8
DCUL	0.871
CDPP	0.763
CCPP	0.798
SME trade credit management effectiveness factors after EFA	Cronbach’s alpha
MTC1	0.914
MTC2	0.766
MTC3	0.907
MTC4	0.837
MTC5	0.781

Source: SPSS calculations.

provides Cronbach’s alpha for SME corruption and trade credit management factors, respectively, after EFA.

Table 1 depicts the results obtained after performing the KMO and BTS tests with results from both tests supporting the appropriateness of the factor analysis technique as explained next. For questionnaire Section B: BTS at 1176 (p = 0.000), and for questionnaire Section C: BTS at 595 (p = 0.000), illustrating that the data were appropriate to perform a factor analysis. The result of the KMO measure of sampling adequacy indicates sufficient items for each factor: 0.860 and 0.934 for questionnaire Sections B and C, respectively, indicating high validity. Reliability results from Table 2 reveal Cronbach’s alpha values close to 1 for all three SME corruption factors, illustrating very high internal consistency ranging between 0.70 and 0.80. Likewise, Cronbach’s alpha is very close to 1 for all five SMEs’ trade credit management factors, indicating that most of the factors are highly reliable, ranging between 0.80 and 0.90 for three out of the five factors with the remaining two factors ranging between 0.70 and 0.80.

4.3. Factor Analysis for Computed SMEs Corruption and Trade Credit Management Effectiveness Factors

EFA was performed on the final 422 questionnaires returned by SMEs in the main survey to test the homogeneity of the underlying constructs. EFA of the responses allowed for construct validity using Cronbach’s alpha to analyse the 49 items in Section B, testing for SMEs corruption variables, and 35 items Section C, testing for SMEs’ trade credit management effectiveness variables and questionnaire components. The set of three independent SME corruption factors, refer to

Table 3. Rotated factor loading values for SMEs corruption factors after EFA.

Factor	Factor Components	1	2	3	4	5	6	7	8	9	10	11	12
DCUL	B8.7 Debtors benefit from the business approving loans that have no potential to be repaid by the debtor	0.021	0.021	0.839	0.099	0.041	−0.010	−0.029	0.079	0.008	−0.063	0.054	−0.073
	B8.6 The business benefits from creditors approving loans that do not adhere to the basic financial criteria	−0.014	−0.007	0.832	−0.011	0.035	0.011	0.065	−0.058	0.001	0.152	−0.018	0.102
	B8.8 The business benefits from creditors approving loans with no potential to be repaid by the business itself	−0.030	−0.027	0.811	0.064	0.011	−0.049	0.025	−0.096	0.000	0.136	−0.007	−0.048
	B8.5 Debtors benefit from the business approving loans that do not adhere to the basic financial criteria	−0.061	0.040	0.774	0.028	−0.048	0.184	0.019	0.132	0.022	−0.046	−0.001	−0.021
CDPP	B8.1 Debtors (corporate and/or government customers) delay payment to the business while having enough funds available for full payment to the business	−0.023	−0.018	−0.009	0.034	−0.084	−0.021	0.035	0.886	0.036	−0.068	0.025	0.085
	B8.3 Debtors (corporate and/or government customers) delay payment while giving preference to another business	−0.033	−0.006	0.041	−0.053	0.086	−0.042	−0.001	0.879	0.040	0.068	0.030	−0.017
CCPP	B8.4 The business avoiding payment to creditors due (corporate and/or government suppliers) while giving preference to other creditors for which payment is not due	0.032	−0.038	0.294	−0.128	−0.041	−0.085	−0.026	0.068	−0.065	0.681	−0.067	0.061
	B8.2 The business avoiding payment to creditors (corporate and/or government suppliers) while having enough funds available for full payment	0.182	−0.118	0.275	−0.125	−0.015	−0.148	−0.004	0.032	−0.079	0.617	0.010	0.117

Source: SPSS calculations.

, included SME and debtor corruption through approving unviable loans (DCUL), corrupt debtor payment practises through forcefully delaying payments due to the SME (CDPP), and corrupt SME payment practises through forcefully delaying payments due to creditors of the SME (CCPP). The set of five dependent factors that formed SMEs trade credit management effectiveness, refer to

Table 4. Rotated factor loading values for SMEs trade credit management effectiveness factors after EFA.

Factor	Factor Components	1	2	3	4	5	6
MTC1	C1.5 Assessing debtors’ financial reserves as ability for repayment	0.824	0.019	0.024	−0.107	−0.286	0.018
	C1.6 Assessing debtors’ financial position as ability for repayment	0.821	−0.050	0.086	−0.105	−0.158	−0.005
	C1.4 Assessing debtors’ capacity in terms of their willingness to repay	0.765	−0.051	0.102	−0.024	0.213	−0.017
	C1.3 Assessing debtors’ character in terms of their willingness to repay	0.763	−0.055	0.093	−0.034	0.259	0.007
	C1.1 Analysing general economic conditions, including the political environment, before granting credit	0.705	0.023	−0.046	−0.110	0.008	0.157
	C1.14 Determining if the debtor(s) possess the collateral needed for repayment	0.549	0.248	0.059	0.084	−0.227	−0.283
	C1.7 Checking debtor orders against credit limits allowed	0.536	0.008	0.018	−0.121	0.210	−0.210
	C1.8 Collecting revenue in line with agreed credit terms, as set out in the credit policy	0.503	−0.039	0.012	−0.186	0.281	−0.227
	C1.2 Administering the sales ledger (e.g., monthly reconciliations of debtor accounts and/or all other administrative duties relating to debtor accounts)	0.480	−0.003	0.167	−0.120	0.420	0.044
MTC2	C1.10 Collecting outstanding debt through the use of collection agencies	−0.026	0.921	−0.072	−0.047	0.083	0.217
	C1.11 Collecting outstanding debt through the use of legal action	−0.001	0.881	−0.004	−0.052	0.195	0.101
	C1.17 Having credit insurance for sales	−0.047	0.539	0.070	0.020	−0.218	−0.281
	C1.18 Imposing statutory interests on late payment	0.036	0.536	0.061	0.011	−0.195	−0.236
MTC3	C2.5 Ensuring effective order and invoice control of all creditor records	−0.027	−0.017	0.900	−0.050	−0.078	0.026
	C2.4 Building a sound and long-term relationship with creditor(s)	−0.077	0.046	0.893	−0.081	−0.172	0.121
	C2.6 Managing creditors actively	−0.046	0.013	0.787	−0.193	−0.134	0.031
	C2.2 Ensuring effective order and invoice control of all debtor records	0.093	−0.029	0.745	−0.003	0.183	−0.061
	C2.3 Managing debtors actively	0.026	−0.006	0.739	−0.035	0.179	−0.072
	C2.1 Building a sound and long-term relationship with debtor(s)	0.072	−0.060	0.732	0.037	0.072	−0.080
MTC4	C3.2 Managing general trade credit practises	0.081	0.039	0.037	−0.773	−0.075	−0.088
	C3.1 Managing cash flow	0.149	−0.065	0.126	−0.757	−0.053	0.049
	C3.4 Managing late payments made to creditors	0.024	0.038	0.211	−0.670	0.016	0.101
	C3.3 Managing late payments received from debtors	0.164	0.041	0.140	−0.511	0.120	−0.154
MTC5	C4.2 Conducting a credit analysis (evaluation of applicants, in order to distinguish between ‘good’ debtors that will pay and potential ‘bad’ debtors that will default)	0.135	−0.047	0.075	−0.066	−0.005	−0.745
	C4.1 Offering a credit period (the period of time in which the buyer needs to repay the outstanding account)	0.038	−0.062	0.238	−0.003	0.118	−0.667

Source: SPSS calculations.

, included SMEs’ effectiveness in providing trade credit management activities (MTC1), mechanism and insurance to assist with the collection of or protection against the risk of outstanding debt (MTC2), SMEs’ effectiveness in managing trade credit management principles (MTC3), SMEs’ effectiveness in managing trade credit management aspects (MTC4), and SMEs’ effectiveness in applying credit policy components when granting credit (MTC5).

4.4. Total Variance Explained for Computed SMEs Corruption and Trade Credit Management Effectiveness Factors

According to the rules of factor analysis only factors that have Eigen values greater than one should be retained. The Initial Eigenvalues for questionnaire Sections B and C cumulative percentages were 70.374% and 65.824%, respectively. Firstly, the Eigen values for the 3 SME corruption factors, DCUL, CDPP, and CCPP are shown in

Table 5. Eigenvalues and total variance explained9 for SMEs corruption factors.

Factor	Eigenvalues Total	Cumulative Variance Explained %
DCUL	3.254	39.487
CDPP	1.635	60.396
CCPP	1.208	65.967
Unlabelled factor10	1.095	68.201

Source: SPSS calculations.

. The total variance explained for the formulated factors totalled 70.374%. Secondly, the Eigen values for the 5 SMEs trade credit management effectiveness factors, MTC1 to MTC5, are shown in

Table 6. Eigenvalues and total variance explained for SMEs trade credit management factors.

Factor	Eigenvalues Total	Cumulative Variance Explained %
MTC1	13.660	39.028
MTC2	3.507	49.049
MTC3	2.059	54.931
MTC4	1.550	59.360
Unlabelled factor11	1.173	62.711
MTC5	1.090	65.824

Source: SPSS calculations.

. The total variance explained for the formulated factors totalled 65.824%. These 5 factors, together with the 3 SME corruption factors, are further confirmed by the rotation sums of squared loading after Oblimin rotation.

4.5. Correlation and Regression Analysis Results

In completing the first secondary objective, namely to empirically test the relationship between corruption and SME trade credit management effectiveness, the findings report on the correlation and regression analysis results between SMEs’ corruption factors and SMEs’ trade credit management effectiveness factors. The section to follow does so by explaining the empirical models.

4.5.1. Models

Five models are reported, with each model containing the same independent variables but a different dependent variable. Models 1 to 5 used in this study can expressed as follows:

Consider the linear regression model (Equation (1)) illustrating the various corruption factors, DCUL, CDPP, and CCPP proposed to impact SMEs’ trade credit management effectiveness (refer to MTC1 in

Table 7. Correlation and regression analysis results between SMEs corruption and SMEs trade credit management effectiveness factors.

Correlation Results
Dependent variables	MTC1		MTC2		MTC3		MTC4		MTC5
Impendent variables	r	Sig.	r	Sig.	r	Sig.	r	Sig.	r	Sig.
DCUL	0.128 ***	0.01	0.272 ***	0.01	0.023	0.05	0.011	0.05	0.099 **	0.05
CDPP	0.215 ***	0.01	0.127 ***	0.01	0.133 ***	0.01	0.119 **	0.05	0.164 ***	0.01
CCPP	0.224 ***	0.01	0.112 **	0.05	0.121 **	0.05	0.153 ***	0.01	0.139 ***	0.01
Regression results
	Model 1:		Model 2:		Model 3:		Model 4:		Model 5:
Dependent variables	MTC1		MTC2		MTC3		MTC4		MTC5
Impendent variables	Std. Beta	Sig.	Std. Beta	Sig.	Std. Beta	Sig.	Std. Beta	Sig.	Std. Beta	Sig.
DCUL12	−0.100 **	0.019	0.144 ***	0.005		-	-	-	-	-
CDPP	0.025	0.530	−0.020	0.677	0.022	0.635	0.039	0.374	0.044	0.354
CCPP	0.142 ***	0.001	−0.001	0.988	0.016	0.722	0.013	0.767	0.021	0.651
R2	0.507		0.265		0.294		0.362		0.274
Adjusted R squared	0.493		0.244		0.278		0.348		0.258
F	35.964 **		12.786 **		18.320 **		24.868 **		16.478 **
N	397		403		405		404		402

Source: SPSS calculations ***, ** indicate significance on a 99% and 95% confidence level, respectively.

):

Y₁ = α + β₁X₁ + β₂X₂ + β₃X₃ + ϵ

(1)

where

• Y₁ = MTC1;
• β_1,2,3 = beta (sample coefficients for internal/external business environmental factors);
• X₁ = CDUL;
• X₂ = CDPP;
• X₃ = CCPP;

Consider the linear regression model (Equation (2)) illustrating the various corruption factors that are proposed to impact SMEs’ trade credit management effectiveness (refer to MTC2 in Table 7):

Y₂ = α + β₁X₁ + β₂X₂ + β₃X₃ + ϵ,

(2)

where

• Y₂ = MTC2 (refer to Table 7);
• β_1,2,3 = beta (sample coefficients for internal/external business environmental factors);
• X_1,2,3 = corruption factors (see Equation (1)).

Model three is as follows:

Y₃ = α + β₁X₁ + β₂X₂ + β₃X₃ + ϵ,

(3)

where

• Y₃ = MTC3 (refer to Table 7);
• β_1,2,3 = beta (sample coefficients for internal/external business environmental factors);
• X_1,2,3 = corruption factors (see Equation (1)).

Consider the fourth linear regression model below provided by Equation (4):

Y₄ = α + β₁X₁ + β₂X₂ + β₃X₃ + ϵ,

(4)

where

• Y₄ = MTC4 (refer to Table 7);
• β_1,2,3 = beta (sample coefficients for internal/external business environmental factors);
• X_1,2,3 = corruption factors (see Equation (1)).

For the final linear regression model, refer to Equation (5), illustrating the three corruption factors proposed to impact SMEs’ trade credit management effectiveness (refer to MTC5 in Table 7):

Y₅ = α + β₁X₁ + β₂X₂ + β₃X₃ + ϵ,

(5)

where

• Y₅ = MTC5;
• β_1,2,3 = beta (sample coefficients for internal/external business environmental factors);
• X_1,2,3 = corruption factors (see Equation (1)).

4.5.2. Reporting of Correlation and Regression Analyses Results

This section reports on the results obtained from the correlation and regression analysis.

For the correlation analysis results tabulated in Table 7, the Pearson correlation was used to test for the direction and strength of the relationship between corruption factors and SMEs trade credit management effectiveness factors. The p-value for each factor was compared against a significance level of 0.05. If the p-value is <0.05, a significant relationship exists between the SME corruption factor and SME trade credit management effectiveness factor. As observed from Table 7, the following corruption factors reveal a weak correlation (r < 0.40) with MTC1, DCUL (r = 0.128; p < 0.01), CDPP (r = 0.215; p < 0.01), CCPP (r = 0.224; p < 0.01). The following corruption factors reveal a weak correlation (r < 0.40) with MTC2, DCUL (r = 0.272; p < 0.01), CDPP (r = 0.127; p < 0.01), CCPP (r = 0.112; p < 0.05). DCUL displays an insignificant correlation with factor MTC3 (r = 0.023; p > 0.05). The following corruption factors reveal a weak correlation (r < 0.40) with MTC3, CDPP (r = 0.133; p < 0.01), and CCPP (r = 0.121; p < 0.05). DCUL displays an insignificant correlation with factor MTC4 (r = 0.011; p > 0.05). The following corruption factors reveal a weak correlation (r < 0.40) with MTC4, CDPP (r = 0.119; p < 0.05), and CCPP (r = 0.153; p < 0.01). The following corruption factors reveal a weak correlation (r < 0.40) with MTC5, DCUL (r = 0.099; p < 0.05), CDPP (r = 0.164 p < 0.01), and CCPP (r = 0.139; p < 0.01).

Concerning the regression analysis results (refer to Table 7), model one reveals that DCUL obtained a significant negative relationship (β = −0.100; p < 0.05) with MTC1. This is in line with expectations given that a corruption-related SME business environmental factor is expected to be detrimental to their trade credit management effectiveness. In continuing, two unexpected results were observed as unexpected: CCPP obtained a significant positive relationship (β = 0.142; p < 0.01) with MTC1 while model two reveals that DCUL obtained a significant positive relationship (β = 0.144; p < 0.05) with MTC2. Both outcomes are somewhat surprising, given the expectation that corruption should impair SMEs’ effectiveness in managing trade credit. However, when considering the reality of corruption being prevalent and affecting entrepreneurship, the trend in results becomes less unexpected from a South African SME perspective.

4.6. Discussion of Results

The study’s primary objective was to determine the impact of corruption on SMEs’ trade credit management effectiveness. The sections below intend to do so through a discussion of the provided results.

The results show that SMEs and SME debtors do decide to participate in corruption-related activities, by approving unviable loans and forcefully delaying payments due to creditors. Therefore, SMEs are more effective in managing trade credit, with specific reference to MTC1 and MTC2, which aligns with the ‘greasing the wheels’ perspective on corruption (Trinh et al. 2024; Kaufmann and Wei 1999). First, DCUL has a statistically significant positive impact on MTC2, which implies the intent of SMEs and SME debtors to partake in corruption-related activities to improve their management of trade credit. Second, CCPP has a statistically significant positive impact on MTC1. Because of this, it can be argued that SMEs do operate in a business environment implicated by corruption in that SMEs are avoiding their obligations payable to creditors while giving preference to other creditors for which payment is not due or simply postponing payment to creditors while having enough funds to make payment. Moreover, in the case of DCUL positively impacting MTC2, results explain that these acts of corruption enable both SME and SME debtor to improve their trade credit management effectiveness, as both parties are involved in acts of corruption through approving unviable loans, that could be an indication of a joint alliance of mutual benefit for both SME and debtor justifying for them their willingness to partake in acts of corruption. In the case of CCPP, SMEs decide to purposefully delay payments due to creditors, while positioned to complete the payment, instead decide to allocate payment to another creditor for whom payment is not due. This corresponds with the previous observation made that SMEs are willing to partake in corruption as they benefit from a joint alliance of mutual benefit favouring their trade credit management effectiveness. The findings by Wellalage and Thrikawala (2021), Wellalage et al. (2019a), Bowen et al. (2012), Weill (2011), and Méon and Weill (2010) support the results. Also aligned with the trend in study results observed, several GEM South African Reports show an increase in SME corruption-related activities while recent corruption indicators testify to the seriousness of corruption (Statistics South Africa 2023; Transparency International 2023; Herrington and Kew 2018). In addition, because SMEs largely use trade credit as a ‘two-way transaction’, the reality of a negative ‘spill-over’ effect on SMEs’ cash-flow cycle can become unavoidable as these spillovers negatively affect SMEs’ cash-flow cycle due to SMEs’ constraints with the management of trade credit. Therefore, apart from those SMEs who choose to partake in corruption to increase their trade credit management effectiveness, as observed from the study findings, for those SMEs not partaking in corruption the choice to do so should have devastating repercussions, which should orientate the enterprise not to get involved in corruption as observed from the results that show a more expected outcome discussed next.

In addition, DCUL reveals a statistically significant negative relationship with MTC1 that aligns with the ‘sanding the wheels’ perspective on corruption (Le and Doan 2020; Reinikka and Svensson 2005). Therefore, an increase in SME and debtor corruption related to approving unviable loans will result in a decrease in SMEs’ trade credit management effectiveness specific to MTC1 and vice versa. Previous studies support this by identifying corruption as harmful and a main obstacle to SME development, given that corruption could influence SMEs’ repayment capacity once credit is granted (Andrade-Rojas and Erskine 2024; Soni and Smallwood 2024; Amin and Motta 2023; Wellalage and Thrikawala 2021; Le and Doan 2020; Barkemeyer et al. 2018; Bryant and Javalgi 2016). In aligning this more expected study finding with the asymmetric information theory on trade credit, postulated by Smith (1987), several authors support the study finding concerning the results in that DCUL has a statistically significant negative relationship with MTC1. This holds true, as supported by the previous literature describing that corruption in SMEs becomes prevalent because of asymmetric information proliferating credit lending uncertainty resulting in debtors default risk that adversely affects SMEs’ trade credit management effectiveness (Otto 2022; Chen et al. 2020; Andrieu et al. 2018; Lourenço et al. 2016; Vander Bauwhede et al. 2015).

5. Recommendations

The following recommendations (refer to secondary objective two) are provided after the completion of the first secondary objective.

Despite the multidimensional trend observed in the results, the study argues in favour of the implementation of successful anti-corruption programmes to produce a more equitable business environment for SMEs, which must be maintained and expanded by several anti-corruption organisations. Therefore, the following recommendations are provided. Firstly, prominent forms of corruption-related activities should be analysed on a broad scale to such an extent as to provide useful policy implementation. Secondly, it is recommended that non-profit and public sector organisations such as Corruption Watch and Action Society, including Small Enterprise Development Agency (SEDA) and Small Enterprise Finance Agency (SEFA), join hands with private sector stakeholders such as SME South Africa and the Small Business Institute (SBI), by ensuring that SMEs firmly understand the seriousness of corruption from a legal perspective and are fully informed as how to report corruption. Thirdly, non-profit organisations, specifically Corruption Watch and Action Society, should lead by example by strategically and deliberately directing awareness to SMEs nationally using radio and television broadcasting, including social media and local platforms. Lastly, all anti-corruption organisations uphold their mandate to eradicate public and private sector corruption, by broadening existing policies to specifically include the investigation of SME and SME debtor corruption relating to improving effectiveness in managing trade credit.

6. Conclusions

This study set out to determine the impact of corruption on SMEs’ trade credit management effectiveness, this was attained. Apart from the study results that align with expectations observed from the previous literature, some results are somewhat unexpected, yet still align with several theories. Expectedly, the results align with conventional wisdom around corruption’s impact on business operations as the results show that the mitigation of corruption (DCUL) is statistically associated with increased SMEs’ trade credit management effectiveness (MTC1). Somewhat unexpectedly and contrary to conventional wisdom, the results also show that an increase in corruption (CCPP and DCUL) is statistically associated with increased SMEs’ trade credit management effectiveness (MTC1 and MTC2). Because of this, the results are indicative of SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit, showing that the roots of corruption are prevalent among SMEs, as confirmed by numerous other studies. The practical value of the article includes the following: firstly, by determining the impact of the significant independent factors on the formulated dependent factors, it broadens the understanding of the association between corruption and SMEs trade credit management effectiveness, which is useful to SMEs operating in a business environment prevalent of corruption. Secondly, the article provides valuable recommendations to SMEs and relevant stakeholders exposed to corruption while operating in distorted credit environments, given their dependency on trade credit.

As for study limitations, although the study’s primary objective was attained, the results do, however, reveal the willingness of SMEs to partake in corruption to increase trade credit management effectiveness, which leaves a void in understanding the reasons for this phenomenon. Therefore, future research can expand on the existing study in the form of an exploratory study for a more detailed understanding of why SMEs would pursue such avenues to improve their trade credit management effectiveness. Lastly, further research can also strive to determine how acts of corruption in SMEs affect their availability of trade credit as access to entrepreneurial finance remains a major cause of failure, as it so regularly constraints the growth of SMEs especially in developing countries.