The impacts on informal financing strategy of small and micro enterprises by interest rate risks and public health emergencies

Abstract

Informal finance is a crucial financing method for small and micro enterprises (SMEs) in most developing countries. Interest rate risks (IRS) should be a vital component of SMEs' informal finance strategies (IFS), and public health emergencies (as COVID-19) is likely to modify IFS due to increased liquidity risks for SMEs. This article examines the relationship between IRS and IFS and its moderated effects by COVID-19. The study develops a structural equation model of SMEs' IFS to investigate SMEs' IFS, the causal relationship between IRS and SMEs' IFS, mediator effects of SMEs' corporate financial status and regional financial and economic conditions, and moderating effects of SMEs' variables. The empirical data is from Wenzhou, China, distributed between 2016 and 2021. The study concludes that IFS will continue to be a financing technique for Chinese SMEs, even if it may have a deterrent effect due to COVID-19. Additionally, there is a strong positive causal association between IFS and SMEs' IRS, as well as moderator and mediator effects. Recommendations for government policy include clarifying the connection between SMEs' IFS and influencing variables, providing scientific and methodical analysis tools for evaluating factors impacting SMEs' IFS, and encouraging the optimization and upgrading of SMEs' IFS.

Introduction

In most developing countries, informal finance has always been an important financing method for small and micro enterprises (SMEs). Interest rate risks (IRS) should be an important component of SMEs’ informal finance strategy (IFS), and public health emergencies (as COVID-19) should modify IFS because it increased SMEs' liquidity risks. (Allen et al., 2019; Nguyen & Canh, 2021; Peng et al., 2019) Thus, the topics of this article include the relationship between the IRS and the IFS, as well as its moderated effects by public health emergencies (as COVID-19), followed by the theoretical assumptions, empirical results, and recommendations for governments and researchers. SMEs' IFS refers to the financial operations of small and medium-sized enterprises that occur outside of the formal financial system that is supervised by the central bank. However, the definition of IFS is ambiguous due to its diversity, and empirical data on it is difficult to obtain. And SMEs' IFS is said to encompass private, illicit, and criminal financing (Jackson & Thompson, 2022; Bruton et al., 2021; Nguyen & Canh, 2021; Allen et al., 2019; Cull et al., 2019; Ojong, 2019; Rahman, 2019; Yao et al., 2019). Mukete et al. (2021) discovered that more than 90% of SMEs in Cameroon's Mezam Division should implement IFS. Naegels et al. (2018) found Tanzanian female entrepreneurs mostly employ IFS.

SMEs can benefit from their IFS due to lower transaction costs resulting from closer interpersonal or communal links between lenders and borrowers, which may facilitate monitoring and enforcement with reduced costs and lower risks of moral hazard or adverse selection. And the lender-SME relationship can be a crucial approach to addressing information gaps and lack of transparency in formal financing for SMEs. This relationship can enable lenders to gather SME-specific data and evaluate their repayment capacity more accurately. (Kabange & Simatele, 2022; Sahar & Anis, 2016; van Klyton & Rutabayiro-Ngoga, 2018) Additionally, informal finance may offer the kinds of financial services that SMEs want but that formal finance may not be able to supply. In contrast, lending organizations or loan brokers for SME informal financing may impose higher interest rates and costs to cover SME information and default risks. And as SMEs would be wary of the hazards and risk-rations of formal finance, risk-related considerations should be the primary element taken into account by SME's IFS. (Allen et al., 2019; Lee & Persson, 2016; Nguyen & Canh, 2021; Wellalage & Locke, 2016).

Enterprises should profit from effectively managed IFS, particularly SMEs, hence the topic of this study is SME IFS and its affecting elements. Due to SMEs' poor position in the supply chain and lack of financial records, obtaining trade credit from suppliers and clients may be difficult. Furthermore, equity financing through equity crowdfunding, angel investors, venture capital, and private equity should be unpopular and least desired for SMEs (De Crescenzo et al., 2022; Cumming & Groh, 2018). As a result, informal loans should be a regular source of external capital for SMEs. (Jackson & Thompson, 2022; Nguyen & Canh, 2021) Kijkasiwat (2021) investigated the impact of SME owner behavioral characteristics on IFS using partial least square structural equation modeling. Attitudes, subjective standards, and perceived behavioral control are its owner's behavioral elements. Retained profits and private equity are its IFSs.

From the results of experts’ survey, SMEs’ IFS should consider its risk factors which should include IRS, so the first sub-purpose of this paper is to investigate the causal relationship between IRS and SMEs’ IFS. There are few literatures available today to theoretically and empirically validate the above assertions, which is one of the objectives for this research.

The second sub-purpose of this article is to investigate the mediator impacts of regional corporate finance and regional finance and economics on the aforementioned causal connection, for regional risks should be informal finance concerns. On the other hand, a growing body of literature suggests that regional corporate finance and regional finance and economics are positively associated to the accessibility of informal finance, since it decreases information asymmetry and opacity among SMEs in the same region. (Kabange & Simatele, 2022; Li (2022) creates a venture risk appraisal method for SMEs using the SMEs' structure and processes, as well as the socioeconomic and technological environment. Dore and Narayanan (2020) discovered that the more the regional economic variety, the more possible yet uneven the access to financing and skills. According to Lee et al. (2019), SMEs in the poorest region would support fewer regional resources than SMEs in other regions. Naegels et al. (2018) shown that SME views of the regional business climate influenced their IFS behavior.

The third sub-purpose of this article is to investigate the moderator effects by the IFS’s period, the IFS’s collateral, the IFS’s purpose, and public health emergencies (as COVID-19) on the abovementioned causal relationship and mediator effects. According to the findings of the literature study, most informal financing is small in size, short in duration, and costly. The reason for examining the moderator effects of public health emergencies, rather than solely focusing on COVID-19, is because the COVID-19 pandemic is not a permanent situation, and public health emergencies will continue to arise in the future. Li (2022) studied the hazards of SME venture financing during the loan duration. Yang et al. (2022) investigated the factors that influence SME patent collateralization for loans. Durango-Gutierrez et al. (2021) discovered that the default likelihood of microcredit financing institution loans may be described by loan amount, arrears payments, guarantees, credit analyst evaluation, borrower gender, and stock exchange index.

This paper aims to provide theoretical and empirical analysis of informal finance concerns in SMEs through a case study of Chinese SMEs, using partial least squares structural equation modeling (PLS-SEM). The second section of this paper reviews the existing literature on SMEs' financing strategy, their use of IFS, and the impact of interest rates and public health emergencies on their financial difficulties. The third section determines the variables and factors affecting SMEs' IFS, using a literature review and an expert survey, and outlines the theoretical framework and structural equation model employed in this study. The fourth section analyzes the PLS-SEM results, examining the causal relationship between IRS and SMEs' IFS, the mediator effects of regional corporate finance and regional finance and economics, and the moderator effects of SME's IFS purpose, period, collateral, and COVID-19 using multi-group analysis. The empirical results are compared and discussed with other researchers, highlighting the contribution of this study. The fifth section includes the conclusion and recommendations, the practical implications of this study, its limitations, and future research prospects.

Literature review and variable assumption

Literature review on the financing strategy of small and micro enterprises

SMEs have been shown to contribute significantly to GDP, employment, poverty alleviation, inequality reduction, and innovation (Huang et al., 2022, 2021; Yang et al., 2021; Faal, 2020; Djoutsa et al., 2017; Djeudja & Kongnyuy, 2018; Woldie et al., 2018; Abraham & Schmukler, 2017) SMEs in China are industrial enterprises with less than RMB 30 million in annual taxable income and fewer than 100 employees, as well as other firms with less than RMB 0.5 million in annual taxable income. Micro enterprises have fewer than 20 workers and an annual revenue of less than RMB 3 million. (The Ministry of Industry And Information Technology of China, 2011) Many studies have shown that SMEs are critical to China's economy, employment, taxation, and patenting (Huang et al., 2022; Yang et al., 2021). Between January and July 2022, there were around 15 million newly registered SMEs in China, accounting for almost 90% of total. As a result, this article used China as a case study to examine the effects of IRS and public health emergencies (as COVID-19) on SME IFS. Kabange and Simatele (2022) believed that SMEs should be the driving force behind Cameroonian progress. According to Nkaku Policy Institute (2019), SMEs account for 50% of employment in Cameroon.

Access to formal and informal financing for SMEs should be a crucial aspect in their performance since it is tied to SMEs' financial management abilities, particularly in developing nations (Boohene, 2018; Fowowe, 2017; Tabiri et al., 2022; Viswanadham, 2017; Woldie et al., 2018). Finance, according to Khatami et al. (2022), is a crucial component of entrepreneurial sustainable innovations. According to Bakhtiari et al. (2020), finance availability is the starting point for SME growth and development. According to Faal (2020), the most pressing development issues for SMEs in Gambia are access to capital and markets, management knowledge and talent, and business development services. Djeudja and Kongnyuy (2018) discovered that the financial performance of SMEs should be connected to financial accessibility, infrastructures, markets, financial regulation, asset usage, and the life cycle of the firm. According to Woldie et al. (2018), SME access to external funding, which should be the most difficult hurdle for SME in emerging economies, is dependent on SME collateral, appealing and bankable initiatives, and entrepreneurial traits.

The issues of SMEs' formal finance include its information asymmetries and monitoring hazards, which may make it impossible for SMEs' collaterals or documents to fulfil the standards of formal finance. On the other hand, SMEs may face significant institutional biases from formal financial institutions, who prefer to lend to large or national firms (Abraham & Schmukler, 2017; Djoutsa et al., 2017; Wang et al., 2021). And these issues are most acute during COVID-19, which is the credit-tightening time. (Tabiri et al.,, 2022) The reasons that SMEs find it difficult to get formal funding, according to Kabange and Simatele (2022), and Jackowicz et al. (2021), include transaction costs and default risks. Turkson et al. (2020) shown that formal financial institutions in Ghana are hesitant to lend to SMEs.

Literature review on the informal financing strategy of small and micro enterprises

Numerous studies have indicated that access to informal finance can impact SME performance in terms of survival rate, reinvestment, and innovation, and that SME financial management efficiency may change depending on performance. Therefore, the significance of SME IFS cannot be overstated. However, Manja and Badjie (2022) found that informal financing may have negative effects on welfare, as also noted by Thu et al. (2020), Allen et al. (2019), and Nguyen (2019). Pangarkar and Elango (2022) found that the timeliness, convenience, and low cost of informal finance may benefit developing market enterprises' exports. Thu et al. (2020) discovered that informal loans may have a considerable impact on the welfare of disadvantaged households and poverty alleviation. Nguyen (2019) discovered that there is a considerable beneficial association between SME informal funding and reinvestment. Peng et al. (2019) found that a firm's client concentration, which can be interpreted as its negotiating power, is positively connected with its IFS. However, Ullah (2019) discovered that informal finance may not have a substantial influence on company innovation in transition economies. Djoutsa et al. (2017) found that SME's debt ratio should boost its chances of survival.

Governments, researchers, and enterprises across many countries have studied SME IFS, but few have investigated the impact of IRS and public health emergencies such as COVID-19 on SME IFS. As shown in Fig. 1, the number of publications on China National Knowledge Infrastructure related to informal finance or private financing is 11,233 as of May 10th, 2022, with the majority of articles discussing the influence of informal financing on the economy, finance, and companies. Chen et al. (2022) suggest that the appropriate allocation of formal and informal financing can facilitate rural revitalization. Huang et al. (2022) found that community-based IFS can have a significant positive impact on family entrepreneurship decision-making. Sun et al. (2021) and Liao et al. (2021) discovered that informal finance can improve the financial and business performance of SMEs. Sun et al. (2020) found that farmers' IFS can help mitigate some of the risks associated with agricultural production and operations. However, Li (2020) argued that informal finance may also lead to regional financial risks.

Fig. 1.

The number of articles with the topic of informal finance or private finance on China National Knowledge Infrastructure in the period of 2001–2022. The number of articles with the topic of informal finance on Web of Science in the period of 2001–2022

Figures 1 and 2 depicts the number of research articles on Web of Science related to informal finance, with a total of 1,763 articles as of May 10, 2022. The majority of the articles have focused on examining the factors that influence the adoption of informal financing. Du and Cheng (2022) found that bank competition and formal financial constraints had a significant impact on household IFS. Falola et al. (2022) discovered that agricultural IFS promoted investment in production and commercial activities, and identified influential factors such as farmer age, income, farm size, interest, agricultural experience, family size, education, and loan term. The study also suggested that governments should provide adequate resources to informal financial providers to bridge the funding gap left by formal financial institutions such as commercial banks. Liu et al. (2022) found that cluster commercial credit, a form of informal financing, could enhance innovation, capital allocation efficiency, and total factor productivity in the manufacturing industry. Manja and Badjie (2022) discovered that formal and informal financing have varied effects on household food and non-food consumption, education spending, total income, and subjective wellbeing evaluation. Pangarkar and Elango (2022) suggested that SMEs in emerging economies should embrace informal finance, which may be quicker, more convenient, and more cost-effective than official funding.

Fig. 2.

The Path Diagrams on the Structural Equation Model of SME’s IFS in response to its IRS

The existing literature on IFS has mainly focused on their operations, relationship with formal finance, and socioeconomic impacts. However, their interactions with internal variables of SMEs have been largely unexplored (Corrado & Corrado, 2017; Khan & Dewan, 2017; Nguyen & Canh, 2021). The operation mechanism of IFS is predominantly based on relationships and reputation and takes various forms, such as direct social lending, private lending service centers, and small loan enterprises (Allen et al., 2019). Informal financing is believed to help SMEs decrease their liquidity risk. Due to information asymmetry, the cost of communication, supervision, and renegotiation between the supply and demand sides of informal financial funding is lower than that of formal financial systems, such as formal financial financing. Prior to financing, suppliers usually possess detailed knowledge of SMEs. During financing, suppliers can monitor managers' on-the-job wasteful consumption, major shareholder encroachment, and other agency issues. Finally, suppliers can renegotiate debt after financing restructuring, which can reduce debt agency costs. The social network of IFS is characterized by knowledge and trust based on community, culture, and conventions. Ojong (2019) highlighted the connections between informal money and intricate social and cultural networks.

Previous studies have shown that most SMEs utilize both formal and informal financing, with varying mechanisms for monitoring and enforcing behavioral rules and incentives (Nguyen & Canh, 2021; Bruton et al., 2021). Khan and Dewan (2017) suggest that the availability of informal loans may affect the repayment of microloans. The majority of IFS activities consist of rotating savings and credit associations (ROSCAs), which may assist those without access to formal funding in developing their financial and saving habits (Eroglu, 2010).

Based on a review of the literature and expert consultation, the socioeconomic implications of IFS are diverse, including impacts on economic growth, economic inequality, and entrepreneurship. Self-employed women, for instance, often rely on their own resources and informal financing, and their access to financial resources is often influenced by factors such as age, education, and marital status (Kasseeah & Tandrayen-Ragoobur, 2015). The benefits and drawbacks of IFS also need to be considered, including risk sharing, consumption smoothing, incomplete financial intermediaries, and less judicial and regulatory oversight. Alvi and Dendir (2009) have noted that informal finance may help increase incomes, reduce income uncertainty, and modify demand and resource proxy measures.

This paper contributes to the existing literature on SMEs’ IFS in several ways. First, it uses a unique combination of PLS-SEM and a case study of Chinese SMEs to provide a more nuanced understanding of the factors that contribute to SMEs' use of informal finance and how they interact with each other. This approach provides a theoretical and empirical analysis of SMEs’ IFS, which is a valuable addition to the literature. Second, this paper expands on the existing literature by focusing on the impact of IRS and public health emergencies on SMEs' IFS. And it provides a more comprehensive analysis of the variables and factors affecting SMEs' IFS. Third, this paper provides future research directions on the existing literature by providing a detailed analysis of the causal relationship between IRS and SMEs' IFS, as well as the mediating effects of regional corporate finance and regional finance and economics. The analysis includes a multi-group analysis of the moderator effects of various factors, such as the purpose, period, collateral, and COVID-19 impact of SMEs' IFS. Overall, this paper contributes valuable insights to the literature on SMEs’ IFS and provides a foundation for further research in this area.

Literature review on the informal financing strategy of small and micro enterprises caused by its interest rate risks and modified by public health emergencies

IRS is a cost for SMEs’ IFS, and the interest rate should reflect the time value of SMEs’ IFS. For IRS is increasing in most of the time, large and medium-sized businesses would utilise financial instruments to control IRS, including new financial futures, options contracts, interest-rate swaps, caps, floors, collars, and swaptions. However, SMEs usually have no way to utilise these technologies. According to theoretical inferences, SMEs employing fixed-rate IFS may repay or swap loans when IRS increases and the interest rate tends to drop in order to reduce capital expenses. SMEs which use floating-rate IFS may repay or switch to floating-rate borrowings when the IRS grows and the interest rate is trending upward, but they may not alter their IFS in other situations (Donoso et al., 2011; Lin & Sun, 2006).

On the other hand, IRS often drives SMEs' IFS. Formal financing can be challenging for SMEs to access due to insufficient collateral, credit history, and financial statements. Consequently, SMEs may resort to IFS from friends, family, and moneylenders who offer loans at higher interest rates. However, these higher rates also expose SMEs to IRS, as the cost of financing can become unsustainable, reducing profitability. While most research on SMEs' IFS focuses on information and default risks, IRS has been overlooked. Du and Cheng (2022) found that household IFS is based on official financial limitations to mitigate default risks. Nguyen (2019) discovered that SMEs may use internal finance to save with lower risks and external finance (i.e., informal finance) to reinvest in risky institutional contexts. According to Ojong (2019), informal money relies on complex social and cultural interactions.

Public health emergencies such as COVID-19, floods, droughts, or wars can create financial difficulties for SMEs, forcing them to resort to using IFS. However, the imposition of IRS may further exacerbate their financial challenges, resulting in reduced IFS amounts. Such emergencies can have significant impacts on SMEs, including reduced sales, disrupted supply chains, and increased operating costs. Although SMEs may employ IFS to deal with the situation, IRS may increase during public health emergencies. This is because lenders may seek higher interest rates to offset increased risk resulting from reduced economic activity, leading to SMEs borrowing at higher interest rates, which worsens their financial situation during such crises.

Informal lenders for SMEs should be small and regional. However, during public health emergencies, SMEs with IFS may all default in a particular region. (Bruton et al., 2021) Calabrese et al. (2022) suggest that the demand, supply, and government share of SME loans may increase with the duration of COVID-19. In a study by Xu and Liu (2021) that used an evolutionary game to investigate SME financing in the post-COVID-19 period, collateral coverage and SME return were identified as the primary concerns.

Most recent literature uses PLS-SEM to analyze informal finance issues in SMEs. Kabange and Simatele (2022) employed a SEM approach to examine the mediating role of social capital for SMEs in Cameroon in the relationships between financial capital and firm performance. Meanwhile, Pangarkar and Elango (2022) employed the Heckman approach twice to investigate the relationship between informal lending and exports from developing markets. Wang and Schøtt (2022) suggested that networks between academics and investors could enhance the coupling of finance and innovation in firms. Mukete et al. (2021) used logit regression analysis to investigate the factors influencing SMEs' access to loan programs. Ullah et al. (2021) used recursive bivariate probit models to examine firms' financing options with credit limits and found that enterprises with credit constraints tended to choose informal finance. Turkson et al. (2020) used the Heckman Selection Technique to simulate the finance selection process for SMEs and addressed issues of reverse causality. Lastly, Boohene (2018) employed structural equation modelling to explore the relationships among SME social capital, access to finance, and growth.

The setup of variables and theoretical framework

The setup of variables

The dependant variables of SME's IFS (${\mathbf{I}\mathbf{F}\mathbf{S}}_0$) are loan amount (${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$) and daily interest (${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$), according to the results of a literature research and expert interviews. Shohibul et al. (2019) proposed that SME demand evaluation might aid in their growth. Wang et al. (2019a) discovered that the volume of informal finance expanded considerably over time. Fogel et al. (2018) investigated the cost and conditions of informal finance using spreads and covenant constraints.

More and more research has shown that SMEs may prefer informal loans with smaller contractual risks, even when formal loans have lower interest rates. IRS (${\mathbf{I}\mathbf{R}\mathbf{S}}_0$) is the independent variable, which may be further subdivided into daily interest value at risk (${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$) and interest rate deviation (${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$). When interest rate market risk variables change, ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$ refers to the probable maximum interest loss of SME's IFS in one day with a confidence level of 99%. Its formula is as follows:

$${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}=\left|{\mathbf{Z}}_{1\%},{\mathbf{\sigma }}_{\mathbf{d}},{\mathbf{P}}_{\mathbf{I}\mathbf{F}},{\mathbf{D}}_{\mathbf{I}\mathbf{F}}\right|$$

where ${\mathbf{Z}}_{1\%}$=-2.33 denotes the Z value with significant at 1%, ${\mathbf{\sigma }}_{\mathbf{d}}$ denotes the daily derivation of IFS's interest rate, ${\mathbf{P}}_{\mathbf{I}\mathbf{F}}$ denotes the principal of SME's IFS, and ${\mathbf{D}}_{\mathbf{I}\mathbf{F}}$ denotes the period of IFS. The term ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$ refers to the difference between loan interest and average interest. Its formula is as follows:

$${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}={\mathit{r}}_{\mathit{d}}-\overline{{\mathit{r}}_{\mathit{d}}}$$

where ${\mathit{r}}_{\mathit{d}}$ is the monthly interest rate of a SME's IFS, $\overline{{\mathit{r}}_{\mathit{d}}}$ is the mean monthly interest rate of a SME's IFS on the same day as ${\mathit{r}}_{\mathit{d}}$.

Tallaki and Bracci (2021) explored risk allocation, transfer, and management in PPP and private financing ventures. According to Liu et al. (2020), IFS hazards encompassed both endogenous and external threats. Interest rate risk, credit risk, moral risk, operational risk, and liquidity risk were among its endogenous hazards. Institutional risk and policy risk were among its exogenous hazards. Naegels et al. (2018) shown that interest rates should be the primary factor for SME informal borrowing. Tanzanian female entrepreneurs primarily rely on informal financing.

The mediator variables are constructed as regional corporate finance (${\mathbf{R}\mathbf{C}\mathbf{F}}_0$) and regional finance and economics (${\mathbf{R}\mathbf{F}\mathbf{E}}_0$) to highlight the mechanism of informal financing (Zhao et al., 2010). Regional enterprise assets (${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$), regional enterprise asset-liability ratio (${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$), and regional enterprise return on assets (${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$) are all subsets of ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$. ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$ is the natural logarithm of regional enterprise total assets; ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$ is regional enterprise total liability divided by total assets; and ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$ is regional corporate profits divided by total assets. According to Li and Tian (2021), geographical disparities in informal financing may be driven by regional variances in risk spillover and credit norms. According to Liu et al. (2019), SME informal finance may boost total factor productivity through technological advancement, with regional variability and improvement over time. According to Islam et al. (2018), a SME's social capital and non-financial performance will mediate the linkages between its financial performance and mobile phone use.

${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ is broken into three components: regional formal financial institution loan amount (${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$), regional GDP growth rate (${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$), and regional enterprise unit number (${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$). ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$ is the sum of regional financial institutions' total loans divided by GDP, and ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$ is the difference between regional GDP in the current quarter and GDP in the previous quarter divided by GDP in the previous quarter. Ullah et al. (2021) discovered that while business size and human capital may have a detrimental impact on informal financing by releasing credit limitations, social capital has a beneficial impact on informal financing.

The moderator variables include COVID-19 period (${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$), SME's IFS financing term (${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$), SME's IFS collateral and substitutes (${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$), and SME's IFS usage (${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$). ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$ is separated into two groups: COVID-before (${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{B}}$) data before January 1, 2020, and COVID-during (${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{D}}$) data after or equal to January 1, 2020. COVID-19 has had a major negative impact on firm expenses and profitability; the liquidity risk of small and micro companies has grown dramatically; and SMEs are having difficulty obtaining funding from formal financial institutions. As a result, it is also a topic highly valued and widely concerned by many developing countries and SMEs, which is related not only to the development and success of SMEs, but also to the development and prosperity of the national economy, but there is little literature discussion at the moment, despite its obvious importance. Wang et al. (2019b) discovered that non-state-owned firms reduced their use of informal financing during the COVID era.

${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$ is subdivided into the long-term category (${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{L}}$) for loans with a tenure of greater than or equal to one year. And the short-term category (${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{S}}$), where the loan period is shorter than a year. According to Khoi et al. (2013), land owning status, interest rate, and loan length are major factors impacting access to informal finance.

${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$ is subdivided into the mortgage/pledge group (${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathbf{M}\mathbf{P}}$), where collaterals are mortgage or pledge. Also included in the collateral replacements category (${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathbf{C}\mathbf{S}}$) are collaterals that are guaranteed or credit. (Mukete et al., 2021; De Haas and Millone (2020) discovered that collateral and guarantee requirements may decrease during the course of the lending transaction. According to Amoako-Adu and Eshun (2018), collateral has been utilised to lessen the credit and information risks of SMEs. Naegels et al. (2018) shown that collateral and guarantee should be the primary considerations for SME informal borrowing.

${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$ is separated into two groups: those whose loan purpose is production (${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{P}}$) and those whose loan purpose is others without production (${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{O}}$). According to Quartey et al. (2017), the size, ownership, legal rights, credit information, export, and top management of SMEs all have an impact on their access to funding. According to Kislat (2015), various household groups used informal loans to get different gains, such as the majority of poor families' farming assets and the majority of affluent households' (food) consumption.

Theoretical framework and descriptive statistics

The concerns of SMEs, financial institutions, and government should be the issues of SME's IFS for the quick transformation and upgrading of informal financing, as well as the efficiency and effectiveness of SMEs, financial institutions, and government. However, there is still a scarcity of literature on the subject. PLS-SEM would be used in this article to explore SME's IFS, its causal relationship of ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$, its mediator effects of ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$ and ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$, and its moderator effects of ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$, ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$. The recommendations will subsequently be used to build SME IFS and informal financing.

The relative advantages of PLS-SEM in this article are the solutions of multiple dependent variables and independent variables, multivariable collinearity, robust interference data and missing values, prediction of potential variables, reactive and formative indicators, and small sample data distribution when compared to other methods. (Costa & Monteiro, 2016; Hernandez-Perlines et al., 2017; Khan et al., 2020; Lin et al., 2020; Yang et al., 2021) Appiah et al. (2022) examined the link between macroenvironmental influences and SME RET investment plans, as well as the moderator effects of entrepreneurial orientation on the aforesaid association, using PLS-SEM. During COVID-19, Behl et al. (2022) utilised PLS-SEM to examine the link between SMEs' big data analytics skills and their long-term competitive advantage. PLS-SEM was used by Haryanto et al. (2022) to investigate the moderator effects of transformational leadership on the association between work conflict and employee performance. PLS-SEM was used to examine the impact of students' inventiveness and altruism on their long-term entrepreneurial ambition.

Figure 3 depicts the route diagrams of SME's IFS's structural equation model in response to its IRS. The model's pathways (equations) are as follows:

$${\mathbf{I}\mathbf{F}\mathbf{S}}_0={{\mathit{\alpha }}_1+\mathbf{\beta }}_1{\mathbf{I}\mathbf{R}\mathbf{S}}_0+{\mathbf{ϵ}}_1$$ 1

$${\mathbf{I}\mathbf{F}\mathbf{S}}_0={{\mathit{\alpha }}_2+\mathbf{\beta }}_2{\mathbf{R}\mathbf{C}\mathbf{F}}_0+{\mathbf{ϵ}}_2$$ 2

$${\mathbf{I}\mathbf{F}\mathbf{S}}_0={{\mathit{\alpha }}_3+\mathbf{\beta }}_3{\mathbf{R}\mathbf{F}\mathbf{E}}_0+{\mathbf{ϵ}}_3$$ 3

$${\mathbf{R}\mathbf{C}\mathbf{F}}_0={{\mathit{\alpha }}_4+\mathbf{\beta }}_4{\mathbf{I}\mathbf{R}\mathbf{S}}_0+{\mathbf{ϵ}}_4$$ 4

$${\mathbf{R}\mathbf{F}\mathbf{E}}_0={{\mathit{\alpha }}_5+\mathbf{\beta }}_5{\mathbf{I}\mathbf{R}\mathbf{S}}_0+{\mathbf{ϵ}}_5$$ 5

$${\mathbf{I}\mathbf{F}\mathbf{S}}_0={{\mathit{\alpha }}_6+\mathbf{\beta }}_6{\mathbf{I}\mathbf{R}\mathbf{S}}_0{+\mathbf{\beta }}_7{\mathbf{R}\mathbf{C}\mathbf{F}}_0{+\mathbf{\beta }}_8{\mathbf{R}\mathbf{F}\mathbf{E}}_0+{\mathbf{ϵ}}_6$$ 6

where ${\mathbf{ϵ}}_1$, ${\mathbf{ϵ}}_2$, ${\mathbf{ϵ}}_3$, ${\mathbf{ϵ}}_4$, ${\mathbf{ϵ}}_5$, and ${\mathrm{ϵ}}_6$ are residual terms.

Fig. 3.

The Path Diagrams on the Structural Equation Model of SME’s IFS in response to its IRS

The purpose of Eq. (1) is to test the alternative hypothesis of ${\mathbf{H}}_1$ that ${\mathbf{H}}_1$: The causal relationship between ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\mathrm{and}{\mathbf{I}\mathbf{F}\mathbf{S}}_0({\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0)$ is significant.

The meaning of Eqs. (2), (4), and (6) is to test the alternative hypothesis of ${\mathbf{H}}_2$ that ${\mathbf{H}}_2$: The mediator effect for ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$ on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$ $({\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$) is significant.

The purpose of Eqs. (3), (5), and (6) is to test the alternative hypothesis of ${\mathbf{H}}_3$ that ${\mathbf{H}}_3$: The mediator effect for${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$ $({\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$).

The meanings of ${\mathbf{H}}_1$ is to prove that the IRS of informal finance can have an impact on SMEs' usage of IFS. In situations where formal finance is unavailable or inaccessible, SMEs may turn to informal finance as a substitute. However, if SMEs face high IRS for informal finance, they should consider decreasing their reliance on informal finance and re-evaluating the cost–benefit of formal and informal finance.

The meanings of ${\mathbf{H}}_2$ is to prove that regional corporate finance could act as a mediator between IRS and SMEs’ IFS. Better regional corporate finance might lead SMEs to decrease their reliance on informal finance and reduce their exposure to the high IRS of informal finance. Additionally, regional corporate finance might also provide SMEs with financial advice and support to help them manage their finances more effectively, leading to better financial outcomes and decreasing their reliance on informal finance.

The meanings of ${\mathbf{H}}_3$ is to prove that the mediator effects of regional finance and economics are significant. Its reason should be that regional finance and economics can influence the availability and accessibility of formal finance, which in turn can impact the usage of SMEs’ IFS. However, the mediator effects of regional finance and economics can vary depending on the specific context and characteristics of the region. For example, regions with stronger financial markets and economics might have a higher level of formal finance accessibility and lower usage of IFS, while regions with weaker financial markets and economics might have a higher usage of IFS due to the institutional voids of formal finance.

Furthermore, the alternative hypothesis of ${\mathbf{H}}_4$, ${\mathbf{H}}_5$ and ${\mathbf{H}}_6$ assumes that there are significantly moderated effects on ${\mathbf{H}}_1$, ${\mathbf{H}}_2$, and ${\mathbf{H}}_3$ with regard to ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$, ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$, such as.

$${\mathbf{H}}_{4\mathbf{a}}:\mathrm{The}\mathrm{moderator}\mathrm{effect}\mathrm{for}{\mathbf{COVID}}_0\mathrm{on}{\mathbf{H}}_1\mathrm{is}\mathrm{signicant}.$$

$${\mathbf{H}}_{5\mathbf{a}}:\mathrm{The}\mathrm{moderator}\mathrm{effect}\mathrm{for}{\mathbf{COVID}}_0\mathrm{on}{\mathbf{H}}_2\mathrm{is}\mathrm{signicant}.$$

$${\mathbf{H}}_{6\mathbf{a}}:\mathrm{The}\mathrm{moderator}\mathrm{effect}\mathrm{for}{\mathbf{COVID}}_0\mathrm{on}{\mathbf{H}}_3\mathrm{is}\mathrm{signicant}.$$

In the same logic, ${\mathbf{H}}_{4\mathbf{b}}$, ${\mathbf{H}}_{5\mathbf{b}}$ and ${\mathbf{H}}_{6\mathbf{b}}$ for ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{H}}_{4\mathbf{c}}$, ${\mathbf{H}}_{5\mathbf{c}}$ and ${\mathbf{H}}_{6\mathbf{c}}$ for ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, ${\mathbf{H}}_{4\mathbf{d}}$, ${\mathbf{H}}_{5\mathbf{d}}$ and ${\mathbf{H}}_{6\mathbf{d}}$ for ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$.

The meanings of ${\mathbf{H}}_{4\mathbf{a}}$, ${\mathbf{H}}_{5\mathbf{a}}$ and ${\mathbf{H}}_{6\mathbf{a}}$ are to prove that moderator effects of COVID-19 pertain to how the pandemic could influence the correlation between SMEs’ IFS and IRS, along with the mediator effects. SMEs have encountered unprecedented challenges due to the pandemic, such as disrupted supply chains, reduced cash flow, and diminished demand for goods and services. These challenges have led to heightened financial difficulties and restricted access to formal finance for SMEs. As a consequence, SMEs that are unable to afford the costs of IRS may be forced to halt operations or withdraw from the market, resulting in a stronger relationship between IRS and IFS. Furthermore, the impact of COVID-19 may vary depending on the industry type, geographical region, and government policies. For example, government relief programs and stimulus packages may affect the utilization of informal finance by SMEs during COVID-19.

The meanings of ${\mathbf{H}}_{4\mathbf{b}}$, ${\mathbf{H}}_{5\mathbf{b}}$ and ${\mathbf{H}}_{6\mathbf{b}}$ are to prove that the moderator effects of the period of SMEs' IFS refer to how the timing of the use of informal finance can impact its relationship with other variables. The period of SMEs' IFS can be influenced by factors such as the duration of the project, the timing of revenue generation, and the maturity of the business. For example, if the period of SMEs' IFS is short-term, the relationship between IRS and IFS may be stronger as the costs of IRS might be higher. However, if the period of SMEs' IFS is long-term, the relationship between IRS and IFS may be weaker as formal finance may be more appropriate for long-term investment projects.

The meanings of ${\mathbf{H}}_{4\mathbf{c}}$, ${\mathbf{H}}_{5\mathbf{c}}$ and ${\mathbf{H}}_{6\mathbf{c}}$ are to prove that the moderator effects by the collateral of SMEs' IFS refer to how the presence or absence of collateral can influence the relationship between IRS and IFS. Collateral refers to assets that are pledged as security for a loan. The collateral requirement can vary depending on the type of finance, lender, and SME characteristics. If the collateral requirement for informal finance is low, the relationship between IRS and IFS may be stronger as the costs of IRS might be higher. Conversely, if the collateral requirement for informal finance is high, the relationship between IRS and IFS might be weaker as SMEs may choose to use formal finance.

The meanings of ${\mathbf{H}}_{4\mathbf{d}}$, ${\mathbf{H}}_{5\mathbf{d}}$ and ${\mathbf{H}}_{6\mathbf{d}}$ are to prove that the moderator effects of the purpose of SMEs' IFS refer to how the intended use of informal finance can impact its relationship with other variables. Various factors such as the nature of their business, their growth stage, and their financial needs can influence the purpose of SMEs' IFS. The purpose of IFS is crucial as it can determine the extent to which IRS influences its usage. For instance, if SMEs' IFS is intended for short-term working capital, the relationship between IRS and IFS may be stronger as the costs of IRS might be higher. Conversely, if the purpose of SMEs' IFS is for long-term investment, the relationship between IRS and IFS may be weaker as formal finance may be more appropriate for such purposes.

The present study draws upon a dataset consisting of 242,769 valid samples obtained from the Wenzhou Index, spanning the period from January 1, 2016, to December 31, 2021. The Wenzhou Index serves as a reliable and up-to-date gauge of the vibrancy and pricing of informal financial activities in Wenzhou, China. The index's underlying data is chiefly sourced from four distinct channels: several hundred enterprise reporting points established by the Wenzhou municipal government, which collect unattributed reports of interest rates paid by informal lenders to local borrowers; weighted averages of interest rates charged by a variety of microloan companies; rates charged by financing guarantee companies, such as pawnshops; and real-time rates reported by private lending service centers. By applying a weighted average to these interest rates, the Wenzhou Index is calculated. The sources of the Wenzhou Index are deemed representative and comprehensive in capturing the full range of informal financial transactions within Wenzhou, China. Furthermore, the index's data has the potential to provide insight into informal financial activities in other regions or countries (Cheng et al., 2022).

Below are the descriptive statistics of IFS for SMEs and the factors that affect it.

1. ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$: its maximum is RMB 150 million, with a mean (S.E.) of RMB 430.1 (1307.9) thousand. This suggests that there are some SMEs with significant financing needs, and most SMEs require smaller financing amounts than larger corporations. The descriptive statistics for ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$ from January 2016 to December 2021 are shown in Table 1. From Table 1, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$ should have a heterogeneous distribution of months and be impacted by COVID-19; its average and total should be at a minimum in 2021. This suggests that there might be various factors influencing the duration of SME loans, and the pandemic has had a significant effect on SME financing.

2. ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$: its maximum is RMB 46.2 thousand, and its mean (S.E.) is RMB 188.1 (613.8). This suggests that certain SMEs face higher financing costs than others. The descriptive data for ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$ from January 2016 to December 2021 are shown in Table 2. According to Table 2, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$ should have a heterogeneous month distribution and be impacted by COVID-19; its total in 2021 is the lowest it can be, but its average and even S.E. are both at their highest in that year. This might indicate a decreased demand for SME financing due to pandemic-related economic uncertainties, but there is the potential volatility in SME financing interest rates.

3. ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$: Its mean (S.E.) and maximum are RMB 2.5 (6.6) million and RMB 527.6 million. These values indicate the average and maximum amounts of funds that are at risk in a single day, which can help financial institutions and investors make informed decisions regarding risk management strategies.

4. ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$: Its maximum and minimum are 36.5‰ and -11.7‰. Its mean (S.E.) is 1×10^-3‰ (3.1‰). These values enable SMEs to anticipate and mitigate any adverse effects of such fluctuations on their investments and overall profitability, and indicate the overall direction and magnitude of the changes in interest rates.

5. ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$: Its mean (S.E.) is 8.4 (1.2), which suggests that the region has a substantial amount of assets, which can contribute to its economic growth and development.

6. ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$: Its maximum and minimum are 74.3% and 48.2%, with a mean (S.E.) of 51.5% (4.4%). These values indicate that the region's enterprises have a moderate level of leverage, and imply that the enterprises may have access to sufficient financial resources to fund their operations and growth, but they should also be cautious about taking on excessive debt.

7. ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$: Its maximum and minimum are 6.5% and 2.0%, and its mean (S.E.) is 3.4% (1.6%). These values suggest that the region's enterprises have a relatively low profitability, and might indicate that the enterprises are facing challenges in generating profits, such as high costs, low productivity, or intense competition.

8. ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$: Its mean (S.E.) is RMB 1.1 (0.2) trillion, with maximum and minimum of RMB 1.6 and 0.8 trillion. These values could help policymakers, financial institutions and SMEs to assess the overall financial health of the region and identify potential gaps in the credit market, and provide insight into the range of loan amounts available in the region.

9. ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$: Its mean (S.E.) is 44.7% (68.0%), with maximum and minimum of 129.9% and -79.5%, which indicate significant fluctuations in economic activity over the period studied, and such fluctuations can have significant implications for business operations, investment decisions, and employment opportunities. (10) ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$: Its mean (S.E.) is 5,338.2 (679.1), with maximum and minimum of 6,724 and 4,582. These values suggest a potentially diverse and dynamic business environment, which can foster innovation, competition, and economic growth. However, the wide range of enterprise unit numbers might indicate varying levels of competitiveness and barriers to entry for businesses in the region.

10. Table 3 is the mean of ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$,${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$ from 2016 to 2021, From Table 3, ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$,${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$ should be affected by COVID-19.

11. ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$: ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{B}}$ and ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{D}}$ accounted for 67.8% and 32.2% of all respondents. These values suggest that the COVID-19 pandemic might has a significant impact on the SMEs’ IFS, for the pandemic has altered economic trends and patterns, and impacted economic and financial decision-making at all levels, as individual consumers, SMEs, large corporations, and governments.

12. ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$: Its mean (S.E.) is 8.1 (4.8) month. ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{S}}$ and ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{L}}$ accounted for 61.8% and 38.2% of all respondents. These values suggest that the majority of loans in SMEs’ IFS are short-term and are likely to be used for immediate financial needs, and SMEs’ IFS might have a higher preference for short-term borrowing over long-term borrowing, and tend to repay their loans within a relatively short period.

13. ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$: ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathbf{M}\mathbf{P}}$ and ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathbf{C}\mathbf{S}}$ accounted for 11.0% and 89.0% of all respondents. These values suggest that the majority of SMEs’ IFS are secured using collateral replacements such as guarantees or credit, and SMEs’ IFS prefer to use alternative forms of collateral rather than pledging their assets such as property or vehicles. (14) ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$: ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{P}}$ and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{O}}$ are 99.3% and 0.7% of all respondents. These values suggest that the vast majority of SMEs’ IFS are intended to finance production-related activities such as investment in machinery, inventory, or labor, and SMEs’ IFS are primarily engaged in production-related activities, and that production is a key driver of SMEs’ economic and financial activities.

Table 1.

The Descriptive Statistics of ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$ from Jan, 2016 to Dec, 2021

		Year
		2016	2017	2018	2019	2020	2021
Month	January	2,056.50	1,433.70	1,483.00	1,372.60	1,131.80	1,163.60
	February	1,287.70	982.1	1,032.60	580.4	246.8	925
	March	2,165.60	1,570.70	1,451.40	1,472.60	1,494.40	1,755.40
	April	1,949.80	1,368.60	1,477.90	1,245.30	1,627.00	1,036.70
	May	2,160.20	1,431.10	1,651.70	1,201.70	1,557.70	1,032.80
	June	2,099.90	1,504.60	1,621.20	1,547.50	1,659.70	1,317.20
	July	1,888.00	1,346.10	1,652.00	1,592.90	1,943.30	1,155.80
	August	1,342.90	1,291.40	1,389.00	1,792.60	2,058.50	933.4
	September	1,974.70	1,619.30	1,463.30	1,956.00	1,702.30	1,330.70
	October	1,423.10	943.8	1,325.10	1,236.80	1,232.00	849.5
	November	1,827.70	1,549.20	1,289.10	1,412.70	1,168.90	1,216.10
	December	1,833.60	1,500.40	1,449.30	1,761.10	1,695.80	1,173.30
Descriptive Statistics	Sum	22,009.70	16,541.20	17,285.60	17,172.20	17,518.20	13,889.50
	Average	1,834.10	1,378.40	1,440.50	1,431.00	1,459.80	1,157.50
	S.E	313.5	216.5	173.5	357.9	476.8	242.3

Table 2.

The Descriptive Statistics of ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$ from Jan, 2016 to Dec, 2021 Unit: RMB Thousand

		Year
		2016	2017	2018	2019	2020	2021
Month	January	941.0	617.8	639.2	596.0	491.4	457.6
	February	579.9	421.8	434.4	243.2	96.2	413.2
	March	979.5	674.2	627.7	650.9	669.3	739.3
	April	907.6	602.1	653.5	544.1	707.5	418.6
	May	1,016.7	622.8	728.7	541.9	700.4	442.5
	June	960.5	660.2	723.4	698.1	733.3	526.3
	July	881.2	600.6	744.8	676.5	869.8	487.7
	August	619.3	556.8	617.9	712.7	929.5	397.7
	September	882.7	724.0	664.3	805.7	665.1	567.2
	October	645.3	426.6	609.0	545.7	458.1	371.4
	November	801.0	706.7	585.9	626.1	474.8	526.3
	December	766.8	646.9	623.9	742.3	647.8	502.1
Descriptive Statistics	Sum	9,981.6	7,260.5	7,652.7	7,383.1	7,443.2	5,850.0
	Average	294.6	229.9	160.1	147.1	160.2	187.6
	S.E	580.5	544.5	590.3	602.4	656.7	697.0

Table 3.

The Mean of ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$,${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$, ${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$ from 2016 to 2021

	2016	2017	2018	2019	2020	2021
${\mathit{I}\mathit{R}\mathit{S}}_{\mathit{V}\mathit{a}\mathit{R}}$	3.9	2.6	2.0	2.2	1.9	2.7
${\mathit{I}\mathit{R}\mathit{S}}_{\mathit{D}\mathit{e}\mathit{v}}$	7.9‰	+	−	−	+	+
${\mathit{R}\mathit{C}\mathit{F}}_{\mathit{A}}$	8.4	8.5	8.5	7.6	8.7	8.8
${\mathit{R}\mathit{C}\mathit{F}}_{\mathit{L}\mathit{e}\mathit{v}}$	51.0%	49.3%	52.4%	52.1%	51.7%	51.2%
${\mathit{R}\mathit{C}\mathit{F}}_{\mathit{R}\mathit{O}\mathit{A}}$	3.4%	3.4%	3.6%	3.7%	2.9%	3.0%
${\mathit{R}\mathit{F}\mathit{E}}_{\mathit{L}}$	0.8	0.8	0.9	1.1	1.3	1.5
${\mathit{R}\mathit{F}\mathit{E}}_{\mathit{G}}$	74.7%	39.1%	42.2%	37.8%	46.7%	29.5%
${\mathit{R}\mathit{F}\mathit{E}}_{\mathit{N}}$	7765.8	8327.5	9381.9	10,963.7	13,134.2	14,798.4

Based on the empirical evidence and relevant research presented, it is inferred that IFS will continue to serve as a financing method for Chinese SMEs. However, its utilization is likely to be influenced by the COVID-19 pandemic, leading to a decline in its quantity, while its interest rate remains unaffected. This reduction can be attributed to SMEs' adoption of risk-averse strategies such as cash flow preservation, cost reduction, efficiency improvement, labor simplification, and investment conservation. The rationale for the increased interest risk (S.E.) can be attributed to the exacerbation of SMEs' operational and financial risks due to COVID-19, as evidenced by the increased daily interest value at risk and interest rate deviation.

The COVID-19 pandemic has implications for regional corporate finance and economics. Particularly, it may affect SMEs’ IFS. The government's SME finance strategy should take into account the importance of safeguarding SMEs' access to informal finance, as many rely on it for emergency cash flow and production. Notably, SMEs’ IFS often require weaker collateral compared to formal financing options, as highlighted in existing literature. Therefore, the government should consider policies to support SMEs' access to informal finance and ensure that they have adequate collateral and credit guarantees.

The structural equation model, multi-group analysis, and discussions

The structural equation model

This article employs the SmartPLS (4.0.67 version) software to investigate the PLS-SEM of Chinese SME's IFS via IRS, which is a reflecting model. The analysis criteria included its dependability, validity, appropriateness, and comparison of several groups, with 1,000 bootstrap replications and the route weighting scheme with the greatest ${\mathbf{R}}^2$ value (Fu et al., 2021; Ringle et al., 2022). Table 4 shows the correlation coefficients of ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$ and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$. According to Table 4, ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$ is impacted significantly by ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$.

Table 4.

The Correlation Coefficients of ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$ and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$

		${\mathit{I}\mathit{F}\mathit{S}}_0$		${\mathit{I}\mathit{R}\mathit{S}}_0$
		${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$
${\mathit{I}\mathit{F}\mathit{S}}_0$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	1
	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	0.942***	1
${\mathit{I}\mathit{R}\mathit{S}}_0$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	0.664***	0.581***	1
	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	-0.034***	0.058***	-0.043***	1

^***p < 0.001

Cronbach's alpha ($\mathbf{\alpha }$), Dillon-rho Goldstein's (rho), composite reliability (CR), variance extracted (AVE), outer loading (OL), and collinearity statistics (VIF) on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$, and ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ are shown in Table 5. And the corrected ${\mathbf{R}}^2$ for ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$, and ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ are 0.403, 0.001, and 0.002, respectively. According to Table 3, the PLS-SEM of Chinese SME's IFS by IRS should be reliable, valid, and suitable.

Table 5.

The Variables’ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$, ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$, and ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$) $\mathbf{\alpha }$, rho, CR, AVE, OL, and VIF

Variables	α	rho	CR	AVE	Indicators	OL	VIF
${\mathbf{I}\mathbf{R}\mathbf{S}}_0$	-0.09	-3.106	0.485	0.500	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	0.987	1.002
					${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	0.983	1.002
${\mathbf{I}\mathbf{F}\mathbf{S}}_0$	0.970	0.983	0.985	0.971	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	1.000	8.917
					${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	-0.029	8.917
${\mathbf{R}\mathbf{C}\mathbf{F}}_0$	0.320	0.590	0.615	0.466	${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{A}}$:	-0.068	1.010
					${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{L}\mathbf{e}\mathbf{v}}$	0.787	1.204
					${\mathbf{R}\mathbf{C}\mathbf{F}}_{\mathbf{R}\mathbf{O}\mathbf{A}}$	0.880	1.204
${\mathbf{R}\mathbf{F}\mathbf{E}}_0$	0.495	0.785	0.605	0.607	${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{L}}$	0.928	5.571
					${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{G}}$	-0.453	1.013
					${\mathbf{R}\mathbf{F}\mathbf{E}}_{\mathbf{N}}$	0.868	5.540

The following are the findings of the PLS-SEM of Chinese SME's IFS via IRS (Fig. 3 and Table 4): (1) ${\mathrm{H}}_1$ is supported since there is a significant positive causal relationship between ${\mathbf{I}\mathbf{R}\mathbf{S}}_0$ and ${\mathbf{I}\mathbf{F}\mathbf{S}}_0$ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$). (2) Because of the significantly negative mediator effects of ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ on the aforementioned causal relationship (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$), ${\mathrm{H}}_3$ is supported. However, it is not for ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$), thus ${\mathrm{H}}_2$ is not supported.

These findings might demonstrate that the IRS has had a major influence on the IFS of Chinese SMEs. However, Chinese SME's IFS would case regarding ${\mathbf{R}\mathbf{F}\mathbf{E}}_0$ but not ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$, and the higher the ${\mathbf{R}\mathbf{C}\mathbf{F}}_0$, the lower the ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$. The reason for these results, according to the experts' survey, should be that SMEs are highly sensitive to financial risks; the purpose of SME's IFS is primarily for its production and emergency cash flow, so SME's IFS would care about regional economics and finance, which directly influence SME's survival, but it may not care about regional corporate finance, which indirectly influences SME's survival.

Multi-group analysis of ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$, ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$

This article used multi-group analysis to investigate on the moderator effects on ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$, ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$. COVID-19's moderating effects (see Table 5) are assessed using a multi-group study that incorporates two-way ANOVA and PLS-SEM. COVID-19 has significant moderating effects on ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, but not on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, according to Table 5. As a result, ${\mathbf{H}}_{4\mathbf{a}}$ and ${\mathbf{H}}_{6\mathbf{a}}$ are supported, but not ${\mathbf{H}}_{5\mathbf{a}}$. Their reasoning should be that SME is a risk sensitizer and averter for COVID-19's risk-amplifier. However, when a SME requires IFS, it should be an emergency, thus the SME is less concerned about ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$.

Multi-group analysis was used to resolve the moderating effects of ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$ (Table 6). Table 6 shows that ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$ has a significant moderating effect on ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, but not on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$. As a result, ${\mathbf{H}}_{4\mathbf{b}}$ and ${\mathbf{H}}_{6\mathbf{b}}$ are supported, but not ${\mathbf{H}}_{5\mathbf{b}}$. Their justification should be that SME's IFS would want to take more IRS for a longer term of IFS.

Table 6.

The PLS-SEM of Chinese SME’s IFS by IRS

	Total Indirect Effect	Total Effect
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$	0.001***	0.635***
${\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$	-0.002
${\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$	-0.014***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0$	-0.026***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0$	-0.042***
	Specific Indirect Effect
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$	$5.2\mathrm{X}{10}^{-5}$
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$	0.001***

^**p < 0.01, ^***p < 0.001

The moderating effects of ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$ were solved by multi-group analysis (Table 7). From Table 7, there are significant moderating effects of ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$ on ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$; but not for ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$ Therefore, ${\mathbf{H}}_{4\mathbf{c}}$ and ${\mathbf{H}}_{6\mathbf{c}}$ are supported, but not for ${\mathbf{H}}_{5\mathbf{c}}$. Their reasons should be SME’s IFS would like take more IRS for collaterals is guarantee or credit, compared with the collaterals is mortgage or pledge.

Table 7.

Multi-Group Analysis of ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$

Variables	Indicators	${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{B}}$	${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_{\mathbf{D}}$	Difference Significance
		Mean	Mean
${\mathbf{I}\mathbf{R}\mathbf{S}}_0$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	2.6	2.2	***
	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	$2.0\mathrm{X}{10}^{-3}$	-1.2 $\mathrm{X}{10}^{-4}$	-
${\mathbf{I}\mathbf{F}\mathbf{S}}_0$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	443.4	402.2	***
	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	196.5	170.5	***
		Coefficient	Coefficient
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.655***	0.601***	***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		-3.2 $\mathrm{X}{10}^{-4}$***	-0.001***	-
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.003***	0.001***	***

^***p < 0.001

Multi-group analysis was used to resolve the moderating effects of ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$ (Table 8). Table 8 shows that ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$ has a significant moderating effect on ${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$, ${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$, ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, and ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$, but not on ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$. As a result, ${\mathbf{H}}_{4\mathbf{d}}$ and ${\mathbf{H}}_{6\mathbf{d}}$ are supported, but not ${\mathbf{H}}_{5\mathbf{d}}$. Their justification should be that SME's IFS would like to take more IRS for the purpose of manufacturing. However, when a SME need IFS for production, it should be an emergency, thus the SME is less concerned with ${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$ Table 9.

Table 8.

Multi-Group Analysis of ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$

Variables	Indicators	${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{S}}$	${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_{\mathbf{L}}$	Difference Significance
		Mean	Mean
${\mathbf{I}\mathbf{R}\mathbf{S}}_0$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	1.7	3.7	***
	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	0.9	-1.5	-
${\mathbf{I}\mathbf{F}\mathbf{S}}_0$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	485.2	341.1	***
	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	224.2	129.8	***
		Coefficient	Coefficient
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.742***	0.927***	***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		-2.7 $\mathrm{X}{10}^{-4}$***	–1.1 $\mathrm{X}{10}^{-4}$***	-
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		-0.002***	0.001***	***

^***p < 0.001

Table 9.

Multi-Group Analysis of ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$

Variables	Indicators	${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{P}}$	${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_{\mathbf{O}}$	Difference Significance
		Mean	Mean
${\mathbf{I}\mathbf{R}\mathbf{S}}_0$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	4.7	1.6	***
	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	-1.0	-1.8	-
${\mathbf{I}\mathbf{F}\mathbf{S}}_0$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	615.4	158.8	***
	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	246.3	61.1	***
		Coefficient	Coefficient
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.637***	0.675***	***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		2.6X ${10}^{-4}$	-0.008	-
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.001*	-0.005	***

^***p < 0.001

Discussions

The following are the explanations of the above empirical results: (1) IFS should be an important financing method for Chinese SMEs, which is comparable to the conclusions of Bruton et al. (2021), Nguyen and Canh (2021), Allen et al. (2019), and Ojong (2019). Its economic reasons and intuition behind the results suggest that IFS should be an important financing method for Chinese SMEs to address the unique challenges they face in accessing traditional financing methods, managing cash flow and credit risk, and tapping into the growth potential of the financing services industry in China. (2) The causal relationship between IFS and its IRS is significantly positive, which is rarely explored in the literature, however this conclusion does not meet the interest rate hypothesis. According to a survey of specialists, its reason might be most SME IFS are anti-IRS or in an emergency. Its economic reasons and intuition might be attributed to the unique characteristics and circumstances of SMEs, which might lead them to prefer IFS over other financing methods, even if the interest rate is higher.

(3) The mediator effects of regional finance and economics are significant, which has received less attention in the literature, but this conclusion should be consistent with the theory of company financing. (Kabange & Simatele, 2022; Li, 2022; Dore & Narayanan, 2020; Lee et al., 2019; Naegels et al., 2018) Its economic reasons and intuition suggest that firms might be influenced by regional factors such as access to financing, economic growth, and market conditions when making financing decisions. (4) The mediator effects of regional corporate finance are not significant, which is rarely emphasised in the literature, but this conclusion should not be consistent with corporation financing theory. According to a survey of experts, there is no relationship between most SMEs' IFS, hence regional corporate finance is not an influencing element in SME IFS. Its economic reasons and intuition suggest that other factors, such as firm-level and regional economic and financial factors, might be more important in driving SMEs' use of IFS (Kabange & Simatele, 2021).

(5) The majority of the moderator effects of IFS characteristics and COVID-19 are significant, which is rarely explored in the literature, but is consistent with the findings of Calabrese et al. (2022), Li (2022), Yang et al. (2022), Xu and Liu (2021), Durango-Gutierrez et al. (2021). Table 10 summarises the hypothesis testing findings from Tables 5 ~ 9. Table 10 demonstrated that IFS features (as ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$) and COVID-19 should greatly mitigate the causative relationship between MSE's IFS and IRS, as well as the mediator effects of MSE's regional finance and economics. However, the mediator effects of MSE's regional corporate finance may not be present. Its economic reasons and intuition suggest that the importance of considering IFS characteristics and COVID-19 when examining the relationships between MSE's IFS, IRS, and regional finance and economics Table 11.

Table 10.

Multi-Group Analysis of ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$

Variables	Indicators	${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathbf{M}\mathbf{P}}$	${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_{\mathit{C}\mathit{S}}$	Difference Significance
		Mean	Mean
${\mathbf{I}\mathbf{R}\mathbf{S}}_0$	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{V}\mathbf{a}\mathbf{R}}$	2.2	4.7	***
	${\mathbf{I}\mathbf{R}\mathbf{S}}_{\mathbf{D}\mathbf{e}\mathbf{v}}$	0.1	-1.0	-
${\mathbf{I}\mathbf{F}\mathbf{S}}_0$	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{A}}$	407.1	615.4	***
	${\mathbf{I}\mathbf{F}\mathbf{S}}_{\mathbf{I}}$	180.9	246.3	***
		Coefficient	Coefficient
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		0.629***	0.807	***
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		-2.9 $\mathrm{X}{10}^{-4}$	2.4X ${10}^{-4}$	-
${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$		-8.5 $\mathrm{X}{10}^{-5}$	-0.002*	***

^*p < 0.05, ^***p < 0.001

Table 11.

Hypothesis Testing Results of ${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$, ${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$, ${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$, and ${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$

	${\mathbf{C}\mathbf{O}\mathbf{V}\mathbf{I}\mathbf{D}}_0$	${\mathbf{P}\mathbf{e}\mathbf{r}\mathbf{i}\mathbf{o}\mathbf{d}}_0$	${\mathbf{C}\mathbf{o}\mathbf{l}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}}_0$	${\mathbf{U}\mathbf{s}\mathbf{a}\mathbf{g}\mathbf{e}}_0$
${\mathbf{H}}_{4\mathbf{a}}$, ${\mathbf{H}}_{4\mathbf{b}}$, ${\mathbf{H}}_{4\mathbf{c}}$,${\mathbf{H}}_{4\mathbf{d}}$ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$)	√	√	√	√
${\mathbf{H}}_{5\mathbf{a}}$, ${\mathbf{H}}_{5\mathbf{b}}$, ${\mathbf{H}}_{5\mathbf{c}}$, ${\mathbf{H}}_{5\mathbf{d}}$ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{C}\mathbf{F}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$)	×	×	×	×
${\mathbf{H}}_{6\mathbf{a}}$, ${\mathbf{H}}_{6\mathbf{b}}$, ${\mathbf{H}}_{6\mathbf{c}}$, ${\mathbf{H}}_{6\mathbf{d}}$ (${\mathbf{I}\mathbf{R}\mathbf{S}}_0\to {\mathbf{R}\mathbf{F}\mathbf{E}}_0\to {\mathbf{I}\mathbf{F}\mathbf{S}}_0$)	√	√	√	√

√: Hypothesis is supported. × : Hypothesis is not supported

The results of a robustness test of Fig. 4, which is the structural equation model of SME's IFS in response to its IRS without taking regional corporate finance into account, are similar to those of this article. As a result, this article will not go into depth about this robustness test.

Fig. 4.

The Path Diagrams on the Structural Equation Model of a Robustness Test

This article presents significant theoretical contributions to the literature on SME financing. Firstly, it complements existing economic and financial theories of SME's IFS by highlighting the potential benefits of IFS as an alternative financing option for these firms. The study also contributes to the development of a more nuanced understanding of the factors that influence the effectiveness of IFS as a financing method for SMEs. Given the increasing prominence of issues concerning SME IFS, particularly during the COVID-19 pandemic, this study focuses on the causative relationship between IRS and SMEs' IFS, as well as the moderator effects of public health emergencies, which have received less attention in the literature.

Secondly, the study supplements the empirical model of SME's IFS by providing important theoretical contributions to the literature on SME financing. Specifically, the study finds that the causal relationship between SMEs’ IFS and its IRS is significantly positive but does not meet the interest rate hypothesis. This finding aligns with the theory of company financing, which posits that the availability and cost of financing are influenced by external factors such as regional economic conditions and financial market conditions. The study suggests that regional economic and financial conditions have a significant impact on the financing decisions of SMEs, possibly through their effect on the availability of funding and the cost of borrowing.

IFS is one of the most pressing challenges in SMEs' finance, but prior research has focused on empirical models of its information hazards, with no empirical model addressing the links between SMEs' IFS, IRS, regional corporate finance, and regional finance and economics. Therefore, this article fills this gap in the literature and contributes to a more comprehensive understanding of the factors that affect SME financing decisions.

Thirdly, this article contributes to the literature on SMEs' IFS by supplementing their perspective on IFS. The study's findings refine our understanding of the factors that influence SMEs' IFS, indicating that most firms may be turning to alternative financing methods due to changes in the financial landscape. The results highlight the importance of considering IFS characteristics and the impact of COVID-19 when examining SMEs' financing choices. This study attempts to investigate IFS from a SME's perspective, which may increase the efficacy of SME's IFS policies.

Fourthly, this article complements the quantitative method used to investigate SMEs' IFS and its impacting elements. The PLS-SEM method is utilized to examine the causal link between the IRS and the IFS of SMEs, as well as the mediator effects of regional corporate finance and regional finance and economics. Furthermore, multi-group analysis is employed to investigate the moderator effects of time, collateral, SME's IFS purpose, and COVID-19.

Conclusion and suggestions

Conclusions

Because formal financial institutions were unable to effectively and completely solve the financing problems of SMEs, informal finance, which has the advantages of information and transaction costs based on the social network relationship of popularity and geography, has become an important channel of SME financing. Informal finance is widespread and significant in China, contributing significantly to economic growth. The presence and growth of informal finance is both necessary and sensible. To encourage economic progress, the government should effectively control informal finance concerns such as its development, disadvantages, and advantages.

According to the results of literature review and expert interviews, the risks of informal finance should be important but discussed with less literature. As SME’s IFS should be easily affected by interest rate risk, and SME’s IFS should have been affected by the COVID-19. Thus, this article used the literature review on SME’s IFS. PLS-SEM, and multi-group analysis to study SME’s IFS, its causal relationship of interest rate risks; its mediator effects of regional corporate finance and regional finance and economics; and its moderator effects of COVID-19 period, SME's IFS financing term, SME's IFS collateral and substitutes, and SME's IFS usage.

The main results are: (1) IFS will continue to be a Chinese SME financing strategy; however, it may be influenced by public health emergencies (as COVID-19); SME IFS should be heterogeneous in terms of months. (2) Public health emergencies (as COVID-19) has an influence on IFS' IRS, regional economics and finance, and regional corporate finance. (3) There is a significant positive causal relationship between IFS and its IRS. (4) Regional economics and finance have considerably negative mediator effects on the aforementioned causal relationship, whereas regional corporate finance does not. (5) Public health emergencies (as COVID-19), IFS’ duration, IFS’ collateral (guarantee or credit), and IFS’ utilisation purpose would all considerably mitigate the foregoing causative relationship and moderator effects.

The impacts on policy practice

The findings of this research provide policy recommendations, including the following:

1. Government and formal financial institutions should conduct research on SME IFS to gain a better understanding of SME willingness to lend, as well as formal and informal financial incentives and complementary financing processes. Jones (2008) asserts that such research is crucial for identifying gaps in the current financing landscape and developing tailored policies to address them. Wang et al. (2019b) highlight that the heterogeneity of information structure, loan technology, and market rivalry between online and offline informal finance can lead to asymmetric effects of monetary policy on these two types of financing. Therefore, policymakers should carefully consider the potential impact of monetary policies on both types of financing. Additionally, Schraader et al. (2010) suggest that official financing can provide support for informal financing dealers, which may help to stabilize the informal finance market.

2. Several policy recommendations for SME financing can be derived from the main results of this study. Firstly, given the potential impact of public health emergencies like COVID-19, it is recommended that SMEs' IFS be made more flexible in terms of duration. Secondly, the impact of public health emergencies on IFS' IRS, regional economics and finance, and regional corporate finance should be closely monitored to ensure that appropriate measures are taken to mitigate potential risks. Thirdly, SMEs should consider measures to mitigate potential IRS, such as improved risk management practices. Fourthly, tailored policies should be developed that account for local economic conditions, with a particular focus on regional economics and finance. Finally, policies designed to support SME financing should take into account factors such as public health emergencies like COVID-19, IFS' duration, collateral (guarantee or credit), and utilisation purpose (Jasso et al., 2022).

3. To mitigate IRS, SMEs might need to consider diversifying their financing sources and seeking professional financial advice to help them manage IRS. They might also need to work on building their credit history and financial statements to improve their chances of accessing formal financing in the future. Its reason is for SMEs’ IFS might not provide the same level of transparency and legal protection as formal financing sources, leaving the enterprise vulnerable to fraud or default (Fay et al., 2021).

4. To mitigate the financial and information risks faced by SMEs, the government should implement regulations that facilitate both informal and formal access to credit and strictly enforce these policies. As noted by Kuada (2021) and Atiase et al. (2018), effective enforcement mechanisms for both informal and formal finance would increase the likelihood of SMEs obtaining financing. Additionally, Wellalage and Locke (2016) have shown how SME informality can impact loan availability, and how government initiatives to promote formal financial platforms could reduce SME informality.

5. Formal and informal financial institutions can utilize computer paradigms and big data analytics approaches to identify SME credit through mobile money and social media transactions, which can help to minimize the asymmetry and opacity of SME financial information. Fintech plays a crucial role in IFS, as stated by Correia et al. (2022). Cash flow financing using big data analytics methodology may alleviate SME financial access difficulties, as it is based on SME cash flows, equity returns and value, and risk-sharing, according to Amoako-Adu and Eshun (2018). SME finance was analyzed by van Klyton and Rutabayiro-Ngoga (2018) using the k-score, which evaluates SME business networks on social media (Facebook, Twitter, and LinkedIn), emails, and mobile phones. Gosavi (2017) discovered that SME adoption of mobile money may improve access to finance, hence improving performance and macroeconomic growth.

The limitations and the directions for future research

While IFS has the potential to serve as an important financing method for certain Chinese SMEs, its appropriateness and availability are constrained by various factors that necessitate individual consideration. The suitability of IFS as a financing option may vary for different firms, based on factors such as their size, financial requirements, and risk profile. Furthermore, IFS may not be readily accessible to all SMEs, particularly those in certain geographic regions or industries, due to various factors like regional economic conditions, regulatory frameworks, and investor interest. As a result, some SMEs may not be able to leverage IFS as a financing option, even if it is suitable for their needs.

This study's interest rate hypothesis, which posits a significantly positive causal relationship between IFS and its IRS, warrants further exploration due to certain limitations. For example, it is possible that most SMEs employing IFS are anti-IRS or are in emergency situations where the cost of borrowing is not a primary concern. These limitations highlight the context-dependent effectiveness of IFS as a financing option for SMEs, emphasizing the need for further research to comprehend the factors that affect the IFS-IRS relationship. Additionally, future research could explore the degree to which interest rate considerations influence SMEs' decision to adopt IFS in different contexts and investigate other potential reasons for the observed relationship.

It is important to note that the findings of this study might be particular to the Chinese context and may not be generalizable to other countries or regions. Further research is needed to examine these limitations and extend the findings to other geographic regions and economic conditions.

In terms of limitations, this article suggests that future research should focus on SME private equity strategies as an alternative form of IFS. While this study only examined SME private loan strategies, it is essential to link private equity plans for SMEs with their pay-back capabilities and experience with formal finance, as well as their empathy, knowledge sharing, and reciprocal benefits with informal finance (Guo et al., 2021; Kijkasiwat, 2021; Koropp et al., 2014). Guo et al. (2021) further investigated the relationships between SME crowdfunding campaigns, social connections, and network centrality, indicating the need for future research to explore the causal relationship, mediator effects, and moderator effects of SME informal financing from family and friends. However, it should be noted that family finance differs from informal finance in that returns are not its primary goal (Lee & Persson, 2016).

Future research could examine SME informal financing in different regions of China, considering the variability in banking systems and intermediary services, laws, and regulations (Allen et al., 2019; Nguyen, 2019). Nguyen (2019) highlighted the significant variability in the relative relevance of local government and informal funding on the reinvestment of SMEs. Moreover, the development of SMEs may vary across different areas of developing economies due to transaction expenses (Nguyen et al., 2018), and non-linear correlations exist between government size, public governance, and private investment in Vietnamese provinces (Su & Bui, 2017).

Future research could also explore the distinction between SME informal financing and household informal finance, as most SME informal money is used for production, while most household informal credit is used for housing (Sheuya, 2007). The decision-making process of household informal finance is influenced by credit restriction, financial literacy, credit discipline, and banking trust (Semenova & Kulikova, 2016). Additionally, social capital with family engagement can assist SMEs in reducing agency issues and improving access to informal funding (Chua et al., 2011), and informal finance has been shown to have an essential link with the urban poor in Turkey through rotating savings and credit groups (Eroglu, 2010).

The interplay between informal finance and formal financing for SMEs presents an area of inquiry for future research. SMEs employ a range of financial strategies, including no external financing, formal financing, informal financing, and both formal and informal financing. Micro-finance or microcredit, with a global size exceeding USD 34 billion, can be considered as a form of formal financing for SMEs (Bruton et al., 2021; Chen et al., 2017; Corrado & Corrado, 2017; Nguyen, 2019; Schraader et al., 2010). Trust-related and economic factors are key drivers for SMEs, as argued by Jackowicz et al. (2021).

Future research may focus on the interrelationships between informal finance and formal financing for SMEs. Viswanadham (2017) identified several significant variables for seed finance, including fairness, interest rates, charity, collateral, processes, and scheduling. The survey found that high lending rates, a scarcity of funds, and unfavorable tax charges were major barriers to SME growth. Khoi et al. (2013) noted that the following characteristics affect accessibility to microcredit: government employment, credit membership, certification, education, working skills, and road access. In India's rural economy, informal financing is expected to play a crucial role, as highlighted by Jones (2008).

Funding

This research was supported by the National Social Science Foundation of China (No.18ZDA093).