The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies

Hiew Ye Soon; Supachaipanya Pattarasaya

doi:doi:10.11648/j.ijefm.20261402.14

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The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies

Hiew Ye Soon

, Supachaipanya Pattarasaya^*

Published in International Journal of Economics, Finance and Management Sciences (Volume 14, Issue 2)

Received: 15 March 2026 Accepted: 13 April 2026 Published: 21 April 2026

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Abstract

As an important institutional innovation to alleviate the financing difficulties of small and medium-sized enterprises (SMEs), supply chain finance (SCF) has attracted increasing attention for its potential to improve firms’ internal capital turnover efficiency. Using a panel dataset of Chinese A-share listed companies from 2000 to 2024, this study employs a two-way fixed-effects model to systematically examine the impact of SCF on SMEs’ working capital efficiency, as measured by the cash conversion cycle (CCC).The empirical results indicate that participation in SCF significantly shortens the cash conversion cycle, thereby enhancing firms’ working capital efficiency. This finding remains robust after a series of robustness checks, including alternative variable specifications and endogeneity tests. Further mechanism analysis reveals that SCF improves working capital efficiency primarily through two channels: alleviating financing constraints and reducing financing costs, which enable firms to optimize their capital allocation and operational processes.Heterogeneity analysis shows that the positive effects of SCF are more pronounced in private enterprises and smaller firms, which typically face greater financing frictions compared to state-owned or larger enterprises. These findings highlight the differential impact of SCF across firm characteristics and emphasize its role in promoting inclusive financial development. Overall, this study provides new empirical evidence on the economic consequences of SCF and offers important policy implications for optimizing supply chain finance systems and supporting the sustainable development of SMEs.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 14, Issue 2)
DOI	10.11648/j.ijefm.20261402.14
Page(s)	153-160
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Supply Chain Finance, Working Capital Efficiency, Cash Conversion Cycle, Financing Constraints, Financing Cost

1. Introduction

Small and medium-sized enterprises (SMEs) are a vital component of the national economy, playing an important role in promoting economic growth, employment, and innovation. However, persistent financing difficulties and high financing costs have long constrained their development. Due to information asymmetry and insufficient collateral, traditional bank lending has struggled to meet SMEs’ financing needs. Against this backdrop, supply chain finance (SCF), supported by real trade transactions and the credit of core enterprises, has emerged as an innovative financing model that broadens SMEs’ financing channels and optimizes capital allocation within supply chains.

Working capital management directly affects corporate liquidity and profitability, and the cash conversion cycle (CCC) is a key indicator of working capital efficiency. Theoretically, SCF may influence the CCC by accelerating accounts receivable turnover and improving accounts payable conditions, thereby enhancing working capital efficiency. However, its actual impact and underlying mechanisms require systematic empirical examination.

Using data from Chinese A-share listed companies from 2000 to 2024, this study empirically investigates the effect of SCF on SMEs’ working capital efficiency. The main contributions are threefold: extending SCF research from the perspective of the cash conversion cycle, incorporating financing constraints and financing costs to uncover the transmission mechanisms, and conducting heterogeneity analysis to provide implications for differentiated financial policies.

2. Literature Review

Research on SCF has mainly focused on its role in alleviating financing difficulties for SMEs. He and Liu find that digital SCF significantly reduces financing constraints for Chinese SMEs

[4]

. Guo and Yao argue that supply chain collaboration and trade credit can strengthen financing support for SMEs

[8]

. Liu et al. further demonstrate that SMEs’ digital supply chain capabilities improve supply chain financing performance, alleviating constraints and enhancing efficiency

[5]

. Liu and Fu also show that SCF improves financing efficiency for technology-oriented SMEs

[11]

Another stream of literature examines the economic consequences of SCF. Wang et al. demonstrate that supply chain finance improves capacity utilization in manufacturing enterprises

[3]

. Lu et al. find that digitalization enhances SCF performance by improving resource allocation

[6]

. Lu et al. also show that supply chain-specific investments can send positive signals to financial institutions and improve financing conditions

[7]

. Lu et al. further analyze the moderating role of ambidextrous innovation in SCF performance

[10]

. Wang et al. confirm that SCF promotes innovation efficiency in manufacturing SMEs

[13]

From the perspective of working capital management, the cash conversion cycle is widely used to measure firms’ capital turnover efficiency. Chen et al. analyze the characteristics of the CCC in Chinese firms

[14]

. Shuburi and Aburumman examine the relationship between the CCC and corporate financial characteristics

[20]

With the development of digital technologies, new forms of supply chain finance have gradually emerged. Ighian et al. show that blockchain technology improves transparency and security in SCF

[1]

. Yang et al. suggest that corporate data assets can serve as new forms of collateral in SCF

[2]

. Jiawen emphasizes the role of big data in supporting SCF systems

[15]

. Xu and Gao also discuss the application of blockchain in supply chain finance

[18]

. Bi et al. analyze the impact of technology investment by logistics firms in platform-based supply chains

[9]

. Zhou and Lee explore SCF business model innovation through e-commerce platforms

[12]

. Yan and Song discuss innovative enterprise financing models based on SCF

[16]

. Pérez-Elizundia et al. provide international evidence from the Maxican factoring market

[17]

. Yang illustrates the application of SCF through a case study of SF Express

[19]

Although existing studies provide valuable insights, relatively few studies explore the relationship between supply chain finance and working capital efficiency from the perspective of the cash conversion cycle. Therefore, this study further investigates the impact and mechanisms of supply chain finance on SMEs' working capital efficiency.

3. Mechanisms and Research Hypotheses

Working capital efficiency is closely related to firms’ liquidty management and financing conditions. CCC reflects the time required for firms to convert investments in inventory and receivables into cash. SCF may influence this process thgrough multiple channels.

First, SCF may alleviate financing constraints. By relying on real trade transactions and the credit of core enterprises, SCF reduces information asymmetry and allows SMEs to convert accounts receivable and inventory into liquid funds. This improves firms’ access to external financing and helps stabillize their capital chain.

Second, SCF may reduce financing costs. By lowering lending risks and improving information transparency, supply chain finance enables financing institutions to provide funding at lower interest rates and with better financing terms. Lower financing costs help firms manage temporary funding gaps and improve the efficiency of capital utilization.

Based on the above analysis, the following hypotheses are proposed:

H1: Participation in SCF shortens the cash conversion cycle and improves working capital efficiency.

H2: SCF enhances working capital efficiency by alleviating constraints.

H3: SCF enhances working capital efficiency by reducing financing costs.

H4: The positive effect of SCF on working capital efficiency is more pronounced in private and smaller enterprises.

4. Research Design and Data

4.1. Model Construction

To test the effect of the supply chain on SMEs’ working capital efficiency, the following model is used:

X+Y=Z

{CCC}_{it} = α_{0} + α_{1} {SCF}_{it} + α_{2} {Controls}_{it} + μ_{i} + λ_{t} + ε_{it}

(1)

In this context, where i and t denote firm and year respectively. represents the cash conversion cycle, denotes supply chian finance participation, represents the set of control variables; and denote firm and year fixed effects, and is the error term:

{SA}_{it} (or {Cost}_{it}) = β_{0} + β_{1} {SCF}_{it} + β_{2} {Controls}_{it} + μ_{i} + λ_{t} + ε_{it}

(2)

{CCC}_{it} = γ_{0} + γ_{1} {SCF}_{it} + γ_{2} {SA}_{it} (o r {Cost}_{it}) + γ_{3} {Controls}_{it} + μ_{i} + λ_{t} + ε_{it}

(3)

4.2. Model Construction

4.2.1. Dependent Variable

Working capital efficiency is measured by the cash conversion cycle (CCC), which reflects the time required for a firm to converts its investments in inventory and receivables into cash. A shorter CCC indicates higher working capital effciency.

4.2.2. Core Independent Variables

SCF is measured by adoption and scale. SCF_dummy equals 1 if the firm reports receivables financing or related activities, and 0 otherwise. SCF_size is measured by the balance of receivables financing, and its logarithm (SCF_size_log) is used in the regressions.

4.2.3. Control Variables

Control variables are selected from two dimensions: firm characteristics and governance. Firm characteristics include leverage (Lev), while governance controls include ownership type (SOE, equal to 1 for state-owned enterprises and 0 otherwise) and customer concentration (Customer), measured by the share of the top five customers.

4.2.4. Mechanism Variables

To examine the underlying mechanisms, this study introduces financing constraints and financing costs. Financing constraints are measured by the SA index (SA = −0.737Size + 0.043Size² − 0.04Age), where a larger value indicates stronger constraints. Financing cost (Cost) is measured as the ratio of financial expenses to average interest-bearing liabilities.

4.3. Data Source

As shown in Table 1, the data for this study were primarily sourced from the CSMAR and RESSET databases, as well as publicly disclosed annual reports, covering financial and governance information for Chinese A-share-listed companies. Financial firms, ST/*ST/PT companies, and observations with missing key variables were excluded, and continuous variables were winsorized at the 1% and 99% levels. The final sample comprises 69,939 firm-year observations.

Table 1. Variable Definitions and Descriptive Statistics.

	N	mean	std	min	25%	50%	75%	max
SCF Dummy Variable	68168	0.263	0.440	0	0	0	1	1
Scale of SCF	68168	45375052	182686404	0	0	0	622958.018	1390158649
Logarithm of SCF Scale	68168	4.552	7.702	0	0	0	13.342	21.053
Financing Cost	68167	0.007	0.035	-0.169	-0.002	0.012	0.027	0.071
CCC	68083	162.948	254.286	-147.334	37.427	97.842	190.376	1678.892
Revenue Growth (%)	62393	16.074	47.464	-70.087	-5.128	9.235	26.032	309.290
Listing Age	68168	9.4904	7.6195	0	3	8	14	34

The results show that the mean of the SCF dummy is 26.3%, indicating that about a quarter of firms participate in SCF. The SCF scale shows a high variation cycle of 163 days (median 98), with a right-skewed distribution, suggesting some firms have long cash turnover periods.

5. Empirical Analysis

5.1. Multicollinearity Test

Table 2 shows the results of the multicollinearity test using the varaince inflation factor (VIF). the VIF values for Total Assets, Total Liablities, Operating Revenue, and Operating Cost exceed the threshold of 10, indicating severe multicollinearity among these variables. This is mainly due to accounting identities and the natural relationship between revenue and cost. The VIF values of the remaining variables are all below 10, suggesting that multicollinearity is not a serious concern in the model.

Table 2. Results of the Multicollinearity Test.

Variable	VIF	VIF Interpretation
Total Assets	2381.724	Severe multicollinearity (VIF>10)
Total Liabilities	2307.749	Severe multicollinearity (VIF>10)
Operating Revenue	31.848	Severe multicollinearity (VIF>10)
Operating Cost	26.624	Severe multicollinearity (VIF>10)
Accounts Payable	3.986	Acceptable
Inventory	1.962	Acceptable
Financial Expenses	1.685	Acceptable
Net Accounts Receivable	1.585	Acceptable
Receivables Financing	1.067	Acceptable

5.2. Correlation Analysis

Based on the correlation matrix in Table 3, Operating Revenue and Operating Cost (0.935), Total Assets and Total Liabilities (0.999), and Financial Expenses and Interest Expenses (0.904) show strong correlations, which is consistent with the multicollinearity test results.

For the key variable, Receivables Financing is sigficantly positively correlated with Accounts Payable, Operating Revenue, and Operating Cost at the 1% level. This suggests that firms with larger operational scales are more likely to partipate in supply chian finance.

It should be noted that correlation analysis reflects only associations between variables and does not imply causality.

Table 3. Correlation Matrix.

Variable	Receivables Financing	Net Accounts Receivable	Inventory	Accounts Payable	Operating Revenue	Operating Cost	Financial Expenses
Receivables Financing	1
Net Accounts Receivable	0.156***	1
Inventory	0.094***	0.323***	1
Accounts Payable	0.219***	0.576***	0.681***	1
Operating Revenue	0.204***	0.427	0.467***	0.692***	1
Operating Cost	0.217***	0.428***	0.505***	0.745***	0.935***	1
Financial Expenses	0.089***	0.342***	0.428***	0.513***	0.575***	0.603***	1

5.3. Fixed Effects Model Test

Table 4 reports benchmark regression results with firm- and year-fixed effects to evaluate the impact of supply chain finance on working capital efficiency, measured by the Cash Conversion Cycle (CCC).

Table 4. Fixed Effects Regression Results.

Variable	Model 1 (SCF_Dummy)	Model 2 (SCF_Size)
	CCC	CCC
	Coefficient/Std. Error	Coefficient/Std. Error
SCF_Dummy	-14.754***
	(5.444)
SCF_Size_log		-0.782**
		(0.313)
Leverage	62.938***	62.651***
	(16.898)	(16.901)
Revenue Growth	-0.475***	-0.475***
	(0.029)	(0.029)
Listing Age	-6.634	-6.462
	(4.489)	(4.483)
Ownership Type	0.510	0.511
	(1.423)	(1.423)
Customer Concentration	-0.098**	-0.098**
	(0.040)	(0.040)
_cons	Controlled	Controlled
Firm Fixed Effects	Yes	Yes
Year Fixed Effects	Yes	Yes
N	58,780	58,780
R²	0.0232	0.0231

^**, **, and *** Indicate significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are reports in parentheses.

Model 1 shows that the coefficient of the supply chain finance dummy variable (SCF_Dummy) is -14.754 and is significant at 1% level. This means that firms using supply chain finance shorten their CCC by about 14.75 days, providing initial support for Hypothesis H1.

In Model 2, the coefficient of supply chain finance size (SCF_Size_log) is -0.782 and is significant at the 5% level. This suggests that the greater the use of supply chain finance, the stronger the improvement effect, which further supports H1. Regarding the control variables, leverage is significantly and positively related to CCC. Revenue growth and customer concentration are significantly and negatively related to CCC. The effects of listing age and ownership type are not statistically significant.

5.4. Model TeRobustness Tests

To make sure the results are reliable, we conducted two robustness tests. The results are listed in Table 5.

Table 5. Robustness Test Results.

Variable	Lagged SCF	Alternative CCC Measure
	CCC	CCC_alt
	Coefficient/Std. Error	Coefficient/Std. Error
SCF_Size_log_lag1	-0.744**
	(0.303)
SCF_Size_log		0.431
		(5.420)
Control Variables	Controlled	Controlled
Firm Fixed Effects	Yes	Yes
Year Fixed Effects	Yes	Yes
N	52,000	58,780
R²	0.0218	0.0152

^**, **, and *** Indicate significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are reports in parentheses.

(1) Lagged variable test: After lagging the core independent variable by one period (SCF_Size_log_lag1), the coefficient is -0.744 and remains significant at 5%. This shows that the main result is robust.

(2) Alternative dependent variable: After replacing CCC with an alternative measure (CCC_alt), the coefficient is negative but not significant. This suggests that the result is somewhat sensitive to the measurement of the dependent variable.

Overall, after considering potential endogeneity, the main conclusion still holds. However, the insignificant result under the alternative measure suggests that the findings should be interpreted with caution.

5.5. Fixed Effects Model Test

The results show that SCF significantly reduces financing costs but does not significantly alleviate financing constraints. After adding these variables into the regression, the coefficient of SCF remains largely unchanged. This suggests that neither financing constraints nor financing costs play a significant mediating role.

Table 6. Mechanism Test Results.

Path	Variable	Coefficient	Std.Error	P-value	Significance
SCF→CCC	SCF_Size_log	-0.884874146	0.304852977	0.00370178	***
SCF→SA	SCF_Size_log	-0.000296975	0.000255064	0.24429994
SCF→Financing Cost	SCF_Size_log	-9.80826E-05	4.43771E-05	0.027094461	**
SCF+SA→CCC	SCF_Size_log	-0.883976712	0.305470518	0.00380718	***
SCF+SA→CCC	SA Index	3.02191489	28.81949694	0.91648992
SCF+ Financing Cost →CCC	SCF_Size_log	-0.875207783	0.305291818	0.00414795	***
SCF+ Financing Cost →CCC	Financing Cost	98.55326638	49.22134825	0.04526256	**

^**, **, and *** Indicate significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are reports in parentheses.

Therefore, SCF may improve working capital efficiency through other channels, such as enchanced in formation transparency and stronger supply chain coordination.

5.6. Model TesHeterogeneity Analysis

We used subgroup regressions to examine differences by ownership type and firm size (Table 7).

For ownership type, the coefficient of SCF is significantly negative in non-state-owned enterprises but only weakly significant in state-owned enterprises. This indicates that SCF plays a stronger role in improving working capital efficiency in non-state-owned firms.

For firm size, the coefficients are negative buit not statistically significant fot both large and small firms. Therefore, hypothesis H4 is only partially supported.

Table 7. Heterogeneity Test Results.

Group	Variable	Coefficient	Std.Error	P-value	Significance
State-owned	SCF_Size_log	-0.758	0.407	0.0635	*
Non-state-owned	SCF_Size_log	-0.842	0.374	0.0247	**
Large firms	SCF_Size_log	-0.557	0.369	0.1309
Small firms	SCF_Size_log	-0.187	0.468	0.6900

^**, **, and *** Indicate significance at the 10%, 5% and 1% levels.

6. Conclusions

This study investigates the impact of supply chain finance (SCF) on the working capital efficiency of SMEs using data from Chinese A-share listed companies from 2000 to 2024. The emprical results show that SCF significantly shortens the cash conversion cycle and improves firms’ working capital efficiency. The results remain robust after a series of robustness tests, although the significance weakens when alternative measures of the dependent variable are used.

Mechanusm analysis indicates that while SCF significantly reduces financing costs, its effect on alleviating financing constraints is not statistically significant. This suggests that the improvement in working capital efficiency may not primarily arise from reduced financing pressure but rather from enchanced supply chain coordination and information integration.

Heterogeneity analysis further shows that the positive effect of SCF is more pronounced in non-state-owned enterprises, while the effect for smaller firms is not significant.

Overall, the findings provide emprical evidence for the role of supply chain finance in improving firms’ operational efficiency and offer policy implications for promoting SCF development and supporting SME growth.

Abbreviations

SCF	Supply Chain Finance
CCC	Cash Conversion Cycle
SMEs	Small and Medium-sized Enterprises

Acknowledgments

This section serves to recognize contributions that do not meet authorship criteria, including technical assistance, donations, or organizational aid. Individuals or organizations should be acknowledged with their full names. The acknowledgments should be placed after the conclusion and before the references section in the manuscript.

Author Contributions

Hiew Ye Soon: Conceptualization, Data curation, Formal Analysis, Investigation, Validation

Supachaipanya Pattarasaya: Data curation, Formal Analysis, Investigation, Validation

Data Availability Statement

The data supporting the outcome of this research work has been reported in this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Soon, H. Y., Pattarasaya, S. (2026). The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. International Journal of Economics, Finance and Management Sciences, 14(2), 153-160. https://doi.org/10.11648/j.ijefm.20261402.14

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Soon, H. Y.; Pattarasaya, S. The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. Int. J. Econ. Finance Manag. Sci. 2026, 14(2), 153-160. doi: 10.11648/j.ijefm.20261402.14

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Soon HY, Pattarasaya S. The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. Int J Econ Finance Manag Sci. 2026;14(2):153-160. doi: 10.11648/j.ijefm.20261402.14

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@article{10.11648/j.ijefm.20261402.14,
  author = {Hiew Ye Soon and Supachaipanya Pattarasaya},
  title = {The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {14},
  number = {2},
  pages = {153-160},
  doi = {10.11648/j.ijefm.20261402.14},
  url = {https://doi.org/10.11648/j.ijefm.20261402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20261402.14},
  abstract = {As an important institutional innovation to alleviate the financing difficulties of small and medium-sized enterprises (SMEs), supply chain finance (SCF) has attracted increasing attention for its potential to improve firms’ internal capital turnover efficiency. Using a panel dataset of Chinese A-share listed companies from 2000 to 2024, this study employs a two-way fixed-effects model to systematically examine the impact of SCF on SMEs’ working capital efficiency, as measured by the cash conversion cycle (CCC).The empirical results indicate that participation in SCF significantly shortens the cash conversion cycle, thereby enhancing firms’ working capital efficiency. This finding remains robust after a series of robustness checks, including alternative variable specifications and endogeneity tests. Further mechanism analysis reveals that SCF improves working capital efficiency primarily through two channels: alleviating financing constraints and reducing financing costs, which enable firms to optimize their capital allocation and operational processes.Heterogeneity analysis shows that the positive effects of SCF are more pronounced in private enterprises and smaller firms, which typically face greater financing frictions compared to state-owned or larger enterprises. These findings highlight the differential impact of SCF across firm characteristics and emphasize its role in promoting inclusive financial development. Overall, this study provides new empirical evidence on the economic consequences of SCF and offers important policy implications for optimizing supply chain finance systems and supporting the sustainable development of SMEs.},
 year = {2026}
}

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SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20261402.14
AB  - As an important institutional innovation to alleviate the financing difficulties of small and medium-sized enterprises (SMEs), supply chain finance (SCF) has attracted increasing attention for its potential to improve firms’ internal capital turnover efficiency. Using a panel dataset of Chinese A-share listed companies from 2000 to 2024, this study employs a two-way fixed-effects model to systematically examine the impact of SCF on SMEs’ working capital efficiency, as measured by the cash conversion cycle (CCC).The empirical results indicate that participation in SCF significantly shortens the cash conversion cycle, thereby enhancing firms’ working capital efficiency. This finding remains robust after a series of robustness checks, including alternative variable specifications and endogeneity tests. Further mechanism analysis reveals that SCF improves working capital efficiency primarily through two channels: alleviating financing constraints and reducing financing costs, which enable firms to optimize their capital allocation and operational processes.Heterogeneity analysis shows that the positive effects of SCF are more pronounced in private enterprises and smaller firms, which typically face greater financing frictions compared to state-owned or larger enterprises. These findings highlight the differential impact of SCF across firm characteristics and emphasize its role in promoting inclusive financial development. Overall, this study provides new empirical evidence on the economic consequences of SCF and offers important policy implications for optimizing supply chain finance systems and supporting the sustainable development of SMEs.
VL  - 14
IS  - 2
ER  -

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Author Information

Hiew Ye Soon

School of Management and E-business, Zhejiang Gongshang University, Hangzhou, China

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http://orcid.org/0009-0008-3482-4039
Supachaipanya Pattarasaya

School of Management and E-business, Zhejiang Gongshang University, Hangzhou, China

Contact Email

http://orcid.org/0009-0009-9662-1461

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APA Style

Soon, H. Y., Pattarasaya, S. (2026). The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. International Journal of Economics, Finance and Management Sciences, 14(2), 153-160. https://doi.org/10.11648/j.ijefm.20261402.14

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ACS Style

Soon, H. Y.; Pattarasaya, S. The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. Int. J. Econ. Finance Manag. Sci. 2026, 14(2), 153-160. doi: 10.11648/j.ijefm.20261402.14

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AMA Style

Soon HY, Pattarasaya S. The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies. Int J Econ Finance Manag Sci. 2026;14(2):153-160. doi: 10.11648/j.ijefm.20261402.14

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@article{10.11648/j.ijefm.20261402.14,
  author = {Hiew Ye Soon and Supachaipanya Pattarasaya},
  title = {The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {14},
  number = {2},
  pages = {153-160},
  doi = {10.11648/j.ijefm.20261402.14},
  url = {https://doi.org/10.11648/j.ijefm.20261402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20261402.14},
  abstract = {As an important institutional innovation to alleviate the financing difficulties of small and medium-sized enterprises (SMEs), supply chain finance (SCF) has attracted increasing attention for its potential to improve firms’ internal capital turnover efficiency. Using a panel dataset of Chinese A-share listed companies from 2000 to 2024, this study employs a two-way fixed-effects model to systematically examine the impact of SCF on SMEs’ working capital efficiency, as measured by the cash conversion cycle (CCC).The empirical results indicate that participation in SCF significantly shortens the cash conversion cycle, thereby enhancing firms’ working capital efficiency. This finding remains robust after a series of robustness checks, including alternative variable specifications and endogeneity tests. Further mechanism analysis reveals that SCF improves working capital efficiency primarily through two channels: alleviating financing constraints and reducing financing costs, which enable firms to optimize their capital allocation and operational processes.Heterogeneity analysis shows that the positive effects of SCF are more pronounced in private enterprises and smaller firms, which typically face greater financing frictions compared to state-owned or larger enterprises. These findings highlight the differential impact of SCF across firm characteristics and emphasize its role in promoting inclusive financial development. Overall, this study provides new empirical evidence on the economic consequences of SCF and offers important policy implications for optimizing supply chain finance systems and supporting the sustainable development of SMEs.},
 year = {2026}
}

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TY  - JOUR
T1  - The Impact of Supply Chain Finance on SMEs’ Working Capital Efficiency: Evidence from A-Share Listed Companies
AU  - Hiew Ye Soon
AU  - Supachaipanya Pattarasaya
Y1  - 2026/04/21
PY  - 2026
N1  - https://doi.org/10.11648/j.ijefm.20261402.14
DO  - 10.11648/j.ijefm.20261402.14
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 153
EP  - 160
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20261402.14
AB  - As an important institutional innovation to alleviate the financing difficulties of small and medium-sized enterprises (SMEs), supply chain finance (SCF) has attracted increasing attention for its potential to improve firms’ internal capital turnover efficiency. Using a panel dataset of Chinese A-share listed companies from 2000 to 2024, this study employs a two-way fixed-effects model to systematically examine the impact of SCF on SMEs’ working capital efficiency, as measured by the cash conversion cycle (CCC).The empirical results indicate that participation in SCF significantly shortens the cash conversion cycle, thereby enhancing firms’ working capital efficiency. This finding remains robust after a series of robustness checks, including alternative variable specifications and endogeneity tests. Further mechanism analysis reveals that SCF improves working capital efficiency primarily through two channels: alleviating financing constraints and reducing financing costs, which enable firms to optimize their capital allocation and operational processes.Heterogeneity analysis shows that the positive effects of SCF are more pronounced in private enterprises and smaller firms, which typically face greater financing frictions compared to state-owned or larger enterprises. These findings highlight the differential impact of SCF across firm characteristics and emphasize its role in promoting inclusive financial development. Overall, this study provides new empirical evidence on the economic consequences of SCF and offers important policy implications for optimizing supply chain finance systems and supporting the sustainable development of SMEs.
VL  - 14
IS  - 2
ER  -

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