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Article

Enhancing Construction Enterprise Financial Performance through Digital Inclusive Finance: An Insight into Supply Chain Finance

by
Wei Yu
1,
Huiqin Huang
1 and
Keying Zhu
2,*
1
Accounting Department, Ningbo University, Ningbo 315211, China
2
Finance Accounting and Economics Department, The University of Nottingham Ningbo China, Ningbo 315100, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(13), 10360; https://doi.org/10.3390/su151310360
Submission received: 18 March 2023 / Revised: 26 June 2023 / Accepted: 28 June 2023 / Published: 30 June 2023
Review Reports Versions Notes

Abstract

:
Digital Inclusive Finance (DIF) is a novel approach that employs digital technology to foster the development of inclusive finance, which can effectively alleviate the financing constraints of enterprises. This paper empirically tests the relationship between DIF and the financial performance of construction enterprises, with a focus of supply chain finance (SCF). The findings indicate that DIF can enhance the financial performance of construction enterprises, and SCF is one of the mechanisms through which DIF affects the financial performance of construction enterprises. Moreover, the cross-sectional analysis reveals that the impact of DIF on financial performance is more pronounced in firms with characteristics of private capital-holding and high operating pressure. This study not only enriches the research perspectives of DIF, but also provides valuable insights for policymakers to formulate effective policies.

1. Introduction

The construction industry is a crucial pillar of China’s economy and plays a significant role in its macroeconomic development. Achieving sustainable and stable growth of the construction industry requires construction enterprises to attain favorable financial performance. Numerous factors affect the financial performance of construction enterprises, including corporate governance [1,2,3], macro policies [4,5,6], and business operations [7,8]. Financing is the core business activity that determines the financial performance of a company [9], forming the foundation of corporate investment decisions, with financing costs exerting a direct impact on firm financial performance. However, construction enterprises face difficulties in obtaining financing for large-scale and long-term investment projects which normally require significant capital to support project development. Moreover, national macroeconomic regulations increase the difficulty of corporate financing, affecting traditional financing channels for construction enterprises [10]. For example, banks have tightened credit to construction enterprises to meet the national macroeconomic regulation requirement of “reducing leverage”, resulting in financing restrictions. On the other hand, small and medium-sized enterprises (SMEs) in the subcontractor and material supply sectors are considered an important player in the supply chain of construction enterprises. With the transactions from and to construction enterprises, they share part of the financial pressure of construction enterprises through account receivables, inventory, and account payables within the supply chain. Therefore, we argue that SMEs’ financing ability brings a direct impact on the operations of those construction enterprises. Nevertheless, they are generally smaller in scale and have fewer assets to collateralize, thereby diminishing the ability to finance their account receivables or payables or inventory to construction enterprises via the supply chain, which, as a result, impacts the business activities and financial performance of construction enterprises.
In 2015, the Chinese State Council issued the “Plan for the Development of Inclusive Finance (2016–2020)” to address the challenges of “difficult financing” and “expensive financing” faced by SMEs. This policy elevated inclusive finance as a core component of supporting financing to SMEs. However, the implementation of inclusive finance policies has been hindered by the constraints of traditional financial technology, resulting in a disparity between policy ideas and actions. The situation is improved with the application of digital technology to the financial sector, as demonstrated by “The G20 High Level Principles on Digital Inclusive Finance” released at the G20 Hangzhou Summit. The 2016 Hangzhou Summit reassured the financing support to nations and corporations in various forms. This policy proposal aimed to expand the space for financial services [11], improve efficiency [12], reduce costs [13], alleviate information asymmetry [14], and broaden financing channels of SMEs [15] through the use of digital technology. Numerous scholars have directly explored the effects of DIF on financial performance of SMEs [16,17,18]; few studies have focused on the positive implications of DIF from a supply chain perspective, especially within the construction industry. Our paper seeks to investigate the effects of DIF on the financial performance of construction enterprises and to elucidate its mechanism from the perspective of the supply chain.
This paper utilizes the DIF index from Peking University which proposed by Guo et al. (2020) [19], as a proxy for DIF, and uses construction enterprises data from 2011 to 2020 provided by CSMAR database, to empirical test the impact of DIF on the financial performance of construction enterprises from the supply chain perspective. The findings of this study support the conclusion that DIF can improve the financial performance of construction enterprises, with the supply chain finance (SCF) serving as a mechanism through which DIF affects the financial performance of construction enterprises. Notably, further analysis indicates that DIF’s positive impact on financial performance is more pronounced in construction enterprises with characteristics of privately held and high operating pressure. The contribution of our paper is threefold. Firstly, it broadens the research perspective of DIF by elaborating the logic of how DIF affects financial performance of construction enterprises from the perspective of the supply chain. This approach is different from traditional empirical studies that directly argue the relationship between DIF and financial performance of construction enterprises, and thus, the paper holds value in exploring DIF from different perspectives. Secondly, our paper enriches the research results of DIF by focusing on construction enterprises. While prior studies have explored the effectiveness of DIF by considering personal income [20], household resource allocation efficiency [21], energy consumption [22], the urban-rural income gap [23], and family debt gap [24], among others, our study provides insights into how DIF can empower the development of construction enterprises, which establishes the paper’s exploration value in terms of research content. Thirdly, our paper provides recommendations to policy-making departments on the rational use of DIF policies to improve the quality and efficiency of construction enterprises. Our paper suggests that financial institutions at all levels should continue to implement and optimize DIF policies, expand their coverage, and improve their implementation efficiency, which will provide useful guidance to the government on how to use financial policies to accurately help construction enterprises improve quality and increase efficiency.

2. Literature and Hypotheses

2.1. DIF and Financial Performance of Construction Enterprises

DIF refers to the application of advanced technology in the financial segment to benefit a larger population in the economy, particularly people with lower income or companies with smaller size who have difficulties in obtaining financial resources from elsewhere. It serves a role as “offering fuel in snowy weather” and offers financial support at affordable costs. DIF benefits the 66% of the enterprises in the world that are not able to borrow from a formal financial sources as World Bank Group’s World Development Report shows [25]. Prior research shows that DIF benefits the economy at worldwide level [26,27]. Evidence from China reveals more insights of DIF. For example, by using the data from China Household Finance Survey, Li et al. (2020) [28] show that DIF promotes households consumption and thus improves the efficiency in household resource allocation [21]. DIF also closes family debt gap [24], and promotes urban innovation [29] and, thus, a more sustainable economic growth [30].
While access to financial resources and, thus, the availability of cash [31] being a vital factor to the economy, adequate liquidity is a prerequisite for construction enterprises to carry out their normal business activities. Nevertheless, construction industry faces more significant financing constraints than other industries [32]. While bank credit and corporate bonds traditionally represent the primary means for construction enterprises to obtain direct financing, these options have been considerably decreased in recent years due to national regulations. In response, many construction enterprises have turned to the SCF model for financing.
SCF is concerned with capital inflow of all the participants within the entire supply chain, including upstream and downstream players and financial service providers, through an inter-organizational approach. Its aim is to improve the general creditability so that each player can support each other to maximize their overall benefits [33]. Unfortunately, SMEs in the supply chain of construction enterprises also face financing difficulties due to the scale of their assets, which hinders their ability to provide effective SCF support for construction enterprises. To address the issue of “difficult financing” and “expensive financing” for SMEs, DIF has emerged as an important guarantee. SMEs use DIF to acquire financial support and invest these cash resources in the production of the supply chain with construction enterprises, which could potentially have a positive impact on the financial performance of construction enterprises.
Firstly, the inclusive nature of DIF can enhance the financing of SMEs in the supply chain, thereby improving their ability to provide commercial credit to construction enterprises and alleviating the financing constraints faced by these enterprises. For instance, SMEs situated at the forefront of the supply chain can utilize the financing acquired through DIF to provide credit to construction enterprises, which alleviate the financing constraints of these enterprises. Similarly, after receiving support from the DIF policy, SMEs located at the backend of the supply chain can offer financing to construction enterprises in the form of prepayment, which mitigate the operating cash pressure of construction enterprises, improve their cash holding level, and alleviate their financing constraints. Using evidence from China, Wu and Huang (2022) [34] and Zhang et al. (2023) [35] show that those companies’ financial performance is supported with less financial constraints.
Secondly, while SMEs are featured with information opacity which is even aggravated with a lack of audit [36], the digital nature of DIF can enhance the transparency of SMEs’ information [37], enabling construction enterprises to evaluate SMEs in the supply chain more objectively and optimize their operational efficiency by integrating the resources of supply chain enterprises so as to enhance the financial performance of construction enterprises. For example, construction enterprises can utilize the information released by DIF to select appropriate partners and enhance the efficiency of resource utilization by coordinating resources in the supply chain to optimize construction projects and improve financial performance.
Based on the aforementioned analysis, we propose the following hypothesis:
Hypothesis 1 (H1). 
DIF can improve the financial performance of Construction enterprises.

2.2. DIF, SCF and Financial Performance of Construction Enterprises

SCF is a financing mode that relies on the supply chain as a vehicle. Construction enterprises utilize their dominant positions in construction engineering operations to organize financial resources by optimizing logistics, commercial flow, and cash flow within the supply chain, which helps construction enterprises to integrate financial resources and serve the construction enterprises [6]. However, due to inadequate information disclosure, high thresholds for bond issuance, and national industry regulation, construction enterprises often face difficulty obtaining financing from commercial banks [38]. Additionally, commercial banks have weakened their willingness to provide credit support to construction enterprises due to the difficulty in effectively assessing the risks involved [39] which, in return, will aggravate the cash pressure of construction enterprises. To address these challenges, construction enterprises leverage DIF policy to ensure stable and sustainable financial performance through SCF. Within the SCF system, construction enterprises can use logistics to integrate the allocation of material resources of enterprises in the supply chain, thus improving the efficiency of resource utilization. By utilizing the “coping” approach, construction enterprises can obtain resources from suppliers and buyers and realize the financing effect of DIF for SMEs through the supply chain so as to reduce the capital pressure ultimately. Similarly, construction enterprises can also use cash flow to integrate the financing needs of enterprises in the supply chain, particularly by obtaining monetary funds from consumers through “pre-collection”, thereby extending the financing effect of DIF for SMEs to construction enterprises through the supply chain. Furthermore, construction enterprises can utilize information flow to break the information barrier in the supply chain, select the best partners, and share resources among enterprises in the supply chain, thus making the “inclusive” and “digital” characteristics of DIF deeply imprinted on enterprises in the supply chain. This eventually will lead to the optimization of construction enterprises by integrating the resources of supply chain enterprises.
Based on the above analysis, we propose the following hypothesis:
Hypothesis 2 (H2). 
The role of DIF in optimizing the financial performance of construction enterprises is achieved by supply chain finance.

3. Research Design

3.1. Sample Source and Data Selection

Our paper draws its sample from Chinese listed construction firms in the Shanghai and Shenzhen Stock Exchange markets with a ten-year period spanning from 2011 to 2020. The objective of this paper is to investigate the relationship between DIF and the financial performance of construction enterprises within the available data domain. In order to ensure the reliability of our findings, we have applied specific exclusion criteria for our research data, which include: (1) excluding observations labelled as “delisted” or “special treatment” firms, and (2) removing observations with negative net assets. The final sample comprises 763 firm-year observations from 98 listed construction enterprises. All financial data are from the Wind and China Stock Market and Accounting Research (CSMAR) databases. Wind database provides financial data that comprehensively covers information including A shares, B shares, macro industry, announcement information, etc., which is also compatible with various other databases. CSMAR is a comprehensive research-oriented database focusing on Chinese listed companies.

3.2. Research Model and Key Variables

We construct the following research model to test the first hypothesis:
Performanceit = a0 + β1Digfincityit + ΣControlsit + β2Year + β3Province + εit
Performance represents the financial performance of construction enterprises, which is measured by ROA and TobinQ [40]. ROA, defined as the ratio of profit before interest and tax to total assets, is a measure of the profitability of business activities. On the other hand, TobinQ, calculated as the ratio of the total market value of equity and debt to total assets, is a reflection of the sales growth ability of construction enterprises. Digfincity is the independent variable of Model (1), which represents the degree of DIF and is measured using the “Peking University digital inclusive finance index” compiled and maintained by the digital finance research center of Peking University. A higher value of Digfincity indicates a greater level of DIF influence on the construction enterprises. To control for potential confounding variables, prior literature [41,42] suggests that firm size (Firmsize), sales growth rate (Salesgrowth), firm leverage (Leverage), level of cash holding (Cashholding), ownership status (SOE), age of the firm (Firmage), board size (Boardsize), shareholder spread (TOP1), and level of marketization (Mktindex) should be included in Model (1). The full definition of each variable is presented in Table 1. Data of all control variables are from Wind database and CSMAR. Additionally, year and province fixed effects are also included in Model (1). Our hypothesis predicts a significantly positive β1 for Model (1).
We construct research Model (2) to test the second hypothesis:
Performanceit = a0 + β1Digfincityit + β2SCFit + β3SCFit × Digfincityit + ΣControlsit +
β4Year + β5Province + εit
SCF represents the supply chain finance in construction industry, which is measured by the sum of short-term loans and bill payable to total asset, and is used to test the effect of DIF on the financial performance of construction enterprises. Our paper uses interaction terms to test the mechanism of the impact of DIF on the financial performance of construction enterprises. If β3 is significantly positive, then the result means DIF can promote the financial performance of construction companies in a favorable supply chain environment, which confirms that SCF is a path for DIF to influence financial performance of construction enterprises.

4. Empirical Results and Discussion

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics for the main variables of interest. To avoid outliers and extreme values, all continuous variables have been winsorized at the 1% and 99% level. Based on the descriptive statistics, the mean values of ROA and TobinQ are 0.023 and 1.587, respectively, with standard deviations of 0.039 and 1.309. The median values for these two variables are 0.023 and 1.241, indicating a wide variation in profitability and sales growth ability among the samples. The mean value of Digfincity is 233.052, with a minimum of 19.200 and a maximum of 340.008, highlighting the significant differences in DIF across different prefecture-level cities, which aligns with the general state of regional financial development in China. The minimum and maximum values of SCF are 0.000 and 0.002, respectively, indicating considerable variation in SCF across the supply chain industry. Additionally, Table 1 displays the distribution and spread of other variables, which is consistent with previous studies [16,37].

4.2. Regression Results of Hypotheses 1

Before using Model (1) to test our first hypothesis, we performed a multiple collinear test on the variables in the model to strengthen the robustness of the empirical test results. We tested the variance inflation factor (VIF) of the independent variables to support the selection of the control variables, and found that the VIF value of the independent variables is 1.341, which is much less than 10, indicating that the issue of multicollinearity does not exist for Model (1). The VIF results for the remaining variables are shown in Table 3.
Table 4 presents the results of the multivariate regression analysis using research Model (1). In all specifications, we employed robust standard errors for firm clustering and report adjusted t-values to account for potential cross-sectional and time-series dependence in the data. Consistent with our hypothesis, our findings reveal a positive association between DIF and financial performance of construction enterprises. Columns 1 and 2 report the direct regression results between DIF and financial performance, while Columns 3 and 4 report the results between DIF and financial performance after controlling for other factors that may influence financial performance. The coefficients of Digfincity in Columns 1 and 2 are both positive and significant, indicating that DIF promotes the profitability of construction enterprises. Similarly, both the coefficients of Digfincity in Columns 3 and 4 are positive, signifying that DIF enhances the sales growth ability of construction enterprises. In summary, our empirical results indicate that higher levels of DIF are associated with better financial performance of construction enterprises, thus verifying the positive impact of DIF on financial performance.

4.3. Robustness Tests

4.3.1. Robustness Test with Reconstructed Key Variables

To enhance the robustness of our findings, our paper conducted tests of our hypothesis using different units of DIF. We replaced the variable of Digfincity with a new independent variable labeled as Digfincounty, which is measured at different geographical levels (County-level). The data for Digfincounty were obtained from the digital finance research center of Peking University for the period 2016–2020. We then applied Model (1) to robustly test the relationship between DIF and the financial performance of construction enterprises. Following the approach of Nakatani (2019) [44], we replaced the measurement method of the dependent variable with ROE and profit margin (Profit-M). We use ROE and profit margin because both are good alternatives of financial performance measures [45]. Again, we used Model (1) to robustly test the relationship between DIF and the financial performance of construction enterprises. The coefficients of Digfincity in Table 5 are all positive significantly, which means our empirical findings are robust and consistent with our prediction even when using alternative measures of the independent variable and the dependent variable.

4.3.2. Controlling the Impact of Macro Influenced Factors

To further test the robustness of our findings, our paper conducted an additional dimension of robustness test by examining the effect of macro-influenced factors on DIF. Prior studies have shown that regional economic development, regional financial development, and regional legal environment are dominant factors that affect the implementation of DIF and may affect the financial performance of construction enterprises [46]. Therefore, we used the two-stage regression method to control some macro-influenced factors. In the first stage, we used Digfincity as the dependent variable and regional legal environment level, regional financial development level, and regional economic development level (measured by GDP) as the independent variables. Then, we defined DIF as DigfincityR using the residual term of the regression. Columns 1 and 2 of Table 5 demonstrate that the coefficients of DigfincityR are significantly positive, indicating that DIF plays a role in improving the financial performance of construction enterprises after controlling for the effect of some macro factors.

4.3.3. Test of IV Two-Stage Least Regression

Finally, to address the potential endogeneity issues stemming from omitted variables, our paper employed a two-stage least squares (2SLS) method with instrumental variables (IV). Specifically, we used the internet penetration rate of prefecture-level cities as the IV of Digfincity. As argued by Xie et al. (2018) [47], the internet penetration rate reflects the utilization rate of internet technology at a prefecture-level city, and, thus, can serve as a convenient means to implement inclusive financial policy. We confirmed that the IV satisfies the criteria of a proper IV, as it is highly related to the independent variable but not correlated with the error term of empirical Model (1). We obtained the data of the internet penetration rate from the statistical yearbook of the local government and include it in Model (1) by two-stage least squares regression. Column 3 of Table 6 reports the result of the first-stage regression, which shows a positive association between the IV and Digfincity at a 1% significant level. The results of the second-stage regression, reported in Column 4 and Column 5, indicate that the relationship between financial performance (ROA and TobinQ) and Digfincity remains consistent with the findings presented in Table 3. Therefore, after addressing the potential endogeneity concern, our conclusion that DIF promotes the financial performance of construction enterprises remains valid.

4.4. Regression Results of Hypotheses 2

Table 7 presents the results of multivariate regression analyses using Model (2). The coefficients of the Digfincity are all significantly positive, which still supports the conclusion that the DIF can improve the financial performance of construction enterprises. The coefficients of interaction term are 0.021 and 0.007, respectively, both passing the significance tests at a 5% significant level. This indicates that the role of DIF in promoting financial performance of construction enterprises is more pronounced in higher supply chain intensity environment. The conclusion verifies the Hypothesis 2 of our paper, that construction enterprises can obtain financing from SMEs through SCF.

4.5. Cross-Section Test

The previous section has verified the impact of DIF on financial performance of construction enterprises. In this section, we further discussed the variability of this impact in different contexts, specifically focusing on two aspects: property ownership and corporate operating pressures.

4.5.1. Impact of Property Ownership

Firms with different property ownership exhibit significant differences in governance structure, decision-making logic, and financing needs, resulting in varying impacts of DIF on their financial performance. Private capital-holding construction enterprises, for example, are more constrained by financing and have stronger incentives to seek financing from SMEs in the supply chain, resulting in a closer relationship between their financial performance and DIF. In contrast, state-owned capital-holding construction enterprises have access to lower-cost credit financing and weaker claims to financing from SMEs in the supply chain [48]. Therefore, when SMEs within the supply chain receive financing from DIF policies, private capital-holding construction firms have stronger incentives to obtain financing from SMEs, and then the impact of DIF on financial performance of construction enterprises are more pronounced [49]. To examine the impact of DIF on different types of construction enterprises, we divide the sample into state-owned capital-holding (SOE) and private capital-holding (non-SOE) construction enterprises and conduct regressions on each group using Model (1). Table 8 reports the results, which show that the regression coefficients of β1 for the sample group of private capital-holding enterprises are significant at the 1% level, while the regression coefficients for the SOE are significant only at the 10% level. The between-group difference test of DIF in columns 1 and 2, and columns 3 and 4 are 1.773 and 2.532, respectively, which shows a significant difference in the effect of DIF on the financial performance of construction enterprises. The effect of DIF in promoting the financial performance of enterprises is more pronounced within non-SOE, confirming that property ownership is indeed a factor influencing the impact of DIF on the financial performance of construction enterprises.

4.5.2. The Impact of Operating Pressure

Construction enterprises with different operating pressures exhibit varying degrees of operating risks, operating strategies and financing needs. These differences subsequently result in financial performance variability for construction enterprises impacted by DIF. In comparison to enterprises with higher operating pressures, those with lower operating pressures face significantly lower operating risks and are considerably less dependent on supply chain resources from SMEs [50]. Additionally, enterprises with less operating pressure are more likely to obtain financing from traditional channels, such as banks, thereby reducing their dependence on SMEs within the supply chain for financing. Consequently, construction enterprises with higher operating pressure have a stronger incentive to obtain financing through the supply chain when SMEs within the supply chain receive financing from DIF policies. Conversely, construction enterprises with lower operating pressure have relatively weaker claims to SCF, which exacerbates the impact of DIF on construction enterprises which has higher operating pressure. In this study, we measured operating pressure by using the return on equity (ROE) of construction enterprises in the previous year, which is in line with previous literature [51,52]. We divided the research sample into two groups based on the mean value of ROE, which represent higher and lower operating pressures. That is, higher ROE represents lower operating pressures and lower ROE represents higher operating pressures. Subsequently, we use Model (1) to regress the two groups respectively. The test results presented in Table 9 reveal that the regression coefficients of DIF in columns 1 to 4 are all significantly positive. The between-group difference test of the DIF in columns 1 and 2, and columns 3 and 4 are 2.072 and 1.889, respectively, indicating that operating pressure is indeed a factor that affects the role of DIF in promoting the financial performance of construction enterprises.

5. Conclusions

Digital Inclusive Finance (DIF) is a new initiative in China that aims to use digital technology to promote inclusive finance, which can improve financing situations for construction enterprises through the supply chain finance (SCF). SCF for construction enterprises is realized through the improved financial situation of upstream suppliers and downstream buyers, including subcontractor, materials suppliers, etc., most of which are small and medium-sized enterprises (SMEs). While SMEs are the primary beneficiaries of DIF, we therefore argue that DIF will eventually improve the financial performance of construction enterprises. This paper selects data of construction enterprises from CSMAR database, together with data of Peking University digital inclusive finance index compiled by the digital finance research center of Peking University, to theoretically induce and empirically test the relationship between DIF and financial performance of construction enterprises. The conclusions of this study are threefold: firstly, DIF can enhance the financial performance of construction enterprises; secondly, DIF continuously improves the financial performance of construction enterprises through SCF; and finally, the role of DIF in promoting financial performance is more pronounced among private capital holdings and enterprises operating under greater pressure.
Our findings have important implications for policy and practice. First, the government should prioritize the continuous promotion of financial system reform and optimize the implementation measures of DIF policies to empower high-quality development of SMEs through digital technology. Second, the optimization of the government’s DIF policy should take into account enterprises with different property rights and operating conditions, particularly privately held enterprises and those with high operating pressure, to strengthen the implementation and coverage of DIF and focus on helping enterprises with operating difficulties while using DIF services for regional economic development. Next, construction enterprises should leverage the favorable opportunity of DIF to provide financing for SMEs in the supply chain, integrate the resources of enterprises in the supply chain to strengthen their financing capacity, and use the information disclosure function of DIF to select partners suitable for the development of construction enterprises to enhance the operation capacity of the entire supply chain and ultimately achieve the goal of improving the financial performance of construction enterprises through DIF. Finally, construction enterprises should make good use of the favorable opportunity of DIF to provide financing for SMEs in the supply chain, strengthen the financing capacity of construction enterprises by integrating the resources of enterprises in the supply chain, and use the information disclosure function of DIF for SMEs to select partners suitable for the development of construction enterprises in order to strengthen the operation capacity of the whole supply chain, and ultimately achieve the goal of construction enterprises using DIF to improve the financial performance of construction enterprises.

Author Contributions

Conceptualization, W.Y., H.H. and K.Z.; methodology, W.Y. and H.H.; software, W.Y.; validation, W.Y., H.H. and K.Z.; formal analysis, W.Y., H.H. and K.Z.; investigation, W.Y.; resources, W.Y., H.H. and K.Z.; data curation, W.Y., H.H. and K.Z.; writing—original draft preparation, H.H.; writing—review and editing, W.Y. and Zhu, K.; visualization, H.H. and K.Z.; supervision, W.Y.; project administration, W.Y. and K.Z.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Academy of Longyuan Construction Financial Research] in the annual project of Financial Support for Technology Innovation in the Construction Industry grant number [LYZDA2302] And Ningbo University Humanities and Social Sciences Cultivation Project “Research on the Performance and Path of Party Organization Governance Mechanism to Promote the Green Transformation of Private Enterprises” [XPYB20002].

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: [http://cndata1.csmar.com/csmar.html?v=1634558712806#/index] and [https://www.wind.com.cn/en/].

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Variable definition.
Table 1. Variable definition.
SymbolDefinition
ROAA ratio of net profit to total assets
TobinQA ratio of market value to replacement cost
DigfincityDigital inclusive financial index at prefecture level
SCFFollowing Wu et al. (2022) [34] that measures SCF, we use the ratio of (account payables + notes payables + advance payment–account receivables–notes receivables–prepayment) to gross sales as the proxy.
FirmsizeThe nature logarithm of total assets
SalesgrowthA ratio of sales growth compared with previous year
LeverageA ratio of total debt to total assets
CashholdingA ratio of total cash and cash equivalents to total assets
SOEA dummy variable, equaling to 0 when the ultimate controlling shareholder of a listed firm is a (central or local) government agency or government-controlled SOE and 1 otherwise
FirmageThe nature logarithm of the age of firm for the year
BoardsizeThe nature logarithm of number of chairman board
Top1The ratio of Shares held by the largest shareholder to total firms’ total shares
MktindexThe index of market quality published in <Marketization Index of China’s Provinces: NERI 2021> by Wang et al. (2021) [43].
Table
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariableMeanS.D.MinMedianMaxN
ROA0.0230.039−0.1790.0230.108763
TobinQ1.5871.3090.8721.2419.893763
Digfincity233.05285.54419.200247.924340.008763
SCF0.0020.520−0.0470.0030.072763
Firmsize22.5981.58018.65222.44525.392763
Salesgrowth0.5000.745−0.7500.3424.514763
Leverage0.1390.2480.0010.0604.466763
Cashholding0.2280.1250.0070.2080.914763
SOE0.5230.5000.0001.0001.000763
Firmage1.9970.8550.0002.1973.296763
Boardsize2.1080.1741.6092.1972.708763
Top137.57115.2054.49035.42078.590763
Mktindex9.6351.5574.1419.80811.934763
Table
Table 3. The result of VIF test for Model (1).
Table 3. The result of VIF test for Model (1).
VariableDigfincityFirmsizeSalesgrowthLeverageCashholdingSOEFirmageBoardsizeTop1Mktindex
VIF1.3414.5202.8325.9852.5877.2264.3886.2311.6072.343
Table
Table 4. Regression result of the Hypothesis 1.
Table 4. Regression result of the Hypothesis 1.
VariableROAROATobinQTobinQ
(1)(2)(3)(4)
Digfincity0.002 ***0.001 **0.005 ***0.004 **
(4.640)(2.226)(3.414)(2.501)
Firmsize 0.003 0.022
(1.434) (1.356)
Salesgrowth 0.005 * 0.008 ***
(1.698) (2.937)
Leverage −0.023 ** −0.234 **
(−2.567) (−1.965)
Cashholding −0.064 −0.562
(−0.722) (−1.068)
SOE 0.008 0.017 *
(1.481) (1.758)
Firmage 0.089 * 0.375 ***
(1.693) (2.689)
Boardsize −0.007 −0.161
(−0.621) (−0.491)
Top1 0.000 0.004
(0.401) (0.915)
Mktindex −0.007 0.281
(−0.814) (1.430)
Constants0.036 ***0.0471.119 **2.403
(3.812)(0.502)(2.339)(1.066)
YearYESYESYESYES
ProvinceYESYESYESYES
N763763763763
Adjected-R20.0590.1640.0870.150
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
Table
Table 5. Robustness test of reconstructed key variables.
Table 5. Robustness test of reconstructed key variables.
VariableROATobinQROEProfit-M
(1)(2)(3)(4)
Digfincounty0.001 **0.005 **
(1.982)(2.098)
Digfincity 0.012 **0.000 ***
(2.392)(4.499)
Firmsize0.004 *0.667 ***0.026 ***0.025 **
(1. 891)(4.834)(3.133)(2.113)
Salesgrowth0.009 *0.247 **0.019 *0.025
(1.739)(2.467)(1.674)(1.257)
Leverage−0.012 *−0.236−0.0320.017
(−1.933)(−0.768)(−1.101)(0.852)
Cashholding−0.062 **−0.869 ***0.180 ***0.100 **
(−2.542)(−3.154)(3.160)(2.427)
SOE0.0010.0920.0190.032
(0.135)(1.159)(0.940)(1.318)
Firmage−0.011 ***−0.410 ***−0.029 *−0.023 **
(−2.622)(−3.337)(−1.822)(−2.040)
Boardsize−0.009−0.384−0.044−0.068
(−0.571)(−0.976)(−1.111)(−1.224)
Top10.000−0.0010.0000.000
(0.272)(−0.167)(0.646)(0.446)
Mktindex−0.015−0.154−0.026−0.030
(−1.176)(−0.767)(−0.736)(−1.083)
Constant0.1270.939−0.146−0.111
(0.914)(0.193)(−0.368)(−0.441)
YearYESYESYESYES
ProvinceYESYESYESYES
N763763452452
Adjected-R20.1720.1660.1120.111
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
Table
Table 6. Robustness test of omitting variables.
Table 6. Robustness test of omitting variables.
VariableROATobinQDigfincityROATobinQ
(1)(2)(3)(4)(5)
IV 17.776 **
(2.064)
DigfincityR0.000 *0.001 **
(1.781)(2.361)
Digfincity 0.005 **0.022 ***
(2.126)(3.515)
Firmsize0.027 ***0.734 ***−0.679 **0.003 **0.670 ***
(4.170)(7.066)(−2.389)(2.099)(3.178)
Salesgrowth0.009*0.0190.0120.0070.216 **
(1.857)(0.418)(0.021)(1.643)(2.116)
Leverage0.0110.5982.567−0.012−0.309
(0.942)(1.277)(0.290)(−1.538)(−0.973)
Cashholding−0.025 ***−0.456 ***−7.0990.057 **−0.711 *
(2.998)(3.583)(−0.120)(2.354)(−1.830)
SOE−0.022 **−0.146 *−0.746−0.003−0.032
(−2.432)(−1.794)(0.232)(−0.633)(−1.010)
Firmage0.025 ***0.267 **−0.219−0.010 ***0.469 **
(3.208)(2.132)(0.123)(−3.070)(2.146)
Boardsize−0.039 *−0.466−0.983−0.009−0.098
(−1.806)(−1.348)(−0.054)(−0.793)(−0.297)
Top10.0000.0060.0130.0000.063
(0.147)(0.935)(0.038)(0.617)(0.884)
Mktindex0.018 ***0.238 **−2.2810.012 *0.237 *
(2.840)(2.398)(−0.239)(1.843)(1.747)
Constant−0.282 *1.908 ***8.913 ***0.0992.957
(−1.690)(6.274)(4.223)(0.188)(0.126)
YearYESYESYESYESYES
ProvinceYESYESYESYESYES
N0.0960.1700.2800.1690.159
Adjected-R2763763763763763
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
Table
Table 7. Regression result of the Hypothesis 2.
Table 7. Regression result of the Hypothesis 2.
VariableROATobinQ
(2)(3)
Digfincity0.000 *0.002 *
(1.677)(1.912)
Digfincity × SCF0.021 **0.007 **
(2.053)(1.966)
SCF0.099 ***0.032 ***
(2.901)(2.717)
Firmsize0.0420.066**
(1.420)(2.511)
Salesgrowth−0.031 ***−0.098
(−3.822)(−1.062)
Leverage−0.102 ***−0.455 ***
(−2.964)(−2.723)
Cashholding−0.066 **−1.211 ***
(−2.226)(−2.932)
SOE−0.010 ***−0.132 ***
(−2.607)(−2.781)
Firmage−0.017 ***−0.099 ***
(−2.692)(−4.004)
Boardsize−0.022 *−0.062
(−1.825)(−1.491)
Top10.001 *0.008 ***
(1.729)(3.741)
Mktindex−0.005−0.184
(−0.792)(0.842)
Constant0.091 ***0.431 **
(2.597)(2.321)
YearYESYES
ProvinceYESYES
N763763
Adjected-R20.1880.201
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
Table
Table 8. The moderating influence of ownership.
Table 8. The moderating influence of ownership.
VariableROAROATobinQTobinQ
(1)(2)(3)(4)
Non-SOESOE Non-SOESOE
Digfincity0.002 ***0.000 *0.006 ***0.003 *
(3.336)(1.752)(2.782)(1.688)
Firmsize0.002 *0.003 **0.704 ***0.493 ***
(1.865)(2.089)(4.589)(3.589)
Salesgrowth0.009 **0.0010.303 ***0.005
(2.557)(0.124)(3.088)(0.072)
Leverage−0.015−0.015−0.004−0.781
(−1.305)(−0.693)(−0.009)(−1.077)
Cashholding0.0440.067−1.404−0.182
(1.375)(0.859)(−1.228)(−0.487)
Firmage−0.001−0.0150.1770.412 *
(−0.287)(−1.505)(1.002)(1.796)
Boardsize−0.007−0.011−0.6940.378
(−0.402)(−0.777)(−1.503)(1.328)
Top10.0000.0000.009−0.001
(0.287)(0.638)(1.195)(−0.182)
Mktindex−0.016 *−0.0020.518 *0.046
(−1.874)(−0.171)(1.849)(0.421)
Constant0.152 *−0.079 ***4.114 ***5.701 ***
(1.689)(3.581)(3.732)(3.635)
YearYESYESYESYES
ProvinceYESYESYESYES
T-test of β11.773 *2.532 **
N0.1740.2300.1750.183
Adjected-R2364399364399
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
Table
Table 9. The moderating influence of operating pressure.
Table 9. The moderating influence of operating pressure.
VariableROAROATobinQTobinQ
(1)(2)(3)(4)
Lower Operating PressureHigher Operating PressureLower Operating PressureHigher Operating Pressure
Digfincity0.003 **0.000 *0.005 ***0.003 *
(2.215)(1.735)(2.669)(1.945)
Firmsize0.0020.0050.581 ***0.698 ***
(0.933)(1.150)(2.787)(7.274)
Salesgrowth0.0020.007−0.144−0.277
(0.890)(1.279)(−1.102)(−1.395)
Leverage−0.029 ***−0.019−0.674 **−0.660 ***
(−3.543)(−1.477)(−2.302)(−3.850)
Cashholding0.070 ***0.051 *1.3000.164
(4.726)(1.854)(1.454)(0.249)
SOE−0.010−0.005−0.700−0.402 *
(−1.448)(−0.627)(−1.200)(−1.921)
Firmage−0.004−0.014 **−0.209 **−0.560 ***
(−1.541)(−2.474)(−2.412)(−5.103)
Boardsize0.004−0.0150.3750.240
(0.284)(−1.054)(0.820)(0.825)
Top10.0000.0000.0010.005
(0.677)(0.208)(0.166)(0.942)
Mktindex0.004−0.0120.3760.233
(0.393)(−1.062)(1.604)(0.965)
Constant0.063 **0.076 ***3.095 ***4.398 ***
(2.525)(3.586)(2.805)(5.053)
YearYESYESYESYES
ProvinceYESYESYESYES
T-test of β12.072 **1.889 *
N342421342421
Adjected-R20.1030.1180.1570.184
***, **, and * indicate the significance levels of 1%, 5%, and 10%, respectively.
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Yu, W.; Huang, H.; Zhu, K. Enhancing Construction Enterprise Financial Performance through Digital Inclusive Finance: An Insight into Supply Chain Finance. Sustainability 2023, 15, 10360. https://doi.org/10.3390/su151310360

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Yu W, Huang H, Zhu K. Enhancing Construction Enterprise Financial Performance through Digital Inclusive Finance: An Insight into Supply Chain Finance. Sustainability. 2023; 15(13):10360. https://doi.org/10.3390/su151310360

Chicago/Turabian Style

Yu, Wei, Huiqin Huang, and Keying Zhu. 2023. "Enhancing Construction Enterprise Financial Performance through Digital Inclusive Finance: An Insight into Supply Chain Finance" Sustainability 15, no. 13: 10360. https://doi.org/10.3390/su151310360

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