How Does Digital Finance Affect Sports Enterprise Innovation? Evidence from Chinese Sports Listed Enterprises

Abstract

In the context of the digital economy, digital finance has emerged as a new driving force for the sustainable and high-quality development of the sports industry. The sports industry promotes economic growth, social well-being, and environmental sustainability. However, the sustainable development of the sports industry faces challenges such as insufficient innovation and a lack of diverse financing channels. This study has investigated the impact of digital finance on sports enterprise innovation in China, focusing on the mediating role of financing constraints. Employing a panel dataset of 95 listed Chinese sports enterprises from 2011 to 2020, we conducted random-effects GLS regression and mediation analyses to examine the interrelationships among digital finance, financing constraints, and sports enterprise innovation. The empirical results have confirmed the positive role of digital finance in promoting sports enterprise innovation and alleviating financing constraints. Financing constraints negatively influence sports enterprise innovation and partially mediate the relationship between digital finance and innovation. The heterogeneity analyses have revealed that the impact of digital finance on innovation was more pronounced in the eastern region of China, and among both the major and small and mid-sized sports enterprises, with the latter potentially benefiting more. Our findings have highlighted the transformative potential of digital finance in driving inclusive innovation within the sports industry by providing accessible financing solutions and reducing information asymmetries. This study has contributed to the literature on digital finance, financing constraints, and enterprise innovation in the sports industry context, offering valuable insights for sports enterprises, policymakers, and financial institutions in leveraging digital finance to foster innovation-driven growth.

1. Introduction

In the digital economy era, digital finance, a product of the deep integration of finance and information technology, has emerged as a new engine to drive high-quality economic development [1]. Gomber et al. (2017) [2] defined digital finance as a financial model that leverages digital technology innovation to effectively integrate financial services, enhancing the financial industry’s operational efficiency, reducing transaction costs, and expanding service coverage. As of May 2023, China’s digital finance market size reached RMB 41.7 trillion, accounting for 15.6% of the global digital finance market size, ranking first globally [3]. The Chinese government has also introduced various policies and initiatives to support digital finance development, such as the “Digital Economy Development Planning (2021–2025)” [4] and the “Overall Layout Planning for the Construction of Digital China” [5]. As its importance has grown, governments worldwide have reinforced policy support for digital innovation [6,7]. Digital finance is crucial in facilitating the transformation and upgrading of various industries, fostering innovative and sustainable development within enterprises.

Extant literature has demonstrated the positive impact of digital finance and digital technologies on different sectors. For instance, Yao and Yang (2022) [8] found that digital finance promotes the growth of small and medium-sized enterprises (SMEs) by alleviating financing constraints. Bolton et al. (2018) [9] found that digital technologies improve operational efficiency and customer experience in both business-to-business and business-to-consumer. However, studies have yet to investigate the effect of digital finance on enterprise innovation from a micro perspective, particularly in the context of the sports industry.

The sports industry plays an increasingly significant role in China’s economy. In 2022, the total output value of China’s sports industry reached RMB 3.3 trillion, accounting for 1.1% of the country’s GDP [10]. The Chinese government has also prioritised the development of the sports industry, aiming to bring the total size of the industry to 5 trillion RMB by 2035, with an added value of 2% of GDP [11]. Despite its economic significance, the sports industry faces challenges such as inadequate innovation and limited business models, which hinder its sustainable growth [12]. This study focuses on the sports industry due to its unique characteristics and potential for theoretical innovation.

The decision problem this study aims to address is as follows: How does digital finance affect sports enterprise innovation, and what role do financing constraints play in this relationship? Specifically, we seek to answer the following three research questions: First, what are the interrelationships among digital finance, financing constraints, and sports enterprise innovation? Specifically, how does digital finance directly influence sports enterprise innovation and financing constraints, and how do financing constraints impact innovation? Second, do financing constraints mediate the relationship between digital finance and sports enterprise innovation? Third, are there heterogeneous effects of digital finance on innovation across different regions in China and enterprise sizes?

This study contributes to the literature in several ways by investigating these questions. First, we systematically explore the impact of digital finance on enterprise innovation in the context of the sports industry, enriching the understanding of the digital finance–innovation nexus from a micro perspective. Second, we examine the mediating role of financing constraints, shedding light on how digital finance influences sports enterprise innovation. Third, we analyse the heterogeneous effects of digital finance on innovation across regions and enterprise sizes, providing nuanced insights into the boundary conditions of the digital finance-innovation relationship.

The remainder of this article is structured as follows. Section 2 reviews the relevant literature and develops the research hypotheses. Section 3 describes the methodology, including data sources, variable measurement, and model specification. Section 4 presents the empirical results and robustness checks. Section 5 discusses the findings and implications. Finally, Section 6 concludes the article.

2. Literature Review and Research Hypotheses

2.1. Digital Finance and Sports Enterprise Innovation

Research on digital finance primarily focuses on theory and empirical analysis, emphasising the application and impact of digital finance. Studies have shown that digital finance has been applied in various fields, including internet finance [13], fintech finance [14], open banking [15], embedded finance [16], decentralised finance [17], blockchain finance [18], artificial intelligence finance [19], and electronic money [20]. These applications have transformed traditional financial services and created new financial inclusion and innovation opportunities. Digital finance can promote financial inclusion [21] and increase consumer spending and investment, stimulating economic growth [22,23]. However, it can also bring new risks and challenges, such as cybersecurity threats [21], data privacy issues [24], and the possibility of digital financial exclusion [21].

Current research on innovation in sports enterprises focuses on internal and external factors. Internal factors include the characteristics and capabilities of sports enterprises and management’s environmental commitment and vision. Huang and Chen (2002) [25] examined the role of organisational resources in supporting green innovation investments in Chinese sports goods manufacturing enterprises, finding that limited internal resources posed significant challenges for these smaller-scale enterprises compared to other manufacturing industries. Managers prioritising sustainable development and long-term growth are more likely to drive innovation within their organisations [26]. External factors include the external environment (government policies, market forces, and stakeholder pressure) and market competition. Industry policies promoting environmental protection and sustainable development drive green innovation in manufacturing sports goods [27]. Intense competition among Chinese sports goods manufacturers compels them to pursue innovation to achieve product differentiation and alleviate market share and profit pressures [25].

Drawing from the asymmetric information theory [28] and transaction costs theory [29], we argue that digital finance can positively influence sports enterprise innovation in the following ways: First, digital finance can alleviate information asymmetry issues, promoting enterprise innovation. Gomber et al. (2018) [30] emphasised that digital finance efficiently gathers valuable information in competitive markets, reducing information asymmetry. This screening and aggregation enable financial service providers to understand enterprise information better and offer targeted credit products, fostering enterprise innovation and development. Second, digital finance enhances the allocation efficiency of economic resources and provides more support for enterprise innovation activities. Fan and Chen (2022) [31] argued that digital finance positively affects resource allocation efficiency through human capital and corporate innovation. Third, digital finance reduces the cost of financial services, freeing up resources for innovation investment. Demertzis et al. (2018) [32] found that digital finance’s convenience lowers service costs and thresholds, enabling financial institutions to foster enterprise innovation through precise risk assessment and comprehensive business processes. Digital finance provides more accessible financing channels at a lower cost, enhancing corporate finance availability [33].

While extant research has examined the impact of digital finance on various aspects of the economy and enterprise development [21,22,23], there is a paucity of studies investigating its specific effect on sports enterprise innovation. This study aims to fill these gaps by directly examining the relationship between digital finance and sports enterprise innovation. Consequently, we propose the following hypothesis:

H1. 

Digital finance plays a positive role in promoting sports enterprise innovation.

2.2. Digital Finance and Financing Constraints

Financial constraints refer to the limiting factors that hinder companies from obtaining funds for all expected investments [34]. Research on financial constraints primarily focuses on empirical studies, examining the measurement, determinants, and consequences of financial constraints. Researchers have developed measurement standards based on firm characteristics, such as the KZ index [35], the WW index [36], and the SA index [37]. The determinants of financial constraints include firm-level factors, macroeconomic factors, corporate governance, and financial market characteristics [37,38,39,40]. The consequences of financial constraints include challenges in financial reporting and auditing, limitations in firm-level operations, difficulties in corporate governance, and effects on other firms’ behaviours and decisions [41,42,43,44,45,46].

Building on the information asymmetry theory [28] and the transaction cost theory [29], we contend that digital finance can alleviate financing constraints for sports enterprises in the following ways: First, digital finance can reduce the information asymmetry between sports enterprises and financial institutions, thus easing financing constraints. Li et al. (2023) [47] demonstrated that digital finance improves information transparency and lowers adverse selection costs, mitigating financing constraints for SMEs. Second, digital finance enhances the allocation efficiency of financial resources and provides more support for sports enterprise financing. Fu (2023) [48] showed that digital finance significantly expands the breadth and depth of financial services, lowering service thresholds and costs and enhancing the convenience and inclusiveness of financial services, thereby helping to alleviate the financing constraints that SMEs face. Third, digital finance reduces the cost of financial services, freeing up resources for sports enterprise financing. Wang (2022) [49] found that the convenience of digital finance lowers service costs and thresholds, enabling financial institutions to foster enterprise financing through precise risk assessment and comprehensive business processes. Digital finance provides more accessible financing channels at a lower cost, enhancing corporate finance availability.

Although prior studies have investigated the impact of digital finance on alleviating financing constraints faced by SMEs [47,48,49], research explicitly examining its role in the context of sports enterprises remains limited. This study addresses these gaps by directly investigating the relationship between digital finance and financing constraints in sports enterprises. Consequently, we propose the following hypothesis:

H2. 

Digital finance plays a positive role in alleviating the financing constraints of sports enterprises.

2.3. Digital Finance, Financing Constraints, and Sports Enterprise Innovation

Drawing from the pecking order theory [50], we posit that financing constraints can negatively affect sports enterprise innovation in the following ways: First, the availability of internal funds affects the scale and intensity of innovation investment in sports enterprises. Guariglia and Liu (2014) [51], using microdata from unlisted Chinese enterprises, found that the availability of internal funds constrains enterprise innovation activities. Second, financing constraints reduce the level of innovation output in sports enterprises. Hai et al. (2022) [52], based on an empirical test of 142,000 Chinese manufacturing enterprises from 1999 to 2009, elucidated the significant impact of financing constraints on enterprise innovation outputs. Third, financing constraints disrupted the rational pace of innovation investment in sports enterprises. Aghion et al. (2012) [53] examined the relationship between credit constraints and R&D investment behaviours in enterprises over the economic cycle, concluding that enterprises without financing constraints demonstrate a more robust and rational R&D investment pattern. Consequently, we propose the following hypothesis:

H3. 

Financing constraints play a negative role in promoting sports enterprise innovation.

Many researchers have explored the relationship between financial development levels and corporate innovation. Zhu et al. (2020) [54] utilised data from 50 countries to demonstrate that overall financial development positively impacts innovation. However, this impact diminishes once financial development exceeds a certain threshold. Digital finance, as a critical component of modern financial innovation, is increasingly notable for its role in alleviating corporate financing constraints. Ding et al. (2022) [55], using data from a 331-city fintech index provided by Ant Financial Services, found that fintech development facilitated business lending and stimulated investment in research and development. Internet credit intensified the competition for bank loans. Lyu et al. (2022) [56] found that FinTech significantly alleviated corporate finance constraints, and its coverage ability is more effective than the depth of its use.

While existing studies have explored the relationship between financial development and corporate innovation [54,55], research investigating the financing constraints’ role in mediating digital finance’s impact on sports enterprise innovation is scarce. Our study seeks to fill this gap by examining the interplays among digital finance, financing constraints, and innovation in the sports industry and uncovering the potential mediating effect of financing constraints. Consequently, we propose the following hypothesis:

H4. 

Financing constraints have a mediating effect between digital finance and sports enterprise innovation.

Figure 1 presents this study’s conceptual framework, an original model designed and constructed by the authors based on synthesising relevant theories and the identified research gaps in the literature.

3. Materials and Methods

3.1. Sample Selection and Data Sources

This study’s research population consisted of all sports companies listed in China from 2011 to 2020, encompassing sports companies in A-share, Hong Kong stock, and U.S. stock markets. To obtain the final sample, we first excluded enterprises with poor performance (ST) and those at risk of delisting (ST*) from the research population. Subsequently, we removed companies with substantially missing values for key variables. Lastly, we Winsorised the main continuous variables at the 1st and 99th percentiles to mitigate the influence of outliers. Following these steps, we obtained a final sample of an unbalanced panel dataset of 688 observations from 95 listed sports companies. We acquired the financial data of these enterprises from the China Stock Market and Accounting Research Database, retrieved the digital finance index from the Institute of Digital Finance Peking University [57], and obtained enterprise-level microdata from the iFinD database. The data were processed using Stata 17.0 statistical software.

3.2. Variable Selection

3.2.1. Explained Variable

Sports Enterprises Innovation: Enterprises encounter various challenges during the process of technological innovation. First, technological innovation results are often challenging to quantify accurately using specific numerical indicators, leading to difficulties in comparing innovation outcomes across different enterprises and a lack of intuitive comparability [58]. Second, during the lengthy cycle of transforming inputs into tangible innovation outcomes, innovators may encounter various uncertain and uncontrollable factors hindering innovation effectiveness. Furthermore, if an enterprise’s innovation management needs more rigour and robustness, it may exacerbate the harmful effects of such uncertainties, significantly impeding enterprises from achieving their expected innovation outputs. Considering the factors above, this study employs R&D investment intensity (i.e., the ratio of R&D expenditures to operating revenues) as a critical indicator for evaluating an enterprise’s level of technological innovation [8]. Based on this criterion, a higher R&D investment intensity indicates that an enterprise has invested more in technological innovation, suggesting a relatively higher level of technological innovation.

3.2.2. Explanatory Variable

Digital Finance: This study employed the Peking University Digital Financial Inclusion Index, jointly developed by the Institute of Digital Finance Peking University and Ant Group. Guo et al. (2020) [59] described a comprehensive methodology for constructing this index, which spans from 2011 to 2020 and includes data from 31 provinces across China. The index measures the extent and effectiveness of digital financial services in fostering financial inclusion within a region. This study used the Digital Financial Inclusion Index composite score to evaluate digital finance. The Digital Financial Inclusion Index consists of the following three primary components:

Breadth of Coverage: This component measures the extent to which digital financial services are available across different demographic and geographic areas. It evaluates the inclusiveness of digital financial services, assessing factors such as the distribution of digital banking access points, the availability of mobile financial services, and the inclusivity of digital finance platforms toward underserved or marginalised groups.

Depth of Use: This metric examines the intensity and frequency with which those with access use digital financial services. It includes indicators such as transaction frequency, diversity of digital financial products used (e.g., payments, savings, credit), and the overall engagement of consumers with digital financial platforms.

Degree of Financial Inclusion Digitalization: This component assesses the extent to which digital services contribute to financial inclusion. It measures how digital innovations in financial services reduce costs, expand access, and provide new opportunities for users.

In this study, all Digital Financial Inclusion Index metrics underwent normalisation processing, ensuring each indicator score was within the 0 to 100 range. This normalisation facilitated comparisons across regions and periods, providing a standardised scale for evaluating progress toward digital financials.

3.2.3. Control Variables

This study incorporated control variables from the related literature that considered corporate finance, operations, and governance aspects to mitigate the impact of omitted variable bias on the model results. First, the asset–liability ratio reflects an enterprise’s financial risk and leverage level, which may influence innovation activities [60]. It is important to note that the gearing ratio itself may not directly capture the impact of digital finance on innovation; instead, it serves as a control variable to account for the influence of other factors on innovation. Second, the sales revenue growth rate reflects an enterprise’s market competitiveness and development dynamics, which may significantly influence innovation activities [61]. Enterprises with high growth rates may be motivated to invest more in innovation to meet market demand. Third, return on equity, which reflects an enterprise’s profitability [62], impacts its ability to undertake innovative activities. Highly profitable enterprises are more likely to invest capital and resources in innovation. Fourth, shareholding concentration, which reflects an enterprise’s ownership structure [63], significantly influences corporate governance and decision-making mechanisms. Enterprises with higher ownership concentration may be more likely to implement innovative strategies, as minority shareholders can reach consensus more quickly. Fifth, the cash flow ratio, which reflects an enterprise’s ability to manage funds [64], may influence its innovation activities. Effective cash flow management ensures enterprises have sufficient funds to support innovation projects. Sixth, management costs reflect an enterprise’s efficiency and cost control [65] and may influence innovation activities. High overheads may affect enterprises’ innovation investments, necessitating their consideration of innovation research.

Based on careful consideration of the theoretical and empirical relationships between these control variables and innovation activities, as well as the characteristics of our data, we determined that using contemporaneous values of the control variables was appropriate for our analysis. This approach is consistent with prior studies examining the relationship between financial indicators and innovation (e.g., Hai et al. (2022) [52], Kijkasiwat and Phuensane (2022) [66]).

3.2.4. Mediating Variable

Financing Constraints: First, digital finance, which leverages advancements in big data and other technologies, can alleviate the financing constraints faced by sports enterprises by offering additional financing channels and addressing financing challenges, encouraging enterprises to engage in innovative activities [67]. Consequently, a sound theoretical basis exists for considering financing constraints as a mediating variable in the relationship between digital finance and innovation. Second, Yao and Yang (2022) [8] demonstrated that digital finance influences corporate financing constraints, mediating enterprise innovation. By selecting financing constraints as a mediating variable, this study could further investigate how digital finance impacts sports enterprise innovation, building upon the existing research. Accordingly, this study employed the SA index developed by Hadlock and Pierce (2010) [37] to measure the level of financing constraints faced by Chinese sports listed companies, calculated as follows:

$$SA=-0.737\times Size+0.043\times Siz{e}^2-0.04\times Age$$

(1)

In Equation (1), Size represents the enterprise’s size, measured as the logarithm of its total assets. At the same time, Age denotes the enterprise’s years of existence, calculated as the current year minus the year of the enterprise’s inception plus one. Smaller and younger firms often face more significant information asymmetry and financing difficulties [68,69], so a higher SA value indicates a higher degree of financing constraints [37]. Previous studies, such as those by Yao and Yang (2022) [8] and Guo et al. (2023) [70], have validated the applicability of the SA index to measure financing constraints in Chinese listed companies, affirming its relevance to this study’s context.

Table 1. Variable names and identification.

Types	Variable Name	Identification	Measurement Method
Dependent variable	Sports enterprise innovation	INNOV	R&D expenditure/sales revenue
Independent variables	Digital finance	FIN	The Peking University Digital Financial Inclusion Index of China (2011–2020)
	Breadth of coverage	BRE
	Depth of use	DEP
Mediating variable	Financing constraint	SA	SA index (See Equation (1))
Control variables	Asset-liability ratio	ALR	Total liability/total assets
	Sales revenue growth rate	SRGR	(Current Sales Revenue—Previous Sales Revenue)/Previous Sales Revenue × 100%
	Return on equity	ROE	Net profit/owners’ equity
	Shareholding concentration	SC	Percentage of shares held by the largest shareholder
	Cash flow ratio	CFR	Net cash flow/total assets × 100%
	Management costs	MC	Natural logarithm of management costs

presents the measurement and Identification of the variables employed in the analysis.

3.3. Model Construction

Following Hypotheses H1–H3, we employed the following random-effects generalised least squares (GLS) regression model to examine the relationship between digital finance, financing constraints, and sports enterprise innovation. The specific model is as follows:

$$\begin{array}{l}INNO{V}_{i,t}={\beta }_0+{\beta }_{1}FI{N}_{i,t}+{\beta }_{2}AL{R}_{i,t}+{\beta }_{3}SRG{R}_{i,t}+{\beta }_{4}RO{E}_{i,t}\\ +{\beta }_{5}S{C}_{i,t}+{\beta }_{6}CF{R}_{i,t}+{\beta }_{7}M{C}_{i,t}+{\alpha }_i+{\epsilon }_{i,t}\end{array}$$

(2)

$$\begin{array}{l}S{A}_{i,t}={\beta }_0+{\beta }_{1}FI{N}_{i,t}+{\beta }_{2}AL{R}_{i,t}+{\beta }_{3}SRG{R}_{i,t}+{\beta }_{4}RO{E}_{i,t}\\ +{\beta }_{5}S{C}_{i,t}+{\beta }_{6}CF{R}_{i,t}+{\beta }_{7}M{C}_{i,t}+{\alpha }_i+{\epsilon }_{i,t}\end{array}$$

(3)

$$\begin{array}{l}INNO{V}_{i,t}={\beta }_0+{\beta }_{1}S{A}_{i,t}+{\beta }_{2}AL{R}_{i,t}+{\beta }_{3}SRG{R}_{i,t}+{\beta }_{4}RO{E}_{i,t}\\ +{\beta }_{5}S{C}_{i,t}+{\beta }_{6}CF{R}_{i,t}+{\beta }_{7}M{C}_{i,t}+{\alpha }_i+{\epsilon }_{i,t}\end{array}$$

(4)

In Equations (2)–(4), INNOV_i,t represents the innovation capacity of sports enterprise i in year t, β₀ is a constant term. β₁, β₂, …β₇ are the parameters to be estimated. FIN_i,t denotes the comprehensive digital finance in the province where enterprise i operates during year t. ALR_i,t, SRGR_i,t, ROE_i,t, SC_i,t, CFR_i,t, MC_i,t are control variables denoting the observed value of the i enterprise in the year t. α_i is an enterprise-specific random effect, denoting the particular impact of the i enterprise. ε_i,t represents the idiosyncratic error term for i enterprise in year t. Given the relatively short time frame of our panel data (10 years), we refrained from making strong assumptions about the distribution of the error term. Instead, we focus on the consistency and robustness of our estimates [71].

Furthermore, to test H4, this study developed the following model:

$$\begin{array}{l}INNO{V}_{i,t}={\beta }_0+{\beta }_{1}FI{N}_{i,t}+{\beta }_{2}AL{R}_{i,t}+{\beta }_{3}SRG{R}_{i,t}+{\beta }_{4}RO{E}_{i,t}\\ +{\beta }_{5}S{C}_{i,t}+{\beta }_{6}CF{R}_{i,t}+{\beta }_{7}M{C}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(5)

$$\begin{array}{l}S{A}_{i,t}={\beta }_0+{\beta }_{1}FI{N}_{i,t}+{\beta }_{2}AL{R}_{i,t}+{\beta }_{3}SRG{R}_{i,t}+{\beta }_{4}RO{E}_{i,t}\\ +{\beta }_{5}S{C}_{i,t}+{\beta }_{6}CF{R}_{i,t}+{\beta }_{7}M{C}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(6)

$$\begin{array}{l}INNO{V}_{i,t}={\beta }_0+{\beta }_{1}FI{N}_{i,t}+{\beta }_{2}S{A}_{i,t}+{\beta }_{3}AL{R}_{i,t}+{\beta }_{4}SRG{R}_{i,t}+{\beta }_{5}RO{E}_{i,t}\\ +{\beta }_{6}S{C}_{i,t}+{\beta }_{7}CF{R}_{i,t}+{\beta }_{8}M{C}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(7)

In Equations (5)–(7), These symbols have the same meaning as above.

4. Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

VarName	Obs	Mean	SD	Min	Median	Max
INNOV	688	3.431	2.546	0.061	3.207	14.643
FIN	688	5.513	0.472	3.652	5.628	6.068
BRE	688	5.438	0.481	3.517	5.557	5.984
DEP	686	5.539	0.451	4.018	5.633	6.192
ALR	688	0.418	0.194	0.069	0.407	0.962
SRGR	688	0.123	0.253	−0.545	0.096	1.030
ROE	688	0.094	0.175	−0.817	0.088	0.578
SC	688	0.371	0.171	0.108	0.350	0.849
CFR	688	2.267	1.798	0.440	1.714	12.742
MC	688	2.466	4.507	0.055	1.154	30.501
SA	688	−2.617	0.737	−4.364	−2.657	−0.402

provides the descriptive statistics for the main variables of this study based on 688 valid observations. The dependent variable, INNOV, has a mean of 3.431 and a standard deviation of 2.546, indicating substantial variation in the innovation investment across sports enterprises. This disparity indicates varying emphasis on innovation and expenditure among sports enterprises, likely due to differences in the industry segments and technological maturity. The explanatory variables, FIN, BRE, and DEP, have means of 5.513, 5.438, and 5.539, respectively, with standard deviations from 0.451 to 0.481. These figures indicate that the sampled sports enterprises are in regions with relatively high levels of digital finance development. However, the standard deviations reveal some regional disparities. The mediating variable, SA, has a mean of −2.617 and a standard deviation of 0.737. Higher SA values indicate more severe financing constraints [37], suggesting that the sampled sports enterprises generally face financing constraints. However, the standard deviation shows variability in the degree of financing constraints among enterprises. The control variables, ALR, SRGR, ROE, SC, CFR, and MC, show considerable variation, as indicated by their standard deviations and the range between their minimum and maximum values. These variations indicate differences among sports enterprises in capital structure, revenue growth, profitability, shareholding structure, short-term solvency, and management efficiency. These differences may stem from enterprise size, industry segment, and development stage.

4.2. Correlation Analysis

To ensure the robustness of our empirical analysis, we examined the correlations between the variables before conducting the regression tests.

Table 3. Correlation analyses.

Variable	INNOV	FIN	BRE	DEP	ALR	SRGR	ROE	SC	CFR	MC	SA
INNOV	1
FIN	0.12 ***	1
BRE	0.11 ***	0.99 ***	1
DEP	0.12 ***	0.97 ***	0.95 ***	1
ALR	−0.18 ***	0.03	0.02	0.00	1
SRGR	0.11 ***	−0.06	−0.06	−0.05	0.01	1
ROE	−0.05	−0.06	−0.06	−0.06	0.05	0.29 ***	1
SC	−0.03	−0.02	−0.02	−0.02	0.02	0.04	0.31 ***	1
CFR	0.15 ***	−0.08 **	−0.07 *	−0.06 *	−0.57 ***	−0.07 *	0.01	−0.04	1
MC	−0.25 ***	0.07 *	0.07 *	0.07 *	0.23 ***	−0.04	0.03	−0.05	−0.12 ***	1
SA	0.31 ***	−0.16 ***	−0.16 ***	−0.14 ***	−0.25 ***	0.09 **	0.10 ***	0.17 ***	0.13 ***	−0.60 ***	1

Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the correlation matrix for the main variables used in this study. Firstly, the correlation coefficients between INNOV and digital finance indicators (FIN, BRE, DEP) at the 1% significance level are 0.12, 0.11, and 0.12, respectively, all significantly positive. This finding preliminarily confirmed the primary hypothesis that digital finance can promote the development of sports enterprise innovation. However, the correlation coefficients only represent the correlation between variables and factors, and we must conduct further in-depth analysis after incorporating control factors. Secondly, the correlation coefficients of the variables are all within a reasonable range, except for the high correlation coefficients (above 0.94) between the digital finance indicators (FIN, BRE, DEP). The correlation coefficients of the other variables are all less than 0.7, indicating the absence of severe multicollinearity among the essential variables.

Furthermore, SA is significantly and positively correlated with INNOV at the 1% level (β = 0.31), suggesting that, contrary to expectations, higher financing constraints, as indicated by higher SA values, are associated with increased innovation investment in sports enterprises. This observation might imply that sports enterprises are leveraging innovation as a strategic response to overcome their financing challenges. Moreover, SA is significantly negatively correlated with digital finance development indicators (FIN, BRE, DEP) at the 1% level (correlation coefficients of −0.16, −0.16, and −0.14, respectively), implying that the development of digital finance contributes to reducing the severity of financing constraints faced by sports enterprises. This finding provides a preliminary basis for further exploring how the development of digital finance promotes innovation in sports enterprises by alleviating financing constraints.

In summary, the results of the correlation analysis between the variables are generally in line with the research hypothesis of this study, laying the foundation for subsequent empirical research. After controlling for relevant variables, we needed to conduct further regression analyses to verify the impact mechanism of digital financial development on sports enterprise innovation.

4.3. Regression Analysis

4.3.1. Multivariate Regression Analysis of Digital Finance on Financing Constraints and Sports Enterprises Innovation

Table 4. The test results show the relationship between digital finance, financing constraints, and sports enterprises innovation.

Variable	(1)	(2)	(3)
	INNOV	SA	INNOV
FIN	0.614 ***	−0.464 ***
	(0.121)	(0.023)
SA			−0.460 ***
			(0.161)
ALR	−1.221 **	−0.051	−1.257 **
	(0.499)	(0.094)	(0.507)
SRGR	−0.269	−0.170 ***	−0.341
	(0.225)	(0.042)	(0.231)
ROE	−0.906 **	0.082	−1.064 ***
	(0.372)	(0.070)	(0.379)
SC	−0.129	0.458 ***	−0.461
	(0.733)	(0.142)	(0.740)
CFR	−0.002	−0.010	−0.020
	(0.044)	(0.008)	(0.044)
MC	−0.059 *	−0.055 ***	−0.067 *
	(0.033)	(0.007)	(0.035)
Constant	0.833	0.039	−1.257 **
	(0.862)	(0.165)	(0.507)
N	688	688	688
R2	0.077	0.298	0.001

Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the empirical results on the interrelationships among digital finance, financing constraints, and sports enterprise innovation. The positive and statistically significant coefficient for FIN on INNOV in column (1) (β = 0.614, p < 0.01) corroborates Hypothesis H1, suggesting that digital finance plays a positive role in promoting sports enterprise innovation. The negative and significant coefficient for FIN on SA in column (2) (β = −0.464, p < 0.01) lends support to Hypothesis H2, indicating that digital finance plays a positive role in alleviating the financing constraints faced by sports enterprises. Lastly, the negative and significant coefficient for SA on INNOV in column (3) (β = −0.460, p < 0.01) confirms Hypothesis H3, demonstrating that financing constraints play a negative role in promoting sports enterprise innovation. These findings underscore the positive influence of digital finance on sports enterprise innovation, both directly and indirectly, through alleviating financing constraints while highlighting the detrimental effect of financing constraints on innovation.

The control variables in Table 4 shed light on the complex factors influencing both INNOV and SA. Regarding INNOV, the negative and significant coefficients for ALR (β = −1.221, p < 0.05 in column 1; β = −1.257, p < 0.05 in column 3), ROE (β = −0.906, p < 0.05 in column 1; β = −1.064, p < 0.01 in column 3), and MC (β = −0.059, p < 0.1 in column 1; β = −0.067, p < 0.1 in column 3) suggest that higher levels of debt, profitability, and management costs may hinder innovation, potentially due to financial constraints, short-term focus, and resource diversion. Turning to SA, the negative and highly significant coefficients for SRGR (β = −0.170, p < 0.01 in column 2) and MC (β = −0.055, p < 0.01 in column 2) indicate that higher sales growth rates and management costs may help alleviate financing constraints, possibly by signalling better financial health and credibility to lenders and investors. In contrast, the positive and highly significant coefficient for SC (β = 0.458, p < 0.01 in column 2) implies that higher shareholding concentration may exacerbate financing constraints due to reduced transparency and increased agency costs. These findings underscore the multifaceted nature of the determinants of innovation and financing constraints in the sports industry, highlighting the interplay among the financial, governance, and organisational factors.

4.3.2. Testing for the Mediating Effect of Financing Constraints

Table 5. The results of the mediation analysis.

Variable	(1)	(2)	(3)
	INNOV	SA	INNOV
FIN	0.798 ***	−0.169 ***	0.960 ***
	(0.195)	(0.046)	(0.192)
SA			0.954 ***
			(0.157)
Controls	Yes	Yes	Yes
Constant	−0.538	−1.432	0.829 ***
	(1.176)	(0.279)	(1.168)
N	688	688	688
R2	0.123	0.411	0.168
Adj.R2	0.114	0.405	0.158
Sobel-Goodman Mediation Tests
Sobel	−0.161 ***	Z: −3.128
	(0.052)
Bootstrap test (Replications = 1000)
Indirect effect	−0.161 ***	Z: −3.230
	(0.050)
Direct effect	0.960 ***	Z: 5.390
	(0.178)
Total effect	0.798 ***	Z: 4.580
	(0.174)

Note: Standard errors are in parentheses. *** p < 0.01.

presents the mediation analysis results, which employed both the Sobel and Bootstrap tests with 1000 replications to investigate the mediating role of SA in the relationship between FIN and INNOV, thereby testing Hypothesis H4. The Sobel test follows a three-step procedure, as outlined by Baron and Kenny (1986) [72]. First, we estimated the effect of FIN on INNOV (β = 0.798, p < 0.01). Second, we assessed the effect of FIN on SA (β = −0.169, p < 0.01). Third, we examined the effect of SA on the INNOV while controlling for FIN (β = 0.954, p < 0.01). In addition to the Sobel test, we conducted a Bootstrap test to calculate the indirect effect (β = −0.161, p < 0.01), direct effect (β = 0.960, p < 0.01), and total effect (β = 0.798, p < 0.01) of the mediation model. The Bootstrap test provides a more robust estimation of the mediation effects, as it does not rely on the assumption of normality for the sampling distribution of the indirect effect [73]. The significant Sobel test coefficient (β = −0.161, p < 0.01; Z = −3.128) and the significant indirect, direct, and total effects in the Bootstrap test collectively demonstrate the significant mediating effect of financing constraints on the relationship between digital finance and sports enterprise innovation, thus validating Hypothesis H4. These findings underscore the crucial role of digital finance in fostering sports enterprises innovation, both directly and indirectly, by alleviating financing constraints faced by sports enterprises.

4.4. Heterogeneity Analysis of Regional Distribution and Enterprise Size

This study examined the heterogeneous effects of digital finance on sports enterprise innovation across different regions and enterprise sizes using group regression analysis, with the results presented in

Table 6. Results of heterogeneity analyses.

	(1)	(2)	(3)	(4)	(5)
	Central	Eastern	Northeast	Major	Small and Mid-Sized
FIN	0.953	0.646 ***	0.112	0.501 ***	1.382 **
	(0.622)	(0.142)	(0.182)	(0.121)	(0.665)
Control	YES	YES	YES	YES	YES
Constant	1.986	0.605	1.906	0.977	−3.262
	(3.542)	(1.036)	(1.336)	(0.869)	(4.147)
N	44	605	32	606	82
R2	<0.001	0.072	0.084	0.059	0.236

Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05.

. Firstly, addressing regional differences, the study divided the sample into central, eastern, and northeastern regions. Regression analysis reveals that digital finance significantly promotes sports enterprise innovation in the eastern region, as evidenced by a cheerful and highly significant coefficient (β = 0.646, p < 0.01). In contrast, the influence of digital finance in the central (β = 0.953) and northeastern regions (β = 0.112) is not statistically significant, suggesting disparities in economic development and the maturity of digital financial infrastructure across regions. The significant impact in the eastern region is attributed to its advanced economic development and well-established digital financial infrastructure, highlighting the crucial role of digital finance in promoting the sustainable development of the sports industry. Conversely, the slower development of the digital economy in the central and northeastern regions may hinder the effectiveness of digital finance in fostering innovation in sports enterprises. These findings underscore the importance of enhancing digital financial infrastructure and improving the accessibility of financial services in the central and northeastern regions to support sustainable economic and social development.

Secondly, regarding the heterogeneity in enterprise size, the study classified the sample into major, small, and mid-sized enterprises. Regression results indicate that digital finance significantly enhances innovation in major sports enterprises, as evidenced by a cheerful and highly significant coefficient (β = 0.501, p < 0.01). Furthermore, the impact of digital finance on small and mid-sized enterprises is also positive and significant (β = 1.382, p < 0.05), suggesting that digital finance can effectively promote innovation in sports enterprises of various sizes. These findings imply that both the major and the small and mid-sized sports enterprises can leverage digital financial resources to foster innovation, which could contribute to sustainable enterprise growth and societal development by improving the eco-friendliness of products and services and encouraging community engagement. However, the magnitude of the impact may vary, with small and mid-sized enterprises potentially benefiting more from digital finance in terms of innovation. This variation highlights the importance of strengthening inclusive finance initiatives, particularly in less developed regions, to enhance the popularity and accessibility of digital finance, remove financial barriers, and boost innovation across enterprises of all sizes.

4.5. Robustness Check

4.5.1. Replacement of Explanatory Variables

This study employed alternative core variables, namely BRE and DEP, by substituting FIN as the explanatory variable in the baseline regression model (Equations (2) and (3)), as shown in

Table 7. Robustness test results after changing the explanatory variables.

Variables	X: BRE		X: DEP
	INNOV	SA	INNOV	SA
X	0.640 ***	−0.497 ***	0.611 ***	−0.514 ***
	(0.121)	(0.023)	(0.128)	(0.025)
Control	Yes	Yes	Yes	Yes
Constant	0.728	0.173	0.880	0.293 *
	(0.852)	(0.167)	(0.898)	(0.177)
N	688	688	688	688
R2	0.072	0.072	0.578	0.578
Sobel-Goodman Mediation Tests
Sobel	−0.167 ***	Z: −2.985	−0.149	Z:−2.598
	(0.056)		(0.057)
Bootstrap test (Replications = 1000)
Indirect effect	−0.167 ***	Z: −3.260	−0.150 ***	Z: −3.010
	(0.051)		(0.050)
Direct effect	0.900 ***	Z: 5.460	0.958 ***	Z: 4.900
	(0.165)		(0.196)
Total effect	0.733 ***	Z: 4.420	0.808 ***	Z: 4.080
	(0.166)		(0.198)

Note: Standard errors are in parentheses. *** p < 0.01, * p < 0.1.

, to enhance the robustness of the findings. The BRE index assesses the improvement and penetration of digital financial infrastructure in each region, while the DEP measures how extensively and intensively various economic agents utilise digital financial services. Higher scores on these indices signify more advanced development and more excellent value of digital finance.

The robustness check indicates that BRE, as an explanatory variable, positively affects INNOV (β = 0.640, p < 0.01), consistent with the benchmark regression results. Similarly, DEP, as the core explanatory variable, yields a positive and significant effect on INNOV (β = 0.611, p < 0.01). These results support Hypothesis H1, stating that digital finance significantly enhances sports enterprises innovation. Concurrently, the regression analysis reveals that both BRE and DEP have significant adverse effects on SA (β = −0.497, p < 0.01 for BRE; β = −0.514, p < 0.01 for DEP), providing evidence for Hypothesis H2, which posits that the development of digital finance alleviates financing constraints in sports enterprises.

Furthermore, the Sobel tests confirm the significant mediating role of financing constraints in the relationship between digital finance and sports enterprise innovation. The Sobel test yields significant coefficients for both BRE (Sobel = −0.167, p < 0.01, Z = −2.985) and DEP (Sobel = −0.149, Z = −2.598), indicating the presence of a mediating effect. The bootstrap tests further support this finding, with significant indirect effects of BRE (β = −0.167, p < 0.01) and DEP (β = −0.150, p < 0.01) on INNOV through SA, as well as significant direct and total effects. These results provide strong evidence for Hypothesis H4, which states that digital finance promotes sports enterprise innovation by alleviating financing constraints.

4.5.2. Endogeneity Test

To address potential endogeneity and enhance the reliability of our findings, we employed a two-stage least squares approach, using lagged values of digital financial development indices as instrumental variables. The lagged variables, reflecting prior periods’ digital financial development, were likely uncorrelated with the current period’s disturbances, meeting the relevance and homogeneity requirements for valid instrumental variables.

Table 8. Endogeneity test results.

Variables	INNOV
	(1)	(2)	(3)
FIN	0.870 ***
	(0.267)
BRE		0.682 ***
		(0.240)
DEP			0.972 ***
			(0.307)
Control	Yes	Yes	Yes
Constant	−0.981	0.141	−1.592
	(1.712)	(1.552)	(1.957)
N	647	647	647
F	34.680	33.680	35.400
Underidentification test
Kleibergen-Paap rk LM statistic	130.865 ***	119.837 ***	181.497 ***
Weak identification test
Kleibergen-Paap rk Wald F statistic	2834.025	1042.039	1133.183
Stock-Yogo critical values: 10% maximal IV size	16.380	16.380	16.380

Note: Robust standard errors in parentheses. *** p < 0.01.

presents the regression results from the endogeneity test using the instrumental variable approach. Controlling for other variables, the coefficients for FIN, BRE, and DEP are positive and significant (β = 0.870, p < 0.01 for FIN; β = 0.682, p < 0.01 for BRE; β = 0.972, p < 0.01 for DEP). These results align well with the benchmark regression findings, underscoring the robustness of the conclusions and confirming that digital finance’s positive impact on sports enterprise innovation is not driven by endogeneity.

We conducted several diagnostic tests to assess the validity of the instrumental variables. The underidentification test results in Table 8 show that the Kleibergen–Paap rk LM statistics are highly significant (p < 0.01) for all three models, indicating the relevance of the instrumental variables used [74]. The weak identification test results reveal that the Kleibergen–Paap rk Wald F statistics substantially exceed the Stock–Yogo critical value of 16.38 at the 10% maximal IV size, confirming the strength of the instrumental variables [75]. These tests confirm that the lagged digital financial development indices are valid and robust instruments for addressing endogeneity concerns.

5. Discussion

5.1. Implications of the Findings

This study has investigated the impact of digital finance on sports enterprise innovation within China’s rapidly evolving digital economy and the growing importance of the sports industry. By systematically analysing the relationships among digital finance, financing constraints, and sports enterprise innovation and employing random-effects GLS and other measurement methods, this research provides empirical evidence based on panel data from 95 listed sports enterprises in China from 2011 to 2020. The findings reveal the relationships among these three focal constructs and offer valuable insights into the mechanisms through which digital finance influences sports enterprise innovation.

Consistent with prior literature (Yang et al., 2021 [76]; Zhao et al., 2023 [77]; Xiong et al., 2023 [78]), our results confirm the positive role of digital finance in promoting sports enterprise innovation (H1). This finding underscores the transformative potential of digital finance in driving innovation within the sports industry. By leveraging advanced technologies such as big data analytics, artificial intelligence, and blockchain, digital finance facilitates information sharing, reduces transaction costs, and enhances risk assessment capabilities [32,79], creating a more conducive environment for sports enterprises to engage in innovative activities. Furthermore, data-driven insights and personalised services enabled by digital finance can help sports enterprises better understand and cater to their customer’s evolving needs and preferences, facilitating the development of innovative products and services that enhance customer engagement and satisfaction [80]. However, it is essential to acknowledge that digital finance also brings new risks and challenges that sports enterprises must navigate. Cybersecurity threats and data privacy issues are prominent concerns in the digital finance landscape [21,24]. Sports enterprises must invest in robust cybersecurity measures and adhere to stringent data protection regulations to safeguard sensitive customer and financial information. Notwithstanding these challenges, the overall positive impact of digital finance on sports enterprise innovation remains significant. By proactively addressing the risks and limitations associated with digital finance, sports enterprises can unlock their full potential to drive innovation and competitiveness in the digital era.

Moreover, our study provides evidence supporting the positive impact of digital finance on alleviating financing constraints faced by sports enterprises (H2). This finding aligns with previous research (Lin and Ma (2022) [81]; Yao and Yang (2022) [8]; Feng et al., 2023 [82]) highlighting the role of digital finance in improving access to financial resources and reducing information asymmetries between lenders and borrowers [31,83]. By leveraging alternative data sources and advanced credit scoring models, digital finance enables sports enterprises, particularly small and medium-sized ones, to overcome traditional barriers to financing and secure the necessary funds for innovation [21]. The increased financial inclusion and efficiency brought about by digital finance can help level the playing field for sports enterprises, regardless of their size or collateral capacity, by providing them with a broader range of financing options tailored to their specific needs and risk profiles. For instance, online lending platforms and peer-to-peer lending networks can connect sports enterprises with a broader pool of investors, enabling them to access funding at more competitive rates and flexible terms. Furthermore, blockchain technology and smart contracts in digital finance can enhance transparency, security, and efficiency in financial transactions, reducing the costs and risks associated with financing activities for sports enterprises.

In addition to the positive impact of digital finance on sports enterprise innovation and its role in alleviating financing constraints, our study confirms the negative influence of financing constraints on sports enterprise innovation (H3). This finding is consistent with the extensive literature (Huo and Li (2022) [84]; Peng et al., 2023 [85]; Hu et al., 2023 [86]) highlighting the detrimental effect of financing constraints on enterprise innovation across various industries. When faced with financing constraints, sports enterprises may need help to allocate sufficient resources to research and development, product and service improvements, and market expansion, hindering their ability to innovate and remain competitive [87]. Financing constraints can arise from information asymmetries, lack of collateral, and limited access to traditional financing channels, disproportionately affecting small and medium-sized sports enterprises. These constraints can force sports enterprises to prioritise short-term survival over long-term innovation, leading to reduced investment in research and development, a delay in adopting new technologies, and a reluctance to explore new markets or business models.

Furthermore, our mediation analysis reveals that financing constraints fully mediate the relationship between digital finance and sports enterprise innovation (H4). This finding sheds light on the crucial mechanism through which digital finance influences innovation in the sports industry. By alleviating financing constraints, digital finance directly supports innovation activities. It indirectly fosters a more favourable environment for sports enterprises to allocate resources towards research and development, product and service improvements, and market expansion [23,88]. The mediation effect highlights the pivotal role of access to finance in enabling sports enterprises to translate the benefits of digital finance into tangible, innovative outcomes. When digital finance effectively reduces financing constraints, sports enterprises can more easily secure the necessary funds to invest in innovation-related activities, such as acquiring new technologies, hiring skilled personnel, and conducting market research. Moreover, alleviating financing constraints through digital finance can help sports enterprises better manage their cash flows, reduce financial risks, and improve their overall financial health, enhancing their ability to pursue innovative projects and seize new market opportunities. The whole mediation effect also suggests that addressing financing constraints is necessary for the positive impact of digital finance on sports enterprise innovation to be unlimited and even negligible.

The heterogeneity analyses offer nuanced insights into the differential effects of digital finance on sports enterprise innovation across regions and enterprise sizes. The findings reveal that the impact of digital finance on innovation is more pronounced in the eastern region of China, which can be attributed to its advanced economic development and well-established digital financial infrastructure. These insights suggest that policymakers should prioritise the development of digital finance in less-developed regions, such as the central and northeastern regions, to promote inclusive growth and innovation in the sports industry. Furthermore, the significant impact of digital finance on innovation among both major and small and mid-sized sports enterprises underscores the broad potential of digital finance in driving innovation across enterprises of various scales. Interestingly, the results indicate that small and mid-sized enterprises may benefit more from digital finance in innovation than major enterprises. This finding highlights the crucial role of digital finance in levelling the playing field for smaller sports enterprises, which often face more significant challenges in accessing traditional financing channels. By providing tailored financial products and services that cater to the specific needs of sports enterprises of different sizes, particularly small and mid-sized enterprises, financial institutions can help bridge the financing gap and support inclusive innovation in the sports industry.

5.2. Practical Implications

Our findings provide practical implications for sports enterprises, policymakers, and financial institutions. First, for sports enterprises, our study highlights the importance of utilising digital finance to overcome financing constraints and promote innovation. Managers should actively explore digital financial platforms and services to access alternative funding, enhance financial management, and mitigate risks associated with innovation. This approach is essential for small and medium-sized sports enterprises, as our heterogeneity analysis shows they may benefit more from digital finance in innovation than larger enterprises. Second, policymakers can use insights from our research to design and implement targeted policies and regulations that promote digital finance development and support sports enterprise innovation. The heterogeneity analysis presented reveals significant regional disparities in the impact of digital finance on sports enterprise innovation, with the eastern region experiencing a more pronounced effect than the central and northeastern regions. This finding underscores the need for policymakers to prioritise the development of digital financial infrastructure and services in less-developed regions to foster inclusive innovation in the sports industry. By providing targeted support and incentives for digital finance adoption in these regions, policymakers can help level the playing field for sports enterprises and encourage innovation-driven growth across the country. Third, financial institutions, including banks and fintech companies, can benefit from our findings by recognising digital finance’s potential to serve the unique needs of sports enterprises, particularly small and medium-sized ones. The heterogeneity analysis based on enterprise size indicates that small and mid-sized sports enterprises may gain more from digital finance in innovation than larger enterprises. This insight should prompt financial institutions to develop tailored digital financial products and services that cater to smaller sports enterprises’ specific needs and challenges. By leveraging digital technologies to assess creditworthiness, reduce information asymmetries, and offer more accessible and affordable financing options, financial institutions can play a crucial role in supporting innovation and sustainable growth in the sports industry.

5.3. Theoretical Contributions

This study makes several notable contributions to the literature on digital finance, financing constraints, and enterprise innovation within the sports industry context. First, our research extends the understanding of digital finance’s impact on enterprise innovation by focusing on the sports industry, which has received limited attention in prior studies. By demonstrating digital finance’s positive influence on sports enterprise innovation, we highlight digital technologies’ transformative potential in driving innovation within this sector. Second, our study contributes to the literature on financing constraints by empirically examining how digital finance alleviates financing constraints for sports enterprises. While previous studies have explored digital finance’s impact on financing constraints in general [8,47], our research provides industry-specific evidence. It elucidates mechanisms through which digital finance reduces financing barriers for sports enterprises. Third, by revealing the mediating role of financing constraints in the relationship between digital finance and sports enterprise innovation, our study offers a nuanced understanding of the complex interplay among these constructs. This finding underscores the importance of considering financing constraints as a key mechanism through which digital finance influences innovation outcomes, particularly in the sports industry. Fourth, our heterogeneity analyses contribute to the literature by revealing the different effects of digital finance on sports enterprise innovation across regions and enterprise sizes. These findings offer a granular understanding of the boundary conditions and contextual factors shaping the impact of digital finance on innovation in the sports industry.

5.4. Scientific Implications

This study provides scientific implications for researchers, students, and the broader academic community. First, this study contributes to the growing body of knowledge about digital finance, financing constraints, and enterprise innovation (especially in the context of the sports industry). Researchers can build on our work by further investigating the nuances and boundary conditions of these relationships and exploring other factors that may influence the impact of digital finance on sports enterprise innovation. Second, this study highlights the importance of considering specific industry contexts when examining the impact of digital finance on enterprise innovation. Future research could apply our conceptual framework and methodology to other industries, such as healthcare, education, or creative industries, to reveal potential differences in the relationship between digital finance, financing constraints, and innovation. Third, this study emphasises the value of interdisciplinary research, as our study draws on knowledge from areas such as finance, innovation management, and sports economics. Students and researchers can use our findings to explore the intersection of these disciplines and develop new theoretical and practical insights. Finally, this study informs researchers and students interested in employing panel data analysis and econometric techniques to study the impact of digital finance on enterprise innovation, providing a methodological template for future research in the field.

5.5. Limitations and Future Research Directions

Despite its contributions, this study has several limitations that offer opportunities for future research. First, our analysis focuses on listed sports enterprises in China, potentially limiting the generalizability of our findings to other contexts or non-listed enterprises. Future studies could extend our research by examining the impact of digital finance on sports enterprise innovation in different countries or by including a broader sample of sports enterprises. Second, although we use a comprehensive set of control variables and conduct robustness checks to address potential endogeneity concerns, our model may not capture other factors influencing sports enterprise innovation. Future research could explore additional moderating or mediating variables, such as corporate governance, entrepreneurial orientation, or government support, to provide a more comprehensive understanding of the complex relationships between digital finance, financing constraints, and innovation. Third, our study relies on secondary data sources and quantitative analysis to examine the impact of digital finance on sports enterprise innovation. Future research could complement our findings by conducting qualitative studies, such as case studies or interviews, to gain deeper insights into the mechanisms and processes through which digital finance influences innovation in the sports industry.

6. Conclusions

This study has examined the relationship between digital finance, financing constraints, and sports enterprise innovation using micro data from listed Chinese sports firms from 2011 to 2020. The study has also investigated the mediating role of financing constraints. Empirical evidence has confirmed that digital finance is crucial in promoting sports enterprise innovation, both directly and indirectly, by alleviating financing constraints. The findings support the hypothesised relationships as follows: digital finance positively influences sports enterprise innovation (H1), mitigates financing constraints faced by sports enterprises (H2), and financing constraints negatively impact sports enterprise innovation (H3). Moreover, the mediation analysis revealed that financing constraints mediate the relationship between digital finance and sports enterprise innovation (H4). The heterogeneity analysis highlighted the disparities in digital finance’s impact across regions and enterprise sizes. The eastern region benefits more from digital finance in terms of innovation. Small and medium-sized enterprises potentially gain more from digital finance than larger ones. Robustness checks, including the Replacement of explanatory variables and endogeneity tests using instrumental variables, support the findings’ reliability. These conclusions emphasise the importance of developing digital financial infrastructure and enhancing the accessibility of digital financial services to encourage innovation and sustainable growth in the sports industry, particularly for small and medium-sized enterprises.