Financial Flexibility and Innovation Efficiency: Pathways and Mechanisms in Chinese A-Share Listed Firms (2013–2022)

Abstract

Applying the resource-based view and dynamic capability theory, this study employs panel data analysis to examine how financial flexibility influences corporate innovation efficiency from an integrated resource-capability perspective. Analyzing data from Chinese A-share listed companies during 2013–2022, we discovered three key results. First, as an organizational liquidity buffer, financial flexibility reduces transaction costs, enhances incentives for technical talent retention, and better aligns executive compensation with innovation objectives. Second, as a manifestation of financial dynamic capabilities, financial flexibility significantly boosts a firm’s overall dynamic capabilities, thereby increasing innovation efficiency. Third, institutional investor engagement positively moderates this relationship through enhanced governance oversight. These investors strengthen governance oversight and reduce information asymmetry. Our findings advance the financial flexibility literature and offer actionable strategies to optimize innovation resource allocation and sustain R&D competitiveness. Companies should strategically build financial reserves to enhance innovation efficiency and achieve sustainable development.

1. Introduction

Although effective financial resource allocation and sound governance mechanisms are crucial for innovation efficiency, prior research has overlooked their interplay. Innovation efficiency is a core ability for gaining sustainable competitive advantages, aligning with UN Sustainable Development Goals. Researchers have focused on it. However, the existing literature mainly delves into R&D inputs and patent outputs [1,2,3,4]. Two significant research gaps persist: (1) the impact of corporate financial resource allocation on innovation efficiency, and (2) whether governance mechanisms like institutional investor engagement can boost the sustainability and efficiency of innovation activities.

This study addresses these gaps by investigating the influence of financial flexibility on corporate innovation efficiency. Financial flexibility is a strategic resource reserve that alleviates financing constraints and strengthens risk resilience [1], influencing corporate innovation efficiency. Innovation efficiency measures how effectively firms convert R&D expenditures into valuable patents [5,6,7,8,9]. Based on the resource-based view (RBV) [10] and dynamic capability theory [11], we propose that financial flexibility not only serves as a resource buffer but also fundamentally enhances firms’ dynamic resource management capabilities, thus improving innovation efficiency.

Our analysis reveals three key findings. First, financial flexibility functions as an organizational liquidity buffer. It improves innovation efficiency by cutting transaction costs, enhancing incentives for retaining technical talent, and aligning executive compensation with long-term innovation goals. Empirically, a one-unit increase in financial flexibility raises innovation efficiency by 0.25%. Second, as an expression of financial dynamic capabilities, it significantly boosts overall dynamic capabilities, driving efficiency improvements in innovation processes. Third, institutional investor research activities positively moderate this relationship by enhancing governance oversight and reducing information asymmetries between firms and external stakeholders.

This study contributes to the academic field in three ways. First, it expands the theoretical applications of RBV and dynamic capability theory in the innovation finance literature. Second, it provides practical guidance for firms on strategically integrating financial flexibility reserves with governance mechanisms to maintain innovation momentum. Third, it validates the positive role of institutional investors in China’s capital market, enriching the understanding of corporate governance in innovation ecosystems. The rest of this paper is organized as follows: Section 2 reviews the literature; Section 3 presents theoretical analysis and research hypotheses; Section 4 describes the research design; Section 5 conducts empirical analysis; and Section 6 concludes.

2. Literature Review

2.1. Theoretical Overview

2.1.1. Resource-Based Theory

Barney advanced the resource-based view (RBV), proposing that a firm’s unique bundle of resources, characterized by value, rarity, and inimitability, enables it to achieve and sustain performance advantages [12]. A firm’s unique bundle of resources is a core determinant of its competitive advantage [13]. From the RBV perspective, the implementation of a firm’s innovation strategy depends on its resource endowment. Innovation value arises only when resources are effectively coordinated and allocated [14]. Innovation is inherently long-term, unpredictable, and uncertain [15]. Consequently, understanding and fostering the fundamental drivers of firm innovation from the perspective of resource replenishment is crucial. Innovation requires investments of labor and capital, where financial and human resources serve as fundamental building blocks for enhancing financial performance, building core competencies, and maintaining competitive advantage [16]. Financial flexibility, representing a firm’s resource availability, provides the necessary capital input for innovation activities [17].

2.1.2. Dynamic Capabilities Theory

The ever-changing market environment necessitates that firms continuously enhance their dynamic adjustment capabilities to seize emerging opportunities and address challenges arising from environmental shifts [18]. Dynamic capabilities contribute to improving both a firm’s financial performance [19] and innovation performance [20]. This theory posits that dynamic capabilities constitute a firm’s ability to build, integrate, and reconfigure resources in response to environmental changes [21], exerting a decisive influence on strategic transformations within the firm. Dynamic capabilities facilitate the effective integration of assets [22]. Financial flexibility is regarded as a specific manifestation of a firm’s dynamic capabilities [23]. By identifying, assimilating, and leveraging external knowledge, firms can propel the implementation of their innovation strategies [24], thereby increasing the efficiency of innovation activities.

2.2. Financial Flexibility and Firm Innovation

Innovation activities are essentially strategic actions driven by entrepreneurship, with the core objective of gaining a competitive edge by enhancing innovation efficiency, helping firms obtain Schumpeter’s rent and achieve value enhancement [25]. However, innovation processes remain inherently challenging due to their elevated risk exposure [26], substantial capital requirements [27], and unpredictable outcomes [28]. These characteristics underscore the necessity of systematically examining how financial resource allocation interacts with innovation efficiency, as this relationship has emerged as a critical determinant of sustained innovation capabilities in firms.

Financial flexibility enables firms to utilize resources and respond to opportunities [29]. Existing research shows significant progress on how it affects corporate innovation. Studies show that financial flexibility helps firms diversify funding sources and stimulate R&D investments. It does this by alleviating financing constraints [30] and mitigating policy uncertainty [4]. The relationship between financial flexibility and innovation investment has been widely explored in the existing literature [31]. Maintaining higher cash reserves and reducing debt to preserve future borrowing capacity can enhance a firm’s capability to raise financial resources, thereby promoting investments in research and development (R&D) [32]. Financial flexibility effectively mitigates underinvestment in innovation by alleviating financing constraints [33]. Innovation and R&D represent a long-term, continuous process that requires substantial and sustained financial commitment. Moreover, the confidential nature of R&D activities may create information asymmetry between the firm and external stakeholders [34], leading to discrepancies in financing costs between internal and external sources [35].

Scholars have also investigated the association between financial flexibility and innovation performance. Empirical evidence suggests that financial flexibility can enhance a firm’s green innovation performance by mitigating risk-taking behaviors and curbing inefficient investments. Research also shows that cash flexibility has a stronger facilitating effect than debt flexibility [36]. When facing financing constraints, maintaining financial flexibility effectively relieves the associated pressures, enables firms to seize promising innovation investment opportunities, and provides stable cash flow for innovation activities, thereby boosting innovation output and efficiency [37]. Financial flexibility not only offers financial security for R&D investment but also helps address challenges posed by technological, market, and organizational uncertainties, thus reducing risks inherent in innovation [38]. Overall, a higher degree of financial flexibility grants firms greater autonomy in innovation and lower innovation-related risks, facilitating the smooth implementation of innovation initiatives and enhancing innovation outcomes [39]. It has been argued that financial flexibility facilitates the dynamic allocation of resources, with moderate financial reserves serving to counteract the constraining effects of strategic divergence on innovation outcomes [40] while elevating innovation productivity [41,42].

In summary, existing research affirms the role of financial flexibility in mitigating financing constraints and responding to environmental changes. As a crucial component of financial resources, the relationship between financial flexibility and innovation input or performance has been relatively well-explored. However, the relationship between financial flexibility and innovation efficiency remains relatively underexplored.

Although empirical evidence from China’s economic context confirms a positive correlation between financial flexibility and innovation efficiency [43], research on the contingent factors and underlying mechanisms of this relationship remains underexplored. Meanwhile, existing scholarship exhibits two principal limitations: insufficient integration of financial flexibility’s dual functions as a resource buffer and dynamic capability enhancer within a unified analytical framework. Consequently, this study investigates the operational mechanisms through which financial flexibility improves innovation efficiency, aiming to inform evidence-based strategies for sustaining firm innovation development. Building upon prior research, this study extends the boundaries of research on financial flexibility and corporate innovation by further exploring its mechanisms and effects specifically concerning innovation efficiency.

2.3. Institutional Investors and Firm Innovation

Institutional investors are important participants in the capital market. Porter argues that institutional investors are important external governance factors in corporate governance and have an impact on the innovation activities of firms through their participation in corporate governance [44]. Existing research reveals that institutional investors influence innovation activities mainly through four governance channels: First, the information efficiency channel, where institutional investors possess greater information advantages [45]. Their professional information gathering capabilities can reduce agency costs [46] and increase information transparency and disclosure quality [47]. Improving financial reporting transparency [48] and thereby optimizing the efficiency of R&D resource allocation [49]. Second, the risk-sharing channel, with a long-term investment perspective and high risk tolerance [50], effectively alleviates the financing constraints of innovation projects [51,52]. Third, strategic oversight channels that intervene in corporate governance through in-depth research [53] curb management short-sightedness [54] and promote ESG-oriented innovation decisions [55]. Fourth, financing channels, institutional investors can broaden the firm’s equity financing channels, thereby promoting innovation activities [56]. The institutional evolution of China’s capital market provides a unique context for testing these mechanisms. Since the Shenzhen Stock Exchange implemented the investor research information disclosure system in 2012, institutional investor research has developed into a governance tool with both information intermediary and strategic advisory functions [57]. Empirical evidence shows that this model of deep engagement can not only curb opportunistic behavior (such as tax evasion) but also promote the improvement of corporate innovation efficiency by optimizing the firm’s investment decisions [58].

3. Theoretical Analysis and Research Hypotheses

3.1. Theoretical Anchoring in SDG Context

The synergistic mechanism between financial flexibility and innovation efficiency profoundly embodies the core of United Nations Sustainable Development Goal 9 (Industry, Innovation and Infrastructure)—facilitating sustainable industrial transformation through optimized resource allocation efficiency. Grounded in resource-based theory and dynamic capability theory, this study constructs a dual-path framework integrating static resource optimization with dynamic capability enhancement (Figure 1).

3.2. Financial Flexibility and Firm Innovation Efficiency

Based on resource-based theory, financial flexibility is a critical element of organizational financial systems. It stabilizes innovation processes. This capital allocation flexibility mitigates financing constraints in R&D activities while safeguarding against innovation discontinuities caused by capital chain disruptions, thereby enhancing innovation efficiency. Empirical evidence [59] demonstrates that robust financial health enables firms to optimize cash flow management and stabilize R&D expenditure patterns. Complementary findings [60] show that sufficient financial reserves ensure R&D continuity by reducing liquidity risk. They also facilitate knowledge iteration and technological upgrading. Together, these factors accelerate innovation commercialization and improve efficiency. In summary, holding financial flexibility not only provides a basic resource guarantee for innovation activities but also promotes the optimization of innovation elements, ultimately achieving an increase in innovation efficiency.

When firms hold financial flexibility, at the resource acquisition level, the accumulation of financial flexibility mainly relies on cash flexibility and liability flexibility: the former is obtained through the accumulation of operating surplus funds and the replacement of cash equivalents, etc. The latter is achieved by using low financial leverage to reserve borrowing capacity for the future. Holding financial flexibility ensures that the firm has ample low-cost funds and enhances its ability to bargain with credit institutions to lower costs. Through flexible management of funds, the efficiency of fund allocation can be optimized and the continuity of fund supply can be guaranteed [61]. At the same time, abundant financial resource reserves provide firms with more strategic options, enabling them to respond more flexibly and quickly to the risks of technological iteration and market uncertainties. This paper argues that in carrying out innovation activities, firms can ensure the stability of the capital chain by using cash flexibility to smooth innovation input, and at the same time build future financing capacity reserves through debt flexibility, achieving dual financial guarantees for innovation activities. This not only effectively alleviates the pressure of financing constraints but also enhances the stability and continuity of capital input, promoting the smooth progress of innovation activities and the improvement of innovation efficiency.

Hypothesis 1:

Financial flexibility boosts the innovation efficiency of firms.

Hypothesis 1-1:

ash flexibility boosts innovation efficiency.

Hypothesis 1-2:

Debt flexibility boosts corporate innovation efficiency.

3.3. Pathways by Which Financial Flexibility Enhances Corporate Efficiency

3.3.1. Resource Path

• Transaction cost reduction mechanism

When the cost of using market price mechanisms for resource allocation is high, firms tend to rely on internal relationships to allocate resources in order to avoid incurring unnecessary external transaction costs. Transaction costs are mainly influenced by factors such as asset specificity, bounded rationality, and opportunism [62]. Financial flexibility, as an important tool in response to market changes [63], mitigates the external shocks faced by firms and makes financial resources an important cornerstone for improving firm performance by maintaining strategic liquidity reserves (cash flexibility) and debt capacity reserves (leverage flexibility). It can help firms enhance their core competitiveness and maintain their competitive edge [16]. Financial flexibility can smooth out the innovation input of firms while ensuring the sustainability of capital input, facilitating the transformation of innovation activities from input to output. In this process, it reduces transaction costs (e.g., time and interest expenses) associated with external financing (e.g., stock or bond issuance), thereby improving resource allocation efficiency.

Hypothesis 2:

Financial flexibility reduces transaction costs, enhancing innovation efficiency.

2.

Human capital incentive strengthening mechanism

Research has indicated that in corporate innovation investment, over 50% of resources are allocated to compensation systems for scientific and technical personnel, with the proportion dedicated to key personnel such as scientists and engineers being particularly significant [64]. Furthermore, studies have found that incentives targeting core technical staff serve as an important mechanism for promoting innovation [65]. Motivating core technical personnel is beneficial for stabilizing R&D teams and promoting the continuous progress of innovation projects [66], and the research also confirms that motivating core technical staff is the key to improving the innovation efficiency of firms [67]. Existing studies have examined the positive effects of employee motivation from the perspectives of stock option incentives, enhanced employee collaboration, and promoted mutual supervision [68,69,70]. It is certain that enhancing the incentives of human capital requires increased investment in corporate funds. Holding financial flexibility increases the flexibility of corporate cash flow. Firms maintaining financial flexibility obtain more disposable funds, enhancing incentives for core employees, stimulate the efficiency and enthusiasm of scientific researchers, promote the input–output transformation of innovation activities as a whole, and improve innovation efficiency. Financial flexibility helps realize the mechanism of “financial flexibility—core employee incentives—innovation efficiency”.

Hypothesis 3:

Financial flexibility can enhance the incentives for core technical employees, thereby improving the innovation efficiency of the firm.

3.

Promotion Mechanism for Executive Risk-taking

Existing studies have shown that the stickiness of executive compensation is conducive to creating an innovation-friendly governance environment. From a human capital perspective, innovation activities are the result of multi-party collaboration between management and employees. The study argues that the nature of innovation is “creative destruction”, with highly uncertain output and significant long-term characteristics in value creation [71]. Therefore, building an executive compensation system that promotes corporate innovation requires the establishment of a dual incentive compatibility mechanism: while providing high rewards for successful innovation investments, it must also be able to tolerate (or even reward) short-term or early failures in innovation. Executive pay stickiness enhances their tolerance for risk, and studies have found positive performance in terms of corporate strategic flexibility and financial investment [72,73]. Compensation stickiness mechanisms can motivate innovation by providing a guarantee of compensation and benefits for managers: (1) establishing a risk hedging mechanism to maintain the continuity of their innovation decisions by maintaining a basic compensation level during the period of failed projects to prevent managers from being overly punished by short-term performance fluctuations; (2) building a long-term incentive structure that links long-term incentive tools such as equity incentives to commercial earnings to form a community of interests for value creation. This system alleviates managers’ concerns about the risks of innovation activities to a certain extent. This drives managers to pursue value-enhancing innovation initiatives, fostering systematic improvements in organizational innovation efficiency through strategic resource alignment. Enhancing executive compensation stickiness requires a high level of financial investment. Holding financial flexibility is more conducive to achieving this goal and can lead to the path of “financial flexibility —enhancing executive compensation stickiness—improving innovation efficiency”.

Hypothesis 4:

Financial flexibility increases managerial pay stickiness, which improves innovation efficiency.

3.3.2. Dynamic Capability Path

Dynamic capability theory posits that organizations must adapt their resource configurations and develop responsive mechanisms to address the dynamic and evolving market landscape. This strategic adaptability enables firms to maintain a sustained competitive edge while progressively enhancing innovation performance through continuous organizational renewal [74]. Existing research has confirmed that by implementing strategic integration of internal and external resources through dynamic capabilities, firms can achieve overall improvement in innovation performance [75], obtain higher innovation benefits [76], and capture customer needs in a timely manner, thereby creatively developing new products or services that conform to consumer preferences [77]. This leads to the sustainable development of the firm. As an extension of dynamic capabilities in the financial field [23], financial flexibility has the dual functions of risk buffering and opportunity capture [29]. We deconstruct dynamic capabilities into innovation, absorption, and adaptation capacities [78], and this framework has received empirical support in the Chinese context [79]. Specifically, innovation focuses on new product development and market expansion, absorption emphasizes the recognition and transformation of external knowledge, and adaptability is reflected in the agility of resource reallocation.

• Holding financial flexibility can enhance a firm’s innovation ability.

Financial flexibility constitutes an organization’s reservoir of allocatable capital resources that serve as critical innovation enablers. These financial reserves synergize with complementary operational assets to facilitate dynamic recalibration of innovation strategies, support evolutionary development of organizational innovation capabilities, and sustain the viability of innovation initiatives. Through this multidimensional resource orchestration, firms enhance innovation efficiency by aligning financial adaptability with strategic technological advancement.

2.

Financial flexibility enables firms to integrate existing and new knowledge.

Absorptive capacity facilitates the execution of organizational innovation strategies through the systematic identification, assimilation, and application of external knowledge [24], while concurrently enhancing operational efficiency in innovation processes. Furthermore, empirical studies have shown that organizations with enhanced absorptive capacity maintain a superior competitive position by converting external technical knowledge into organizational intellectual assets [80]. In this process, firms need to integrate internal resources such as finance with external technical knowledge to achieve the innovation effect [81]. Therefore, holding financial flexibility can promote innovation efficiency by enhancing absorptive capacity.

3.

Financial flexibility manifests through firms’ capacity for strategic deployment of leverage.

Organizations maintaining such flexibility can strategically amplify operational leverage during phases of market growth or demand surges, thereby enabling business expansion and market penetration. Conversely, during economic contractions, cost escalations, or market downturns, these firms demonstrate adaptive capacity through operational downsizing, achieving cost containment while preserving capital reserves. This dual-capacity mechanism illustrates how financial flexibility serves as a dynamic risk management instrument across business cycles. When faced with the need for corporate strategic decisions and innovation activities, holding financial flexibility can also respond promptly, demonstrating greater adaptability, facilitating the input–output transformation in innovation activities, and enhancing innovation efficiency. To sum up, based on the dynamic capability theory framework, financial flexibility, as an important dimension of firm strategic resources, can shorten the capital allocation cycle, improve the conversion rate of R&D investment, form the material basis for organizational learning, and achieve the accumulation of technical experience and the integration of knowledge through a continuous supply of funds. Financial flexibility allows firms to use their own cash reserves and surplus borrowing capacity to cushion the volatility and high risk of innovation activities. Therefore, this paper argues that holding financial flexibility can enhance innovation efficiency by strengthening the dynamic capabilities of firms. To sum up, this paper puts forward the following research hypothesis:

Hypothesis 5:

Financial flexibility enhances a firm’s dynamic capabilities, thereby improving innovation efficiency.

3.4. The Moderating Role of Institutional Investor Research

Existing studies suggest that institutional investors play an active governance role when participating in corporate governance. This study argues that research activities by institutional investors represent active governance behavior and continuation of the positive governance perspective. Under the premise that institutional investors actively engage in corporate governance, this paper explores how their research conduct influences the relationship between financial flexibility and innovation efficiency. The study posits that institutional investor research can affect corporate innovation activities through the following two channels.

3.4.1. Mitigating Information Asymmetry in Corporate Governance

Institutional investor research can increase the amount of information disclosed by relevant firms and alleviate information asymmetry in corporate governance to a certain extent. Institutional investors are often composed of groups of experts with rich investor experience and information processing capabilities, effectively reducing the cost of information search. Studies demonstrated that institutional investors’ analytical activities enhance corporate transparency through improved disclosure practices, consequently alleviating information asymmetry in capital markets [82]. This empirical evidence underscores the market-stabilizing function of professional investment analysis. The research behavior of institutional investors can uncover more internal information of the relevant firms, and at the same time, for the purpose of long-term value acquisition, it will prompt institutional investors to participate more actively in corporate governance and supervision [83]. Through research activities, institutional investors can build direct communication channels, gain initiative in information acquisition through interactive forms such as executive interviews and field visits [84], and the acquisition of more information can accurately assess the feasibility of the firm’s innovation strategy and form a pricing mechanism for value discovery [54]. It is also possible to mine non-public information and use professional analytical capabilities to identify governance signals outside financial statements [82]. Through research activities, institutional investors can achieve cross-verification of public and non-public information and form external supervision of management decisions [78]. This paper posits that institutional investor site visits could mitigate corporate information asymmetry, enhance governance engagement, and incentivize firms to pursue innovation efficiency improvements for competitive advantages.

3.4.2. Promote the Matching of Partners

On the other hand, institutional investor research can help firms improve the efficiency of innovation activities and save the cost of innovation activities. Innovation activities require firms to accumulate knowledge, and in this process, firms often need to seek suitable partners to carry out innovation activities together. Therefore, institutional investor research helps to form a link between the external governance and internal innovation synergy of firms. This governance participation significantly reduces the cost of cooperation search for firms and avoids the risk of adverse selection due to incomplete information. This information interaction not only reduces agency costs but also provides market validation for innovative decisions by identifying the implementation details of the firm’s technology strategy through a professional analytical framework.

This study proposes that institutional investor engagement contributes to strengthening the sustainability and core competitiveness of corporate innovation activities, while also positively moderating the relationship between financial flexibility and innovation efficiency. Based on this rationale, the following hypotheses are formulated:

Hypothesis 6:

The frequency of institutional investor visits positively moderates the relationship between financial flexibility and corporate innovation efficiency.

Hypothesis 6-1:

The frequency of institutional investor visits positively moderates the relationship between cash flexibility and corporate innovation efficiency.

Hypothesis 6-2:

The frequency of institutional investor visits positively moderates the relationship between liability flexibility and corporate innovation efficiency.

4. Research Design

4.1. Variable Definitions

4.1.1. Definition of the Variable Being Explained

The explained variable is the innovation efficiency of firms. Innovation is the core competitiveness of a firm, while innovation efficiency is the quantification of innovation ability. Efficiency indicators combine production and operation organically [8]. The ratio of the number of patent applications to R&D investment is used to measure the innovation output per unit of R&D investment, which is equal to the ratio of the natural logarithm of the number of patent applications plus 1 to the natural logarithm of R&D investment plus 1. The larger the value, the higher the innovation efficiency.

4.1.2. Definition of the Explanatory Variable

Financial flexibility level measures reserved financial capacity. When measuring financial flexibility, a single metric is mainly used [9,85] or a combination of multiple indicators [4,86]. As in this paper, financial flexibility is obtained by the sum of cash flexibility and liability flexibility, where cash flexibility = firm cash holding level − industry average cash holding level, liability flexibility = MAX {0, Industry average debt ratio − firm debt ratio} [4].

4.1.3. Moderating Variables

When measuring the number of research visits by institutional investors, this paper uses the relevant data of investor relations in the CSMAR database to sum up the number of research activities recorded by the same listed firm within one year and take the logarithm [87,88].

4.1.4. Mediating Variables

This paper argues that holding financial flexibility can promote innovation efficiency by increasing incentives for core technical personnel, enhancing executive compensation stickiness, reducing transaction costs, and enhancing the dynamic capabilities of the firm. This paper selects the proportion of financial expenses to total liabilities as an indicator of firm transaction costs (TCs) [89]. For the incentives of core technical personnel, this study measures the rights of core technical personnel by adding 1 [65,90]. When measuring the stickiness of executive compensation, the executive compensation—firm performance sensitivity and its average value during the rise and fall of firm performance were calculated, respectively [72,91]. Executive compensation stickiness is the difference between the mean sensitivity when corporate performance rose and the mean sensitivity when corporate performance fell. When measuring dynamic capabilities, Wang suggests that the dynamic capabilities of a firm can be expanded from three aspects: innovation ability, absorption ability, and adaptability [79]. This study examines the impact of financial flexibility on innovation ability, absorption ability, and adaptability one by one. When measuring innovation ability, two indicators, namely the annual R&D investment intensity of the sample firm and the proportion of technical personnel, were used for comprehensive evaluation. The data of these two indicators were standardized, respectively, and then summed up to obtain the comprehensive value of innovation ability (dc1) [79], namely

$$\mathrm{d}\mathrm{c}1={\displaystyle \frac{{\mathrm{X}}_{\mathrm{r}\mathrm{d}}-{\mathrm{M}\mathrm{I}\mathrm{N}}_{\mathrm{r}\mathrm{d}}}{{\mathrm{M}\mathrm{A}\mathrm{X}}_{\mathrm{r}\mathrm{d}}-{\mathrm{M}\mathrm{I}\mathrm{N}}_{\mathrm{r}\mathrm{d}}}}+{\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}\mathrm{t}}-{\mathrm{M}\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}}{{\mathrm{M}\mathrm{A}\mathrm{X}}_{\mathrm{i}\mathrm{t}}-{\mathrm{M}\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}}}$$

(1)

In this paper, the absorptive capacity dc2 is measured by the intensity of R&D expenditure, that is, the ratio of the annual R&D expenditure to the operating income of the sample companies [80].

When measuring adaptability (dc3), it mainly includes two dimensions: One is whether the firm can increase the operating profit margin during the prosperous period of the market. The second is whether firms can increase their working capital investment in a timely manner during the period of economic prosperity [24]. According to these two dimensions, the proxy variables for building adaptability are as follows:

$$\mathrm{d}\mathrm{c}3=\max \left(\mathrm{g}\mathrm{p}\mathrm{r}-\overline{\mathrm{g}\mathrm{p}\mathrm{r}},0\right)\times \mathrm{w}\mathrm{c}\mathrm{r}+\mathrm{g}\mathrm{p}\mathrm{r}\times \max \left(\mathrm{w}\mathrm{c}\mathrm{r}-\overline{\mathrm{w}\mathrm{c}\mathrm{r}},0\right)+\max \left(\mathrm{g}\mathrm{p}\mathrm{r}-\overline{\mathrm{g}\mathrm{p}\mathrm{r}},0\right)\times \max \left(\mathrm{w}\mathrm{c}\mathrm{r}-\overline{\mathrm{w}\mathrm{c}\mathrm{r}},0\right)$$

(2)

Among them, gpr (gross profit rate) is the gross profit margin of the firm, and wcr (working capital ratio) is the proportion of the firm’s working capital. The proxy variable for adaptability consists of three parts: (1) the first part indicates that when the gross profit rate is above average, the firm can increase its working capital investment; (2) the second part indicates that when the working capital is high, the firm can regulate the profit margin to a higher level; (3) the third part is a cross effect project of the first two. Among them, the benchmark for comparing the gross profit margin and working capital ratio of firms is the average within the industry. The higher the dc3 indicator, the more capable the firm is of increasing capital investment or creating product premium during prosperous market periods.

4.1.5. Control Variables

The selection of control variables in this paper refers to the research on financial flexibility, institutional investor research, and firm innovation. Some other factors that have been found to affect the innovation efficiency of firms are controlled around the level of firm characteristics and corporate governance mechanisms [4,41]. This paper selected the proportion of independent directors, net profit margin of total assets, asset size, firm age, equity balance degree, growth rate of operating income, net cash flow from operating activities, the combination of two positions, and the repayment multiple of fixed expenses. The cash dividend payout ratio was used as the control variable. The specific variable definitions in this paper are shown in

Table 1. Variable definitions.

Variable	Name	Definition Method	References	Data Sources
Explanatory variables	L1ff	Cash flexibility + debt flexibility	[4]	Csmar Database
	C1ff	Firm cash holding level − Industry average cash holding level	[4]	Csmar Database
	D1ff	MAX {0, industry average debt ratio − Firm debt ratio}	[4]	Csmar Database
Explained variable	Y1	Equal to the ratio of the natural logarithm of patent applications plus 1 to the natural logarithm of research and development investment plus 1	[30]	Csmar Database
Moderating variables	insnum1	Take the logarithm by adding one to the number of institutional investor research visits	[89]	Csmar Database
Control variables	idr	Proportion of independent directors	[23]	Csmar Database
	ROA	Net profit margin on total assets	[40]	Csmar Database
	CGC	The positions of chairman and general manager are combined	[40]	Csmar Database
	GOI	Growth rate of operating income	[4]	Csmar Database
	Age	Firm age	[4]	Csmar Database
	AS	Asset size	[4]	Csmar Database
	EBD	Equity balance degree of top 5 shareholders/Equity balance degree of the largest shareholder	[4]	Csmar Database
	OCF	Operating net cash flow	[4]	Csmar Database
	FCC	Fixed cost	[92]	Csmar Database
	CDP	Cash dividend payout ratio	[93]	Csmar Database

. Existing research has identified that at the firm level, variables such as fixed costs and dividend payout can influence corporate innovation activities. Similarly, at the corporate governance level, the presence of independent directors and CEO duality may also impact innovation. Therefore, we incorporate all these factors as control variables. To mitigate the effects of macroeconomic fluctuations—such as GDP growth and interest rate levels—which affect all firms in the sample, we control for year fixed effects. Furthermore, considering heterogeneity across industries due to differences in technological intensity, profit margins, and business models, we also control for industry fixed effects.

4.2. Model Design

This study uses OLS regression for empirical analysis. To verify Hypothesis 1, Model (1) is constructed in this study. If the coefficient of financial flexibility ff is positive, Hypothesis 1 holds. When testing Hypotheses H1-1 and H1-2, the financial flexibility ff in Model (1) is replaced with cash flexibility c1ff or liability flexibility d1ff [94].

$${Y1}_{i,t}=f({l1ff}_{i,t},{Control Variables}_{i,t})+{\epsilon }_{i,t}$$

(3)

In examining the mechanism by which financial flexibility promotes innovation efficiency, this paper builds on Model (1) to examine the impact of financial flexibility on mediating variables, thereby verifying the existence of the mechanism by which financial flexibility promotes innovation efficiency in Hypotheses 2 to 5. Mk represents the mediating variable, and in verifying Hypotheses 2 to 5, dynamic capabilities, transaction costs, core technical personnel incentives, and executive compensation stickiness are, respectively, substituted for empirical analysis [95].

$$M_{k,i,t}=f(l1{ff}_{i,t},{Control Variables}_{i,t})+{\epsilon }_{i,t}$$

(4)

To test Hypothesis 6, Model (3) is constructed in this study. If the interaction term between financial flexibility ff and institutional investor insnum1 is positive, Hypothesis 6 holds. When testing hypotheses H6-1 and H6-2, the financial flexibility ff in Model (1) is replaced with cash flexibility c1ff or liability flexibility d1ff.

$${Y1}_{i,t}=f(l1{ff}_{i,t},{{l1ff}_{i,t}\times {insnum1}_{i,t},{insnum1}_{i,t},Control Variables}_{i,t})+{\epsilon }_{i,t}$$

(5)

4.3. Sample Selection

We obtained data from the CSMAR database. The sample range with firm innovation efficiency data as the explained variable was limited from 2013 to 2022, and the initially obtained samples were processed according to the following criteria: (1) Gold industry with ST and ST* excluded. (2) Excluding listed companies in the financial and insurance sectors. (3) Excluding listed companies that issue both B shares and H shares. (4) Excluding insolvent companies—Annual observations. (5) Exclude companies with missing data—Annual observations.

5. Empirical Analysis

5.1. Descriptive Statistics

Table 2. Descriptive statistical table.

Variable	Mean	SD	Min	p50	Max	N
y1	0.0299	0.0726	0	0	0.283	28,000
l1ff	0.0445	1.393	−2.109	−0.258	7.826	28,000
c1ff	−0.0435	1.315	−2.129	−0.306	7.394	28,000
d1ff	0.0831	0.108	0	0.0191	0.426	28,000
insnum1	1.360	1.903	0	0	10.60	28,000
idr	0.378	0.0539	0.333	0.364	0.571	28,000
ROA	0.0348	0.0654	−0.275	0.0358	0.200	28,000
CGC	0.293	0.455	0	0	1	28,000
GOI	0.350	0.901	−0.708	0.130	6.220	28,000
Age	10.79	7.718	1	9	28	28,000
AS	22.32	1.296	19.99	22.12	26.37	28,000
EBD	0.756	0.610	0.0323	0.593	2.825	28,000
OCF	8.000 $\times$ 108	2.500 $\times$ 109	−1.900 $\times$ 109	1.600 $\times$ 108	1.900 $\times$ 1010	28,000
FCC	3.590	10.37	−47.11	2.482	58.06	28,000
CDP	0.265	0.303	−0.0179	0.208	1.802	28,000

presents the descriptive statistics on the main variables of this study, with a total of 28,000 samples. Our dependent variable is innovation efficiency. Over the past decade, Chinese listed firms prioritized innovation efficiency differently. Sample firms ranged from 0 to 0.283 in innovation efficiency. This indicates that there is a significant gap in innovation efficiency among Chinese listed companies, which may be related to the nature of the companies and may be influenced by the industry in which the companies are located or the innovation incentive policies in the regions where the companies are located. The explanatory variable for this study, financial flexibility, is the sum of cash flexibility and liability flexibility, with a mean of 0.0445, a median of −0.258, and a maximum of 7.826, indicating that many companies do not have sufficient financial flexibility reserves, but some companies have higher levels of financial flexibility reserves, up to 7.826. The median of cash flexibility is −0.306, with a maximum of 7.394, and the holding levels of cash flexibility vary greatly. The mean of debt flexibility was 0.0831, with a median of 0.0191, indicating that most of the sample companies retained “residual borrowing capacity”. The moderating variable is the number of institutional investor visits, which averages 1.360 but has a median of 0, indicating that although institutional investors were active in the capital market, their target companies were not widely distributed among all listed companies, and institutional investors only conducted research on some of the sample companies.

5.2. Multiple Regression Analysis

This study employs multivariate regression analysis to examine the relationship between financial flexibility and innovation outcomes.

Table 3. Multiple regression results of financial flexibility and innovation efficiency.

	(1)	(2)	(3)
	y1	y1	y1
l1ff	0.0025 ***
	(7.9630)
c1ff		0.0026 ***
		(7.8611)
d1ff			0.0248 ***
			(5.6054)
idr	−0.0298 ***	−0.0297 ***	−0.0294 ***
	(−3.7031)	(−3.6960)	(−3.6568)
ROA	0.0276 ***	0.0280 ***	0.0286 ***
	(3.7514)	(3.8139)	(3.8717)
CGC	−0.0006	−0.0006	−0.0006
	(−0.6482)	(−0.6514)	(−0.5732)
GOI	0.0017 ***	0.0016 ***	0.0017 ***
	(3.2348)	(3.2289)	(3.3286)
Age	−0.0004 ***	−0.0004 ***	−0.0005 ***
	(−6.4743)	(−6.4938)	(−6.7713)
AS	0.0073 ***	0.0072 ***	0.0074 ***
	(15.5307)	(15.4262)	(15.1622)
EBD	−0.0016 **	−0.0016 **	−0.0016 **
	(−2.2757)	(−2.2611)	(−2.1871)
OCF	−0.0000	−0.0000	−0.0000
	(−1.5726)	(−1.5505)	(−1.5188)
FCC	−0.0001	−0.0001	−0.0001
	(−1.1699)	(−1.1674)	(−1.2330)
CDP	−0.0003	−0.0002	−0.0001
	(−0.1836)	(−0.1368)	(−0.0869)
Year FE	YES	YES	YES
Ind FE	YES	YES	YES
_cons	−0.1284 ***	−0.1264 ***	−0.1345 ***
	(−10.5380)	(−10.4124)	(−10.5797)
N	27,832	27,832	27,832
adj. R2	0.071	0.071	0.070

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the empirical results for Model (3), confirming Hypothesis 1. After controlling for year and industry fixed effects, Table 3 shows that financial flexibility positively affects innovation efficiency (p < 0.01). Both cash flexibility and debt flexibility also show significant positive effects. For every additional unit of financial flexibility, the innovation efficiency of a firm can increase by 0.25%. The empirical findings support hypotheses H1, H1-1, and H1-2, confirming that Chinese listed companies can effectively improve innovation performance through maintaining financial flexibility. These results reveal a robust positive association between corporate financial flexibility and innovation efficiency in China’s capital market.

Table 4. Mechanism test of how fnancial flexibility promotes innovation efficiency.

	(1)	(2)	(3)	(4)	(5)	(6)
	tc	Lnh	Ecs	dc1	dc2	dc3
l1ff	−0.0166 ***	0.0426 *	0.0883 *	0.0292 ***	0.0063 ***	2.0890 ***
	(−92.7835)	(1.8594)	(1.7272)	(28.5069)	(36.4052)	(55.4544)
idr	0.0059	1.6533 ***	0.9469	0.0759 ***	0.0084 *	−0.5307
	(1.3212)	(2.8614)	(0.8231)	(2.9240)	(1.9202)	(−0.5590)
ROA	−0.0812 ***	7.3489 ***	3.3348 ***	−0.2027 ***	−0.0625 ***	40.6407 ***
	(−19.7176)	(13.9163)	(3.0294)	(−8.4166)	(−15.3699)	(46.8405)
CGC	−0.0003	0.4627 ***	0.0711	0.0163 ***	0.0041 ***	0.4196 ***
	(−0.6280)	(6.5999)	(0.4950)	(5.1790)	(7.7006)	(3.6421)
GOI	0.0004	0.0358	−0.0408	0.0254 ***	0.0018 ***	0.4301 ***
	(1.2455)	(0.9740)	(−0.5742)	(15.0909)	(6.4411)	(7.1324)
Age	−0.0001 ***	−0.0982 ***	−0.0080	−0.0024 ***	−0.0007 ***	−0.1373 ***
	(−2.6245)	(−20.5098)	(−0.7938)	(−11.0855)	(−18.8558)	(−17.4522)
AS	0.0011 ***	0.3073 ***	0.2373 ***	0.0055 ***	0.0001	−1.2308 ***
	(4.3527)	(9.1427)	(3.5317)	(3.6291)	(0.4445)	(−22.2839)
EBD	0.0005	0.3547 ***	−0.2523 **	0.0168 ***	0.0035 ***	0.1794 **
	(1.3129)	(6.8923)	(−2.4539)	(7.2669)	(9.0859)	(2.1214)
OCF	−0.0000 ***	−0.0000	0.0000 *	−0.0000 ***	−0.0000	0.0000
	(−3.1651)	(−0.0571)	(1.7390)	(−3.0710)	(−1.3705)	(1.5917)
FCC	0.0002 ***	0.0041	0.0017	0.0001	−0.0001 **	0.0231 ***
	(9.9525)	(1.3087)	(0.2253)	(0.3783)	(−2.2176)	(4.5034)
CDP	−0.0062 ***	−0.2379 **	−0.2445	−0.0120 **	−0.0026 ***	1.7419 ***
	(−7.5836)	(−2.2787)	(−1.2472)	(−2.5696)	(−3.2833)	(10.1565)
Year FE	YES	YES	YES	YES	YES	YES
Ind FE	YES	YES	YES	YES	YES	YES
_cons	−0.0251 ***	−6.5712 ***	−1.2600	0.0388	0.0212 ***	39.1214 ***
	(−3.6785)	(−7.5052)	(−0.7257)	(0.9855)	(3.1904)	(27.1950)
N	27,792	27,832	18,443	27,252	27,252	27,832
adj. R2	0.329	0.090	0.014	0.551	0.471	0.733

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the empirical results for Model (4). Column 1 examines the mechanism through which financial flexibility enhances innovation efficiency. It reports the impact of financial flexibility on corporate financing costs and shows that overall financial flexibility significantly reduces the financial expense ratio. This indicates that holding financial flexibility promotes innovation efficiency by alleviating transaction costs associated with financing expenses, thus supporting Hypothesis 2. Column 2 tests the role of incentives for core technical personnel as a mechanism. The results show that holding financial flexibility significantly increases the incentive level for core technical personnel, thereby improving innovation efficiency via the pathway of “financial flexibility → incentives for core technical personnel → innovation efficiency”, which supports Hypothesis 3. Column 3 reports the results for executive compensation stickiness as a mechanism. It finds that financial flexibility positively enhances executive compensation stickiness, reduces the sensitivity of executive compensation, bolsters executive confidence and focus on innovation activities, and consequently promotes innovation efficiency, thereby supporting Hypothesis 4.

Columns 4 to 6 examine the mechanisms related to dynamic capabilities. Specifically, Column 4 focuses on innovation capability, Column 5 on absorptive capacity, and Column 6 on adaptive capacity. The results reveal that holding financial flexibility positively enhances a firm’s innovation capability, absorptive capacity, and adaptive capacity. This aligns with the view that financial flexibility possesses attributes of “utilization” and “responsiveness”. Holding financial flexibility can promptly respond to the strategic needs of the firm when market demand changes by flexibly adjusting and responding. It not only enhances the innovation and absorption capabilities of firms but also strengthens their adaptability in dynamic environments, that is, the firm can improve its innovation efficiency through the action path of “financial flexibility—dynamic capabilities—innovation efficiency”. The empirical results also indirectly affirm the importance of financial flexibility in enhancing the dynamic capabilities of firms; Hypothesis 5 has been verified.

Table 5. Multiple regression analysis of financial flexibility, institutional investor research, and innovation efficiency.

	(1)	(2)	(3)
	y1	y1	y1
l1ff	0.0022 ***
	(6.7209)
l1ff $\times$ insnum1	0.0005 ***
	(3.2080)
c1ff		0.0023 ***
		(6.6471)
c1ff $\times$ insnum1		0.0005 ***
		(2.9306)
d1ff			0.0244 ***
			(5.5086)
d1ff $\times$ insnum1			0.0083 ***
			(3.9630)
insnum1	0.001 8***	0.0018 ***	0.0019 ***
	(7.4894)	(7.4902)	(7.7414)
idr	−0.0291 ***	−0.0291 ***	−0.0290 ***
	(−3.6224)	(−3.6193)	(−3.6044)
ROA	0.0198 ***	0.0202 ***	0.0196 ***
	(2.6748)	(2.7288)	(2.6282)
CGC	−0.0010	−0.0010	−0.0010
	(−1.0518)	(−1.0537)	(−0.9832)
GOI	0.0017 ***	0.0017 ***	0.0018 ***
	(3.3965)	(3.3910)	(3.4782)
Age	−0.0004 ***	−0.0004 ***	−0.0004 ***
	(−5.2390)	(−5.2592)	(−5.4087)
AS	0.0067 ***	0.0066 ***	0.0069 ***
	(14.1068)	(14.0262)	(13.9054)
EBD	−0.0020 ***	−0.0020 ***	−0.0020 ***
	(−2.8504)	(−2.8344)	(−2.8088)
OCF	−0.0000	−0.0000	−0.0000
	(−1.1967)	(−1.1788)	(−1.1371)
FCC	−0.0001	−0.0001	−0.0001
	(−1.2050)	(−1.2031)	(−1.3766)
CDP	0.0001	0.0001	0.0002
	(0.0582)	(0.0948)	(0.1323)
Year FE	YES	YES	YES
Ind FE	YES	YES	YES
_cons	−0.1176 ***	−0.1159 ***	−0.1245 ***
	(−9.6046)	(−9.5005)	(−9.7438)
N	27832	27832	27832
adj. R2	0.074	0.073	0.073

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

reports the empirical results for Model (5), presenting evidence that institutional investors’ research frequency serves as a positive moderator in the relationship between corporate financial flexibility (measured using aggregate financial flexibility, cash reserves, and debt restructuring capacity) and innovation efficiency. We find that increased self-initiated research activities by institutional investors amplify the innovation-enhancing effects stemming from financial flexibility mechanisms. This pattern validates hypotheses H6, H6-1, and H6-2, confirming the effective monitoring role of institutional investors in optimizing the innovation returns derived from financial flexibility strategies.

5.3. Endogeneity Test

In the baseline regression results, this paper concludes that holding financial flexibility significantly promotes corporate innovation efficiency, but this result may still be disturbed by endogeneity issues such as reverse causality and omitted variables. In specifying the baseline regression model, this study addressed the issue of partial reciprocal causality by lagging explanatory variables by one period. Additionally, instrumental variable techniques were employed in this section to mitigate the impact of endogeneity on the study’s findings.

The primary endogeneity issues in this study arise from omitted variables and measurement errors, and we used instrumental variables to address endogeneity concerns [96]. Theoretically, due to the prevalence of the same group effect, firms in the same region face the same operating environment and regional regulatory policies, so there is a correlation among them in the decision-making of capital structure, specifically in this study, in the holding level of financial flexibility, which meets the requirements of instrumental variable correlation. On the other hand, no study has shown that the capital structure of the same region in the same year affects the financial decisions of the target firm, so it basically meets the requirement of exogeneity of the instrumental variable. Therefore, this paper selects the financial flexible holding level of the same year and the same region as the instrumental variable, which can affect the holding level of the financial flexibility of the sample firm, but it is difficult to have an impact on the firm value of the explained variable of the sample firm, which conforms to the selection criteria of the instrumental variable.

It is important to emphasize that all research hypotheses in this study are grounded in Hypothesis 1, which establishes the relationship between financial flexibility and innovation efficiency. Consequently, our endogeneity tests focus on validating Hypothesis 1 specifically. Given that this core relationship serves as the foundation for subsequent hypotheses, we prioritize addressing potential endogeneity challenges in the link between corporate financial flexibility and innovation efficiency. By rigorously mitigating potential confounding factors through endogeneity testing of this primary association, we infer that endogeneity concerns are likely reduced in the derivative hypotheses.

Table 6. Endogeneity test of instrumental variable method.

	(1)	(2)	(3)	(4)	(5)	(6)
	l1ff	y1	c1ff	y1	d1ff	y1
otherpromean1	0.9397 ***
	(19.7163)
ivff01		0.0103 ***
		(3.7772)
ivc			0.9582 ***
			(19.9010)
ivcff01				0.0110 ***
				(3.8997)
ivd					0.7215 ***
					(17.7942)
ivdff01						0.0698 *
						(1.6731)
idr	0.2590 *	−0.0323 ***	0.2249	−0.0322 ***	0.0190 *	−0.0303 ***
	(1.7231)	(−3.9938)	(1.5724)	(−3.9829)	(1.7559)	(−3.7456)
ROA	2.9913 ***	0.0043	2.7197 ***	0.0050	0.2620 ***	0.0166
	(21.9689)	(0.3935)	(20.9916)	(0.4728)	(26.6561)	(1.2460)
CGC	0.0195	−0.0009	0.0204	−0.0009	−0.0007	−0.0006
	(1.0665)	(−0.8901)	(1.1753)	(−0.9149)	(−0.5184)	(−0.5841)
GOI	0.0226 **	0.0015 ***	0.0227 **	0.0015 ***	0.0004	0.0017 ***
	(2.3694)	(2.9090)	(2.4988)	(2.8684)	(0.5913)	(3.3169)
Age	−0.0162 ***	−0.0003 ***	−0.0152 ***	−0.0003 ***	−0.0009 ***	−0.0004 ***
	(−13.0661)	(−3.6615)	(−12.8958)	(−3.6782)	(−9.9015)	(−5.2033)
AS	−0.3286 ***	0.0098 ***	−0.2879 ***	0.0096 ***	−0.0400 ***	0.0092 ***
	(−38.5813)	(9.7376)	(−35.5218)	(10.2519)	(−64.9936)	(5.2994)
EBD	0.0757 ***	−0.0023 ***	0.0692 ***	−0.0022 ***	0.0053 ***	−0.0018 **
	(5.6584)	(−3.0073)	(5.4383)	(−3.0008)	(5.5193)	(−2.4162)
OCF	0.0000 ***	−0.0000 ***	0.0000 ***	−0.0000 ***	0.0000 ***	−0.0000 *
	(8.1943)	(−2.6072)	(7.8419)	(−2.6002)	(10.7017)	(−1.8614)
FCC	−0.0005	−0.0000	−0.0005	−0.0000	0.0001	−0.0001
	(−0.6278)	(−1.0705)	(−0.6967)	(−1.0553)	(1.0893)	(−1.2877)
CDP	0.4256 ***	−0.0036 *	0.3853 ***	−0.0034 *	0.0373 ***	−0.0018
	(15.7472)	(−1.9184)	(14.9826)	(−1.8932)	(19.0990)	(−0.8544)
Year FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
_cons	6.9186 ***	−0.1821 ***	6.0459 ***	−0.1767 ***	0.8853 ***	−0.1778 ***
	(30.8997)	(−8.1344)	(28.3755)	(−8.5199)	(52.9565)	(−4.2473)
N	27,832	27,832	27,832	27,832	27,832	27,832
adj. R2	0.130	0.070	0.116	0.070	0.243	0.069

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

demonstrates statistically significant positive coefficients for the instrumental variables (IVs) in the first-stage regressions. The first-stage F-statistics (exceeding the 10% threshold) combined with p-values below 0.001 collectively confirm the instruments’ strength and validity, thereby rejecting weak IV concerns. Furthermore, the second-stage estimates for Hypothesis 1 retain statistical reliability after implementing causal identification strategies to mitigate endogeneity, showing congruence with baseline model specifications.

5.4. Robustness Test

Robustness test 1: Province Fixed Effects. China is a vast country with a relatively independent innovation incentive policy among different provinces. At the same time, companies in different regions face different external environments, and the cost of accessing innovation activities varies. To reduce the impact of the differences among provinces on the research results, we added provincial fixed effect and examined whether the principal regression pattern still exists after adding the provincial fixed effect.

Table 7. Robustness tests 1: Provincial fixed effect.

	(1)	(2)	(3)	(4)	(5)	(6)
	y1	y1	y1	y1	y1	y1
l1ff	0.0024 ***			0.0021 ***
	(7.5101)			(6.3324)
c1ff		0.0025 ***			0.0022 ***
		(7.4121)			(6.2649)
d1ff			0.0232 ***			0.0226 ***
			(5.2200)			(5.1002)
l1ff $\times$ insnum1				0.0005 ***
				(3.0663)
c1ff $\times$ insnum1					0.0004 ***
					(2.7895)
d1ff $\times$ insnum1						0.0078 ***
						(3.7553)
insnum1				0.0019 ***	0.0019 ***	0.0019 ***
				(7.6683)	(7.6732)	(7.8649)
idr	−0.0320 ***	−0.0320 ***	−0.0319 ***	−0.0311 ***	−0.0311 ***	−0.0311 ***
	(−3.9772)	(−3.9698)	(−3.9559)	(−3.8686)	(−3.8652)	(−3.8691)
ROA	0.0297 ***	0.0301 ***	0.0310 ***	0.0215 ***	0.0219 ***	0.0216 ***
	(4.0308)	(4.0905)	(4.1858)	(2.8844)	(2.9352)	(2.8818)
CGC	−0.0008	−0.0008	−0.0008	−0.0012	−0.0012	−0.0011
	(−0.8454)	(−0.8480)	(−0.7789)	(−1.2265)	(−1.2282)	(−1.1667)
GOI	0.0017 ***	0.0017 ***	0.0017 ***	0.0017 ***	0.0017 ***	0.0018 ***
	(3.2532)	(3.2473)	(3.3288)	(3.3914)	(3.3849)	(3.4615)
Age	−0.0004 ***	−0.0004 ***	−0.0004 ***	−0.0003 ***	−0.0003 ***	−0.0003 ***
	(−5.8215)	(−5.8388)	(−6.1248)	(−4.6884)	(−4.7067)	(−4.8757)
AS	0.0070 ***	0.0069 ***	0.0071 ***	0.0064 ***	0.0063 ***	0.0066 ***
	(14.8785)	(14.7804)	(14.4852)	(13.4392)	(13.3628)	(13.2077)
EBD	−0.0016 **	−0.0016 **	−0.0015 **	−0.0020 ***	−0.0020 ***	−0.0019 ***
	(−2.1874)	(−2.1732)	(−2.1030)	(−2.7311)	(−2.7151)	(−2.6930)
OCF	−0.0000 **	−0.0000 **	−0.0000 **	−0.0000	−0.0000	−0.0000
	(−2.0083)	(−1.9888)	(−1.9731)	(−1.5784)	(−1.5630)	(−1.5325)
FCC	−0.0001	−0.0001	−0.0001	−0.0001	−0.0001	−0.0001
	(−1.1954)	(−1.1930)	(−1.2588)	(−1.2294)	(−1.2281)	(−1.3932)
CDP	−0.0000	0.0001	0.0002	0.0003	0.0004	0.0005
	(−0.0087)	(0.0361)	(0.1084)	(0.2347)	(0.2701)	(0.3266)
Year FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES
_cons	−0.1129 ***	−0.1111 ***	−0.1185 ***	−0.1017 ***	−0.1002 ***	−0.1080 ***
	(−9.0994)	(−8.9779)	(−9.1389)	(−8.1558)	(−8.0535)	(−8.2887)
N	27,832	27,832	27,832	27,832	27,832	27,832
adj. R2	0.076	0.076	0.075	0.078	0.078	0.077

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

reports the regression results incorporating provincial fixed effects. Columns 1–3 present robustness tests for Hypotheses H1, H1-1, and H1-2, respectively. Columns 4–6 present robustness tests for Hypotheses H6, H6-1, and H6-2, respectively. The results reveal that the statistically significant relationships supporting hypotheses H1 and H6 remain consistent with the established analytical framework, thus demonstrating methodological robustness across alternative model specifications.

Robustness test 2: Change the measurement method of the explained variable. In reality, the accumulation of innovation results is discrete rather than gradual. Thus, the capitalization of eligible development-stage expenditures also serves as a financial metric for evaluating a firm’s phased innovation progress. The improvement in corporate innovation efficiency can be operationally quantified by the commercial effectiveness of R&D expenditures, specifically measured by the proportion of total R&D investment that qualifies for capitalization. This metric reflects a firm’s capacity to convert research inputs into marketable outputs or scalable production advantages. The study uses the ratio of current capitalization expenditure to R&D investment to measure capital conversion efficiency and replaces the explained variable with capital conversion rate for robustness tests. The empirical findings summarized in

Table 8. Robustness test 2: Changing the measurement method of the explained variable.

	(1)	(2)	(3)	(4)	(5)	(6)
	y3	y3	y3	y3	y3	y3
l1ff	0.2397 ***			0.1639 *
	(2.7069)			(1.7573)
c1ff		0.2391 **			0.1593
		(2.5710)			(1.6219)
d1ff			2.6496 **			2.2916 *
			(2.0920)			(1.8003)
l1ff $\times$ insnum1				0.1041 **
				(2.5641)
c1ff $\times$ insnum1					0.1082 **
					(2.5133)
d1ff $\times$ insnum1						1.5473 ***
						(2.7448)
insnum1				0.0214	0.0210	0.0305
				(0.3408)	(0.3350)	(0.4864)
idr	−0.8977	−0.8948	−0.9078	−0.8943	−0.8944	−0.9166
	(−0.4080)	(−0.4067)	(−0.4125)	(−0.4065)	(−0.4065)	(−0.4166)
ROA	−17.1304 ***	−17.0692 ***	−17.0973 ***	−17.2319 ***	−17.1714 ***	−17.3742 ***
	(−8.7098)	(−8.6843)	(−8.6571)	(−8.6523)	(−8.6274)	(−8.6843)
CGC	0.3282	0.3285	0.3335	0.3301	0.3311	0.3343
	(1.2773)	(1.2787)	(1.2979)	(1.2822)	(1.2860)	(1.2983)
GOI	0.9148 ***	0.9148 ***	0.9168 ***	0.9216 ***	0.9217 ***	0.9215 ***
	(5.7897)	(5.7898)	(5.8018)	(5.8299)	(5.8307)	(5.8300)
Age	0.1277 ***	0.1275 ***	0.1268 ***	0.1275 ***	0.1273 ***	0.1270 ***
	(6.7063)	(6.6940)	(6.6592)	(6.6164)	(6.6076)	(6.5955)
AS	2.2819 ***	2.2724 ***	2.3094 ***	2.2674 ***	2.2597 ***	2.3011 ***
	(16.7231)	(16.7069)	(16.2148)	(16.3187)	(16.3153)	(15.8924)
EBD	0.5450 ***	0.5469 ***	0.5419 ***	0.5316 ***	0.5328 ***	0.5289 ***
	(2.8572)	(2.8668)	(2.8389)	(2.7814)	(2.7872)	(2.7655)
OCF	0.0000	0.0000 *	0.0000 *	0.0000 *	0.0000 *	0.0000 *
	(1.6408)	(1.6517)	(1.6563)	(1.6647)	(1.6713)	(1.6970)
FCC	−0.0059	−0.0059	−0.0063	−0.0047	−0.0047	−0.0062
	(−0.4991)	(−0.4955)	(−0.5276)	(−0.3985)	(−0.3932)	(−0.5226)
CDP	−1.9275 ***	−1.9179 ***	−1.9235 ***	−1.8969 ***	−1.8900 ***	−1.8946 ***
	(−4.9624)	(−4.9395)	(−4.9400)	(−4.8810)	(−4.8656)	(−4.8638)
Year FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
_cons	−31.6232 ***	−31.3819 ***	−32.2967 ***	−31.4448 ***	−31.2439 ***	−32.2561 ***
	(−8.7281)	(−8.6868)	(−8.5794)	(−8.6150)	(−8.5847)	(−8.5101)
N	18,830	18,830	18,830	18,830	18,830	18,830
adj. R2	0.133	0.133	0.133	0.133	0.133	0.133

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

demonstrate the robustness of the core hypotheses when subjected to alternative measures of the dependent variables. Columns 1–3 present robustness tests for hypotheses H1, H1-1, and H1-2, respectively. Columns 4–6 present robustness tests for hypotheses H6, H6-1, and H6-2, respectively. Specifically, the statistical significance and directional effects of the relationships posited in hypotheses H1 and H6 remain stable across different model specifications, thus confirming the robustness of the core findings.

Robustness test 3: Change the sample group: Given that innovation demands vary significantly across industries, with certain sectors prioritizing innovation efficiency more intensively, this study focus on innovation-driven manufacturing sectors, such as computer and electronic equipment production, instrumentation manufacturing, transportation equipment fabrication (railway, maritime, aerospace), automotive manufacturing, petroleum refining and nuclear fuel processing, electrical machinery production, chemical materials and products manufacturing, general-purpose and specialized equipment production, and pharmaceutical manufacturing. Columns (1)–(3) present robustness tests for H1, H1-1, and H1-2, respectively. Columns (4)–(6) report robustness tests for H6, H6-1, and H6-2. Robustness was further validated using representative advanced manufacturing sectors as samples, with detailed results provided in

Table 9. Robustness test 3: Change the sample.

	(1)	(2)	(3)	(4)	(5)	(6)
	y1	y1	y1	y1	y1	y1
l1ff	0.0036 ***			0.0032 ***
	(6.7698)			(5.7515)
c1ff		0.0037 ***			0.0033 ***
		(6.7015)			(5.6710)
d1ff			0.0408 ***			0.0386 ***
			(4.8961)			(4.6080)
l1ff $\times$ insnum1				0.0005 *
				(1.8943)
c1ff $\times$ insnum1					0.0005 *
					(1.8932)
d1ff $\times$ insnum1						0.0065 *
						(1.8033)
insnum1				0.0010 **	0.0010 **	0.0011 ***
				(2.4355)	(2.4280)	(2.6598)
idr	−0.0124	−0.0125	−0.0126	−0.0128	−0.0129	−0.0127
	(−0.8818)	(−0.8839)	(−0.8932)	(−0.9111)	(−0.9143)	(−0.8986)
ROA	0.0304 **	0.0311 **	0.0300 **	0.0262 **	0.0269 **	0.0245 *
	(2.3466)	(2.4058)	(2.2878)	(2.0023)	(2.0601)	(1.8498)
CGC	−0.0018	−0.0018	−0.0015	−0.0019	−0.0019	−0.0017
	(−1.1051)	(−1.1115)	(−0.9569)	(−1.1751)	(−1.1816)	(−1.0389)
GOI	0.0044 ***	0.0045 ***	0.0045 ***	0.0046 ***	0.0046 ***	0.0046 ***
	(4.0252)	(4.0346)	(4.0477)	(4.1414)	(4.1496)	(4.1628)
Age	−0.0007 ***	−0.0007 ***	−0.0007 ***	−0.0007 ***	−0.0007 ***	−0.0007 ***
	(−5.7480)	(−5.7704)	(−5.9961)	(−5.2547)	(−5.2747)	(−5.4359)
AS	0.0106 ***	0.0105 ***	0.0110 ***	0.0102 ***	0.0101 ***	0.0106 ***
	(12.3943)	(12.3099)	(12.2576)	(11.7576)	(11.6825)	(11.6714)
EBD	−0.0009	−0.0009	−0.0008	−0.0012	−0.0012	−0.0011
	(−0.7656)	(−0.7508)	(−0.6760)	(−0.9879)	(−0.9727)	(−0.9178)
OCF	−0.0000	−0.0000	−0.0000	−0.0000	−0.0000	−0.0000
	(−0.3067)	(−0.2899)	(−0.2715)	(−0.2899)	(−0.2797)	(−0.2066)
FCC	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
	(0.1332)	(0.1414)	(0.1870)	(0.1844)	(0.1936)	(0.1700)
CDP	0.0011	0.0012	0.0011	0.0014	0.0015	0.0013
	(0.4436)	(0.4842)	(0.4274)	(0.5563)	(0.5931)	(0.5332)
Year FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
_cons	−0.2066 ***	−0.2038 ***	−0.2198 ***	−0.1989 ***	−0.1963 ***	−0.2122 ***
	(−10.0327)	(−9.9315)	(−10.0838)	(−9.5649)	(−9.4737)	(−9.6535)
N	12,864	12,864	12,864	12,864	12,864	12,864
adj. R2	0.038	0.038	0.037	0.039	0.039	0.037

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

. As shown in the table, the regression outcomes for Hypothesis 1 and Hypothesis 6 align with prior findings and demonstrate strong robustness.

6. Conclusions and Prospect

6.1. Research Conclusions

Our study reached three main conclusions. First, maintaining financial flexibility in China’s capital market can improve corporate innovation efficiency. Second, financial flexibility boosts innovation efficiency through two channels: resource allocation and capability enhancement. Financially flexible companies can reduce transaction costs, increase incentives for core technical staff, and make senior executives’ wages more stable. This confirms that financial flexibility is a strategic financial resource. At the same time, as a sign of dynamic capabilities, financial flexibility improves innovation efficiency by enhancing innovation, absorption, and adaptation capabilities, validating its “utilization” and “response” features. Third, institutional investors, as important market participants, provide effective supervision. Through research activities, they actively participate in corporate governance, reduce information asymmetry, and improve governance efficiency. This strengthens the positive link between financial flexibility and corporate innovation efficiency.

These findings enrich the corporate innovation literature by highlighting the crucial role of financial flexibility in improving innovation efficiency. They also offer practical guidance for organizational decision-making. Moreover, the study deepens our understanding of institutional investors’ governance effectiveness in China’s capital market, clarifying their supervisory roles and implications for corporate governance.

6.2. Research Limitations and Future Prospects

Our study relies solely on public data from listed firms, making our conclusions dependent on secondary sources. Also, since the sample only includes Chinese A-share listed firms, it does not represent small- and medium-sized enterprises (SMEs). SMEs face more severe financing constraints and have different financial flexibility setups compared to listed companies. Thus, more research is needed to validate and interpret the relationship between financial flexibility and innovation outcomes in SMEs.

For future research, scholars can use surveys and case studies to expand the sample. Including SMEs and emerging markets will help analyze the link between financial flexibility and innovation more deeply. Comparative studies across emerging markets or different countries can also reveal whether the impacts and mechanisms of financial flexibility on innovation efficiency differ under various institutional environments. Such cross-context validation will make the findings more applicable.

6.3. Research Suggestions

Firms should strategically leverage financial flexibility to sustain innovation. First, they should optimize financial resource allocation. By adjusting financial flexibility levels according to their capital structure, companies can take advantage of it to reduce transaction costs and retain talent more effectively. Second, managers need to strengthen the coordination between financial flexibility and corporate strategy. They should recognize that financial flexibility reflects dynamic capabilities and use it as a key financial resource to drive sustainable development. Also, firms should improve the governance of innovation activities by disclosing information promptly. This promotes interaction between internal and external governance mechanisms.

Investors, especially institutional investors, should actively supervise. They can actively participate in governance through mechanisms such as exercising voting rights. By using new channels like digital platforms, they can improve information collection and analysis. This will ultimately boost corporate innovation efficiency. Specifically, institutional investors can form investment teams with industry-specific technical expertise. These teams can check the authenticity of corporate financial flexibility reserves and the rationality of innovation plans. They should also monitor how companies use financial flexibility to ensure optimal resource allocation.