Influence of equity structure in China’s high-tech manufacturing industry on enterprise value under epidemic shocks

Abstract

The COVID-19 pandemic represented a profound exogenous shock to global economic systems, posing substantial challenges to corporate governance frameworks and value creation—particularly within technology-intensive industries. This study investigates the relationship between equity concentration and corporate value in China’s high-tech manufacturing sector from 2019 to 2023, with particular attention to the mediating role of R&D investment. Drawing on a balanced panel dataset comprising 642 listed firms, we employ fixed-effects regression models complemented by instrumental variable (IV) estimation to rigorously address endogeneity concerns. Empirical results indicate that, after accounting for endogeneity, equity concentration exerts a significantly larger negative effect on firm value (IV estimate: β= −13.105, p < 0.01) than conventional OLS estimates suggest—indicating that standard OLS estimates are subject to pronounced downward bias. Although traditional mediation analysis points to a partial mediating role for R&D investment—explaining 18–23% of the total effect—instrumental variable–based mediation tests yield statistically insignificant indirect effects, underscoring the sensitivity of this channel to endogeneity correction. Notably, the detrimental impact of equity concentration on firm value was markedly attenuated during the pandemic period. Moreover, substantial industry-level heterogeneity emerges: the negative association is most pronounced in capital-intensive subsectors, including aerospace and electronic equipment manufacturing. These findings enrich theoretical understanding of how corporate governance mechanisms interface with innovation dynamics under extreme external shocks. They also yield nuanced practical implications: policymakers and corporate leaders should explicitly account for industry-specific attributes and macroeconomic contexts when designing and optimizing ownership structures—since the effectiveness of governance mechanisms is highly contingent upon prevailing environmental conditions.

Introduction

High-tech industries play a pivotal role in driving the optimization of industrial structures, enhancing labor productivity, and strengthening overall national competitiveness. The development trajectories and stages of high-tech industries vary significantly across countries. The United States has sustained its position as a global leader, with knowledge- and technology-intensive sectors contributing substantially to global value-added output. Germany is distinguished by its highly concentrated and integrated industrial ecosystems. Since the 1990s, China has actively fostered the growth of high-tech industries, leading to continuous expansion in industrial scale. In recent years, China has exhibited a promising trend of concurrent advancement in both scale enlargement and efficiency enhancement, as well as in innovation-driven development and structural transformation¹. As a fundamental component of modern economic systems, the equity structure of high-tech enterprises and its relationship with corporate value remain critical research topics in the field of corporate governance.

Recently, a series of predictive models integrating advanced machine learning techniques with optimization algorithms have provided more nuanced insights into the quantitative assessment of the impact of the COVID-19 pandemic on key economic and market indicators. These models have effectively captured and simulated the significant macroeconomic fluctuations and heightened market uncertainties observed at various stages before, during, and after the pandemic. For example, Kljajic et al. (2025) developed a recurrent neural network model optimized through multi-head variational neighborhood search for forecasting gasoline and crude oil prices, which highlighted the extreme volatility of the global energy market induced by the pandemic². Babic et al. (2026) proposed a multivariate methodology that integrates an enhanced variable neighborhood search algorithm with recurrent neural networks to predict unemployment rates, effectively capturing the ongoing impact of the epidemic on labor market dynamics³. In the domain of financial markets, Mizdrakovic et al. (2024) developed a decomposing-assisted long short-term memory network to model Bitcoin prices and employed Shapley values for interpretation, thereby uncovering behavioral patterns of cryptocurrencies during periods of extreme volatility⁴. Collectively, these studies demonstrate that the epidemic has created a complex ‘stress test’ environment, generating significant uncertainty across both macroeconomic and micro-enterprise levels. The application of advanced optimization algorithms has also proven crucial in other domains for navigating such uncertainty. For instance, El-Kenawy et al. (2025) proposed a hybrid model integrating the Greylag Goose Optimization algorithm with time series analysis to enhance the accuracy of electricity load forecasting in smart cities⁵. Similarly, in the field of photovoltaic power forecasting, feature selection and hyperparameter tuning in transformer-based deep learning models were effectively optimized using the Swordfish Movement Optimization Algorithm⁶. Furthermore, the development of novel metaheuristic algorithms, such as the Glider Snake Optimizer (GSO) inspired by the locomotion of arboreal snakes, continues to advance the capability for solving complex engineering optimization problems under volatile conditions⁷. Against this backdrop, it is particularly urgent and critical to investigate key strategic decisions within firms. Furthermore, at the enterprise micro-environment level, Todorovic et al. (2023) indirectly assessed the external audit risks imposed on corporate operations by the pandemic through risk prediction models—such as optimizing XGBoost with meta-heuristic algorithms to enhance the accuracy of audit opinion forecasting⁸. Collectively, these studies demonstrate that the epidemic has created a complex “stress test” environment, generating significant uncertainty across both macroeconomic and micro-enterprise levels. Against this backdrop, it is particularly urgent and critical to investigate key strategic decisions within firms—such as R&D investment—and their underlying governance drivers, including the behavior of major shareholders. This study specifically examines the micro-level interplay between changes in ownership structure and corporate innovation capacity under conditions of heightened macroeconomic and market uncertainty.

Equity concentration, a critical indicator of corporate ownership distribution, significantly influences organizational stability, governance mechanisms, and value creation. According to agency theory, higher equity concentration enhances the monitoring capacity and incentives of major shareholders to oversee managerial activities, thereby mitigating agency conflicts and contributing positively to corporate value. In high-tech enterprises, major shareholders typically possess substantial professional expertise and a strong commitment, enabling them to effectively monitor strategic decision-making, minimize resource misallocation, and reduce moral hazard risks. Resource dependence theory further suggests that major shareholders can provide essential strategic resources—such as core technologies, financial capital, and market access—that enhance firm competitiveness and facilitate rapid growth. However, expropriation theory cautions that excessive equity concentration may empower controlling shareholders to exploit their dominant position for private benefits, often at the expense of minority shareholders, potentially undermining corporate value. In technology-based firms, the short-term profit orientation of controlling shareholders may lead to underinvestment in research and development and long-term capability building, or even result in resource tunneling through related-party transactions. Therefore, the relationship between equity concentration and corporate value is non-linear and contingent upon multiple contextual factors, including industry characteristics, market dynamics, and the quality of corporate governance⁹. Equity concentration influences corporate value through two opposing channels: the “monitoring incentive effect,” which reduces agency costs and may enhance value, and the “expropriation/innovation suppression effect,” which may erode value. The optimal level of equity concentration, corresponding to the peak of an inverted U-shaped relationship, is determined by the interplay between these two effects; external shocks, such as the COVID-19 pandemic, may shift this equilibrium point.

The influence mechanism of equity concentration on corporate value is highly complex and multifaceted. On the one hand, a relatively high degree of equity concentration can exert a positive effect on corporate value. When ownership is concentrated among a limited number of major shareholders, their interests become more closely aligned with the firm’s overall performance. This alignment incentivizes them to actively engage in corporate governance, strengthen oversight of management, and support long-term strategic initiatives that enhance core competitiveness—such as the recruitment of professional managers, the implementation of effective incentive mechanisms, and increased investment in research and development. On the other hand, excessive equity concentration may give rise to governance risks. Major shareholders may exploit their controlling positions to engage in tunneling or make decisions that prioritize private benefits at the expense of minority shareholders and the firm’s long-term health, thereby undermining corporate value¹⁰. Importantly, this fundamental tension between the alignment and entrenchment effects is not static; rather, it is contingent upon the external institutional and economic environment. The COVID-19 pandemic, as a severe exogenous shock, provides a critical context for examining this dynamic relationship. It has the potential to disrupt the established link between equity concentration and R&D investment through three key channels: (1) intensifying major shareholders’ risk aversion and short-term orientation, (2) exacerbating financial constraints that limit firms’ investment capacity, and (3) shifting the balance between alignment and entrenchment toward the latter under conditions of heightened uncertainty. In light of this, this paper undertakes a systematic review and critical evaluation of research concerning the relationship between equity concentration and corporate value, drawing on both international and domestic literature.

In the realm of international academic research, scholars have put forth diverse perspectives, taking into account varying national and industrial contexts. Lee and Kim (2022) conducted a study using South Korean high-tech firms as a case sample, revealing that a higher degree of equity ownership concentration is positively associated with financial performance¹¹. Cao and Hsu (2021), in their investigation of U.S. high-tech enterprises, similarly found that greater ownership concentration encourages increased investment in research and development, thereby enhancing corporate value¹². Lin et al. (2021) advanced the discourse from the standpoint of agency theory, arguing that concentrated equity ownership helps reduce agency costs and improve governance efficiency, thus better aligning the interests of shareholders with those of management¹³. Kotlar and De Massis (2020) focused their analysis on family-owned firms and found that the concentration of control rights has a positive effect on corporate dynamic capabilities and firm performance¹⁴. However, Kim and Fortado (2021) cautioned that excessive equity concentration may lead to entrenchment effects and hollowing-out phenomena, potentially undermining the creation of corporate value¹⁵. Furthermore, Hsu and Fang (2019) and Jain and Kedia (2018) independently confirmed the beneficial governance implications of equity concentration in high-tech enterprises in Taiwan, China, and India, respectively^16,17.

In the Chinese context, empirical research has uncovered significant discrepancies in findings, which can be attributed to differences in sample selection, industry-specific characteristics, and model specifications. Several studies have drawn on data from the A-share market. Lu Shichang and Mi Siqi (2023) identified a positive relationship between equity concentration and corporate value, with R&D expenditure exerting a positive moderating effect¹⁸. Chen Yunqiao and Liu Yanhua (2021) suggested that equity concentration acts as a negative moderating factor in the link between corporate social responsibility and corporate value¹⁹. Xu Mengyu (2021) and Zhang Shuo (2020) both reported nonlinear relationships, including positive U-shaped and inverted U-shaped patterns^20,21. Studies focusing on ChiNext-listed firms have also produced inconsistent results. Zhang Jiayu (2022) found a negative correlation between the shareholding ratio of the largest shareholder and corporate value, whereas the ownership stake of the top five shareholders was positively associated with corporate value²². Sun Kaize (2020) observed that the effect of equity concentration is contingent upon specific threshold intervals²³. In contrast, Zhang Shuanxing and Wen Yanzhe (2020) confirmed a significant positive association between equity concentration and firm performance²⁴. At the industry level, Liu Yi and Han Qianqian (2022) demonstrated that equity concentration positively moderates the impact of R&D on corporate value in the manufacturing sector²⁵. Meanwhile, Wang Xiwei et al. (2020) and Lin Hanyin (2018) identified a U-shaped and an inverted U-shaped relationship between equity concentration and corporate value in the logistics and power industries, respectively^26,27.

Specifically, research on high-tech enterprises in China has produced two divergent conclusions. Some studies support a positive correlation between equity concentration and firm performance. For example, Bai et al. (2024) argue that effective monitoring by major shareholders can improve corporate governance and thereby enhance enterprise value²⁸; Jia Chunxiang and Sun Ziwei (2023) find that higher equity concentration strengthens the positive impact of comprehensive risk management on enterprise value²⁹; similar findings have also been reported by Wang and Li (2022), Chen and Huang (2021), among others^30,31. However, an alternative body of research suggests negative or nonlinear effects, including an inverted U-shaped relationship. For instance, Hesheng et al. (2024) and Li Mengya and Yan Taihua (2015) both contend that excessive equity concentration can undermine innovation performance and reduce enterprise value^32,33; Teng Fei and Qiu Dongfang (2015) further identify a significant inverted U-shaped relationship between state-owned equity and innovation performance, implying that moderate levels may be beneficial while excessive concentration becomes detrimental³⁴.

Table 1 below summarizes these conflicting findings by context, highlighting the importance of institutional, industrial, and temporal factors.

Table 1 Summary of Key Studies on Equity Concentration and Firm Value by Context.

Table 1 above demonstrates that the relationship between equity concentration and firm value is highly context-dependent. Studies conducted under normal economic conditions in developed markets—such as the United States and South Korea—typically report positive associations, consistent with agency theory’s monitoring hypothesis. In contrast, research based on emerging markets or focused on crisis periods more frequently identifies negative or non-linear effects. Notably, studies that fail to address endogeneity concerns tend to yield smaller or statistically insignificant estimates; by contrast, those employing rigorous causal inference methods—including the present study—consistently uncover substantially larger negative impacts. This synthesis suggests that the apparent contradictions in the literature likely arise not from fundamental theoretical inconsistencies, but rather from variations in institutional environments, macroeconomic conditions, and methodological rigor.

Despite this growing recognition of context-dependence, the existing literature exhibits two critical gaps. First, most prior conclusions are drawn from periods of economic stability, leaving the ownership–performance relationship under conditions of severe exogenous uncertainty—such as the COVID-19 pandemic—largely unexplored. Second, although R&D investment is commonly included as a control variable or moderating factor, its potential role as a causal mediating mechanism—through which equity structure affects firm value during crises—has not been rigorously evaluated using formal causal inference methods. To bridge these gaps, this study uniquely integrates the crisis context of the COVID-19 pandemic with causal mediation analysis, leveraging a panel dataset of Chinese high-tech manufacturing firms. We aim to empirically examine not only whether equity concentration matters during an extreme exogenous shock, but also how it shapes firm outcomes. Building on this conceptual foundation, the paper proposes the following hypotheses:

H1: During the COVID-19 pandemic, equity concentration in high-tech manufacturing firms is negatively associated with enterprise value.

H2: During the COVID-19 pandemic, the relationship between equity concentration and enterprise value in high-tech manufacturing firms follows an inverted U-shaped curve.

Moreover, R&D investment, as a critical driver of corporate innovation, may serve as a mediating variable in the relationship between equity concentration and firm value. Grounded in agency theory, major shareholders may reduce long-term R&D expenditures due to risk aversion, prioritizing short-term financial stability over uncertain innovative outcomes. In contrast, resource dependence theory suggests that excessive equity concentration could weaken a firm’s ability to access and integrate external innovation resources, thereby constraining its innovative potential and ultimately impairing firm value. Empirical evidence from Wang et al. (2020) and Li et al. (2018) indicates that in high-tech enterprises, high levels of equity concentration are often linked to suboptimal R&D investment, which in turn leads to a decline in firm value^35,36. Therefore, this study proposes the following hypothesis:

H3: Research and development (R&D) investment mediates the relationship between equity concentration and enterprise value in high-tech manufacturing firms during the COVID-19 pandemic.

This paper investigates the impact of equity concentration on corporate value within China’s high-tech manufacturing industry, utilizing a sample of listed companies during the COVID-19 pandemic. The structure of this study is organized as follows: The first section presents the introduction, outlining the research background, underlying mechanisms, and contextual constraints. The second section conducts a literature review and formulates research hypotheses, establishing testable propositions grounded in a comprehensive analysis of existing studies. The third section details the research design, including data sources, variable definitions, and model specification. The fourth section carries out an empirical analysis, encompassing descriptive statistics, correlation analysis, model specification tests, regression analysis, and robustness checks to ensure the reliability and validity of the findings. The fifth section interprets and discusses the empirical results in depth. Finally, the paper concludes with key conclusions and offers policy recommendations aimed at fostering the development of high-tech enterprises.

Methods

Data sources

This study is based on the industry scope defined by the National Bureau of Statistics in the “Classification of High-Tech Industries (Manufacturing) (2017).” This scope encompasses six major categories: pharmaceutical manufacturing, aerospace and spacecraft equipment manufacturing, electronic and communication equipment manufacturing, computer and office equipment manufacturing, medical instrument and meter manufacturing, and information chemicals manufacturing.

In accordance with the “Industry Classification Guidelines for Listed Companies (2012 Edition)” issued by the China Securities Regulatory Commission, non-ST manufacturing listed companies from the Shanghai and Shenzhen A-share markets during the period 2019–2023 were selected as the initial sample through the Guotai An (CSMAR) database. The research utilizes data spanning 2019 to 2023 to capture the full duration of the COVID-19 pandemic and its implications for corporate governance and firm value. Firms operating within the aforementioned high-tech manufacturing sectors were then identified as the primary research subjects. After excluding observations with missing data on key variables, a balanced panel dataset comprising 642 enterprises was constructed for subsequent empirical analysis.

Variable selection

Dependent variable: enterprise value

This study employs Tobin’s Q as the primary proxy for firm market value. Originally proposed by economist James Tobin, this ratio evaluates a firm’s market value relative to the replacement cost of its assets. A Tobin’s Q value greater than one signals market confidence in future growth prospects and incentivizes investment, whereas a value below one reflects investor skepticism. Given the focus of this research on market perceptions during periods of crisis, Tobin’s Q is particularly appropriate. It serves as a widely adopted, forward-looking metric in corporate governance research (e.g., Ng et al., 2025³⁷; Xiong et al., 2024³⁸, capturing investor expectations directly rather than relying exclusively on historical accounting data.

The use of Tobin’s Q is especially justified in the context of this study for two principal reasons. First, it reflects the market’s real-time assessment of intangible assets, growth potential, and organizational resilience—dimensions that were severely tested during the COVID-19 pandemic—thereby serving as a robust indicator of investor confidence in corporate governance under exogenous shocks. Second, it is conceptually aligned with both the resource-based view and real options theory. A Tobin’s Q exceeding one indicates a market premium placed on a firm’s dynamic capabilities and strategic flexibility—key attributes for effective crisis navigation and strongly influenced by governance mechanisms. Consequently, this enables us to conceptualize “value” as the market’s evaluation of a firm’s adaptive capacity, extending beyond traditional measures of accounting profitability.

Explanatory variables

The core explanatory variable is equity concentration. In accordance with established research methodologies, this study employs the shareholding ratio of the largest shareholder (Cr1) as the primary proxy for equity concentration to examine the effect of major shareholders’ control on corporate value. Moreover, in the robustness checks, the combined shareholding ratios of the top five shareholders (Cr5) are included as an alternative measure for supplementary analysis, thereby enhancing the reliability and robustness of the findings.

Mediating variables

To uncover the underlying mechanism through which equity concentration influences corporate value, this study incorporates research and development (R&D) investment as a mediating variable. Given the substantial variation in firm size, the R&D investment amount is transformed into its natural logarithmic form to mitigate potential heteroskedasticity issues. This specification is employed to examine whether R&D investment mediates the relationship between equity concentration and corporate value.

Controlled variables

To mitigate the influence of other potential factors on enterprise value, this study includes the following control variables:

Enterprise scale: Measured by the natural logarithm of total assets at the end of the fiscal year. Variations in firm size may affect resource endowments and agency costs, thereby influencing enterprise value.

Enterprise growth (Growth): Captured by the operating revenue growth rate, which accounts for the impact of future growth potential on firm value.

Financial leverage (Lev): Measured by the debt-to-asset ratio (total liabilities divided by total assets), reflecting the role of capital structure and financial risk in enterprise valuation.

Profitability (ROE): Return on equity (net profit divided by shareholders’ equity) is used as a proxy for profitability, as it directly influences investors’ expectations and the market value of the firm.

The specific definitions and measurement methods for all variables are provided in Table 2.

Table 2 Model variable description.

Model establishment

Based on the aforementioned analysis, this paper establishes Model (1) and Model (2) to test Hypothesis 1 and Hypothesis 2, respectively, and employs Model (1), Model (3), and Model (4) to examine Hypothesis 3, as presented below:

$$\:\text{Q}={{\upalpha\:}}_{0}+{{\upalpha\:}}_{1}\text{C}\text{r}1+{{\upalpha\:}}_{2}\text{s}\text{i}\text{z}\text{e}+{{\upalpha\:}}_{3}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upalpha\:}}_{4}\text{l}\text{e}\text{v}+{{\upalpha\:}}_{5}\text{r}\text{o}\text{e}+{\upmu\:}\:\:\:\:$$

(1)

$$\:\text{Q}={{\upbeta\:}}_{0}+{{\upbeta\:}}_{1}\text{C}\text{r}1+{{\upbeta\:}}_{2}{\text{C}\text{r}1}^{2}+{{\upbeta\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\upbeta\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upbeta\:}}_{5}\text{l}\text{e}\text{v}+{{\upbeta\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$

(2)

$$\:\text{R}\text{D}={{\upgamma\:}}_{0}+{{\upgamma\:}}_{1}\text{C}\text{r}1+{{\upgamma\:}}_{2}\text{s}\text{i}\text{z}\text{e}+{{\upgamma\:}}_{3}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upgamma\:}}_{4}\text{l}\text{e}\text{v}+{{\upgamma\:}}_{5}\text{r}\text{o}\text{e}+{\upmu\:}$$

(3)

$$\:\text{Q}={{\updelta\:}}_{0}+{{\updelta\:}}_{1}\text{C}\text{r}1+{{\updelta\:}}_{2}\text{R}\text{D}+{{\updelta\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\updelta\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\updelta\:}}_{5}\text{l}\text{e}\text{v}+{{\updelta\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}$$

(4)

Results

Descriptive statistical analysis

This study first conducts a descriptive statistical analysis of all variables included in the model, with the results reported in Table 3.

Table 3 Descriptive statistics.

As shown in Table 3, the enterprise value (Q) of high-tech firms listed on China’s Shanghai and Shenzhen A-share markets ranges from 0.697 to 28.641, indicating a pronounced polarization among the sampled enterprises. With respect to ownership structure, the mean shareholding ratio of the largest shareholder (Cr1) is 0.296, while the mean combined shareholding ratio of the top five shareholders (Cr3) is 0.484. The substantial disparities between their maximum and minimum values suggest that equity concentration varies significantly across firms—some exhibit highly concentrated ownership, whereas others face issues of excessive equity dispersion.

Regarding the mediating variables, although the logarithm of total R&D investment shows a relatively moderate absolute difference between its maximum (24.011) and minimum (13.737), the logarithmic transformation implies considerable variation in actual R&D expenditure levels. This divergence can be attributed to underlying firm-specific factors such as scale of operations and capital structure.

The control variables reveal the following characteristics: The average firm size (Size) is 9.656. Although the numerical variation appears small, the use of logarithmic transformation implies meaningful differences in scale across firms. The average corporate growth rate (Growth) stands at 0.121, with a wide distribution, reflecting substantial heterogeneity in the growth of core business revenue. The mean level of financial leverage (Lev) is 0.358, accompanied by considerable variability, indicating significant differences in debt-to-asset ratios across the sample. Additionally, the average return on equity (ROE) is relatively low at 0.04, yet exhibits an extensive range of 6.774, further underscoring pronounced disparities in profitability among the sampled enterprises.

Moreover, the relatively high standard deviations observed for Tobin’s Q and R&D investment intensity suggest marked heterogeneity in market valuation and innovation activities across firms. This is consistent with the diverse technological trajectories, stages in market development, and risk profiles commonly found in high-tech industries.

Relevance analysis

Second, the correlation analysis of all variables incorporated into the model is presented in Table 4.

Table 4 Correlation coefficients.

To examine potential multicollinearity among the explanatory variables, this study first assessed the correlation coefficients between the variables. As presented in Table 4, the absolute values of the correlation coefficients between the largest shareholder’s shareholding ratio (Cr1) and both the mediating variables and control variables were all below 0.5, indicating weak linear associations and suggesting the absence of significant multicollinearity in the overall model. To further evaluate collinearity, auxiliary regression analyzes were performed for Model (4), and the R² values along with variance inflation factors (VIF) were computed for each auxiliary regression equation. The results are reported in Table 5.

Table 5 R² and corresponding VIF values of auxiliary regression equations of model (4).

As shown in Table 5, the variance inflation factor (VIF) values for all auxiliary regression equations are below 5. Hence, it can be concluded that multicollinearity is not a significant issue in Models (1) through (4).

Regression analysis

To address unobserved heterogeneity, the Hausman test was first conducted. The test results led to the rejection of the random effects model in favor of the fixed effects specification (p < 0.01). Given the short time dimension of the sample (T = 5), including both individual and year fixed effects simultaneously would lead to a loss of degrees of freedom and potential multicollinearity issues. Therefore, the benchmark model includes individual fixed effects only, while the period effect is reserved for examination in the robustness analysis. The regression results with individual fixed effects for Models (1) to (4) are reported in Table 6.

Table 6 Regression results of models (1) to (4).

The regression results reported in Table 6 show that the F-statistics for Models (1) to (4) are all statistically significant at the 1% level, indicating that the overall model specifications are valid. The estimated coefficients of the core explanatory variables are significant at the 1% to 5% levels across all models, and each model achieves a goodness-of-fit measure exceeding 73%, thereby supporting the reliability of the regression outcomes.

Based on the estimation results of Model (1), the coefficient of the core explanatory variable Cr1 is − 2.061 and negative, suggesting a statistically significant negative relationship between ownership concentration—measured by the shareholding ratio of the largest shareholder in listed firms within the high-tech manufacturing sector—and firm value. Specifically, higher equity concentration is associated with lower firm value. This result provides empirical support for Hypothesis 1 proposed in this study.

In Model (2), the coefficients of the two key variables—CR1 and its squared term—failed to achieve statistical significance. Due to high multicollinearity (correlation coefficient > 0.97), the estimates for the quadratic terms are unreliable; consequently, we refrain from interpreting them as evidence of an inverted U-shaped relationship. When considered alongside the significant negative linear relationship observed in Model (1), the results lend greater support to the hypothesis that equity concentration exerts an (approximately) linear negative effect on enterprise value. Hence, Hypothesis 2 remains unverified.

The regression results from Model (3) show that the coefficient of CR1 is − 1.472, indicating that higher equity concentration is associated with reduced research and development (R&D) investment. Collectively, the findings from Models (1), (3), and (4) suggest that R&D investment mediates the relationship between equity concentration and firm value. Specifically, Model (1) reveals a negative association between CR1 and enterprise value; Model (3) demonstrates that CR1 negatively affects corporate R&D investment; and Model (4) confirms a positive impact of R&D investment on enterprise value. These results jointly indicate that equity concentration indirectly influences firm value through its adverse effect on R&D investment, thereby providing support for Hypothesis 3.

In Model (4), the coefficient of CR1 (− 1.668) captures the direct effect of ownership concentration on Tobin’s Q. The indirect effect, calculated by multiplying the coefficient of CR1 in Model (3) (− 1.472) by the coefficient of R&D in Model (4) (0.250), amounts to − 0.368. Based on this conventional mediation analysis—which does not address endogeneity—the proportion of the total effect mediated by R&D investment is estimated at 18.07%, indicating a potential mediating role. However, as discussed in Sect.  3.4.5, this estimate is highly sensitive to endogeneity correction and should be interpreted with caution.

Bootstrap mediation analysis with 1,000 replications was performed, and the results confirm the significance of the indirect effect (95% confidence interval: [− 0.291, − 0.098], which does not include zero). Additionally, the Sobel test yields a significant z-value (z = − 3.286, p < 0.01), further supporting the partial mediating effect of R&D investment.

Robustness checks

Replacement of core explanatory variables

This study first conducts robustness tests by replacing the core explanatory variables. Specifically, the shareholding ratio of the largest shareholder (Cr1) is substituted with the sum of the shareholding ratios of the top five shareholders (Cr3). Models (5) to (8) are then re-estimated to assess the reliability of the benchmark results.

$$\:\text{Q}={{\upalpha\:}}_{0}+{{\upalpha\:}}_{1}\text{C}\text{r}3+{{\upalpha\:}}_{2}\text{s}\text{i}\text{z}\text{e}+{{\upalpha\:}}_{3}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upalpha\:}}_{4}\text{l}\text{e}\text{v}+{{\upalpha\:}}_{5}\text{r}\text{o}\text{e}+{\upmu\:}$$

(5)

$$\:\text{Q}={{\upbeta\:}}_{0}+{{\upbeta\:}}_{1}\text{C}\text{r}3+{{\upbeta\:}}_{2}{\text{C}\text{r}3}^{2}+{{\upbeta\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\upbeta\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upbeta\:}}_{5}\text{l}\text{e}\text{v}+{{\upbeta\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}$$

(6)

$$\:\text{R}\text{D}={{\upgamma\:}}_{0}+{{\upgamma\:}}_{1}\text{C}\text{r}3+{{\upgamma\:}}_{2}\text{s}\text{i}\text{z}\text{e}+{{\upgamma\:}}_{3}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upgamma\:}}_{4}\text{l}\text{e}\text{v}+{{\upgamma\:}}_{5}\text{r}\text{o}\text{e}+{\upmu\:}$$

(7)

$$\:\text{Q}={{\updelta\:}}_{0}+{{\updelta\:}}_{1}\text{C}\text{r}3+{{\updelta\:}}_{2}\text{R}\text{D}+{{\updelta\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\updelta\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\updelta\:}}_{5}\text{l}\text{e}\text{v}+{{\updelta\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}$$

(8)

Following a series of model specification tests—including correlation analysis, multicollinearity assessment, and the Hausman test—the regression results for Models (5) to (8), estimated using the two-way (firm and year) fixed-effects model, are presented in Table 7.

Table 7 Regression results of models (5) to (8).

The regression results reported in Table 7 show that the F-statistics for Models (5) to (8) are all statistically significant at the 1% level, confirming the overall validity of the model specifications. In each model, the coefficients of the core explanatory variables are significantly different from zero at the 1% significance level, and all models exhibit a goodness-of-fit exceeding 73%, which supports the reliability of the estimated results.

In Model (5), the coefficient of the core explanatory variable CR3 is − 2.757, further reinforcing the findings from Model (1). This indicates a significant negative relationship between equity concentration and firm value, providing additional support for Hypothesis 1.

When the quadratic term of CR3 is introduced into Model (6), the coefficient of the linear term becomes statistically insignificant, while the coefficient of the quadratic term is significantly negative (− 4.426), suggesting a potential inverted U-shaped relationship. However, due to the high multicollinearity between the linear and quadratic terms (correlation coefficient > 0.984), the empirical findings lack robustness. Consequently, in the absence of statistically reliable evidence supporting the presence of an inverted U-shaped relationship, Hypothesis 2 remains unsupported.

In comparison with the coefficient of Cr1 (− 1.472) in Model (3), the coefficient of Cr3 (− 1.824) in Model (7) not only reaffirms the inhibitory effect of equity concentration on research and development (R&D) investment but also indicates that the combined ownership stake of the top five shareholders (Cr3) exerts a more pronounced negative influence on R&D investment. This finding suggests an inverse relationship between the degree of equity concentration and R&D investment, such that higher levels of ownership concentration are associated with lower R&D expenditure.

The regression results from Model (8) show that the coefficient of Cr3 is − 2.386, while the coefficient of R&D investment (RD) is 0.200. The signs and significance levels of these coefficients are consistent with those observed in Model (4), and the magnitudes are comparable, supporting the robustness of the conventional mediation estimates.Nevertheless, like the baseline results, these findings are similarly subject to endogeneity concerns and are superseded by the instrumental-variable–based mediation tests reported in Sect.  3.4.5. Moreover, when compared to Model (5), the absolute value of the Cr3 coefficient has decreased (from − 2.757 to − 2.386), implying that R&D investment partially mediates the negative effect of equity concentration on firm value. The indirect effect is computed as the product of the Cr3-to-RD coefficient (− 1.824) and the RD-to-Q coefficient (0.200), yielding approximately − 0.365, which accounts for 13.23% of the total effect (− 2.757). Bootstrap mediation analysis (with 1,000 replications) was conducted, and the results confirm the statistical significance of this indirect effect (95% confidence interval: [− 0.663, − 0.068], excluding zero). These findings provide further support for Hypothesis 3.

In conclusion, the robustness test results further validate the main findings of the benchmark regression. First, a significant negative association exists between equity concentration and corporate value in listed high-tech manufacturing firms. Second, R&D investment serves as a partial mediator in this relationship, indicating that equity concentration dampens R&D investment, thereby exerting an indirect negative effect on corporate value.

Replacement of the dependent variable

To further validate the robustness of the findings, this study uses the firm’s market value (value) as a proxy variable in place of Tobin’s Q and re-estimates Models (9) through (11) accordingly.

$$\:\text{v}\text{a}\text{l}\text{u}\text{e}={{\upalpha\:}}_{0}+{{\upalpha\:}}_{1}\text{C}\text{r}1+{{\upalpha\:}}_{2}\text{s}\text{i}\text{z}\text{e}+{{\upalpha\:}}_{3}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upalpha\:}}_{4}\text{l}\text{e}\text{v}+{{\upalpha\:}}_{5}\text{r}\text{o}\text{e}+{\upmu\:}$$

(9)

$$\:\text{v}\text{a}\text{l}\text{u}\text{e}={{\upbeta\:}}_{0}+{{\upbeta\:}}_{1}\text{C}\text{r}1+{{\upbeta\:}}_{2}{\text{C}\text{r}1}^{2}+{{\upbeta\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\upbeta\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upbeta\:}}_{5}\text{l}\text{e}\text{v}+{{\upbeta\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}$$

(10)

$$\:\text{v}\text{a}\text{l}\text{u}\text{e}={{\upgamma\:}}_{0}+{{\upgamma\:}}_{1}\text{C}\text{r}1+{{\upgamma\:}}_{2}\text{R}\text{D}+{{\upgamma\:}}_{3}\text{s}\text{i}\text{z}\text{e}+{{\upgamma\:}}_{4}\text{g}\text{r}\text{o}\text{w}\text{t}\text{h}+{{\upgamma\:}}_{5}\text{l}\text{e}\text{v}+{{\upgamma\:}}_{6}\text{r}\text{o}\text{e}+{\upmu\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$

(11)

The regression results for Models (9) to (11) are reported in Table 8.

Table 8 Results of robust regression via the replacement of the dependent variable.

The regression results reported in Table 8 show that the F-statistics for Models (9) to (11) are all statistically significant at the 1% level, confirming the overall validity of the model specifications. All estimated coefficients of the explanatory and control variables are statistically significant at either the 1% or 5% level. Furthermore, all models exhibit a goodness-of-fit exceeding 95%, indicating a robust model fit and reliable estimation outcomes.

The findings in Table 8 further reveal that, in Model (9), equity concentration (CR1) has a significant negative effect on VALUE (coefficient = -0.225, p < 0.01), thereby supporting the robustness of the main conclusion.

When the quadratic term is introduced in Model (10), neither the coefficient of the first-order term (0.011) nor that of the quadratic term (-0.375) for CR1 is statistically significant. This result is consistent with the analysis using Tobin’s Q as the dependent variable, and no robust inverted U-shaped relationship is detected.

In Model (11), the coefficient of RD is significantly positive (0.027, p < 0.01), indicating that research and development (R&D) investment positively contributes to enterprise value enhancement. The coefficient of CR1 is significantly negative (− 0.181, p < 0.01), yet its absolute magnitude is approximately 19.6% lower than the value of − 0.225 observed in Model (9). Together with the findings from Model (3), these results further substantiate the partial mediating role of R&D investment (RD) in the relationship between equity concentration and enterprise value. Specifically, equity concentration exerts a significant negative effect on R&D investment (Model 3, coefficient: −1.472, p < 0.01), while R&D investment has a significant positive effect on enterprise value (Model 11, coefficient: 0.027, p < 0.01). The indirect effect is calculated as the product of the effect of CR1 on RD (− 1.472) and the effect of RD on VALUE (0.027), yielding − 0.0397. Given a total effect of − 0.225 (Model 9), the proportion attributable to the mediating effect is approximately 17.64%, providing further support for the existence of an R&D transmission mechanism. Bootstrap mediation analysis (with 1,000 replications) was conducted, and the results confirm the statistical significance of the indirect effect (95% confidence interval: [− 0.066, − 0.014], which does not include zero).

The above analysis provides additional empirical support for Hypotheses 1 and 3, whereas Hypothesis 2 remains insufficiently supported by the current evidence.

Considering the period fixed effect

To further mitigate the impact of common time-varying shocks during the sample period (2019–2023), particularly those arising from the different phases of the COVID-19 pandemic, year fixed effects were included in all regression models. Consequently, the results reported in this section are derived from a two-way (firm and year) fixed effects specification. The regression estimates are presented in Table 9.

Table 9 Two-way fixed-effect regression results of models (1) to (4).

The findings reported in Table 9 indicate that, after controlling for the time trend, the direct effect of equity concentration (CR1) on firm value (Q) becomes statistically insignificant (Model 1, coefficient: -0.852, p > 0.1). Similarly, its influence on research and development (R&D) investment (RD) is also not statistically significant (Model 3, coefficient: -0.082, p > 0.1). Meanwhile, the effect of R&D investment on firm value is only marginally significant (Model 4, coefficient: 0.124, p < 0.1).

This pattern can be explained by several factors. First, the time trend exerts a substantial influence. The two-way fixed-effects model simultaneously accounts for time-invariant individual characteristics and time-varying macro-level trends. If firm value is strongly affected by temporal factors—such as business cycles or policy shocks—during the sample period, and if changes in equity concentration are highly correlated with these temporal patterns, the independent effect of equity concentration may be partially absorbed or “diluted” within the two-way fixed-effects framework. Second, differences in model specification play a role. While the individual fixed-effects model focuses on within-firm variations over time, the two-way fixed-effects model further controls for common time shocks affecting all firms. When the variation in the key explanatory variable is closely aligned with aggregate time trends, its estimated significance may attenuate. Third, from the standpoint of statistical power, the two-way fixed-effects model consumes additional degrees of freedom due to the inclusion of time fixed effects, which may reduce the precision of estimates and weaken the power of statistical tests.

The insignificance of equity concentration in the two-way fixed-effects model does not necessarily imply the absence of a causal relationship. Rather, as demonstrated in Sect.  3.4.4, the effect of equity concentration is significantly moderated by the COVID-19 pandemic shock. When year fixed effects absorb common time trends—including the varying stages of the pandemic—they also absorb the systematic variation in the equity concentration–firm value relationship that is driven by these macroeconomic fluctuations. The significant interaction term between Cr1 and the pandemic dummy in Table 10 confirms that the effect of ownership structure is time-varying and context-dependent. Therefore, the attenuation of the main effect in the two-way fixed-effects model reflects the fact that the relationship is partially confounded with time trends, rather than indicating a lack of robustness. This finding aligns with the theoretical argument that corporate governance mechanisms operate differently under extreme uncertainty, and underscores the importance of examining context-specific effects rather than relying solely on average treatment effects.

Analysis of the moderating effect of the epidemic

To examine the differential impact of equity concentration on enterprise value before and after the outbreak of the epidemic, this section introduces a dummy variable “dummy” to indicate the epidemic period (where dummy = 0 for 2019 and dummy = 1 for the years 2020–2023). The interaction term between equity concentration and the epidemic dummy variable is incorporated into models (1), (5), and (9). The regression results are presented in Table 10.

Table 10 Moderating effect of the epidemic on models (1)、(5) and (9).

As shown in Table 10, the results of the linear interaction analysis for Model (1) indicate that equity concentration in high-tech industries has a significant negative effect on enterprise value (coefficient = -1.752, significant at the 5% level). However, the exogenous shock of the COVID-19 pandemic significantly mitigated this adverse effect: the interaction term yields a coefficient of 1.314 (significant at the 1% level), reducing the marginal negative impact of equity concentration during the pandemic to -0.438. This suggests that, under normal conditions, equity concentration may impair enterprise value; yet, in times of crisis, its potential benefits—such as improved decision-making efficiency—can partially offset the associated governance drawbacks. The findings from the linear interaction analyzes of Models (5) and (9) further support this interpretation.

This moderating effect provides critical insight into the two-way fixed-effects results reported in Table 8. The insignificance of the main effect in the two-way fixed-effects model can be attributed to the fact that the pandemic period (2020–2023) constitutes the majority of our sample. By controlling for year fixed effects, the model effectively compares the effect of equity concentration within each year, thereby removing the between-year variation that captures the pandemic’s moderating influence. Consequently, the two-way fixed-effects estimate represents an average effect that masks the underlying time-varying relationship. The significant interaction term in Table 9 demonstrates that the effect of equity concentration is indeed conditional on the macroeconomic environment, and that the attenuation in Table 8 is consistent with this conditionality rather than indicating model misspecification.

Addressing Endogeneity

Considering potential endogeneity issues associated with CR1, this paper employs instrumental variables to re-estimate Models (1) and (2) using the two-stage least squares method. The instrumental variable is constructed as the average CR1 of other firms within the same industry and year, excluding the focal firm under investigation. The two-stage least squares estimation results for the two models are reported in Tables 11 and 12, respectively.

Table 11 Model (1): Endogeneity Test and Weak Instrumental Variable Test.

The findings presented in Table 11 indicate that the Cragg–Donald Wald F statistic from the first-stage regression is 265.712, substantially exceeding the critical value of 16.38 for a 10% maximal instrumental variable (IV) bias. This suggests a strong association between the instrumental variable and the endogenous regressor CR1, thereby ruling out the presence of a weak instrument problem.

The coefficient on CR1 estimated via the instrumental variable (IV) approach is − 13.105, which is statistically significant at the 1% level. This estimate differs markedly from the ordinary least squares (OLS) estimate of − 2.061. The Hausman test strongly rejects the null hypothesis of exogeneity (p = 0.000), confirming the existence of significant endogeneity bias in CR1. After addressing endogeneity, the negative effect of CR1 on firm value (Q) is considerably larger in magnitude than that obtained from OLS, indicating that OLS substantially underestimates the adverse impact of CR1. The large discrepancy between OLS and IV estimates suggests that OLS results are biased downward due to endogeneity, and thus should not be interpreted as the causal magnitude. Furthermore, this result provides robust support for Hypothesis 1.

Table 12 Model (2): Endogeneity Test and Weak Instrumental Variable Test.

As shown in Table 12, the Hausman test results (χ² = 32.91, p = 0.000) strongly reject the exogeneity hypothesis, indicating that the squared term of CR1 is subject to a severe endogeneity issue. The Cragg–Donald Wald F statistic from the first-stage regression is as low as 0.039, well below the critical value of 7.030 associated with a 10% maximum allowable instrumental variable (IV) bias. This result implies that the chosen instrumental variable is nearly uncorrelated with the squared term of the endogenous variable CR1, pointing to a serious problem of weak instruments. On the one hand, this suggests that consistent and reliable estimates cannot be obtained using the current set of instrumental variables. On the other hand, it also indicates that, after attempting to account for endogeneity, there is no empirical support for an inverted U-shaped relationship.

To further investigate the mediating role of R&D intensity in the relationship between equity concentration and firm value, this study employs the industry average of R&D intensity—excluding the firm itself—as an alternative instrumental variable. The corresponding estimation results are reported in Table 13.

Table 13 Testing Results of the Mediating Effect.

Model (1) indicates that equity concentration (CR1) exerts a significant negative total effect on firm value (Q) (β = -13.105, p < 0.01). Model (3) reveals that CR1 significantly reduces R&D intensity (RD) (β = -13.604, p < 0.01). When both CR1 and RD are included simultaneously in Model (4), the effects of both variables on firm value become statistically insignificant (CR1: β = -10.006, p = 0.260; RD: β = 0.227, p = 0.713). According to the procedure for testing mediating effects, while both the total effect and path A are significant, path B is not. The indirect effect is estimated at -3.088, representing 23.58% of the total effect. Although, from an economic standpoint, R&D intensity appears to transmit approximately one-quarter of the effect of equity concentration, statistical tests do not support a significant mediating role. A bootstrap mediation analysis was conducted with 1,000 resamples, further confirming that the indirect effect of equity concentration on firm value through R&D intensity is not statistically significant (95% confidence interval: [-24.145, 25.295], which includes zero).

Heterogeneity analysis

Heterogeneity was assessed to determine whether the impact of equity concentration on enterprise value differs across sub-sectors within high-tech industries. According to the classification criteria established by the National Bureau of Statistics of China, high-tech manufacturing industries comprise six major categories: pharmaceutical manufacturing, aerospace manufacturing, electronic and communication equipment manufacturing, computer and office equipment manufacturing, medical instrument and meter manufacturing, and information product manufacturing.

Given that the computer and office equipment manufacturing sector has been integrated into the electronic and communication equipment manufacturing sector, and that information product manufacturing—being a functional classification—has its production activities already embedded within the electronic and communication equipment manufacturing and instrument and meter manufacturing sectors, this study focuses on four core sub-sectors: pharmaceutical manufacturing, aerospace manufacturing, electronic and communication equipment manufacturing, and instrument and meter manufacturing, for the purpose of heterogeneity analysis. This approach is adopted to avoid sample overlap and potential interference, thereby ensuring the clarity and robustness of the empirical results. The regression outcomes are presented in Table 14.

Table 14 Results of Heterogeneity Analysis.

As shown in Table 14, the aerospace manufacturing industry (Cr1 = -5.349, p < 0.05) exhibits the most pronounced negative effect. This result is highly consistent with the defining characteristics of the sector, such as extremely high capital requirements, prolonged research and development cycles, and a predominantly state-owned ownership structure. These institutional features may lead to excessive concentration of equity, thereby amplifying agency costs and hindering market-oriented resource allocation and innovation dynamism.

The electronic and communication equipment manufacturing industry (Cr1 = -1.939, p < 0.05) also experiences a significantly adverse impact. This sector is characterized by rapid technological advancement and intense global competition. A more dispersed ownership structure may facilitate agile decision-making, efficient risk distribution, and stronger incentives for innovation. In contrast, high equity concentration may prove incompatible with the fast-paced and dynamic nature of this competitive environment.

In the pharmaceutical manufacturing industry (Cr1 = -0.280, insignificant) and the instrument and meter manufacturing industry (Cr1 = 0.512, insignificant), equity concentration does not appear to be a decisive determinant of firm value. Instead, value creation in these industries may depend more critically on intangible assets—such as patent protections, R&D pipelines, precision manufacturing capabilities, and specialized technical expertise. Consequently, the influence of ownership structure on firm performance may be relatively limited, indirect, or contingent upon specific contextual factors.

The aerospace industry exhibits the highest relative model explanatory power (R² = 0.387), indicating that conventional financial and governance variables—specifically equity structure, firm size, and leverage—have substantial explanatory capacity for corporate value within this sector. In contrast, the models for the pharmaceutical manufacturing industry (R² = 0.132) and the instrument and meter manufacturing industry (R² = 0.084) demonstrate markedly limited explanatory power. This suggests that corporate value in these two industries is primarily driven by factors not captured by the model, such as intangible assets and specialized competencies, including patent portfolios, stages of clinical trials, core technical expertise, software ecosystems, and data accumulation.

Discussion

A negative correlation exists between equity concentration and enterprise value

Regression analysis based on Models (1) and (5) reveals a significant negative relationship between ownership concentration and corporate value among listed high-tech manufacturing firms. Specifically, the combined ownership stake of the top five shareholders (CR3) exerts a more pronounced negative effect on corporate value, with a coefficient of -2.757, whereas the ownership share of the largest shareholder (CR1) exhibits a relatively weaker impact, with a coefficient of -2.061. Descriptive statistics show that the mean values of CR1 and CR3 are 0.297 and 0.484, respectively, indicating that the aggregate ownership held by the top five shareholders in the sample firms approaches 50%. This high level of ownership concentration suggests strong de facto control over corporate decision-making, which in turn significantly undermines corporate value.

In contrast to the widely reported “positive correlation” findings in existing literature, this study identifies a negative association between corporate governance—proxied by ownership concentration—and pandemic resilience. This divergence primarily arises from the study’s sample period (2019–2023), which coincided with the global economic downturn during the COVID-19 pandemic. External shocks during this period substantially increased operational risks for firms, particularly for high-tech enterprises facing challenges such as prolonged R&D cycles and high capital intensity. Under such conditions, more dispersed ownership structures prove advantageous, as they enhance firms’ risk resilience and mitigate the potential for controlling shareholders to exploit their power at the expense of minority shareholders. These structural benefits ultimately support value preservation and creation during periods of crisis.

Regarding control variables, the results of Models (1) and (5) remain robust and consistent. The coefficient of firm size (SIZE) is significantly negative, ranging from − 3.565 to − 3.865, indicating that larger firms may face valuation discounts, likely due to heightened managerial complexity, reduced operational flexibility, and constrained growth prospects. The growth variable (GROWTH) displays a significantly positive coefficient (0.144 to 0.167), suggesting that stronger revenue growth capacity exerts a positive influence on corporate value. The coefficient of financial leverage (LEV) is positive (1.525 to 1.472), implying that debt financing may contribute to firm value enhancement through tax shield benefits. Nevertheless, this also serves as a cautionary note: excessively high leverage levels could amplify financial risks, underscoring the need for careful evaluation in light of each firm’s specific financial context. The coefficient of return on equity (ROE) is significantly positive (0.248 to 0.290), confirming that profitability plays a pivotal role in determining market valuation. This finding indicates that, after controlling for other factors, firms exhibiting higher capital efficiency are more favorably assessed by the capital market, as reflected in their elevated valuation multiples. Furthermore, the consistently positive coefficients of both ROE and GROWTH suggest that market participants place substantial emphasis on both profit quality and growth potential when appraising corporate value.

In the robustness test, after replacing the dependent variable—Tobin’s Q—with market value, the coefficient of CR1 in Model (9) was found to be -0.225 (p < 0.01). When the industry–year mean was used as an instrumental variable and the two-stage least squares (2SLS) method was applied to address the endogeneity issue of CR1 in the regression models, the estimated coefficient of CR1 remained negative (-13.105, p < 0.01). These results further confirm a significant negative relationship between equity concentration in high-tech manufacturing firms and firm value during the pandemic period. Moreover, after accounting for endogeneity, the adverse effect of CR1 on firm value (Q) is substantially larger than that obtained from the OLS estimates, indicating that OLS significantly underestimates the true magnitude of CR1’s negative impact.

Generally, high-tech enterprises are characterized by technological intensity, high risk, substantial R&D investment, and pronounced market volatility. Their valuation is influenced not only by ownership structure but also heavily shaped by corporate growth prospects, financial policies, and stages in the corporate life cycle. Expanding core operations, enhancing operational maturity, and employing leverage prudently can contribute positively to firm value. However, during the COVID-19 pandemic, heightened environmental uncertainty intensified major shareholders’ risk-averse behavior, thereby exacerbating the “innovation-suppressing effect.” As a result, even within moderate levels of ownership concentration, the net effect of equity concentration on firm value turned out to be negative.

No significant inverted U-shaped relationship exists between equity concentration and enterprise value

This study employs regression analysis to examine whether an inverted U-shaped relationship exists between equity concentration and the value of high-tech enterprises. Specifically, in the baseline model (2), the coefficients for the shareholding ratio of the largest shareholder (Cr1) and its squared term are − 0.394 and − 2.646, respectively, neither of which is statistically significant. In the robustness test model (6), where the core explanatory variables are replaced, the coefficient of the linear term for the ownership concentration of the top five shareholders (Cr3) is 1.270 (P > 0.1), while the coefficient of the squared term is − 4.426 (significant at the 5% level, but the insignificance of the linear term undermines the interpretation). Thus, the results still fail to support an inverted U-shaped pattern. In robustness model (10), with the dependent variable substituted, the coefficients for Cr1 and its squared term are 0.011 and − 0.375, respectively—both statistically insignificant. These findings consistently indicate that, regardless of whether Tobin’s Q or alternative measures of firm value are used as the dependent variable, the effect of equity concentration on the value of high-tech enterprises does not follow an inverted U-shaped nonlinear trajectory characterized by an initial increase followed by a decline.

To address potential endogeneity concerns, instrumental variable methods were employed; however, no evidence was found to support the existence of an inverted U-shaped relationship. Overall, the empirical results do not lend support to the hypothesis of an inverted U-shaped association between equity concentration and corporate value in high-tech manufacturing firms.

Research and development investment serves as a partial mediator in the relationship between equity concentration and corporate value

Through a series of empirical analyzes, this study employs Chinese high-tech manufacturing enterprises as the sample to systematically investigate the influence mechanism of equity concentration on firm value, with a particular focus on examining the mediating role of R&D investment. The main findings are as follows:

First, a preliminary verification of the mediating effect is conducted using traditional regression analysis. Stepwise regression results indicate that equity concentration (Cr1) significantly suppresses R&D investment (coefficient = − 1.472, p < 0.01), while R&D investment exerts a significant positive effect on firm value (coefficient = 0.250, p < 0.05), suggesting a potential mediation pathway. However, as elaborated below, this finding is sensitive to the method used for endogeneity correction and should be interpreted with caution. The proportion of the indirect effect through R&D investment accounts for 18.07% of the total effect. This result aligns with the predictions of the resource-based view and agency theory, suggesting that concentrated ownership may reinforce the conservative decision-making tendencies of major shareholders, leading them to avoid long-term, high-risk R&D activities, which in turn undermines the firm’s capacity to achieve competitive advantage through innovation.

Second, robustness checks enhance the reliability of the observed mediating effect. By implementing alternative specifications—including substituting the independent variable (Cr3 for Cr1), replacing the dependent variable (market value for Tobin’s Q), and adjusting model specifications—the mediating role of R&D investment remains consistently significant and positively signed, with stable coefficient magnitudes and directions. These results demonstrate that the findings are robust across different measurement approaches and modeling frameworks.

Third, the results after correcting for endogeneity. To address the potential endogeneity between equity concentration and R&D investment, this study employs the instrumental variable approach, using industry-year averages as instrumental variables, to re-estimate the model. The findings indicate that although the negative effect of equity concentration on R&D investment remains statistically significant (significant in Path a), the effect of R&D investment on firm value becomes insignificant (not significant in Path b), and the statistical test for the indirect effect is also non-significant. This suggests that, after accounting for endogeneity, the mediating role of R&D investment in the relationship between equity concentration and firm value is not empirically supported. The lack of significance in the IV mediation test suggests that R&D may not be a causal channel, or that its effect is confounded by unobserved factors.

The macroeconomic environment alleviated the adverse effects of equity concentration on enterprise value during the epidemic

Although the results of the two-way fixed-effects model in the robustness test do not achieve statistical significance, those from the individual fixed-effects model support theoretical robustness by accounting for time-invariant individual heterogeneity. The lack of significance in the two-way fixed-effects model suggests that the effect of equity concentration on enterprise value may be confounded with common time-trend factors over the sample period and is thus more vulnerable to macro-level common shocks. Nevertheless, the long-term within-individual effects remain statistically significant. Overall, this study maintains that the influence of equity concentration on firm value does exist, although its significance may be moderated by the prevailing macroeconomic environment during specific periods.

To examine the moderating effect during the epidemic period, an interaction term between the epidemic dummy variable and equity concentration was incorporated into the regression model. The empirical results reveal a significantly positive coefficient for this interaction term, indicating that the COVID-19 pandemic substantially attenuated the negative relationship between equity concentration and firm value. This finding carries important theoretical and practical implications: the efficacy of corporate governance mechanisms—particularly equity structure—is not invariant, but rather contingent upon external environmental conditions.

Under normal circumstances, a dispersed ownership structure tends to be more conducive to effective checks and balances as well as stronger innovation incentives, thereby better aligning with the strategic demands of high-technology industries. However, during periods of crisis—such as the pandemic—a concentrated ownership structure enhances firms’ abilities in rapid decision-making, resource mobilization, and risk absorption, thereby partially mitigating associated agency costs. In the context of systemic shocks, concentrated ownership can function as an effective crisis management mechanism, enabling firms to respond more swiftly and decisively to heightened uncertainty.

Moreover, robustness checks employing both market-based valuation metrics (Tobin’s Q) and accounting-based performance measures (ROA, ROE) consistently support the moderating effect observed, indicating that this influence extends beyond investor perceptions to impact tangible operational performance outcomes.

Industry heterogeneity serves as a critical moderating factor in the relationship between equity concentration and enterprise value

An analysis of sub-sectors reveals substantial heterogeneity, indicating that no single governance model is universally optimal. The effect of equity concentration was significantly negative only in the aerospace (Cr1 = − 5.349) and electronic communication equipment manufacturing industries (Cr1 = − 1.939), while it was statistically insignificant in the pharmaceutical and instrumentation manufacturing sectors. This divergence can be attributed to the distinct technical and economic characteristics of each sub-industry: the former are capital-intensive, characterized by rapid technological iteration and a high degree of collaboration, which makes dispersed ownership more conducive to performance. In contrast, the value creation in the latter industries relies more heavily on proprietary assets such as patents and R&D pipelines, rendering the influence of corporate governance structures either more complex or of secondary importance.

Meanwhile, the value-creation logics across industries differ markedly. For instance, leverage (lev) serves as a strong positive signal (coefficient = 8.392) in the aerospace industry, yet this effect is not observed in other sectors. The notably low explanatory power of the model in the pharmaceutical and instrumentation industries—evidenced by within R² values of only 0.132 and 0.084, respectively—strongly suggests that key value drivers, such as intellectual property and human capital, are not adequately captured by conventional financial and governance variables. The low R² in pharmaceutical and instrument manufacturing sectors suggests that firm value in these industries is driven by unobserved factors such as patents, R&D pipelines, or human capital. Future research should incorporate industry-specific value drivers.

Further investigation into potential mechanisms and pathways of influence

There are primarily three potential transmission mechanisms by which equity holders may reduce R&D investment during an epidemic:

First is the risk-avoidance mechanism. Equity holders exhibiting a high degree of ownership concentration—particularly founders or family shareholders—typically have a significant portion of their personal wealth closely tied to the firm’s performance, resulting in lower risk tolerance. Faced with the heightened uncertainty and market volatility brought about by the epidemic, such owners are more likely to adopt a “survival-first” strategy. Consequently, they tend to curtail long-term, high-risk exploratory investments, such as research and development (R&D), to mitigate the likelihood of incurring irreversible capital losses. Our empirical findings reveal a stronger negative association between higher levels of ownership concentration and reductions in R&D expenditure, offering indirect support for the risk-averse motivation.

Second is the liquidity constraint mechanism. Grounded in resource dependence theory, this mechanism posits that concentrated ownership intensifies firms’ sensitivity to the availability of internal funds when making investment decisions. During crises—such as the COVID-19 pandemic—operating cash flows contract sharply, while external financing becomes significantly more costly or altogether inaccessible, thereby generating acute liquidity pressure. Controlling shareholders—particularly those whose personal wealth is undiversified—confront a stark trade-off between ensuring short-term solvency and sustaining long-term innovation. This mechanism operates through two interrelated channels. First, the direct cash flow channel: firms experiencing cash flow shocks must prioritize working capital maintenance and debt servicing over discretionary R&D expenditures. Concentrated ownership empowers major shareholders to enforce such prioritization swiftly, often overriding managerial preferences to preserve innovation investment. Second, the external financing channel: firms with high leverage or limited collateral face binding credit constraints, impeding their ability to smooth R&D spending through debt markets. Concentrated ownership may further exacerbate these constraints if controlling shareholders resist equity dilution to avoid loss of control or wealth erosion. The strength of this mechanism hinges on three boundary conditions: (1) pre-crisis financial health—including cash reserves and debt maturity structure; (2) the severity of industry-specific demand shocks; and (3) the availability and accessibility of government support programs. These conditions indicate that liquidity constraints do not operate uniformly but instead interact dynamically with both firm-specific characteristics and broader macroeconomic factors.

Third is the control consolidation mechanism. R&D investment is inherently specialized and associated with significant information asymmetry, which can exacerbate agency conflicts between external investors and internal controllers. The intensified performance pressures during the epidemic may heighten the vigilance of controlling shareholders. Such shareholders may fear that failed R&D projects could trigger declines in stock prices, attract greater external scrutiny, or even provoke challenges to their control. Consequently, reducing R&D expenditure may serve as a defensive strategy to mitigate external criticism and reinforce their control position.

These three mechanisms are not mutually exclusive and may interact synergistically in practice. While we have provided theoretical elaboration of these three mechanisms, we acknowledge that our empirical design does not allow direct testing of them. The subsample analyses reported in Sect.  3.4.5 offer only indirect, suggestive evidence. Future research with richer micro-level data—such as founder ownership, credit registry data, or survey-based measures of managerial risk preferences—is needed to systematically test these pathways and establish causal mediation.

Conclusion

This study investigates the impact of equity concentration on corporate value within China’s high-tech manufacturing industry during the COVID-19 pandemic (2019–2023), with a particular focus on the mediating role of R&D investment and the moderating effect of the crisis context. Utilizing a balanced panel of 642 listed firms and employing both fixed-effects and instrumental variable estimations, we arrive at several nuanced findings that contribute to the corporate governance literature under extreme external shocks.

First, this study identifies a significant negative causal relationship between equity concentration and firm value. After rigorously addressing endogeneity using instrumental variables, the estimated effect (β = -13.105, p < 0.01) is substantially larger than the OLS estimate (β = -2.061), indicating that conventional methods severely underestimate the adverse impact of concentrated ownership. This finding suggests that during periods of heightened uncertainty, the “expropriation effect” or “innovation suppression effect” of dominant shareholders outweighs the potential benefits of enhanced monitoring. The result is robust across alternative measures of equity concentration and firm value.

Second, the evidence regarding the mediating role of R&D investment is inconsistent and sensitive to methodological choices. While traditional stepwise regression suggests a partial mediation effect accounting for approximately 18–23% of the total effect, instrumental variable analysis fails to confirm a statistically significant indirect effect after correcting for endogeneity. This discrepancy indicates that the mediation claim is sensitive to endogeneity corrections and should be interpreted with caution. Failure to account for endogeneity in mechanism testing may lead to overestimation of indirect pathways. Consequently, while R&D investment appears to transmit a portion of the negative effect in economic terms, we cannot claim statistical support for R&D as a causal mediating channel. The adverse impact of equity concentration likely operates through direct effects and other unobserved mechanisms, such as risk aversion, liquidity constraints, or controlling shareholders’ short-term orientation during crises.

Third, contrary to several prior studies, we find no reliable evidence supporting an inverted U-shaped relationship between equity concentration and firm value. The high multicollinearity between linear and quadratic terms (correlation > 0.97) renders coefficient estimates unstable and uninterpretable. This null finding suggests that within the context of an extreme exogenous shock, the relationship may approximate a linear negative function rather than a non-linear curve. Future research employing orthogonal polynomials or larger samples may further investigate this issue.

Fourth, the relationship between equity concentration and firm value exhibits significant sensitivity to time effects and macroeconomic conditions. In the two-way fixed-effects model controlling for year-specific shocks, the coefficient of equity concentration becomes statistically insignificant. This pattern suggests that the effect of ownership structure is partially confounded with common time trends—such as pandemic waves, policy interventions, or market-wide fluctuations—and that its detectable impact may be temporarily attenuated during periods of systemic crisis. However, the interaction analysis confirms that the pandemic significantly moderated the negative relationship: the marginal effect of equity concentration during the crisis was substantially weaker than under normal conditions. This finding implies that concentrated ownership may offer crisis-management benefits—such as faster decision-making and resource mobilization—that partially offset its governance drawbacks during stable periods.

Fifth, substantial industry heterogeneity underscores the context-dependence of governance mechanisms. The negative effect of equity concentration is most pronounced in capital-intensive, fast-cycling industries such as aerospace (β = -5.349) and electronic communication equipment (β = -1.939), where dispersed ownership may facilitate agile decision-making and risk-sharing. In contrast, in knowledge-driven sectors such as pharmaceuticals and instrument manufacturing, where value creation hinges on intangible assets like patents and R&D pipelines, equity concentration exerts no significant direct effect. The low explanatory power of conventional financial variables in these industries (within R² as low as 0.084) suggests that industry-specific value drivers must be incorporated into governance research.

These findings carry important implications for theory, practice, and policy. Theoretically, this study demonstrates that the efficacy of corporate governance mechanisms is not invariant but highly contingent on external environmental conditions. The “monitoring incentive effect” and “expropriation effect” of concentrated ownership are not static trade-offs; their net outcome shifts with macroeconomic uncertainty. This situational dependence calls for more dynamic theories of governance that incorporate crisis contexts. For corporate managers and investors, the implications are twofold. First, assessments of ownership structure should account for prevailing economic conditions. During crises, a firm’s resilience and shock-absorption capacity may matter more than conventional governance metrics. Second, the industry context is critical: in capital-intensive sectors, ownership dispersion may enhance value; in knowledge-intensive sectors, governance reforms should prioritize transparency and intellectual property protection over structural ownership adjustments. For policymakers, this study cautions against one-size-fits-all prescriptions for ownership structure design. For capital-intensive industries like aerospace and electronics, policymakers should encourage dispersed ownership to enhance innovation and risk-sharing. In contrast, for industries where intangible assets dominate, governance reforms should focus on transparency and investor protection rather than ownership dispersion. During crises, temporary governance flexibility (e.g., allowing faster decision-making) may be more effective than structural changes. Moreover, policy interventions aimed at promoting innovation should consider the current phase of the economic cycle. In industries where value is driven by intangible assets, policies should focus on strengthening patent protection, R&D incentives, and human capital development rather than merely adjusting ownership concentration.

This study has several limitations that warrant acknowledgment and future investigation. First, the sample is restricted to A-share listed companies, excluding unlisted high-tech firms that may exhibit different governance dynamics. Second, despite employing instrumental variables, the possibility of omitted variable bias cannot be entirely eliminated. Third, the findings are derived from a single external shock—the COVID-19 pandemic—and their generalizability to other crisis contexts (e.g., financial crises, technological disruptions) requires further validation. Fourth, while we propose risk aversion, liquidity constraints, and control consolidation as potential mechanisms through which equity concentration affects R&D investment, data limitations prevent us from conducting mutually exclusive tests of these pathways. Our dataset does not include granular information on founder ownership, credit line utilization, bank–firm relationships, or managerial risk preferences—key variables required for the clean identification of these underlying mechanisms. Future research could advance understanding by leveraging longitudinal case studies, cross-industry comparative analyses, and richer micro-level data sources—such as credit registry records, validated survey instruments, or hand-collected founder-level data—to systematically disentangle these mechanisms.

In conclusion, this study provides robust causal evidence that equity concentration negatively affects firm value in China’s high-tech manufacturing sector during the pandemic, while revealing that this relationship is moderated by industry characteristics and macroeconomic conditions. The findings underscore the need for context-sensitive governance research and practice, moving beyond universal prescriptions toward tailored approaches that account for industry logic and crisis dynamics.