The Effect of ESG on Firms’ Product Market Performance and Supply Chain Spillover Effects

Abstract

In product manufacturing and operations, firms increasingly treat Environmental, Social, and Governance (ESG) ratings as strategically important. This differs from earlier views that framed ESG mainly as a burden, whereas recent studies suggest that ESG can enhance firm value. Using panel data on Chinese A-share listed firms over 2009–2022, this study examines whether ESG ratings affect product-market performance. A two-way fixed-effects model shows that better ESG ratings significantly increase market share, mainly by signaling stronger product quality and service capability. While findings from this emerging market context may have limited generalizability, results consistently show that ESG performance bolsters competitiveness, particularly in high-tech and consumer-facing sectors. Moreover, improvements in ESG ratings are positively associated with net market-share growth. The benefits extend beyond the focal firm and generate positive spillovers for downstream customers. The three ESG dimensions do not contribute equally: the Environmental (E) and Governance (G) dimensions exert stronger effects on product-market performance than the Social (S) dimension. This study provides a new perspective on understanding the value creation mechanism of ESG investment.

1. Introduction

Against the backdrop of intensifying climate challenges and rising social inequality, ESG investing has drawn increasing global attention [1,2]. According to the Global Sustainable Investment Alliance (GSIA), ESG investments in major economies—including the United States, the European Union, Canada, Australia, New Zealand, and Japan—reached USD 30.3 trillion in 2022, representing a 128% increase over 2012 and an average annual growth rate of 8.6%. As environmental, social, and governance considerations become more deeply embedded in corporate operations and management, ESG is increasingly linked to firm identity and value creation. Yang et al. [3] show that ESG practices improve information transparency, lower operating risk, and restrain managerial short-termism, thereby helping firms obtain trade credit and alleviate financing constraints. ESG can also strengthen capital-market support. Dhaliwal et al. [4] find that firms disclosing social-responsibility information are more likely to attract institutional investors than otherwise comparable non-disclosing firms. In addition, ESG has been shown to promote technological innovation [5], strengthen resilience [6,7], improve labor-investment efficiency [8], and attract talented employees [9]. Distinguishing itself from traditional Corporate Social Responsibility (CSR), which emphasizes ethical voluntarism, ESG integrates Governance into its core framework, marking a paradigm shift from qualitative narratives to quantitative risk management [10]. This evolution enables non-financial performance to translate more directly into financial materiality. At the same time, the literature also points to a potential “dark side” of ESG. Wu et al. and Kim et al. [11,12] argue that ESG controversies can damage reputational capital and significantly reduce firm value. Derrien et al. [13] further show that negative ESG news depresses stock prices by weakening analysts’ expectations of future cash flows, while La Rosa and Bernini [14] find that such controversies can substantially raise the cost of equity capital. Since firm value growth ultimately depends on sales and profits, and since ESG practices are closely related to product attributes, it is important to ask whether ESG affects product-market performance. To our knowledge, this question has not been examined directly. This study, therefore, investigates ESG value creation from a product-market perspective.

We argue that ESG practices can improve a firm’s product-market performance through multiple channels. From the perspective of stakeholder theory [15], firms may strengthen ESG performance to meet the expectations of key stakeholders, including consumers who prefer environmentally friendly products [16] and investors who value long-term risk reduction [17]. This can generate customer loyalty and financial support, both of which strengthen product-market competitiveness. From the resource-based view [18], ESG practices also promote green technological innovation [19] and help firms attract high-quality talent [9]. These efforts allow firms to accumulate rare and hard-to-imitate technological and human resources, thereby building differentiated competitive advantages and expanding market share. Accordingly, this paper addresses three questions: whether ESG ratings improve product-market performance, how large that effect is, and through which mechanisms it operates.

To answer these questions, we examine Chinese A-share listed firms across a wide range of sectors, including manufacturing, services, and high-tech industries. The sample spans 2009–2022, a period during which ESG investing developed rapidly in China. To enhance identification, the model controls for time-invariant firm characteristics, common macroeconomic shocks, and firm-level factors such as size, financial condition, and governance characteristics. Our empirical analysis uses leading domestic third-party ESG rating systems [20]. The dependent variable is product-market share measured under the industry-classification standards of the China Association for Public Companies [21,22].

The results indicate that an improvement in a firm’s ESG rating significantly increases product-market share. To assess generalizability, we perform cross-validations using ESG ratings from alternative providers and recompute competitive positions under multiple industry-classification standards. After controlling for unobserved firm heterogeneity and year effects, the positive association between ESG performance and product-market competitiveness remains robust. These findings suggest that ESG practices operate not only as a compliance requirement but also as a strategic resource that can strengthen market position.

In the empirical analysis, we implement multiple robustness checks to address potential endogeneity concerns. Simulation exercises indicate that the observed market-share gains are unlikely to be driven by random variation. In addition, exogenous-shock tests and instrumental-variable estimates support a causal interpretation of the link between ESG performance and market share. By comparing matched firms with similar characteristics, we also show that the positive ESG effect is not merely attributable to differences in firm size or financial condition. Taken together, these tests strengthen the reliability of our conclusions.

To examine the underlying mechanisms, this study draws on signaling theory and the resource-based view and focuses on two channels: product quality and service capability. Prior research suggests that strong non-financial performance can reduce information asymmetry and attract innovative resources [18]. Our results indicate that high ESG ratings send favorable signals to the market, encourage green innovation, and thereby enhance product competitiveness. At the same time, stronger ESG practices improve service capability by optimizing human-capital structure, which in turn raises customer satisfaction and expands market presence.

In addition, this study shows that the value-creation effect of ESG differs across firm types. In technology-intensive industries, the impact on market standing is more pronounced as ESG performance helps attract top-tier talent and accelerate innovation. From a value-chain perspective, firms closer to end-consumers can better leverage a responsible brand image to gain consumer trust, effectively translating ESG advantages into stronger purchase intentions.

We also examine three related issues: whether ESG has a long-term effect, whether ESG practices generate spillover effects, and whether the three ESG dimensions create value to different degrees. The results indicate that the contribution of ESG to market share persists over time rather than reflecting a temporary fluctuation. Importantly, the positive effect is not limited to the focal firm but also spills over along the supply chain and improves the market performance of downstream customers. In addition, the three ESG pillars do not contribute equally to market competitiveness, with the environmental and governance dimensions exerting the strongest influence.

This study makes three principal contributions. First, it extends the ESG value-creation literature by focusing on product-market competition. Second, it introduces value-chain position as an additional lens for understanding heterogeneity in corporate ESG strategies. Third, it documents spillover effects within the supply chain and thereby provides evidence on the broader externalities of corporate sustainability strategies.

2. Literature and Hypothesis Development

2.1. The Impact of ESG Ratings on Firm Performance

Existing studies mainly examine how ESG ratings affect firms in areas such as financial performance, capital markets, risk management, operating efficiency, and technological innovation. The relationship between ESG and financial performance remains contested. Most studies report a positive association between ESG performance and firms’ financial outcomes [23,24]. For instance, Friede et al. [25], after reviewing more than 2000 empirical studies, concluded that roughly 90% document a positive link between ESG performance and financial results. However, some studies present opposing views. Using a sample of 104 multinational firms from Chile, Brazil, Mexico, Colombia, and Peru over 2011–2015, Grisales and Aguilera-Caracuel [26] find a significant negative relationship between ESG scores and financial outcomes. Additionally, Andreou and Kellard [27] also show, using data from the EU Emissions Trading System, that greenhouse-gas reduction efforts may coincide with weaker financial performance. Other studies suggest that the relationship may be nonlinear or statistically insignificant. For example, Nollet et al. [28] document substantial heterogeneity across ESG dimensions. The governance (G) dimension exhibits a U-shaped relationship with financial outcome, while the environmental (E) and social (S) dimensions show no significant correlation.

By contrast, there is much broader agreement that ESG improves capital-market outcomes. Recent studies suggest that ESG can mitigate the adverse effect of the COVID-19 shock on stock-market capitalization [29,30,31]. ESG also improves stock liquidity [32,33] and reduces crash risk [34]. ESG also contributes positively to risk management. Wu et al. [35] show that ESG improves firms’ risk-bearing capacity by lowering information asymmetry and increasing investment efficiency. From the export perspective, Ma et al. [6] find that ESG strengthens firm reputation, relaxes financing constraints, and improves export resilience, thereby enhancing firms’ ability to withstand external shocks. ESG also improves firms’ operational efficiency on multiple fronts. ESG also improves operational efficiency on multiple dimensions. Studies show that firms with higher ESG ratings exhibit better labor-investment efficiency [36], higher total factor productivity [37], and greater employee satisfaction [38]. One of the key challenges to operational efficiency is the agency conflict between executives and shareholders. He et al. [39] point out that ESG practices can restrain managerial misconduct and support sustainable development. ESG also impacts firm operations through technological innovation. Gao et al. [5] find that ESG promotes green innovation by reducing the cost of debt financing, mitigating management’s tendency towards short-termism, and optimizing supply chain financing. Hao et al. [40] likewise find that ESG promotes digital innovation.

2.2. Determinants of Product Market Performance and the Signaling Advantages of ESG

Product-market performance is a core indicator of firm competitiveness and is shaped by multiple factors. Traditional economic theories emphasize the roles of price, quality, and brand equity in shaping product market performance [41,42,43]. For example, firms often enhance product appeal by increasing technological content or introducing design innovations to gain a competitive edge [44,45]. Huang and Sarigöllü [46], using survey data and market data together with causal-inference methods, show that brand awareness positively affects product-market share. With the development of consumer-behavior theory, scholars have increasingly examined demand-side factors such as customer satisfaction [47,48,49], firms’ social responsibility perception [50], and perceived customer value [51]. For instance, Rego et al. [48] find that firms with higher customer satisfaction tend to have greater market share, especially when switching costs are low. Bae et al. [50] show that strong firms’ social responsibility helps highly leveraged firms reduce market share losses. Rahman et al. [51] reveal a positive relationship between environmental performance and market share, which becomes stronger when customers have a higher perceived value of a firm’s products.

Firms’ capital structure is another key factor. Studies by Campello [52] and Opler and Titman [53] find that highly leveraged firms face greater financial vulnerability and higher bankruptcy risk. As a result, customers may worry about post-purchase service reliability, leading to market share decline. Similarly, Fresard [54] finds a significant positive correlation between cash holdings and product market performance. Some studies explore product market performance from other perspectives. Through a quasi-natural experiment based on the merger of financial institutions, He and Huang [55] demonstrate that cross-shareholdings promote direct cooperation between firms and increase market share. Liu et al. [56] show that direct investment in suppliers for technological upgrades enhances firms’ product market performance. A review of the literature shows that existing studies have examined a wide range of factors affecting product market performance, including traditional elements (e.g., price and quality), financial indicators (e.g., leverage and cash holdings), and non-financial indicators (e.g., firms’ social responsibility and environmental performance).

According to signaling theory, a signal is credible when low-quality firms face high costs of imitation, that is, when “cost asymmetry” exists [57]. Compared to traditional signals such as advertising expenditures, which involve lower sunk costs and are highly susceptible to manipulation [58], ESG practices—encompassing green supply chain transformation and governance structure optimization—entail significant irreversible sunk costs and compliance risks. This prohibitive imitation cost enables ESG ratings to facilitate a “separating equilibrium,” effectively distinguishing high-quality firms with long-term operational stability from those merely employing short-term public relations to mask underlying issues. Consequently, this mechanism fosters deeper consumer trust within the product market [59].

Furthermore, this ESG-based value creation is extending across supply chain networks. Literature indicates that a firm’s sustainability performance generates significant positive externalities. For instance, informal institutions like social trust can drive collaborative ESG improvements across the industrial chain [60]. Regarding financial interconnections, credit or liquidity improvements of one party can support partners through transactional relationships [61]. Similarly, investments in green innovation significantly enhance the trade credit acquisition capabilities of affiliated firms [62]. Moreover, increased supply chain transparency amplifies the signaling effects of ESG performance, strengthening partners’ financial outcomes [63]. In summary, although existing research has identified the risk-mitigating and credit-enhancing roles of ESG within supply chains, direct evidence on whether ESG expands downstream customers’ market share through signaling mechanisms remains limited. This study seeks to address that gap.

2.3. Research Hypothesis

As sustainable development receives greater global attention, firms’ ESG performance has become an increasingly prominent public concern [64,65]. In recent years, more and more firms have incorporated ESG into their strategic planning to enhance core competitiveness. This paper, grounded in stakeholder theory and resource-based theory, examines how firms’ ESG ratings influence product market performance.

First, according to Stakeholder Theory [15], corporate ESG practices enhance product market competitiveness by fulfilling the value propositions of two pivotal stakeholders: customers and investors. At the consumer level, proactive ESG engagement bolsters brand image, recognition, and loyalty [66], thereby increasing purchase intentions and improving overall market performance [67,68]. Furthermore, ESG initiatives help alleviate the moral anxiety consumers experience when confronted with negative social externalities, such as environmental pollution, which in turn facilitates repurchase intentions [16]. At the investor level, ESG disclosure reduces information-processing costs and attracts value-oriented institutional investors and analysts. Research indicates that firms actively disclosing such information secure substantially larger financing scales compared to non-disclosing peers [4,17]. By obtaining financial support from investors, firms can strengthen their cash flow positions and fund research and development projects; this resource security derived from capital markets ultimately translates into sustained product competitiveness within the market.

Barney [18]’s Resource-Based View (RBV) argues that a firm’s sustainable competitive advantage stems from its control over VRIN resources—those that are Valuable, Rare, Imperfectly Imitable, and Non-substitutable. ESG practices can generate economic value by improving production efficiency and reducing pollution [69]. ESG also drives green innovation, creating rare and hard-to-replicate resources [19]. Furthermore, the effectiveness of ESG depends on a unique organizational culture [70] and high-quality management [71]. Strong ESG performance reflects the presence of these inimitable cultural and human resources, providing firms with a sustained competitive edge. These advantages help firms differentiate themselves from competitors and gain greater market share. Therefore, we propose the following hypothesis:

Hypothesis 1:

A higher ESG rating leads to better product market performance for a firm.

Product quality and service capability are two important determinants of a firm’s product-market performance. With respect to product quality, higher-quality products improve customer satisfaction, increase repeat purchases, strengthen brand image, and ultimately raise market share [72]. In highly competitive markets, offering superior products gives firms a competitive edge and helps expand their market share [73,74]. Regarding service capability, studies show that high-quality service significantly improves customer experience, increases customer retention in competitive environments, and drives growth in product market share [49,75,76]. However, due to information asymmetry, customers often cannot assess a firm’s product quality and service capability before making a purchase. This creates search costs and may limit purchasing behavior. Finding effective ways to reduce this information gap becomes a key challenge for improving product market performance.

According to signaling theory, ESG practices may offer a potential solution. Signaling theory, introduced by Spence [57], explains how individuals or organizations convey unobservable characteristics to stakeholders through observable signals in situations of information asymmetry. We argue that ESG can serve as a credible signal of both product quality and service capability, thereby reducing information asymmetry. Regarding product quality, ESG ratings reflect long-term competitiveness and lower information-screening costs for institutional investors, which makes it easier to attract long-term value-oriented investors [17,77]. This improves access to capital for product development and quality upgrading. Firms with high ESG ratings also tend to show stronger social responsibility and greater investment in green R&D [5], which can support the production of safe, low-carbon, and environmentally friendly products [19,78]. Consumers may therefore associate strong ESG performance with higher product quality, safety, and environmental reliability [79,80]. Regarding service capability, firms with strong ESG ratings often invest more in employee development by improving working conditions and career advancement opportunities. For example, some firms provide training programs and strengthen corporate governance so that employee feedback is heard and valued, which raises employee satisfaction and productivity [38]. Higher employee satisfaction usually translates into better customer service, improved customer satisfaction, and stronger sales [81]. Strong ESG ratings can also attract talented employees and thereby improve service capability [9]. Based on the above theoretical analysis, ESG practices serve as effective signals of product quality and service capability, which enhance a firm’s product market performance. Therefore, we propose the following hypotheses:

Hypothesis 2:

A firm’s ESG rating positively influences product market performance by signaling product quality.

Hypothesis 3:

A firm’s ESG rating positively influences product market performance by signaling service capability.

3. Research Design

3.1. Sample Selection and Data Sources

This study uses Chinese A-share listed firms from 2009 to 2022 as the initial sample. We begin in 2009 because ESG rating data for Chinese A-share-listed firms are available from that year onward. We end in 2022 for two reasons. First, because corporate financial data and ESG ratings are released with a lag, 2022 is the latest complete fiscal year for which comprehensive financial indicators, industry-classification data, and supply-chain linkage information are jointly available. This helps preserve the rigor of lagged-variable design and robustness tests. Second, because China’s ESG disclosure standards were substantially adjusted after 2023, restricting the sample to 2022 helps us examine the long-run market effects of ESG within a relatively stable regulatory environment and reduces interference from short-term policy changes. We apply three screening rules: (1) financial firms are excluded because their accounting rules, business models, capital structures, and sales-based measures of market share are not directly comparable to those of non-financial firms; (2) firms with ST, *ST, or PT status during the sample period are removed; and (3) observations with missing key variables are deleted. To limit the influence of outliers, all continuous variables are winsorized at the 1st and 99th percentiles. The final sample contains 48,767 firm-year observations from 4876 firms.

This study relies on several databases. The main ESG measure is taken from Sino-Securities Index Information Service (Shanghai) Co., Ltd. (the “Sino-Securities ESG Index”). This index covers all Chinese A-share listed firms, provides a long historical series beginning in 2009, and uses an industry-weighted scoring method that aggregates bottom-level indicators into issue, theme, and E/S/G dimension scores. The resulting firm-level ESG score is reported on a 100-point scale, with ratings ranging from AAA to C. This database is widely used in the related literature [20,82]. Although domestic rating systems may differ from international standards such as MSCI and Bloomberg in indicator weighting, the CSI index is more closely aligned with China’s institutional environment, including policy-driven social responsibilities, and therefore provides a more representative measure of local firms’ non-financial performance. For robustness tests, we additionally use ESG data from Shanghai Wind Information Co., Ltd., Bloomberg L.P., and SynTao Green Finance. Green patent-application data are obtained from the China National Research Data Service Platform (CNRDS), employee-education data come from Wind, and the remaining firm-level variables are drawn from the China Stock Market & Accounting Research (CSMAR) database, including annual sales, total industry sales, firm size, employment, age, return on assets, leverage, fixed assets, ownership concentration, board size, the proportion of independent directors, and ownership type.

3.2. Variable Definitions and Model Specifications

3.2.1. Dependent Variable

Product Market Performance. Following Mitani [21] and Morgan and Rego [22], we use market share to measure a firm’s product-market performance. Specifically, MktShare equals a firm’s annual sales divided by the total sales of all firms in the same industry-year. A larger MktShare, therefore, indicates stronger product-market performance. Our baseline industry classification follows the “Industry Classification Guidelines for Listed Companies in China” issued by the China Association for Public Companies. To ensure that the results are not driven by a particular classification scheme, we also use the industry classifications published by the China Securities Regulatory Commission, Shenwan Hongyuan Securities Co., Ltd., China Securities Index Co., Ltd., and CITIC Securities Co., Ltd. in the robustness tests.

3.2.2. Independent Variable

Firms’ ESG Performance. We measure ESG performance using the Sino-Securities ESG Index. To remove scale effects, we divide the original score by 100 and denote the resulting continuous variable, which ranges from 0 to 1, as ESG. Values closer to 1 indicate stronger overall performance in environmental protection, social responsibility, and corporate governance, whereas values closer to 0 indicate weaker ESG performance.

3.2.3. Mediation Variables

Product Quality. Under the theoretical framework discussed above, ESG ratings can help firms obtain investor funding. Those funds strengthen competitive barriers, allow firms to command premiums above marginal cost through product uniqueness and brand credibility, and support product-quality improvement through green technological innovation. To test this channel, we use enterprise markup (Markup) and green patent applications (Green_Patent) as indicators of product quality [83]. Markup captures the pricing premium associated with product-quality improvement, while Green_Patent reflects observable innovation outcomes related to such improvement. Using both proxies helps reduce the measurement error associated with any single indicator. Markup is calculated using the De Loecker and Warzynski [84] method, which estimates output elasticity with respect to variable inputs from the log production function and then combines that estimate with the ratio of operating revenue to variable cost. Green_Patent is measured as ln(1 + number of green patents filed by the firm in a given year).

Service Level. In organizational-behavior research, service level is often proxied by employee satisfaction [85]. Because employee satisfaction is a subjective perception and difficult to observe directly, we follow [86] and use employee turnover (Labor_Flow) as an alternative proxy. Labor_Flow equals 1 when the number of employees at year-end exceeds the corresponding number in the previous year, and 0 otherwise. Following Sun et al. [87], we also use employee education (Labor_Edu) as a proxy for service level. According to the “Employee-Customer Value Chain” perspective, lower employee turnover reflects stronger workforce incentives under internal governance, and stable labor relations form the basis for consistent, high-quality customer service. A higher education level captures the stock of intellectual capital and thus affects a firm’s capacity to handle complex service demand and provide specialized support. Although high educational attainment does not necessarily imply stronger service orientation, it can be regarded as a threshold condition for service capability. Labor_Edu is measured as employees with a master’s degree or above divided by total employees. A larger value indicates higher employee quality within the firm.

3.2.4. Control Variables

To control for other factors that may affect product-market performance, we include several covariates. Following Czarnitzki and Thorwarth [44], we first control for firm size (SIZE), employee size (Employee), and firm age (AGE). SIZE is measured as ln(year-end total assets), and Employee is measured as ln(number of employees at year-end). These two variables capture firm scale. AGE is measured as ln(current year—listing year + 1), which serves as a rough proxy for market experience and reputation.

Fresard [54] shows that product-market performance is also related to financial strength. Accordingly, we control for several variables that capture the firm’s financial condition: return on assets (ROA), measured as the ratio of net profit to total assets; leverage (LEV), measured as the ratio of total liabilities to total assets; and fixed asset ratio (Fixed), measured as the ratio of fixed assets to total assets. These variables help capture the firm’s financial situation.

In addition, because management can influence product decisions and market entry [88,89], we include ownership concentration (TOP1), defined as the shareholding ratio of the largest shareholder; board size (Board), measured as the natural logarithm of the number of board members; and the proportion of independent directors (Indep), measured as the ratio of independent directors to total board members. These variables help reflect the potential impact of corporate governance structure on firm decisions.

Finally, considering the potential influence of political factors on firms’ decisions and market performance, we include the firms’ ownership type (SOE) variable in the model. This variable is coded according to shareholder type: SOE equals 1 for state-owned firms and 0 otherwise (see

Table 1. Definitions of Main Variables.

Variable Type	Variable Name	Symbol	Description
Dependent Variable	Product Market Performance	MktShare	firm annual sales/total sales of all firms in the same industry-year
Independent Variable	Firms’ ESG Performance	ESG	Measured by the CSI ESG rating/index
Mediation Variables	Product Quality	Markup	Output elasticity with respect to variable inputs is estimated from a translog production function, and markup is then derived using the ratio of operating revenue to variable cost
		Green_Patent	ln(1 + number of green patent applications filed in year t)
	Service Level	Labor_Flow	1 if Employees_t > Employees_(t − 1); 0 otherwise
		Labor_Edu	employees with a master’s degree or above/total employees
Control Variables	Firm size	SIZE	ln(year-end total assets)
	employee size	Employee	ln(number of employees at year-end)
	firm age	AGE	ln(current year—listing year + 1)
	return on assets	ROA	net profit/total assets
	leverage	LEV	total liabilities/total assets
	fixed asset ratio	Fixed	fixed assets/total assets
	ownership concentration	TOP1	shares held by the largest shareholder/total shares
	board size	Board	ln(number of board members)
	the proportion of independent directors	Indep	independent directors/total board members

).

3.2.5. Model Setting

To examine whether a firm’s ESG rating affects subsequent product-market performance, we estimate the following regression model using ordinary least squares (OLS):

$$MktShar{e}_{i,t+1}={\alpha }_0+{\alpha }_{1}ES{G}_{i,t}+{\displaystyle \sum Control{s}_{i,t}+FirmFE+YearFE+{\epsilon }_{i,t}}$$

(1)

$$Mediato{r}_{i,t}={\beta }_0+{\beta }_{1}ES{G}_{i,t}+{\displaystyle \sum Control{s}_{i,t}+FirmFE+YearFE+{\epsilon }_{i,t}}$$

(2)

$$MktShar{e}_{i,t+1}={\gamma }_0+{\gamma }_{1}ES{G}_{i,t}+{\gamma }_{2}Mediato{r}_{i,t}+{\displaystyle \sum Control{s}_{i,t}+FirmFE+YearFE+{\epsilon }_{i,t}}$$

(3)

In these equations, i and t denote the firm and the year, respectively. MktShare is the dependent variable capturing product-market performance, ESG denotes the firm’s ESG rating, Controls represent the control variables defined above, and the error term captures unexplained variation. To enhance robustness, the baseline specification includes both firm fixed effects (FirmFE) and year fixed effects (YearFE), and standard errors are clustered at the firm level. In model (1), a significantly positive coefficient on ESG indicates that a firm’s ESG rating improves product-market performance (H1). To test H2 and H3, we additionally estimate models (2) and (3), in which Mediator denotes the mediating variable used to examine the mechanism through which ESG affects product-market performance.

3.3. Descriptive Statistical Analysis and Correlation Analysis

Table A1. Descriptive statistics.

Variables	N	Mean	SD	Median	Min	Max
MktShare	39,823	0.992	1.743	0.397	0.008	12.377
ESG	39,823	0.731	0.051	0.734	0.550	0.855
SIZE	39,823	22.163	1.298	21.965	19.531	26.456
Employee	39,823	7.612	1.258	7.534	3.871	11.181
AGE	39,823	2.048	0.931	2.197	0.000	3.401
ROA	39,823	0.036	0.066	0.038	−0.493	0.222
LEV	39,823	0.420	0.208	0.412	0.028	0.927
Fixed	39,823	0.207	0.158	0.173	0.002	0.772
TOP1	39,823	34.265	15.048	32.020	0.290	89.990
Board	39,823	2.123	0.199	2.197	1.609	2.708
Indep	39,823	37.591	5.579	36.360	0.000	80.000
SOE	39,823	0.347	0.476	0.000	0.000	1.000

reports descriptive statistics for the main variables. The mean value of MktShare is 0.992, with a standard deviation of 1.743, a minimum of 0.008, and a maximum of 12.377, indicating substantial variation in product-market performance across the sampled firms and suggesting that most firms hold relatively modest market positions. The mean ESG score is 0.731, with a standard deviation of 0.051, which indicates that the overall ESG performance of Chinese A-share listed firms is relatively strong. ESG ranges from 0.550 to 0.855, showing meaningful cross-firm variation and providing a sufficient basis for empirical analysis.

Table A2. Correlation analysis.

Variables	MktShare	ESG	SIZE	Employee	AGE	ROA	LEV
MktShare	1.000
ESG	0.195 ***	1.000
SIZE	0.619 ***	0.195 ***	1.000
Employee	0.581 ***	0.171 ***	0.721 ***	1.000
AGE	0.224 ***	−0.123 ***	0.435 ***	0.275 ***	1.000
ROA	0.069 ***	0.241 ***	−0.001	0.053 ***	−0.217 ***	1.000
LEV	0.275 ***	−0.097 ***	0.496 ***	0.352 ***	0.407 ***	−0.356 ***	1.000
Fixed	0.018 ***	−0.060 ***	0.103 ***	0.234 ***	0.136 ***	−0.058 ***	0.099 ***
TOP1	0.103 ***	0.108 ***	0.185 ***	0.168 ***	−0.076 ***	0.147 ***	0.045 ***
Board	0.148 ***	0.029 ***	0.255 ***	0.243 ***	0.142 ***	0.022 ***	0.150 ***
Indep	0.040 ***	0.069 ***	0.010 **	−0.010 *	−0.012 **	−0.024 ***	−0.011 **
SOE	0.200 ***	0.058 ***	0.345 ***	0.253 ***	0.433 ***	−0.072 ***	0.295 ***
Variables	Fixed		TOP1	Board	Indep		SOE
Fixed	1.000
TOP1	0.087 ***		1.000
Board	0.148 ***		0.023 ***	1.000
Indep	−0.049 ***		0.040 ***	−0.530 ***	1.000
SOE	0.202 ***		0.224 ***	0.271 ***	−0.060 ***		1.000

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

reports Pearson correlation coefficients for the main variables. All coefficients are below 0.8, which suggests that multicollinearity is not severe and supports the reliability of the subsequent regression analysis. We further compute variance inflation factors (VIFs): the maximum VIF is 2.93 and the mean VIF is 1.62, both well below the conventional threshold of 10, indicating no serious multicollinearity problem.

4. Results

4.1. Main Findings

Table 2. Baseline regression results.

Variables	F.MktShare	F.MktShare
	(1)	(2)
ESG	1.699 ***	0.856 ***
	(8.340)	(4.456)
SIZE		0.502 ***
		(12.383)
Employee		0.165 ***
		(5.939)
AGE		−0.039
		(−1.340)
ROA		0.857 ***
		(8.069)
LEV		0.095
		(1.122)
Fixed		0.163
		(1.446)
TOP1		−0.001
		(−0.771)
Board		−0.089
		(−0.886)
Indep		−0.002
		(−0.590)
SOE		−0.149 ***
		(−2.709)
Firm FE	YES	YES
Year FE	YES	YES
N	34,057	34,057
adj. R2	0.842	0.862

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The reported t-statistics, adjusted for clustering at the firm level, are enclosed in parentheses.

reports the baseline regression results for the effect of ESG ratings on firm product-market performance. Column (1) includes only year effects and firm fixed effects, whereas column (2) additionally controls for firm-level factors. In column (2), the coefficient on ESG is 0.856 (t = 4.456, p < 0.01), indicating that ESG ratings have a significantly positive effect on product-market performance (MktShare) at the 1% level. The effect is also economically meaningful: a one-standard-deviation increase in ESG is associated with an increase of 4.36 percentage points in market share (0.856 × 0.051). These results provide strong support for Hypothesis 1 and indicate that improvements in ESG performance enhance product-market competitiveness.

4.2. Robustness Checks

4.2.1. Replace Core Variables

To reduce potential measurement bias, we re-estimate the model using alternative proxies for both product-market performance and ESG ratings. First, to address possible measurement differences stemming from industry-classification standards, we recalculate firm market share using the classification systems issued by the China Securities Regulatory Commission, Shenwan Hongyuan Securities Co., Ltd., China Securities Index Co., Ltd., and CITIC Securities Co., Ltd., and denote the resulting measures as MktShare1, MktShare2, MktShare3, and MktShare4. Panel A of

Table 3. Regression results for alternative core variables.

Panel A: Alternative Measures of MktShare
Variables	F.MktShare1	F.MktShare2	F.MktShare3	F.MktShare4
	(1)	(2)	(3)	(4)
ESG	0.843 ***	0.827 ***	0.911 ***	0.957 ***
	(3.832)	(4.437)	(4.158)	(3.919)
Controls	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	34,043	34,043	34,043	34,043
adj. R2	0.840	0.864	0.872	0.804
Panel B: Alternative measures of ESG
Variables	F.MktShare1	F.MktShare2	F.MktShare3	F.MktShare4
	(1)	(2)	(3)	(4)
ESG1	0.038 ***
	(4.479)
Wind_ESG		2.981 ***
		(5.904)
Bloomberg_ESG			0.580 ***
			(3.039)
SynTao_ESG				0.059 *
				(1.707)
Controls	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	34,057	12,556	13,105	4309
adj. R2	0.862	0.865	0.943	0.931

Note: *** and * indicate significance at the 1% and 10% levels, respectively.

shows that the main conclusions remain unchanged under these alternative classification schemes. We then replace the baseline ESG measure with several alternatives. First, based on the Sino-Securities Index Information Service (Shanghai) Co., Ltd. rating system, we convert the nine ESG grades (AAA, AA, A, BBB, BB, B, CCC, C, C) into a 9-point score [20], with AAA = 9 and the lowest grade = 1, and denote this variable ESG1. Second, we use ESG scores from Wind, Bloomberg, and SynTao Green Finance, denoted as Wind_ESG, Bloomberg_ESG, and SynTao_ESG. Panel B of Table 3 shows that the coefficient on the core explanatory variable remains significantly positive regardless of the ESG data source. Overall, these alternative-proxy tests support the robustness of the main findings.

4.2.2. High-Dimensional Fixed Effects and Clustered Robust Standard Errors

To further reduce the influence of regional disparities, industry differences, and possible sample autocorrelation, we perform additional robustness checks by introducing high-dimensional fixed effects and changing the clustering level of the standard errors. The results are reported in

Table 4. Change the regression model.

Variables	F.MktShare	F.MktShare	F.MktShare	F.MktShare	F.MktShare
	(1)	(2)	(3)	(4)	(5)
ESG	0.798 ***	0.829 ***	0.777 ***	0.777 ***	0.777 ***
	(3.852)	(4.439)	(3.840)	(3.620)	(4.082)
Controls	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
City × Year FE	YES	NO	YES	YES	YES
Industry × Year FE	NO	YES	YES	YES	YES
cluster_level	Firm	Firm	Firm	City	Industry
N	32,279	33,995	32,217	32,217	32,217
adj. R2	0.858	0.872	0.868	0.868	0.869

Note: *** indicate significance at the 1% levels.

. Building on the baseline specification with firm and year fixed effects, we progressively add City x Year and Industry x Year interaction fixed effects to absorb time-varying unobservable shocks at the city and industry levels. Columns (1) and (2) report the estimates with City x Year and Industry x Year fixed effects, respectively. The estimated ESG coefficients are 0.798 (t = 3.852) and 0.829 (t = 4.439), and both remain significantly positive at the 1% level. Column (3) includes both sets of high-dimensional fixed effects simultaneously. Even under this more stringent specification, the ESG coefficient remains 0.777 (t = 3.840, p < 0.01). These results indicate that the positive effect of ESG on market share remains robust after controlling for time-varying macroeconomic and industry-specific disturbances.

In the baseline regressions, we cluster robust standard errors at the firm level. However, residual correlation may still exist among firms within the same city or industry. To address this concern, we re-cluster the standard errors in the specification reported in column (3) at the city level (column 4) and at the industry level (column 5). The results show that changing the clustering dimension slightly affects the standard errors but does not alter the main conclusion: when errors are clustered at the city level, the ESG coefficient is 0.777 with a t-value of 3.620, and when they are clustered at the industry level, the ESG coefficient remains 0.777 with a t-value of 4.082. Under both clustering approaches, the positive effect of ESG remains significant at the 1% level, indicating that the main results are not driven by within-city or within-industry clustering.

4.2.3. Placebo Test

To assess whether the effect of a firm’s ESG score on product-market performance could be driven by random factors, we conduct a placebo test. The logic is straightforward: if product-market performance were driven by factors other than ESG, randomly reassigning ESG scores across firms should yield similar effects. We therefore run 1000 regressions using randomly matched ESG scores and examine the kernel density of the resulting coefficients in Appendix B, Figure A1. The simulated coefficients are approximately normally distributed around zero and remain far below the true regression coefficient, which is indicated by the vertical line. This pattern is consistent with the expected placebo outcome and further supports the robustness of the baseline results.

4.2.4. Instrumental Variables (IV) Method

A remaining concern is reverse causality: firms with stronger product-market performance may possess greater resources and managerial capability for ESG implementation, which in turn could lead to higher ESG ratings. To address this endogeneity concern, we estimate a two-stage least-squares (2SLS) model with instrumental variables. Following Jin et al. [90], we use the number of shares held by “ESG-themed investment funds” (Fundnumber) as an instrument for firm ESG rating (We first select and identify the list of ESG-themed investment funds based on the “China Responsible Investment Annual Report 2020.” Then, we extract the number of shares held by each ESG-themed investment fund in publicly listed firms from the firms’ fund holdings files in the CSMAR database, and apply a logarithmic transformation).

Regarding relevance, Dyck et al. [91] show, using data on publicly listed firms in 41 countries, that firms with more institutional investors exhibit better environmental and social performance. As institutional investors, ESG investment funds may participate in corporate governance and influence firm decision-making [92], thereby introducing ESG principles into the firm and improving ESG performance. Regarding exogeneity, the investment decisions of ESG funds primarily depend on a firm’s ESG characteristics rather than directly on its product-market performance.

Table 5. Instrumental variable (IV) test results.

Variables	The First Stage	The Second Stage
	ESG	F.MktShare
	(1)	(2)
Fundnumber	0.000 ***
	(4.893)
ESG		29.406 ***
		(3.797)
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES
N	37,233	32,197

Note: *** indicate significance at the 1% levels.

reports the IV results. In column (1), the first-stage coefficient on Fundnumber is significantly positive at the 1% level, indicating a strong positive association between ESG-themed fund holdings and firm ESG ratings, as expected. This suggests that there is no reverse causality issue between ESG ratings and product market performance.

We further evaluate the validity of the instrumental variable through standard diagnostics. First, instrument strength is assessed using the Kleibergen–Paap Wald rk F-statistic. Under heteroskedasticity- and autocorrelation-robust standard errors, the statistic equals 26.53, which is well above both the rule-of-thumb threshold of 10 [93] and the Stock–Yogo critical value of 16.38 for a 10% maximum relative bias. We therefore reject the weak-instrument concern (p < 0.01). Second, we test for underidentification using the Kleibergen–Paap rk LM statistic. The LM statistic is 26.11 (p = 0.000), which rejects the null hypothesis of underidentification at the 1% level.

4.2.5. Identification Strategy

To strengthen causal inference, we further employ a quasi-natural experiment in addition to the instrumental-variables approach. Following Bai et al. [94] and Wang et al. [95], we use the release of ESG ratings for A-share listed firms by SynTao Green Finance in December 2015 as an exogenous shock and construct model (4) for estimation:

$$MktShar{e}_{i,t+1}={\alpha }_0+{\alpha }_{1}ESG\_DI{D}_{i,t}+{\displaystyle \sum Control{s}_{i,t}+FirmFE+YearFE+{\epsilon }_{i,t}}$$

(4)

In this model, ESG_DID is a dummy variable equal to 1 for firm i in period t and in all subsequent periods once its ESG rating is released, and 0 otherwise. The remaining variables are defined as above. Column (1) of

Table 6. DID and PSM Test results.

Variables	F.MktShare	F.MktShare
	DID	PSM
	(1)	(2)
ESG_DID	0.261 ***
	(5.689)
ESG		0.439 **
		(2.344)
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES
N	34,057	26,147
adj. R2	0.862	0.962

Note: *** and ** indicate significance at the 1% and 5% levels, respectively.

shows that ESG_DID is significantly positive at the 1% level, indicating that ESG ratings significantly improve firms’ product-market performance. To validate the DID specification, we perform a parallel-trend test. Figure A2 in Appendix C reports the dynamic coefficients. In all pre-event periods (from −13 to 0), the estimated coefficients fluctuate around zero, and their 95% confidence intervals cross the zero line, indicating no statistically significant pre-trend. This supports the parallel-trend assumption and suggests that the results are not driven by prior differences between the treatment and control groups. After the rating release (from period 1 to 6), the coefficients become significantly positive and rise over time, which supports a causal interpretation of the observed product-market improvement.

4.2.6. Propensity Score Matching (PSM) Method

Besides reverse causality, the estimates may also be affected by omitted-variable bias and sample-selection bias. To address these concerns, we apply propensity score matching (PSM). The matching procedure is implemented in three steps. First, firms are divided each year into a treatment group and a control group according to whether the firm’s ESG rating is above the industry-year median. Firms above the median are classified as the treatment group (high-ESG firms), and the remaining firms form the control group. Second, using the control variables from model (1) as covariates, we adopt 1:2 nearest-neighbor matching with replacement on an annual basis. Third, we re-estimate the regression using matched observations with non-zero weights. Balance tests before and after matching are reported in Appendix D. After matching, the absolute differences in firm characteristics between the treatment and control groups decline by 59.2% to 97.1%. The standardized bias of the matched covariates is below 5% (maximum = 3.4%), and for most covariates, the t-tests fail to reject equality between the two groups. These results indicate that observable differences have been largely removed and that the matching procedure is successful. Column (2) of Table 6 shows that the ESG coefficient remains significantly positive after matching, which further supports the robustness of the findings.

4.3. Mechanism Analysis

4.3.1. Product Quality Channel

Based on the analysis in Section 3.2.3, we use Markup and Green_Patent as mediating variables to test Hypothesis 2.

Table 7. Product quality.

Variables	F.MktShare	Markup	F.MktShare	Green_Patent	F.MktShare
	(1)	(2)	(3)	(4)	(5)
ESG	0.856 ***	0.079 ***	0.864 ***	0.756 ***	0.769 ***
	(4.456)	(3.706)	(4.321)	(7.019)	(4.145)
Markup			0.326 **
			(2.354)
Green_Patent					0.107 ***
					(5.454)
Controls	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
N	34,057	35,482	30,659	39,336	34,057
adj. R2	0.862	0.708	0.868	0.643	0.862

Note: *** and ** indicate significance at the 1% and 5% levels, respectively.

reports the results. Column (1) reproduces the benchmark regression and captures the overall effect of ESG ratings on product-market performance, thereby providing preliminary support for Hypothesis 1. Column (2) reports the regression in which Markup is the dependent variable and shows that the coefficient on ESG is significantly positive, suggesting that firms with higher ESG ratings tend to have higher markups. Column (3) shows that Markup is significantly positively associated with product-market performance. After controlling for Markup, the ESG coefficient remains significantly positive, indicating that ESG ratings may improve product-market performance in part by signaling product-quality advantages. Columns (4) and (5) use Green_Patent as the proxy for product quality and yield similar conclusions. Overall, these findings provide strong support for Hypothesis 2.

4.3.2. Service Capability Channel

Based on the analysis in Section 3.2.3, we use employee education (Labor_Edu) and employee turnover (Labor_Flow) as mediating variables to test Hypothesis 3.

Table 8. Service level.

Variables	F.MktShare	Labor_Edu	F.MktShare	Labor_Flow	F.MktShare
	(1)	(2)	(3)	(5)	(6)
ESG	0.856 ***	1.228 **	0.706 ***	0.166 **	0.851 ***
	(4.456)	(2.494)	(3.157)	(2.337)	(4.439)
Labor_Edu			0.018 ***
			(3.218)
Labor_Flow					0.038 ***
					(3.818)
Controls	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
N	34,057	26,999	22,746	39,338	34,057
adj. R2	0.862	0.883	0.889	0.219	0.862

Note: *** and ** indicate significance at the 1% and 5% levels, respectively.

reports the service-level results. As in the earlier analysis, column (1) presents the benchmark regression and reflects the overall effect of ESG ratings on product-market performance. Column (2) reports the regression in which Labor_Edu is the dependent variable and shows that the coefficient on ESG is significantly positive, suggesting that firms with higher ESG ratings tend to employ better-educated workers and thus exhibit stronger service quality. Column (3) shows that, after controlling for Labor_Edu, the ESG coefficient remains significantly positive. This indicates that ESG ratings may also improve product-market performance by signaling service-quality advantages to the market. Columns (4) to (6), which use Labor_Flow as the proxy, yield highly similar conclusions. These empirical results strongly support Hypothesis 3.

4.4. Moderating Effect Analysis

4.4.1. High-Tech Firms

In a highly competitive business environment, high-tech firms typically rely on technological innovation and skilled labor to secure a competitive advantage. The preceding analysis shows that firms with stronger ESG performance are more successful in attracting and retaining talent and in promoting technological innovation. We therefore examine whether the positive effect of ESG ratings on product-market performance is stronger among high-tech firms. Following the “Classification of High-tech Industries (Services) (2018)” and the “Classification of High-tech Industries (Manufacturing) (2017)” issued by the National Bureau of Statistics of China, we identify high-tech enterprises and construct a dummy variable, High_Tec, which equals 1 if a firm belongs to a high-tech industry and 0 otherwise. Column (1) of

Table 9. Moderation effect analysis.

Variables	F.MktShare	F.MktShare
	(1)	(2)
ESG	0.500 *	0.510 *
	(1.946)	(1.723)
High_Tec	0.340 ***
	(4.436)
ESG × High_Tec	0.727 *
	(1.927)
Down_Idu		0.233 ***
		(4.043)
ESG × Down_Idu		0.769 **
		(2.004)
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES
N	34,057	34,057
adj. R2	0.862	0.862

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

shows that the interaction term ESG × High_Tec is significantly positive. This result is consistent with expectations and suggests that high-tech firms are better able to translate their technological and human-resource advantages into market competitiveness through ESG strategies.

4.4.2. The Position of the Firm in the Industry Chain

With the spread of the internet, individual consumers are often influenced by information circulating on social media when making purchase decisions [96,97]. As a result, firms serving individual consumers typically need to pay closer attention to brand image and reputation [98,99]. A higher ESG rating can strengthen consumer trust in and favorability toward the firm, thereby increasing purchase intention. We therefore expect the positive effect of ESG ratings on product markets to be stronger for firms that are closer to individual consumers. To test this idea, we follow [100,101] to calculate firms’ upstream degree (The calculation method for the firm’s upstream degree is provided in Appendix E). Firms are classified as downstream or upstream (Down_Idu) according to the sample median of upstream degree. If a firm’s upstream degree is below the median, Down_Idu equals 1, indicating that the firm is closer to individual consumers; otherwise, Down_Idu equals 0. Column (2) of Table 9 shows that the interaction term ESG x Down_Idu is significantly positive at the 5% level. This indicates that firms closer to individual consumers obtain a stronger positive product-market effect from the brand image and consumer loyalty associated with ESG ratings.

4.5. Further Analysis

4.5.1. ESG Ratings and Changes in Market Share

To further examine how ESG ratings affect market competitiveness, we analyze the effect of ESG on the net growth of market share. Following He and Huang [55], net market-share growth is defined as the change in market share across periods. Column (1) of

Table 10. Further analyses.

Variables	F.NetMktShare	F.Customer_MktShare	F.MktShare	F.MktShare	F.MktShare
	(1)	(2)	(3)	(4)	(5)
ESG	0.203 ***	1.262 *
	(3.685)	(1.723)
E			0.460 ***
			(2.754)
S				0.201 *
				(1.897)
G					0.474 ***
					(3.925)
Controls	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
N	34,057	782	34,057	34,057	34,057
adj. R2	0.070	0.626	0.861	0.861	0.861

Note: *** and * indicate significance at the 1% and 10% levels, respectively.

reports the regression results. We find a significantly positive relationship between ESG and net market-share growth, indicating that ESG ratings contribute to the expansion of firms’ market share. Economically, a one-standard-deviation increase in ESG raises net market-share growth by 1.04 percentage points. This result suggests that ESG practices improve not only the level of market share but also its continued growth, highlighting their long-run competitive value.

4.5.2. The Spillover Effect of ESG Ratings

Building on the preceding analysis, we note that firms with high ESG ratings generally exhibit better product quality and stronger service capability. This raises a further question: can customers of high-ESG firms gain product-market advantages through supply-chain spillovers? To address this issue, we extend the analysis to the supply-chain context and examine whether a supplier’s ESG performance affects the market share of its downstream customers. Specifically, we extract firm-customer pair data from the CSMAR database and, due to data availability, restrict the customer sample to A-share listed firms. Customer_MktShare is defined in the same way as the main dependent variable, namely customer firm sales divided by total industry sales under the “Industry Classification Guidelines for Listed Companies in China.” Column (2) of Table 10 reports the regression results. We find a significantly positive relationship between supplier ESG and customer product-market performance. Quantitatively, a one-unit increase in a supplier’s ESG rating is associated with an average increase of 1.27 units in the customer’s market share. This finding supports the existence of an ESG spillover effect and suggests that high-ESG firms can improve downstream customers’ product competitiveness by supplying higher-quality intermediate goods or services.

4.5.3. The Impact of E, S, and G on Product Market Performance

More specifically, improvement in the Environmental (E) dimension may map directly onto consumer demand for product safety and low-carbon attributes, whereas Corporate Governance (G) can support the stability of firm performance and commitment by improving managerial efficiency. By contrast, the Social (S) dimension often includes external philanthropic activities whose conversion into product-market share is relatively indirect. We therefore expect the three ESG pillars to differ in their contributions to market performance.

To further examine the effects of different ESG dimensions on product-market performance, we re-estimate model (1) using the E, S, and G scores as the core explanatory variables. Columns (3)–(5) of Table 10 show that the lagged coefficients of all three dimensions are significantly positive, indicating that each ESG dimension contributes positively to product-market performance. A closer comparison shows that the coefficients on E and G are larger and more statistically significant than that on S. This pattern is consistent with the earlier mechanism analysis. E factors affect product quality more directly because stronger environmental performance is often accompanied by higher standards in production processes, resource use, and product design. G factors are closely related to service capability, since stronger governance improves operating efficiency, raises service quality, and thereby enhances market performance.

5. Discussion

5.1. Discussion of Main Findings

Overall, the evidence indicates that ESG ratings significantly improve a firm’s product-market performance. This finding is broadly consistent with, and also extends, the existing literature that links ESG to firm outcomes, especially financial performance [25] and capital-market or risk-management benefits [6,35]. Although some studies emphasize the potential costs or “dark side” of ESG [26,27], our results suggest that, in product markets, positive ESG signals mainly benefit firms by easing demand-side concerns and increasing market share.

Regarding the underlying mechanisms, our analysis suggests that ESG improves product-market performance mainly by serving as a credible signal of product quality and service capability. This result is consistent with the signaling-theory framework discussed earlier. Because traditional market signals such as advertising can be manipulated relatively easily and often involve lower sunk costs [58], high ESG ratings function as a more difficult-to-imitate, cost-asymmetric signal [57] that consumers are more likely to trust [59]. This mechanism also accords with earlier findings that demand-side factors, especially environmental performance and customer satisfaction, are important drivers of market-share growth [48,51].

Finally, the spillover analysis provides additional insight. We identify a robust supply-chain spillover effect whereby a supplier’s ESG commitment improves the market share of its downstream customers. This finding is in line with, and further extends, the Operations and Supply Chain Management literature. Whereas previous studies show that a firm’s sustainability performance can generate positive externalities through social trust [60], transactional credit [61], and financial outcomes [63], our results indicate that ESG can also promote tangible market-share gains across the supply chain, thus addressing the gap identified in our literature review.

5.2. Theoretical Contributions

This study makes three main theoretical contributions to the literature on corporate sustainability and operations management. First, it broadens the discussion of ESG value creation by shifting the focus from capital-market performance and internal operating efficiency to product-market competitiveness. Whereas prior studies mainly examine how ESG helps firms secure financing, promote innovation, or improve labor efficiency, this study introduces signaling theory to explain how ESG practices signal product quality and service capability and thereby support market-share expansion. Second, this study introduces supply-chain position as an additional perspective for explaining heterogeneity in corporate ESG strategies. Moving beyond conventional explanations based mainly on industry pollution characteristics or external regulatory pressure, our analysis shows that the closer a firm is to final consumers, the more effectively its ESG practices translate into market advantage. This provides a structural explanation for why firms adopt differentiated ESG-investment strategies. Third, this research extends the Operations and Supply Chain Management framework by documenting the spillover effects of ESG practices across supply-chain networks. Existing theories often confine analysis to the cost-benefit balance within a single firm. By contrast, this study broadens the scope to a cross-firm supply-chain ecosystem and shows that a supplier’s ESG performance can generate positive network externalities that improve downstream customers’ product-market performance. This not only addresses the limited attention paid to ESG spillovers in current supply-chain research but also provides stronger theoretical support for understanding the broader externalities of corporate sustainability strategies.

5.3. Practical and Regulatory Implications

This study offers several practical and regulatory implications. For corporate managers, the first implication is that ESG should be viewed not only as beneficial in capital and labor markets, but also as a source of brand image and competitiveness in the sales market. Second, firms should incorporate ESG practices into their marketing strategy by disclosing product-related ESG information—such as green-innovation investment and human-capital structure—in order to signal product quality and service capability to consumers and differentiate themselves in competitive markets. Third, because the environmental (E) and governance (G) dimensions exert stronger effects on product-market share, firms seeking market-performance gains should prioritize green R&D, pollution control, and governance systems, while still recognizing the longer-term value of the social (S) dimension. Finally, firms should evaluate ESG projects in light of industry characteristics. High-tech firms (e.g., internet, electronics, or biomedicine firms) and firms closer to final consumers (e.g., e-commerce, furniture, or food-service firms) may benefit from greater ESG investment. For regulators, our findings suggest that ESG disclosure can improve resource allocation in product markets. Policymakers may therefore consider a gradual expansion of mandatory ESG disclosure, especially in industries where sustainability performance can become a source of competitive advantage. Such disclosure may help curb greenwashing, lower consumers’ information-search costs, and create a fairer and more transparent market environment.

6. Conclusions

6.1. Summary

Drawing on stakeholder theory and the resource-based view, this study empirically examines how ESG ratings affect firm-level product-market performance and through which mechanisms. Using panel data on Chinese A-share listed firms from 2009 to 2022, we show that ESG ratings significantly improve product-market performance by signaling product quality and service capability. Among the three ESG dimensions, the environmental (E) and governance (G) components exert stronger effects than the social (S) component. The moderation analysis further indicates that the positive effect of ESG is more pronounced for high-tech firms and for firms located closer to final consumers. From the perspective of dynamic market-share change, we also find that ESG ratings significantly promote net growth in product-market share. Moreover, firm commitment to ESG generates supply-chain spillovers: ESG improves not only the focal firm’s own product-market performance but also the market share of its downstream customers.

6.2. Limitations and Future Research

Although we control for many factors in data cleaning and conduct several tests to assess the causal link between ESG and product-market performance, this study still has several limitations. First, the analysis focuses on two mechanisms through which ESG may improve product-market performance, but other channels remain open for future research. Second, we examine only linear relationships between environmental, social, and governance factors and product-market performance; future work could explore nonlinear relationships and more detailed industry-specific heterogeneity. Third, the samples used to test supply-chain spillovers and mediation mechanisms are smaller than the main sample because of data constraints. In addition, although the proxies for quality and service level follow common academic practice, they remain indirect measures and may not fully capture micro-level perceptual variation. Future studies could use questionnaires or experimental methods and further examine possible nonlinear associations between ESG and market performance.