Seeing Is More than Believing: ESG Performance Aspiration Gap and Institutional Investors’ Site Visits

Abstract

ESG has profoundly influenced asset pricing and resource allocation in capital markets. However, existing research largely focuses on the economic consequences of absolute ESG levels, with limited attention paid to how the gap between corporate ESG performance and aspiration levels—the ESG performance aspiration gap—is related to the behavior of information intermediaries in the capital market. Using a sample of A-share non-financial listed companies on the Shenzhen Stock Exchange from 2013 to 2024, this paper empirically examines the relation between the ESG performance aspiration gap and institutional investors’ site visits and its potential underlying channel. The findings are as follows. First, the ESG performance aspiration gap is positively correlated with institutional investors’ site visits. This conclusion remains robust after a series of robustness tests. Second, the mechanism analysis is consistent with information asymmetry serving as a potential channel linking the ESG performance aspiration gap and institutional investors’ site visits. Third, the moderating effect analysis shows that both marketization level and analyst coverage negatively moderate the positive relation between the ESG performance aspiration gap and institutional investors’ site visits. This paper extends the research boundaries of the economic consequences of ESG and institutional investors’ information search behavior, providing a new theoretical explanation for how the capital market responds to the dynamic changes in corporate ESG performance. It also offers policy implications for improving the ESG information disclosure system and enhancing the information efficiency of the capital market.

1. Introduction

Globally, environmental, social, and governance (ESG) principles have profoundly evolved into a core dimension for capital allocation and risk assessment in financial markets, and integrating ESG factors into investment decisions has become common practice among mainstream institutional investors (Amel-Zadeh & Serafeim, 2018). Existing research has demonstrated that ESG performance is highly correlated with corporate financial performance (Friede et al., 2015), making ESG no longer merely a matter of corporate reputation but one that directly affects asset pricing and portfolio resilience. This global trend is particularly pronounced in the Chinese market: as the “dual carbon” goals have been elevated to a national strategy, China has established an ESG policy framework. The China Securities Regulatory Commission (CSRC) and stock exchanges have continuously strengthened ESG disclosure requirements for listed companies, and the State-owned Assets Supervision and Administration Commission (SASAC) has further pushed for full ESG reporting coverage among state-owned listed enterprises. However, the capital market’s response to corporate ESG performance depends not only on absolute performance levels but is more profoundly influenced by the relative gap between actual performance and “aspiration performance.”

Organizational aspiration theory posits that the firm is a goal-oriented behavioral system, and decision-makers perceive performance not in absolute terms but through comparison with a certain “aspiration level.” When actual performance falls below the aspiration level, a “performance aspiration gap” arises-a gap that drives the firm to take actions to close the gap. This logic has been fully validated in the domain of financial performance, yet extending it to the non-financial performance dimension of ESG reveals a more complex scenario. ESG performance aspiration gap refers to the gap formed when a firm’s actual ESG performance falls below the combination of its own historical level and the industry average. Unlike financial performance aspiration gaps, ESG aspiration gaps not only reflect resource constraints or capability deficiencies but also carry a strong crisis signal: they may imply that the firm’s commitment to sustainability issues has wavered, that internal management has loopholes, or even that undisclosed environmental or social risks exist (Hawn & Ioannou, 2016). From an information asymmetry perspective, ESG performance aspiration gaps dramatically amplify the information gap between investors and firms. When a firm’s ESG performance falls short of aspiration, public ESG reports, ratings, or news may have already released some negative signals, but such public information is often lagging, ambiguous, and susceptible to managerial impression management. It is difficult for investors to judge, based solely on such information, whether the gap represents a temporary performance fluctuation or a deep-seated systemic governance deficiency. Therefore, an ESG performance aspiration gap essentially means that the firm has failed to meet the normative aspiration of key stakeholders, which undermines corporate reputation and capital market recognition, exposing the firm to stricter regulatory scrutiny, more fragile brand loyalty, and potential capital market penalties.

To address ESG performance aspiration gaps, site visits can transform information acquisition from a “narrative-receiving” mode into “active verification.” Unlike the passive reception of public information such as financial reports or ESG disclosures, site visits afford investors a firsthand verification opportunity grounded in “seeing for oneself” (Cheng et al., 2016). During such visits, investors can directly engage with management and, to the extent that ESG issues are on the agenda, may discuss the causes and remediation of ESG performance gaps. More broadly, visits allow investors to personally observe firm operations—potentially including production processes, environmental protection equipment, and employee safety conditions—as well as to capture the tone and demeanor of management when responding to sensitive questions. Although we measure the total number of institutional investor visits and do not allow us to identify whether each visit specifically focused on ESG topics, site visits as a general private information acquisition channel can still reduce the uncertainty surrounding ESG gaps. By observing the firm’s operational reality and interacting with management, investors can obtain incremental, difficult-to-quantify information that helps them judge whether an ESG gap reflects temporary misallocation and genuine willingness to rectify, or systemic governance failure and opportunistic “greenwashing” (Hawn & Ioannou, 2016; E.-H. Kim & Lyon, 2015). Thus, site visits serve as a natural strategy for institutional investors to mitigate the information asymmetry engendered by ESG performance aspiration gaps.

Therefore, we pose the core question of this paper: What is the relationship between the gap in ESG performance aspiration and institutional investors’ site visits? What are the influencing mechanisms? Are there differences in different situations? Based on these questions, the research objective of this paper is to construct a model to analyze the economic consequences of the gap in ESG performance aspiration at the level of institutional investors’ site visits, test the mechanism by which information asymmetry plays a role, and exploratively investigate the heterogeneity of marketization degree and analyst coverage.

Although existing research has found that institutional investors’ site visits can improve corporate ESG ratings (Tang et al., 2025), prevent corporate ESG decoupling (X. Zhang et al., 2026), and mitigate ESG rating disagreement (W. Wang et al., 2026), these studies primarily proceed from a corporate governance perspective, overlooking the relationship between the ESG performance aspiration gaps and institutional investors’ behavioral decision-making. In particular, the specific relationship between an ESG performance aspiration gap and subsequent site visit behavior remains unexplored. The potential contributions of this paper are fourfold. First, this paper extends the organizational aspiration model from the traditional financial performance domain to the non-financial ESG dimension, offering a new analytical perspective for research on the economic consequences of ESG. It reveals that, besides ESG ratings and ESG rating divergence, the gap between a firm’s ESG performance and its historical or industry aspirations also constitutes an independent information signal that can substantially influence the behavioral decisions of information intermediaries in the capital market, thereby enriching the application boundaries of performance feedback theory. Second, although existing studies have found that ESG information may influence institutional investors’ site visits, such as ESG rating divergence intensifying institutional investors’ site visits (Q. Zhang et al., 2025), this paper examines how ESG information affects institutional investors’ site visits from another distinct ESG perspective. Third, this paper reveals the transmission mechanism through which the ESG performance aspiration gap affects institutional investors’ site visits from the perspective of information asymmetry, providing micro-level evidence for understanding the logic behind institutional investors’ ESG information search behavior. Fourth, through heterogeneity analysis, this paper delineates the boundary conditions of the impact of the ESG performance aspiration gap on institutional investors’ site visits. It provides an empirical reference for promoting region-specific ESG disclosure reforms and developing regional information intermediary service systems, and offers contextualized empirical evidence for the policy design of ESG information governance.

2. Literature Review

2.1. Economic Consequences of Performance Aspiration Gaps

The economic consequences of performance aspiration gaps constitute one of the most active research streams in organizational behavior theory and strategic management. Existing studies have primarily focused on financial performance aspiration gaps. In the domain of risk-taking and strategic change, research based on U.S. manufacturing firms has found that when a firm’s actual return on assets falls below the industry-mean social aspiration level, its subsequent risk-taking propensity increases significantly, manifested in higher R&D intensity (Bromiley, 1991). Similarly, historical aspiration gaps can predict strategic expansion into new market domains and risk-seeking decisions (Greve, 1998). In terms of misconduct and information manipulation, empirical analyses of firms engaging in financial restatements and accounting violations reveal that when firm performance falls substantially short of analyst consensus expectations, the probability of financial misreporting and earnings manipulation rises significantly, indicating that aspiration gaps not only trigger legitimate strategic adjustments but may also drive managers to cross ethical and compliance boundaries, adopting opportunistic behaviors to conceal true performance (Harris & Bromiley, 2007). In the domain of innovation and investment, existing research has further refined the effects of aspiration gaps, identifying an inverted U-shaped relationship between performance aspiration gaps and R&D search intensity—that is, moderate gaps stimulate innovation search, while excessively deep gaps may suppress innovation inputs due to resource depletion and survival threats (W.-R. Chen & Miller, 2007). Notably, a growing body of literature has recently begun to explore the economic consequences of non-financial performance aspiration gaps, such as environmental performance gaps and social responsibility performance aspiration gaps (M. Zhang et al., 2023; H. Wang et al., 2022; Pan et al., 2023). In the ESG domain, existing research has found that the ESG performance aspiration gap can reduce supply chain stability and impede firms’ total factor productivity (Dong, 2026; H. Zhang & Wu, 2025). The longer the ESG underperformance persists, the lower the level of greenhushing (Z. Zhang et al., 2026).

2.2. Economic Consequences of ESG

2.2.1. Economic Consequences of ESG Rating

The existing literature has examined the substantive impact of ESG ratings as a single composite indicator on firms and markets from multiple dimensions, including financial performance, financing costs, risk buffering, and investor behavior. Research has shown that firms with superior ESG performance demonstrate better long-term stock market and accounting performance. From the perspective of equity financing costs, firms with strong ESG performance enjoy significantly lower cost of equity capital, as good ESG practices broaden the investor base and reduce perceived risk (El Ghoul et al., 2011). In credit markets, banks impose higher loan spreads and stricter non-price terms on firms with poor ESG performance, indicating that creditors’ pricing of ESG risk has been embedded in credit decision-making processes (Goss & Roberts, 2011). Regarding risk resilience, firms with high social capital earned significantly higher stock returns during the financial crisis, as such firms had built stronger trust-based relationships with stakeholders, providing a buffer against extreme negative shocks (Lins et al., 2017). At the investment practice level, institutional investors have widely incorporated ESG information into portfolio risk assessment, sector allocation, and active engagement, with ESG ratings transitioning from a peripheral ethical consideration to a core signal driving capital flows (Amel-Zadeh & Serafeim, 2018). The above studies, from the perspective of ESG ratings, have explored how ESG performance affects firms and capital markets. However, ESG ratings are a single, static indicator. For a firm that has long maintained a high level of ESG performance, the same ESG score may signify mediocre performance, whereas for another firm with a historically poor track record, it may represent significant improvement. Similarly, the same rating may be seen as falling short of aspiration by an industry leader, while being regarded as more than satisfactory by an industry laggard. Therefore, focusing solely on the absolute level of ESG fails to capture the more nuanced information dimension of a firm’s performance gap relative to its own history or peer group.

2.2.2. Economic Consequences of ESG Rating Disagreement

Despite the growing importance of ESG ratings in capital markets, significant divergence often exists among different rating agencies in their assessments of the same firm—a phenomenon that is increasingly becoming a frontier topic in ESG research. A systematic decomposition of the sources of rating disagreement shows that approximately 56% of ESG rating divergence can be attributed to measurement divergence. This implies that ESG disagreement is not random noise but a structural problem (Berg et al., 2022). The economic consequences of such rating disagreement are multifaceted and far-reaching. At the asset pricing level, ESG rating disagreement significantly raises firms’ discount rates and risk premiums, suggesting that the market negatively prices uncertainty about the quality of ESG information (Avramov et al., 2022; Li et al., 2026). This will introduce noise into the equity pricing process, thereby increasing stock price crash risk and exacerbating stock price synchronicity (Guo et al., 2026; Yin et al., 2026). Regarding bond research, it has been found that the differences in ESG ratings increase the risk of bond default (Dai & Zhong, 2026). In terms of information quality, ESG rating disagreement is positively correlated with the future incidence of negative ESG news and controversies, indicating that disagreement itself conveys a signal that casts doubt on the authenticity of a firm’s ESG practices (Gibson Brandon et al., 2021). With respect to investment behavior, when ESG rating disagreement is high, institutional investors’ willingness to hold shares is significantly suppressed, and this avoidance effect is particularly pronounced among investors with ESG preferences (Christensen et al., 2022). Moreover, ESG rating disagreement exacerbates information frictions between firms and investors, weakens the signaling function of ratings in coordinating expectations, and thereby reduces the information efficiency of capital markets (Chatterji et al., 2016). Research on corporate financing has found that differences in ESG ratings will reduce a company’s access to commercial credit and bank loans (L. Wang & Yang, 2026). The above literature focuses on ESG rating disagreement, revealing how inconsistencies among rating agencies exert negative impacts on capital markets. Although both ESG rating disagreement and ESG performance aspiration gaps involve “inconsistency” or “gaps” the former represents an external “horizontal noise” that confuses capital markets as to which rating is accurate, while the latter is a “vertical gap” that alerts investors to whether a firm is experiencing problems or regressing in its ESG practices. Thus, ESG performance aspiration gaps constitute an information dimension that is both related to yet distinct from ESG rating disagreement.

2.3. Determinants of Institutional Investors’ Site Visits

The determinants of institutional investors’ site visits have received extensive attention in academia, with existing research primarily focusing on two dimensions: corporate information environment and governance characteristics. From the perspective of the information environment, when firms exhibit a high degree of information asymmetry or poor quality of public disclosure, investors are more motivated to acquire private information through site visits to compensate for the inadequacy of public information (Bushee et al., 2018). Firms with insufficient analyst coverage tend to have lower information transparency, and institutional investors are more inclined to obtain incremental information and reduce valuation uncertainty through on-site visits (Han et al., 2018). A more favorable local business environment is also likely to attract institutional investors to conduct site visits (Xu et al., 2025). From the governance characteristics perspective, ownership structure has a significant impact on the demand for site visits: when firms have large shareholders or a high proportion of institutional investor shareholding, institutional investors tend to increase the frequency of site visits to fulfill monitoring duties and assess management capability and strategy execution (X. Chen et al., 2007). Additionally, corporate geographic location is also an important factor; firms located closer to institutional investors are more frequently visited due to lower access costs (Baik et al., 2010). Regarding managerial characteristics, a stronger willingness of management to engage in voluntary disclosure and more open interaction with investors make firms more likely to attract institutional investors’ site visits (Solomon & Soltes, 2015). Political factors, such as trade sanctions, are also likely to weaken institutional investors’ site visits (X. Wang & Sun, 2025). The above literature largely focuses on financial information and corporate governance characteristics, with limited attention paid to how the non-financial dimension of ESG performance aspiration gap affects institutional investors’ site visit behavior.

3. Theoretical Analysis and Hypotheses Development

3.1. The Effect of ESG Performance Aspiration Gap on Institutional Investors’ Site Visits

Investors do not perceive performance in absolute terms; rather, they make relative judgments based on a reference point. When actual performance falls below the aspiration level, a “performance aspiration gap” emerges, triggering external information search and internal organizational change. Extending this logic to the ESG domain, when a firm’s actual environmental, social, or governance performance falls below the combination of its own historical level and the industry average, it not only signals an execution gap in sustainability commitments but also carries deep-seated signals of management loopholes, accumulating compliance risks, and even inadequately disclosed negative ESG information (Hawn & Ioannou, 2016). On the one hand, facing such a gap, firms have incentives to engage in impression management through selective disclosure, tone manipulation, or symbolic communication (E.-H. Kim & Lyon, 2015). Particularly in the Chinese market, where ESG disclosure is characterized by a coexistence of substantive and symbolic elements (Marquis & Qian, 2014), public information alone makes it difficult for investors to discern whether the gap is a temporary fluctuation or a systemic governance deficiency. The resulting intensification of information asymmetry directly raises perceived risk and the marginal benefit of private information search. On the other hand, compared to individual investors, institutional investors possess far stronger capabilities in capital mobilization, professional analysis, and information acquisition and interpretation. As sophisticated and influential market participants, after capturing the gap signal, they not only need to “understand” its causes but are also motivated to “intervene” in its trajectory through active stewardship. Prior research indicates that firms with poor ESG performance are more likely to become targets of shareholder proposals, opposing votes, and public pressure (Dimson et al., 2015). Regardless of whether the ultimate choice is engagement, exit, or pressure, the prerequisite for any decision rests on deep, private information about the true state of ESG. Among various information acquisition channels, site visits, with their unique on-site verification function, play an irreplaceable role in mitigating such information asymmetry. Moreover, the multi-dimensional information intake during visits expands the boundaries of obtaining private incremental information. Studies have found that site visits can improve the corporate information environment and confer an informational advantage to investors (Han et al., 2018). Based on the above theoretical analysis, this paper proposes the following research hypothesis.

Based on the above analysis, this study proposes the following hypothesis:

H1. 

The ESG performance aspiration gap is positively correlated with institutional investors’ site visits.

3.2. The Mediating Role of Information Asymmetry

When a firm’s ESG performance falls below the aspiration level, this signal tends to trigger market questioning of managerial competence and commitment. Confronted with the ensuing legitimacy challenges and intensified market scrutiny, management has strong incentives to engage in selective disclosure or strategic obfuscation of information in order to delay the diffusion of negative signals and buy time for corrective actions. Against the backdrop of the long-standing coexistence of substantive and symbolic ESG disclosure in China’s capital market, such behavior directly undermines the credibility of public ESG reports and rating data, making it impossible for investors to judge from public information alone whether the gap is a temporary fluctuation or the tip of an iceberg of systemic governance deficiencies. This ambiguous signal significantly raises the degree of information asymmetry between investors and the firm (E.-H. Kim & Lyon, 2015; Marquis & Qian, 2014). The intensification of information asymmetry, in turn, precisely drives institutional investors to increase site visits. When the decision usefulness of public information declines due to the credibility crisis induced by the aspiration gap, institutional investors turn to first-hand channels to acquire verifiable private information. The on-site verification function of site visits allows investors to personally visit production and operation sites, observe the operation of environmental facilities, and talk to employees to cross-validate the truthfulness of management representations, thereby effectively reducing the uncertainty caused by the gap (Cheng et al., 2016; Solomon & Soltes, 2015).

Based on the above analysis, this study proposes the second hypothesis:

H2. 

A larger ESG performance aspiration gap is related to greater information asymmetry, and greater information asymmetry is in turn related to more frequent site visits.

4. Research Design

4.1. Sample Selection

In July 2012, the Shenzhen Stock Exchange issued a requirement that listed companies mandatorily disclose information related to institutional investors’ site visits starting from 2013, whereas the Shanghai Stock Exchange did not impose such mandatory disclosure requirements. Therefore, this study takes A-share listed companies on the Shenzhen Stock Exchange from 2013 to 2024 as the initial research sample. To avoid the influence of outliers, the following samples are excluded: listed companies under ST or *ST status; listed companies in the financial and insurance industries; and listed companies with missing financial data. To reduce the influence of extreme values, all continuous variables are winsorized at the 1st and 99th percentiles. This yielded a final sample of 18,576 observations. Institutional investors’ site visit data come from the CNRDS database, ESG data are from Huazheng, and other data are obtained from the CSMAR database. The final panel is unbalanced.

Table 1. Sample selection.

Step	Description	Observations
1	Initial firm-year observations	25,033
2	Less: missing Huazheng ESG	(3354)
3	Less: financial and insurance industries	(311)
4	Less: ST or *ST firms	(1201)
5	Less: missing data for key variables	(1591)
6	Final sample	18,576

reports sample selection.

4.2. Variable Definition

4.2.1. Dependent Variable: Institutional Investors’ Site Visits (Visit)

Following the existing literature (Cheng et al., 2019), institutional investors’ site visits are measured as the natural logarithm of one plus the number of site visits made by institutional investors to the firm in a year. In this paper, institutional investors are defined to include insurance companies, funds, securities firms, and other similar entities, while excluding individual investors, the media, government, and public institutions. It should be noted that the dependent variable measures general institutional investor visits rather than visits demonstrably focused on ESG matters. Furthermore, the dependent variable only includes physical visits and does not include virtual research methods such as telephone meetings and roadshows. This paper focuses on the frequency of institutional investors’ site visits rather than the number of institutions involved. A meeting attended by several institutional investors is counted as one visit event. Since the Shenzhen Stock Exchange required listed companies to mandatorily disclose information on institutional investors’ site visits beginning in 2013, firm-year observations without any disclosed visit records in the database are coded as zero visits.

4.2.2. ESG Performance Aspiration Gap (ESG_GAP)

The independent variable of this paper is the listed company’s ESG performance aspiration gap (ESG_GAP). According to organizational aspiration theory, performance aspiration gaps are primarily influenced by two reference points: the historical aspiration level and the industry aspiration level. According to the existing research (Dong, 2026; H. Zhang & Wu, 2025; Z. Zhang et al., 2026), this paper constructs Formula (1) based on the target firm’s own historical ESG performance and the average ESG performance of its industry.

$${\mathrm{GAP}}_{\mathrm{i},\mathrm{t}} = {\mathrm{FirmESG}}_{\mathrm{i},\mathrm{t}}-{\mathsf{\alpha }\mathrm{FirmESG}}_{\mathrm{i},\mathrm{t}-1}-{(1-\mathsf{\alpha })\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$$

(1)

$${\text{ESG\_GAP}}_{\mathrm{i},\mathrm{t}} = \mathrm{I} \times {\mathrm{GAP}}_{\mathrm{i},\mathrm{t}}$$

(2)

Regarding the construction of the core explanatory variable: ${\mathrm{FirmESG}}_{\mathrm{i},\mathrm{t}}$ denotes the ESG performance of firm i in year t, and ${\mathrm{FirmESG}}_{\mathrm{i},\mathrm{t}-1}$ denotes the ESG performance of firm i in year t − 1, which serves as the historical aspiration level. ${\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$ is the leave-one-out industry mean ESG performance in year t, calculated as the mean ESG performance of all firms in firm i’s industry except firm i, representing the industry aspiration level. We convert these categories into a nine-point scale by assigning values from 1 to 9, where a higher value indicates better ESG performance. The Huazheng ESG nine rating categories are converted as follows: C = 1, CC = 2, CCC = 3, B = 4, BB = 5, BBB = 6, A = 7, AA = 8, and AAA = 9. ESG performance in this paper is measured using the Huazheng ESG rating. Since the Huazheng ESG evaluation system releases ratings on a quarterly basis, we calculate the annual ESG performance of a firm as the average of its four quarterly ratings in a given calendar year. The parameter α is a weight coefficient ranging between 0 and 1. Different values of α determine whether the ESG performance aspiration gap places more emphasis on the deviation from the historical aspiration level or from the industry aspiration level. In the main test, α is set to 0.5, assigning equal influence weights to the firm’s own historical ESG level and the industry average ESG level. We define a dummy variable I that takes the value 1 when ${\mathrm{GAP}}_{\mathrm{i},\mathrm{t}}$ is less than 0—indicating the existence of a performance aspiration gap—and 0 otherwise. The interaction term of the dummy variable I and ${\mathrm{GAP}}_{\mathrm{i},\mathrm{t}}$ forms the independent variable of this paper, the ESG performance aspiration gap (ESG_GAP). For ease of economic interpretation, we take the absolute value of ESG_GAP. Thus, a larger value of this variable indicates a greater ESG performance aspiration gap for the firm. To further enhance the robustness of this paper, in the robustness test section, we use the fourth-quarter ESG score instead of the annual average to measure the ESG performance aspiration gap, and we change the industry average ESG to the previous year t − 1.

4.2.3. Mediating Variable: Information Asymmetry (IA)

Information Asymmetry is measured using the KV index. The core idea of this index is to analyze the sensitivity of stock returns to trading volume and thereby inversely infer the degree of information asymmetry of the firm (O. Kim & Verrecchia, 2001). A higher sensitivity indicates that public information is less sufficient and that investors rely more heavily on private information conveyed through trading volume, implying a higher degree of information asymmetry. This index has been widely adopted in existing research (Daske et al., 2008; Balakrishnan et al., 2014).

The model for constructing the KV index is as follows:

$$\mathrm{L}\mathrm{n}\left|\right({\mathrm{P}}_{\mathrm{t}}-{\mathrm{P}}_{\mathrm{t}-1})/{\mathrm{P}}_{\mathrm{t}-1}|={\mathsf{\lambda }}_0+\mathsf{\lambda }({\mathrm{V}\mathrm{o}\mathrm{l}}_{\mathrm{t}}/{\mathrm{V}\mathrm{o}\mathrm{l}}_0-1)+\mathsf{\epsilon }$$

(3)

In Equation (3), ${\mathrm{P}}_{\mathrm{t}}$ and ${\mathrm{V}\mathrm{o}\mathrm{l}}_{\mathrm{t}}$ denote the closing price and trading volume (number of shares) on day t, respectively, and ${\mathrm{V}\mathrm{o}\mathrm{l}}_0$ is the average daily trading volume over the fiscal year. For each listed firm, the coefficient λ estimated by ordinary least squares is used to construct the KV index, with negative values of λ not considered. A smaller λ indicates more adequate information disclosure; therefore, a higher KV value represents lower disclosure quality (O. Kim & Verrecchia, 2001).

The KV index is defined as:

$$\mathrm{KV} = \mathsf{\lambda } \times {10}^6$$

The scaling by 10⁶ is used only for reporting convenience and has no effect on statistical inference.

When calculating the annual KV index, we require that a stock have at least 120 valid trading days in the given fiscal year.

4.2.4. Control Variables

This paper selects a series of control variables. This paper also controls for year fixed effects and firm fixed effects.

The definitions and calculation methods of all variables are presented in

Table 2. Variable definitions.

Variables	Symbol	Definition
Institutional Investors’ Site Visits	Visit	Natural logarithm of (1 + number of institutional investors’ site visits)
ESG Performance Aspiration Gap	ESG_GAP	See the preceding description.
Information Asymmetry	IA	KV index
Listing age	AGE	Natural logarithm of (firm listing age + 1)
Enterprise size	Size	Natural logarithm of total assets at the end of the fiscal year
Return on Assets	ROA	Annual net income/total assets at the end of the fiscal year
Asset-Liability Ratio	Lev	Total liabilities/Total assets
Proportion of Independent Directors	Indir	Independent directors/total board directors
Growth Capability	Growth	Operating revenue in the current year/Operating revenue in the previous year − 1
Stock Turnover Rate	Turnover	annual trading volume/outstanding shares
Institutional Investor Shareholding	Insthold	The proportion of a listed company’s shares held by institutional investors
Ownership Nature	SOE	Equals 1 for state-owned enterprises and 0 for non-state-owned enterprises
Board size	Board	Natural logarithm of (1 + the number of board directors).
Audit Quality	Big4	Equals 1 if the auditor is from a Big Four accounting firm, and 0 otherwise.

.

4.2.5. Model

Based on the above analysis, in order to accurately identify the relationship between the ESG performance aspiration gap and institutional investors’ site visits, this paper constructs the following basic econometric models:

Baseline Regression Model:

$${\mathrm{Visit}}_{\mathrm{i},\mathrm{t}} = {\mathsf{\alpha }}_0 + {\mathsf{\alpha }}_1{\text{ESG\_GAP}}_{\mathrm{i},\mathrm{t}-1} + {\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1} + \mathrm{Year} + \mathrm{Firm} + \mathsf{\epsilon }$$

(4)

Mechanism Test Models:

$${\mathrm{IA}}_{\mathrm{i},\mathrm{t}} = {\mathsf{\beta }}_0 +{ \mathsf{\beta }}_1{\text{ESG\_GAP}}_{\mathrm{i},\mathrm{t}-1} + {\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1} + \mathrm{Year} + \mathrm{Firm} + \mathsf{\epsilon }$$

(5)

$${\mathrm{Visit}}_{\mathrm{i},\mathrm{t}} = {\mathsf{\gamma }}_0 + {\mathsf{\gamma }}_1{\text{ESG\_GAP}}_{\mathrm{i},\mathrm{t}-1} + {\mathsf{\gamma }}_2{\mathrm{IA}}_{\mathrm{i},\mathrm{t}} + {\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1} + \mathrm{Year} + \mathrm{Firm} + \mathsf{\epsilon }$$

(6)

Furthermore, in order to address heteroscedasticity and enhance the accuracy and reliability of the measurements, this paper employs cluster standard error handling at the firm level.

4.3. Empirical Results

4.3.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variable	N	Mean	Sd	Min	Max
Visit	18,576	1.127	0.989	0.000	4.407
ESG_GAP	18,576	0.265	0.274	0.000	1.913
IA	18,576	0.417	0.259	0.000	1.462
AGE	18,576	2.228	0.507	1.099	3.497
Size	18,576	22.651	1.156	20.131	26.360
ROA	18,576	0.029	0.059	−0.218	0.239
Lev	18,576	0.459	0.178	0.052	0.827
Indir	18,576	0.374	0.053	0.333	0.571
Growth	18,576	0.155	0.502	−0.391	2.629
Turnover	18,576	2.851	2.439	0.337	11.405
Insthold	18,576	0.388	0.242	0.002	0.903
SOE	18,576	0.372	0.483	0.000	1.000
Board	18,576	2.252	0.186	1.609	2.944
Big4	18,576	0.058	0.231	0.000	1.000

reports the descriptive statistics of the main variables. The mean value of institutional investors’ site visits is 1.127, with a standard deviation of 0.989, indicating considerable variation in visit intensity across sample firms. The mean value of the ESG performance aspiration gap is 0.265, with a standard deviation of 0.274 and a maximum value of 1.913. The descriptive statistics of the ESG performance aspiration gap are largely consistent with those reported in existing studies on ESG performance aspiration gaps (Dong, 2026; H. Zhang & Wu, 2025). This pattern indicates that the ESG performance of most firms remains near the aspiration level, while a small number of firms experience substantial aspiration deviations. Information asymmetry, measured by the KV index, has a mean of 0.417, a standard deviation of 0.259, a minimum of 0.000, and a maximum of 1.462, suggesting significant divergence in the quality of the information environment across sample firms. The remaining control variables are generally consistent with existing studies.

4.3.2. Main Test

Table 4. Results of the main regression test.

	(1)	(2)	(3)
	Visit	Visit	Visit
ESG_GAP	0.298 ***	0.214 ***	0.152 ***
	(5.67)	(4.62)	(3.29)
AGE		−0.072 ***	−0.058 ***
		(−3.31)	(−2.84)
Size		−0.006	−0.005
		(−0.83)	(−0.71)
ROA		0.338 ***	0.285 ***
		(3.02)	(2.57)
Lev		0.031	0.026
		(0.85)	(0.64)
Indir		0.017	0.015
		(0.14)	(0.11)
Growth		0.046 *	0.030 *
		(1.68)	(1.67)
Turnover		0.016 ***	0.012 ***
		(4.30)	(3.26)
Insthold		0.098 ***	0.082 ***
		(3.52)	(3.16)
SOE		−0.056 *	−0.025 *
		(−1.79)	(−1.71)
Board		0.005	0.003
		(0.13)	(0.08)
Big4		0.139 ***	0.117 ***
		(3.48)	(3.61)
_cons	1.048 ***	1.273 ***	0.427 ***
	(16.98)	(12.02)	(4.46)
Year/Firm	NO	NO	YES
Adj R2	0.007	0.234	0.302
N	18,576	18,576	18,576

Note: t-statistics in parentheses. * p < 0.1, *** p < 0.01.

reports the baseline regression results of the relationship between ESG performance aspiration gap and institutional investors’ site visits. Column (1) includes only the ESG performance aspiration gap, without adding control variables or controlling for year and firm fixed effects. The result shows that the coefficient of the ESG performance aspiration gap is 0.298 and significantly positive at the 1% level, indicating a significant positive correlation between the ESG performance aspiration gap and institutional investors’ site visits when other factors are not considered. Column (2) adds control variables on the basis of Column (1) but still does not control for fixed effects; the coefficient of ESG performance aspiration gap is 0.214 and significantly positive at the 1% level. Column (3) further controls for year and firm fixed effects as the complete main test model of this paper, and the coefficient of ESG performance aspiration gap is 0.152, remaining significantly positive at the 1% level. From column (1) to column (3), the coefficient of ESG_GAP gradually declines from 0.298 to 0.152, suggesting that controlling for firm characteristics and fixed effects partially mitigates concerns about omitted variable bias, and the core effect becomes more conservative yet remains robust. The consistent findings across specifications indicate that the positive relationship between ESG performance aspiration gap and institutional investors’ site visits is robust, thereby supporting Hypothesis H1. This finding is consistent with the existing literature (Cheng et al., 2016). Column (3) shows that a one-standard-deviation increase in the gap corresponds approximately to a 4.2% increase in 1 + the number of visits.

4.3.3. Mechanism Test

We use a nonparametric bootstrap that uses firm-level cluster resampling with 5000 replications. The reported 95% confidence interval is constructed using the percentile method applied to the empirical distribution of the bootstrap indirect effects.

Table 5. Mechanism test—Regression results.

	(1)	(2)
	IA	Visit
ESG_GAP	0.617 ***	0.121 ***
	(4.11)	(2.64)
IA		0.051 ***
		(2.89)
Controls	YES	YES
_cons	0.503 ***	0.412 ***
	(6.59)	(4.28)
Year/Firm	YES	YES
Adj R2	0.194	0.309
N	18,576	18,576
Bootstrap 95% CI	[0.014, 0.063]

Note: t-statistics in parentheses. *** p < 0.01.

reports the results of the mechanism analysis. As shown in Column (1), ESG_GAP has a positive and statistically significant effect on information asymmetry at the 1% level. The result indicates that the ESG performance aspiration gap significantly exacerbates information asymmetry. When ESG_GAP and IA are jointly included in Column (2), both coefficients remain significantly positive at the 1% level, consistent with an information-asymmetry channel.

4.3.4. Robustness Test

This study employs a variety of robustness tests to ensure the robustness of the research conclusions.

First, we use alternative measures of the independent variable.

(1) Changing the weights

In the main test, the weight coefficient assigned to the ESG performance aspiration gap is set at 0.5, treating historical ESG performance and industry ESG performance as equally important reference components. In this section, the weight coefficient is adjusted to 0.3 and 0.7, respectively, to recalculate the ESG performance aspiration gap. When the weight coefficient is 0.3, the ESG performance aspiration gap places greater emphasis on the gap relative to industry ESG performance; when the weight coefficient is 0.7, it places greater emphasis on the gap relative to historical ESG performance. The results in Column (1) of

Table 6. Robustness test—Alternative independent-variable measures.

	(1) α = 0.3	(2) α = 0.7	(3) α = 0	(4) α = 1	(5) t − 1 Industry	(6) Q4-ESG
	Visit	Visit	Visit	Visit	Visit	Visit
ESG_GAP	0.118 ***	0.171 ***	0.109 **	0.184 ***	0.137 ***	0.146 ***
	(2.65)	(3.66)	(2.41)	(3.85)	(2.94)	(3.17)
Controls	YES	YES	YES	YES	YES	YES
_cons	0.418 ***	0.433 ***	0.425 ***	0.427 ***	0.422 ***	0.429 ***
	(4.35)	(4.52)	(4.30)	(4.67)	(4.39)	(4.52)
Year/Firm	YES	YES	YES	YES	YES	YES
Adj R2	0.302	0.302	0.303	0.301	0.301	0.297
N	18,576	18,576	18,576	18,576	18,576	18,576

Note: t-statistics in parentheses. ** p < 0.05, *** p < 0.01.

show that when the weight coefficient is adjusted to 0.3, the coefficient of the ESG performance aspiration gap is 0.118, which is significantly positive at the 1% level. The results in Column (2) indicate that when the weight coefficient is adjusted to 0.7, the coefficient of the ESG performance aspiration gap is 0.171, also significantly positive at the 1% level. The above results indicate that after altering the measurement of the ESG performance aspiration gap, it remains significantly positively related to institutional investors’ site visits, and the core conclusion of this paper remains unchanged.

To further distinguish the roles of the historical-aspiration and industry-aspiration components, we also set the weight coefficient to its two extreme values. Specifically, when the weight coefficient equals 0, the ESG performance aspiration gap is constructed entirely relative to the industry, representing the pure industry-aspiration gap. When the weight coefficient equals 1, the gap is constructed entirely relative to the firm’s own historical ESG performance, representing the pure historical-aspiration gap. The corresponding results are reported in Columns (3) and (4) of Table 6. The coefficient of the pure industry-aspiration gap (α = 0) is 0.109, significant at the 5% level, while the coefficient of the pure historical-aspiration gap (α = 1) is 0.184, significant at the 1% level. These results indicate that both components are positively related to institutional investors’ site visits, but the historical-aspiration component appears to be more pronounced.

(2) Using the industry average value from period t − 1

In the main test, the measurement method for the ESG performance aspiration gap uses the industry average value for the t year. In this section, we use the industry average value of the t − 1 year. The results in Column (5) indicate that the coefficient is 0.137, remaining significantly positive at the 1% level. This indicates that the core conclusion remains unchanged.

(3) Using fourth-quarter Huazheng ESG rating

In the main test, the measurement method for the ESG performance aspiration gap uses the average value for the t − 1 year. In this section, we use the fourth-quarter ESG rating of year t − 1 instead of the annual average of year t − 1. The results in Column (6) indicate that the coefficient is 0.146, remaining significantly positive at the 1% level. This indicates that the core conclusion remains unchanged.

Second, considering that during the COVID-19 outbreak and containment period in 2020–2021, site visit activities were significantly disrupted by multiple factors such as offline travel restrictions, quarantine policies, and corporate shutdowns, institutional investors’ visit methods, frequency, and information demands may have experienced atypical fluctuations, which could pose a potential threat to the robustness of the core conclusion of this paper. To this end, this paper excludes the 2020–2021 sample and re-estimates the regression. The results in

Table 7. Robustness test—Changing the sample period.

	Visit
ESG_GAP	0.158 ***
	(3.74)
Controls	YES
_cons	0.423 ***
	(4.37)
Year/Firm	YES
Adj R2	0.296
N	15,304

Note: t-statistics in parentheses. *** p < 0.01.

show that after excluding the pandemic sample, the regression coefficient of the ESG performance aspiration gap is 0.158, remaining significantly positive at the 1% level. The above results indicate that after ruling out the interference of the external shock during the special pandemic period, the positive relation between the ESG performance aspiration gap and institutional investors’ site visits remains robust, and the core conclusion of this paper is not affected by the abnormal events during the sample period.

Third, to demonstrate that the core conclusion does not depend on a particular temporal specification and to examine the timely response to the ESG performance aspiration gap, the robustness checks further report a contemporaneous model in which both the ESG performance aspiration gap and institutional investors’ site visits are measured at period t. The results in

Table 8. Robustness test—Using the same period as the independent variable.

	Visit
ESG_GAP	0.163 ***
	(3.52)
Controls	YES
_cons	0.425 ***
	(4.01)
Year/Firm	YES
Adj R2	0.299
N	20,112

Note: t-statistics in parentheses. *** p < 0.01.

show that the coefficient of ESG_GAP is 0.163 and is significantly positive at the 1% level, consistent with the main test. This indicates that the positive relation between the ESG performance aspiration gap and institutional investors’ site visits holds robustly regardless of whether the gap is measured contemporaneously or with a one-period lag.

Finally, to verify that the core conclusion is not sensitive to the model specification, this paper further employs fixed-effects Poisson regression for a robustness test.

For the Poisson robustness test, the dependent variable is the raw count of institutional investors’ site visits. We estimate a fixed-effects Poisson model using the conditional maximum likelihood estimator. Firm fixed effects are absorbed through the conditional likelihood, and year dummies are included to capture common time effects. Standard errors are clustered at the firm level. Firms with no within-firm variation in the visit count are dropped automatically by the estimator. The results in

Table 9. Robustness test—Poisson estimates.

	Visit
ESG_GAP	0.296 ***
	(3.21)
Controls	YES
Year/Firm	YES
Pseudo R2	0.282
N	15,234

Note: Due to the use of Poisson estimates, the values within the model brackets are z-values. *** p < 0.01.

show that the regression coefficient of the ESG performance aspiration gap is 0.296, significantly positive at the 1% level. After applying the Poisson estimation, the significance and direction of the coefficient of the ESG performance aspiration gap remain the same as those of the main test in this paper. This indicates that the results of this paper remain robust even after changing the model.

4.3.5. Heterogeneity Analysis

To further explore whether the positive relation between the ESG performance aspiration gap and institutional investors’ site visits differs across different contexts, this study conducts moderating effect analysis from two dimensions: marketization level and analyst coverage. The regression results are presented in

Table 10. Heterogeneity analysis.

	(1)	(2)
	Visit	Visit
ESG_GAP	0.167 ***	0.172 ***
	(3.47)	(3.54)
Mkt	0.015 ***
	(3.14)
ESG_GAP × Mkt	−0.043 ***
	(−3.08)
Analyst		0.018 ***
		(3.62)
ESG_GAP × Analyst		−0.020 **
		(−2.05)
Controls	YES	YES
_cons	0.424 ***	0.421 ***
	(4.41)	(4.36)
Year/Firm	YES	YES
Adj R2	0.303	0.301
N	18,576	18,576

Note: t-statistics in parentheses. ** p < 0.05, *** p < 0.01.

.

First, the relationship between the ESG performance aspiration gap and institutional investors’ site visits may differ significantly depending on the level of marketization in the region where the firm is located. The existing literature indicates that the degree of marketization largely reflects the accessibility of regional information (H. Wang & Qian, 2011). In regions with a lower level of marketization, legal enforcement efficiency is relatively inadequate, the development of information intermediaries lags behind, and government intervention and administrative barriers are higher. Consequently, the quality of firms’ public information disclosure tends to be low, and the cost for investors to obtain reliable information through formal institutional channels rises significantly. When firms in such regions fall into an ESG performance aspiration gap, publicly disclosed ESG information is more likely to have been selectively packaged or deliberately obscured, making it difficult for investors to accurately judge the true causes and risk levels behind the gap based on annual reports, ESG reports, or regular announcements. Under these circumstances, institutional investors’ reliance on site visits as an informal channel for acquiring private information will be substantially enhanced. In contrast, in regions with a higher level of marketization, well-developed legal systems, advanced intermediary service networks, and efficient information dissemination mechanisms provide solid institutional infrastructure for the generation and transmission of corporate ESG information. Investors can acquire and verify corporate ESG information relatively adequately through multiple public channels. Even if a firm experiences an ESG performance aspiration gap, investors in most cases can rely on the mature market information system to form relatively reliable risk judgments, thus making their marginal demand for site visits relatively limited. Using the data of the *China Provincial Marketization Index Report*, we include the interaction term between the ESG performance aspiration gap and the marketization index in the full sample regression. The results are presented in Column (1) of Table 10. The coefficient of the interaction term (ESG_GAP × Mkt) is −0.043 and is significant at the 1% level, indicating that the positive relation between the ESG performance aspiration gap and institutional investors’ site visits becomes significantly weaker as the level of marketization improves.

Finally, analysts, as key information intermediaries connecting firms and investors in the capital market, directly influence the quality of a firm’s information environment through the extent of their coverage. When a firm is followed by numerous analysts, professional analyst teams continuously mine, interpret, and disseminate the firm’s financial and non-financial information. Changes in ESG performance are thus more likely to be captured in a timely manner and reflected in research reports, earnings forecast revisions, and market pricing. Analyst coverage essentially constructs an efficient information production and distribution network for the firm, enabling investors to obtain relatively sufficient and professionally processed information about the firm’s ESG conditions through public channels, even without conducting on-site visits. Conversely, for firms with low analyst coverage, the supply of public information is severely inadequate, and the mining of ESG-related information remains nearly absent. When such firms experience an ESG performance aspiration gap, the risk signals are difficult for the market to identify promptly, let alone be effectively interpreted and disseminated. Under these circumstances, the failure of public information channels renders investors unable to rely on analysts’ professional judgment to reduce cognitive uncertainty. Site visits, as a proactive and self-sufficient means of acquiring private information, thus see their marginal value significantly enhanced. We include the interaction term between the ESG performance aspiration gap and lagged analyst coverage in the full sample regression. The results are presented in Column (2) of Table 10. The coefficient of the interaction term (ESG_GAP × Analyst) is −0.020 and is significant at the 5% level, indicating that the positive relation between the ESG performance aspiration gap and institutional investors’ site visits becomes significantly weaker as analyst coverage increases.

5. Conclusions and Implications

5.1. Research Conclusions

This paper introduces the non-financial performance dimension of ESG into the organizational aspiration model and empirically examines the relation between the ESG performance aspiration gap and institutional investors’ site visits. The findings are as follows. First, the ESG performance aspiration gap is positively correlated with institutional investors’ site visits. That is, the greater the extent to which a firm’s ESG performance falls below its historical and industry aspirations, the higher the frequency of institutional investors’ site visits. This correlation remains robust after a series of robustness tests. Second, the results are consistent with an information-asymmetry channel, in which a larger ESG performance aspiration gap is related to greater information asymmetry, and greater information asymmetry is in turn related to more frequent institutional investors’ site visits. Specifically, the ESG performance aspiration gap is positively correlated with information asymmetry between firms and investors, and higher information asymmetry is in turn positively correlated with more frequent site visits, consistent with the interpretation that investors rely more on site visits to obtain private incremental information and reduce decision-making uncertainty. Third, the moderating effect analysis shows that both marketization level and analyst coverage negatively moderate the positive relation between the ESG performance aspiration gap and institutional investors’ site visits.

5.2. Policy Implications

This study provides several implications for regulatory authorities, listed firms, and institutional investors:

(1) Further improving the standardization and credibility of ESG information disclosure may help mitigate the information environment problems correlated with ESG performance aspiration gaps.

Regulatory authorities could consider further refining unified ESG disclosure standards, specifying quantitative indicators, calculation scopes, and disclosure templates for key issues tailored to different industries. Such measures may reduce firms’ discretion in issue selection and data presentation, thereby narrowing the room for selective disclosure and impression management. On this basis, ESG information disclosure could be progressively transitioned from a voluntary approach toward a “comply or explain” mechanism and, where appropriate, toward mandatory disclosure, particularly for high-emission industries and firms with elevated environmental risks. In addition, drawing on the institutional experience of financial auditing, a third-party assurance mechanism for ESG information could be explored, enabling independent verification of key environmental data and social responsibility indicators. These steps may enhance the reliability and credibility of disclosed information, potentially mitigating at its source the information asymmetry correlated with ESG performance aspiration gaps, reducing investors’ reliance on private information search channels such as site visits, and contributing to the information efficiency of the capital market.

(2) Strengthening disclosure supervision for firms experiencing an ESG performance aspiration gap may reduce the scope for managerial impression management.

Regulatory authorities could implement more targeted disclosure supervision for firms with a significant ESG performance aspiration gap, for example, by requiring them to explain the causes of the gap, the corrective measures already taken, and the expected recovery timeline in their periodic reports, and to provide explanations for abnormal changes in key quantitative data. For firms with a large or prolonged gap, stock exchanges may use existing mechanisms, such as inquiry letters, to request supplementary disclosures and to track the quality of responses over time. Such measures may help reduce management’s ability to exploit information ambiguity, thereby enabling public information to reflect firms’ ESG risk profiles more truthfully and promptly, and potentially weakening the correlation between the gap and information asymmetry.

(3) Developing information intermediary service systems may contribute to a more efficient capital market information environment.

In regions with lower levels of marketization, it may be beneficial to advance the rule of law and factor market development, thereby reducing distortions in information transmission and creating a more favorable institutional ecosystem for corporate disclosure quality. At the same time, regional financial information service platforms could be further developed, and information intermediaries—such as securities analysts, financial media, and third-party rating agencies—could be encouraged to expand their coverage and analysis of listed companies in less developed regions. Through professional information production and dissemination, such efforts may lower the barriers to investor access to information and allow risk signals such as the ESG performance aspiration gap to be reflected in market prices more timely and comprehensively.

5.3. Limitations and Future Directions

Despite this study providing capital market-level empirical evidence for understanding how the ESG performance aspiration gap affects institutional investor behavior, this study has several limitations that suggest directions for future research:

First, our annual ESG performance measure is constructed by averaging quarterly Huazheng ESG rating categories. Although this approach is commonly used in the literature and allows us to maintain a consistent firm-year panel, it may not fully capture the ordinal nature of ESG ratings or intra-year fluctuations in ESG performance. Future research could adopt more granular ESG scores or alternative rating data to improve measurement precision.

Second, as for the dependent variable, it is measured as the total annual number of institutional investors’ site visits. Future research could use keyword-based or textual analysis to identify ESG-related visits and more precisely test the verification channel. The measure does not distinguish between investor demand for access and firm willingness to host a visit, so future studies could attempt to separate demand-driven visits from supply-driven visits.

Third, this paper focuses on ESG underperformance rather than general performance deviation and therefore discards information on positive performance feedback. Future research could construct separate measures for performance below and above aspirations to more completely characterize how relative ESG performance—whether gaps or surpluses—is related to institutional investors’ information acquisition decisions.

Finally, this paper focuses on the site visit behavior of institutional investors as a core information intermediary, but the risk signals released by the ESG performance aspiration gap may also trigger the attention and actions of other market participants. For instance, analysts may adjust their coverage strategies or the tone of their research reports as the ESG gap heightens earnings forecast uncertainty; auditors may increase audit effort and fees due to the compliance risks implied by the ESG gap; and financial media may increase reporting frequency because the gap possesses news value as a “deviation from aspiration.” Future research could construct a response framework for the ESG performance aspiration gap that encompasses multiple information intermediaries, thereby enabling a more comprehensive understanding of how capital markets digest ESG risk signals through different information channels, and providing more targeted policy references for regulators to improve ESG information governance from multiple dimensions.