Investor attention and environmental performance of Chinese high-tech companies: the moderating effects of media attention and coverage sentiment

China’s rapid industrialization has produced severe environmental challenges (air and water pollution, hazardous waste, high carbon emissions). High-tech enterprises, despite driving growth and innovation, contribute materially to environmental pressures and face increasing scrutiny and regulatory pressure. Prior emphasis on economic growth over environmental stewardship, uneven regulatory enforcement, and low transparency have exacerbated issues. Investors increasingly consider environmental performance alongside returns, and social media has transformed how information spreads among stakeholders. This study asks whether and how investor attention affects the environmental performance (EP) of Chinese high-tech firms, and whether media attention and coverage sentiment moderate this relationship. The paper motivates a focus on high-tech firms due to their significant footprints in supply chains and potential to implement innovative environmental solutions. It contributes by centering an external driver—investor attention—examining media attention and sentiment as moderators, and focusing specifically on high-tech enterprises in China.

Corporate environmental performance (EP) captures a firm’s environmental impacts, resource use, and related financial aspects, often measured via pollutant emissions, ESG databases (e.g., ASSET4), or the Sino-Securities Index (SNSI). Prior work links EP to factors such as digital technologies, governance, and R&D, while informal external regulation (media scrutiny, community activism) also plays a critical role in shaping EP. Investor attention—proxied by search indices or online activity—can influence EP, but evidence is mixed. Positive channels include enhanced transparency, monitoring, and incentives to prevent reputational loss. Negative channels include short-termism, cost-cutting of environmental expenditures, and superficial compliance. Based on this ambiguity, the authors propose H1a (positive effect) and H1b (negative effect). Media attention, defined as sustained coverage, can shape legitimacy pressures and moderate the investor attention–EP link (H2). Coverage sentiment (tone) can also moderate the relationship (H3), with positive sentiment potentially amplifying supportive effects on EP. The research gap includes uncertainty about the net effect of investor attention on EP, limited work on media moderators, and scant focus on high-tech enterprises.

The authors estimate dynamic panel models for corporate environmental performance (EP) among Chinese high-tech listed firms from 2011–2022. Baseline specification includes investor attention (IA) and firm controls: size (SIZE), employees (EMP), Tobin’s Q (TOBINQ), listing age (LISTAGE), and return on equity (ROE). A dynamic panel model adds lagged EP to address persistence. Moderation models introduce interaction terms for media attention (MA) and media coverage sentiment (MCS): EP_it = β0 + γ EP_it-1 + β1 IA_it + β2 MA_it (or MCS_it) + β3 IA_it×MA_it (or IA_it×MCS_it) + controls + ε_it. Marginal effects are computed as ∂EP/∂IA = β1 + β3×Moderator, with significance assessed at min/mean/max moderator values. Estimation uses difference and system GMM to address endogeneity and dynamic panel bias, with Hansen tests for over-identifying restrictions and AR(1)/AR(2) tests for serial correlation. Measures: EP is the “environmental” score from the Sino-Securities Index (SNSI) ESG Ratings. IA is the log count of investor comments from the Guba Database (CNRDS). MA is the log frequency of news mentions (CNRDS). MCS is the Janis–Fadner coefficient constructed from counts of positive and negative news (values near 1 indicate more positive, −1 more negative). Controls are from CSMAR: SIZE (log total assets), EMP (log employees), TOBINQ, LISTAGE (ln(current year − incorporation year + 1)), and ROE. Sample: high-tech firms listed in Shanghai/Shenzhen, identified per China’s “Administrative Measures for the Recognition of High-tech Enterprises,” excluding ST/PT, financials/insurers, firms with missing data, and outliers. Final sample: 463 firms, 5,556 firm-year observations (2011–2022).

- Descriptive statistics show moderate EP levels (mean ~38.96) and variation in IA, MA, and MCS; correlations do not indicate severe multicollinearity. - Benchmark GMM regressions (difference and system GMM) show IA has a statistically significant negative effect on EP for high-tech listed companies. Example IA coefficients range approximately from −0.52 to −0.79 (Table 3). Diagnostic tests support model validity (Hansen not rejected; AR(1) present, AR(2) absent). - Robustness: Excluding COVID-19 period (2011–2019) maintains the negative IA effect on EP; IA ≈ −1.89 (1% significance) in SYS-GMM (Table 4). Using environmental information disclosure (EID) as an alternative dependent variable also yields negative IA coefficients (e.g., −0.22, 1% significance in SYS-GMM two-step; Table 5). - Moderation: Interaction terms indicate mitigating roles of media factors. IA×MA is positive and significant across models, implying higher media attention weakens the negative IA–EP relationship. IA×MCS shows similar mitigation (positive moderation), indicating that more favorable coverage sentiment reduces the adverse effect of IA on EP. Marginal effect analysis shows a rising marginal effect of IA on EP with higher MA/MCS; at mean levels, a 1% rise in IA corresponds to a 2.01% increase in annual EP growth; at maximum levels, a 1% IA rise corresponds to a 5.95% EP increase. - Heterogeneity by industry: In high-tech manufacturing, MA and MCS mitigate the negative IA–EP link (e.g., positive and significant interactions). In IT services, media attention intensifies the negative IA–EP effect (IA×MA ≈ 4.54), and MCS has no significant moderating effect. - Heterogeneity by pollution intensity: IA negatively affects EP in both polluted and non-polluted samples; MA mitigates the negative effect in both. In polluted firms, positive coverage sentiment alleviates the negative relationship; in non-polluted firms, IA×MCS ≈ −11.91 suggests sentiment exacerbates the negative IA–EP impact. - Heterogeneity by ownership: IA is negative for both state-owned and private firms. MA mitigates the negative IA–EP effect in both. For state-owned enterprises, IA×MCS ≈ −5.22 indicates positive sentiment can exacerbate the negative IA–EP relationship; in private firms, sentiment mitigates it. Overall, results support H1b (negative effect of investor attention on EP) and confirm moderating roles for H2 (media attention) and H3 (coverage sentiment), with nuanced differences across industries, pollution intensity, and ownership.

The findings indicate that heightened investor attention can push high-tech firms toward short-term financial priorities at the expense of environmental initiatives, aligning with theories of short-termism and evidence of earnings management under pressure. At the same time, media attention and favorable sentiment function as informal governance mechanisms that enhance transparency, accountability, and legitimacy, mitigating the adverse impact of investor pressure on environmental practices. Legitimacy theory helps explain how sustained and credible media scrutiny elevates societal expectations and compels firms to improve environmental strategies, thereby moderating the investor attention–EP relationship. Sectoral, pollution-intensity, and ownership heterogeneity show that the effectiveness of media mechanisms depends on the visibility and tangibility of environmental impacts and on institutional contexts (e.g., state-owned versus private governance). These results address the research question by demonstrating a predominantly negative baseline effect of investor attention on EP in Chinese high-tech firms, and by identifying media attention and sentiment as meaningful moderators that can counterbalance or, in some contexts, intensify this relationship.

The study contributes by documenting a statistically significant negative effect of investor attention on the environmental performance of Chinese high-tech firms and by establishing that media attention and media coverage sentiment can mitigate this adverse effect on average. Using dynamic panel GMM on 463 firms (2011–2022), the results are robust to alternative periods (excluding COVID-19) and to alternative measures of environmental outcomes (EID). Heterogeneity analyses reveal that the moderating role of media differs across industries (manufacturing vs. IT services), pollution intensity (polluted vs. non-polluted), and ownership (state-owned vs. private). Policy and managerial implications include: enhancing ESG reporting standards to improve transparency; developing green indices and benchmarks to channel capital toward environmentally responsible firms; and offering incentives for green technologies and practices. Policymakers can also foster a media environment that supports accurate, thorough environmental reporting to align investor perceptions with sustainability. Firms should proactively engage with media and communicate environmental strategies transparently to leverage positive sentiment and attention. Future research should explore additional moderating mechanisms and contextual factors that can dampen the adverse effects of investor attention on environmental outcomes and broaden samples to improve generalizability.

The study excludes certain enterprises due to data availability (e.g., ST/PT firms, financials/insurers, incomplete data), which may affect generalizability. While GMM addresses endogeneity concerns, unobserved confounders may remain. Media measures, sentiment classification, and EP proxies (SNSI, EID) may introduce measurement error. Results are specific to Chinese high-tech listed firms and time period. Future work should incorporate additional moderating factors, alternative media/attention measures, broader sectors and geographies, and causal identification strategies.