ESG and customer stability: a perspective based on external and internal supervision and reputation mechanisms

The study investigates whether and how corporate ESG performance enhances customer stability within supply chains, an issue of importance given ongoing instability in global and Chinese supply chains and China’s policy emphasis on resilience and dual circulation. The paper posits, drawing on external pressure theory and reputation theory, that improved ESG alleviates information asymmetry, lowers customers’ monitoring costs, and sends credible signals of sustainable operations, thereby fostering stable customer relationships. The research aims to quantify the relationship between ESG and customer stability among Chinese A-share listed firms and to examine mechanisms via internal/external supervision and corporate reputation. It also explores heterogeneity by governance level, firm size, and ESG sub-dimensions (E, S, G).

Grounded in external pressure theory, stakeholders impose environmental and social expectations that prompt firms to undertake ESG activities to meet evolving demands and reduce regulatory pressures (Porter 2007; Weber 2014; Lee and Raschke 2023; La Torre et al. 2021). ESG disclosure mitigates information asymmetry, facilitating external stakeholders’ supervision and reducing monitoring costs (Healy and Palepu 2001; Bilyay-Erdogan et al. 2024). Internally, ESG promotes better governance, stronger internal controls, and higher information transparency (Cantista and Tylecote 2008; Crace and Gehman 2023; Harasheh and Provasi 2023). Reputation theory views ESG as a source of reputational capital enhancing brand image, investor confidence, and customer loyalty (Highhouse et al. 2009; Pástor et al. 2021; Uyar et al. 2022). Prior work links ESG to supply chain transparency and performance (Zhang et al. 2024; Zeng et al. 2022) and to reduced risk and improved financial outcomes (Saini et al. 2022; Broadstock et al. 2021). However, direct evidence on ESG and customer stability is limited. The paper formulates hypotheses: H1, ESG is positively correlated with customer stability; H2, ESG enhances customer stability via strengthened internal and external regulation (internal control quality and information transparency); H3, ESG enhances customer stability via improved corporate reputation (media attention and positive coverage).

Sample and data: All A-share listed companies (Shanghai and Shenzhen) from 2011–2022. Exclusions: financial/insurance and real estate industries; abnormal firms (ST, ST*, PT); firms not disclosing specific names and sales of top five clients; observations with missing controls or outliers. Final panel: 10,840 firm-year observations. Customer data (top five customers, repeat identification, sales) were manually compiled; financial and firm characteristics from CSMAR; ESG scores primarily from Bloomberg, with robustness using Hexun, CESG (CNRDS), and Sino-Securities. Variables: Dependent variables (customer stability): CustomerNum = number of repeat customers among current-year top five vs. previous year, divided by 5; CustomerSales = share of sales to repeat customers in current-year total sales of top five. Key regressor: ESG = ln(Bloomberg ESG score + 1). Controls: Size (ln employees +1), Lev (liabilities/assets), ROA, ATO, Cashflow (operating cash flow/assets), FirmAge (ln years since establishment +1), Top1 (largest shareholder ownership), Balance (second-largest/largest ownership). Mechanism variables: Internal control index (Inter), information transparency (trans) for internal/external regulation; Baidu search index (baidu) and positive media coverage (report) for reputation. Econometric design: Two-way fixed effects model with firm and year FEs; robust standard errors clustered at industry-year level. Hausman tests support FE (CustomerNum chi2=311.45, p<0.001; CustomerSales chi2=302.74, p<0.001). Baseline model: Y_it = β0 + β1 ESG_it + γ X_it + μ_i + σ_t + ε_it, where Y is CustomerNum or CustomerSales. Multicollinearity was low (average VIF=1.54). Tests indicated autocorrelation and heteroscedasticity; robust clustering used. Mechanism tests: Three-step mediation within two-way FE: (2) M_it = b0 + β1 ESG_it + φ X_it + μ_i + σ_t + ε_it; (3) Y_it = b0 + β2 M_it + β3 ESG_it + φ X_it + μ_i + σ_t + ε_it, for M in {Inter, trans, baidu, report}. Endogeneity: Instrumental variables (IV) approach using interaction of industry-year average ESG score and Confucian cultural influence (proxied by number of Confucian temples/academies within 200 km of firm’s registered location). First-stage relevance confirmed; weak IV ruled out (first-stage F=107.02; Cragg-Donald Wald F=107.02; Kleibergen-Paap LM=430.63). Second-stage 2SLS estimated effect of ESG on customer stability (firm and year FEs; controls). Robustness: (i) Alternative ESG measures (Hexun, CESG, Sino-Securities); (ii) controlling for time trends using third-order polynomial interactions of controls with time and interactions with year dummies (following Moser and Voena 2012); (iii) re-clustering at the firm level; results remained stable. Heterogeneity: Split by corporate governance level (median split) and by firm size (small vs large). ESG sub-dimensions (E, S, G) tested separately against customer stability.

- Baseline effects: ESG positively and significantly predicts customer stability. In two-way FE models (industry-year clustered SEs), ESG coefficients range from about 0.056 to 0.076 and are significant at the 5% level for both CustomerNum and CustomerSales (Table 4). Adj. R² around 0.41–0.42; N=10,840. - Mechanisms—internal and external regulation: ESG strongly increases Internal control index (Inter) (β≈0.420, p<0.01) and information transparency (trans) (β≈0.055, p<0.01). Inter and trans, in turn, significantly increase customer stability (e.g., Inter β≈0.021, p<0.01; trans β≈0.084, p<0.05). When included, ESG remains positive, indicating partial mediation (Table 5). - Mechanisms—reputation: ESG raises Baidu search index (β≈0.187, p<0.01) and positive media coverage (β≈0.127, p<0.01). Reputation metrics significantly predict higher customer stability (baidu β≈0.053, p<0.01; report β≈0.022, p<0.10), with ESG remaining positive, consistent with partial mediation (Table 6). - Endogeneity (IV): Using the interaction of industry-year average ESG and Confucian cultural proximity as instruments, first-stage tests show strong relevance (F=107.02; Stock-Yogo thresholds exceeded) and no weak IV concerns. Second-stage results show ESG positively affects customer stability (CustomerNum β≈0.286, p<0.05; CustomerSales β≈0.302, p<0.05), confirming causal interpretation (Table 7). - Robustness: Results hold with alternative ESG ratings (Hexun, CESG, Sino-Securities), with time-trend controls, and with alternative clustering; coefficients remain positive and significant. - Heterogeneity: Effects are stronger and significant in firms with high governance levels; insignificant in low-governance firms (Table 11). ESG significantly improves customer stability in small firms (p<0.10) but not in large firms (Table 12). - ESG sub-dimensions: Environmental (E) and Social (S) scores significantly increase customer stability (E: p<0.05; S: p<0.10), whereas Governance (G) does not show a significant effect (Table 13). - Descriptive context: Mean customer stability metrics (CustomerNum and CustomerSales) ≈0.443; sample shows wide dispersion (0–1). Mean ESG (ln-transformed Bloomberg+1) ≈3.066.

The findings support the hypotheses that stronger ESG performance enhances customer stability and that this relationship is channeled through improved internal/external supervision and heightened corporate reputation. ESG disclosure reduces information asymmetry for both internal principals and external stakeholders, elevating internal control quality and transparency, which in turn reduces customers’ supervision costs and uncertainty about the firm’s reliability. Concurrently, ESG builds reputational capital via increased media attention and positive coverage, improving brand image and trustworthiness, which fosters customer loyalty and longer-term cooperation. The stronger effects in highly governed and smaller firms suggest the ESG signal is more credible and impactful where governance structures are effective or where firms face greater resource constraints and visibility benefits from ESG are more salient. The dominance of environmental and social dimensions in driving stability aligns with external pressure theory: these pillars are more visible to stakeholders and more likely to influence customer perceptions and choices. Overall, the study indicates ESG functions as an operational lever—not only an investment criterion—by helping firms stabilize their customer base and, by extension, their supply chains.

This study demonstrates that better corporate ESG performance significantly improves customer stability among Chinese A-share listed firms. The relationship is robust across multiple specifications, alternative ESG measures, and endogeneity-corrected IV estimates. Mechanism analyses show ESG enhances stability via stronger internal/external regulation and via improved corporate reputation. The effects are more pronounced in firms with higher governance quality and smaller size; environmental and social pillars drive the effect, while governance scores do not. Contributions include extending external pressure and reputation theories to customer stability, identifying ESG as a determinant of customer relationship outcomes, and clarifying channels and boundary conditions. Practical implications advise firms to invest in ESG—especially environmental and social initiatives—strengthen internal controls and transparency, and actively manage reputation to stabilize customer relationships and supply chains. Future research could expand cross-country comparisons, explore industry-specific dynamics, and examine harmonization and comparability across ESG rating methodologies to further unpack ESG-customer stability linkages.

- External validity: The sample comprises only Chinese A-share listed firms; cross-country institutional differences may limit generalizability. - Measurement heterogeneity: Different ESG rating agencies can yield divergent scores; despite robustness checks using Hexun, CESG, and Sino-Securities, rating philosophy differences may affect results. - Data constraints: Customer stability relies on disclosure of top five customers; firms without such disclosure are excluded, potentially introducing selection bias. - Mechanism proxies: Internal control, transparency, and reputation are proxied measures (e.g., Baidu index, media coverage) that may imperfectly capture underlying constructs.