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competition and may introduce new risks if not supported by effective governance (Vives,
2019). As a result, the stabilizing effect of digitalization depends on whether banks
maintain strong managerial discipline and cost control.
2.4. Hypotheses development
The preceding discussion suggests that bank stability is shaped by governance at
multiple levels, including ESG performance, institutional quality, and operational
efficiency. From stakeholder and legitimacy perspectives, ESG performance strengthens
governance structures, enhances transparency, and builds stakeholder trust. These
mechanisms support more prudent risk management and improve resilience.
H1: ESG performance has a positive effect on bank stability.
Institutional theory highlights the role of the regulatory environment in shaping
financial outcomes. Stronger institutional quality improves enforcement and reduces
governance distortions, thereby enhancing stability.
H2: Higher institutional quality (lower corruption) is associated with greater bank
stability.
Operational efficiency reflects managerial discipline and effective cost control.
Banks with higher inefficiency tend to exhibit weaker profitability and lower resilience to
shocks.
H3: Higher cost inefficiency is associated with lower bank stability.
3. Research methodology
3.1. Data
This study uses a panel dataset of 144 listed commercial banks across eight Asian
countries over the period 2021 to 2024, including Indonesia, Japan, Korea, Malaysia, the
Philippines, Singapore, Thailand, and Vietnam. The sample consists of 576 bank-year
observations and captures both cross-sectional and time variation in bank stability and
ESG performance.
Bank-level financial data and ESG scores are obtained from Bloomberg, while
macroeconomic variables such as GDP growth and inflation are collected from the World
Bank’s WDI. Institutional quality is measured using the Corruption Perceptions Index (CPI),
with the Rule of Law indicator from the Worldwide Governance Indicators used for
robustness. In both cases, higher values indicate better institutional quality.
Bank stability (LN1ZSCORE) is defined as the natural logarithm of one plus the Z-
score, which reflects the distance to default based on profitability, capitalization, and
earnings volatility. This transformation helps reduce the influence of outliers and improve
distributional properties (Laeven and Levine, 2009). ESG performance is the main
explanatory variable, while corruption and other bank-level and macroeconomic factors
are included as controls.
To limit the impact of extreme values, EFFR and EA are winsorized at the 1st and
99th percentiles prior to estimation.
3.2. Methodology
To examine the relationship between ESG performance and bank stability, this
study estimates the following baseline model:
LN1ZSCOREit​ = β0 + β1​ ESGit​ + β2​ CORRUPTIONct ​ + β3LN_TAit ​ + β4EAit ​ +

β5EFFRit ​ + β6LSit ​ + β7GDPGct ​ + β8INFLATIONct​ + μi ​ + λt​ + εit​ .
where:
denotes banks and denotes year
LN1ZSCOREit represents bank stability

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