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AI adoption in Indonesian mining firms is linked to stronger ESG performance, and those ESG gains are associated with higher firm value; study based on PLS-SEM analysis of 225 firms (2020–2023) shows correlation but does not establish causality.

Artificial Intelligence and ESG Performance: Empirical Evidence from Indonesia-Listed Company
Rizky Windar Amelia, Abdul Hadi Hari, Syska Lady Sulistyowatie · December 31, 2025 · JAS (Jurnal Akuntansi Syariah)
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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In a sample of 225 Indonesian mining firms (2020–2023), higher AI adoption is associated with better ESG performance, and ESG performance statistically mediates the positive association between AI adoption and firm value.

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Artificial intelligence (AI) quick development has become a major factor in determining how well businesses perform in terms of environmental, social, and governance (ESG). The rapid development of AI is reshaping the global economic, and also social structure, and its wide application empowers the sustainable development of enterprises. This research aims to explore the influence of AI adoption on ESG performance and further assess the mediation effect of ESG performance in the relation between AI adoption and firm value. The research was carried out from 2020 to 2023 on mining sector companies in Indonesia, yielding 225 observational data points. A multivariate analysis was performed utilising partial least squares structural equation modelling (PLS-SEM) to assess the hypothesis. The research findings from hypothesis testing demonstrate that performance, firm size, debt to assets ratio, and also return on equity have a significant positive impact on firm value of mining sector companies. Furthermore, the impact of AI adoption on firm value can be more effectively mediated by ESG performance. By serving as a strategic resource, increasing productivity, and promoting sustainability to satisfy stakeholder expectations, AI improves ESG performance and raises business value. For AI-driven company sustainability, this research promotes standardized policies, management integration, and government support.

Summary

Main Finding

AI adoption in Indonesian mining firms (2020–2023) improves firm value primarily by raising ESG performance: ESG acts as an effective mediator between AI adoption and firm value. In addition, firm size, debt-to-assets ratio, and return on equity (ROE) are positively associated with firm value.

Key Points

  • Sample and scope: 225 observations from mining-sector firms in Indonesia, 2020–2023.
  • Core causal chain: AI adoption → better ESG performance → higher firm value (mediation effect).
  • Control/firm-level predictors: firm size, debt-to-assets ratio, and ROE each have a significant positive effect on firm value.
  • Mechanisms: AI functions as a strategic resource that increases productivity and promotes sustainability, helping firms meet stakeholder expectations and thereby enhancing valuation.
  • Practical recommendations from the study: standardize AI/ESG policies, integrate AI into management practices, and promote government support for AI-driven sustainability.

Data & Methods

  • Data: 225 firm-year observations from Indonesian mining companies covering 2020–2023.
  • Key variables:
    • Independent: AI adoption (measure not detailed in summary).
    • Mediator: ESG performance.
    • Dependent: firm value (unspecified metric).
    • Controls: firm size, debt-to-assets ratio, return on equity, possibly other controls (not listed).
  • Empirical approach: Partial Least Squares Structural Equation Modeling (PLS-SEM) used for multivariate hypothesis testing and mediation analysis.
  • Findings derived from hypothesis tests within the PLS-SEM framework (statistical significance reported for stated relationships).

Implications for AI Economics

  • Valuation channels: The study highlights a non-financial channel (ESG performance) through which AI adoption can raise firm value—important for models of firm valuation that incorporate intangible and stakeholder-related factors.
  • Complementarities: Positive roles for firm size, leverage (debt/assets), and ROE suggest complementarities between AI adoption and existing firm financial/organizational characteristics; economic models should account for heterogeneity in firm capacity to extract value from AI.
  • Policy design: Results support targeted public policies (standards, incentives, capacity building) to accelerate AI adoption for sustainability outcomes—especially in resource-intensive sectors.
  • Measurement & research agenda:
    • Need for standardized, transparent measures of AI adoption and of ESG outcomes to enable cross-sector and cross-country comparisons.
    • Causal identification: future work should address endogeneity (selection into AI adoption), long-run effects, and external validity beyond mining and Indonesia.
    • Disaggregation: separate effects on environmental, social, and governance dimensions could reveal which ESG sub-components most strongly mediate value creation.
  • Managerial practice: Firms should integrate AI into sustainability strategy and reporting to capture valuation benefits; governance and training are key complements.

Limitations to note (implicit from study design) - Sector- and country-specific sample (Indonesian mining) limits generalizability. - Observational design and PLS-SEM limit causal claims—possible omitted variables or reverse causality. - Summary lacked specification of measurement details for AI adoption and firm value; interpreting magnitudes is therefore not possible from the provided text.

Assessment

Paper Typecorrelational Evidence Strengthlow — The study is based on observational data from a single sector and country (225 observations) and relies on PLS-SEM to claim mediation; without exogenous variation, instruments, difference-in-differences, or robustness checks for reverse causality and omitted variables, causal interpretation is weak. Methods Rigorlow — PLS-SEM is an acceptable tool for exploratory structural relationships but is sensitive to measurement choices and model specification; the paper appears to lack strong identification strategies (e.g., IVs, panel fixed effects, pre/post variation), potential measurement validity and endogeneity concerns are not addressed, sample is limited to one sector/country and relatively small for complex SEM, and robustness checks are not described. Sample225 firm-level observations of mining sector companies in Indonesia covering 2020–2023; variables include measures of AI adoption (unspecified measurement), ESG performance (unspecified scoring/indicator source), firm value (outcome), and controls such as firm size, debt-to-assets ratio, and return on equity. Themesadoption governance IdentificationObservational firm-level analysis using partial least squares structural equation modeling (PLS-SEM) on cross-sectional/panel data (2020–2023) to estimate associations and test mediation of ESG between AI adoption and firm value; no quasi-experimental variation, instrumental variables, or strategies to address endogeneity are reported. GeneralizabilitySingle sector (mining) limits transferability to manufacturing, services, or tech-intensive industries, Single country (Indonesia) — results may not hold in different regulatory, market, or institutional contexts, Modest sample size (225) and likely non-random selection of firms restrict external validity, Short and recent time window (2020–2023) includes pandemic-related shocks that may confound relationships, Measurement of AI adoption and ESG not standardized or validated (likely self-reported or proprietary scores) which limits comparability

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The study used 225 observational data points from mining sector companies in Indonesia collected between 2020 and 2023. Other null_result sample size / data collection
Reading fidelity high
Study strength high
n=225
0.5
A multivariate analysis was performed using partial least squares structural equation modelling (PLS-SEM) to assess the hypotheses. Other null_result analysis method
Reading fidelity high
Study strength high
n=225
0.5
AI adoption has a positive influence on ESG performance. Organizational Efficiency positive ESG performance
Reading fidelity high
Study strength medium
n=225
0.3
ESG performance mediates the relationship between AI adoption and firm value (the impact of AI adoption on firm value is more effectively mediated by ESG performance). Firm Productivity positive firm value (mediated effect)
Reading fidelity high
Study strength medium
n=225
0.3
ESG performance (referred to in the paper as 'performance') has a significant positive impact on firm value of mining sector companies. Firm Productivity positive firm value (predictor: ESG performance)
Reading fidelity medium
Study strength medium
n=225
0.18
Firm size has a significant positive impact on firm value of mining sector companies. Firm Productivity positive firm value (predictor: firm size)
Reading fidelity high
Study strength medium
n=225
0.3
Debt-to-assets ratio (leverage) has a significant positive impact on firm value of mining sector companies. Firm Productivity positive firm value (predictor: debt-to-assets ratio)
Reading fidelity high
Study strength medium
n=225
0.3
Return on equity (ROE) has a significant positive impact on firm value of mining sector companies. Firm Productivity positive firm value (predictor: ROE)
Reading fidelity high
Study strength medium
n=225
0.3
AI acts as a strategic resource that increases productivity and promotes sustainability, thereby improving ESG performance and raising business value. Organizational Efficiency positive mechanism connecting AI to ESG performance and firm value
Reading fidelity medium
Study strength speculative
n=225
0.03
To support AI-driven company sustainability, the research recommends standardized policies, management integration, and government support. Governance And Regulation positive policy recommendations for AI-driven sustainability
Reading fidelity high
Study strength speculative
n=225
0.05

Notes