Artificial Intelligence and Sustainable Governance: The Moderating Role of Board Gender and Age Diversity

As artificial intelligence (AI) reshapes corporate operations, its role in fostering sustainability has garnered increasing attention. This study investigates how AI application affects firms’ Environmental, Social, and Governance (ESG) performance, and how board-level demographic diversity, specifically gender and age, moderates this relationship. Drawing on panel data from 5,637 non-financial A-share listed firms in China (2014–2023), we employ fixed effects and PSM-DID models to address endogeneity. Results reveal that AI application significantly enhances ESG performance. Board gender diversity positively moderates this relationship, which underscores the strategic value of inclusive leadership. In contrast, board age diversity negatively moderates the effect, suggesting that intergenerational misalignment may hinder the effective implementation of AI-driven sustainability. Our findings are grounded in the Resource-Based View, Upper Echelons Theory and further enriched by Tokenism Theory. This study contributes to the governance literature by showing that board diversity is not uniformly beneficial, its impact varies by dimension and context. The findings highlight the need for boards aligned with technological change and support gender-inclusive governance as a lever for sustainable development.

Authors

Institutions

Publication Details

Journal
Journal of Management & Governance
Published
2026-04-01
DOI
https://doi.org/10.1007/s10997-026-09783-1
Primary Topic
Gender Diversity and Inequality
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence and Sustainable Governance: The Moderating Role of Board Gender and Age Diversity

Bin Wang, Husni Samara, Norkhairul Hafiz Bajuri, Shanshan Yue
Journal of Management & Governance
Gender Diversity and Inequality
article

Artificial Intelligence and Sustainable Governance: The Moderating Role of Board Gender and Age Diversity

Bin Wang, Husni Samara, Norkhairul Hafiz Bajuri, Shanshan Yue
article en

Abstract

As artificial intelligence (AI) reshapes corporate operations, its role in fostering sustainability has garnered increasing attention. This study investigates how AI application affects firms’ Environmental, Social, and Governance (ESG) performance, and how board-level demographic diversity, specifically gender and age, moderates this relationship. Drawing on panel data from 5,637 non-financial A-share listed firms in China (2014–2023), we employ fixed effects and PSM-DID models to address endogeneity. Results reveal that AI application significantly enhances ESG performance. Board gender diversity positively moderates this relationship, which underscores the strategic value of inclusive leadership. In contrast, board age diversity negatively moderates the effect, suggesting that intergenerational misalignment may hinder the effective implementation of AI-driven sustainability. Our findings are grounded in the Resource-Based View, Upper Echelons Theory and further enriched by Tokenism Theory. This study contributes to the governance literature by showing that board diversity is not uniformly beneficial, its impact varies by dimension and context. The findings highlight the need for boards aligned with technological change and support gender-inclusive governance as a lever for sustainable development.

Journal of Management & Governance
Universiti Putra Malaysia (MY), University of Technology Malaysia (MY), Henan University of Economic and Law (CN)
Universiti Teknologi Malaysia
Openalex Percentile: Top 29%
Gender Diversity and Inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.