A Structural Equation Model of Artificial Intelligence Capability and Institutional Sustainability Performance under the ESG Framework in Private Higher Education Institutions in Thailand

บทคัดย่อ

The rapid digital transformation of higher education has elevated the strategic role of artificial intelligence (AI) in institutional governance and sustainability management. Concurrently, Environmental, Social, and Governance (ESG) principles have become key benchmarks for evaluating institutional responsibility and long-term performance. However, prior research in higher education has largely focused on sustainability reporting, with limited attention to the structural mechanisms through which AI capability drives ESG-related sustainability outcomes. Grounded in the Resource-Based View (RBV) and sustainable governance theory, this study aims to examine the level of artificial intelligence capability in private higher education institutions in Thailand, to examine the level of institutional sustainability performance under the ESG framework, to develop and empirically validate a structural equation model explaining the effect of artificial intelligence capability on institutional sustainability performance under the ESG framework in private higher education institutions in Thailand, and to conduct a multi-group analysis by comparing institutions located in Bangkok and those in provincial regions. This study employed a quantitative cross-sectional survey design. Data were collected from 412 administrators and senior academic staff across eight private higher education institutions in Thailand using a multi-stage stratified sampling technique to ensure institutional representation. Covariance-based Structural Equation Modeling was applied to test both measurement and structural models. Reliability and validity were assessed using Confirmatory Factor Analysis (CFA), Composite Reliability (CR), Average Variance Extracted (AVE), and standard model fit indices.

The institutional sustainability performance is at a high level (3.92). The next most important factor is artificial intelligence capability, which is also at a high level (3.87). The structural model revealed that artificial intelligence capability has a significant positive effect on institutional sustainability performance (β=0.64, p<0.001), indicating a large effect size. The model explained 52% of the variance in sustainability performance (R²= 0.52), reflecting substantial explanatory power. Additional robust analysis confirmed the stability of the model across institutional contexts. The findings indicate that the relationship between artificial intelligence capability and institutional sustainability performance is positive and significant in both groups. The findings indicate that artificial intelligence capability functions as a strategic organizational resource that enhances environmental efficiency, social inclusiveness, and governance transparency. By shifting the focus from ESG reporting to AI-enabled ESG integration, this study contributes to the advancement of sustainable digital governance theory in higher education. The results highlight the importance of aligning AI investment strategies with ESG-oriented institutional transformation to achieve long-term sustainability outcomes.