Drivers and Success of Artificial Intelligence Integration for Organizational Sustainability in Thai Businesses: An Application of the Technology-Organization-Environment Framework in the Green Economy Era

Abstract

This research aims to 1) investigate the drivers and structural barriers of Artificial Intelligence (AI) integration for sustainability in Thai business organizations, 2) develop and validate a causal model using the Technology-Organization-Environment (TOE) framework integrated with the Resource-Based View (RBV) and Institutional Theory, and 3) propose strategic guidelines and policy recommendations. The study employs an Exploratory Sequential Mixed-Methods design. Phase 1 involved qualitative research through in-depth interviews with 25 executives and experts to explore context-specific variables. Phase 2 involved quantitative research via a survey of 340 organizations in BCG target industries (Agriculture & Food, Energy & Materials, Health & Wellness, and Tourism), with data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Qualitative findings reveal that AI integration in Thailand is driven more by Global Supply Chain Pressure and measures like CBAM than by internal motivation. The study identifies three novel structural barriers: the "Language Trap" (Localization Complexity), which entails high hidden costs; the "Verification Cost Barrier," which hinders SMEs; and the "Twin Skills Crisis." Quantitative results confirm that Supply Chain Pressure (β = 0.345) and Twin Skills Readiness (β = 0.312) have the strongest positive influence on success. In contrast, Localization Complexity (β = -0.245) and Verification Costs (β= -0.265) significantly exert negative effects. This research proposes a success model emphasizing the need to unlock structural constraints, recommending the establishment of a National Carbon Data Verification Platform and support for Open Thai LLM development to reduce transaction costs and enhance sustainable competitiveness.