The twin transition in emerging economies: Synergizing artificial intelligence and sustainable business model innovation in Thailand’s BCG economy

This study investigates the convergence of digitalization and sustainability—termed the ''Twin Transition''—within the context of Thailand’s Bio-Circular-Green (BCG) Economy. While Artificial Intelligence (AI) offers significant potential to accelerate sustainable development, research in emerging economies remains fragmented, often lacking empirical grounding in the specific institutional realities of the Global South. Integrating Institutional Theory and Dynamic Capabilities Theory (DCT), this research employs a rigorous mixed-methods sequential exploratory design (Qual → QUAN) to unpack the mechanisms driving this convergence. Phase 1 involved a reflexive thematic analysis of 30 in-depth interviews with executives. This phase revealed a ''Compliance Plus'' mindset—where firms evolve from regulatory adherence to competitive innovation—alongside critical structural barriers, such as the ''Data-Sustainability Paradox.'' Subsequently, Phase 2 analyzed survey data from 425 firms using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings confirm that Institutional pressures (INP) (coercive, normative, and mimetic) are strong antecedents of AI Capability (AIC), which in turn significantly drives Sustainable Business Model Innovation (SBMI). Crucially, Environmental Turbulence (ENT) positively moderates this relationship, highlighting AI as a vital mechanism for organizational resilience in volatile markets rather than merely for operational efficiency. The study also addresses the ''Dark Side'' of AI, proposing a ''Net-Positive'' framework to mitigate energy consumption and algorithmic bias. These results challenge techno-centric views, offering policymakers actionable insights to bridge the ''Data Gap'' and ''Talent Gap,'' positioning the Twin Transition as a critical lever for emerging economies to escape the Middle-Income Trap.