AI-ENABLED COST ACCOUNTING IN THE GIG ECONOMY: A MULTI-COUNTRY MODEL FOR DYNAMIC COST ALLOCATION

Global digital markets are transforming cost coordination, contracting, and decision-making in ways that traditional transaction cost perspectives cannot fully explain. This study analyzed multi-country datasets from OECD, ILO, and Fairwork to model algorithmic coordination efficiency using advanced structural equation modeling. Findings showed that algorithmic cost allocation (β = 0.41), predictive cost efficiency (β = 0.29), and automated monitoring (β = 0.22) significantly reduced transaction inefficiencies, while governance mechanisms amplified cost predictability and transparency (R² = 0.78). The results confirmed that algorithmic coordination reshapes transaction costs by embedding trust and adaptive efficiency into digital ecosystems. This research contributes to theory by extending Transaction Cost Theory through the addition of algorithmic governance, thereby broadening its explanatory scope and offering a refined framework for understanding digital coordination and cost optimization in global platform economies. The study recommends integrating AI transparency standards into international regulatory systems to enhance accountability and cross-border collaboration. Findings provide evidence that algorithmic governance not only lowers transaction costs but also fosters equitable digital participation across economies. The implications span theory, management, and global policy, shaping how institutions adapt to automated cost management and governance integration.