In recent years, the rapid expansion of cryptocurrencies and blockchain-based technologies has brought significant transformations to corporate payment systems and financial management practices. The adoption of cryptocurrencies as an innovative payment instrument can reshape cash flow structures, liquidity cycles, and working capital management strategies within firms. However, the highly volatile nature of cryptocurrencies and the uncertainty surrounding their acceptance make the assessment of their financial implications complex and challenging. In addition, differences in institutional environments and regulatory frameworks across countries have led to heterogeneous effects of cryptocurrencies on corporate financial performance. In this context, the application of advanced probabilistic modeling approaches is essential for a more accurate understanding of these relationships.
This study aims to investigate the impact of cryptocurrency adoption on corporate liquidity management using a Bayesian network approach to model probabilistic relationships among key variables. This method enables the analysis of causal and nonlinear dependencies between cryptocurrency adoption, operating cash flows, cash conversion cycles, and liquidity risk. Moreover, Bayesian networks are capable of structuring and handling uncertainty in financial and environmental data while simulating different decision-making scenarios. This feature enhances the flexibility and accuracy of the results compared to traditional econometric models.
The conceptual findings suggest that cryptocurrency adoption may improve liquidity management by reducing transaction costs, accelerating settlement processes, and enhancing access to international markets. At the same time, price volatility and regulatory risks may negatively affect liquidity stability. The results indicate that the overall impact of cryptocurrency adoption on liquidity management is nonlinear and context-dependent. Overall, the Bayesian network approach provides a more comprehensive framework for analyzing uncertainty and capturing complex interdependencies among financial variables in the cryptocurrency environment.