blockchain applications in value added tax refund: a deep learning-based dual-stage sem-ann analysis

This study examines the determinants of businesses' readiness to implement blockchain technology for value-added tax (vat) refunds. this study enhances technology acceptance model 2 (tam2) by incorporating the concept of perceived risk into the framework. this study utilized a two-stage approach that integrated partial least squares structural equation modeling (pls-sem) with artificial neural network (ann) predictive analytics to test the proposed hypotheses. the ann technique was employed to identify and analyze the potential nonlinear effects within the model. a total of 175 self-administered questionnaires were used for the analysis. the study found that a significant relationship between tam2s' constructs and intention to use blockchain technology can markedly enhance the efficiency of vat refund operations from a managerial standpoint. the theoretical implications of blockchain technology are substantial, as it introduces novel concepts of trust and accountability in tax interactions, disrupts traditional intermediaries, modifies the balance of information, and redefines contract enforcement.