Corporate Failure Prediction: A Literature Review of Altman Z-Score and Machine Learning Models Within a Technology Adoption Framework

Abstract

Research on corporate failure prediction is focused on increasing the model’s statistical accuracy, most recently via the introduction of a variety of machine learning (ML)-based models, often overlooking the practical appeal and potential adoption barriers in the context of corporate management. This literature review compares ML models with the classic, widely accepted Altman Z-score through a technology adoption lens. We map how technological features, organizational readiness, environmental pressure and user perceptions shape adoption using an integrated technology adoption framework that combines the Technology–Organization–Environment framework with the Technology Acceptance Model. The analysis shows that Z-score models offer simplicity, interpretability and low cost, suiting firms with limited analytical resources, whereas ML models deliver superior accuracy and adaptability but require advanced data infrastructure, specialized expertise and regulatory clarity. By linking the models’ characteristics with adoption determinants, the study clarifies when each model is most appropriate and sets a research agenda for long-horizon forecasting, explainable artificial intelligence and context-specific model design. These insights help managers choose failure prediction tools that fit their strategic objectives and implementation capacity.