Development of a Cash Flow Growth Pathway Model for Predicting Lifecycle Transitions in Software SMEs

This study develops a cash flow growth pathway model to analyze and predict the dynamic and nonlinear lifecycle trajectories of software SMEs. Specifically, it identifies lifecycle stages using cash flow patterns, maps transition pathways empirically, and develops a forecasting framework to predict subsequent lifecycle states using prior-period financial information. By extending the conventional lifecycle framework, the model captures heterogeneity within transitional and contractionary phases, which are particularly relevant in the software industry. The results show that cash flow-based lifecycle scoring is economically meaningful and significantly distinguishes high- and low-growth firms. Moreover, the transition pathway analysis indicates that firm development is not strictly sequential. Firms exhibit downward transitions and meaningful recovery pathways, in addition to strong persistence in expansionary stages. In the forecasting analysis, predictive performance varies substantially across alternative models and class-imbalance treatments. Resampling-based models consistently outperform those estimated on the original dataset, indicating that class imbalance is a critical issue in lifecycle prediction. Among the alternative specifications, the best-performing model achieves strong predictive performance, suggesting that prior-period lifecycle states and financial characteristics contain meaningful forward-looking information. This study contributes to the literature by combining lifecycle theory, cash flow analysis, pathway modeling, and predictive analytics within a unified framework. It also offers practical implications for managers, investors, and policymakers by providing a preliminary basis for monitoring transition-related risks and identifying firms with recovery potential in the software sector.