Journal of Airline Operations and Aviation Management

Hierarchical multiple regression (HRM) is applied to examine the incremental influence of aviation market identity, revenue generation, and cost structures on airline net income across U.S.-based carriers from 2022 to 2024. This study utilizes archival data obtained from an aviation database that complies with the U.S. Department of Transportation Form 41 airline financial disclosures (AviationDB, 2024). The analysis categorizes ten independent variables into three theory-informed sets aligned with the income statement equation: Set A (Aviation Market Identity), Set B (Revenues and Incomes), and Set C (Expenses and Costs). All regression assumptions were rigorously assessed and met, with model stability confirmed via multicollinearity diagnostics, linearity, reliability, validity, and residual analysis. Results demonstrated that aviation market identity alone explained a modest portion of the variance (R² = 0.01). The inclusion of revenue-related variables added 0.13 explanatory power, while the final step of introducing expense variables contributed an additional 0.81, resulting in a highly predictive model (R² = 0.95). Operating revenues and expenses emerged as the strongest predictors. The findings emphasize the dominant role of cost management in driving air carrier profitability and net income, reinforcing the strategic value of HMR in disentangling financial drivers within complex commercial systems. This research contributes to aviation financial modeling by offering empirical insight into income dynamics and supporting data-driven decision-making for air carrier executives, regulators, and aviation financial policy strategists.