Nonstatistical Factors Influencing Predictions of Financial Distress and Managerial Implications in the All-Cargo Airline Industry

All-cargo airlines carry over 50% of global airfreight, yet they are prone to bankruptcy. Many financial models are designed to predict a firms\u27 financial health, but they do not assess many nonstatistical factors that influence the prediction capability of these models. In this study, qualitative grounded theory design was used to identify nonstatistical factors and explore how they influence bankruptcy prediction models in the all-cargo airline industry. In the first phase of the study, financial data from 2005 to 2009 for 17 all-cargo U.S. airlines were used to determine the bankruptcy prediction ability of the Kroeze financial bankruptcy model. A sample of six all-cargo airlines (ABX Air, Arrow Air, Atlas Air, Cargo 360, Gemini Air Cargo, and Kitty Hawk Air Cargo) were selected containing a mixture of airlines for which the Kroeze model correctly and incorrectly predicted bankruptcy. The sample was used as the starting point to explore the nonstatistical factors using grounded theory. Data were obtained on the six airlines from company annual reports, SEC 10K annual reports, reports from professional journals such as Air Transport Intelligence and Traffic World, news reports and company press releases. The data were coded and grouped into conceptual categories, which were used in theory generation to support the emerging theory. Six categories (management, risk, operations, competitive advantage, financial, and external factors) that relate to the financial stability of an all-cargo airline emerged during the research. Three themes emerged that may improve current quantitative bankruptcy prediction models. The three themes are airline fleet type, type of aircraft flown, and aircraft utilization. The three themes relate to the type, use, and make up of an airline’s fleet. These themes influence bankruptcy prediction model and should be incorporated into failure prediction models to improve their overall accuracy. Future research should be conducted to verify these findings on a larger population, such as all-cargo airlines that operate outside the United States. These airlines operate under different financial regimes that may affect the prediction models differently