Using Financial Distress Models in Indonesian F&B Companies During Multidimensional Crisis: Covid-19 Period to the Israeli-Palestinian Conflict
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Abstract
This study aims to compare the effectiveness of financial distress prediction models (Altman Z-Score, Springate, Zmijewski, Taffler, Grover, and Ohlson) for food and beverage companies in Indonesia impacted by multidimensional crises: the COVID-19 pandemic, community activity restrictions (PPKM), and the boycott of pro-Israel products. The research objects include four companies listed on the Indonesia Stock Exchange (MAPI, MAPB, PZZA, FAST) that faced operational and financial pressures due to these phenomena. A quantitative method with a causal-comparative design was employed, analyzing secondary financial report data from 2020–2023 through descriptive statistics and predictive analysis. Results revealed variations in model accuracy depending on the crisis context: Springate achieved the highest overall accuracy (75%), attributed to its integration of liquidity, profitability, and leverage variables relevant during the pandemic and PPKM. Meanwhile, Ohlson excelled during the 2023 boycott period (100% accuracy) due to its sensitivity to non-financial factors like reputation and demand shifts. Conversely, Zmijewski (31%) and Grover (44%) underperformed, limited by their inability to capture external dynamics. The implications emphasize the importance of selecting context-specific models—liquidity-focused models (e.g., Taffler) for sudden economic shocks and multifactor probabilistic models (e.g., Ohlson) for geopolitical disruptions. For practitioners, product diversification and cost efficiency are key to resilience, while investors should integrate non-financial risks into decision-making. This study contributes to literature by integrating geopolitical crises into financial distress analysis, though limited by its small sample and historical data. Future research should expand sector coverage and develop hybrid models incorporating qualitative metrics.
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