Alvany T. Wanma
Fakultas Ekonomi dan Bisnis Universitas Papua, Manokwari, Indonesia

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Predictive Analysis of Financial Distress in Construction Service Companies Using the Altman, Springate, Grover, and Zmijewski Models Yakoba Delatasya Basna; Camelia L. Numberi; Alvany T. Wanma
Advances in Economics & Financial Studies Vol. 4 No. 3 (2026): June - September
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/aefs.v4i3.958

Abstract

Purpose: This study aims to compare the results of financial distress classification produced by the Altman Z-Score (Modified), Springate, Grover, and Zmijewski models for service companies in the construction sector listed on the Indonesia Stock Exchange during the 2022–2024 period. Research Method: The study employed a comparative quantitative approach with a saturated sample of 21 companies (63 company-year observations). Secondary data in the form of annual financial reports were analyzed using the four models and then evaluated through descriptive analysis, Cohen’s Kappa, the Friedman test, and the Wilcoxon Signed-Rank test. Results and Discussion: The results of the study show that the four models produce different classification distributions, with varying levels of agreement among the models. The Friedman test revealed significant differences in classification (p < 0.001), indicating that differences in formulas, financial ratios, and threshold values cause each model to provide a different interpretation of the company’s financial condition. Implications: The research findings provide insights for investors, creditors, and management to consider using multiple models when evaluating a company’s financial condition. Originality: The novelty of this study lies in its comparative analysis of four financial distress classification models in Indonesia’s construction sector during the post-pandemic period, with an emphasis on the differences in classification results and the degree of agreement among the models.