Ardhia Prameswari Regita Cahyani
Universitas Hayam Wuruk Perbanas Surabaya

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STUDI FINANCIAL DISTRESS PADA PERUSAHAAN TRANSPORTASI & LOGISTIK DI INDONESIA : KONDISI PANDEMI BERDAMPAK-KAH? Ardhia Prameswari Regita Cahyani; Rr. Iramani
Jurnal Ilmu Manajemen Vol 10 No 4 (2022)
Publisher : UNESA In Collaboration With APSMBI (Aliansi Program Studi dan Bisnis Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (380.312 KB) | DOI: 10.26740/jim.v10n4.p1073-1086

Abstract

The world was shocked by a case originating from Wuhan, China, in December 2019, which was identified as coronavirus disease (Covid-19). On January 30, 2020, WHO designated this virus as a Public Health Emergency of International Concern because the spread and increase in Covid-19 cases occurred very quickly and spread between countries, including Indonesia. The Government of Indonesia's efforts to suppress the spread and increase in the number of Covid-19 cases are to establish a policy of Large-Scale Social Restrictions (PSBB) to the Enforcement of Restrictions on Community Activities (PPKM). The crisis caused by the Covid-19 pandemic can cause companies to experience financial distress and lead to bankruptcy. This study examines the impact of pandemic conditions, liquidity, solvency, activity and profitability in predicting financial distress. The research sample uses purposive sampling with the criteria that the company publishes audited financial statements for 2018-2021 successively on the website www.idx.co.id. 26 transportation and logistics companies meet the criteria—the research analysis technique used logistic regression. There are 2 measurements of financial distress in this study. The first measurement uses the industry median, which shows the results of solvency, activity, and profitability can be used as predictors of financial distress with a predictive power of 65.4%. The second measurement uses the industry average, which shows the results of liquidity, solvency, activity, and profitability can be used as a predictor of financial distress with a predictive power of 78.2%.