JOURNAL ARTICLE

Speed of Adjustment of Trade Receivables: The Case of a Developing Country.

  • Published In: Journal of International Commerce, Economics & Policy, 2025, v. 16, n. 2. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Nguyen, Liem 3 of 3

Abstract

Examining the mean reversion behavior and the adjustment speed of trade receivables of nonfinancial listed firms in Vietnam from 2010 to 2022, this is the first study to investigate whether firms feature a tendency to move toward target trade credit level, and how COVID-19 influences trade credit supply in a dynamic setting in a developing market. The research results suggest that firms do have a target trade receivables level and the speed of adjustment toward this target level is quite significant. Further, we show that firms demonstrate a faster adjustment speed toward target trade credit receivables during the COVID-19 pandemic. We show that lower profitability and higher allowance for bad trade receivables can positively affect the adjustment speed. As the COVID-19 pandemic tends to affect profitability and the level of bad trade receivables, we consider these factors as channels that explain why firms tend to adjust faster toward their target trade credit during the pandemic. The study offers implications for achieving sustainable development and improving business performance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of International Commerce, Economics & Policy. 2025/06, Vol. 16, Issue 2, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1793-9933
  • DOI:10.1142/S1793993325500061
  • Accession Number:188764499
  • Copyright Statement:Copyright of Journal of International Commerce, Economics & Policy is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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