Measuring dynamic supply chain risks for the offshoring decision in the post‐COVID‐19 era: A longitudinal study.
Published In: Transportation Journal (Wiley-Blackwell), 2024, v. 63, n. 3. P. 188 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Min, Hokey; Ahn, Young‐Hyo; Ma, Jin‐Hee 3 of 3
Abstract
In times of prolonged economic doldrums across the globe, multinational firms (MNFs) offshoring blunders can undermine their competitiveness in the marketplace. To help the MNF formulate a more resilient offshoring strategy and identify the most desirable offshoring destination, this article aims to identify dynamic risk factors that significantly hinder the efficiency of offshoring and then measure specific offshoring risks over time using two different versions of data envelopment analysis (DEA) models and Malmquist productivity index (MPI). After assessing the degree of risk resiliency of the offshoring host countries over extended periods and then conducting Tobit regression analysis to identify key factors that significantly influence offshoring risks, we found that the host country's logistics efficiency (i.e., logistics performance index [LPI]) and domestic market size were critical indicators of offshoring success in that country. Since low‐cost sourcing countries (LCCs) tend to have relatively low LPIs and smaller domestic market sizes, they are not attractive offshoring destinations. This finding defies conventional wisdom. This article is one of the first longitudinal studies to assess the comprehensive risk resilience of 87 different offshoring destinations (countries) during multiple periods (6‐year span). [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Transportation Journal (Wiley-Blackwell). 2024/07, Vol. 63, Issue 3, p188
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2024
- ISSN:0041-1612
- DOI:10.1002/tjo3.12026
- Accession Number:178316273
- Copyright Statement:Copyright of Transportation Journal (Wiley-Blackwell) is the property of Wiley-Blackwell 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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