Evidence map›Paper›PMID 40435355›Full record

ArticlePloS one2025

Performance evaluation of Vietnamese industrial goods and services during and post-COVID-19 era based on multi-criteria decision-making methods.

Chia-Nan Wang, Tram Thi Mai Nguyen, Nhat-Luong Nhieu, Yu-Chi Chung

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chia-Nan WangDepartment of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.ORCID https://orcid.org/0000-0002-2374-3830
Tram Thi Mai NguyenDepartment of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.ORCID https://orcid.org/0009-0000-3353-5851
Nhat-Luong NhieuDepartment of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Yu-Chi ChungDepartment of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The industrial goods and services sector is crucial for the advancement of the Vietnamese economy in terms of its substantial economic contribution and positive impact on employment. Performance evaluation has become critical in this industry, which has constantly developed and had an intensive rivalry. This paper aims to analyze the performance of industrial goods and services firms during and after COVID-19 using an objective integrated multi-criteria decision-making technique. This study suggests a three-phase model. Criteria Importance Through Intercriteria Correlation (CRITIC) eliminates human judgment errors, increases accuracy, and maintains objectivity in the evaluation variable weighting phase. Then, Evaluation based on Distance from Average Solution (EDAS) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) are used as effective cross-validation techniques to evaluate and rank forty-five Vietnam Stock Exchanges-listed firms for each year from 2020 to 2022. The reliability of the CRITIC-based weights is verified by the Statistical Variance Procedure. The research results reveal that the debt term structure is the most vital among the fifteen financial research indicators reflecting a business's solvency, profitability, growth, operating efficiency, and capital structure. Additionally, the research findings indicate discrepancies in the rankings produced by EDAS and TOPSIS. However, the disparities are not grave, and the top and bottom positions, in particular, remain consistent between the two approaches. PDN was the best firm during COVID-19 and was succeeded by CIA after the pandemic. Pursuing digital transformation, sustainable development, and keeping inventory turnover at high levels are common characteristics of successful businesses in this industry. For the first time, the article provides a performance analysis of Vietnamese industrial goods and services firms. It is a significant reference for domestic and international investors in portfolio selection, financial institutions in loan approval, managers and policymakers in planning and policy development, and researchers conducting investigations within this domain.

Indexed as

COVID-19Decision MakingIndustryHumansVietnam

Identifiers

PMID40435355
PMCPMC12119014

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.