Evidence map›Paper›PMID 34831837›Full record

ArticleInternational journal of environmental research and public health2021

An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model.

Lijiao Yang, Yishuang Qi, Xinyu Jiang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lijiao YangSchool of Management, Wuhan University of Technology, Wuhan 430070, China.
Yishuang QiSchool of Management, Wuhan University of Technology, Wuhan 430070, China.ORCID 0000-0003-1079-0790
Xinyu JiangSchool of Management, Wuhan University of Technology, Wuhan 430070, China.

Funding

Fundamental Research Funds for Central Universities,China WUT: 2020IVB023National Natural Science Foundation of China 41807504National Natural Science Foundation of China 41907393
6 · The paper itself

Abstract

COVID-19 has had a great impact on the economy, society, and people's lives in China and globally. The production and operations of Chinese enterprises have also faced tremendous challenges. To understand the economic impact of COVID-19 on enterprises and the key affecting factors, this study adds to the literature by investigating the business recovery process of enterprises from the micro perspective. Specific attention is paid to the initial stage of business recovery. A questionnaire survey of 750 enterprises explored the impact during the pandemic period from July to September 2020. An accelerated failure time model in survival analysis was adopted to analyze the data. The results show that the manufacturing industry is mainly faced by affecting factors such as enterprise ownership, employees' panic and order cancellation on initial enterprise recovery. As for the non-manufacturing industry, more factors, including clients' distribution, employees' panic, raw material shortage, cash flow shortage and order cancellation, are found to be significant. Acceleration factors that estimate the effects of those covariates on acceleration/deceleration of the recovery time are presented. For instance, the acceleration factor of employees' panic is 1.319 for non-manufacturing, which implies that, compared with enterprises where employees are less panicked, enterprises with employees obviously panicked will recover 1.319 times slower at any quantile of probability of recovery time. This study provides a scientific reference for the post-pandemic recovery of enterprises, and can support the formulation of government policies and enterprise decisions.

Indexed as

COVID-19ChinaCommerceGovernmentHumansPandemicsSARS-CoV-2accelerated failure time modelaffecting factorsCOVID-19initial recoverysurvival analysis

Identifiers

PMID34831837
PMCPMC8619245

What OpenQuestion holds

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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.