Evidence map›Paper›PMID 34898758›Full record

ArticleTechnology in society2021

The challenges of COVID-19 control policies for sustainable development of business: Evidence from service industries.

Ji Chen, Jiayan Huang, Weihua Su, Dalia Štreimikienė, Tomas Baležentis

Abstract read
In one paragraph

Article in Technology in society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 citing papers in PubMed.

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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

5 authors.

Ji ChenSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.
Jiayan HuangSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.
Weihua SuSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.
Dalia ŠtreimikienėLithuanian Centre for Social Sciences, Vilnius, Lithuania.
Tomas BaležentisLithuanian Centre for Social Sciences, Vilnius, Lithuania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Such large-scale disruptions as the pandemic increase the uncertainty and risk related to business. Therefore, the business continuity management (BCM) has become an essential technical solution for enterprise emergency response. Since the beginning of 2020, the COVID-19 has spread worldwide at an alarming rate causing many threats to sustainable development of the business sector. The decline in consumer demand has hugely impacted service industries, such as wholesale and retail sales, tourism. Enterprise production and operations have faced severe challenges. In this study, we develop a risk factor analysis of BCM under the presence of COVID-19 in China. Based on a statistical survey of 940 enterprises in Hangzhou City, China, this study employs ordinal logistic regression to explore the hindering effect of risk factors introduced by the epidemic on business performance. Then, the interpretive structure model (ISM) is applied to analyze the hierarchical structure of the factors under examination. The key factors influencing the enterprise production and operation during COVID-19 outbreak significantly differ across the sub-sectors of the service industry. Therefore, this paper assesses the resilience of the productive technologies and business models of different industries amid the pandemic. This paper proposes epidemic prevention and control strategy focusing on investment and government regulation to ensure sustainable business development.

Indexed as

BusinessCOVID-19EnterpriseInterpretive structure modelOrdinal logistic regressionProduction and operationService industry

Identifiers

PMID34898758
PMCPMC8646580

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.