Evidence map›Paper›PMID 34629691›Full record

ArticleComputers & industrial engineering2021

Investigating the COVID-19 pandemic's impact on sustainable supplier selection in the Nigerian manufacturing sector.

Ifeyinwa Juliet Orji, Frank Ojadi

Abstract read
In one paragraph

Article in Computers & industrial engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
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

2 authors.

Ifeyinwa Juliet OrjiResearch Center for Smarter Supply Chain, Dongwu Business School, Soochow University, Suzhou 215006, PR China.
Frank OjadiDepartment of Operations Management, Lagos Business School, Pan-Atlantic University (formerly Pan-African University), Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the years, sustainable supplier selection (SSS) has become increasingly popular among scholars and practitioners as a viable means to actualize supply chain sustainability. Little, however, is known about the impact of the COVID-19 pandemic on sustainable supplier selection particularly in the manufacturing sector. In this paper, we present pandemic response strategies as a significant aspect of the COVID-19 pandemic's impact and investigate the relative importance of such strategies in SSS implementation. Drawing upon a rich data pool from the Nigerian manufacturing sector, we proposed an integrated multi-criteria decision making (MCDM) methodology to analyse the interrelationships between the COVID-19 pandemic response strategies and Triple-Bottom-Line (TBL) criteria for SSS. Our analysis shows that economic criteria and pandemic response strategies are the highest ranked in terms of relative importance and thereby pinpoints the need for manufacturing firms to emphasize such during SSS implementation in the COVID-19 pandemic. Specifically, quality, cost, use of personal protective equipment and use of information technologies for customer demand prediction are inferred as highly significant in SSS implementation in the COVID-19 pandemic era. Furthermore, the efficiency of the proposed methodology was validated by a comparative analysis with other MCDM methods. Therefore, this study presents implications on the significance of pandemic response strategies in SSS and thereby enriches literature on the COVID-19 pandemic's impact on supply chains.

Indexed as

COVID-19 pandemicManufacturing sectorSupply chain sustainabilitySustainable supplier selectionTriple-Bottom-Line (TBL)

Identifiers

PMID34629691
PMCPMC8490952

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.