Evidence map›Paper›PMID 36127921›Full record

ArticleHeliyon2022

Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers' perspectives.

Md Rukon Miah, Afzal Hossain, Rony Shikder, Tama Saha, Meher Neger

Abstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

Md Rukon MiahDepartment of Marketing, Comilla University, Cumilla, Bangladesh.
Afzal HossainDepartment of Business Administration, Trust University, Barishal, Bangladesh.
Rony ShikderDepartment of Marketing, Comilla University, Cumilla, Bangladesh.
Tama SahaDepartment of Marketing, Comilla University, Cumilla, Bangladesh.
Meher NegerDepartment of Marketing, Comilla University, Cumilla, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background of the study: Nowadays, the business pattern is changing globally. The business organization is influenced customers to purchase their necessary goods and services from online businesses. The online-based business takes promotional activities through social media platforms like Facebook, Twitter, Instagram, and Pinterest. Purpose: The aim of the research was to investigate the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers. Research methods: Quantitative type research was applied and the study used descriptive research design. A standardized questionnaire was used to collect 350 data points from Bangladeshi consumers using an online purposive sampling method. A partial least square structural equation modeling (PLS-SEM) approach was used to evaluate the data and test the hypotheses. Results: PLS-SEM analysis method demonstrated that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior during the COVID-19 pandemic in the perspective of Bangladesh. Conclusion: The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. The customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

Indexed as

Bangladeshi consumersCOVID-19 pandemicOnline shoppingPLS-SEMSocial media

Identifiers

PMID36127921
PMCPMC9477614

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.