Evidence map›Paper›PMID 34434152›Full record

ArticleFrontiers in psychology2021

Evading the Entrepreneurship: A Study to Discover Implementable Online Approaches to Avoid Greenhouse Consequences.

Lian Feng, Hu Wenting, Tazeem Akhter, Gadah Albasher, Alamzeb Aamir, Asma Imran

Abstract read
In one paragraph

Article in Frontiers in psychology, 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
–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

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

6 authors.

Lian FengSchool of Philosophy and Social Development, Shandong University, Shandong, China.
Hu WentingBusiness School, Nanjing University, Nanjing, China.
Tazeem AkhterUniversity of Health Science, Lahore, Pakistan.
Gadah AlbasherDepartment of Zoology, College of Sciences, King Saud University, Riyadh, Saudi Arabia.
Alamzeb AamirDepartment of Management Science, FATA University, Kohat, Pakistan.
Asma ImranDepartment of Management Sciences, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Greenhouse gases emissions due to climate change are a continuous threat to the global world, mainly relying on the pervasive consumption of numerous products, including synthetic and non-synthetic products. This research focused on the green purchase intentions of students in Pakistan towards different products, which are related to minimising the greenhouse effect and are available for sale on numerous e-commerce websites, ultimately proceeding to green entrepreneurship. The main objective of this study was to determine which methodology was better among product listing, social media advertising, and online virtual community to enhance customer online green purchase intention while considering online information about the greenhouse effect as a mediating variable. The AMOS 24 was used for this research. SEM was performed with the help of bootstrap methodology. The research was conducted on 280 students at different educational institutes in Pakistan, using a simple random sampling technique. A finding of this study suggested that all three methods positively impacted the green purchase intention of consumers and green entrepreneurship, but online virtual communities could be considered in a more effective way to enhance the green purchase intention of its targeted customers.

Indexed as

e-commercegreen entrepreneurship green productsgreenhouse gassesgreen purchase intentionmitigation

Identifiers

PMID34434152
PMCPMC8381331

What OpenQuestion holds

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LicenceCC BY
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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.