Evidence map›Paper›PMID 39262971›Full record

ArticleHeliyon2024

Sustainable performance in SMEs using big data analytics for closed-loop supply chains and reverse omnichannel.

Syed Abdul Rehman Khan, Muhammad Sohail Tahir, Adnan Ahmed Sheikh

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

3 authors.

Syed Abdul Rehman KhanSchool of Management and Engineering, Xuzhou University of Technology, Xuzhou, China.
Muhammad Sohail TahirComsats University Islamabad, Vehari Campus, Pakistan.
Adnan Ahmed SheikhDepartment of Business Administration, Air University Islamabad, Multan Campus, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aims to examine the advantages of utilizing big data analytics (BDA) on circular economy (CE) dual systems, which are closed-loop supply chain (CLSC) and the reverse omnichannel (ROC), aiming to enhance sustainable firm performance (SFP) of the small and medium based enterprises (SMEs). The product return knowledge (PRK) can reinforce the proposed network of relationships and facilitate the approach of active returns in CE. Using the structural equation model (SEM) in AMOS v24, the researchers examined the hypotheses using data from a simple random sample of 232 SMEs in Pakistan. Though ROC solutions provide rare performance-related benefits, however, BDA makes the CE system more efficient. CLSC gets strengthened when the PRK is developed. The improved PRK facilitates the CLSC network and enhances its performance. This study adopts a novel approach to studying CE by considering the dual system of CE in the forms of CLSC and ROC. The research investigates whether the BDA facilitates improved product return processes by improving CLSC operations and achieving service capabilities using ROC. Finally, the proposed framework is the first to investigate the benefits evolving from the PRK, which enhance the capability to sense, seize, and reconfigure the process and facilitate the approach of active return in CE. The findings suggest that firms must decide carefully between CLSC and ROC. Choosing CLSC requires higher operational capabilities, whereas deciding on ROC requires higher service capabilities. Lastly, PRK is necessary for achieving the firm's performance objectives.

Indexed as

Big data analyticsCircular economyClose-loop supply chainProduct return knowledgeReverse omnichannel

Identifiers

PMID39262971
PMCPMC11388499

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.