Evidence map›Paper›PMID 36124174›Full record

ArticleComputational and mathematical methods in medicine2022

The Development Relationship between Cross-Border e-Commerce and Internet of Things Technology Coupling in Digital Economy Based on Neural Network Model.

Qi Liu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Qi LiuGuangdong University of Science and Technology, Dongguan, Guangdong 523083, China.ORCID https://orcid.org/0000-0001-5432-5300

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compared with the traditional trading model, the problem of information asymmetry in export cross-border e-commerce (CBEC) transactions actually exists. Since the buyers and sellers of export CBEC are located in different countries, it is difficult for both parties to make accurate judgments on each other's credit. Besides, the number of export CBEC financing is relatively small, and the financing situation is not optimistic. Fortunately, the Internet of Things (IOT) technology has a wide range of applications and strong social penetration, especially in e-commerce, which can effectively improve payment, logistics, and distribution. By employing the concept of the IOT, this paper expounds the application of the IOT in the e-commerce transaction process and the impact of the IOT on the development of e-commerce. Besides, this paper also proposes some security issues in the application of the IOT in e-commerce. In addition, this paper analyzes the relationship and development trend of CBEC and IOT technology; meanwhile, it also points out the practical problems currently faced by combining the regional advantage of a certain region. The e-commerce industry develops IOT technology and drives innovation-driven development in the region. In addition, this paper builds an evaluation index system for the development of export CBEC enterprises based on the analysis of the problems and reasons by using the sample data to train the BP neural network and using the test samples to test the model. It is found that the model accuracy rate is 89.47%. Then, this paper also takes the export CBEC company A as an example to conduct empirical research and to prove the operability of the credit evaluation model. Finally, it provides relevant suggestions for improving the credit evaluation system of export CBEC enterprises.

Indexed as

CommerceInternet of ThingsData CollectionHumansNeural Networks, ComputerTechnology

Identifiers

PMID36124174
PMCPMC9482480

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.