Evidence map›Paper›PMID 36647351›Full record

ArticleHeliyon2023

Research on the effectiveness of online food safety supervision under the existence of settled enterprises' myopic cognitive bias.

Cong Shen, Bing Lei, Chang Lu, Feng Zhou

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

4 authors.

Cong ShenSchool of Management, Henan University of Technology, Zhengzhou, China.
Bing LeiSchool of Management, Henan University of Technology, Zhengzhou, China.
Chang LuSchool of Education, Shanghai Jiao Tong University, Shanghai, China.
Feng ZhouZhengzhou Administration for Market Regulation, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the scale of China's online food industry grew rapidly in recent years, the risky operational practices of business were not adequately prohibited. Based on the repeated illegal behaviors of online food enterprises, this study constructs the intertemporal utility model of online food enterprises from the perspective of behavioral economics. It was discovered that the presence of cognitive biases with a short-term focus in online food businesses would result in dangerous food sales behavior. The myopic unsafe online food quantity deviation of businesses would decrease as the cognitive deviation coefficient and penalty factor of online food businesses increase. Due to the existence of myopic cognitive bias, it is difficult for online food businesses to perceive the difference between the discount factor in the short term and the long term during the decision-making process. Therefore, they will not choose to sell a significant quantity of dangerous food. However, during the sales behavior phase, food-settled enterprises would opt to offer more dangerous food due to myopic cognitive bias. According to the different behaviors of online food enterprises in different periods, this study provides some corresponding supervision strategies for government regulators in the near and far future. Even though government departments could act quickly to investigate and deal with food safety incidents that are less harmful, the long-term solutions to ensure continued online food safety and optimize the online business environment are to build comprehensive system of online food safety regulation, improve information disclosure of online food enterprises, and enhance the government online governance structures.

Indexed as

Myopic cognitive biasOnline food safetySupervision

Identifiers

PMID36647351
PMCPMC9840359

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.