Evidence map›Paper›PMID 39787121›Full record

ArticlePloS one2025

Digital technology innovation, supply chain resilience and enterprise performance-The case of listed automotive parts manufacturing companies.

Xiangbing Chen, Chen Sun, Fang Wang

Abstract read
In one paragraph

Article in PloS one, 2025. 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.

Xiangbing ChenWuhan University of Science and Technology, Hubei Province, China.
Chen SunWuhan University of Science and Technology, Hubei Province, China.ORCID 0009-0002-0811-3117
Fang WangWuhan University of Science and Technology, Hubei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital technology innovation (DTI) is the core driving force for the development of the digital economy. This paper brings digital technology innovation and the supply chain of auto parts manufacturing under the same framework. This paper uses Stata 18 to empirically analyze the panel data of 130 A-share auto parts listed companies in Shanghai and Shenzhen from 2010 to 2022. The digital technology innovation indicator is divided into three levels: substantial digital technology innovation (SDTI), non-substantial digital technology innovation (NDTI), overall digital technology innovation (ODTI). To explore its impact mechanism on enterprise performance. The empirical results show that: (1) Digital technological innovation (DTI) has a positive and significant impact on enterprise performance, and supply chain resilience plays a mediating role in the relationship between digital technology innovation and enterprise performance, and R&D investment (RDI) positively moderates the role of supply chain resilience in promoting enterprise performance. (2) Heterogeneity analysis showed that the impact of supply chain resilience on enterprise performance was more significant in small and medium-sized enterprises. There are significant differences between different groups of business ownership. In economically underdeveloped regions, the effect of digital technology innovation on enterprise performance is more significant. This paper complements the perspective of supply chain to study the relationship between digital technology innovation and enterprise performance, expands the existing research, and its heterogeneity analysis provides new insights for understanding China's auto parts manufacturing industry. This provides a basis for strengthening digital technology innovation and promoting the sustainable development of the auto parts industry.

Indexed as

AutomobilesDigital TechnologyInventionsChinaManufacturing Industry

Identifiers

PMID39787121
PMCPMC11717258

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.