Evidence map›Paper›PMID 41875170›Full record

ArticlePloS one2026

Digital transformation, dynamic capabilities and new quality productive forces: Empirical data from listed Chinese manufacturing companies.

Junling Wang, Yongjun Zhang, Ruifen Zhao

Abstract read
In one paragraph

Article in PloS one, 2026. 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.

Junling WangManagement School, Hebei GEO University, Shijiazhuang, China.
Yongjun ZhangManagement School, Hebei GEO University, Shijiazhuang, China.
Ruifen ZhaoEconomics School, Hebei GEO University, Shijiazhuang, China.ORCID https://orcid.org/0009-0005-5463-3115

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of the rapid growth of the digital economy, it is of great significance to explore how digital transformation can promote the development of new quality productive forces (Nqpf). Drawing on data from A share manufacturing listed companies in Shanghai and Shenzhen between 2011 and 2022, this study empirically analyzes the effect of digital transformation in the manufacturing industry on emerging Nqpf. The results reveal that digital transformation in the manufacturing industry has a significant positive impact on enhancing the level of Nqpf. Mechanism tests show that digital transformation in the manufacturing industry can promote the growth of Nqpf by strengthening companies' innovation, absorptive, and adaptive capabilities. Heterogeneity tests further indicate that the influence of digital transformation on advancing Nqpf is particularly pronounced in Non-state-owned, High-tech, and Growth stage manufacturing companies. The research results uncover the underlying mechanisms through which digital transformation in the manufacturing industry influences Nqpf, and offering valuable theoretical and practical insights for manufacturing companies on how to leverage digital transformation to promote the development of Nqpf.

Identifiers

PMID41875170
PMCPMC13012489

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Registered trials

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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.