Evidence map›Paper›PMID 41865027›Full record

ArticleScientific reports2026

Leveraging Industry 4.0 technologies for organizational sustainability performance in Chinese firms: an NRBV-mediated model advancing UN SDGs 9 and 12.

Yan Gao, Jinlong Wang, Xiangtang Chen, Shouping Peng, Sohaib Mustafa

Abstract read
In one paragraph

Article in Scientific reports, 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

5 authors.

Yan GaoCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
Jinlong WangSchool of Education, Qufu Normal University, Qufu, 276826, China. wang-jin-long@qq.com.
Xiangtang ChenLibrary, Wenzhou University of Technology, Wenzhou, 325027, China.
Shouping PengSchool of Economics and Trade Management, Wenzhou Vocational College of Science and Technology, Wenzhou, 325000, China. nrskqfzly@163.com.
Sohaib MustafaCollege of Economics and Management, Beijing University of Technology, Beijing, People's Republic of China.

Funding

Shandong Provincial Education Science "14th Five-Year Plan 2023ZC048
6 · The paper itself

Abstract

The rapid integration of Industry 4.0 technologies offers new pathways for Chinese manufacturers to reconcile operational efficiency with environmental stewardship. Grounded in the Natural Resource-Based View (NRBV), this study investigates how digital technologies, Artificial Intelligence/Machine Learning (AIA), Digital Twin Usage (DTU), IoT Integration (IOTI), and Green Technology Adoption (GTA), serve as strategic resources to enhance Sustainable Manufacturing Practices (SMP) and drive Organizational Sustainability Performance (OSP). Analyzing data from 719 firms through structural equation modeling (SEM), the findings reveal significant direct effects of AIA, DTU, GTA, and IOTI on SMP, with SMP fully mediating their impact on OSP. The study advances NRBV by demonstrating how digital technologies function as dynamic capabilities that reconfigure operational practices to achieve sustainability outcomes. Managers should prioritize integrated IoT networks for real‑time monitoring, AI/ML for predictive quality control, digital twins for lifecycle optimization, and green‑technology implementations for energy and material efficiency. While this study does not directly measure SDG-specific indicators, the observed improvements in resource efficiency, emissions reduction, lifecycle optimization, and eco-efficient production practices are conceptually consistent with the objectives of UN Sustainable Development Goals (SDG 9 and 12). Accordingly, SDG alignment should be interpreted as an inferential linkage grounded in firm-level sustainability outcomes rather than as direct evidence of SDG attainment.

Indexed as

AI/ML adoptionDigital twin usageGreen technology adoptionIndustry 4.0Internet of Things (IoT) integrationOrganizational sustainability performanceSDGs 9 and 12Sustainable manufacturing practices

Identifiers

PMID41865027
PMCPMC13149516

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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.