Evidence map›Paper›PMID 41102239›Full record

ArticleScientific reports2025

Longitudinal insights into the industrial IoT-green intellectual capital Nexus in the post-COVID era using SEM‑ANN approach.

Yang Cao, Lin Xinwen

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Yang CaoFaculty of Economic and Management, The National University of Malaysia, Bangi, 43600, Selangor, Malaysia.
Lin XinwenXiamen City University, Xiamen, 361008, China. linxinwen@xmcu.edu.cn.

Funding

Xiamen City University 20105035
6 · The paper itself

Abstract

In the post-COVID-19 era, the tourism industry faces urgent sustainability challenges stemming from carbon-intensive operations and inefficient resource use. While the industrial internet of things has the potential to drive green transformation, how it reconstructs green intellectual capital to foster green innovation remains underexplored. This study addresses this critical gap by adopting a three-wave longitudinal survey of 543 Chinese star-rated hotels and applying a hybrid SEM-ANN model to analyze both linear and nonlinear dynamics among industrial internet of things, green intellectual capital, and green innovation. The results reveal that industrial internet of things integration directly promotes green innovation (β = 0.214, p < 0.001) and indirectly drives green innovation by enhancing green intellectual capital dimensions: green human capital (β = 0.324) through digital skill upgrading, green structural capital (β = 0.450) via improved digital governance, and green relational capital (β = 0.360) by enabling cross-sectoral partnerships. Mediation analysis confirms that green intellectual capital partially mediates the IIoT-GI relationship (p < 0.05). Notably, ANN analysis shows that green relational capital exerts the strongest nonlinear influence on green innovation. By extending dynamic capabilities theory, this research proposes a novel IIoT-GIC-GI integration model, offering a strategic roadmap for green digital transformation in hospitality. Practically, the findings inform China's tourism policy goals for 2023-2025 and provide actionable insights for enhancing low-carbon development in star-rated hotels.

Indexed as

Digital transformationGreen innovationGreen intellectual capitalIndustrial internet of thingsLongitudinal studyPost-Covid-19SEM-ANN modelSustainable development

Identifiers

PMID41102239
PMCPMC12532556

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.