Evidence map›Paper›PMID 40175456›Full record

ArticleScientific reports2025

Sustainability in construction economics as a barrier to cloud computing adoption in small-scale Building projects.

Zonghui Wang, Olga Veniaminovna Kalugina, Olga Vladimirovna Volichenko, Ivan Khalil, Haytham F Isleem

Erratum issuedAbstract 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. An erratum has been issued. 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

5 · Who and what money

Authors and funding

5 authors.

Zonghui WangDepartment of Architecture Restoration and Design, Peoples Friendship University of Russia (RUDN University), Moscow, 117198, Russia. 1042228158@pfur.ru.
Olga Veniaminovna KaluginaDepartment of Architecture Restoration and Design, Peoples Friendship University of Russia (RUDN University), Moscow, 117198, Russia.
Olga Vladimirovna VolichenkoDepartment of Architecture Restoration and Design, Peoples Friendship University of Russia (RUDN University), Moscow, 117198, Russia.
Ivan KhalilDepartment of Architecture Restoration and Design, Peoples Friendship University of Russia (RUDN University), Moscow, 117198, Russia.
Haytham F IsleemDepartment of computer Science, University of York, YO105DD, York, United Kingdom. mps565@york.ac.uk.ORCID http://orcid.org/0000-0002-4826-7420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of intelligent technology to enhance decision-making, optimize processes, and boost project economics and sustainability has the potential to significantly revolutionize the construction industry. However, there are several barriers to its use in small-scale construction projects in China. This study aims to identify these challenges and provide solutions. Using a mixed-methods approach that incorporates quantitative analysis, structural equation modeling, and a comprehensive literature review, the study highlights key problems. These include specialized challenges, difficulty with data integration, financial and cultural constraints, privacy and ethical issues, limited data accessibility, and problems with scalability and connection. The findings demonstrate how important it is to get rid of these barriers to fully utilize intelligent computing in the construction sector. There are recommendations and practical strategies provided to help industry participants get over these challenges. Although the study's geographical emphasis and cross-sectional approach are limitations, they also offer opportunities for further investigation. This study contributes significantly to the growing body of knowledge on intelligent computing in small-scale construction projects and offers practical guidance on how businesses might leverage their transformative potential.

Indexed as

ConstructionEconomicsIntelligent computingSmall constructionSustainability

Identifiers

PMID40175456
PMCPMC11965287

What OpenQuestion holds

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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.