Evidence map›Paper›PMID 39435064›Full record

ArticleHeliyon2024

Double-edged sword of industrial internet of things: Empirical evidence from green transformation of enterprises.

Liqun Liu, Lin Liu, Kai Liu, Ana Isabel Jiménez-Zarco

Abstract read
In one paragraph

Article in Heliyon, 2024. 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
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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

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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

4 authors.

Liqun LiuCollege of Economics and Management, Shihezi University, Shihezi, China.
Lin LiuCollege of Economics and Management, Shihezi University, Shihezi, China.
Kai LiuCollege of Economics and Management, Shihezi University, Shihezi, China.
Ana Isabel Jiménez-ZarcoEconomic and Business Department, Open University of Catalunya, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The industrial revolution based on the Internet of Things brings new opportunities and challenges for enterprises' green and sustainable development. Therefore, this study explores the relationship between the Industrial Internet of Things (IIOT) and the Green Transformation of Enterprises (GT) based on the data of China's A-share listed companies from 2009 to 2023 using two-way fixed, mediated effect, and moderated effect models. The results show that for every unit increase in IIOT, the GT increases by 28.89 percent on average, indicating that IIOT can break down information barriers and effectively promote GT. However, after disaggregating the GT, it was found that the IIOT guided companies to implement the Symbolic Green Transformation (SGT) instead of adopting the Realistic Green Transformation (RGT). This strategic choice preference is more pronounced among firms that are larger, in energy-intensive industries, in regions with weaker environmental regulations and higher levels of development. The intermediary mechanism test proved that the IIOT can drive firms to implement comprehensive GT through paths that enhance investment attractiveness, productivity, technological innovation, and pay gaps. In particular, the masking effect reminds us that smart technology is a "double-edged sword". The negative effects of smart technologies hinder the implementation of RGT. Further analysis reveals an inverted U-shaped relationship between IIOT and RGT preferences. In addition, in companies that focus on Environmental, Social and Governance (ESG) performance and avoid financial speculation, the dark side of intelligent technology is suppressed. The findings of this study enrich the research scope of smart technology, GT, and provide important insights for enterprises in emerging economies to rationally utilize IIOT to seek green and sustainable development under the wave of rapid development of smart technology.

Indexed as

Corporate green transformationDouble-edged swordIndustrial internet of thingsRealistic green transformationSymbolic green transformation

Identifiers

PMID39435064
PMCPMC11492445

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.