Evidence map›Paper›PMID 38694071›Full record

ArticleHeliyon2024

A meta-analysis of the causal interpretation of enterprise green innovation: A structural theoretical model.

Fanbo Li, Hongfeng Zhang, Linlu Weng, Haoqun Yan

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
–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

4 authors.

Fanbo LiSchool of Social Sciences, Tsing Hua University, China.
Hongfeng ZhangFaculty of Humanities and Social Sciences, Macao Polytechnic University, China.
Linlu WengFaculty of Humanities and Social Sciences, Macao Polytechnic University, China.
Haoqun YanFaculty of Humanities and Social Sciences, Macao Polytechnic University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The escalating environmental challenges have compelled corporations to embark on green innovation initiatives, establishing this as a pivotal strategy for attaining economic sustainability. Yet, there remains a lack of consensus within the scholarly community regarding the precursors and outcomes of green innovation. This research leverages Giddens' structuration theory and employs meta-analytical methods to elucidate the determinants and effects of corporate green innovation. Initially, the study synthesizes 288 effect sizes from 161 distinct scholarly articles, spanning from 2012 to early 2023, guided by the structuration framework. This comprehensive analysis corroborates the influence of several structuration theory antecedents on green innovation, thereby offering fresh empirical backing for the theory. Subsequently, it scrutinizes the link between green innovation and its impacts, evaluated through economic and environmental performance lenses. Furthermore, the research contrasts the meta-analytical findings across large-scale and smaller enterprises, underscoring notable disparities in the dynamics of green innovation across different organizational contexts. This inquiry not only reaffirms the theoretical constructs of structuration theory, such as spatialization, subjectification, and structuration, but also integrates these notions with quantifiable variable models. The paper posits that structuration theory could underpin a theoretical framework for dissecting the influential variables associated with green innovation, thereby fostering further academic investigation into corporate environmental innovation.

Indexed as

Enterprise sizeGreen innovationMeta-analysisTheory of structuration

Identifiers

PMID38694071
PMCPMC11061688

What OpenQuestion holds

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LicenceCC BY-NC
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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.