Evidence map›Paper›PMID 39584125›Full record

ArticleHeliyon2024

Are enterprises embedding in digital platform ecosystems greener? A resource curse view.

Qian Zhao, Chenfang Zhao, Chengcheng Song

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

3 authors.

Qian ZhaoSchool of Economics, Zhongnan University of Economics and Law, 430073, Wuhan, China.
Chenfang ZhaoSchool of Business Administration, Southwestern University of Finance and Economics, 611130, Chengdu, China.
Chengcheng SongSchool of International Relations and Public Affairs, Fudan University, 200433, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving the global climate mitigation goals requires corporate green innovation, especially substantive green innovation, to address the resource constraints. In this paper, we analyze the impact of the depth of enterprise embeddedness in digital platform ecosystems (DPEs) on substantive green innovation of enterprises. Previous research findings suggest that enterprises' involvement in DPEs can overcome the resource constraints and increase resource accumulation required for the substantive green innovation of enterprises. However, we suggest that this positive effect may also trigger the resource curse and inhibit substantive green innovation of enterprises. By crawling the word frequencies related to enterprise embedding in the DPE in enterprise annual reports, matching the CSMAR and CNRDS databases, this paper constructs a sample of 15,828 Chinese industrial A-share listed firms from 2010 to 2020, and empirically tests the above theoretical hypotheses by using a quadratic polynomial regression model. We find that there is a significant inverted U-shaped effect of the depth of enterprise embeddedness in DPEs on firms' substantive green innovation, and the optimal depth of embeddedness is 0.66 %. The results of the moderating mechanism test suggest that the family-owned equity of firms weakens, whereas state-owned equity enhances, the inverted U-shaped effect. Our study introduces the resource blessing and resource curse perspectives into the study of DPEs, revealing that moderate depth of enterprise embeddedness in DPEs promotes substantive green innovation of enterprises, whereas excessive depth of embeddedness hinders it.

Indexed as

Depth of enterprise embeddedness in the digital platform ecosystemFamily-owned equityResource curseState-owned equitySubstantive green innovation

Identifiers

PMID39584125
PMCPMC11583716

What OpenQuestion holds

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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.