Evidence map›Paper›PMID 42220396›Full record

ArticleFrontiers in psychology2026

From "scientific leadership" to sustainable innovation: the shaping mechanism of talent ecosystem and green innovation capability in Chinese knowledge-intensive firms.

Rongcheng Liang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. 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

1 author.

Rongcheng LiangEmergency Management Training Department, Party School of Shandong Provincial Committee of the Communist Party of China (Shandong Academy of Governance), Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of global sustainable development, knowledge-intensive firms face unprecedented challenges in cultivating green innovation capabilities. Drawing upon the theoretical foundations of talent ecosystem theory and dynamic capability theory, this study investigates the shaping mechanism through which talent ecosystems influence green innovation capability in Chinese knowledge-intensive enterprises. By analyzing the interplay among scientific leadership, talent density, and organizational learning climate, it develops a conceptual framework that elucidates how human capital serves as the core driving force for organizational sustainable development. Our findings reveal that a robust talent ecosystem, characterized by mission-driven leadership and collaborative knowledge networks, significantly enhances firms' green innovation performance through the mediation of organizational learning mechanisms. Furthermore, scientific leadership emerges as a critical boundary condition that amplifies the positive relationship between talent ecosystem characteristics and green innovation outcomes. This research contributes to the social sustainability literature by highlighting the critical role of talent development in achieving environmental sustainability goals, offering practical implications for managers seeking to build sustainable competitive advantages through strategic human resource management. The study advances our understanding of the micro-foundations of sustainable innovation and provides actionable insights for organizations navigating the transition toward environmentally responsible business models.

Indexed as

green innovation capabilityknowledge-intensive firmsorganizational learningscientific leadershiptalent ecosystem

Identifiers

PMID42220396
PMCPMC13218847

What OpenQuestion holds

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LicenceCC BY
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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.