Evidence map›Paper›PMID 41951698›Full record

ArticleScientific reports2026

Artificial intelligence assimilation shapes sustainable performance through dynamic capabilities.

Mingyang Zhang, Wenwen Dong

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Mingyang ZhangSchool of Public Administration, Sichuan University, Chengdu, China. mingyangzhang1239@gmail.com.
Wenwen DongSchool of Public Affairs, Xiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Beyond the hype of disruptive technologies, achieving sustainability requires assimilating artificial intelligence (AI) into organizational dynamic capabilities. Yet conflicting evidence on the sustainability outcomes of AI assimilation (AIA) motivates this study to unveil its underlying capability logic. Grounded in the Natural Resource-Based View and Dynamic Capabilities Theory, this study employs meta-analytic structural equation modeling (MASEM) to systematically investigate the intrinsic mechanisms through which AIA affects firms’ sustainable performance (SP). The findings reveal several key insights. First, AIA directly enhances SP and exerts indirect effects through the multiple mediating pathways of organizational agility (OA), green innovation (GI), and organizational resilience (OR). Second, GI serves as a core mediating pathway, playing a pivotal role in connecting AIA with sustainable development goals. Third, OA generates performance gains through short-cycle resource reconfiguration, whereas OR provides stable support in uncertain environments; these two capabilities exhibit complementary characteristics across different time scales. Finally, the research elucidates the complete pathway wherein AIA influences SP through the serial mediation of OA, GI, and OR. This reveals the underlying logic of how AI drives sustainable development through the evolution of dynamic capabilities. By integrating findings across existing studies, this research helps to reconcile discrepancies in prior conclusions. It provides a novel theoretical explanation for understanding the complex mechanisms of AI-enabled corporate sustainable development and also offers practical implications for businesses advancing digital and intelligent transformation.

Indexed as

AI assimilationGreen innovationOrganizational agilityOrganizational resilienceSustainable performance

Identifiers

PMID41951698
PMCPMC13219588

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.