Evidence map›Paper›PMID 42848773›Full record

ArticlePloS one2026

AI empowerment and supervisory board effectiveness: Evidence from an information processing framework.

Ruisheng Qin, Jiaxing Qin, Shiquan Wang

Abstract read
In one paragraph

Article in PloS one, 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

3 authors.

Ruisheng QinSchool of Business Administration, Northeastern University, Shenyang, China.
Jiaxing QinPersonnel Office, Guangxi International Business Vocational College, Nanning, China.ORCID https://orcid.org/0009-0000-8834-2627
Shiquan WangSchool of Business Administration, Northeastern University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of rapid digitalization and intelligent governance, artificial intelligence (AI) technologies are increasingly embedded in organizational supervision and decision-making processes, fundamentally reshaping how supervisory boards process information and perform monitoring tasks. Drawing on information processing theory, this study develops a moderated mediation model to examine how AI empowerment is associated with supervisory board effectiveness through information transparency, and how this process is contingent upon organizational learning capability. Using survey data collected from 382 supervisory board members and senior managers across multiple industries, this study adopts an organizational-level perspective and employs hierarchical regression and bootstrap analyses to test the proposed hypotheses. The results show that AI empowerment significantly associated with both information transparency and supervisory board effectiveness. Information transparency partially mediates the relationship between AI empowerment and supervisory effectiveness, indicating that AI is positively associated with supervisory performance by optimizing organizational information-processing mechanisms. Moreover, organizational learning capability positively moderates the relationship between AI empowerment and information transparency. However, the moderated mediation effect is not statistically significant, indicating that the indirect effect of AI empowerment on supervisory effectiveness through information transparency remains relatively stable across different levels of organizational learning capability. These findings suggest that organizational learning capability shapes the relationship between AI empowerment and information transparency, but does not significantly alter the overall indirect pathway to supervisory effectiveness.

Indexed as

Artificial IntelligenceEmpowermentHumans

Identifiers

PMID42848773
PMCPMC13649021

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.