Evidence map›Paper›PMID 40567644›Full record

ArticlePeerJ. Computer science2025

Can executives' digital background develop the level of AI utilization in enterprises.

Ruichao Yu, Linrong Wu, Guiying Li, Zixin Wang

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2025. 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
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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

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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.

Ruichao YuSchool of Economic and Management, Guangxi Normal University, Guilin, GuangXi, China.
Linrong WuSchool of Management and Engineering, Guilin University, Guilin, GuangXi, China.
Guiying LiSchool of Economic and Management, Guangxi Normal University, Guilin, GuangXi, China.
Zixin WangCollege of Foreign Studies, Guangxi Normal University, Guilin, GuangXi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital talent has emerged as a pivotal resource for advancing artificial intelligence (AI) within enterprises, and it constitutes a critical digital asset that firms prioritize in their AI development strategies, aiming to attract and retain such talent. To elucidate the interconnections and mechanisms linking the digital backgrounds of executives with the level of AI utilization in enterprises, this study undertakes an examination of the relationship between these two variables, drawing on a sample of Chinese A-share listed companies spanning from 2012 to 2022. The research findings are as follows: (1) The presence of a substantial number of executives with digital backgrounds within a company significantly enhances the development of AI utilization within the enterprise. (2) The influence of executives' digital backgrounds on the level of AI utilization in enterprises is modulated through the enhancement of the enterprise's total factor productivity. (3) Executives' digital backgrounds elevate the level of AI utilization by bolstering their proficiency in applying digital technology. (4) Heterogeneity analysis, conducted based on the nature of enterprise property rights, geographical regions, and technological proficiency, reveals that the positive effect of executives' digital backgrounds on AI utilization is more pronounced in private enterprises compared to state-owned enterprises. Regionally, the impact of executives' digital backgrounds on AI utilization diminishes progressively from the eastern to the central and western regions. Robustness checks, including the substitution of key variables, model alteration, the application of the instrumental variable method, and the Heckman two-stage method, confirm the persistence of these findings. This article contributes to the understanding of the impact and pathways through which executives' digital backgrounds influence AI utilization in enterprises against the backdrop of high-quality economic development. It enriches the scholarly discourse on executives' digital backgrounds and AI utilization in enterprises, offering both theoretical support and practical insights for the advancement of AI within enterprises.

Indexed as

Application of digital technologyArtificial intelligenceExecutive digital backgroundHigh quality developmentTotal factor productivity

Identifiers

PMID40567644
PMCPMC12192996

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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.