Evidence map›Paper›PMID 39292695›Full record

ArticlePloS one2024

Digital transformation and corporate tax avoidance: An analysis based on multiple perspectives and mechanisms.

Qi Zhang, Jinghuai She

Abstract read
In one paragraph

Article in PloS one, 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

2 authors.

Qi ZhangCollege of Business Administration, Capital University of Economics and Business, Beijing, China.ORCID 0009-0008-7994-4552
Jinghuai SheCollege of Business Administration, Capital University of Economics and Business, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Promoting the seamless integration of the digital economy with the real economy, mitigating the adverse impacts of widespread corporate tax avoidance, and optimizing tax governance are critical imperatives in the era of digital economy. This study examines all A-share listed companies from 2007 to 2022 as research samples. It utilizes multiple perspectives including signal theory, information asymmetry theory, and the T-O-E (Technology-Organisation-Environment) framework to investigate the primary impacts of digital transformation on corporate tax avoidance, along with the intermediate mechanisms and foundational conditions that influence its effectiveness. After conducting both theoretical and empirical analyses, this paper presents the following conclusions. (1) The implementation of digital transformation significantly reduces corporate tax avoidance, a conclusion supported by rigorous robustness tests. Moreover, digital transformation enhances corporate productivity through the suppression of tax avoidance. (2) Digital transformation diminishes corporate tax avoidance through enhanced innovation and efficiency in resource allocation (technology level), improved quality of internal controls (organization level), and decreased industry competition (environment level). (3) The impact of digital transformation in reducing tax avoidance is significantly greater for enterprises in their growth phase, experiencing lower financing constraints, particularly those situated in the central and western regions. (4) Lower business risk is essential for maximizing the effectiveness of digital transformation and reducing corporate tax avoidance. This is crucial for governments seeking to improve tax administration, guide market and regional development, and enhance the impact of corporate digital transformation on mitigating tax avoidance.

Indexed as

TaxesCommerceHumans

Identifiers

PMID39292695
PMCPMC11410206

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.