Evidence map›Paper›PMID 38128046›Full record

ArticlePloS one2023

Geographical peer effect in serial mergers and acquisitions: The influence of social learning and director network.

Xiaoxu Zhang, Yu Song, Qiaoran Liao

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. 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
0.5field-weighted citation impact, top 28% of its field
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, 1 citations in OpenAlex.

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 at 1 institution in 1 country.

Xiaoxu ZhangSchool of Business Administration, University of Science and Technology Liaoning, Anshan, China.
Yu SongSchool of Business Administration, University of Science and Technology Liaoning, Anshan, China.ORCID 0009-0006-2809-8857
Qiaoran LiaoSchool of Business Administration, University of Science and Technology Liaoning, Anshan, China.
University of Science and Technology Liaoning · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Based on the serial mergers and acquisitions(M&A) data of Chinese A-share listed companies from 2010 to 2019, this paper discusses whether there are geographical peer effects in serial M&A, and tests its mechanism and influence path. The empirical study shows that geographical peer decision-making has geographical peer effects in the decision-making of serial M&A, and the average number of serial M&A of geographical peers has a significant positive impact on the serial M&A decision-making of focal firms. Social learning and director networks are essential to produce geographical peer effects in serial M&A. The external learning mechanism shows that the geographical peer effect of serial M&A decision-making is partly caused by backward firms' social learning to the leading peer firms' decision-making, but vice versa. The director network relationship of focal firms can positively moderate the relationship between the geographical peer firms' average serial M&A decision-making and the focal firm's serial M&A decision-making. This paper reveals the mechanism of geographical peer effect in serial M&A decision-making and provides a new perspective to understand the motivation of serial M&A decision-making. These empirical findings not only provide important empirical evidence on serial M&A from developing countries such as China, but also provide a valuable reference for decision-makers and researchers of serial M&A in the future.

Indexed as

Social LearningChinaLearningMotivation

Identifiers

PMID38128046
PMCPMC10735032
OpenAlexW4390047180

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.