Evidence map›Paper›PMID 40760215›Full record

ArticlePsychonomic bulletin & review2025

Does your surname undermine your research impact?

Yuejun Lawrance Cai, Kin Fai Ellick Wong, Jessica Y Y Kwong

Abstract read
In one paragraph

Article in Psychonomic bulletin & review, 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
–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.

Yuejun Lawrance Cai *Department of Management and Strategy, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Kin Fai Ellick Wong *Department of Management, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. mnewong@ust.hk.
Jessica Y Y KwongDepartment of Marketing, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong.

Funding

General Research Fund by RGC Hong Kong #14612118
6 · The paper itself

Abstract

Citation frequency is widely recognized as a crucial metric for assessing academic impact. Previous studies analyzing data from citation databases have observed a surname order bias-a phenomenon where the alphabetical ordering of researchers' surnames negatively impacts their citation counts. However, the underlying mechanisms driving this bias, the causality behind it, and its implications for in-text citation practices remain poorly understood. Therefore, the present research aims to address these gaps through two preregistered studies. Study 1 replicates and extends the work of Stevens and Duque (Psychonomic Bulletin & Review, 26, 1020-1026, 2019), using a larger sample of 446,755 articles and controlling for surname initial frequency and publication year. Study 2 is an experiment with 307 valid responses from academics holding doctoral degrees, manipulating both citation systems and surname alphabetical order. Consistent and robust findings emerged across both studies: articles authored by individuals with surnames appearing earlier in the alphabet were more likely to be cited. This effect was especially pronounced in the context of alphabetical citation systems, compared with numerical citation systems. The current research provides a testable, reliable explanation for the surname order bias and establishes a causal link between surname alphabetical order and citation frequency. Implications for theory and academic practice are discussed.

Indexed as

BibliometricsHumansNamesAlphabetical orderCitation frequencyPrimary effectResearch evaluation

Identifiers

PMID40760215
PMCPMC12627127

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.