RESEARCH ARTICLE
Can the presence of an author photograph and
biography have an impact on article citations?
The case of chemistry and chemical engineering
a n o p e n a c c e s s
j o u r n a l
Department of Communication and Learning in Science, Chalmers University of Technology, Gothenburg, Sweden
Tahereh Dehdarirad
Keywords: author characteristics, citation practices, multilevel regression analysis, photograph and
biography, random effect within-between (REWB) models, repeated measures
ABSTRACT
The aim of this study was to investigate whether the presence of an author photograph and
biography in scientific articles could have an impact on article citations. The impact of a
photograph and biography, in combination with certain author characteristics (i.e., gender,
affiliation country (measured as whether the author was affiliated with a high-income country
or not), and scientific impact (measured as whether the author was a high-impact author or
not)), was also examined, while controlling for several covariates. This study focused on a
sample of articles published in the time span of 2016–2018 in chemistry and chemical
engineering journals by Elsevier. The articles were downloaded from Scopus. The analysis was
done using random effects within-between model analyses. Within authors, the results showed
no significant impact of author photograph and biography on citations. Different patterns were
found for visibility of articles when the presence of an author photograph and biography was
combined with author characteristics. While being affiliated to a high-income country and
being a high-impact author had a positive impact on citations, gender (female) had a negative
impact. For gender, there was a small citation disadvantage of 5% for female authors when
they provided a photograph and biography.
1.
INTRODUCTION
The process of scientific citations and visibility in the scientific community seems to be subject
to implicit biases (Dion & Mitchell, 2020). Previous research has shown that author character-
istics such as academic age (Mahbuba & Rousseau, 2011) or the prestige of affiliated country or
institutions (Didegah, Bowman, & Holmberg, 2018) might affect the visibility of scientific works
in terms of citations. For example, a scientific work published by a famous person (Mahbuba &
Rousseau, 2011), or a country or an institute with high prestige in terms of citation (Didegah
et al., 2018) might have higher visibility in terms of citations. The result of a more recent study
by Nielsen and Andersen (2021) showed that the top 1% most-cited scientists, who are often
affiliated with high-ranking institutions in the Western world, receive more citations.
The Matthew effect has been applied to explain these inequalities and systematic deviations
in the number of citations from that of expected citations (Vinkler, 2010). The Matthew effect
refers to “greater increments of recognition for particular scientific contributions to scientists of
considerable repute and the withholding of such recognition from scientists who have not yet
made their mark” (Merton, 1973).
Citation: Dehdarirad, T. (2022). Can the
presence of an author photograph and
biography have an impact on article
citations? The case of chemistry and
chemical engineering. Quantitative
Science Studies, 3(4), 1024–1039.
https://doi.org/10.1162/qss_a_00219
DOI:
https://doi.org/10.1162/qss_a_00219
Peer Review:
https://publons.com/publon/10.1162
/qss_a_00219
Supporting Information:
https://doi.org/10.1162/qss_a_00219
Received: 19 March 2022
Accepted: 17 September 2022
Corresponding Author:
Tahereh Dehdarirad
Tahereh@chalmers.se
Handling Editor:
Ludo Waltman
Copyright: © 2022 Tahereh Dehdarirad.
Published under a Creative Commons
Attribution 4.0 International (CC BY 4.0)
license.
The MIT Press
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Can the presence of an author photograph and biography have an impact on article citations?
In addition to discrimination based on the abovementioned variables, reduced recognition
might arise from other factors, such as gender (Mahbuba & Rousseau, 2011). According to a
League of European Research Universities report by Gvozdanović and Maes (2018), implicit
bias has been a significant impediment to women’s advancement in academic careers.
Implicit biases might affect citation practices in a way that make men’s research be viewed
as more central or important in a scientific field (the Matthew effect), whereas women’s work
would be underrecognized (the Matilda effect) (Ghiasi, Mongeon et al., 2018; Dion, Sumner,
& Mitchell, 2018; Dion & Mitchell, 2020). Previous research has shown that in disciplines
such as social and political sciences where there are more female scholars, women receive
a similar or higher number of citations. This might mean a reduction of the Matthew effect.
However, gender citation gaps may persist if the Matilda effect and implicit biases in citation
practices occur (Dion et al., 2018).
Similarly, publications from countries with higher research and development investment
(Mahbuba & Rousseau, 2011) might receive more citations or attention on social media. Harris,
Macinko et al. (2017) used the Implicit Association Test with healthcare professionals to study
the association between good research and richness of countries (measured as their gross
domestic product per capita [GDP]). The results showed that the majority of participants asso-
ciated good research with rich countries. Implicit associations such as these might disfavor
research from lower income countries in research evaluation and citation practices, and
consequently their visibility (Harris et al., 2017). Mishra and Wang (2021) studied 55 million
publications covering STEM research from 218 countries. The results showed that while there
is a clear convergence (in terms of output and impact) among the high-income and upper
middle income countries, there is a widening gap that segregates the lower middle and
low-income regions from the higher income regions.
The presence of an author photograph and biography in scientific articles is another
characteristic that could potentially create bias in citation practices and sharing or engaging
with articles on social media platforms. Previous research has shown that the way
researchers present themselves through their profile image or biography could have an
impact on credibility and trust formation (Francke, 2021). However, the way authors are
perceived through their photograph or biography could be affected by author characteristics
such as gender, country, or scientific impact, which have been previously shown to create
bias in citations. In fact, appearance has been shown to have a different impact on the
perceived likelihood of being a scientist for male and female academics (Banchefsky,
Westfall et al., 2016).
While many studies have examined factors causing bias in scientific practices, and more
generally the driving factors of citations (Didegah et al., 2018), the potential impact of authors’
photographs and biographies on citation has remained a not very well explored research ques-
tion. Fidrmuc and Paphawasit (2018) studied the impact of physical attractiveness on the pro-
ductivity of authors in the economics field. While the results showed a significantly positive
effect of authors’ attractiveness on both journal quality and citations, the impact on citations
disappeared after controlling for journal quality. The result of a study by Dehdarirad and
Sotudeh (2017) in chemistry showed a very small citation superiority in favor of women in
papers where authors included their photograph and biography. This study aims to extend
the latter line of research by investigating whether, and, if so, how, the presence of author pho-
tographs and biographies in scientific articles can have an impact on the visibility of articles in
terms of citation. To ensure that author photographs and biographies were presented in the
same way, this study focused on a sample of articles in chemistry and chemical engineering
journals published by Elsevier.
Quantitative Science Studies
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Can the presence of an author photograph and biography have an impact on article citations?
To achieve the aim, the following research question is addressed:
(cid:129) Is there any difference in log transformed citations received by papers based on gender,
country, and impact of authors when they shared their photograph and biography, in
comparison with when they did not?
While addressing the question, the impact of other covariates that might influence the
number of citations was controlled (see Section 2.2).
2. METHODOLOGY
2.1. Data Collection
The data in this study is based on articles in 121 chemistry and chemical engineering journals
published by Elsevier, which are indexed in the Scopus database. The data were retrieved from
Scopus in February 2021. The studied time period was 2016–2018.
By consulting the author information pack of each journal, the journal articles were divided
into two groups:
(cid:129) Set 1: Authors were asked to provide a photograph and biography.
(cid:129) Set 2: Authors were not asked to provide a photograph and biography.
As, in some cases, the authors might not provide their photograph, all articles in set 1 were
double-checked for the existence of photographs using the ScienceDirect Elsevier Object
Retrieval API (https://dev.elsevier.com/sciencedirect.html#/Object_Retrieval).
In the next step, articles in set 1 were paired with articles in set 2, based on a common
author. In other words, the articles in both sets were written by a common author under two
different conditions. In set 1, authors provided a biography and photograph, whereas in set
2, authors did not. As a result, the data for this study comprised two sets. Set 1 comprised
4,572 articles, whereas set 2 comprised 9,292 articles. This accounted for 6,284 authors and
32,005 observations, which were used in citation analysis (Models 0 and 1). Author disam-
biguation was mainly done using a combination of Scopus authors IDs and ORCID, where
both were available. If ORCID was not available, then Scopus author ID was used. In the
few cases where the name of an author appeared under two author IDs, a manual check
was done by searching online for authors’ web pages, where they provided a list of their
publications. This was done to check whether the publications belonged to the same author
or not.
The common authors were either first or second authors on a paper. The reason for
selecting these authors was their higher visibility in a paper where authors provided their
photograph and biography. Additionally, this made it possible to control for the impact of
author byline order in both sets.
2.2. Covariates
In this section, details regarding data collection and processing of the covariates used in the
regression analyses (see Section 2.3) are provided. Reasons are also provided why they were
included as covariates in the regression models.
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Can the presence of an author photograph and biography have an impact on article citations?
Several of the covariates included in the regression models capture different aspects that
might have an impact on the number of citations.
Collaboration, measured as number of authors and number of countries, and journal
impact, measured as source normalized impact per paper (SNIP), have been among the most
important factors associated with citations (Didegah & Thelwall, 2013; Didegah et al.,
2018).
As reputation can be considered as an important signal of trustworthiness and quality
(Petersen, Fortunato et al., 2014), it is expected that papers by well-reputed authors or from
rich countries (high income) will receive more citations. Previous research has shown a rela-
tionship between authors’ and countries’ prestige and the number of citations (Didegah et al.,
2018; Dehdarirad & Karlsson, 2021). Gender has also been shown to affect citation counts in
the field of chemistry (Day, Corbett, & Boyle, 2020). Thus, in this paper, these author charac-
teristics for common authors (gender, scientific age, and GDP level of affiliation country) were
controlled. Additionally, the impact of the rest of the authors (coauthors of the common author)
on each paper was controlled in terms of these characteristics.
To detect the gender of authors, a combination of gender API (https://gender-api.com/) and
manual checking was carried out. First, gender API was used to conduct a search using the first
name of authors. Then, in cases of gender-neutral, unknown, initials, or where the accuracy
was lower than 80%, the names were checked manually using internet searches. For common
authors, gender was treated as a binary variable with male (0) and female (1). For the other
authors, the female proportion per paper was calculated. In the regression models this variable
is named “female proportion.”
The scientific age of authors was calculated by using the geometric mean of citations for
both common (named “Author impact”) and other authors (named “Rest of authors’ impact”).
The number or log-transformed number of publications and citations of an author has been
previously defined as the professional or scientific age of an author (Mishra, Fegley et al.,
2018; Andersen, Schneider et al., 2019). For this paper, the authors were divided into quartiles
based on their geometric mean of citations. Then, a dummy variable was created which
showed whether the author belonged to the top high-impact quartile (1) or not (0).
GDP data were gathered from the World Data Bank website (https://data.worldbank.org
/indicator/ NY.GDP.MKTP.CD). The World Bank assigns the world’s economies into four
income groups—high, upper middle, lower middle, and low. For common authors, a dummy
variable was created that indicated whether the studied author was affiliated to a high-income
country (1) or not (0). This is named GDP level in the regression models. To control for invest-
ment in GDP for the other authors on a paper, the GDP values for their affiliated countries
during 2016–2018 were obtained and an average for this time period was calculated. Then
for each paper, GDP average values for their corresponding countries were averaged. This is
named Rest of authors’ GDP in the regression models.
Open access has been shown to be an important factor for citations (Eysenbach, 2006;
Hajjem, Harnad, & Gingras, 2006; Gargouri, Hajjem et al., 2010). In this study, open access
was treated as a binary variable with OA (1) and non-OA (0).
As article topic has been shown to influence citation counts (Gallivan, 2012; Wang, Jiao
et al., 2020), a categorical variable was created which grouped articles into five broad
headings: “Biochemistry,” “Applied,” “Physical, Inorganic, and Analytical,” “Organic,”
and “Macromolecular.” The Chemical Abstracts Service headings were used to tag the
articles.
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Can the presence of an author photograph and biography have an impact on article citations?
Regarding abstract readability, the Flesch Reading Ease Score was used as it is the most
used measure of text readability and has been used in other studies (Didegah et al., 2018).
The R quanteda package (Benoit, Watanabe et al., 2018) was used to calculate this score
for each abstract. The highest possible score is 121.22, and there is no lower limit. The higher
the score, the easier the text is to understand.
Finally, in the regression models, time since publication was considered as an offset
variable.
2.3. Data Analysis
2.3.1. Models 1 and 2: Mixed and hybrid (within-between) models
In both models in this section, the outcome variable (ln(citation + 1)) is a continuous variable
and there is a random effect associated with the intercepts for each author (Author_ID).
Authors are also nested within countries (Country/Author_ID). Furthermore, there are several
fixed effects predictors with fixed slopes (see Table 1).
There are two types of predictors (time-varying and time-invariant) in the models. In the
context of applying multilevel models to repeated measure (longitudinal) data problems,
time-varying predictors will appear at level 1, because they are associated with specific mea-
surements, whereas time-invariant predictors will appear at level 2 or higher, because they are
associated with the individual (or a higher data level) across all measurement conditions
(Finch, Bolin, & Kelley, 2019). Thus, in this study, photograph and biography, GDP for rest
of authors, open access, readability, rest of authors’ impact, female proportion, number of
countries, number of authors, and topic are level 1 predictors. Gender, Journal, author impact,
and GDP level are level 2 predictors.
First a linear mixed model (LMM) using the lme4 R package (Bates, Mächler et al., 2015)
was applied to the ln (citation + 1). Citation distributions follow close to a discretized lognor-
mal distribution. Thus, for regression analyses, the best option is to use ordinary least squares
regression applied to the natural logarithm of citation counts plus one (Thelwall, 2016). The
log transformation prevents individual highly cited articles from dominating the results
(Thelwall & Sud, 2020). To compare all models and to decide which variables to include in
the models, the anova() function was used. It was also checked to whether keep the random
effects for authors and journals in the models or not. To do this, for both authors and journals, a
linear model (LM) without random effect was fitted to the data using the lm function. Then, LM
and LMM models were compared in terms of their BIC (Bayesian information criterion) and
Variable type
Random factor
Fixed factors
Repeated measures
Dependent variable
Table 1.
Variables by type
Country/Author_ID
Variable
Gender, female proportion, GDP level of affiliation country, rest of authors’ GDP,
author impact, rest of authors’ impact, open access, Journal, readability,
number of countries, number of authors, topic
Photograph and biography
ln(citation + 1)
Note. In the models, “rest of authors’ impact” was not entered as it did not improve the models.
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Can the presence of an author photograph and biography have an impact on article citations?
RMSE (root mean squared error) using the Compare_performance command in the R perfor-
mance package (Lüdecke, Ben-Shachar et al., 2021). For authors, the results showed that the
LMM model had lower values of BIC and RMSE and a 100% performance score in comparison
to the LM model. The result of a likelihood ratio test for authors using the rand function in the
lmerTest package (Kuznetsova, Brockhoff, & Christensen, 2017) also showed a significant
p-value (p < 0.001). Thus, the random effect for authors was retained in the model. For jour-
nals, the results showed no difference between the LMM and LM models. Thus, the simpler
model without the random effect for journals was preferred. Thus, journal was entered as a
factor variable in the models. The inclusion of journal fixed effects accounted for factors that
do not vary within a journal, such as its specialty or impact, that might be correlated with
citations.
Of the models fitted, the mixed model with the command below was the most parsimoni-
ous for the data.
Model 1=lmer (Log_normalized_citations ∼ photo&bio + offset(log(time)) + author_gender*
photo&bio + female_proportion + GDP level + GDP_rest_of_authors + OA + author_impac*
photo&bio + GDP*photo&bio + number_of_authors + Topic + Factor(Journal) + readability +
number_of_countries + (1 | country/AU_ID), data=citation_data, REML=FALSE, control=
lmerControl(optimizer="optimx",optCtrl=list(method=’nlminb’)))
The R performance package was used to check the model fit in terms of outliers and influ-
ential observations, normality of residuals for both fixed and random effects, collinearity, and
heteroscedasticity (see Supplementary material, Section 1).
LMMs are statistical models for continuous outcome variables, in which the residuals are
normally distributed but may not be independent or have constant variance. Studies with
clustered, longitudinal, or repeated measures data may be appropriately analyzed using
LMMs (West, Welch, & Gałecki, 2015). The case in this study involves repeated measures
data, in which multiple measurements regarding the citations were made on the same subject
(authors) under two different conditions (with and without photograph and biography).
Repeated measures data sets can be a type of two-level data, in which Level 2 represents
the subjects (authors in this case) and Level 1 represents the repeated measurements made
on each subject (observations) (see Table 1). Covariates measured at Level 2 of the data
describe between-subject variation and Level 1 covariates describe within-subject variables
(West et al., 2015). These models are called mixed effects, as they combine both fixed effects
and random effects. Random effects are estimated with shrinkage (partial pooling), while
fixed effects are estimated using least squares (or, more generally, maximum likelihood)
(no pooling). No pooling assumes that the residual variance is the same within each group
(Gelman & Hill, 2008) whereas partial pooling estimates combine within and between-group
variance.
After fitting a mixed effect model (Model 1), it was investigated whether within and
between effects were equal. Mixed effect models assume that between and within effects
are equal (Bell, Fairbrother, & Jones, 2019, p. 1057). In mixed effect models, covariates at
different levels can be related to outcome variables in quite different ways (Bell et al.,
2019). When within and between are not equal, the model will produce a weighted average
of the two, which will have little substantive meaning (Raudenbush & Bryk, 2002, p. 138; Bell
et al., 2019). Additionally, in the latter case, combining these two relationships inevitably leads
to biased estimates of both, as each will be drawn towards the other (Bell, Jones, & Fairbrother,
2018). This assumption was checked by testing the equality of the coefficients in a random
Quantitative Science Studies
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Can the presence of an author photograph and biography have an impact on article citations?
effect within-between model (REWB) (Model 2). As there were differences between the terms,
the REWB model was used. Additionally, the performance of Models 1 and 2 was compared in
terms of BIC and RMSE. As Model 2 showed 100% better performance, the results of the
REWB model (Model 2) were reported in the paper. Model 2 was fitted with the command
below:
Model 2=lmer (Log_normalized_citations ∼ photo&bio (within) + photo&bio (between) +
offset(log(time)) + within (product of author_gender and photo&bio) + between (product of
author_gender and photo&bio) + female_proportion + GDP level + within (GDP_rest_
of_authors) + between (GDP_rest_of_authors) + within (OA) + between (OA) + within
(product of photo&bio and author impact) + between (product of photo&bio and author
impact) + within (product of photo&bio and GDP) + between (product of photo&bio and
GDP) + within (number_of_authors) + between (number_of_authors) + Topic + Factor( Jour-
nal) + within (readability) + between(readability) + within (number_of_countries) + between
(number_of_countries) + (1 | country/AU_ID), data=citation_data, REML= FALSE, control=
lmerControl(optimizer="optimx",optCtrl=list(method=’nlminb’)))
See Supplementary material, Section 2 for diagnostics of Model 2.
Log-normalized-citationi ∼ N μ; σ2
(cid:1)
(cid:3)
μ ¼ αj i½ (cid:2);k i½ (cid:2) þ β
10 x10ð
þ β
1 x1ð
Þ þ β
Þ þ β
ð
11 x11
2 x2ð
Þ þ β
3 x3ð
12 x12ð
Þ þ β
Þ þ β
4 x4ð
13 x13ð
Þ þ β
Þ þ β
5 x5ð
Þ þ β
14 x14ð
6 x6ð
Þ þ β
Þ þ β
Þ þ β
8 x8ð
Þ þ β
Þ
Þ
15−134 as: factor x15
ð
7 x7ð
ð
Þ þ β
9 x9ð
Þ
αj ∼ Nðγα
þ γα
9 x24ð
0
1 x16ð
þ γα
Þ þ γα
10 x25ð
Þ þ γα
2 x17ð
Þ þ γα
Þ þ γα
11 x26ð
3 x18ð
Þ þ γα
Þ þ γα
12 x27ð
4 x19ð
Þ; σ2
αj
Þ þ γα
5 x20ð
Þ þ γα
6 x21ð
Þ; for authors : countries j ¼ 1; …; J
7 x22ð
Þ þ γα
Þ þ γα
Þ
8 x23ð
(cid:4)
αk ∼ N γα
0
þ γα
1 x28ð
Þ; σ2
αk
(cid:5)
; for number of countries k ¼ 1; …; K
where:
X1: Within_Photo&Bio
X2: Within (Photo&Bio and gender)
X3: Within-Female-proportion
X4: Within (Photo &Bio and High-level income country)
X5: Within (Photo&Bio and High-impact-author)
X6: Within_Number-of-authors
X7: Within_Number-of-countries
X8: Within_Rest-of-authors-GDP
X9: Within_Open-Access
X10: Within_Readability
X11–X14 Topic- Applied; Topic- Physical, inorganic, and analytical; Topic- Organic; Topic-
Macromolecular
X15: Journal
X16: Between_Photo &Bio
X17: Between (Photo&Bio and gender)
X18: Gender
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X19: Between_Female-proportion
X20: Between (Photo&Bio and High-level income country)
X21: Between (Photo&Bio and High-impact-author)
X22: Between_Number-of-authors
X23: Between_Number-of-countries
X24: Between_Rest-of-authors-GDP
X25: Between_Open-Access
X26: Between_Readability
X27: High-impact-author
X28: High-level income country
REWB models (also known as hybrid models) and correlated random-effects models (CRE)
are flexible modeling specifications that separate within and between-cluster effects and allow
for both consistent estimation of Level 1 effects and inclusion of Level 2 variables (Schunck,
2013). The between effect in these models cannot indicate causality because it only represents
the mean difference (between groups) over all measurement points. In hybrid models, similar
to FE (fixed effect) models, a within effect estimator only uses the within variance. FE (within)
models are not generally suited for estimating absolute group differences. However, using
interaction between group dummies (e.g., gender) and time-varying variables (picture and
photograph) in FE models, it was possible to estimate the differences in the coefficients of
covariates by group (Collischon & Eberl, 2020). This methodology has also been applied in
econometrics studies such as Wooldridge (2010). Additionally, hybrid models allow for the
possibility of including a random intercept and random slopes to control for random effects
and thus allow the Level 1 (within) estimators to vary between individuals (Schumann &
Kuchinke, 2020).
To estimate between and within effects in one model, the cluster-specific mean for time
varying variables was calculated (between values) first. The second step was to create the
deviation scores, which is also known as group mean centering (within values) (Schunck,
2013). The final step was regarding interaction terms. In this paper, there are three interaction
terms for the time-varying variable (photograph and biography) with time-invariant variables
(gender, being high-impact author, being affiliated with a high-income country). Following the
methodology in Schunck (2013), three new variables were calculated by creating the product
of the time-varying variable (photograph and biography) and each of the time-invariant vari-
ables. Then, for each of these three variables, their individual cluster mean and deviation
scores were calculated and entered in Models 2 and 3.
Finally, Model 3 was fitted using the marginaleffects R package (Arel-Bundock, 2022),
which shows the within-between model with marginal effects. This was done by restricting
the data to those authors whose status changed in terms of providing a photograph and biog-
raphy. Thus, the authors who always provided a photograph and biography and those that
never did were excluded from the analysis. As a result, the number of observations reduced
to 29,134, accounting for 4,855 authors.
3. RESULTS
3.1. REWB model: Models 2a, 2b, and 3
In the models, the effect of photograph and biography is separated in both within and between
parts. The effect for the other time-varying variables is also separated in within and between
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Can the presence of an author photograph and biography have an impact on article citations?
Table 2.
Random effects within-between model for comparison of authors with and without photograph and biography
Predictors
(Intercept)
Within (Photo & Bio) [Yes]
Between (Photo& Bio) [Yes]
Gender (Female)
High level Income [Yes]
High-Impact Author [Yes]
Within (Female proportion)
Between (Female proportion)
Within (Rest of authors’ GDP)
Between (Rest of authors’ GDP)
Within (Number of authors)
Between (Number of authors)
Within (Number of countries)
Between (Number of countries)
Within (Open Access) [Yes]
Between (Open Access) [Yes]
Within (Readability)
Between (Readability)
Topic [Applied]
Topic [Physical, inorganic, and analytical]
Topic [Organic]
Topic [Macromolecular]
Within [(Photo & Bio) [Yes] × Gender [female]]
Between [(Photo & Bio) [Yes] × Gender [female]]
Within [(Photo & Bio) [Yes] × High Level Income [Yes]]
Between [(Photo & Bio) [Yes] × High Level Income [Yes]]
Within [(Photo & Bio) [Yes] × High-Impact Author]
Between [(Photo & Bio) [Yes] × High-Impact Author]
Model 2a: REWB model
with no covariates
CI
1.35–1.49
Estimates
1.42***
0.08***
0.07
0.07–0.09
−0.01–0.14
Model2b: REWB model
with covariates
Estimates
1.71***
−0.31
−0.31
0.08*
−0.12*
0.20***
0.06***
−0.10*
−0.18***
−0.15*
0.45***
0.52***
0.07
0.68***
−0.04***
−0.10***
0.01
−0.54***
0.05***
0.02
−0.03*
−0.12
−0.05***
−0.06
0.05***
−0.01
0.02*
0.06
CI
1.55–1.86
−1.20–0.58
−1.19–0.57
0.00–0.16
−0.24 – −0.01
0.13–0.27
0.03–0.08
−0.18 – −0.02
−0.26 – −0.10
−0.27 – −0.02
0.38–0.53
0.33–0.71
−0.04–0.18
0.28–1.07
−0.06 – −0.02
−0.16 – −0.05
−0.05–0.08
−0.74 – −0.33
0.03–0.07
−0.01–0.04
−0.05 – −0.00
−0.45–0.22
−0.07 – −0.03
−0.20–0.08
0.03–0.06
−0.13–0.12
0.00–0.04
−0.06–0.19
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Can the presence of an author photograph and biography have an impact on article citations?
Predictors
Random effects
σ2
τ00
N
Observations
Marginal R2/Conditional R2
*p < 0.05; **p < 0.01; ***p < 0.001.
Note. Ref group for topics was Biochemistry.
Table 2.
(continued )
Model 2a: REWB model
with no covariates
CI
Estimates
Model2b: REWB model
with covariates
Estimates
CI
0.16
0.12
0.46AuthorID:country
0.30AuthorID:country
0.05Country
6,284AuthorID
87Country
32,005
0.002/0.754
0.02Country
6,284AuthorID
87Country
32,005
0.269/0.805
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parts in Models 2b and 3. In the regression tables, marginal R2 provides the variance explained
only by fixed effects and conditional R2 provides the variance explained by the entire model
(i.e., both fixed effects and random effects). σ 2 is the within-author variance whereas τ00 is the
between-author variance.
Looking at the within coefficient in Model 2a (see Table 2), the impact of photograph
and biography was significant. In other words, providing a photograph and biography was
associated with an increase by 8% in citations in the model without covariates. However,
after controlling for the covariates, the effect became insignificant. As can been seen from
Models 2b and 3 (see Table 3), both within and between coefficients for photograph and
biography were not significant. Moving from Model 2a to Model 2b, marginal R2
increased from 0.002 to 0.27. This means that 27% of the variance in Model 2b is
explained by the inclusion of fixed effect predictors.
For within coefficients, when looking at the interaction terms of photograph and biography
with gender, affiliation to a high-income country, and being a high-impact author, the impact
was significant. While both GDP level (being affiliated to a high-income country) and author
impact had a positive mediating impact on citations, gender (female) had a negative impact.
For gender, there was a small citation disadvantage of 5% for female authors when they pro-
vided a photograph and biography.
There was a small citation advantage of 5% for being affiliated with a high-income country
when authors provided a photograph and biography, compared to when they did not. For
being a high-impact author, the within authors coefficient indicated a very small citation
advantage of 2% when authors provided a photograph and biography.
Model 3 shows the within-between model with marginal effects. When comparing the
results in Models 3 and 2b, it can be seen that the within coefficients for photograph and biog-
raphy and its interaction terms remained identical.
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Can the presence of an author photograph and biography have an impact on article citations?
Table 3. Marginal effects for Model 3, examining the effects of change in making photograph and biography available
Predictors
(Intercept)
Within (Photo & Bio) [Yes]
Between (Photo & Bio) [Yes]
Gender (Female)
High-Income [Yes]
High-Impact Author [Yes]
Within (Female proportion)
Between (Female proportion)
Within (Rest of authors’ GDP)
Between (Rest of authors’ GDP)
Within (Number of authors)
Between (Number of authors)
Within (Number of countries)
Between (Number of countries)
Within (Open Access) [Yes]
Between (Open Access) [Yes]
Within (Readability)
Between (Readability)
Topic [Applied]
Topic [Physical, inorganic, and analytical]
Topic [Organic]
Topic [Macromolecular]
Within [(Photo & Bio) [Yes] × Gender [female]]
Between [(Photo & Bio) [Yes] × Gender [female]]
Within [(Photo & Bio) [Yes] × High-Income [Yes]]
Between [(Photo & Bio) [Yes] × High-Income [Yes]]
Within [(Photo & Bio) [Yes] × High-Impact Author]
Between [(Photo & Bio) [Yes] × High-Impact Author]
Model 3: REWB with marginal effects
CI
1.56–2.05
Estimates
1.80***
−0.30
−0.41
0.13
−0.19
0.29**
0.05***
−0.10*
−0.20***
−0.13
0.49***
0.50***
0.06
0.89***
−0.04***
−0.14***
−0.00
−0.64***
0.05***
0.01
−0.03**
−0.12
−0.05***
−0.19
0.05***
0.14
0.02*
−0.18
−1.17–0.58
−1.37–0.55
−0.09–0.35
−0.41–0.02
0.09–0.50
0.03–0.08
−0.19 – −0.01
−0.27 – −0.12
−0.28–0.02
0.41–0.57
0.28–0.71
−0.05–0.17
0.42–1.36
−0.06 – −0.02
−0.21 – −0.07
−0.07–0.07
−0.88 – −0.40
0.02–0.07
−0.01–0.04
−0.05 – −0.01
−0.46–0.21
−0.07 – −0.03
−0.66–0.28
0.03–0.06
−0.25–0.54
0.00–0.04
−0.59–0.24
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Predictors
Random effects
σ2
τ00 Author_ID:Coutnry
τ00 Country
Nauthors_ID
NCountry
Observations
Marginal R2/Conditional R2
*p < 0.05; **p < 0.01; ***p < 0.001.
Table 3.
(continued )
Model 3: REWB with marginal effects
CI
Estimates
0.12
0.30
0.03
4,855
86
29,134
0.272/0.806
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4. DISCUSSION AND CONCLUSION
The aim of this study was to investigate whether, and, if so, how, the presence of an author
photograph and biography in scientific articles can have an impact on the visibility of articles
in terms of citation. This was done using random effects within-between model analyses. To
ensure that authors presented their photographs and biographies in the same way, this study
focused on a sample of articles in chemistry and chemical engineering journals published by
Elsevier. The impact of photographs and biographies in combination with authors character-
izes, for example, gender and affiliation country (GDP level); scientific impact was also exam-
ined while controlling for several covariates. The findings regarding these are briefly presented
and discussed below.
The results of the within model showed lower citations for papers where authors provided a
photograph and biography. However, the effect was insignificant both within and between
models when controlling for covariates.
When examining the interaction terms of common authors’ characteristics with photograph
and biography, the impact of gender (female) was significantly negative. Additionally, the find-
ing showed that when female authors provided a photograph and biography, the estimated
citation counts decreased by 5%. This finding is interesting, as chemistry and chemical engi-
neering have been described as examples of relatively high gender-diverse fields (Blosser,
2017; Jarboe, 2019). As indicated in Dion et al.’s (2018) study, while improvements in gender
diversity in academia might increase the visibility and impact of scholarly work by women,
implicit biases in citation practices might persist. As in this study, the authors provided their
photograph and biography in the same way, the finding could be related to implicit bias and
how women are perceived and judged by their appearance. The result of this study is to some
extent in line with a study by Banchefsky et al. (2016), which suggested that for men there was
no impact of gendered appearance on the perceived likelihood of being a scientist, whereas
for women their appearance mattered. According to this study, feminine appearance may erro-
neously signal that women are not well suited for science. As appearance has a powerful and
immediate effect on a person’s perception of others, variations in facial appearance based on
gender could automatically activate gender stereotypes (Banchefsky et al., 2016; Ito & Urland
2003), thereby affecting citation decision-making practice. In social science research, this
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Can the presence of an author photograph and biography have an impact on article citations?
could be referred to as an “amplification process,” which allows categorical differences asso-
ciated with gender to expand in their range of application (Ridgeway, 2011). For example,
gendered stereotypes about visual representation of men and women can be transferred to
evaluations of the products they produce (articles in this study), with women being disadvan-
taged (Tak, Correll, & Soule, 2019). The finding is, however, in contrast with Dehdarirad and
Sotudeh (2017), who found a very small citation superiority in favor of women in papers
where authors provided a photograph and biography. The reason for this contrast could be
due to the different methodologies, samples, and time spans used in these studies, as in this
paper, by using REWB regression models, it was possible to control for several covariates and
estimate Level 1 effects while including Level 2 variables. Additionally, Dehdarirad and
Sotudeh (2017) examined a data set of papers published by highly productive authors,
whereas the current study did not.
Regarding the interaction terms of photograph and biography with two other studied author
characteristics (i.e., being a high-impact author and being affiliated with a high-income coun-
try), the results showed a significant positive impact for both these variables. The findings
showed small citation advantages of 5% and 2% for being affiliated with a high-income coun-
try and being a high-impact author, respectively. The findings seem to suggest that a photo-
graph and biography in combination with author characteristics (being a high-impact author
and being affiliated with a high-income country) could have an amplifying bias effect on the
number of citations received. This amplifying effect may disfavor research from lower income
countries or authors with lower impact to a greater extent in research evaluation and citation
practice (Harris et al., 2017). These findings could be explained by a combination of “status
characteristics theory” and “amplification process” mechanisms. As mentioned in Nielsen,
Baker et al. (2021), a scientist’s country of affiliation could be considered as a “status signal.”
Thus, being affiliated with a high-income country might implicitly influence evaluation prac-
tices. Additionally, facial appearance is important in the perception of academics and their
dissemination of knowledge (Bi, Chan, & Torgler, 2020). Based on the “amplification process”
mechanism, these implicit biases about affiliation countries of authors, combined with phys-
ical appearance biases, might be transferred to the evaluation of their papers and thereby affect
citation practice.
In general, the findings of this study add to the literature on the role that photographs and
biographies, in combination with author characteristics, could play in the visibility of articles.
The findings are important as they provide insights into how citation practices could be biased by
the effect of authors’ photographs and biographies in combination with author characteristics.
Using an REWB model in this paper, it was possible to estimate the effects of time-invariant
as well as time-varying predictors in the analysis. It was also possible to include random slopes
in the models, thereby allowing the effects of Level 1 variables to vary between clusters. As
bibliometric data has a multilevel structure (Mutz & Daniel, 2019), these models could be
extended and used in other bibliometrics studies to correct for the dependence of multiple
observations per individual. It also allows us to estimate the effect of Level 2 variables while
providing effect estimates of Level 1 variables, which are unbiased by a possible correlation
with the Level 2 error (Schunck, 2013). The usefulness of statistical analysis of citations using
causality in bibliometric studies has also been previously investigated and shown (Bittmann,
Tekles, & Bornmann, 2021; Mutz, Wolbring, & Daniel, 2017). As suggested in Bittmann et al.
(2021), these techniques could be used as an approach in bibliometrics to estimate effects and
remove bias.
In this paper, the visibility of articles in terms of citations was studied from the point of view
of photographs and biographies, in combination with author characteristics. However,
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Can the presence of an author photograph and biography have an impact on article citations?
engagement with articles might also be dependent on the characteristics of the people who
engage with the paper (readers). Thus, in future research, I aim to consider the effect of readers’
characteristics too.
This study has some limitations. The findings in this study are limited to a sample of articles
in chemistry and chemical engineering. Thus, generalization of the findings to other fields is
not advised. From a methodological viewpoint, it is important to remember that despite the
advantages of mixed and REWB models, these models cannot control for unmeasured time-
varying confounding variables (Bell et al., 2019; Gunasekara, Richardson et al., 2013). How-
ever, I attempted to control for several important variables in the models. By doing so, it was
possible to increase the possibility of obtaining more precise and reliable results.
ACKNOWLEDGMENT
The author would like to thank Mr. Jonathan Freer at Chalmers University of Technology for
proofreading the article.
FUNDING INFORMATION
The author did not receive any funding for this work.
COMPETING INTERESTS
The author has no competing interests.
DATA AVAILABILITY
All relevant data regarding the variables, how they were obtained, R packages, and commands
used for regression analysis are within the paper. The diagnostics for regression models are
also in the Supplementary material. However, restrictions apply to the availability of the bib-
liometric data. These data were downloaded under the provision of the institutional standard
contract held by the Chalmers University of Technology to Scopus, Elsevier API (https://
dev.elsevier.com/sciencedirect.html#/Object_Retrieval), and SciVal API (https://dev.elsevier
.com/scival.html). I did not have any special access privileges to these databases. Interested
researchers may access the Scopus, Elsevier, and SciVal APIs in the same way I did.
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