Make It Short and Easy: Username Complexity Determines Trustworthiness Above and Beyond Objective Reputation

Abstract

Can the mere name of a seller determine his trustworthiness in the eye of the consumer? In 10 studies (total N = 608) we explored username complexity and trustworthiness of eBay seller profiles. Name complexity was manipulated through variations in username pronounceability and length. These dimensions had strong, independent effects on trustworthiness, with sellers with easy-to-pronounce or short usernames being rated as more trustworthy than sellers with difficult-to-pronounce or long usernames, respectively. Both effects were repeatedly found even when objective information about seller reputation was available. We hypothesized the effect of name complexity on trustworthiness to be based on the experience of high vs. low processing fluency, with little awareness of the underlying process. Supporting this, participants could not correct for the impact of username complexity when explicitly asked to do so. Three alternative explanations based on attributions of the variations in name complexity to seller origin (ingroup vs. outgroup), username generation method (seller personal choice vs. computer algorithm) and age of the eBay profiles (10 years vs. 1 year) were tested and ruled out. Finally, we show that manipulating the ease of reading product descriptions instead of the sellers’ names also impacts the trust ascribed to the sellers.

Full text

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ORIGINAL RESEARCH
published: 19 December 2017
doi: 10.3389/fpsyg.2017.02200
Edited by:
John F. Rauthmann,
Wake Forest University, United States
Reviewed by:
Béla Birkás,
University of Pécs, Hungary
Karl-Andrew Woltin,
University of Roehampton,
United Kingdom
*Correspondence:
Rita R. Silva
rita.silva@uni-koeln.de
Specialty section:
This article was submitted to
Personality and Social Psychology,
a section of the journal
Frontiers in Psychology
Received: 02 October 2017
Accepted: 04 December 2017
Published: 19 December 2017
Citation:
Silva RR, Chrobot N, Newman E,
Schwarz N and Topolinski S (2017)
Make It Short and Easy: Username
Complexity Determines
Trustworthiness Above and Beyond
Objective Reputation.
Front. Psychol. 8:2200.
doi: 10.3389/fpsyg.2017.02200
Make It Short and Easy: Username
Complexity Determines
Trustworthiness Above and Beyond
Objective Reputation
Rita R. Silva1*, Nina Chrobot2, Eryn Newman3, Norbert Schwarz4and Sascha Topolinski1
1Social Cognition Center Cologne, University of Cologne, Cologne, Germany, 2Department of Psychology, SWPS University
of Social Sciences and Humanities, Warsaw, Poland, 3Mind and Society Center, University of Southern California,
Los Angeles, CA, United States, 4Department of Psychology, University of Southern California, Los Angeles, CA,
United States
Can the mere name of a seller determine his trustworthiness in the eye of the
consumer? In 10 studies (total N= 608) we explored username complexity and
trustworthiness of eBay seller profiles. Name complexity was manipulated through
variations in username pronounceability and length. These dimensions had strong,
independent effects on trustworthiness, with sellers with easy-to-pronounce or short
usernames being rated as more trustworthy than sellers with difficult-to-pronounce or
long usernames, respectively. Both effects were repeatedly found even when objective
information about seller reputation was available. We hypothesized the effect of name
complexity on trustworthiness to be based on the experience of high vs. low processing
fluency, with little awareness of the underlying process. Supporting this, participants
could not correct for the impact of username complexity when explicitly asked to do so.
Three alternative explanations based on attributions of the variations in name complexity
to seller origin (ingroup vs. outgroup), username generation method (seller personal
choice vs. computer algorithm) and age of the eBay profiles (10 years vs. 1 year) were
tested and ruled out. Finally, we show that manipulating the ease of reading product
descriptions instead of the sellers’ names also impacts the trust ascribed to the sellers.
Keywords: name pronounceability, name length, fluency, trustworthiness, reputation
INTRODUCTION
Can the mere name of a seller determine his trustworthiness in the eye of the consumer? Surely this
is the case when the name is familiar and is reminiscent of prior interactions, or when its semantic
meaning bears some emotional associations, such as a person named Johnny Goodfaith. But apart
from that, can meaningless and superficial features of a seller’s name determine consumer attitudes?
The present research explored this for the complexity of seller names. As an exemplary domain, we
chose interactions in online marketplaces because in these contexts there are only few other pieces
of information available than the name of a seller.
Commercial transactions frequently encompass some degree of uncertainty and consequently
of risk for the buyer. For example, it is often the case that the seller has more information about the
quality of the product or service he is providing than the buyer does. This information asymmetry
opens the way for opportunistic behavior from a seller who might just take advantage of the other
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Silva et al. Username Complexity and Seller Trustworthiness
part (Akerlof, 1970;Mishra et al., 1998). Thus, as in other
situations where risk is present, trust is a key aspect in buyer-
seller relations, acting as a complexity reduction mechanism (e.g.,
Luhmann, 1979;Lewis and Weigert, 1985;Milne and Boza, 1999;
Gefen, 2000) and enabling transactions when buyers cannot fully
predict or control the intentions and actions of the sellers (e.g.,
Schurr and Ozanne, 1985;Yamagishi and Yamagishi, 1994;Mayer
et al., 1995;Doney and Cannon, 1997;Gefen, 2000;Evans and
Krueger, 2009, 2011;Thielmann and Hilbig, 2015).
When buyer-seller transactions occur in online marketplaces,
uncertainty and risk might be even greater than in the traditional
markets (Ridings et al., 2002;Grabner-Kräuter and Kaluscha,
2003;Beldad et al., 2010). This is due to the impersonal nature
of online environments: usually, buyers and sellers never meet
face-to-face, making it difficult to bind an online seller to
a specific identity (Ridings et al., 2002;Beldad et al., 2010);
and there is no information about the sellers’ standing in the
community or the professionalism of the place from where
they operate (online private sellers often run their business
from their own home; Resnick et al., 2006). Also, there is
not even the possibility to inspect the product personally and
have a concrete idea of its real quality (Ba and Pavlou, 2002).
This anonymous and impersonal context both increases the
chances for dishonest and opportunistic behavior from the
sellers’ side and makes the assessments of seller trustworthiness
more difficult (Metzger et al., 2010). For these reasons, many
online marketplaces like eBay or Amazon.com have put effort
in developing systems that allow extracting the information that
a seller will not take advantage of buyers. These reputation
mechanisms gather the ratings or feedback scores of the online
sellers’ previous interactions and transactions (for detailed
descriptions of eBay’s reputation system, see Resnick et al., 2006;
Cabral and Hortaçsu, 2010). There are already a significant
number of studies investigating seller reputation showing that
overall a good reputation increases not only the probability of
sale but also final prices (see the summary of studies presented
by Resnick et al., 2006).
To our knowledge, reputation seems to be the seller
characteristic that has received most attention in the literature
regarding trust building in online marketplaces (Ang et al., 2001;
Shankar et al., 2002;Yoon, 2002;Corritore et al., 2003;Yousafzai
et al., 2003;Cheema, 2008;Beldad et al., 2010;Metzger et al.,
2010). Little is known about other features of the sellers that
might affect perceptions about their trustworthiness and the
likelihood that the buyers will trust and engage in transactions
with them. But one indispensable and inevitable characteristic
that buyers in online transactions have access to is the name of
a seller. Based on psychological research on mental ease, which
is outlined in the following section, we predicted that the mere
complexity of a seller’s name will affect perceptions of his/her
trustworthiness.
Processing Fluency
Individuals’ attitudes are often influenced by the cognitive
feelings triggered by the dynamics of their mental operations
during information processing (Schwarz, 2004, 2015;Avnet et al.,
2012). One particularly potent cognitive feeling is processing
fluency, that is, the subjective feeling of mental ease that people
experience while processing information (Reber et al., 2004;Alter
and Oppenheimer, 2009). Mental ease can be elicited by different
aspects of information, such as repetition, perceptual clarity, or
writing font (for a review, see Alter and Oppenheimer, 2009).
A high compared to low processing fluency triggers positive
feelings (Garcia-Marques and Mackie, 2000), higher liking
judgments (Reber et al., 1998), increases the truth-value ascribed
to trivia statements (Reber and Schwarz, 1999;Silva et al., 2016,
2017) and elicits facial muscular activity specific to positive
experiences (Harmon-Jones and Allen, 2001;Winkielman and
Cacioppo, 2001;Lee and Labroo, 2004;Labroo et al., 2008;
Topolinski et al., 2009). Thus, fluency seems to be inherently
positive and that aura of positivity leads to more favorable
evaluations of fluent as compared to disfluent information (the
hedonic marking of fluency, Winkielman et al., 2003).
Word and Name Pronounceability
Regarding the name of a seller, one key determinant of its
fluency is its pronounceability. In psychological research, word
pronounceability has been shown to affect consumer attitudes.
For instance, Alter and Oppenheimer (2006) showed that the
pronounceability of ticker codes of shares predicts short-term
stock prices fluctuations both in the laboratory and with real-
world stock market data, with stocks with easy-to-pronounce
names outperforming stocks with difficult-to-pronounce names.
Moreover, Song and Schwarz (2009) found that food additives are
judged as more dangerous when they have difficult-to-pronounce
rather than easy-to-pronounce names. Regarding person names
more directly, Laham et al. (2012) showed that persons with easy-
to-pronounce names are judged more positively and, according
to a real world data analysis, they seem to occupy superior
positions in companies’ hierarchies than persons with difficult-
to-pronounce names. Also relevant to the present work, Newman
et al. (2014) found that statements about different topics are given
higher levels of truth when attributed to individuals with easy-to-
pronounce names, and very recently Zürn and Topolinski (2017)
showed that in economic trust games individuals trust more in
partners bearing easy-to-pronounce names (for related effects of
language articulation on attitudes, see Topolinski et al., 2014,
2015a,b;Topolinski and Boecker, 2016a,b).
Most recently Topolinski et al. (2016) have explored the
impact of another, independent, contribution to processing
fluency of nonsense words, namely the mere length of a
word. They found that short anagrams were judged as more
likely to be solved and as requiring less solvability effort than
long anagrams. This demonstration, however, did not address
attitudes but judgments of cognitive solvability of abstract
intellectual problems.
The Present Experiments
The present research explored whether mere complexity of seller
names would influence perceived trustworthiness of those sellers.
We chose the context of online marketplaces, such as the auction
website eBay (Roth and Ockenfels, 2002;Ockenfels and Roth,
2006), because name complexity most likely plays an important
role in this domain, due to the following. Usually, one searches
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Silva et al. Username Complexity and Seller Trustworthiness
in eBay for a certain product and the search generates a list of
all available offers. When clicking through the offers, the obvious
parameters of price and seller reputation vary across offers, but
also the names of the sellers. Imagine you come across the seller
SIBU and the seller VLEGTIQCLAPL. Both usernames are fairly
meaningless and could simply be the result of a contraction of
the sellers’ given and family names. However, given all other
trustworthiness parameters being constant, would you expect one
of them to be more trustworthy than the other?
Building on the evidence cited above, we predicted that: (1)
Online seller profiles with easy- as compared to difficult-to-
pronounce usernames will be evaluated as more trustworthy,
and (2) Online seller profiles with short as compared to long
usernames will be evaluated as more trustworthy. We tested these
hypotheses in a series of experiments in which participants were
presented with screenshots of ostensibly real profiles of eBay
private sellers, rating them for trustworthiness.
Crucially, we explored the impact of username complexity in
the face of more diagnostic information, with seller reputation
being the most important one. Previous studies showed that
private eBay sellers with an established good reputation receive a
premium of up to 8.1 percent of the average selling price of their
products, have a higher probability that individual bidders enter
their auctions and also receive more bids (e.g., McDonald and
Slawson, 2002;Bajari and Hortacsu, 2003;Resnick et al., 2006).
More relevant to our study, buyers’ perceptions of eBay sellers’
trustworthiness are also positively affected by their reputation
(Ba and Pavlou, 2002). Therefore, we manipulated the sellers’
reputation independently from name complexity and predicted
that: (3) Online seller profiles with better as compared to worse
reputation will be evaluated as more trustworthy.
Given the strong positive effect that seller reputation seems
to have on seller success, it is possible that the effect of
username complexity on trustworthiness evaluations is rather
weak. Thus, finding evidence of such an effect will be quite
remarkable. It suggests that the subjective experience of fluency
associated with username complexity can influence buyers’
judgments of seller trustworthiness despite the presence of strong
objective information attesting or disconfirming it. However, we
hypothesize the effect of username complexity on consumers’
attitudes to be associated with the bias to evaluate fluently
processed stimuli more positively (e.g., Winkielman et al.,
2003;Lee and Labroo, 2004;Labroo et al., 2008), exerting
its effects independent of the more systematic, conscious and
deliberative use of objective arguments such as a seller’s prior
reputation (Maheswaran et al., 1992;Bohner et al., 1994;
Smith and DeCoster, 2000;Suri and Monroe, 2003;Strack
and Deutsch, 2004;Strack et al., 2006;Novak and Hoffman,
2009). Moreover, these two different types of influences seem
to relate to two different and independent types of trust,
namely emotional trust (elicited by positive feelings about
the target person, product or brand) and cognitive trust
(elicited by rational arguments about the person, product
or brand characteristics) (Lewis and Weigert, 1985). Thus,
we also predicted that: (4) Name complexity effects will not
be substantially qualified by seller reputation, in that seller
reputation will not alter or reverse the predicted pattern of
username pronounceability and length effects on trustworthiness
(the two factors should have additive rather than interactive
effects).
Data Treatment and Power Analysis
We report all measures and conditions that were run in
each Experiment. We report and justify exclusion of data
(if any). We calculated required sample sizes a priori using
G∗Power (Faul et al., 2007). To estimate the required sample
size in a conservative fashion, we used the smallest effect of
pronounceability found in Topolinski et al. (2016, Experiment
5) for the basic effect of pronounceability on anagram solvability
difficulty, which was η2
p= 0.19. To replicate this effect with a
power of 0.80, required sample size is N= 37. As the effect of
pronounceability on perceptions of trustworthiness in consumer
choices is unknown, we arbitrarily over-powered most of the
present Experiments, with the smallest sample size being N= 38.
Across all Experiments, analyses were run only after the full final
sample size was collected.
Ethics Statements and Open Data
Participation in all Experiments was voluntary. Participants
gave their consent either when approached directly on the
University campus or by clicking on the web link leading to
the Experiments when invitations were disseminated online.
Participants did not provide any identifying information and they
could terminate their participation at any time. Experiments 8–10
were reviewed and approved by the Institutional Review Board
at the University of Southern California (approval numbers:
UP-15-00402 and UP-14-00630). Experiments 1–7 took place
at the University of Cologne, Germany. These Experiments
were reviewed and approved as part of the funding process by
expert committees at the Department of Innovation, Science,
and Research of the state of North Rhine-Westphalia (the
German system does not require subsequent ethics review of
individual studies and does not maintain institutional review
boards for psychological experiments at the local university
level).
All materials and databases are available online at https://osf.
io/7mgaq.
EXPERIMENT 1
In the first Experiment we orthogonally manipulated username
pronounceability and seller reputation using simulated eBay
profiles. We also explored the impact of seller repetition.
It is often the case that individuals encounter the same
sellers more than once when searching for a product in
online auction sites. Repetition has been consistently shown
to increase positive attitudes toward neutral and meaningless
stimuli (the mere exposure effect, Zajonc, 1968;Monahan
et al., 2000), other people (Rhodes et al., 2001;Zebrowitz
et al., 2008) and also product brands (Janiszewski, 1993;
Baker, 1999;Skurnik et al., 2005). Therefore, participants were
requested to rate all the sellers twice. Based on the extensive
previous evidence of repeated exposure effects, in addition to
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the predicted (1) pronounceability and (3) reputation effects,
we predicted that: (5) Perceptions of trustworthiness will
increase from the first to the second exposure to the eBay
profiles.
Method
Participants
Fifty-eight participants (32 men, Mage = 24 years, SD = 1) were
assigned to the conditions of a 3 (Pronounceability: easy- vs.
medium- vs. difficult-to-pronounce usernames) ×2 (Reputation:
good vs. bad) ×2 (Repetition: first evaluation vs. second
evaluation of the profiles) factorial design, with all independent
variables manipulated within-participants. Participants were
volunteers recruited online by dissemination of the study in
online platforms and mailing lists directed at university students
in Germany.
Materials and Procedure
Usernames
Name stimuli were used as the eBay sellers’ usernames. A large
pool of letter strings that formed meaningless words in the
German language were construed so as to have a high to
low vowel-consonant ratio and then arranging the letters into
sequences that either did or did not conform to German
phonotactic constraints (Brent and Cartwright, 1996;Saffran,
2003;Warker and Dell, 2006) according to the authors’ subjective
judgment. For example, the word SIBU has two vowels and two
consonants, thus having a high vowel-consonant ratio and a
consonant is always followed by a vowel, making it fairly easy
to read. In the word PTONBIA, the ratio is three vowels to four
consonants, and two consonants appear in sequence before a
vowel is added. Finally, the word VLEGTIQCLAPL has a ratio
of only three vowels to nine consonants, and the letters are
rearranged so that there is at least one sequence of three straight
consonants. In an independent pilot study, these letter strings
were pre-tested in how difficult they were to pronounce by an
independent group of N= 13 judges (10 point scale, in which
1 = not difficult at all, and 10 = very difficult). The resulting
ratings ranged from M= 1.00, SD = 0.00 and M= 9.92, SD = 0.28.
We selected the 10 easiest (M= 1.30, SD = 0.77), 10 medium
(M= 5.25, SD = 2.51) and the 10 most difficult (M= 9.44,
SD = 1.00) to pronounce items to be used as usernames in the
Experiment, making a total of 30 name stimuli (Supplementary
Table 1 presents the complete list of usernames for Experiments
1 and 2).
To increase the credibility of our cover story that the profiles
belonged to real eBay private sellers, random combinations of
three digits were added to the usernames. This is a strategy
often used by individuals when they register in a website and
the username they chose is already in use (e.g., a user wanting to
register as Ricot on eBay changes the username to Ricot083 when
the original version is already taken by someone else).
Reputation
We manipulated seller reputation with a five star rating system
(Thoma and Williams, 2013), which we believed would be
very easy to understand by our participants given its wide use
in several commercial domains (from hotels classification to
reviews of both products and sellers in online marketplaces). The
silhouette of five starts was presented in each seller profile. For
good reputation sellers, either 5 or 4.5 of the star silhouettes were
filled with a yellow-golden color; for bad reputation sellers only
3 or 3.5 star silhouettes were colored. The two different ratings
in each reputation level were used to make the sellers slightly
more diverse and were randomly assigned to the sellers. We opted
for not having extremely bad reputation ratings of only 1 or
1.5 stars because feedback on real eBay users is most frequently
quite positive and sellers get negative ratings only 1% of the time
(Resnick et al., 2006).
Thirty eBay profiles were created using as a basis a screenshot
of the standard seller profile image eBay presents next to
every product that is auctioned (Supplementary Figure 1). The
combination of the two independent variables resulted in six
possible profiles: easy-pronunciation with good-reputation; easy-
pronunciation with bad-reputation; medium-pronunciation
with good-reputation; medium-pronunciation with bad-
reputation; difficult-pronunciation with good-reputation;
difficult-pronunciation with bad-reputation. There were five
sellers for each type of profile, resulting in a total of 30 profiles.
To assure that each of the easy-, medium- and difficult-to-
pronounce usernames was rated for trustworthiness both with
a good and a bad reputation score, two stimuli lists counter-
balanced the assignment of the usernames to the two reputation
conditions.
Procedure
Instructions informed participants that they were going to see
several screenshots of private eBay sellers and that they should
indicate how trustworthy they perceived each to be. Participants
used a Likert-type rating scale presented below every eBay profile
with the question “How trustworthy is this seller?” The scale
ranged from 1 – not at all to 10 – very much. To guarantee
that participants attended to the usernames and not only to their
reputation (since it is likely that objective information about
previous behavior has a great weight in trust related assessments,
e.g., Ba and Pavlou, 2002;Resnick et al., 2006), participants were
asked to carefully attend to all the information provided about
the sellers, including the usernames as these could play a role
later in the experiment1. In the first exposure, participants judged
the 30 profiles in a random order for each participant. After
this, without any break or further instruction, all 30 profiles were
presented and judged again in a different new random order for
each participant. After completing the evaluation, participants
reported age and gender, and were asked to state what factors had
influenced their evaluations.
1Specific instructions in Experiments 1–7 were: “In this study we explore how
individuals form attitudes about the private sellers in Internet auction sites. You
will see several screenshots of eBay sellers with some information about them. For
each seller, you should indicate how trustworthy you think they are. To give your
response, please use the slider that is provided bellow each seller. The scale goes
from 1 to 10. The more trustworthy you think the seller is, the higher should be the
number you select in the slider. Observe all the information about the seller. Pay
attention to the name of the sellers, as this may play a role later in the experiment.”
In each experiment, small adjustments were made to accommodate changes made
to the design.
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Silva et al. Username Complexity and Seller Trustworthiness
FIGURE 1 | Average trustworthiness ratings in Experiment 1, by Pronounceability and Reputation conditions; error bars denote standard errors.
Results
The mean trustworthiness ratings for the six
Pronounceability ×Reputation conditions are shown in
Figure 1 (across repetition levels). A 3 (Pronounceability:
easy- vs. medium- vs. difficult-to-pronounce usernames) ×2
(Reputation: good vs. bad) ×2 (Repetition: first evaluation
vs. second evaluation) repeated measures ANOVA (all factors
within-participants) found the predicted main effects for
Pronounceability, F(2,114) = 52.27, p<0.001, η2
p= 0.48, and
for Reputation, F(1,57) = 94.13, p<0.001, η2
p= 0.62. There was
also an interaction between the two factors, F(2,114) = 17.38,
p<0.001, η2
p= 0.23. The main effect of repetition was not
statistically significant, F(1,57) = 1.50, p= 0.227, as well as none
of the interactions involving this factor (all Fs<2, p>0.300).
As Figure 1 shows, eBay seller profiles with easy-to-pronounce
usernames (M= 5.18, SE = 0.23) were considered more
trustworthy than profiles with medium-difficult-to-pronounce
usernames (M= 4.55, SE = 0.24), t(57) = 5.58, p<0.001, dz= 0.73,
95% CI [0.40, 0.85], which in turn were considered more
trustworthy than profiles with difficult-to-pronounce usernames
(M= 3.97, SE = 0.26), t(57) = 7.71, p<0.001, dz= 1.01,
95% CI [0.43, 0.74]. The main effect associated with reputation
shows that across all pronounceability conditions profiles with
good reputation (M= 5.44, SE = 0.30) were considered more
trustworthy than profiles with bad reputation (M= 3.69,
SE = 0.19). The pronounceability ×reputation interaction shows
that the impact of pronounceability was more extreme in the
case of good reputation sellers. But the differences between easy-
and medium-to-pronounce, as well as between medium- and
difficult-to-pronounce usernames were highly significant in each
of the reputation levels (all ts>4.00, ps<0.001).
Previous research shows that gender influences trusting
behavior in economic contexts such as the Investment Game or
the Trust Game, namely that men seem to trust more than women
and women seem to be more trustworthy than men (Croson
and Buchan, 1999;Buchan et al., 2008). Thus, we tested whether
gender differences would emerge in our experimental paradigm.
For each participant, we calculated a difference score between
the trustworthiness ratings given to the easy-to-pronounce
usernames and the difficult-to-pronounce usernames, collapsing
across all other factors. We ran an independent samples t-test
on this difference score, entering Participant gender (male
vs. female) as the between-participants factor. Results showed
that men and women did not differ in their trustworthiness
evaluations, t<1.2
Discussion
These results clearly suggest that username pronounceability has
a strong impact on how trustworthy an eBay seller may seem to
potential buyers. The effect sizes we found were much larger than
those in previous studies on pronounceability (e.g., Topolinski
et al., 2016). Moreover, this effect emerged in a context with
clear and unambiguous objective information about the sellers’
reputation. The absence of a repetition effect, however, was quite
surprising in light of extensive work on the mere exposure
effect (see Bornstein, 1989). But recent work has clarified that
mere exposure effects are strong when repetition is varied
within-participants, but weak or absent when repetition is varied
between-participants (Dechêne et al., 2009, 2010; see also Hansen
et al., 2008). This presumably reflects that the fluency experience
varies from stimulus to stimulus in within-participants designs,
where some stimuli are repeated and others are not. In contrast,
repeating all or none of the stimuli reduces the variation in
participants’ fluency experiences, thus making the experiential
signal less informative. From this perspective, a repetition effect
was not obtained in Experiment 1 because repetition did not
vary from stimulus to stimulus. In contrast, pronounceability
did and so it was able to elicit fluency effects. Experiment 2 was
designed to address this issue and at the same time replicate the
pronounceability effect.
2We also tested for gender differences in Experiments 8–10, as the experimental
paradigm was largely modified, and again there were no differences between male
and female participants (all ps>0.470).
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FIGURE 2 | Average trustworthiness ratings in Experiment 2, by Pronounceability, Reputation and Repetition conditions; error bars denote standard errors.
EXPERIMENT 2
Experiment 2 was a close replication of the previous Experiment,
except that repetition’s effect was assessed by comparing
trustworthiness ratings given to repeated vs. new seller profiles.
Method
Participants
Fifty-eight participants (14 men, Mage = 22, SD = 3) were assigned
to the conditions of a 3 (Pronounceability: easy vs. medium vs.
difficult to pronounce usernames) ×2 (Reputation: good vs.
bad) ×2 (Repetition: repeated vs. new profiles) factorial design,
with all independent variables manipulated within-participants.
Participants were student volunteers recruited on the university
campus of a German university.
Materials and Procedure
To implement the changes in our design, 30 new eBay seller
profiles were added to the previous ones. Thus, 30 new
meaningless letter strings varying in pronunciation fluency (10
easy-, 10 medium- and 10 difficult-to-pronounce items, created
with the same method described in Experiment 1) were added
to the original pool of usernames. Four list of material were
created so that repeated vs. new and good vs. bad status of
the profiles were completely counterbalanced in the three levels
of pronounceability. The procedure of Experiment 1 was fully
replicated, except that on the second round of trustworthiness
evaluations participants rated 30 new seller profiles mixed with
the 30 old profiles.
Results
We analyzed only the trustworthiness ratings of the second block
of stimuli (with repeated and new profiles) because the effect of
repetition could be assessed only in this block. The conditional
means are shown in Figure 2. A 3 (Pronounceability: easy vs.
medium vs. difficult to pronounce usernames) ×2 (Reputation:
good vs. bad) ×2 (Repetition: repeated vs. new profiles) ANOVA
(all factors within-participants) revealed again the main effect
for Pronounceability, F(2,114) = 75.41, p<0.001, η2
p= 0.57,
the main effect for Reputation, F(1,57) = 88.25, p<0.001,
η2
p= 0.61, and the same interaction between the two factors,
F(2,114) = 27.77, p<0.001, η2
p= 0.33. Replicating Experiment
1, eBay sellers with easy-to-pronounce usernames (M= 5.50,
SE = 0.23) were considered more trustworthy than sellers with
medium-difficult-to-pronounce usernames (M= 4.51, SE = 0.24),
t(57) = 9.01, p<0.001, dz= 1.18, 95% CI [0.77, 1.21],
which in turn were considered more trustworthy than sellers
with difficult-to-pronounce usernames (M= 3.95, SE = 0.26),
t(57) = 6.56, p<0.001, dz= 0.86, 95% CI [0.39, 0.72]. Also as in
Experiment 1, profiles with good reputation (M= 5.58, SE = 0.31)
received higher ratings of trustworthiness than profiles with bad
reputation (M= 3.73, SE = 0.19), across all pronounceability
levels. The interaction between Pronounceability and Reputation
factors also replicated the pattern found in Experiment 1,
showing that the differences between the different levels of
pronounceability were larger for good reputation sellers. Paired
sample t-tests again demonstrated that the differences between
easy- and medium-, as well as between medium- and difficult-
to-pronounce usernames were highly significant in each of the
two reputation levels and both for repeated and new profiles
(all ts>5.00, ps<0.001). The main effect of repetition was
now statistically significant, F(1,57) = 6.13, p= 0.016, η2
p= 0.10.
Repeated profiles (M= 4.70, SE = 0.24) were considered more
trustworthy than new profiles (M= 4.61, SE = 0.23). None of the
interactions involving Repetition was significant (all Fs<1).
Discussion
Results perfectly replicate Experiment 1 and show also an effect
of repetition. Together, Experiments 1 and 2 suggest that fluency
effects are more robust when fluency is manipulated within-
participants and varies across stimuli (see Dechêne et al., 2009,
2010). Again, eBay profiles with easy-to-pronounce usernames
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were considered more trustworthy than profiles with difficult-
to pronounce usernames, independently of their reputation and
their familiarity. In Experiment 3 we explored the effect further,
disentangling two independent parameters of name complexity,
namely pronounceability and length.
EXPERIMENT 3
In Experiments 1 and 2 participants judged eBay profiles with
usernames that varied in pronunciation ease. But in those
two Experiments these easy-, medium- and hard-to-pronounce
letter strings had systematic variation in number of letters, or
simply length: the easy-to-pronounce usernames were shorter
(4 letters) than the medium-difficult-to-pronounce usernames
(7 letters), which were in turn shorter that the difficult-to-
pronounce usernames (12 letters). Given that most recent basic
cognitive research studies showed that word pronounceability
and word length have similar but independent effects in problem
solving judgments (Topolinski et al., 2016), in Experiment
3 username pronounceability and length were manipulated
orthogonally to disentangle their effects in perceptions of
seller trustworthiness. Note that this manipulation is the
first to disentangle the respective contributions of word
pronounceability and word length on attitudes (Alter and
Oppenheimer, 2006;Song and Schwarz, 2009;Laham et al.,
2012;Newman et al., 2014). Thus, in this new Experiment,
besides the replication of (1) pronounceability and (3) reputation
effects, we also predicted (2) seller profiles with short usernames
to be evaluated as more trustworthy than profiles with long
usernames.
Given that the previous experiences revealed comparable
significant differences on trustworthiness ratings between the
three pronounceability levels, this Experiment focused solely on
the comparison of the two extreme levels of that dimension (i.e.,
easy vs. difficult pronunciation).
Method
Participants
Forty-eight participants (8 men, Mage = 23, SD = 4) were
assigned to the conditions of a 2 (Pronounceability: easy- vs.
difficult-to-pronounce usernames) ×2 (Length: short vs. long
usernames) ×2 (Reputation: good vs. bad) factorial design,
with all independent variables manipulated within-participants.
Participants were student volunteers recruited on a German
university campus.
Materials and Procedure
To manipulate username pronounceability and length
independently, we used the 100 letter strings provided in
Topolinski et al. (2016) for the experimental condition of
unsolvable anagrams (i.e., non-anagrams; Supplementary
Table 2). These letter strings were again meaningless and could
not be re-arranged into real German words. Fifty of these
letter strings were easy-to-pronounce and the other 50 were
difficult-to-pronounce. In each pronounceability level, 25 items
were short (6–8 letters) and 25 were long (9–11 letters) (for
further details on how the material was created, see Topolinski
et al., 2016). We again added random combinations of three
digits to the usernames. The procedure was exactly like in
Experiments 1 and 2, but the 25 usernames in each of the four
Pronounceability ×Length conditions were randomly associated
with good vs. bad reputation profiles for each participant
(eliminating the necessity to create multiple material lists
counterbalancing the stimuli across conditions). The only other
change was that the trustworthiness rating scale ranged from
1 to 9.
Results
The mean trustworthiness ratings for the
Pronounceability ×Length ×Reputation conditions are
shown in Figure 3. A 2 (Pronounceability: easy- vs. difficult-
to-pronounce usernames) ×2 (Length: short vs. long) ×2
(Reputation: good vs. bad) repeated measures ANOVA
(all factors within-participants) found a main effect for
Pronounceability, F(1,47) = 18.16, p<0.001, η2
p= 0.28, a
main effect for Length, F(1,47) = 17.04, p<0.001, η2
p= 0.27,
and a main effect for Reputation, F(1,47) = 73.61, p<0.001,
η2
p= 0.61. Sellers with easy-to-pronounce usernames (M= 4.92,
SE = 0.20) were again considered more trustworthy than sellers
with difficult-to-pronounce usernames (M= 4.51, SE = 0.24),
as were sellers with good reputation (M= 5.54, SE = 0.27) as
compared to sellers with bad reputation (M= 3.89, SE = 0.16).
The effect of Length was also as predicted, with short-username
profiles (M= 4.88, SE = 0.18) being considered more trustworthy
than long-username profiles (M= 4.55, SE = 0.21). None of the
interactions between the three independent variables reached
statistical significance (all Fs<2).
Discussion
This Experiment shows that username pronounceability and
length have strong, independent effects on the level of perceived
trustworthiness attributed to online sellers. By disentangling
the two dimensions and manipulating them orthogonally,
the effect sizes were somewhat smaller than in the previous
Experiments (except for the effect of Reputation), suggesting
that indeed length might have contributed to the stronger effects
previously observed for pronounceability. But note again that
the two effects persisted despite the presence of the much
more informative and seemingly powerful information of seller
reputation.
After demonstrating the basic existence of the effects
of username complexity (pronounceability and length) on
trustworthiness, the remaining Experiments tested possible
driving mechanisms.
EXPERIMENT 4
The three previous Experiments show that username
pronounceability and length are strong cues that individuals use
to evaluate the trustworthiness of eBay sellers. In principle, these
cues may exert their influence through a deliberate or intuitive
pathway. On the one hand, participants may deliberately attend
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FIGURE 3 | Average trustworthiness ratings in Experiment 3, by Pronounceability, Length and Reputation conditions; error bars denote standard errors.
to the seller’s name and infer from high complexity that there
is something odd about the seller. If so, explicitly informing
participants that name complexity is unrelated to sellers’
trustworthiness should attenuate or eliminate the observed
effects. On the other hand, name complexity may exert its
influence through processing fluency, as we suggest. From this
perspective, fluent processing elicits a positive response and is
associated with a sense of familiarity, which foster increased
trust. If so, telling people that name complexity (the external
cue) is unrelated to sellers’ trustworthiness should have little
effect if the judgment is based on the subjective experience
it elicits (for a framework on how fluency and affect inform
intuitive cognitive judgments, see Topolinski and Strack,
2009a,b,c).
Method
Participants
Eighty-five participants (12 men, Mage = 23, SD = 5) were
assigned to the conditions of a 2 (Pronounceability: easy-
vs. difficult-to-pronounce usernames) ×2 (Length: short
vs. long usernames) ×2 (Reputation: good vs. bad) ×3
(Correction instructions: pronounceability vs. length vs. no-
correction; between-subjects) mixed factorial design. Participants
were student volunteers recruited on a German university
campus.
Materials and Procedure
Materials and procedure were identical to Experiment 3, except
that different instructions were given to participants. Participants
were randomly assigned to one of the following three conditions.
In the pronounceability-correction condition (n= 27), participants
were warned that the usernames varied in pronounceability,
but that this factor has nothing to do with how trustworthy a
seller is and so they should try to not let their judgments be
affected by it. In the length-correction condition (n= 29), the same
type of instructions was given for the length of the usernames.
Finally, in the no-correction condition (n= 29) there was no
particular instruction beyond the general information given in all
the previous Experiments.
Results
The mean trustworthiness ratings for the
Pronounceability ×Length ×Reputation conditions are shown
in Figures 4–6, (pronounceability-correction, length-correction
and no-correction, respectively). A 2 (Pronounceability: easy-
vs. difficult-to-pronounce usernames) ×2 (Length: short vs.
long usernames) ×2 (Reputation: good vs. bad) ×3 (Correction
instructions: pronounceability-correction vs. length-correction
vs. no-correction) mixed ANOVA (last factor between-
participants) revealed once more strong main effects associated
with Pronounceability, F(1,82) = 34.17, p<0.001, η2
p= 0.29,
Length, F(1,82) = 31.41, p<0.001, η2
p= 0.28, and Reputation,
F(1,82) = 192.49, p<0.001, η2
p= 0.70. These effects replicate
what was found in the previous Experiments: participants
attributed higher trustworthiness to sellers with easy- (M= 5.50,
SE = 0.13) as compared to difficult-to-pronounce usernames
(M= 5.05, SE = 0.15); to sellers with short- (M= 5.35, SE = 0.13)
as compared to long-usernames (M= 4.15, SE = 0.14); and to
sellers with a good (M= 6.14, SE = 0.18) as compared to bad
(M= 4.36, SE = 0.11) reputation.
In addition, a significant interaction between
Pronounceability and Length was found, F(1,82) = 5.70,
p = 0.019, η2
p= 0.07. The pattern of means associated with this
interaction shows only that although the difference between
easy- and difficult-to-pronounce usernames is always significant,
it is larger for short (Measy = 5.59, SE = 0.13, Mdifficult = 5.11,
SE = 0.15, t(82) = 5.62, p<0.000, dz= 0.61, 95% CI [1.86, 3.90])
than for long usernames (Measy = 5.31, SE = 0.14, Mdifficult = 4.98,
SE = 0.15, t(82) = 4.96, p<0.000, dz= 0.54, 95% CI [1.17, 2.74]).
This interaction was further qualified, albeit only marginally, by
Reputation, F(1,82) = 3.77, p= 0.056, η2
p= 0.04, in that only for
sellers with good reputation was the Pronounceability ×Length
interaction significant [F(1,82) = 6.12, p= 0.015; interaction for
bad reputation sellers, F<1].
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FIGURE 4 | Average trustworthiness ratings in the Pronounceability-correction condition of Experiment 4; error bars denote standard errors.
FIGURE 5 | Average trustworthiness ratings in the Length-correction condition of Experiment 4; error bars denote standard errors.
The independent effect of the Correction instruction was not
significant (F<1). Most importantly for this experiment, it also
did not qualify the effects promoted by pronounceability, length
and reputation (all Fs<2). As Figures 4–6 show, the exact same
pattern of effects was found in the three Correction instruction
conditions. Instructing participants to not let their judgments be
influenced by the pronounceability or length of the usernames
did not eliminate the seemingly heuristic, irrational bias that
these dimensions bear on trustworthiness evaluations.
Discussion
This Experiment showed that username pronounceability and
length influence evaluations of seller trustworthiness even when
participants were explicitly asked to correct for them. These
results suggest that name complexity effects on attitudes toward
online sellers are not caused by a conscious and deliberative use of
pronounceability or length as cues to infer trustworthiness. The
route through which username complexity exerts its effects seems
to be rather experiential (Novak and Hoffman, 2009), stemming
from the positivity that is associated with fluent processing (e.g.,
Winkielman and Cacioppo, 2001;Winkielman et al., 2003; see
also Topolinski and Strack, 2009a,b,c).
An alternative explanation is that participants maybe
attributed the differences in username pronounceability and
length to other characteristics of the sellers. If so, simply being
warned that the usernames differed in how easy they were to
pronounce or in their length could not eliminate the effects
of other possible attributions participants might have made
during their judgments. In the next three Experiments we
addressed possible rational attribution processes, to further
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FIGURE 6 | Average trustworthiness ratings in the No-correction condition of Experiment 4; error bars denote standard errors.
clarify the nature of name complexity influences on perceived
trustworthiness.
EXPERIMENT 5
In this Experiment we tested whether participants attributed
name complexity to the origin of the sellers. Because the difficult-
to-pronounce usernames do not conform to German phonotactic
constraints and all the participants in previous Experiments were
German students, it may have been that the more complex
usernames were interpreted as belonging to sellers from foreign
backgrounds with different language rules. Given that individuals
hold more positive attitudes toward members of their ingroup
than members of an outgroup (ingroup bias;Brewer, 1999;Everett
et al., 2015), including higher expectations regarding ingroup
members’ trustworthiness (see Balliet et al., 2014), it is possible
that in the previous Experiments sellers with complex usernames
were judged as less trustworthy because they were perceived
as members of an outgroup and not because and not because
their usernames were difficult-to-pronounce. This possibility
finds support in studies using the economic Trust Game to
explore trust and trustworthiness toward ingroup and outgroups
members, which found evidence of ingroup bias in both measures
(but only in Western countries like the United States of America;
Buchan et al., 2006).
To test this hypothesis, we informed participants that some
of the sellers were ostensibly located in Germany and some
in Poland. Poles were chosen as the outgroup to the German
participants because many negative stereotypes about this
national group (such as “drunk and thieves”) are still present
in the contemporary German society (Sakson, 2012). If the
name complexity effects found so far are a result of ingroup–
outgroup biases processes, then they should be eliminated
or at least qualified by the information about the sellers’
origin.
Method
Participants
Seventy-five participants (12 men, Mage = 23, SD = 4)
were assigned to the conditions of a 2 (Pronounceability:
easy- vs. difficult-to-pronounce usernames) ×2 (Length: short
vs. long username) ×2 (Reputation: good vs. bad) ×2
(Seller origin: German vs. Poles) factorial design (all variables
manipulated within-participants). Participants were student
volunteers recruited on a German university campus.
Materials and Procedure
In this Experiment half of the eBay profiles were described
as belonging to sellers from Germany and the other half to
sellers from Poland. Thus, after the general instructions for
the task, participants were told that the eBay sellers they were
going to evaluate were German. Participants then evaluated
half of the profiles (a random sample of 48 profiles across the
Pronounceability ×Length ×Reputation conditions). After that
a new instruction was given, saying that the next sellers were
Polish. Sequence of the German vs. Polish block was randomly
assigned to participants. Because we wanted to assure that
the possible effects of ingroup–outgroup biases would not be
corrected by participants wishing to answer in a more socially
accepted manner, participants were instructed to evaluate the
sellers spontaneously.
Results
The mean trustworthiness ratings for the
Pronounceability ×Length ×Reputation ×Seller origin
conditions are shown in Figure 7. A 2 (Pronounceability: easy-
vs. difficult-to-pronounce usernames) ×2 (Length: short vs.
long usernames) ×2 (Reputation: good vs. bad) ×2 (Seller
origin: German vs. Poles) repeated measures ANOVA (all
factors within-participants) was performed on the averaged
trustworthiness ratings. The analysis revealed the same three
main effects associated with Pronounceability, F(1,74) = 29.14,
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FIGURE 7 | Average trustworthiness ratings in Experiment 5, by Seller Origin, Length, Pronounceability, and Reputation condition; error bars denote standard errors.
p<0.001, η2
p= 0.28 (Measy = 4.82, SE = 0.18; Mdifficult = 4.34,
SE = 0.20), Length, F(1,74) = 25.31, p<0.001, η2
p= 0.26
(Mshort = 4.71, SE = 0.19; Mlong = 4.45, SE = 0.19), and
Reputation, F(1,74) = 69.95, p<0.001, η2
p= 0.49 (Mgood = 5.25,
SE = 0.25; Mbad = 3.91, SE = 0.14). The only significant effect
associated with the Seller origin factor was a weak Length ×Seller
origin interaction, F(1,74) = 4.85, p= 0.031, η2
p= 0.06. However,
this interaction only shows that the statistically significant
difference between short and long usernames was larger when
sellers were from Poland (Mshort = 4.76, SE = 0.19, Mlong = 4.44,
SE = .19, t(74) = 5.20, p<0.000, dz= 0.60, 95% CI [0.78, 1.75])
rather than from Germany (Mshort = 4.66, SE = 0.20, Mlong = 4.46,
SE = 0.20, t(74) = 3.64, p<0.000, dz= 0.42, 95% CI [0.36, 1.23]).
No other effects reached significance (all Fs <2.5, all ps >.125).3
Discussion
This Experiment ruled out the hypothesis that the effects of name
complexity on trustworthiness ratings are due to participants
attributing complex usernames to foreigners or outgroup
members (Laham et al., 2012). Seller origin did not qualify the
effects of pronounceability or length in any conceptually relevant
way. Thus, the ingroup bias hypothesis seems to be discarded as
an alternative explanation. In Experiments 6 and 7, we explored
two other possible seller attributes that participants might infer
from our manipulations of username pronounceability and
length.
EXPERIMENT 6
In this Experiment we tested the hypothesis that participants
rated sellers with complex usernames as less trustworthy because
3Order of the Ingroup vs. Outgroup blocks did not qualify the main effects of
Pronounceability [F(1,73) = 3.69, p = 0.059], Length [F(1,73) = 3.10, p= 0.082]
or Reputation (F<1) on trustworthiness ratings.
they infer that if sellers created usernames with seemingly
random and non-linguistic combinations of letters, then this
randomness or carelessness might transfer to the way they handle
transactions. We tested this hypothesis by actively manipulating
participants’ beliefs about username generation process. We
informed participants either that the usernames presented in the
profiles had been personally created and chosen by the sellers
themselves, or that they had been automatically generated by an
algorithm designed by eBay. If the effects we have observed in
all the previous Experiments were in fact due to the inference
that some sellers were so careless that they created such complex
usernames, then pronounceability and length should impact
trustworthiness ratings only in the condition where usernames
were a personal choice of the sellers. When usernames are created
through an automatic algorithm, it implies by necessity a lack of
agency on the side of the sellers and consequently seller laziness
or carelessness cannot play a role.
Method
Participants
Forty participants (6 men, Mage = 22, SD = 3) were assigned
to the conditions of a 2 (Pronounceability: easy- vs. difficult-to-
pronounce usernames) ×2 (Length: short vs. long username) ×2
(Reputation: good vs. bad) ×2 (Belief about username generation
method: personal choice vs. computer algorithm) factorial design
(all variables manipulated within-participants). Participants were
student volunteers recruited on the university campus to take
part in a multiple experiments session in one of the university
laboratories.
Materials and Procedure
Now participants were told that the profiles they were going
to evaluate either had usernames the sellers had personally
chosen and composed or usernames generated by eBay through
an algorithm with no influence whatsoever from the sellers.
Sequence of the Personal choice vs. Computer algorithm block
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FIGURE 8 | Average trustworthiness ratings in Experiment 6, by Belief in username generation method, Length, Pronounceability and Reputation conditions; error
bars denote standard errors.
was randomly attributed to participants. To assure participants
kept in mind each of the two different instructions about
username generation method, in each block they were asked to
memorize how the usernames had been created. A manipulation
check was introduced at the end of each block, asking participants
to write the username generation method of the sellers they had
just evaluated.
Results
Manipulation Check
Only 52.5% (n= 21) of the participants correctly reported
different username generation methods for the two blocks.
Probably due to a misunderstanding of the question at the end of
each block (“How were the usernames in this block construed?”),
many participants did not report the exact information that had
been given. An inspection of participants’ responses shows that
they focused on the objective characteristics of the usernames,
and thus most often replied “random letters and three digits.”
To address this low rate of the manipulation check and avoid
the noise it could bring to our data, we coded the participants in
two groups according to whether they responded correctly to the
manipulation checks (n= 21) or not (n= 19). We first analyzed
the data including this variable as a between-participants factor to
check for its potential effects. However, there was neither a main
effect nor any significant interactions associated with this factor,
and thus we removed it from the main statistical analysis.
Main Results
The mean trustworthiness ratings for the
Pronounceability ×Length ×Reputation ×Username
generation method conditions are shown in Figure 8.
A 2 (Pronounceability: easy- vs. difficult-to-pronounce
usernames) ×2 (Length: short vs. long usernames) ×2
(Reputation: good vs. bad) ×2 (Belief about username
generation method: personal choice vs. computer algorithm)
repeated measures ANOVA (all factors within-participants
showed the same strong three main effects associated with
username Pronounceability, F(1,39) = 22.92, p<0.001, η2
p= 0.37
(Measy = 4.70, SE = 0.20; Mdifficult = 3.96, SE = 0.22), Length,
F(1,39) = 21.52, p<0.001, η2
p= 0.36 (Mshort = 4.48, SE = 0.20;
Mlong = 4.18, SE = 0.20), and Reputation, F(1,39) = 60.35,
p<0.001, η2
p= 0.61 (Mgood = 5.05, SE = 0.26; Mbad = 3.61,
SE = 0.17). The only significant effect associated with the Belief
about username generation method was an interaction between
this factor and Pronounceability, F(1,39) = 14.80, p<0.001,
η2
p= 0.28. This interaction only shows that the significant
difference between easy- and difficult-to-pronounce usernames
was larger when participants were told that the usernames had
been personally chosen by the sellers (Measy = 4.71, SE = 0.22,
Mdifficult = 3.78, SE = 0.22, t(39) = 5.17, p<0.001, dz= 0.82, 95%
CI [2.36, 5.08]) rather than randomly generated by an algorithm
(Measy = 4.68, SE = 0.21, Mdifficult = 4.14, SE = 0.24, t(39) = 3.52,
p<0.001, dz= 0.56, 95% CI [0.93, 3.45]). No other effects
reached significance (all Fs <2, all ps >0.20).4
Discussion
As in Experiment 5, the alternative explanation that username
complexity affected trustworthiness ratings due to some strategic
attribution was shown to be unlikely. We observed reliable and
strong significant effects for both name pronounceability and
name length independent of whether participants thought the
usernames had been created by the sellers themselves or by an
automatic algorithm. It is true that the pronounceability effects
was even stronger when participants thought the sellers had
created the usernames themselves, but it was still significant
when participants thought the usernames had been created by
an algorithm. Also, the other determinant of name complexity,
length, did not interact with belief of username generation
method. So, once more, a mechanistic assumption is bolstered
in that the fluency experience caused by pronounceability and
4Order of the Personal choice vs. Computer algorithm username blocks did
not qualify the main effects of Pronounceability, Length or Reputation on
trustworthiness ratings (F<1, for all interactions).
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length seems to directly influence trustworthiness ratings in an
experiential manner (Novak and Hoffman, 2009).
EXPERIMENT 7
One other possible rational inference participants may render
from username complexity is to attribute it to the experience
of the seller on eBay, with the following logic. As more
and more sellers register with a website, the availability of
usernames decreases and new users need to find more diverse
alternatives for a name. This often leads to the need of
creating distinct and extreme names that necessarily depart
more and more from linguistic norms. Thus, participants
might have inferred that seller profiles with difficult-to-
pronounce and long usernames had been created more
recently than easy-to-pronounce and short usernames. This
in turn might have led to the assumption that sellers with
more recently created accounts would be less experienced
and thus less trustworthy (e.g., it is assumed that more
experienced sellers have better quality products and are also
better in describing their products accurately; Resnick et al.,
2006).
To address this hypothesis, we manipulated the belief
participants held about the year in which the eBay profiles were
registered. If the level of experience that the sellers have was
inferred from our name complexity manipulations, then the
effects we observed previously should be at least qualified by that
factor.
Method
Participants
Thirty-eight participants (8 men, Mage = 22, SD = 3) were
assigned to the conditions of a 2 (Pronounceability: easy-
vs. difficult-to-pronounce usernames) ×2 (Length: short vs.
long username) ×2 (Reputation: good vs. bad) ×2 (Age
of eBay profile: 10 years vs. 1 year) factorial design (all
variables manipulated within-participants). Participants were
student volunteers recruited on the university campus to take
part in a multiple experiments session in one of the university
laboratories. Due to an error of codification, two participants
were recorded with the same ID number and had to be deleted.
Therefore, the final sample size was N= 36.
Materials and Procedure
Materials and procedure replicated Experiment 6, except that
now the eBay profiles were presented either as having been
registered in the year 2005 (older, well-established accounts) or
in the year 2015 (accounts created close to one year before at
the date of data collection). Sequence of Age of the eBay profiles
blocks was randomly attributed to participants.
Results
Manipulation Check
Seven participants did not enter the correct responses for the Age
of eBay profiles manipulation checks and were thus discarded.
Final sample size considered for the main analysis is N= 29.
Main Results
The mean trustworthiness ratings for the
Pronounceability ×Length ×Reputation ×Age of eBay
profile conditions are shown in Figure 9. A 2 (Pronounceability:
easy- vs. difficult-to-pronounce usernames) ×2 (Length:
short vs. long usernames) ×2 (Reputation: good vs. bad) ×2
(Age of eBay profile: 10 years vs. 1 year) repeated measures
ANOVA (all factors within-participants) again revealed the
same main effects associated with username Pronounceability,
F(1,28) = 18.95, p<0.001, η2
p= 0.40 (Measy = 4.70, SE = 0.31;
Mdifficult = 4.07, SE = 0.33), Length, F(1,28) = 17.44, p<0.001,
η2
p= 0.38 (Mshort = 4.54, SE = .30; Mlong = 4.23, SE = 0.32),
and Reputation, F(1,28) = 43.59, p<0.001, η2
p= 0.61
(Mgood = 5.29, SE = 0.42; Mbad = 3.48, SE = 0.23). The
interaction between Pronounceability ×Reputation was also
significant, F(1,28) = 13.88, p<0.001, η2
p= 0.33, showing
only that the significant difference between easy- and difficult-
to-pronounce usernames was larger for good (Measy = 5.67,
SE = 0.40, Mdifficult = 4.92, SE = 0.45, t(28) = 4.95, p<0.001,
dz= 0.92, 95% CI [1.74, 4.23]) than for bad reputation sellers
(Measy = 3.73, SE = 0.26, Mdifficult = 3.22, SE = 0.23, t(39) = 3.50,
p<0.001, dz= 0.65, 95% CI [0.83, 3.18]). No other effects
reached significance (all Fs <2.5, all ps >.110).5
Discussion
We found no moderation of the effects of name complexity
by the Age of accounts factor. This suggests that the biasing
effects of username pronounceability and length did not affect
trustworthiness via attributions of less experience to sellers with
more complex usernames.
EXPERIMENTS 8 AND 9
In Experiments 8 and 9, we examined the generalizability of
the username complexity effect. We used stimuli sampled from
the real world and a different measure of seller trustworthiness.
In Experiment 8, we asked people to evaluate real eBay seller
usernames to examine whether the effect held for real eBay
accounts. In Experiment 9, we asked people to evaluate real
names rather than seller usernames. In both Experiments, we also
examined whether the name complexity effect would hold when
people evaluated how concerned they were about purchasing
from these sellers, rather than directly asking them about
trustworthiness. Since in Experiments 3–7 pronounceability
and length of the seller usernames promoted independent and
5An exploration of the data entering Order of the New account vs. Old accounts
blocks as a between-participants factor shows that this variable qualified the main
effects of Pronounceability [F(1,27) = 5.11, p = 0.032, η2
p= 0.16) and Length
[F(1,27) = 7.23, p = 0.012, η2
p= 0.21]. However, these interactions show only
that the differences between easy- and difficult-to-pronounce usernames follow the
pattern of the main effect (easy >difficult), but the difference was larger when New
accounts were shown first (Measy = 5.18, SE = 0.32, Mdifficult = 4.30, SE = 0.41) than
second (Measy = 4.01, SE = 0.54, Mdifficult = 3.75, SE = 0.54). The same happens for
the difference between short and long usernames (New accounts first: Mshort = 4.98,
SE = 0.32, Mlong = 4.51, SE = 0.40; New accounts second: Mshort = 3.93, SE = 0.53,
Mlong = 3.83, SE = 0.53). Sequence of New vs. Old accounts blocks did not qualify
Reputation’s effect (F<1).
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Silva et al. Username Complexity and Seller Trustworthiness
FIGURE 9 | Average trustworthiness ratings in Experiment 7, by Age of the eBay accounts, Length, Pronounceability and Reputation conditions; error bars denote
standard errors.
comparable effects, in the next Experiments we focused solely on
the pronounceability component of name complexity.
Experiment 8
Method
Participants
Fifty-four participants (35 men, Mage = 34, SD = 9) completed
the study and rated names on two dimensions (concern
about purchasing, and ease of pronunciation). Participants were
recruited through Amazon’s Mechanical Turk.
Materials and procedure
We created a list of real eBay seller usernames by searching
for digital cameras on eBay. We selected the first 40 usernames
from the category of cameras priced at between $199 and $500
(Supplementary Table 3). Participants were told that they were
going to see a series of e-commerce seller names from eBay.
They were asked to imagine that they were trading with each
seller. Then, participants completed two rating tasks. First, we
asked them to estimate how concerned they would be about
making a purchase from each of the sellers on a 7 point scale
(1 = not concerned at all, 7 = very concerned). Second, we asked
participants to rate how easy it was to pronounce each name,
using a 7 point scale (1 = very difficult, 7 = very easy).
Results
We first examined whether participants’ ratings of the
pronounceability of a seller’s username were associated with
participants’ concern about buying from the seller. We used
an item analysis approach and correlated the mean rating of
pronunciation ease with the mean rating of concern of buying
for each seller username. As expected, we found that the more
difficult it was to pronounce a name, the more concerned people
were about purchasing from that seller, r(38) = −0.85, p<0.001.
Discussion
These results replicate the findings in Experiments 1–7 and
extend those findings to real eBay usernames. The finding that the
seller username pronounceability influenced people’s concern in
interacting with the seller suggests that the previously observed
effect of name complexity and disfluency on trustworthiness
extends to more general concerns about the seller.
Experiment 9
Method
Participants
Fifty-three participants (36 men, Mage = 32, SD = 10) were
assigned to the conditions of a 2 (Pronounceability: easy- vs.
difficult-to-pronounce usernames) within-participants design.
Participants were recruited through Amazon’s Mechanical Turk.
Materials and procedure
We created a database of names by searching online newspapers
from all over the world. From this database, we selected easy- and
difficult-to-pronounce names from different regions of the world,
until we had a set of 23 pairs of names with a similar number of
letters and syllables. One constraint in selecting the names was
that there was always an easy and difficult name from each region.
We normed these names with 44 Mechanical Turk workers.
Subjects saw 46 names in total and rated how easy it was to
pronounce each name on a scale from 1 (easy) to 5 (difficult). The
order of names was randomized for each subject. Using an item
analysis we found that the difficult names (M= 3.01, SE = 0.10)
were significantly more difficult to pronounce than the easy
names (M= 2.06, SE = 0.09), t(44) = 7.26, p<0.001, dz= 1.08,
95% CI [0.71, 1.45]. We use these pronunciation categories (easy-
vs. difficult-to-pronounce names) to group names for analysis in
Experiment 9 (Supplementary Table 4).
We told participants that “In a moment, we will show you a
series of names from around the world. Each of these names is a
seller that you can buy from on eBay or Etsy. Your task today is
to imagine you are going to make a purchase from these sellers.
For each seller name, we would like you to rate how concerned
or cautious you would be about making a purchase from that
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Silva et al. Username Complexity and Seller Trustworthiness
person.” Participants saw 46 names, half easy- and half difficult-
to-pronounce, in a random order. Participants were asked to
rate on a 5-point scale, how concerned they would be about
buying a product from each seller (1 = not concerned to 5 = very
concerned).
Results
Replicating Experiments 1–8, we found that participants were
less concerned to buy from sellers with easy-to-pronounce names
(M= 2.68, SE = 0.12) than sellers with difficult-to-pronounce
names (M= 2.83, SE = 0.12), t(52) = 3.57, p= 0.001, dz= 0.49,
95% CI [0.20, 0.77].
We also examined the influence of the region from which the
names originated. It is possible that region somehow interacted
with the ease of pronunciation to eliminate effects of name
pronounceability for certain countries. A 2 (Pronounceability:
easy- vs. difficult-to-pronounce usernames) ×7 (Region:
Eastern Asia, Southern Asia, Eastern Europe, Western
Europe, Middle East, Southeastern Africa, Northern Africa)
repeated measures ANOVA (all factors within-participants)
revealed a main effect of Pronounceability, F(1,32) = 8.78,
p= 0.01, η2
p= 0.22 (Measy−to−pronounce = 2.68, SE = 0.04;
Mdifficult−to−pronounce = 2.83, SE = 0.04), and Region,
F(6,32) = 5.66, p<0.001, η2
p= 0.52. The Pronounceability
x Region interaction was not significant, F(6,32) = 1.24, p= 0.31,
η2
p= 0.19. The main effect of Region shows that people were
most concerned about buying from sellers with Middle Eastern
names (M= 2.90, SE = 0.07) and least concerned about buying
from sellers with Western European names (M= 2.47, SE = 0.06).
But the absence of an interaction indicates that the effect of
pronunciation held within each region, further illustrating its
robustness.
Discussion
These findings demonstrate that the effect of pronunciation on
trust is not unique to seller usernames, and suggest that when
the seller’s actual name is available, it may influence people in
the same direction. These findings also serve as a conceptual
replication of Experiment 5 where the effect of pronunciation
ease held, regardless of the perceived—or in Experiment 9—real
origin of the names.
EXPERIMENT 10
So far, all fluency manipulations pertained to the name of the
seller. In the final Experiment we examined whether the ease of
processing product information, rather than seller information,
may also influence trust and willingness to buy from the seller. If
the disfluency of product information is experienced as a problem
signal, it may not only hurt the evaluation of the product but also
consumers’ trust in the seller who offers it.
Method
Participants
Ninety-nine participants (25 men, Mage = 20, SD = 2)
were assigned to the condition of a 2 (Seller: B vs. C;
within-participants) ×2 (Processing ease condition: Seller
B easy-to-read/Seller C difficult-to-read vs. Seller B difficult-
to-read/Seller C easy-to-read; between-participants) factorial
design. Participants were student volunteers recruited on the
University of Southern California campus to take part in a
multiple experiments session in one of the university laboratories.
Materials and Procedure
We created three mockup eBay product offers. The first offer
(Seller A) always served as a filler; it described a tripod and
was easy to read. The second (Seller B) and third (Seller C)
offers described compact digital cameras and were our critical
items. One of these descriptions was presented in an easy-to-
read style, and one was presented in a difficult-to-read style (e.g.,
italicized font, low contrast between text and background; see
Supplementary Figures 2, 3). We counterbalanced so that each of
the critical descriptions (Sellers B and C) appeared equally often
in each processing ease condition.
Each offer appeared as a full-screen webpage. Participants
were asked to study each offer as if they were actually thinking
about buying one of the products. After studying these webpages,
participants were asked to make several ratings about the camera
offers. While making these ratings on 7-point scales participants
saw a small thumbnail picture of each webpage as a reminder.
The ratings pertained to: (1) how much they trusted the seller
(1 = a little, 7 = a lot); (2) provided they would like to buy a
camera, would they buy it from this seller (1 = not very likely,
7 = Very likely); (3) the reliability of the information presented on
the website (1 = not reliable at all, 7 = very reliable); (4) how safe
their credit card information would be at this website (1 = not safe
at all, 7 = very safe); (5) how much they liked the webpage they
had seen (1 = dislike 7 = like a lot), (6); how attractive each camera
was, (1 = not attractive, 7 = very attractive), and (7) how easy
it was to process information on the product page (1 = difficult,
7 = easy).
Results
The ratings given to the two target product descriptions were
analyzed with a mixed design repeated-measures ANOVA with
Seller (B vs. C) as a within-participants factor and Processing
ease condition (Seller B easy-to-read/Seller C difficult-to-read
vs. Seller B difficult-to-read/Seller C easy-to-read) as a between-
participants factor. Mean ratings for the fluent and disfluent
seller, the interaction terms of the ANOVA and the follow-
up simple t-tests are presented in Table 1. Confirming the
effectiveness of the fluency manipulation, results showed that
participants rated the offer in the easy-to-read style as easier to
process than the offer in the difficult-to-read style (see bottom
row of Table 1).
Table 1 further shows that the seller with the easy-to-read
product description had an advantage over the seller with the
difficult-to-read description on each of the dependent variables.
Most importantly, participants trusted seller B more than seller
C when offer B was easier to read, but trusted seller C more than
B when offer C was easier to read. Going beyond the measures
used in the earlier Experiments, participants also reported a
higher likelihood to purchase from the seller with the easier to
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Silva et al. Username Complexity and Seller Trustworthiness
read offer, considered the product information more reliable, and
their credit card information in safer hands. They also liked the
camera, as well as the webpage, more when the product offer
was easy to read, consistent with earlier findings on the beneficial
effects of processing fluency on esthetic appreciation (Reber et al.,
2004).
Discussion
Whereas the preceding Experiments manipulated ease of
processing by varying the ease with which a seller’s username
could be pronounced, Experiment 10 varied the ease of reading
product descriptions. It found beneficial effects of processing
fluency across a range of dependent variables. Consistent with the
preceding Experiments, when the product description was easy
to read, participants reported more trust in the seller, a higher
likelihood of buying from the seller and fewer concerns about
the safety of their credit card information than when the same
product description was difficult to read. To our knowledge, this
is the first study to document such pervasive effects of the ease of
processing product information on the perceived trustworthiness
of the seller. As observed in other domains (for a review, see
Schwarz, 2015), people are likely to bring their metacognitive
experiences to bear on any judgment to which they are applicable,
giving the print font of a product description the potential
to affect the trustworthiness of the seller (as observed in this
Experiment) and the ease of pronouncing the seller’s username
the potential to affect judgments of the product.
GENERAL DISCUSSION
We tested the impact of username complexity of eBay seller
profiles on trustworthiness perceptions, using different measures
of trust in market place interactions. Across nine Experiments
we found that eBay profiles with less complex usernames were
consistently rated as more trustworthy than profiles with more
complex usernames. This was even observed when diagnostic
information about the good or bad reputation of the sellers was
available (Experiments 1–7). Two of our Experiments established
external validity of the stimuli by sampling real usernames from
eBay (Experiment 8) and real person names from different world
regions (Experiment 9). Experiments 8–9 generalized the findings
to older participants (Mage of 34 and 32 years) than those in
Experiments 1–7 and 10 (Mage between 20 and 24 years), which
is in line with findings in economic games showing that trust
and trustworthiness remain constant across adulthood (Sutter
TABLE 1 | Mean ratings for the fluent and disfluent sellers in Experiment 10, interaction terms of the ANOVA and follow-up analyses.
DVs Seller Analyses Analyses
B C Interaction term Follow-up
M SE M SE
Trust in seller Fluent 5.33 0.160 5.31 0.170 F(1,97) = 60.18, p<0.001;
η2
p= 0.38
Bfluent >CDisfluent:t(47) = 6.19, p<0.001,
dz= 0.90, 95% CI [0.55, 1.23]
Disfluent 4.49 0.200 3.73 0.230 Cfluent >BDisfluent:t(50) = 4.55, p<0.001,
dz= 0.64, 95% CI [0.33, 0.94]
Willingness-to-buy Fluent 5.06 0.220 4.98 0.200 F(1,97) = 40.58, p<0.001,
η2
p= 0.30
Bfluent >CDisfluent :t(47) = 5.01, p<0.001,
dz= 0.72, 95% CI [0.40, 1.04]
Disfluent 4.02 0.240 3.44 0.220 Cfluent >BDisfluent :t(50) = 3.86, p<.001,
dz= 0.54, 95% CI [0.24, 0.83]
Reliability of information Fluent 5.42 0.964 5.25 1.146 F(1,97) = 34.51, p<0.001,
η2
p= 0.26
Bfluent >CDisfluent :t(47) = 4.92, p<0.001,
dz= 0.71, 95% CI [0.39, 1.02]
Disfluent 4.73 1.429 3.96 1.584 Cfluent >BDisfluent :t(50) = 3.05, p<0.001,
dz= 0.43, 95% CI [0.14, 0.71]
Credit card information safety Fluent 5.08 1.285 4.80 1.265 F(1,97) = 22.32, p<0.001,
η2
p= 0.19
Bfluent >CDisfluent :t(47) = 4.44, p<0.001,
dz= 0.64, 95% CI [0.33, 0.95]
Disfluent 4.55 1.527 4.02 1.828 Cfluent >BDisfluent:t(50) = 1.69, p= 0.10,
dz= 0.24, 95% CI [−0.04, 0.51]
Website liking Fluent 4.94 1.040 5.02 1.122 F(1,97) = 80.12, p<0.001,
η2
p= 0.45
Bfluent >CDisfluent :t(47) = 6.32, p<0.001,
dz= 0.91, 95% CI [0.57, 1.25]
Disfluent 3.37 1.612 3.29 1.637 Cfluent >BDisfluent:t(50) = 6.35, p<0.001,
dz= 0.89, 95% CI [0.56, 1.25]
Cameras attractiveness Fluent 5.10 1.225 4.61 1.429 F(1,97) = 36.47, p<0.001,
η2
p= 0.27
Bfluent >CDisfluent :t(47) = 4.18, p<.001,
dz= 0.60, 95% CI [0.29, 0.91]
Disfluent 3.71 1.628 4.06 1.420 Cfluent >BDisfluent :t(50) = 4.37, p<0.001,
dz= 0.61, 95% CI [0.31, 0.91]
Ease of processing Fluent 4.89 0.230 5.65 0.160 F(1,97) = 40.22, p<0.001,
η2
p= 0.29
Bfluent >CDisfluent :t(47) = 2.55, p= 0.01,
dz= 0.37, 95% CI [0.07, 0.66]
Disfluent 3.86 0.280 4.31 0.230 Cfluent >BDisfluent :t(50) = 6.12, p<0.001,
dz= 0.86, 95% CI [0.53, 1.18]
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Silva et al. Username Complexity and Seller Trustworthiness
and Kocher, 2007). Going beyond the ease of processing a seller’s
name, we further found that difficulty in processing a product
description can also adversely affect perceptions of a seller’s
trustworthiness (Experiment 10).
Our name complexity studies are the first to systematically
disentangle two aspects of name complexity, namely the
contributions of name pronounceability and name length (Song
and Schwarz, 2009;Laham et al., 2012;Newman et al., 2014).
They show that both components exert independent effects,
with easy-to-pronounce and short names being rated as more
trustworthy than hard-to-pronounce and long names. We further
tested whether participants had insight into the name complexity
effect and could correct for it when explicitly warned that they
should not let their evaluations be influenced by the seller’s name
(Experiment 4). This was not the case. From the perspective of
mental correction models (Strack and Hannover, 1996;Wegener
and Petty, 1997; for a review, see Schwarz, 2015) this suggests
that people lack a lay theory that entails that difficult names
impair trust and hence cannot systematically correct for the
influence of this variable when made aware of it. Note that such
warnings are different from misattribution manipulations, which
explicitly offer an alternative source for experienced difficulty,
thus undermining the informational value of the experience (e.g.,
Novemsky et al., 2007). Hence, misattribution manipulations can
elicit discounting effects even when people are unaware that the
experiential input may influence their judgment – they simply
change the diagnosticity of the experiential input. In contrast,
warnings merely draw attention to a potential input and any
correction requires insight into the direction and size of its
likely impact. Accordingly, the influence of ease of processing
variables can be more easily eliminated through misattribution
manipulations (e.g., Schwarz et al., 1991;Sanna et al., 2002;
Novemsky et al., 2007) than corrected in response to warnings
(Experiment 4). A misattribution of affect manipulation could
also help to clarify the role that the experience of positive
affect that is associated with fluent processing (Winkielman and
Cacioppo, 2001) plays on the effects of our Experiments.
Our studies further indicate that the name complexity effect
cannot be traced to inferences about objective features of the
seller that may bear on the seller’s perceived trustworthiness,
including origin and carefulness of the seller and age of
the online account; neither of these variables moderated the
name complexity effect (Experiments 5–7). This observation
is consistent with the conceptual rationale of fluency based
judgment: fluently processed material appears more familiar
than disfluently processed material (e.g., Song and Schwarz,
2009), elicits a more positive affective response (e.g., Winkielman
and Cacioppo, 2001), and is associated with less attention to
details of the message (e.g., Song and Schwarz, 2008) and higher
acceptance of the message as true (e.g., Reber and Schwarz,
1999;McGlone and Tofighbakhsh, 2000;Silva et al., 2016; for a
review, see Schwarz, 2015). As observed in Experiment 10, seller
trustworthiness also benefits from fluent processing even when
the processed information is about the product rather than the
seller him- or herself.
To our knowledge, this set of studies is the first demonstration
of how other seller characteristics beyond the well-researched
objective reputation information (Ba and Pavlou, 2002;Corritore
et al., 2003;Resnick et al., 2006;Cheema, 2008) can determine
trustworthiness in online marketplaces. As such, our findings
are an important addition to other research dealing with trust
building in online commercial contexts (e.g., Ang et al., 2001;
Shankar et al., 2002;Yoon, 2002;Corritore et al., 2003;Grabner-
Kräuter and Kaluscha, 2003;Yousafzai et al., 2003;Beldad et al.,
2010;Metzger et al., 2010). Our Experiments provide the first
evidence that subjective experiences of mental ease associated
with the mere reading of easy-to-pronounce seller (user)names
and of product descriptions increases perceptions of online
trustworthiness, and can thus ultimately increase the likelihood
that a seller is chosen as a transaction partner (trustworthiness
signals approach behavior; see Todorov, 2008).
Implications of the Present Findings
Our findings have important practical implications in many
consumer-related contexts. The most obvious is of course
connected with name composition and choice. In many everyday
situations, the first information that is available about others is
their name, be it in form of an email, when the employee of a
customer care service identifies himself, or in the nametags worn
during professional meetings. Globally, our results suggest that
individuals should strive for simple, easy-to-pronounce names as
a way to increase perceived trustworthiness, which in turn may
contribute to positive outcomes. Similar to Laham et al. (2012)
findings that individuals with easy-to-pronounce names are liked
more and attain higher positions in their companies’ hierarchy,
it is possible that consumers are more persuaded to try a new
product in their local store when approached by a saleswoman
with an easy-to-pronounce name depicted in her nametag.
The importance of choosing a fairly simple, short and easy-
to-pronounce name might be even greater in e-commerce
and online markets. Because in these contexts there are even
fewer pieces of information both about transaction partners
and the quality of the products (Degeratu et al., 2000),
an easy-to-pronounce username might just boost perceptions
of trustworthiness enough to enable more opportunities for
successful transactions. An easy-to-pronounce name might thus
work as a success-enabling tool. One way in which these effects
could be assessed is by gathering real data from auction sites
like eBay or Amazon.com to analyze the correlation between
sale success, price premiums, and username pronounceability
(as often done in research on seller reputation; Cabral and
Hortaçsu, 2010). Future research should also take the form
of field experiments and actively manipulate the complexity
of seller usernames to address the causal relation between
name complexity and sale success. Field experiments may
also allow overcoming the fact that our participants did not
face any real (financial) risk by rating one seller higher on
trustworthiness than another one, and such risk may lead to
different choices/outcomes in real commercial contexts. Future
experiments could also explore the effect of name complexity in
the presence of other variables that influence trustworthiness in
consumer domains, such as the consistency between the valences
of product ratings and of the text in the written reviews about
the product (Tsang and Prendergast, 2009), or the popularity of
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