The Impact of Fake Reviewson Demand and Welfare*
Jesper Akesson, Robert W. Hahn,
Robert D. Metcalfe, and Manuel Monti-Nussbaum†
20 July 2022
Abstract
Although fake online customer reviews have become prevalent on platforms such as
Amazon, Google, and Facebook, little is known about how these reviews influence consumer behavior. This paper provides the first experimental estimates of the effects of fakereviews on individual demand and welfare. We conduct an incentive-compatible online
experiment with a nationally representative sample of respondents from the United
Kingdom (n = 10,000). Consumers are asked to choose a product category, browse aplatform resembling Amazon, and to select one of five products. One of the products is ofinferior quality, one is of superior quality, and three are of average quality. We randomly
allocate participants to variants of the platform. Five treatment groups see positive fakereviews for an inferior product, and the control group does not see fake reviews. Someparticipants are randomly selected to receive an educational intervention that aims to
mitigate the potential effects of fake reviews. Our analysis of the experimental data
yields four findings. First, fake reviews make consumers more likely to choose lower
quality products. Second, we estimate that welfare losses from such reviews may be
important—on the order of $.12 for each dollar spent in the setting we study. Third,
we find that fake reviews have heterogeneous effects. For example, the effect of fakereviews is smaller for those who do not trust customer reviews, which is consistent with amodel we develop. Fake reviews also have larger effects on those who shop online more
frequently. Fourth, we show that the educational intervention reduces the adverse welfare
impact of fake reviews by 44%.
Keywords: Fake reviews, consumer protection, field experiment, online retail
JEL Codes: M30, D18, C90* We would like to thank the teams at Which? and The Behaviouralist for working with us to design, imple-

ment, and analyze the experiment. We also thank Iranzu Monreal, JingKai Ong and Senan Hogan-Hennessy
for excellent research assistance. Sandro Ambühl, Rocio Concha Chris Dellarocas, Chiara Farronato, Andrey
Fradkin, Matt Gardner, Ginger Jin, Matt Kahn, Tai Lam, Jonathan Libgober, John List, Stephen McDonald, Dina
Mayzlin, Davide Proserpio, Itzhak Rasooly Chris Stanton, and Scott Wallsten provided excellent and helpfulcomments. Any opinions expressed in this paper are those of the authors and do not necessarily represent thoseof the institutions with which they are affiliated. This research was funded by Which?. Declarations of interest:
none. AEA trial registration: AEARCTR-0008343.
† Akesson, The Behaviouralist; Hahn, University of Oxford & Technology Policy Institute; Metcalfe, University of Southern California & NBER; Monti-Nussbaum, The Behaviouralist.

1

Introduction

E-commerce represented about 14% of all U.S. retail consumption in 2020–2021 (USCB,
2021), and online customer reviews play an important role in many of these purchasing decisions. Major retailers and search engines, such as Amazon, Apple, and Google routinely
display reviews on their websites, and over 82% of American adults read customer reviews
before purchasing products online for the first time (Smith et al., 2016). In theory, genuine
online customer reviews can inform consumers about the quality of products and can reduce
issues related to moral hazard and adverse selection (Bajari and Hortaçsu, 2004; Chevalierand Mayzlin, 2006; Dellarocas, 2006; Jin and Kato, 2006; Resnick et al., 2006; Cabral andHortacsu, 2010; Anderson and Magruder, 2012; Klein et al., 2016; Luca, 2016; Belleflamme
and Peitz, 2018; Acemoglu et al., 2019; Farronato and Zervas, 2019; Goldfarb and Tucker,
2019; Lewis and Zervas, 2019; Bonatti and Cisternas, 2020; Reimers and Waldfogel, 2021).
However, recent research shows that many reviews are fake and that they promote
low quality—and sometimes even dangerous—products (Hu et al., 2011b; Anderson andSimester, 2014; Mayzlin et al., 2014; Luca and Zervas, 2016; KC and Mukherjee, 2016;
Dwoskin and Timberg, 2018; Zinman and Zitzewitz, 2016; Which?, 2020c; He et al., 2021).
For example, an examination of 720 million customer reviews on Amazon found that around
42% were fake or unreliable (Lee, 2020). Moreover, Lappas et al. (2016) show that fake reviews increase the visibility of products and services, which suggests that some companies
may benefit from posting positive fake reviews.
Although fake reviews appear to be widespread, little is known about how they influence consumer demand and economic welfare. On the one hand, past research shows that(genuine) reviews can change consumer behavior and welfare (see, e.g., Chevalier and Mayzlin (2006) or Reimers and Waldfogel (2021)), and the U.S. Federal Trade Commission argues
that fake reviews reduce consumer welfare (FTC, 2021). On the other hand, it is also possiblethat consumers can distinguish between fake and genuine reviews, which may mean that fakereviews have small effects on consumer welfare.
This paper contributes to our understanding of how fake reviews influence consumers’
demand and welfare in two ways. First, we propose a simple Bayesian model of the effect offake reviews on consumers’ demand. The model predicts that fake reviews inflate consumer
beliefs about product quality even if consumers believe that the reviews are likely fake. The
reason is that the customer cannot be sure whether reviews are fake, and thus, they may
contain some valuable information. Furthermore, the model suggests that a prior skepticism
toward the genuineness of reviews will attenuate their effect on demand.
Second, we test the predictions of our model by conducting the first incentive compatible1

field experiment on fake reviews. We recruited a representative sample of 10,000 consumers
in the United Kingdom (UK) and asked them to complete a common shopping task on an online platform resembling Amazon. The platform offers three types of products: dash-cams,
headphones, or cordless vacuum cleaners.1 Participants are first asked to choose a productcategory. Each participant is then shown a ‘search page’ that displays five products in a particular category, such as five cordless vacuum cleaners, which we refer to as their ‘consideration
set’ (Honka et al., 2019). They can then view one or more product pages before deciding
which product to purchase. This type of shopping scenario is similar to how consumers use
the Amazon platform (Forbes, 2019).2 While all of the products have the same sale price,
one has been classified as a Don’t Buy product by the UK consumer protection organization
Which?; one has been classified as a Best Buy product by Which?; and the remaining threereceived mediocre product ratings.3
In the field experiment, we examine the effects of the types of fake reviews that havebeen identified in the academic literature (see, e.g., Kumar and Shah (2018)). For example,
we make use of inflated star ratings. Inflating the star ratings of products is among the mostcommon fake review strategies, and it has been documented in several instances, especially as
product quality decreases (Which?, 2020c; Hu et al., 2011b,a). Another common feature that
we study is overly positive reviews, which typically employ exaggerated language and repetitive phrases. Ott et al. (2011) and Anderson and Simester (2014) find that fake reviews areless descriptive of the actual product than real reviews, and Hu et al. (2012) shows that fakereviews are less precise in their description of the products and are generally more positive.4
A third set of features we incorporate is the use of ‘sloppy’ (but positive) fake written
reviews, where the reviewer may admit that they were paid to write the review, or where they
1 These categories were selected because a leading UK consumer group, Which?, had documented the use of

fake reviews for low quality products in these categories.
2 The reason for developing a platform resembling a provider like Amazon is that commercial platforms
were unlikely to agree to run a natural field experiment on this subject, and there was not sufficient exogenous
variation present in observational data to answer our research questions. This online approach has also been
used, for instance, to study how consumers respond to product taxes (Taubinsky and Rees-Jones, 2018). See
section 5 for a discussion on the external validity of our experimental design.
3 Which?
is the largest consumer advocacy group in the UK. For more information see
https://www.which.co.uk/. Consumers who use the actual Amazon platform do not see Which? product ratings, and we do not display these ratings to participants in our experiment (nor do we display the Best Buy or
Don’t Buy classifications). Participants do, however, have access to the same type of information that is displayedon Amazon’s product pages, and can search online for more information if they wish.
4 We conducted an assessment of the veracity of reviews when determining whether to include them inthe experiment. More specifically, we classified a review as genuine if there was a strong positive correlation
between the score from the product testing conducted by Which? and the content of the review. Reviews weredeemed to be fake if there was no correlation or a negative correlation between the aforementioned factors, andif they exhibited typical signs of fakery. Of course, one can never be certain if a review that we collect online isfake or not, or if it is simply wrong or uninformed. Thus, our experiment estimates the effect of adding positive
inaccurate reviews that are very likely to be fake. Ultimately, however, it does not really matter whether thereviews are truly fake or not, as long as they are similar to a fake review.

2

mistakenly reviewed the wrong product. Fake reviews with these characteristics have beenfound across several platforms (Which?, 2020c), and Dwoskin and Timberg (2018) showsthat there is great variation in the sophistication in the language of fake reviews. Finally, we
make use of a platform endorsement. Retail platforms, such as Amazon, often use algorithms
to endorse products with good ratings (other platforms use the same approach to endorse
workers). Some studies suggest that these algorithms may be beneficial (Adomavicius andTuzhilin, 2005; Panniello et al., 2014; Horton, 2017). For example, Gao et al. (2015) andNosko and Tadelis (2015) show that promoting higher quality sellers leads to greater welfare,and Barach et al. (2020) find that cheap talk signals used by online platforms, such as “recommended”, are effective in steering demand. We, however, wanted to determine if this typeof endorsement might be welfare-reducing if applied to a low-quality product, which has
been shown to occur in cases where a low-quality product receives many fake, overly positive
reviews (Which?, 2020b).
We randomized consumers into six different groups. We then compared purchasing
decisions across these groups. Group 1—the control condition—was only shown informative
reviews (i.e., reviews where the assessments are strongly and positively correlated with thequality of the product). Group 2 was shown the same reviews as Group 1, but the Don’t Buy
product has inflated star ratings, distributed in a way that is typical for products with fakereviews (i.e., mostly 5-star ratings). Group 3 was shown the same information as Group 2, butwith the addition of fake and overly positive written reviews on the product page of the Don’t
Buy product. Group 4 was shown similar information to Group 3, with the main difference
being that the fake written reviews are more easily identifiable as being fake (e.g., stating
the reviewer was paid for writing the review). Group 5 was shown the same information asGroup 3, with the addition of a platform endorsement for the low-quality product.5
Our final treatment group (Group 6) was exposed to the same information as Group5, but with the addition of an educational intervention that warned participants that somereviews were false, and it provided them with some tips on spotting fake reviews. The in5 Groups 3-6 saw slightly modified versions of fake written reviews that actually existed for these productson the Amazon platform at the time of the experiment. We made minor changes to the written reviews toensure consistency across product categories. The star ratings that participants were shown in Groups 2-6 were,
however, inflated and exceeded the true star ratings found on Amazon. Those in Group 1 were not exposed toany fake material, and they were only shown the ’true’ Amazon star rating as well as genuine written reviews
that we found on the Amazon platform. In other words, when designing the experiment, we tried to reduce
deception to the greatest extent possible while still being able to answer our research questions. It is also
important to note that we never told participants that the product pages and reviews that they were shown werereal. We debriefed everyone at the end of the survey experiment on its purpose, and everybody who participatedin the experiment was better off financially. They all received a flat fee for participation, and they had a chanceof receiving a product of their choice for free (the fake reviews could only influence their choice of the freeproduct). In conclusion, we believe that our research design is appropriate, as it helps us answer several key
research questions while directly making our participants better off.

3

tervention appeared at the top of the search page and did not target a particular product,
meaning that it should be straightforward to implement in other settings. Moreover, thisintervention may have desirable properties as it tries to reduce the adverse effects of fake reviews without needing to remove those reviews. Glazer et al. (2020) provides a theoreticalargument for why it may be welfare-decreasing to remove fake reviews, and Yasui (2021) hypothesizes that an educational approach may be better than trying to remove them. Such an
educational approach has also been recommended as a way of countering fake news (Lazeret al., 2018), but we have no prior experimental evidence on whether this approach changes
behavior with respect to fake reviews.6
To translate differences in purchasing decisions across groups into estimates of changes
in economic welfare, we conducted a companion survey with a representative sample of 1,000
UK adults. In the survey, we presented participants with full and accurate information about
the products available in the main experiment, and we elicited their willingness to pay (WTP)
for these products using the incentive-compatible Becker–DeGroot–Marschak mechanism.
We used the WTP estimates to create a new outcome variable for our experimental analysis–
the dollar value of the decisions participants made.7 In other words, if a participant picks theBest Buy headphones in the main experiment and if we predict that they would value theseheadphones at $30 (if they had full information), the participant is recorded as having gained$30 in welfare (because their price is zero). If fake reviews induce this participant to insteadchoose the Don’t Buy headphones—which for the sake of argument they would value at $10 ifthey had full information—we conclude that fake reviews reduced this consumer’s welfare by$20 (i.e., $30-$10). This calculation captures changes in welfare in our setting as participants
have to choose a product, and the price of all products is zero (in reality, all products usedin the respective product categories have around the same sale price).8 To the best of ourknowledge, this is the first attempt to estimate the welfare loss from fake reviews.
Our empirical analysis yields four main findings. First, we find that fake reviews influence consumer choice in the way predicted by our model. More specifically, the inflated star
ratings make consumers 5.8 percentage points (standard error of 1.2) more likely to choose
the Don’t Buy product (a 55% increase relative to Group 1’s demand for the Don’t Buy product,
the condition with no fake reviews). We found that a one-star increase in the rating of a Don’t
Buy product increases demand for that product by 38%, and that the elasticity of demand

6 There is ongoing research that tries to understand the effect of warnings related to partisan political infor-

mation that is fake or misleading (e.g., Chan et al. (2017); Pennycook et al. (2020); Andı and Akesson (2020)).
7 For our welfare calculations, pound values are converted to dollars using the exchange rate on 1 December
2021 (£1 = $1.33)
8 While it is common for searches to return several products at or near the same price point on Amazon, oursetting is somewhat special as all products have exactly the same price.

4with respect to stars for the low quality product is 1.21.9 Adding the fake written reviews(i.e., comparing Group 2 to Group 3) further increases the share of participants that purchase
Don’t Buy products by around 6 percentage points (standard error of 1.3), and reduces theshare of consumers that purchase Best Buy products by around 3.6 percentage points (standard error of 1.5). Adding the ‘sloppy’ fake reviews has similar effects on consumption.10
All of these results are robust to adjusting the standard errors based on multiple hypothesis
testing.
To understand the mechanisms underlying the effects we observe, we elicited the following information: how confident participants were that they made the right decision, theirperceived ease of making an informed choice, what information they based their choice on,whether they read (and trusted) the customer reviews, and how much time they spent on the
shopping task. We find that those who were allocated to the sloppy fake review group (Group4) were 3.8 percentage points less likely to say that they read the reviews (p = 0.007) thanthose in the control group. We do find evidence, with lower power, suggesting that those allocated to the inflated stars group (Group 2) were 3.1 percentage points (p = 0.069) less likely
to say that they based their choice on the product description and were 2.7 percentage points
less likely to say that they read the written reviews (p = 0.055).11
Second, we estimate that being exposed to both inflated star ratings and fake text reviews(comparing Group 1 to Group 3) translates into a welfare loss of around $.12 per $1 that
consumers spend on the platform. A one-star increase in the rating of the Don’t Buy products
reduces consumer surplus by $.03 per dollar spent on the platform. The presence of fake text
reviews (comparing Groups 2 and 3) reduces consumer surplus by $.07 per dollar spent on
the platform. We also find that fake reviews have negative effects for those who choose thedash-cams, cordless vacuum cleaners, and headphone product categories, suggesting that our
results may generalize to many different products. However, fake reviews have slightly larger
9 We obtain the elasticity of demand with respect to stars by computing the average percent increase in stars

that individuals in Group 2 are exposed to and by comparing that to the average percent change in demandthat we observe relative to Group 1. Our findings and effect sizes corroborate previous work that found thathigher star ratings increase demand (Jin and Leslie, 2003; Anderson and Magruder, 2012; Luca and Zervas,
2016; Lewis and Zervas, 2019), and that employee ratings influence the hiring of workers (Pallais, 2014; Stanton
and Thomas, 2016; Benson et al., 2020).
10 A concern one might have is that individuals think that all customer reviews are accurate in the experiment,
while they actually think that a lower proportion of reviews are accurate in the “real world”. If this is the case,
we may overestimate the effects of fake reviews on consumption, as consumers would be overly gullible in our
experimental setting. However, we asked participants whether they trust reviews on Amazon and whether they
trusted the reviews in the experiment, and we find a striking similarity in their answers to both questions,suggesting that this is unlikely to be a concern. We address other concerns related to external validity in section
5.
11 More specifically, if those who did not purchase a Don’t Buy product ended up not viewing the Don’t Buy
product page, they would not have been exposed to any fake reviews (with the exception of the inflated star
ratings that appeared on the search page).

5effects for cordless vacuum cleaners (the most expensive product), suggesting that people are
not better at identifying fake reviews as the cost of misinterpreting the fake review increases.
Taken together, these results imply that fake reviews influence consumer demand and could
cause meaningful consumer harm.
Third, we find that fake reviews have heterogeneous effects. More specifically, those
who do not trust reviews are less likely to be influenced by them—which is consistent withthe predictions of our model. For example, the inflated star ratings do not appear to haveeconomically meaningful effects on these participants, and the fake written reviews (Group 3)
only increase the share that purchase Don’t Buy products by 4 percentage points (i.e., the effectof fake written reviews is 50% lower for this group relative to the general population). The
loss in consumer surplus from fake star and written reviews (i.e., comparing Groups 1 and 3)
for this segment is $0.08 per $1 spent on the platform (for comparison, it is $0.13 for those
who trust reviews). These results hold even when controlling for a range of demographic
variables and the frequency with which participants shop online.
In addition, we find that fake reviews have larger negative effects for those who use
Amazon (and e-commerce) more frequently. In other words, experience does not seem totranslate into sophistication when it comes to fake reviews. Those who are more experienced
also spend less time when completing the shopping task. This suggests that the problems associated with fake reviews will not solve themselves as consumers become more accustomed
to shopping online. Rather, the opposite is more likely to happen, which emphasizes the needfor initiatives aimed at curbing the presence—and effects—of fake reviews. This finding runs
contrary to the literature on fake news, which shows that less experienced users are more
likely to believe, share, and disseminate false information (see, e.g., Guess et al. (2020)). It
is also a surprising finding in light of work by List (2003), which suggests that consumers
become better at navigating the complexities of markets the more they engage with them.
Fourth, we show that the educational intervention reduces, but does not eliminate, thenegative effects of the fake reviews. More specifically, the intervention reduces the sharethat buy the Don’t Buy product by 23%, increases the share that buy a mediocre product by
12%, and does not influence the share that buy the Best Buy product. Further, on average,
being exposed to the intervention increases consumer welfare by $4, which means that it
reduces the negative impacts of fake reviews by around 44%. In other words, the intervention
increases consumer welfare by $0.06 per dollar spent on the platform.12 We also find thatthe educational warning is effective for all types of consumers (there are, for example, noheterogeneous differences by income and education). The lack of heterogeneous effects is
12 This treatment effect is approximately equivalent to the difference between those who do and do not trust

the reviews. In other words, the intervention has roughly the same (positive) effect on consumer welfare as goingfrom being trusting of reviews to being skeptical about their informativeness.

6

encouraging, as past research in economics suggests that educational warnings work best for
highly-educated people, meaning that they may exacerbate economic inequalities (Hastings
et al., 2013).13 Our findings indicate that there may be simple steps that retailers can take todesign their online platform to curb some of the adverse effects of fake reviews and improve
consumer welfare. These findings are in line with research suggesting that the design of theonline platform can affect search and demand (even in the absence of physical search costs)(Dinerstein et al., 2018).
In an attempt to understand why the intervention changed behavior, we also examine
whether it influenced the strategies that participants adopted, their beliefs, and their attitudes. Our analysis shows that those in the education intervention condition (Group 6) were
4 percentage points less likely to base their product choice on star ratings (p = 0.019) and thatthey were 4 percentage points more likely to base their choice on the brand of the product(p = 0.017) than those in Group 5. These effects are consistent with the recommendations presented in the intervention, which amongst other things emphasized that consumers should“Inspect the comments (don’t rely on star ratings alone)”.
This paper contributes to the literature on fake reviews and search behavior in five main
ways.14 First, our paper reinforces the findings of a directly related quasi-experimental study
by He et al. (2021), which suggests that fake reviews are likely to harm consumers. That
study finds that fake reviews are primarily applied to low-quality products and that theirapplication is associated with increases in sales. Our results are also in line with those of
Zhuang et al. (2018), which finds that adding fake positive reviews to the description of ahotel may increase consumers’ stated intention to stay at that hotel.
Second, we contribute to the theoretical literature on fake reviews by presenting andtesting a simple Bayesian model of consumer behavior. Past studies in this area have primarily
focused on platform design, the design of optimal review systems, and the design of optimalsocial learning (see, for example, Glazer et al. (2020), Luca (2017), Donaker et al. (2019), Daiet al. (2018), Yasui (2021), Che and Hörner (2018), and Banerjee et al. (2021)).15
Third, to the best of our knowledge, this study is the first to causally demonstrate how to
reduce the negative effects of fake reviews. The intervention that we test bears some resem13 It is important to remember that the intervention reduces the effects of fake reviews, even though the prod-

uct with the fake reviews has been endorsed by the very platform that is displaying the intervention.
14 Our paper is also related to the broader literature on customer reviews and advertising, which includes
studies on the number of reviews and review inflation (Aral, 2014; Filippas et al., 2018; Fradkin and Holtz,
2021), customer reviews and discrimination (Botelho and Gertsberg, 2021; Cui et al., 2020), and how companies
respond to reviews (Chevalier et al., 2018). Our paper is also related to List (2006) who finds that private markets
can solve the lemons problem through third-party verification on the quality of the product and reviews.
15 Our results also have relevance to the theoretical literature on mis-specified information and false positives (Libgober, 2019; Frick et al., 2021), in that we see whether people internalize the possibility of biased
information when there is an incentive for bias by sellers.

7

blance to low-cost digital interventions used to tackle the negative effects of fake news (see,for example, Andı and Akesson (2020); Pennycook et al. (2020)). Moreover, the intervention
provides an easy-to-implement complement to efforts aimed at detecting and removing fakereviews (see Zhang et al. (2016) and Yao et al. (2017) for examples of how to improve thedetection of fake reviews).16 Our paper differs from previous literature in that we show that
our educational intervention works equally for all types of people–young and old, rich andpoor, and experienced and inexperienced.
Fourth, we find that it may be in the short-term interest of firms to produce fake reviews
to increase expected profits, supporting previous research showing the reasons why firms
might engage in misleading marketing (Mayzlin, 2006; Bordalo et al., 2015; Piccolo et al.,2018; Aköz et al., 2020; Yasui, 2021; Knapp et al., 2021). This finding holds particularly true
as we find that fake reviews have greater effects on demand for those who shop on online
platforms, like Amazon, more frequently.
Fifth, our study relates to the broader literature on how online platforms influence consumer search and demand. Some studies have used structural and experimental models toestimate how changes in the design of online platforms affect consumer search and demand(Dinerstein et al., 2018; Ursu, 2018; Hodgson and Lewis, 2020; Gardete and Hunter, 2020;
Sahni and Nair, 2020; Lam, 2021). Our paper is consistent with these studies as it also showsthat small changes in platform design can lead to meaningful changes in consumer demand.
The remainder of this paper is structured as follows. Section 2 presents a formal model
illustrating how fake reviews could influence consumer decision-making. Section 3 outlines
the experimental design. Section 4 discusses the empirical analysis and section 5 discusses
the external validity of our results. Finally, Section 6 concludes and reviews areas for future
research.

2

A model of how fake reviews influence consumers’ assessment of product quality

In this section, we provide a simple model of the effect of fake reviews on the beliefs of a
Bayesian consumer. We show how fake reviews could positively affect a consumer’s assessment of product quality, how greater uncertainty about product quality could increase theimpact of a fake review, and how skepticism regarding the genuineness of a review will re16 Ananthakrishnan et al. (2020) conduct an experiment (n = 238) and find that restaurants with fake reviews

that have been labeled as being fake are less likely to be chosen than restaurants without any fake reviews.
However, they do not compare the ’labeling’ intervention to a control group with unlabeled fake reviews, so it
is impossible to assess the efficacy of the intervention.

8

duce its impact on consumer behavior. We abstract from many of the details of actual online
reviews for the sake of clarity. For example, the consumer in our model simply views a binary
review X whose content may be either positive (X = 1) or negative (X = 0). Nonetheless, manyof the key results of our model can be generalized to more complex environments. Moreover,as we are primarily interested in explaining how consumers may be affected by fake reviews
when making a one-off purchase, we do not provide a full general equilibrium analysis, as in
Glazer et al. (2020), nor a dynamic analysis of learning, as in Acemoglu et al. (2019).17
We consider a decision-maker (DM) who is initially uncertain about the quality of a
product. We will measure the product’s quality using the proportion θ ∈ [0, 1] of genuine
reviewers who would give the product a good rating. In other words, θ is the probability
that a (genuine) review would be positive if such a review were randomly sampled from the
population of genuine reviewers.
We formalize the DM’s initial uncertainty about θ using a prior p : [0, 1] → R+ . Importantly, we make almost no assumptions about the shape of the DM’s prior, requiring onlythat p(θ) > 0 for all θ ∈ [0, 1] (i.e., the prior has full support). We also assume that the DM isprincipally interested in the expected value of θ (which provides a natural summary statistic
for their beliefs about product quality).
The DM believes the reviews are generated in the following way. With probability q ∈(0, 1), the review is genuine, in which case it is positive with probability θ. Otherwise, thereview is fake, in which case it is definitely positive about the product.18 Thus, q is theDM’s subjective probability that the review that they see is genuine. Furthermore, fixing anyθ ∈ [0, 1], the DM believes that there is a (prior) probability qθ + 1 − q that the review they will
see is positive.
We begin by examining whether fake reviews ‘work’. More formally, given an arbitraryprior p and subjective probability q, we want to know whether viewing a positive review(which the DM knows may be fake) increases their expected value of θ. The next proposition
verifies that this is indeed the case.
Proposition 1. E[θ|X = 1] > E[θ].
Proposition 1 is intuitive. When the DM receives a positive review, there are two possible
explanations: (1) the review is fake; or (2) the review is genuine and happens to be positive.
17 In a dynamic environment, it is possible that consumers learn over time, eventually becoming better atdistinguishing between fake and genuine reviews. However, as can be seen in our heterogeneity analysis in
Section 4.2, it does not seem like consumers become better at distinguishing between fake and real reviews overtime. Indeed, the opposite seems more likely.
18 In this paper, we focus on fake reviews that are positive. Nonetheless, this type of model can be expanded
to analyze the effects of fake reviews that are negative.

9

In the first case, the DM has learned nothing: fake reviews are always positive, so the fact thatthe fake review is positive cannot provide novel information. In the second case, however, theDM has learned something: the fact that a genuine review is positive suggests a high θ. Thus,
as long as the DM thinks there is some chance that a review is genuine, no matter how small,a fake review will increase her assessment of the product’s quality (which presumably also
increases the likelihood that she purchases the product).
While we have proven Proposition 1 in the simplest of contexts, the basic idea extends tomore complex environments. For example, suppose that the DM observes not one but n ≥ 1
positive (but fake) reviews. Assuming that the reviews are ‘exchangeable’ (e.g., because they
are i.i.d.), it is equivalent to consider the DM updating her prior n times, once for each of thereviews. But then one can apply the logic of Proposition 1 iteratively, allowing one to see thatthe total effect of the reviews is increasing in n.19 For example, while one fake (and positive)
review inflates the DM’s expectation of θ, two fake (positive) reviews do so by even more.
We thus conclude that fake reviews ‘work’ even if the DM believes that they are very
likely to be fake (q ≈ 0). It is natural to think, however, that skepticism about the honesty ofreviews should attenuate their effect. In other words, we might think that the lower the DM
deems q, the smaller the effect that fake reviews have. The next proposition verifies that thisis also the case.
Proposition 2. The effect of a fake review E[θ|X = 1] − E[θ] is strictly increasing in both q andVar(θ).
Proposition 2 is also intuitive. The higher q, the more likely the DM is to think that thereview is genuine. But the possibility that the review is genuine is precisely the reason why
it contains useful information. Given this, it is not surprising that the effect of the review
should be larger the less likely the DM is to think that it is fake.
Proposition 2 also shows that the effect of fake reviews is increasing in the varianceof their prior. One way of understanding this is to view the variance as a measure of theDM’s initial uncertainty (as in, e.g., Augenblick and Rabin (2019)). On this interpretation,
Proposition 2 tells us the effect of fake reviews is larger on consumers who are initially more
uncertain about the quality of the product—these are, after all, the consumers who havethe most to learn from the review. We provide proofs in Appendix A and an illustrative
parametric example of our model in Appendix B.

19 To update in light of the kth review, one needs to interpret the ‘prior’ as the posterior conditional on theprevious k − 1 reviews.

103

Experimental design

The experiment was conducted in February 2020 and took place within an online survey
coded using Qualtrics. We recruited a nationally representative sample of 10,000 UK adults
to take part in the experiment. On average, the survey took 11 minutes to complete, andparticipants were paid £2 in exchange for their participation. Some randomly chosen participants also were sent the product that they choose. The sample was recruited via the panel
provider Dynata, and respondents could complete the survey via desktop or mobile devices.
Participants began by responding to questions about their socio-demographic characteristics and online shopping behavior, and then completed a shopping task in an environment
resembling the Amazon platform (please see the Appendix F for a full list of survey questions). After completing the shopping task, we asked participants questions about whether
they searched for more information online when completing the task, whether they spent asmuch time and effort when choosing a product as they would have when normally shopping
online, in addition to some questions about how they chose a product and their beliefs. We
randomly allocated one sixth of the sample to a group that completed a version of the shopping task that did not include any fake reviews (Group 1). The remainder of the sample wasallocated to one of five treatment groups, and these participants were exposed to fake reviews
when completing the task (Groups 2-6). Those in Group 6 were also shown an educational
intervention. The shopping task, and the randomization, are summarized in Figure 1 and theexperimental conditions are described in Appendix C.
We also conducted a companion survey with a nationally representative sample of UK
residents (n = 1,000) to measure consumers’ willingness to pay for the products used in theexperiment. Participants in this survey were provided with ‘full information’ about the products, and we then elicited their willingness to pay in an incentive-compatible way (using a
BDM mechanism). This survey was also coded using Qualtrics (please see the Appendix G
the full survey used to elicit participants’ willingness to pay).
In the remainder of this section, we describe the shopping task that those in Group 1(the condition without fake reviews) completed, explain how the task varied for those in thetreatment conditions, and discuss our data.20 The section concludes with an overview of themethodology used in the willingness to pay survey.

20 We refer to those allocated to Group 1 as “Group 1” throughout the paper for sake of simplicity.

11

Figure 1: Summary of the experimental design

Notes. In this figure, we present the randomization and steps involved in the main experiment.

12

3.1

Experimental groups

3.1.1

Group 1: No fake reviews

The shopping task began with participants choosing among three product categories: headphones, dash-cams, and cordless vacuum cleaners. These categories were selected because
Which? had documented the use of fake reviews for low quality products in these categories.
Once participants had chosen a product category, they were shown a search page that included five products from said category—i.e., their consideration set. Participants were thenasked to select the product that they preferred in that set and were told that they would beentered into a lottery to win the product of their choice. If they won, we would ship it to theirhome once they completed the survey.
Participants could gather information about the products by looking at the search page,
as well as by clicking on and viewing the product pages for the five respective products in theconsideration set. Figure 2 presents an example of what the search page looked like.21
Participants did not have to view all the product pages. They did, however, need tonavigate to a product page to select a product and complete the survey. The product pages
included various types of information, such as seven written reviews, a star rating, and a
product description. The ratings and written reviews were positively correlated with the true
quality of the products.22 Please see Figure 3 for an example of a shortened product page (thefull page can be seen in Figure A2 in the Appendix).

21 One drawback of our design is that we are unable to study if fake reviews and the educational intervention

influence whether consumers purchase anything at all. While there are many situations where individuals havecommitted to buying something (i.e., a situation like the one replicated in the experiment), it would nonethelessbe interesting to conduct future experiments that allow for an “outside option”.
22 The reviews displayed in the experiment were actual user reviews with real star ratings copied from theproduct pages on Amazon (these were collected in early February 2020). We only included written reviews thatappeared to be real (i.e., reviews that did not exhibit clear signs of being fake) and that aligned with Which?’s
product ratings. The star ratings for these products were also positively correlated with the Which? productratings.

13

Figure 2: Example of the search page

Notes. In this figure, we present an example of the search page shown to participants.

14

Figure 3: Example of a product page

Notes. In this figure, we present an example of a product page shown to participants, which
they could navigate to from the search page.

15

The five products that were displayed on the search page had the same price in theexperiment.23 More specifically, the headphones were listed at £25, the dash-cams listed
at £50, and the cordless vacuum cleaners all listed at £150 (or roughly $34, $68, $204 in
dollar terms).24 Further, within each product category, we included one product that Which?
had classified as a Best Buy and one product that they had classified as a Don’t Buy. The
three remaining products always had product ratings in between the Best Buy and Don’t Buy
products. Participants were not shown any information about the Which? reviews or scoresfor any of the products, but they could have searched for this information online.25 Please
see Tables A17-A19 in the Appendix for a list of all products and product characteristics.
3.1.2

Group 2: Inflated star ratings

Group 2 was asked to complete the same shopping task as Group 1 (the condition with no
fake reviews), with one main difference: the Don’t Buy product had an inflated number offive star reviews, bringing its average rating to 4.8 out of 5 (an average increase of 1.43 stars).
Those in Group 2 were shown artificially inflated star ratings for these products, and thosein Group 1 saw the actual star ratings. We altered the distribution of the ratings for Group
2 by increasing the share of reviews with 5 stars and reducing the share of reviews withlow ratings. More specifically, the Don’t Buy dash-cam had a rating of 3.3 in Group 1, theheadphones had a rating of 2.9, and the cordless vacuum cleaner had a rating of 3.8. All
Don’t Buy products had a rating of 4.8 in Group 2. In addition, the Don’t Buy product was
moved higher up in the search ranking, as this was sorted by star rating. Figure 4 compares
the star ratings of the Don’t Buy dash-cam between Group 1 and Group 2.

23 The five products within each category were available at or close to the same price point online, which

respondents could have discovered if they searched for the product names.
24 Using the exchange rate between GBP and USD on 7 October 2021.
25 Which? conducts rigorous testing, rates products, and publishes their recommendations and warnings ontheir website (Which?, 2020d). The very best products in their respective categories are awarded Best Buy status.
Their tests are completely independent and based on benchmarks determined by impartial experts. Which?
provides a warning about low-quality products by labeling them as Don’t Buy products. Typically, the Don’t Buylabel is used for products with an overall test score of less than 40% or 45%, or when serious health and safetyissues are detected by their tests.

16

Figure 4: Star ratings of the Don’t Buy dash-cam in Group 1 and Group 2

Notes. In this figure, we present the star ratings of the Don’t Buy dash-cam product in two experimentalgroups. Those in Group 1 (the condition with no fake reviews) were shown the star ratings on the lefthand-side, and those in Group 2 (the inflated star ratings group) were shown the star ratings on the righthand-side. The star ratings shown to Group 2 exhibit a distribution that could be suspicious, with a verylarge percentage of 5-star ratings and a very low percentage of 1-star to 3-star ratings.

Inflating the star ratings of products is among the most common fake review strategies,
and it has been documented in several instances (Which?, 2020c; Hu et al., 2011b,a). While
fake reviews could be applied to either high-quality or low-quality items, empirical evidenceshows that the degree of fakery typically increases as product quality decreases (i.e., the sellers of low-quality products seem to utilize fake reviews more than others) (Hu et al., 2011b).
Intuitively, this makes sense as those selling high-quality products should have the smallest
incentives to produce fake reviews.
3.1.3

Group 3: Fake review text

Group 3 was exposed to the same information as Group 2, with the addition of fake written
customer reviews displayed on the Don’t Buy product pages. More specifically, we replaced
the written reviews that were displayed to Groups 1 and 2 with reviews that were highly favorable toward the Don’t Buy product (as in Group 1, participants in Group 3 were also shown
7 written reviews per product). To avoid simply recreating the effects of positive reviews moregenerally, we deliberately included reviews with elements that might raise suspicion and thatare common in actual fake reviews, including the following: exaggerated language, repetitivephrases and formatting, fewer ‘verified purchase’ reviews, several reviews left on the samedate, the same reviewer leaving two reviews, a review left by someone called ‘PlatformCustomer’. We also included one negative review contradicting the otherwise overwhelmingly17positive feedback (this is common for products with many fake reviews) (Which?, 2020d).26
Figure 5 presents an example of the fake review text used for Group 3.27
Figure 5: Example of fake written reviews in Group 3

Notes. In this figure, we present an example of two fake customer reviews that were shown
to Group 3. These fake reviews contain various suspicious elements. More specifically, both
reviews were left on the same date and contain the repetitive use of the exclamation point
as well as improper and inconsistent use of punctuation and capitalization. Additionally, thesecond review was left by someone called ‘PlatformCustomer’.

26 Ott et al. (2011) and Anderson and Simester (2014) investigate reviews that cannot be real, finding thatthey are less descriptive of the real product than real reviews. Hu et al. (2012) also shows that fake reviews areless exact in their description of the products and are generally more positive (by textual sentiment).
27 We did not include any other text, like a QA section, which has shown to be important for people’s search(Banerjee et al., 2021).

18

3.1.4

Group 4: Highly suspicious fake review text

The shopping task for Group 4 was the same as the shopping task for Group 3, with thedifference being that the written fake reviews were even more suspicious and exaggerated.
More specifically, the written reviews for the Don’t Buy products included: two five-star rated
face cream reviews (even though the products were not face creams); positive reviews where
the reviewer admitted to being offered money to leave positive reviews; and negative reviewsclaiming that the reviewer have been offered incentives to change their reviews. Fake reviews
with these characteristics have been found across platforms (Which?, 2020c), and Dwoskin
and Timberg (2018) shows that there is great variation in the sophistication of fake reviews.
Figure 6 provides an example of the highly suspicious written fake review treatment. All of
the written Don’t Buy product reviews for the respective treatment conditions and productcategories are displayed in Figures A7-A15.
3.1.5

Group 5: Platform endorsement

Group 5 was asked to complete the same shopping task as Group 3, but was also exposed to a‘platform endorsement’. Some online review platforms provide endorsement labels for products or services that receive particularly good customer feedback, providing a potential routefor those manipulating reviews to extend the influence of that manipulation. To investigate
the impact of platform endorsements on consumer decision-making, we add an “Amazon’s
Choice”-style treatment for Group 5. While such endorsements are not an element of fake reviews per se, some of Which?’s investigative research has found instances where fake reviews
had contributed to products receiving a platform endorsement (Which?, 2020a). The endorsement logos were placed next to the product on the search page and on the product page aswell (please see Figures A3 and A4 for examples of how the endorsements were placed).
3.1.6

Group 6: Educational intervention

Group 6 received the same information as Group 5, but were also shown an educational
intervention in the form of a warning banner. The banner, displayed in Figure 7, contained awarning about the possible presence of fake reviews, and offers advice to consumers on how
to avoid being influenced by these reviews. It was added to the top of the search and productpages. The advice presented on the banner was taken from Which?’s guide on identifying fakereviews. Please see Figures A5 and A6 for examples of how the intervention was displayedon search and product pages.

19Figure 6: Example of highly suspicious fake written reviews in Group 4Notes. In this figure, we present an example of three fake written customer reviews that were shown
to Group 4. These fake reviews contain some highly suspicious elements. More specifically, the firstreviewer mentioned that he left the review as requested by the seller in exchange for a free gift while
the second review is completely irrelevant as it is about face cream rather than any of the products
included in the experiment. Additionally, the third review revealed that the reviewer was asked by
the seller to change her negative review to a positive one.

20

Figure 7: Details of the warning bannerNotes. In this figure, we display the warning banner that was included at the top of the search and productpages for those in Group 6.

3.2

Data and outcomes of interest

All data for this experiment are collected through the online survey experiment and the companion willingness to pay survey. The main outcome of interest in this experiment is the
product choice that the participant made. More specifically, we are interested in whether
they chose a Best Buy, Don’t Buy, or mediocre product. In addition to the outcomes of interest, we collected demographic data (e.g., gender, age, education, income, and the region
in the UK that they live in). We also collected data on participants’ online shopping habits,
whether they trust reviews on Amazon, the share of reviews on Amazon they think are fake,and whether they think it is easy to spot fake reviews on Amazon. These variables are used
to conduct heterogeneity analyses that test the theory outlined in Section 2. Moreover, wecollected data on whether participants searched for more information online; whether they
spend as much time when completing the task as they would have when normally shopping
online; how confident they were in their decision, their perceived ease of making an informed
choice; what information they based their choice on; whether they read customer reviews, andhow much time they spent on the shopping task. Please see Appendix F for a full list of survey questions. The experimental groups are balanced on observables. We present balancetables and summary statistics for all variables in Appendix D.

21

3.3

Sample characteristics and statistics related to survey completion

The survey included 10,000 participants recruited via a reputable online survey panel (Dynata). The sample is representative of the UK population in terms of age, gender, and regional distribution of the population. Table A3 presents the demographic characteristics ofthe sample, and Table A4 presents key statistics about survey completion: respondents spentan average of 11 minutes on the entire survey; 51% answered the survey on a mobile phone;their average age is 43 years old; their average income is around £29,500 per year; and 51%
are female. Most participants were experienced with online shopping: 97% report that theyuse Amazon, and over 80% shop online at least once a month. Moreover, 60% of participants
claim that they spent as much time when completing the task as they would have when normally shopping online, and 18% said that they searched for more information online whencompleting the task (we do not find differences in these variables by treatment condition).

3.4

Measuring consumers’ willingness to pay

To understand the welfare implications of fake reviews, we also conducted a companion survey where we measure participants’ willingness to pay for the products included in the experiment.
3.4.1

Methodology

We recruited a nationally representative sample of 1,000 UK adults to take part in the survey.
The survey was coded using Qualtrics and participants were recruited via Prolific. The survey
began by presenting users with instructions (see appendix for full instructions) explaining
how we would be eliciting their willingness to pay and that it was in their best interest toreport the true values.
Before eliciting their willingness to pay, participants were required to complete a shortpractice exercise and answer a few comprehension questions to make sure that they understood the instructions. In addition to ensuring that participants fully understood the exercise,the comprehension test (along with other variables such as the time spent reading instructions) allowed us to conduct robustness analyses that examine how the willingness to paychanges when those who do not understand the instructions are excluded.
We then proceeded with the survey by asking the participants to choose a product category, like in the main experiment. Based on the chosen product category, we showed theparticipants the five products included in the main experiment (in a random order) in a22

table. The table was designed to look like one typically seen on product comparison websites, which listed the product names, pictures of the products, and the following characteristics: brands, colors, weights or dimensions, Which? recommendations and test scores, andproduct-specific features.
We elicited participants’ willingness to pay using the strategy proof Becker–DeGroot–
Marschak (Becker et al., 1964) method. To ensure that participants truthfully reported their
willingness to pay for all products, we explained that we would randomly pick one productand a random price point for that product, and then implement it (if they won the lottery).
Those who won the lottery: 1) received the product and the difference between the lottery
amount and the randomly chosen price point if their willingness to pay exceeded the randomly chosen price, or 2) the lottery amount if their willingness to pay was lower than the
randomly chosen price.
Finally, after eliciting the willingness to pay, we asked the same set of demographic
and background questions as in the main experiment (e.g., gender, age, income, education,
where participants live, how often participants shop online, and how often participants use
Amazon).
The data collected from this companion survey allows us to verify whether the Best Buy
product indeed dominates that Don’t Buy product when consumers have full information.28
It also provides us with an estimate of the magnitude of the benefit when participants switchfrom a Don’t Buy to a Best Buy product.
Crucially, the data from this survey allow us to study the negative welfare impact offake reviews and positive welfare impact the intervention. We do this by taking the ‘full
information’ willingness-to-pay estimates from the survey and using these to create a new
outcome variable that we use when analyzing the data from the experiment—the amountthat participants would be willing to pay for the product that they chose if they had access tofull, objective information about all relevant product characteristics. We calculate the valueof this variable by conducting regressions that estimate the association between participants’characteristics and their willingness to pay for the respective products. We then use thesepredictive models to estimate participants’ willingness to pay for the product they selectedin the main experiment. We also use the simple average WTPs for the products in the survey,
rather than a predictive model, as a robustness check.
To provide an illustrative example, imagine that all high-income individuals in the survey valued the Best Buy headphone at $30. Furthermore, we find that a high-income individ28 We are, however, limited by the fact that the participants do not actually have the products in their hands

and so they do not have all information. However, this choice situation is very similar to buying on Amazon
where you cannot see and feel the product.

23

ual in the Group 1 in the experiment (no fake reviews) picked the Best Buy headphones. We
then conclude that their willingness to pay for the product they chose would have been $30
if they had access to full information. Now imagine that the same individual was placed in a
fake review condition, and instead chose the Don’t Buy headphones, which are assumed to be
worth $10 to that individual, based on our survey. We would then conclude that fake reviews
reduced this consumer’s welfare by $20 ($30-$10). This calculation assumes that the products
were the same price, which is the case in our experimental design.29
3.4.2

Survey analysis

Table 1 presents the average willingness to pay (WTP) for the products tested in the experiment. As predicted, we find that the Best Buy products clearly dominate the Don’t Buy products. Indeed, we also find that the Best Buy products dominate all mediocre products.
We conduct robustness analyses of participants’ willingness to pay (please see the appendix), which involves dropping those who did not comprehend the instructions or who
sped through the survey. These robustness checks do not meaningfully alter our results.
Finally, we conduct regressions predicting participants’ willingness to pay for the products in the survey, which we use to estimate participants’ willingness to pay in the main
experiment (see Tables A7-A9 in the appendix). As can be seen in the appendix, the demographic characteristics significantly predict participants’ WTP, and can thus be used when
conducting multiple imputations.

29 A limitation of our analysis is that the sample for the companion survey is not the same as the sample

for the main experiment. Nonetheless, we believe that our approach can provide a reasonable estimate of thepossible efficiency gains from interventions aimed at reducing the adverse effects of fake reviews because bothsurveys use a nationally representative sample. See the conclusion for a discussion of how these estimates might
be refined.

24

Table 1: Average WTP for all products
VariableMedianMean (£)

Std DevN

Headphones
Best Buy

26

25.78

8.67

354

Don’t Buy

6

6.49

6.96

354

Mediocre 1

14

15.63

9.63

354

Mediocre 2

14

15.37

8.33

354

Mediocre 3

18

17.20

9.25

354

Dash cams
Best Buy

59

53.67

19.27

288

Don’t Buy

3

14.01

17.08

288

Mediocre 1

27

31.94

19.63

288

Mediocre 2

35

35.79

18.94

288

Mediocre 3

43

39.04

17.30

288

Cordless vacuum cleaners
Best Buy129

131.88

54.18

356

Don’t Buy

9

36.38

45.64

356

Mediocre 1

81

83.04

52.78

356

Mediocre 2

129120.9661.30

356

Mediocre 3

81

83.45

53.44

356

Notes. In this table, we present summary statistics relating to participants’ willingness to pay for the fifteen products used in the main experiments (5 per
product category). As in the main experiment, participants were first asked to choose a product category,after which we elicited their willingness to pay for thefive products in their chosen category. Please see theappendix for the questions used to elicit participants’
WTP.

4

Results

In this section, we present our analysis of the experimental data. We begin by examining
the effects of fake reviews on participants’ consumption choices. We then conduct a heterogeneity analysis that reveals if fake reviews have differential impacts for different population
segments. Finally, we estimate the effect of the educational intervention on participant behavior. We conduct all analyses using linear probability models (LPM) and ordinary leastsquares (OLS). Three of our main outcomes are binary—whether participants chose a Don’t

25

Buy, Best Buy, or a mediocre product.30
Our fourth (and final) outcome of interest is continuous—participants’ willingness topay for the product that they chose when completing the shopping task. We used two methods of coding this outcome. The first method involves conducting regressions associating
participants’ willingness to pay with their demographic characteristics in the companion survey, and then using these demographic characteristics to impute participants’ predicted willingness to pay in the experiment. Using this method, the outcome of interest we use in our
analysis below is the predicted willingness to pay for the product that participants ended
up choosing, as this represents the value that participants obtained in the experiment (theproducts were free). The second method is similar, but instead involves imputing the average willingness to pay obtained in the companion survey (i.e., we do not predict the WTP ofindividual participants, and instead simply use the average WTP that we measured for the
product they chose–these values are listed in Table 1).

4.1

Fake reviews influence consumer behavior

We begin by evaluating the effects of fake reviews on the products selected by participants
and on consumer welfare. We do so by comparing the share of consumers in the respective
experimental conditions that chose Don’t Buy, Best Buy, and mediocre products, as well as
their WTP for the product they chose. Note that the relevant comparisons differ for eachgroup. Group 2 was shown the same information as Group 1, with the addition of inflated
star ratings. This means that Group 1 is the relevant comparison group for Group 2. However, Group 3 was shown the same information as Group 2, with the addition of fake written
reviews. This means that Group 2 is the relevant comparison group for Group 3 (if we wantto learn about the incremental effect of adding fake written reviews). We can, of course,
compare Group 3 to Group 1, but we then estimate the combined effect of both inflated star
ratings and fake written reviews on consumer choice. Finally, the relevant comparison groupfor Group 4 is also Group 2, and the relevant comparison group for Group 5 is Group 3 (seeFigure 1 for an overview of the relevant comparison groups for all conditions).
Table 2 shows that 10% of Group 1 (the condition with no fake reviews) chose the Don’t
Buy product (see the constant coefficient in column 1); in contrast 16% chose the Don’t Buy
product in Group 2 (adding that constant coefficient to the Group 2 coefficient in column1). In contrast, Group 2 is significantly less likely than Group 1 to purchase a mediocre
product, and is about as likely to purchase a Best Buy product. These findings demonstrate
30 Effect estimates are rounded to the nearest integer value when we report our results.

values throughout this paper because the experiment was conducted with UK participants.

26

We also use GBPTable 2: The effects of fake reviews
(1)
Chose
Don’t Buy
Group 20.058***(G1 + fake stars)(0.012)†††
Group 30.126***(G2 + fake reviews)(0.013)†††
Group 40.110***(G2 + sloppy reviews)(0.013)†††
Group 50.143***(G3 + endorsement)(0.013)†††
Constant
0.105***(0.008)Observations
8326
2
R
0.017

(2)
Chose
Mediocre-0.041**(0.017)††-0.074***(0.017)†††-0.067***(0.017)†††-0.106***(0.017)†††0.612***(0.012)
8326
0.005

(3)
Chose
Best Buy-0.016(0.016)(4)
WTP(predicted)-2.776(1.710)-0.052***(0.015)†††-0.043***(0.015)†††-0.036**(0.015)††0.283***(0.011)8326
0.002-7.156***(1.684)†††-6.184***(1.688)†††-7.165***(1.668)†††58.852***(1.220)
7423
0.004

(5)
WTP
(average)-2.931*(1.519)†-6.687***(1.494)†††-6.676***(1.484)†††-7.251***(1.478)†††57.137***(1.086)8326
0.004

Notes. In this table, we present the effects of being randomly assigned to Groups 2-5 on the share ofparticipant that chose Don’t Buy, Mediocre, and Best Buy products, as well as participants’ WTP for
the product they chose. The regressions are conducted using LPM and OLS. The omitted group ineach regression is Group 1 (the control group with no fake reviews). There are some missing values
in Column (4) as we are missing demographic data for some participants in the main experiment.
Standard errors in parentheses (* p<0.1 ** p<0.05 *** p<0.01). The significance levels of our coefficients do not change if we adjust for multiple hypothesis testing († p<0.1 †† p<0.05 † † † p<0.01
using the multiple hypothesis testing correction introduced by List et al. (2021)).

that inflated star ratings matter, even when applied to low-quality products. Tables A14-A16
in the Appendix present a heterogeneity analysis that shows that fake reviews matter for all
product categories (recall that participants could choose dash-cams, headphones, or cordlessvacuum clears), suggesting that the effects that we record are generalizable. We do, however,find evidence suggesting that fake reviews have a larger impact on demand and welfare in
the high-cost product category (cordless vacuum cleaners).31
Given that the average star rating of the low-quality product increased by 1.46 stars (a46% increase relative to Group 1), and that demand increased for this product by 58%, weestimate that the elasticity of demand for the low-quality product with regard to stars is 1.21
(i.e., 58/46). Moreover, assuming that star ratings have linear impacts on consumption, we
31 This result is consistent with Lewis and Zervas (2019) who find that ratings have a higher impact on de-mand for more expensive hotel rooms than for cheap hotel rooms. However, as only 1910 individuals picked
the dash-cam product category, we do not have sufficient power to detect small differences in this sub-group.
Nonetheless, our coefficient estimates are in the expected direction, and we find, for example, that those assigned
to Group 5 are 8 percentage points more likely to choose the Don’t Buy product.

27

can conclude that increasing the star rating by one star increases demand for the low-quality
product by 38%. This, in turn, translates into a welfare loss of $.03 per dollar spent on theplatform for every one-star increase in the rating of the low-quality product.
While we find that inflated star ratings influence consumer behavior, we find that fakewritten review have an even greater impact. Group 3 (fake written reviews) is 7 percentage points more likely to choose the Don’t Buy product than Group 2 (inflated star ratings).
We also find that highly suspicious fake reviews influence consumer decision-making, withGroup 4 being 5 percentage points more likely to choose the Don’t Buy product than Group 2(inflated star ratings). Furthermore, unlike the inflated star ratings, we find that the written
reviews make participants significantly less likely to purchase a Best Buy product, suggestingthat written fake reviews cause consumer harm.
Strikingly, we find that fake written reviews and inflated star ratings have a very large
combined effect on consumer decision-making. For example, Group 3 is 12.6 percentage
points more likely to purchase a Don’t Buy product than Group 1 (those who were shown no
fake reviews). This corresponds to a 120% increase in the share of participants that purchasea low-quality product. Group 3 is also 7.4 percentage points less likely to purchase a mediocre
product, and 5.2 percentage points less likely to purchase a Best Buy product. Taken together,
this translates into around a £7 loss in welfare per participant in that group.
Finally, Table 2 shows that the platform endorsement matters. More specifically, 14%of Group 5 purchased the Don’t Buy product, which is 2 percentage points more than Group
3. However, those in the platform endorsement group were also more likely than Group 3 topurchase a Best Buy product. Consequently, adding the endorsement only generated a modest
loss in consumer welfare (around £1).
To understand the mechanisms underlying these results, we elicited people’s confidence
in their purchase, the ease of making an informed choice, what information they based theirchoice on, and whether they read the customer reviews. We find that those who were allocatedto the sloppy fake review group (Group 4) were 3.8 percentage points less likely to say thatthey read the reviews (p = 0.007) than those in the control group. While there are no otherstatistically significant relationships between treatment assignment and these outcomes, we
do find evidence suggesting that those allocated to the inflated stars group (Group 2) were
3.1 percentage points (p = 0.069) less likely to say that they based their choice on the productdescription and were 2.7 percentage points less likely to say that they read the written reviews(p = 0.055).32 See Tables A18 and A19 for more information.
32 It is also possible that we failed to detect statistically significant effects on these variables due to a lack ofstatistical power. More specifically, if those who did not purchase a Don’t Buy product ended up not viewing
the Don’t Buy product page, they would not have been exposed to any fake reviews (with the exception of theinflated star ratings that appeared on the search page).

28

4.2

Not all consumers are equally gullible

Next, we test a part of Proposition 2 in Section 2, which states that the fake reviews have alower impact on consumers who think that these reviews are more likely to be fake. We do so
by examining whether the effects of fake reviews are smaller for those who say that they do
not trust reviews on Amazon.
Table 3 shows that those who trust and those who do not trust reviews in Group 1 (thecondition with no fake reviews) are roughly as likely to purchase the Don’t Buy product.
However, the table also reveals that fake reviews have a dramatically larger effect for those
who trust customer reviews. More specifically, those who do not trust reviews are not significantly influenced by inflated star ratings in this context. Furthermore, they are less susceptible to fake written reviews than their more trusting peers. Finally, those who do not trust
reviews are not significantly influenced by the platform endorsement, unlike those who trust
reviews.33
Next, we examine the extent to which experienced Amazon users are likely to fall for
fake reviews. The motivation for conducting this analysis is that past work in economics (see,e.g., List (2003)) shows that consumers often become more sophisticated the more they engage
in a market. As Table A12 shows, we find that fake reviews have a larger negative impact on
those who use the Amazon.co.uk platform more than once per month relative to those who
use it less often. This result is perhaps surprising, and suggests that more experience does
not translate into greater ’sophistication’ when it comes to fake reviews. This result may,in part, be explained by the fact that those who use Amazon more often spent significantly
less time when completing the shopping task (30 seconds, to be exact) than those who use
it less frequently. Moreover, those who use Amazon more frequently were also more likely
to say that they based their choice on star ratings, the number of reviews, and the content ofreviews—factors which were manipulated in the fake review treatment groups.
Finally, we examine whether fake reviews have different effects for consumers who do
not have a college degree, who have lower incomes, and those who use Amazon more frequently. This analysis can help inform the targeting of interventions aimed at addressing
fake reviews. Tables A10 and A11 in the Appendix present the results of these heterogeneity
analyses. For example, consumers without a college degree are more likely to be influenced
by the platform endorsement (Group 4). We do not find any consistent associations between
income and the effects of fake reviews.

33 These results hold even when controlling for a range of demographic factors. See Table A13.

29

Table 3: The effects of fake reviews for those who (dis)trust reviews
(1)
Chose
Don’t Buy
Does not trust reviews -0.0267*(0.0147)
Group 20.0252*(G1 + fake stars)(0.0129)
Group 2 × no trust-0.0418*(0.0251)
Group 30.101***(G2 + fake reviews)(0.0142)
Group 3 × no trust-0.0678**(0.0278)
Group 40.0696***(G2 + sloppy reviews)(0.0137)
Group 4 × no trust
0.00235(0.0285)
Group 50.114***(G3 + endorsement)(0.0141)
Group 5 × no trust-0.0724**(0.0284)
Constant
0.153***(0.00715)
Observations
9,643
2
R
0.015

(2)
(3)
(4)
(5)
Chose
Chose
WTP
WTP
MediocreBest Buy (predicted) (average)
0.0312-0.00447
1.330
3.172*(0.0213)(0.0191)(2.048)(1.876)-0.0326*
0.00741-0.947-0.741(0.0170)(0.0152)(1.670)(1.482)0.0426-0.000810-1.052-1.706(0.0369)(0.0334)(3.611)(3.279)-0.0691*** -0.0320**-5.260*** -4.878***(0.0172)(0.0148)(1.643)(1.447)0.05950.00827-0.2170.630(0.0370)(0.0324)(3.507)(3.221)-0.0577***-0.0119-3.364**-3.459**(0.0171)(0.0150)(1.670)(1.462)0.0461-0.0484-3.298-4.685(0.0366)(0.0312)(3.420)(3.077)-0.0994***-0.0144-6.078*** -6.071***(0.0169)(0.0148)(1.601)(1.414)0.0666*0.00580
2.888
4.166(0.0376)(0.0332)(3.492)(3.246)
0.584***
0.263***
56.38***
54.39***(0.00980) (0.00875)(0.974)(0.860)
9,643
9,643
8,677
9,643
0.007
0.001
0.003
0.004

Notes. In this table, we present the interaction between trusting reviews on Amazon and fake reviews.
The LPM and OLS regressions are conducted by interacting the treatment terms with a dummy variableindicating whether participants trust reviews on Amazon. We do not control for any other factors in these
regressions (see the Appendix for the results when controlling for other variables). The omitted group inthese regressions is Group 1 (the control condition, which was not shown any fake reviews). * p<0.1 **p<0.05 *** p<0.01.

30

4.3

Educational interventions can reduce the effects of fake reviews
Table 4: The marginal effects of the education intervention
(1)
Chose
Don’t Buy
Group 6-0.058***(G5 + education)(0.014)
Constant
0.247***(0.010)Observations
3355
2
R
0.005

(2)
(3)
Chose
Chose
Mediocre Best Buy
0.065***-0.007(0.017)(0.015)0.506*** 0.246***(0.012)(0.010)
3355
3355
0.004
0.000

(4)
(5)
WTP
WTP
(predicted) (average)
3.012*
3.064**(1.634)(1.028)51.687*** 49.886***(1.137)(1.086)
3008
3355
0.001
0.001

Notes. In this table, we present the effects of being assigned to Groups 6 (the educational intervention) on the share of participant that chose Don’t Buy, mediocre, and Best Buy products,
and on their willingness to pay for the product they chose. The regressions are conductedusing Linear Probability Models. The comparison group in each regression is Group 5 (theendorsement group). Standard errors in parenthesis. * p<0.1 ** p<0.05 *** p<0.01. The significance levels of our coefficients do not change if we adjust for multiple hypothesis testing
using List et al. (2021).

In this sub-section we evaluate the effects of the educational intervention. We do so bycomparing consumer behavior between those in the educational intervention group (Group6) and those in the platform endorsement group (Group 5), as those in Group 5 and 6 wereshown the same information (with the exception of the educational intervention). Table 4shows that the intervention makes participants 6 percentage points less likely to purchase a
Don’t Buy product, and it makes them around 7 percentage points more likely to purchase a
mediocre product. The intervention does not, however, make participants significantly more
likely to purchase a Best Buy product. Nonetheless, we find that the intervention increases average consumer welfare by £3, as the intervention increases the predicted value of the productthat participants chose by that magnitude. This is roughly half of the welfare loss incurredby the consumers in the the group with inflated star ratings, fake text reviews, and a platform
endorsement of the Don’t Buy product (Group 5), suggesting that educational interventions
can meaningfully address the negative effects of fake reviews.
We also examine the heterogeneous treatment effects of the educational intervention in
Table A17. We show that the educational intervention works equally well for college educated and non-college educated, and those with low- and above-median incomes. It also has
statistically indistinguishable effects for those who trust and do not trust reviews on Amazon
and for those who use Amazon more or less frequently.
Finally, we examine why the educational intervention changed participant behavior. To
do so, we look at whether those in the intervention condition adopted different strategies,31

held different beliefs, or took different amounts of time relative to those in Group 5. Our
analysis shows that those in their intervention condition (Group 6) were 4 percentage points
less likely to base their product choice on star ratings (p = 0.019) and that they were 4 percentage points more likely to base their choice on the brand of the product (p = 0.017) than thosein Group 5 (see Tables A20 and A21 for more information). These effects are consistent withthe recommendations presented in the intervention, which among other things emphasizedthat consumers should “Inspect the comments (don’t rely on star ratings alone)”.

5

External validity

To help address the external validity of our results, we use the Selection, Attrition, Naturalness, and Scaling (SANS) framework of List (2020). First, to address Selection, we sampleda large nationally representative sample of UK adults. Moreover, our sample includes many
individuals who use online platforms, and who may be influenced by fake reviews in theireveryday lives. One common issue related to recruiting participants from online survey panels is that they exclude those who do not use the internet. However, that is not a problem inour case, as we are only interested in individuals who use the internet. Second, we have no
Attrition in our experiment.
Third, we believe that we have achieved a high degree of Naturalness in the choice task,
setting, and time-frame for our experiment. The choice task closely mimics how consumers
interact with the Amazon platform. People searched for a product category, could visit one ormore product pages, and then selected the product they wished to purchase. Further, participants were incentivized to choose the product that they liked the most. One key difference
to the ’real world’ is that participants were spending the experimenters’ money rather thantheir own (as we were paying for the product).
Participants in our experiment had to buy a product (i.e., they could not choose to buy
nothing and to pocket the cost of the product). Moreover, they had to buy the product on
our platform (they could not shop around for better deals with other online retailers). This
set-up is, of course, not reflective of all online shopping journeys. That said, many individuals
do begin a shopping journey determined to purchase a product, and many Amazon users do
not look for products on other platforms before making a purchase. In fact, a recent surveyof online shopping behavior found 77% of respondents go directly to Amazon when they are
ready to buy a product (without checking other websites) (Forbes, 2019). We thus believe thatthe choice task is representative of important types of real-world shopping behavior.
The setting of the task was also realistic. People were on their computer, laptop, or on

32

their smartphone when completing the task in our experiment, just as they would have been
when using Amazon in their everyday lives. Moreover, the feel and design of the platform
closely resembled the Amazon platform, and we presented the same type of information as
exists on Amazon’s product and search pages. Similar to actual shopping on online marketplaces, participants could search for products online when using our platform (if they, for example, wanted more information about the products)—and around 18% did. The time-frameof choice was also identical to the real-world decision of buying on a platform (participants
could spend as long as they liked, just as when shopping in other settings).
With regards to the issue of Scaling, it is clearly the case that fake reviews ’scale’ (asthey exist across a range of platforms), and we find that they affect purchasing decisions fordifferent population segments and product categories. We also believe that our educational
intervention could be taken to scale, as it is simple to implement and does not require any
targeting (either at an individual or product level). The main factor that may inhibit thescaling of the educational intervention is that platforms would have to dedicate space to it
in a prominent place on their websites (and this is expensive real estate for many online
marketplaces).
Overall, we believe that our results likely have a high degree of external validity basedon these criteria. While we are assuming that the treatments work in the same way for those
who did and did not sign up for the study, we do not see why treatment effects would differ
for these groups. Moreover, there is no attrition, and the choice task closely resembles how
people shop online in their everyday lives.

6

Conclusion

Online commerce provides large benefits to consumers (Dolfen et al., 2019), and (genuine)
online customer reviews are supposed to help consumers navigate this rich—and sometimes
challenging—marketplace. Research has shown, however, that many reviews are fake, andsome argue that such reviews are likely to harm consumers. Indeed, both firms and regulators
are working on policy responses to deal with fake reviews. For example, regulators in the
UK opened an investigation into whether Amazon and Google are adequately addressing
fake reviews (Calatayud and Hoppe, 2021), and the U.S. Federal Trade Commission has put
businesses on notice that they will pay a price if they promote fake reviews (FTC, 2021).
Amazon, for its part, states that it removed more than 200 million suspicious reviews in 2020
before they were ever seen by customers (Amazon, 2021).
While there has been a significant debate about the presence of fake reviews and the33best ways of dealing with them, we know very little about whether such reviews actuallyaffect consumer behavior and welfare. This paper attempts to address this research gap. We
developed a theoretical model of how consumers could update their assessment of productquality based on fake reviews. The model suggests that a person’s beliefs about whether areview is fake, and a person’s belief about whether an unbiased product review would bepositive, are key variables in a customer’s purchasing decision. The predictions of the model
were found to be consistent with our large-scale randomized online experiment.
To the best or our knowledge, this study provided the first experimental estimates ofthe effects of fake reviews on individual consumption choices. The analysis of the experiment produced four main findings. First, fake reviews make consumers more likely to choose
inferior products. We found that these reviews have similar effects for those who choose
the dash-cams, cordless vacuum cleaners, and headphone product categories, suggesting that
our results may generalize. Second, we estimate that welfare losses from such reviews may be
important—on the order of $.12 for each dollar spent on our platform. Taken together withthe fact that fake reviews are typically applied to lower-quality products in the ’real world’(see, e.g., (He et al., 2021)) these findings suggest that such reviews can generate substantial
consumer harm. Furthermore, the results imply that the impacts of fake reviews should betaken into account when assessing the overall welfare impacts of online platforms with rating systems (see, e.g.,, Wu et al. (2015), Lewis and Zervas (2019), and Reimers and Waldfogel(2021) for examples of studies that assess the welfare impacts of reviews in online platforms).
Third, the effect of fake reviews is smaller for skeptical consumers, which is consistent with our model. Moreover, those who use Amazon more frequently are more likely tochanges their purchasing decision in response to fake reviews. Fourth, educational interventions have the potential to dramatically reduce the effects that fake reviews have on consumption choices. More specifically, the intervention reduces the negative welfare impact of fakereviews by around 44% in this context.
We suggest three areas of future research that will help us further understand the welfare consequences of fake reviews. The first is to consider doing natural field experiments on
how fake reviews affect consumer behavior on platforms that sell directly to consumers. Natural field experiments should be conducted because fake reviews may have effects on actual
platforms that we were unable to capture in this experiment. For example, trust is important for the functioning of online markets (Einav et al., 2016; Tadelis, 2016), but fake reviews
could undermine consumer trust in reviews in general. This loss in trust could induce consumers to rely on other sources of information that may be less informative and more costly
to access than honest reviews, and they might choose other platforms. Consumers who fall
for fake reviews may also post negative reviews if they end up disliking the product they pur-34

chased, which may dampen the long-run effects of fake reviews.34 That said, we believe that
our field experiment is the first step in the ’low cost wind tunnel’ of generating replicableevidence (List, 2022).
The second suggestion is to understand the relationship between the extent of fake reviews and consumer demand and welfare. For example, it would be useful to understand how
many fake reviews are required to influence consumer demand.35 It would also be useful todisentangle the marginal impact of fake review text on demand from the marginal impact offake star ratings on demand. Further, it would be interesting to record how consumers interact with platforms in the presence of fake reviews, as this would help us better understandthe mechanisms underlying the observed impact on demand.36
The third suggestion is to measure the economic impact on both consumers and producers of various interventions aimed at curbing fake reviews. While we found that our
educational intervention reduces the impact of fake reviews, more interventions should betested, and their impact on economic welfare should be assessed. This stream of work would
build on recent studies focused on estimating the welfare effects of nudges and other similarinterventions (Allcott and Kessler, 2019; Butera et al., 2022).

34 Note, however, that this dynamic depends on 1) consumers’ ability to assess product quality, and on 2)

their willingness to go through the hassle of posting a review online.
35 A recent paper by Fradkin and Holtz (2021) found that the causal impact of one additional review (fromzero to one) on Airbnb led to no overall changes in demand.
36 Other questions that would be interesting to explore include whether it is better to have platforms with: 1)
no reviews at all or 2) fake and real reviews, and under which circumstances either of the two options dominate.
This type of question could be explored using the design presented in this study, if one was to add a group thatwas shown no reviews at all.

35

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43

Appendices1. We begin by presenting proofs for the model presented in Section 2 and a parametricexample in Appendix A and B.
2. In Appendix C, we present the information that participants were shown when navigating through the experiment.
3. Appendix D includes descriptive statistics and a balance table.
4. Appendix E includes the heterogeneity analyses described in Section 4.2.
5. Finally, Appendix F lists the survey questions used in the experiment and Appendix G
lists the questions used in the companion survey.

A

Proofs

Proof of Proposition 1. By Bayes theorem,p(θ|X = 1) =(qθ + 1 − q)p(θ)
p(X = 1|θ)p(θ)= R1.
p(X = 1)(qθ + 1 − q)p(θ)dθ

(1)

0

Thus,
Z1
E[θ|X = 1] =p(θ|X = 1)θdθ

0
R1(qθ + 1 − q)p(θ)θdθ= R0 1(qθ + 1 − q)p(θ)dθ0
R1R1
q 0 θ 2 p(θ)dθ + (1 − q) 0 θp(θ)dθ= R1
R1
q 0 θp(θ)dθ + (1 − q) 0 p(θ)dθqE[θ 2 ] + (1 − q)E[θ]=qE[θ] + 1 − q(2).

As a result,qE[θ 2 ] + (1 − q)E[θ]− E[θ]
qE[θ] + 1 − q
qE[θ 2 ] + (1 − q)E[θ] − E[θ](qE[θ] + 1 − q)=qE[θ] + 1 − qq E[θ 2 ] − E[θ]2=>0
qE[θ] + 1 − q

E[θ|X = 1] − E[θ] =

44

(3)

where the inequality holds since E[θ 2 ] − E[θ]2 = Var(θ) > 0, q ∈ (0, 1) and E[θ] > 0. We thus
conclude that E[θ|X = 1] > E[θ].
Proof of Proposition 2. From (3),E[θ|X = 1] − E[θ] =qVar(θ).
1 − q(1 − E[θ])(4)

Since the numerator is increasing in q but the denominator is decreasing in q, it follows immediately that E[θ|X = 1] − E[θ] is increasing in q. Since both numerator and denominatorare positive, it is also strictly increasing in Var(θ).

45

BParameteric example

In this section we briefly illustrate our findings with a parametric example. To do so, assume
that the DM’s prior takes the form of a (standard) uniform distribution. By integrating, one
finds that the expected value of the posterior is given by
E[θ|X = 1] =3−q.
3(2 − q)Figure A1 illustrates. As can be seen, the expected value of the posterior always lies above theexpected value of the prior (1/2), indicating that the fake review increases the DM’s assessment of the product’s quality (as in Proposition 1). However, the curve is also upward sloping,indicating that the effect of fake reviews is smaller the lower the subjective probability thatthe review is genuine (as in Proposition 2). Indeed, when q = 0 (all reviews are thought tobe fake), the prior is the same as the posterior, so the review has no effect whatsoever on theDM’s beliefs. (At the other extreme of q = 1, when all reviews are thought to be genuine, thereview increases the DM’s expectation from 1/2 to 2/3).
Figure A1: The effect of q on the expected value of the posterior
Posterior

0.65

0.60

0.55

0.50
0.0

0.2

0.4

0.6

46

0.8

1.0

q

C

Experimental design
Table A1: Products with fake review elements
Group

Which?
Best Buy

Group 1
Group 2
Group 3
Group 4
Group 5
Group 6

Which?
Don’t Buy

MediocreProduct 1

MediocreProduct 2

MediocreProduct 3

X
X
X
X
X

Table A2: Treatment elements applied in the treatment groups
Group

Group 1
Group 2
Group 3
Group 4
Group 5
Group 6

Inflated
rating

X
X
X
X
X

Fake
review

Highlysuspicious
fake review

Platform
endorsement

Warningbanner

X
X

X

X
X
X
X

47

Figure A2: Example of a full product page

Notes. In this figure, we present an example ofa product page shown to participants, which they
could navigate to from the search page. The actual
pages shown to participants differed slightly (i.e.,
missing the information was filled in).

48

Figure A3: Example of the Amazon’s Choice platform endorsement on a product page

Notes. In this figure, we show how the platform endorsements were displayed on the product
page.

49

Figure A4: Example of the Amazon’s Choice platform endorsement on a search page

Notes. In this figure, we show how the platform endorsements were displayed on the search page.

50

Figure A5: Example of the warning banner displayed on a product page

Notes. In this figure, we display the warning banner that was included at the top of
the search and product pages for those in Group 6.

51

Figure A6: Example of the warning banner displayed on a search page

Notes. In this figure, we display the warning banner that was included at the top of
the search and product pages for those in Group 6.

52

Figure A7: Reviews displayed to Group 1: Headphones

Notes. In this Figure, we present the reviews displayed for the Don’t Buy headphone product in Group 1 (thecontrol group).

53

Figure A8: Reviews displayed to Group 3: Headphones

Notes. In this Figure, we present the reviews displayed for the Don’t Buy headphone product in Group 3 (thefake written review group).

54

Figure A9: Reviews displayed to Group 4: Headphones

Notes. In this Figure, we present the reviews displayed for the Don’t Buy headphone product in Group 4 (the’sloppy’ fake written review group).

55

Figure A10: Reviews displayed to Group 1: Dash-cam

Notes. In this Figure, we present the reviews displayed for the Don’t Buy dash-cam product in Group 1 (thecontrol group).

56

Figure A11: Reviews displayed to Group 3: Dash-cam

Notes. In this Figure, we present the reviews displayed for the Don’t Buy dash-cam product in Group 3 (thefake written review group).

57

Figure A12: Reviews displayed to Group 4: Dash-cam

Notes. In this Figure, we present the reviews displayed for the Don’t Buy dash-cam product in Group 4 (the’sloppy’ fake written review group).

Figure A13: Reviews displayed to Group 1: Cordless vacuum

Notes. In this Figure, we present the reviews displayed for the Don’t Buy cordless vacuum cleaner product inGroup 1 (the control group).

58

Figure A14: Reviews displayed to Group 3: Cordless vacuum

Notes. In this Figure, we present the reviews displayed for the Don’t Buy cordless vacuum cleaner product inGroup 3 (the fake written review group).

Figure A15: Reviews displayed to Group 4: Cordless vacuum

Notes. In this Figure, we present the reviews displayed for the Don’t Buy cordless vacuum cleaner product inGroup 4 (the ’sloppy’ fake written review group).

59

D

Descriptive statistics and balance
Table A3: Descriptive statistics for the experiment (1)VariableMean

Std Dev

N

FemaleAge: 18 to 24
Age: 25 to 34
Age: 35 to 44
Age: 45 to 54
Age: 55 to 64
Age: 65+
Income: <£21k
Income: £21k to £41k
Income: £42k to £69k
Income: >£69k
Primary school
Secondary school
Secondary education
University
Post-graduate degree
Uses Amazon
Daily internet use: <2hrs
Daily internet use: 2 to 5hrs
Daily internet use: >5hrs
Purchases online once per month or more
Purchases on Amazon once per month or more
When buying on Amz: Reads customer reviews
When buying on Amz: Looks at profile of reviewers
When buying on Amz: Reads most critical reviews
When buying on Amz: Looks at average star rating
When buying on Amz: Looks at distribution of stars
When buying on Amz: Looks at number of reviews
When buying on Amz: Looks at date of reviews
Does not trust reviews on Amazon
Believes 30% or more reviews on Amazon are fake
It is easy to tell if a review on Amazon is fake

0.512
0.120
0.171
0.179
0.177
0.151
0.203
0.403
0.372
0.156
0.068
0.007
0.210
0.257
0.388
0.138
0.967
0.249
0.521
0.230
0.779
0.595
0.677
0.171
0.434
0.558
0.479
0.498
0.499
0.206
0.452
0.313

0.500
0.325
0.376
0.383
0.381
0.358
0.402
0.491
0.483
0.363
0.252
0.086
0.407
0.437
0.487
0.345
0.178
0.432
0.500
0.421
0.415
0.491
0.468
0.377
0.496
0.497
0.500
0.500
0.500
0.405
0.498
0.464

9988
9988
9988
9988
9988
9988
9988
9093
9093
9093
9093
9849
9849
9849
9849
9849
9988
9865
9865
9865
9881
9863
9662
9662
9662
9662
9662
9662
9662
9643
9967
9764

60

Table A4: Descriptive statistics for the experiment (2)Variable

Mean

Duration (minutes)
11.47347
Took survey on mobile device
0.505206
Product category: Headphones
0.401882
Product category: Dash cam
0.191229
Product category: Vacuum cleaner
0.406888
Chose inferior product0.19203
Chose mediocre product0.557269
Chose best product0.250701
Confidence in choice (0-100%)
70.63182
Found it easy to pick the product0.725443
Would put a lot more effort in real life
0.233188
Read reviews when choosing
0.789996
Searched product online when choosing 0.304361

61

Std Dev

N

93.65499
0.499998
0.490303
0.393289
0.491278
0.393917
0.496734
0.433438
23.84171
0.446313
0.422883
0.407332
0.460159

9988
9988
9988
9988
9988
9988
9988
9988
9987
9987
9636
9976
9975

Table A5: Balance table for the experiment62

FemaleAge: 18 to 24
Age: 25 to 34
Age: 35 to 44
Age: 45 to 54
Age: 55 to 64
Age: 65+
Income: <£21k
Income: £21k to £41k
Income: £42k to £69k
Income: >£69k
Primary school
Secondary school
Secondary education
University
Post-graduate degree
Uses Amazon
Daily internet use: <2hrs
Daily internet use: 2 to 5hrs
Daily internet use: >5hrs
Purchases online once per month or more
Purchases on Amazon once per month or more
When buying on Amz: Reads customer reviews
When buying on Amz: Looks at profile of reviewers
When buying on Amz: Reads most critical reviews
When buying on Amz: Looks at average star rating
When buying on Amz: Looks at distribution of stars
When buying on Amz: Looks at number of reviews
When buying on Amz: Looks at date of reviews
Does not trust reviews on Amazon

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

P-value

0.499
0.117
0.161
0.184
0.175
0.149
0.214
0.393
0.377
0.165
0.066
0.008
0.213
0.250
0.384
0.145
0.972
0.252
0.518
0.230
0.784
0.588
0.700
0.179
0.444
0.583
0.483
0.516
0.499
0.199

0.517
0.123
0.165
0.178
0.181
0.158
0.194
0.404
0.370
0.149
0.077
0.004
0.215
0.255
0.383
0.142
0.973
0.243
0.530
0.227
0.778
0.591
0.660
0.165
0.424
0.540
0.480
0.485
0.512
0.204

0.520
0.120
0.165
0.179
0.185
0.155
0.196
0.384
0.363
0.180
0.073
0.010
0.218
0.232
0.401
0.139
0.971
0.252
0.515
0.233
0.801
0.609
0.655
0.173
0.425
0.558
0.462
0.500
0.463
0.209

0.507
0.119
0.188
0.175
0.178
0.137
0.202
0.406
0.386
0.142
0.065
0.007
0.204
0.291
0.372
0.127
0.966
0.254
0.523
0.223
0.775
0.579
0.672
0.160
0.420
0.554
0.471
0.489
0.503
0.216

0.530
0.117
0.172
0.181
0.175
0.148
0.207
0.428
0.358
0.152
0.061
0.005
0.206
0.257
0.402
0.130
0.957
0.246
0.511
0.243
0.766
0.593
0.690
0.173
0.453
0.578
0.504
0.508
0.514
0.193

0.498
0.125
0.174
0.177
0.167
0.156
0.202
0.403
0.380
0.152
0.065
0.010
0.203
0.259
0.384
0.143
0.965
0.244
0.530
0.226
0.772
0.608
0.684
0.180
0.438
0.538
0.472
0.491
0.500
0.216

0.347
0.980
0.372
0.992
0.822
0.559
0.749
0.210
0.592
0.063
0.523
0.205
0.838
0.007
0.431
0.552
0.085
0.966
0.853
0.807
0.205
0.448
0.046
0.627
0.341
0.040
0.230
0.476
0.056
0.530

Table A5: Balance table for the experiment

Believes 30% or more reviews on Amazon are fake
It is easy to tell if a review on Amzon is fakeNGroup 1

Group 2

Group 3

Group 4

Group 5

Group 6

P-value

0.443
0.314
1648

0.444
0.298
1667

0.469
0.321
1647

0.454
0.306
1671

0.440
0.331
1693

0.466
0.312
1662

0.398
0.397

63

Table A6: Descriptive statistics for the WTP survey
Variable
FemaleAge: 18 to 24
Age: 25 to 34
Age: 35 to 44
Age: 45 to 54
Age: 55 to 64
Age: 65+
Income: <£21k
Income: £21k to £41k
Income: £42k to £69k
Income: >£69k
Primary school
Secondary school
Secondary education
University
Post-graduate degree
Uses Amazon
Daily internet use: <2hrs
Daily internet use: 2 to 5hrs
Daily internet use: >5hrs
Shops online > once per month
Shops on Amazon > once per month
Duration (minutes)
Product category: Headphones
Product category: Dash cam
Product category: Vacuum cleaner

64

Mean
0.51
0.11
0.19
0.18
0.17
0.23
0.11
0.34
0.37
0.10
0.17
0.00
0.09
0.23
0.48
0.20
0.98
0.12
0.45
0.43
0.85
0.64
8.44
0.35
0.12
0.33

Std Dev
0.50
0.32
0.39
0.39
0.37
0.42
0.32
0.47
0.48
0.30
0.37
0.04
0.28
0.42
0.50
0.40
0.15
0.33
0.50
0.50
0.36
0.48
4.22
0.48
0.32
0.47

N
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998
998

Table A7: Predicting WTP for headphones(1)(2)
Best Buy Don’t Buy
Female0.73-1.36*(0.461)(0.076)
Age = 18-24
-3.50
-0.42
(0.147)(0.858)Age = 25-34
-3.21
-1.29
(0.176)(0.584)
Age = 35-44
-3.48
0.53
(0.160)(0.820)Age = 45-54
-1.64
-2.38
(0.501)(0.287)
Age = 55-64
-0.80
-1.76
(0.743)(0.451)
Income <£21k
1.87
3.64
(0.783)(0.113)
Income = £21-41k
1.26
3.07
(0.852)(0.184)
Income = £42-69k
2.72
1.90
(0.693)(0.442)
Primary school
26.98***
8.61***(0.000)(0.000)
Secondary school
14.27***
8.58***(0.000)(0.001)
Secondary education
11.96***
5.28**(0.000)(0.010)
University
12.56***
5.38***(0.000)(0.005)
Post-graduate degree
11.66***
6.57***(0.000)(0.003)Uses Amazon
1.46
-1.95
(0.675)(0.507)
Daily internet use: <2hrs
1.76
9.13***(0.567)(0.001)
Daily internet use: 2 to 5hrs
1.31
10.61***(0.620)(0.000)Daily internet use: >5hrs
1.54
9.94***(0.539)(0.000)Shops online >once per month
-2.25
-1.98
(0.125)(0.119)Shops on Amazon >once per month
1.86
1.97**(0.125)(0.025)Constant
11.89
-8.79
(0.177)(0.119)Observations
354
354

65

(3)
(4)
(5)
Mediocre 1 Mediocre 2 Mediocre 3-1.97*
0.17-1.91*(0.072)(0.859)(0.068)2.70-4.37*-0.86(0.318)(0.080)(0.726)0.88-4.56*-3.96(0.742)(0.066)(0.109)3.01-4.21*-0.49(0.253)(0.096)(0.843)
0.50
-3.42
-2.04
(0.855)(0.176)(0.413)
0.89
-2.96
-2.51
(0.740)(0.235)(0.310)
1.09
6.16
6.74
(0.840)(0.210)(0.168)
0.61
4.29
6.42
(0.910)(0.380)(0.186)
0.17
4.85
6.55
(0.976)(0.334)(0.194)19.91***
31.89***
28.42***(0.000)(0.000)(0.000)20.76***
17.74***
22.09***(0.000)(0.000)(0.000)14.77***
16.04***
17.36***(0.000)(0.000)(0.000)13.30***
16.07***
17.09***(0.000)(0.000)(0.000)15.45***
17.69***
17.43***(0.000)(0.000)(0.000)
2.36
-0.90
2.18
(0.553)(0.788)(0.711)
1.28
5.55
12.37***(0.710)(0.112)(0.000)
3.09
6.38**
12.26***(0.320)(0.040)(0.000)2.73
5.57*
11.99***(0.375)(0.070)(0.000)-1.88-3.72**-2.33(0.275)(0.014)(0.169)
2.27
1.38
0.79
(0.101)(0.167)(0.533)-5.22-4.79-16.52*(0.529)(0.540)(0.063)
354
354
354

Table A8: Predicting WTP for dash cams
(1)
(2)
Best Buy Don’t Buy
Female-0.62-3.85*(0.804)(0.086)
Age = 25-34
-3.71
-2.56
(0.494)(0.586)
Age = 35-44
2.43
0.97
(0.639)(0.830)Age = 45-54
-2.64
0.78
(0.635)(0.873)Age = 55-64
-1.60
-2.57
(0.752)(0.573)Age = 65+
4.23
-3.59
(0.434)(0.484)
Income <£21k
-7.09
-7.08
(0.321)(0.459)
Income = £21-41k
-0.82
-9.34
(0.902)(0.317)
Income = £42-69k
-4.85
-6.68
(0.518)(0.485)
Primary school
10.62
-7.88
(0.165)(0.556)
Secondary school
23.03***
2.37(0.000)(0.843)
Secondary education
17.90***
10.68(0.000)(0.344)
University
15.68***
4.28(0.000)(0.701)
Post-graduate degree
18.13***
4.33(0.000)(0.702)
Uses Amazon
3.08
-5.38
(0.501)(0.456)
Daily internet use: 2 to 5hrs
4.79
3.33
(0.172)(0.295)
Daily internet use: >5hrs
3.79
-0.21
(0.302)(0.947)
Shops online >once per month
-3.19
-2.07
(0.331)(0.529)Shops on Amazon >once per month
1.22
5.31**(0.690)(0.029)Constant
36.26***
22.60(0.000)(0.170)Observations
288
288

66

(3)
(4)
(5)
Mediocre 1 Mediocre 2 Mediocre 3-8.02***-4.02*-2.74(0.001)(0.100)(0.222)
-3.52
-1.63
-7.42
(0.545)(0.737)(0.104)
-1.17
6.88
1.07
(0.839)(0.139)(0.812)
-2.47
-0.44
-1.96
(0.660)(0.926)(0.684)-9.63*-1.76-4.09(0.069)(0.684)(0.350)
-3.43
-0.37
-0.35
(0.566)(0.942)(0.946)
5.63
6.55-11.89***(0.651)(0.663)(0.003)4.51
6.25-10.04***(0.715)(0.674)(0.004)5.11
0.23-14.02***(0.685)(0.988)(0.002)8.13-2.30
15.55*(0.388)(0.782)(0.074)27.27***
22.34***
24.85***(0.000)(0.000)(0.000)24.14***
21.51***
26.56***(0.000)(0.000)(0.000)19.84***
18.41***
22.98***(0.001)(0.000)(0.000)18.12***
19.11***
23.51***(0.004)(0.000)(0.000)
0.05
1.77
0.42
(0.993)(0.772)(0.939)
5.97
3.85
-0.05
(0.110)(0.321)(0.987)
2.37
3.13
-1.41
(0.528)(0.428)(0.673)-3.51
1.89-5.42*(0.302)(0.579)(0.057)
4.50
-1.69
2.82
(0.121)(0.584)(0.267)10.38
6.63
32.85***(0.489)(0.687)(0.000)
288
288
288

Table A9: Predicting WTP for cordless vacuum cleaners

FemaleAge = 25-34
Age = 35-44
Age = 45-54
Age = 55-64
Age = 65+
Income <£21k
Income = £21-41k
Income = £42-69k
Secondary school
Secondary education
University
Post-graduate degree
Uses Amazon
Daily internet use: 2 to 5hrs
Daily internet use: >5hrs
Shops online >once per month
Shops on Amazon >once per month
Constant
Observations

(1)
(2)
(3)
Best Buy Don’t Buy Mediocre 1
10.04
5.83
7.77
(0.129)(0.256)(0.205)14.98
18.03*
26.35**(0.381)(0.097)(0.029)19.29
13.21
39.51***(0.245)(0.173)(0.001)
26.04
25.23**
42.01***(0.113)(0.023)(0.000)17.49
7.39
26.17**(0.266)(0.442)(0.021)
3.82
2.40
22.17*(0.820)(0.809)(0.067)
4.16-13.182.84(0.787)(0.253)(0.834)
10.94
-6.77
5.28
(0.476)(0.559)(0.695)
-4.47
-9.29
-6.41
(0.799)(0.470)(0.680)-42.38**19.25
4.15(0.047)(0.266)(0.840)-52.31***
4.40-11.40(0.008)(0.763)(0.546)-45.01**
6.02-8.87(0.025)(0.678)(0.632)-52.37***
2.59-22.58(0.004)(0.849)(0.188)-35.37**-24.32-13.68(0.025)(0.232)(0.554)80.53***
22.22***
78.03***(0.000)(0.008)(0.000)61.78***
16.00*
67.71***(0.000)(0.081)(0.000)
6.47
-3.09
7.11
(0.500)(0.709)(0.475)
1.81
10.31*-3.69(0.807)(0.075)(0.618)106.57***
24.79
-7.64
(0.000)(0.316)(0.788)
356
356
356

67

(4)
Mediocre 2
12.69*(0.062)29.28(0.119)46.38**(0.010)51.10***(0.004)34.20**(0.050)9.20(0.610)9.63(0.646)23.19(0.262)11.14(0.620)-11.15(0.626)-5.83(0.783)-18.85(0.377)-28.51(0.138)-44.35***(0.008)106.30***(0.000)95.24***(0.000)0.55(0.961)9.74(0.236)15.79(0.621)356(5)
Mediocre 3
5.65(0.361)4.31(0.776)12.64(0.390)21.73(0.138)11.75(0.408)-5.96(0.681)7.06(0.689)15.78(0.367)12.62(0.512)8.77(0.665)12.68(0.515)6.26(0.748)-3.41(0.847)-30.17**(0.049)68.77***(0.000)59.52***(0.000)-4.47(0.653)15.48**(0.043)13.59(0.632)356

E

Supplementary analyses
Table A10: Heterogeneity analysis (1)Group 2
Group 3
Group 4
Group 5
College degree
Group 2 # College degree
Group 3 # College degree
Group 4 # College degree
Group 5 # College degree
Constant
Observations
R-squared(1)
Chose Don’t Buy
0.056***(0.017)0.126***(0.019)0.097***(0.018)0.112***(0.019)-0.016(0.015)0.003(0.024)-0.000(0.026)0.025(0.025)0.059**(0.026)
0.113***(0.011)
8296
0.018

68

(2)
Chose Mediocre-0.032(0.025)-0.076***(0.025)-0.051**(0.024)-0.085***(0.025)-0.002(0.024)-0.016(0.034)0.004(0.034)-0.030(0.034)-0.037(0.034)0.614***(0.017)
8296
0.006

(3)
Chose Best Buy-0.024(0.022)-0.050**(0.022)-0.046**(0.022)-0.026(0.022)0.018(0.022)0.014(0.031)-0.004(0.030)0.004(0.031)-0.022(0.031)0.273***(0.016)8296
0.002

Table A11: Heterogeneity analysis (2)Group 2
Group 3
Group 4
Group 5
Income: £21k to £41k
Income: £42k to £69k
Income: >£69k
Group 2 # Income: £21k to £41k
Group 2 # Income: £42k to £69k
Group 2 # Income: >£69k
Group 3 # Income: £21k to £41k
Group 3 # Income: £42k to £69k
Group 3 # Income: >£69k
Group 4 # Income: £21k to £41k
Group 4 # Income: £42k to £69k
Group 4 # Income: >£69k
Group 5 # Income: £21k to £41k
Group 5 # Income: £42k to £69k
Group 5 # Income: >£69k
Constant
Observations
R-squared(1)
Chose Don’t Buy
0.059***(0.020)0.124***(0.022)0.119***(0.021)0.130***(0.021)-0.024(0.018)-0.013(0.023)0.073*(0.041)
0.004(0.028)0.030(0.038)-0.046(0.031)
0.028(0.031)
0.013(0.039)-0.115*(0.029)-0.019(0.029)0.014(0.041)-0.109*(0.030)0.007(0.030)0.025(0.040)0.041(0.066)0.111***(0.013)
7572
0.020
69

(2)
Chose Mediocre-0.025(0.028)-0.069**(0.029)-0.062**(0.028)-0.118***(0.028)-0.010(0.029)-0.004(0.037)0.009(0.053)0.002(0.041)-0.056(0.054)-0.085(0.041)
0.007(0.041)-0.032(0.052)-0.040(0.041)0.001(0.041)-0.036(0.054)0.011(0.041)0.074*(0.041)-0.021(0.053)-0.140*(0.075)0.613***(0.020)
7572
0.008

(3)
Chose Best Buy-0.034(0.025)-0.055**(0.025)-0.056**(0.025)-0.012(0.025)0.034(0.027)0.017(0.034)-0.082*(0.044)-0.005(0.037)0.026(0.049)0.131**(0.037)-0.035(0.037)0.019(0.047)0.155**(0.037)0.018(0.037)0.022(0.049)0.097(0.036)-0.081**(0.036)-0.004(0.048)0.099(0.066)
0.276***(0.018)7572
0.005

Table A12: Heterogeneity analysis (3)Group 2
Group 3
Group 4
Group 5Uses Amazon > once/monthGroup 2 # Uses Amazon > once/monthGroup 3 # Uses Amazon > once/monthGroup 4 # Uses Amazon > once/monthGroup 5 # Uses Amazon > once/month
Constant
Observations
R-squared

70

(1)
(2)
Chose
Chose
Don’t Buy Mediocre
0.028-0.033(0.019)(0.027)0.071***-0.048*(0.020)(0.027)0.079***-0.048*(0.020)(0.027)
0.091***-0.046*(0.020)(0.027)-0.033**-0.015(0.016)(0.024)0.051**-0.020(0.024)(0.035)0.092***-0.046(0.026)(0.035)0.053**-0.038(0.026)(0.035)0.085*** -0.102***(0.026)(0.035)0.125***
0.622***(0.013)(0.019)
8225
8225
0.020
0.009

(3)
Chose
Best Buy
0.005(0.024)-0.023(0.024)-0.030(0.023)-0.046**(0.023)0.048**(0.022)-0.031(0.031)-0.046(0.031)-0.016(0.031)
0.017(0.031)0.253***(0.017)8225
0.004

Table A13: Heterogeneity with controls
(1)
(2)
(3)
Chose
Chose
Chose
Don’t Buy Mediocre Best Buy
Does not trust reviews -0.0268*
0.02120.00563(0.0157)(0.0227)(0.0205)
Group 20.0284**-0.0324*0.00405(G1 + fake stars)(0.0136)(0.0178)(0.0158)
Group 2 × no trust-0.0334
0.0410-0.00753(0.0267)(0.0390)(0.0350)
Group 30.102*** -0.0719*** -0.0301**(G2 + fake reviews)(0.0149)(0.0179)(0.0153)
Group 3 × no trust-0.0586**
0.05500.00365(0.0296)(0.0392)(0.0342)
Group 40.0668*** -0.0606*** -0.00618(G2 + sloppy reviews)(0.0145)(0.0180)(0.0158)
Group 4 × no trust
0.007210.0540-0.0612*(0.0301)(0.0388)(0.0330)
Group 50.111***-0.102*** -0.00887(G3 + endorsement)(0.0147)(0.0175)(0.0153)
Group 5 × no trust-0.0750**
0.0765*-0.00150(0.0293)(0.0393)(0.0348)
Constant
0.162***
0.598***
0.240***(0.0229)(0.0283)(0.0246)
Controls
Yes
Yes
Yes
Observations
8,723
8,723
8,723
R2
0.020
0.021
0.021

71

(4)
(5)
WTP
WTP
(predicted) (average)
3.059
3.333*(1.986)(1.962)-1.402-1.060(1.572)(1.502)0.154-2.161(3.473)(3.406)-5.901*** -5.481***(1.554)(1.471)0.936-0.242(3.429)(3.371)-3.888**-3.591**(1.592)(1.506)-2.508-3.519(3.317)(3.255)-6.645*** -6.275***(1.514)(1.434)
3.661
2.938(3.398)(3.348)33.88***
50.34***(2.230)(2.335)
Yes
Yes
8,677
8,723
0.099
0.059

Table A14: Treatment effects (headphones)
(1)
Chose
Don’t Buy
Group 20.039**(0.017)
Group 30.108***(0.019)Group 40.087***(0.019)Group 50.114***(0.019)Group 60.078***(0.018)Constant
0.091***(0.011)
Controls
No
Observations
4014
R-squared
0.012

(2)
Chose
Mediocre
0.000(0.027)-0.071***(0.027)-0.036(0.027)-0.106***(0.027)-0.073***(0.027)0.492***(0.020)
No
4014
0.006

72

(3)
(4)
Chose
WTP
Best Buy (predicted)-0.039-0.676*(0.027)(0.396)-0.037-1.381***(0.027)(0.415)-0.052*-1.185***(0.027)(0.412)-0.008-1.269***(0.027)(0.423)-0.005-0.801*(0.027)(0.410)0.416*** 20.041***(0.019)(0.276)
No
No
4014
3557
0.002
0.004

(5)
WTP
(average)-0.683**(0.348)-1.372***(0.367)-1.285***(0.359)-1.137***(0.370)-0.762**(0.363)19.211***(0.241)
No
4014
0.005

Table A15: Treatment effects (dash cams)
(1)
Chose
Don’t Buy
Group 2-0.005(0.029)Group 30.044
(0.031)
Group 40.028
(0.030)Group 50.080***(0.031)
Group 60.020
(0.030)Constant
0.156***(0.021)
Controls
No
Observations
1910
R-squared
0.006

(2)
Chose
Mediocre-0.028(0.039)-0.034(0.039)-0.046(0.039)-0.073*(0.039)-0.012(0.039)0.621***(0.027)
No
1910
0.002

73

(3)
(4)
Chose
WTP
Best Buy (predicted)
0.033
0.094
(0.034)(1.188)-0.010-2.201*(0.033)(1.211)0.018-0.381(0.033)(1.224)-0.008-1.927(0.033)(1.198)-0.008-0.290(0.033)(1.183)0.223*** 37.501***(0.024)(0.831)
No
No
1910
1702
0.001
0.004

(5)
WTP
(average)
0.659(0.991)-1.052(1.015)-0.263(0.997)-1.680*(1.018)-0.097(0.993)35.758***(0.693)
No
1910
0.004

Table A16: Treatment effects (cordless vacuum cleaners)
(1)
Chose
Don’t Buy
Group 20.105***(0.019)Group 30.181***(0.021)
Group 40.174***(0.021)
Group 50.200***(0.021)
Group 60.120***(0.019)Constant
0.095***(0.011)
Controls
No
Observations
4064
R-squared
0.026

(2)
Chose
Mediocre-0.087***(0.025)-0.091***(0.025)-0.106***(0.025)-0.120***(0.025)-0.022(0.025)0.725***(0.017)
No
4064
0.009

74

(3)
(4)
Chose
WTP
Best Buy (predicted)-0.018-6.448***(0.020)(2.209)-0.091*** -13.395***(0.018)(2.289)-0.068*** -12.370***(0.019)(2.298)-0.080*** -14.109***(0.019)(2.276)-0.099*** -9.327***(0.018)(2.217)0.180*** 105.955***(0.015)(1.469)
No
No
4064
3648
0.013
0.014

(5)
WTP
(average)-6.562***(1.731)-13.657***(1.803)-13.295***(1.801)-14.995***(1.810)-9.554***(1.722)103.983***(1.105)
No
4064
0.021

Table A17: Heterogeneous effects of the education intervention
Interaction with. . .
Dummies for age brackets
Dummies for educational attainment
Frequency of Amazon use
Dummies for income ranges
Trusts reviews

75

Effects
No significant interactive effects
No significant interactive effects
No significant interactive effects
No significant interactive effects
No significant interactive effects

Table A18: Mechanisms (1)
(1)

(2)

(3)
(4)
(5)
(6)
Based product choice on. . .
Star#
Review
Look
Prod.
ratings
reviews
content of prod.
descr.
Brand
Group 2-0.0108 0.00451 -0.0128 -0.00714 -0.0313* -0.00757(G1 + fake stars)(0.0171) (0.0158) (0.0172) (0.0171) (0.0172) (0.0170)
Group 3-0.00156 0.0123-0.0252 -0.00278 -0.00207 0.00207(G2 + fake reviews)(0.0172) (0.0159) (0.0172) (0.0172) (0.0172) (0.0171)
Group 4-0.00285 -0.00517 -0.0168 -0.00333 -0.00626 0.00465(G2 + sloppy reviews) (0.0172) (0.0157) (0.0172) (0.0171) (0.0171) (0.0170)
Group 50.0112 -0.00118 -0.00282 -0.00396 -0.0226-0.0124(G3 + endorsement)(0.0171) (0.0157) (0.0172) (0.0170) (0.0171) (0.0169)
Constant
0.424*** 0.289*** 0.439*** 0.416*** 0.585*** 0.401***(0.0122) (0.0112) (0.0122) (0.0121) (0.0121) (0.0121)
Observations
8,326
8,326
8,326
8,326
8,326
8,326
2
R
0.000
0.000
0.000
0.000
0.001
0.000

76

Table A19: Mechanisms (2)Group 2(G1 + fake stars)
Group 3(G2 + fake reviews)
Group 4(G2 + sloppy reviews)
Group 5
(G3 + endorsement)
Constant
Observations
R2

Group 2(G1 + fake stars)
Group 3(G2 + fake reviews)
Group 4(G2 + sloppy reviews)
Group 5
(G3 + endorsement)
Constant
Observations
R2(1)
Trust rev.
in exp.
-0.0157(0.0161)-0.0190(0.0162)-0.0287*(0.0162)-0.00659(0.0160)
0.692***(0.0114)
8,326
0.000

(2)
Duration(minutes)
0.331(2.387)1.924(2.941)
4.579(4.471)-1.144(2.058)10.69***(1.831)
8,326
0.000

(3)
Easy tochoose-0.000439(0.0156)
0.0223(0.0154)-0.0111(0.0156)
0.00855(0.0154)0.723***(0.0110)
8,326
0.001

(4)
Confident
in choice-0.0250(0.0165)
0.00648(0.0164)-0.00380(0.0164)-0.00179(0.0163)0.667***(0.0116)
8,326
0.001

(5)
More
effort IRL-0.0245(0.0165)-0.00707(0.0167)-0.0175(0.0166)-0.0137(0.0165)
0.358***(0.0118)
8,326
0.000

(6)
Buy prod.
again-0.00539(0.0152)-0.0217(0.0151)-0.00600(0.0152)-0.0194(0.0150)
0.262***(0.0108)
8,326
0.000

(7)
Easy spot
fake rev.
-0.0143(0.0159)
0.00504(0.0161)-0.00842(0.0160)
0.0137(0.0161)
0.308***(0.0114)
8,326
0.000

(8)
Trust rev.
on Amz-0.00703(0.0146)-0.00985(0.0147)-0.0190(0.0147)0.00482(0.0144)0.774***(0.0103)
8,326
0.000

(9)
Read
prod. rev.
-0.0269*(0.0140)-0.0224-0.014-0.0381***(0.0141)-0.0131(0.0138)
0.809***(0.00969)
8,317
0.001

(10)
Searched
online0.000560(0.0159)
0.00943(0.0160)
0.00967(0.0160)-0.00160(0.0158)
0.298***(0.0113)
8,316
0.000

77

Table A20: Mechanisms (3)(1)

Group 6(G5 + education)
Constant
Observations
R2

(2)

(3)
(4)
(5)
Based product choice on. . .
Star#
Review
Look
Prod.
ratings
reviews content of prod.
descr.
-0.0400** -0.0145 -0.0219 0.00587 0.0105(0.0170) (0.0155) (0.0171) (0.0170) (0.0171)
0.435*** 0.288*** 0.437*** 0.412*** 0.562***(0.0121) (0.0110) (0.0121) (0.0120) (0.0121)
3,355
3,355
3,355
3,355
3,355
0.002
0.000
0.000
0.000
0.000

(6)

Brand
0.0403**(0.0170)0.389***(0.0119)
3,355
0.002

Table A21: Mechanisms (3)(1)
Trust rev.
in exp.
-0.0155(0.0161)
0.686***(0.0113)
3,355
0.000

(2)
Duration(minutes)
0.193(2.003)9.543***(0.940)
3,355
0.000

(3)
Easy tochoose-0.0112(0.0154)0.731***(0.0108)
3,354
0.000

(4)
(5)
ConfidentMorein choice effort IRL-0.02330.00161(0.0164)(0.0164)0.666***
0.344***(0.0115)(0.0116)
3,354
3,355
0.001
0.000

(6)
Buy prod.
againGroup 60.0238(G5 + education) (0.0150)
Constant
0.243***(0.0104)
Observations
3,355
2
R
0.001

(7)
Easy spot
fake rev.
-0.0169(0.0160)
0.321***(0.0114)
3,355
0.000

(8)
Trust rev.
on Amz-0.0216(0.0146)
0.779***(0.0101)
3,355
0.001

(9)
Read
prod. rev.
0.00124(0.0139)
0.796***(0.00980)
3,352
0.000

Group 6(G5 + education)
Constant
Observations
R2

78

(10)
Searched
online0.0211(0.0159)
0.297***(0.0111)
3,352
0.001

F

Survey questions - Main experiment

F.1

Introduction

Welcome and thanks for participating!
This is a study about purchasing habits on Amazon.co.uk.
In the study, you will be asked to choose a product category that you are interested in. You
will then be shown a list of products, and will be asked to select the product that you would
most like to receive.
All respondents that complete the survey will be entered into a prize draw to receive theproduct that they choose. Ten respondents will be randomly selected as winners at the endof the survey, and will be prompted to enter their address in order to claim their prize. Winners will receive their prizes within 6 weeks of completing the survey. The prize draw iscompletely random, and the distribution of prizes is administered by The Behaviouralist Ltd(contact: info@thebehaviouralist.com).
The survey should take around 5-10 minutes to complete.
The survey must be completed before 13 March 2020 in order to be eligible for the prize draw.
By clicking the button below, you acknowledge that your participation in the study is voluntary, that you are at least 18 years of age, and that you are aware that you can end yourparticipation in the study at any time and for any reason.

F.2

Demographics

To start, please answer the following questions about yourself.
What is your age?
• 18 - 24• 25 - 34• 35 - 44• 45 - 54
79• 55 - 64
• 65 - 74• 75 - 84• 85 or older

What is your gender?
• Male• Female• Other

Where do you live?
• East of England• East Midlands• Greater London• North East England• North West England• Northern Ireland• Scotland• South East England• South West England• Wales• West Midlands• Yorkshire and the Humber

What is your yearly income?
• Up to £7,000• £7,001 - £14,00080• £14,001 - £21,000• £21,001 - £28,000• £28,001 - £34,000• £34,001 - £41,000• £41,001 - £48,000• £48,001 - £55,000• £55,001 - £62,000• £62,001 - £69,000• £69,001 - £76,000• £76,001 - £83,000• £83,001+• Prefer not to say

What is your highest level of education?
• Primary school• Secondary school up to 16 years• Higher or secondary or further education (A-levels, BTEC etc)
• College or university• Post-graduate degree• Prefer not to say

On average, how much time, if any, do you spend online every day? By this, we mean using
the internet or an internet-enabled device, such as a computer, smartphone or tablet.
• Less than 1 hour
• 1 – 2 hours• 2 – 3 hours• 3 – 5 hours• More than 5 hours
81• Don’t know

How often do you purchase items online?
• Most days
• About once or twice per week
• About once or twice per month• About once every three months• About once every six months• About once every year
• Less often than once a year
• Never• Don’t know

How often do you purchase items on Amazon.co.uk?
• Most days
• About once or twice per week
• About once or twice per month• About once every three months• About once every six months• About once every year
• Less often than once a year
• Never• Don’t know

82

F.3

Product category choice

We will now begin the study.
Please choose the product category you are most interested in.
• Headphones• Dash Cams
• Cordless Vacuum Cleaners

F.4

Product choice

We would like you to decide what [product category] you would want to buy from Amazon.co.uk. Please do this exercise as you would in real-life.
If you are having trouble reading the text in the images you can zoom in by pressing COMMAND + "+" on a Mac, and CTRL + "+" on a PC.
Click next to begin
Please consider the products shown in the image. You can learn more about the products byselecting them in the question section below the image. You are able to view more than one
product before you make your choice.
[An image of product search page shown here]
Select a product below to learn more about it• Product 1• Product 2• Product 3• Product 4• Product 5

Explore the product page. Below you can choose to select this product or to go back andcontinue shopping.
[An image of product page shown here]83

Do you want to select this product? [if you want to continue shopping click on the backbutton]• Select Product

F.5

Post-experiment questions

You are almost there! Just a few more questions about your experience.
How confident are you that you chose the best product for you?
• Extremely confident• Very confident• Moderately confident• Slightly confident• Not confident at all

How easy or difficult was it to assess the products’ value for money?
• Extremely easy
• Somewhat easy
• Neither easy nor difficult• Somewhat difficult• Extremely difficult

Thinking about the effort you put into choosing the [product] you selected, how does thiscompare to the amount of effort you would put into choosing and purchasing [product] inreal life?
• I would put a LOT MORE effort into choosing in real life
• I would put a little more effort into choosing in real life84• I would put the same amount of effort into choosing in real life
• I would put a little less effort into choosing in real life
• I would put a LOT LESS effort into choosing in real life
• Don’t know

Imagine that you were given [£24.99/£49.99/£149.99] and asked to buy [a pair of headphones/a dash cam/a cordless vacuum cleaner]. You would have free access to any online
retail platform, such as Amazon.co.uk or Argos. Do you think that you would have chosen adifferent product to the one that you just chose?
• Yes
• No
• Don’t know

What did you base your choice on? (choose all that apply)• The star rating• The number of reviews• The content of the reviews• Information in the product description• Brand• The look of the product• Other

Did you read any customer reviews when choosing a product?
• Yes
• No
[If yes] To what extent did you trust the customer reviews of the product that you selected?
• A great deal
• A fair amount85

• Not very much
• Not at all• Don’t know

Did you search online for more information about the products?
• Yes
• No
[If yes] Where did you search for more information? (choose all that apply)• Search engine• Expert review site
• Retailer website• Manufacturer website• Amazon.co.uk• Other

To what extent do you generally trust customer reviews on Amazon.co.uk?
• A great deal
• A fair amount• Not very much
• Not at all• Don’t know
• I don’t shop on Amazon.co.uk

How easy or hard is it to tell if a customer review on Amazon.co.uk is fake?
• Extremely easy
• Somewhat easy
• Neither easy nor difficult86• Somewhat difficult• Extremely difficult• I don’t shop on Amazon.co.uk

What share of reviews on Amazon.co.uk do you believe are fake?
[Choose on a scale of 0 - 100]
Do you typically do any of the following when buying products on Amazon.co.uk? (choose
all that apply)• Read customer reviews• Look at the profiles of customers who have left reviews on the product• Find and read the most critical customer reviews (e.g., 1-star reviews)• Look at the average star rating• Look at the distribution of star ratings (e.g. how many gave 5-stars compared to 1-stars)• Look at the number of reviews• Look at the date of reviews• Use online tools to help determine the quality of reviews• None of the above• I don’t shop on Amazon.co.uk

In the last 12 months, have you bought or searched online for [product category]?
• Yes
• No
• Don’t know

[If respondent wins the prize draw]
Congratulations! You have been selected to receive the prize.
Please provide your name and email address so we can contact you with further details toclaim your prize.
Your address information will only be used for the purpose of shipping your reward. We may
contact you through your email if we run into issues with the shipment of the prize.
87• Full Name
• Email Address• Address line 1• Address line 2• City• County• Post Code

End of Survey

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G

Survey questions - Eliciting willingness to pay

G.1

Introduction

Welcome and thanks for participating!
This short survey is being conducted by researchers at The Behaviouralist to gain a better
understanding of the prices consumers are willing to pay for different products. The survey
should take around 5-10 minutes to complete.
Please note that by clicking the button below to proceed, you acknowledge that your participation in the study is voluntary, that you are at least 18 years of age, and that you are awarethat you can end your participation in the study at any time and for any reason.
We will start by explaining how the exercise in this survey works. Please go through the
explanations carefully as your responses will be considered invalid if you do not understandhow the exercise works.
We will then give you a short exercise for practice and ask you a few comprehension questions
to make sure that you understand the instructions. We will not tell you if you passed thecomprehension question, so it is essential that you pay attention.
Once you have finished the comprehension questions, the actual survey will then begin. You
will first be asked to choose a product category that you are interested in.
You will then be shown a table containing information about five products within your chosen
product category. Please take your time to look at all five products and the information about
their features, ratings, and reviews.
We will then ask you how much you are be willing to pay for each of the five products. We
will do this by listing a number of prices and asking what the highest price is that you arewilling to pay.
All respondents who complete the survey will be entered into a prize draw for £300. Winners
will be randomly selected at the end of the survey, and the payment will be made via the
Prolific platform.
If you win the prize draw, we will randomly select one of the five products that you wereshown. We will then randomly choose a price point and will implement your choice for thatprice point.
Please see below for an example of this works:
Imagine that we randomly select the Bose Headphones. We then go on to randomly select a89

price point of £25 for that product. Then:
1. If you said that you are willing to pay £25 or more for Bose Headphones, we will send you
Bose Headphones and the remainder of your prize money (which is £275).
2. If you said that you are not willing to pay £25 or more for Bose Headphones, we will only
send you the prize money (which is £300).
It is therefore in your best interest to answer as honestly as you can about the price that you
are willing to pay for each of the five products.

G.2

Comprehension questions

Before we proceed with the survey, we will ask you to complete a practice exercise to makesure you fully understand the instructions. We may exclude your responses from our analysis
if you fail to answer the practice questions correctly.
The table below shows two bluetooth speakers and their product features and other details.
Please go through the information and answer the questions below.

90Figure A16: Table for comprehension questions

Comprehension Question 1: How much is the JBL Charge 4 bluetooth speaker worth toyou (in £)?
Comprehension Question 2: What is the highest amount you would pay for the JBL Charge
4 bluetooth speaker?
For example, if the product is worth £100 to you, you should choose that your highest willingness to pay is £85.
Comprehension Question 3: How much is the Roberts Beacon 310 bluetooth speaker
worth to you (in £)?
Comprehension Question 4: What is the highest amount you would pay for the Roberts
Beacon 310 bluetooth speaker?
Even if you prefer one product to the other, you should still tell us how much you are will91

ing to pay for both, as we will randomly select ONE product and ONE price point for that
product.
Comprehension Question 5: Imagine that you won the prize draw (for £300) and we randomly picked the JBL Charge 4 bluetooth speaker. We also randomly picked the pricepoint of £60. Based on the choices that you made above, what will you receive?
Thank you for your responses to the practice questions!

G.3

Product category choice

We will now begin the study.
Please choose the product category you are most interested in.
• Headphones• Dash Cams
• Cordless Vacuum Cleaners

G.4

Willingness to Pay

The table on the next page shows five [product category] and their product features and otherdetails. Please go through the information carefully and answer the questions below the table.
To help you evaluate the products, the table includes information about product recommendations and test scores from Which?. Which? is a UK’s consumer protection organisation that
conducts rigorous testing, rates products, and publishes their recommendations and warnings on their website.
Which? recommendation shows whether a product is labelled ’Best Buy’ or ’Don’t Buy’ by
Which?. ’Best Buy’ products are the very best products that have satisfied or exceeded specificcriteria based on the results of rigorous comparative tests and analysis carried out by Which?.
’Don’t Buy’ products are the very poor products that are a waste of money at best and a threat
to safety at worst.
Which? test score is based on rigorous independent lab tests, which measure and assess eachof the most important aspects of usability and performance. The results of the expert tests
are combined to create a unique overall test score for the product.

92

Figure A17: Table for headphones

Notes. The product names, brand names, and product photos are available upon request.

93

Figure A18: Table for dash cams

Notes. The product names, brand names, and product photos are available upon request.

94

Figure A19: Table for cordless vacuum cleaners

Notes. The product names, brand names, and product photos are available upon request.

What is the highest price you are willing to pay for these products?
Remember that we will randomly select ONE product and ONE price point, so it is importantthat you provide us with your willingness to pay for all five products.
[Participants are shown a list of ten prices for each of the five products according to the
product category they have chosen.]
95

G.5

Demographics

Before we end the survey, please answer the following questions about yourself.
What is your age?
• 18 - 24• 25 - 34• 35 - 44• 45 - 54• 55 - 64• 65 - 74• 75 - 84• 85 or older

What is your gender?
• Male• Female• Other

Where do you live?
• East of England• East Midlands• Greater London• North East England• North West England• Northern Ireland96• Scotland• South East England• South West England• Wales• West Midlands• Yorkshire and the Humber

What is your yearly income?
• Up to £7,000• £7,001 - £14,000• £14,001 - £21,000• £21,001 - £28,000• £28,001 - £34,000• £34,001 - £41,000• £41,001 - £48,000• £48,001 - £55,000• £55,001 - £62,000• £62,001 - £69,000• £69,001 - £76,000• £76,001 - £83,000• £83,001+• Prefer not to say

What is your highest level of education?
• Primary school• Secondary school up to 16 years• Higher or secondary or further education (A-levels, BTEC etc)97• College or university• Post-graduate degree• Prefer not to say

On average, how much time, if any, do you spend online every day? By this, we mean using
the internet or an internet-enabled device, such as a computer, smartphone or tablet.
• Less than 1 hour
• 1 – 2 hours• 2 – 3 hours• 3 – 5 hours• More than 5 hours• Don’t know

How often do you purchase items online?
• Most days
• About once or twice per week
• About once or twice per month• About once every three months• About once every six months• About once every year
• Less often than once a year
• Never• Don’t know

How often do you purchase items on Amazon.co.uk?
• Most days
• About once or twice per week98• About once or twice per month• About once every three months• About once every six months• About once every year
• Less often than once a year
• Never• Don’t know

End of Survey

99