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BEYOND WORDS: UNVEILING THE IMPLICATIONS OF BLANK REVIEWS IN ONLINE RATING
SYSTEMS

 * Original Research
 * Open access
 * Published: 18 September 2024

 * (2024)
 * Cite this article

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Information Technology & Tourism Aims and scope Submit manuscript
Beyond words: unveiling the implications of blank reviews in online rating
systems
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 * Juan Pedro Mellinas  ORCID: orcid.org/0000-0002-4390-82921 &
 * Veronica Leoni  ORCID: orcid.org/0000-0001-8419-42412,3 

 * 170 Accesses

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ABSTRACT

This study analyzes how review length relates to numerical scores on online
platforms, conducting separate analyses for positive and negative comments and
accounting for non-linearities in the relationship. Moreover, we consider the
role played by blank reviews, i.e. those ratings without textual content, a
topic that has been largely overlooked in previous works. Our findings suggest
that blank reviews are positively correlated with higher scores, which has
important implications for the ordering of reviews on online platforms. We
propose that these results can be explained by social exchange theory, which
suggests that less strict review policies could increase engagement and lead to
a more balanced evaluation of establishments. This could offset the tendency of
dissatisfied guests to disproportionately report negative experiences. Future
studies should compare the composition of guest reviews on platforms adopting
differing review policies.




SIMILAR CONTENT BEING VIEWED BY OTHERS


ANALYZING THE IMPACT OF REVIEW RECENCY ON HELPFULNESS THROUGH ECONOMETRIC
MODELING

Article 08 June 2020


DO SAME-LEVEL REVIEW RATINGS HAVE THE SAME LEVEL OF REVIEW HELPFULNESS? THE ROLE
OF INFORMATION DIAGNOSTICITY IN ONLINE REVIEWS

Article 16 November 2020


THE INTERACTIVE EFFECT OF REVIEW RATING AND TEXT SENTIMENT ON REVIEW HELPFULNESS

Chapter © 2015
Use our pre-submission checklist

Avoid common mistakes on your manuscript.




1 INTRODUCTION

Online reviews have become an increasingly important factor driving consumers’
purchasing decisions. The impact of online generated content on consumer
behaviour has been at the centre of academic interest, especially within the
fields of tourism studies. Online reviews on Online Travel Agencies (OTA)
websites, allow consumers to share their experiences and opinions about products
and services with a wider audience. Not only do they provide valuable
information to potential buyers, but they can also serve as a form of social
proof, helping to shape the reputation of a company.

Most studies in the existing literature focus on the effect of numerical ratings
on key aspects of products and services, such as prices (Rezvani and Rojas
2020), performance (Anagnostopoulou et al. 2019), and survivorship (Leoni 2020).
Numerical scores offer consumers a simple and efficient method to evaluate the
quality of products or services and make informed decisions (Acemoglu et al.
2022; Confente and Vigolo 2018), especially for experience goods whose true
quality can only be determined through use (Nelson 1970). However, the
accompanying text is just as important, as it allows the reviewer to provide
more details and context about their experiences. Research suggests that reviews
that include both ratings and text are more valuable to users than those without
text (S. Lee and Choeh 2016). Additionally, longer reviews that provide more
information are perceived as more helpful by the end-user (Li and Huang 2020).

While it is quite straightforward to imagine that the length of a review could
affect its perceived usefulness, previous research on this topic has also
explored on the relationship between length and the score magnitude. Empirical
research has consistently shown that reviews containing a greater volume of text
are more likely to convey negative sentiments and receive lower ratings. One
plausible explanation is that negative experience tends to trigger stronger
emotional responses, which in turn may motivate individuals to write more
extensively about their dissatisfaction (Chevalier and Mayzlin 2006).

The design of review provisions varies significantly across online platforms.
Some platforms like TripAdvisor, for tourism-related products, and IMDB, for
entertainment content, have a minimum character requirement for submitting a
rating. On the other hand, platforms like Amazon (for commodities), Booking.com
(for tourism products), and Google (for both goods and services) provide
additional options for consumers to assess products and services, allowing users
to submit scores without any accompanying text. The differences in review
provisions across platforms may have important implications for the quality and
usefulness of the reviews. However, there is a gap in the academic literature,
especially when it comes to blank reviews i.e. those rating without any textual
content. Applied studies on online reviews tend to systematically exclude such
reviews (Antonio et al. 2018), resulting in potentially myopic conclusions. On
these premises, this examination seeks to provide a more comprehensive
understanding of the workings and dynamics of online reviews, given their
pivotal role in shaping consumer decision-making process.

To this purpose, we work on a rich dataset of more than 450,000 individual
reviews, provided by consumers staying in hotels booked through Booking.com, the
leading European OTA. Each record consists in three main parts: (i) reviewing
guest characteristics; (ii) numerical score rating; (iii) (optional) written
textual content. Applying linear regression model, we test the relationship
between the reviews’ length and the numerical score, taking advantage of the
unique structure of Booking.com reviews which have separate sections for pros
and cons. This allows also us to analyse positive and negative comments
separately. We consider the possibility of non-linear relationships between the
number of (positive and/or negative) words and the score.

Importantly, we focus on the association between numerical scores and blank
reviews. This aspect underscores the originality of our work, as we reveal a
significant association: blank reviews often coincide with exceptionally high
scores. In this regard, we discuss the potential relationship between the
increasing use of mobile devices for writing reviews and the presence of
text-less the reviews. Moreover, we contribute to a critical debate on the role
of review ordering, especially in the presence of a significant proportion of
blank reviews, considering their tendency to correlate with high scores.


2 LITERATURE REVIEW

Empirical research has consistently shown that the presence of text in a review
enhances its value to other users (Lee and Choeh 2016; Ludwig et al. 2013).
Moreover, the length of textual content has also been found to positively impact
their perceived helpfulness (Li and Huang 2020; Park and Nicolau 2015). Longer
reviews are not only viewed as more useful in determining product quality, but
they are also perceived as more credible signal than shorter reviews (Filieri
2016).

In the realm of tourism, the motivations of users to share their experiences
through online reviews have been studied using various theories from the fields
of psychology, sociology, and consumer behaviour. Among them are the Social
Cognitive theory (Munar and Jacobsen 2014), Extended Unified Theory of
Acceptance and Use of Technology (Herrero et al. 2017), Social Identity theory
(Lee et al. 2014), Social Influence theory (Oliveira et al. 2020) and
Self-determination theory (Hew et al. 2017). But Social Exchange Theory is
widely considered the most prevalent theoretical framework used to explain the
motivations behind writing online reviews (Blau 1964; Homans 1958). According to
this theory, individuals’ decision to share information is based on a
cost-benefit analysis of the sharing process (Benoit et al. 2016). Therefore,
such theory has a good fit describing the propensity and motivations to share
content on social media and other reviewing platforms (Cheung and Lee 2012; Wang
et al. 2019; Liu et al. 2019). Within this framework, when the perceived
expenses associated with sharing exceed the potential advantages, individuals
are less inclined to write an online review.

The degree of consumers’ satisfaction/dissatisfaction with a product or service,
also plays a fundamental role. When individuals are particularly dissatisfied
with a product or brand, they are more likely to express their negative
experiences to others (Bakshi et al. 2021; Eslami et al. 2018; Gretzel and Yoo
2008; Hennig-Thurau et al. 2004). This is in line with the theory of social
sharing of emotions, which posits that individuals are more willing to share
their negative emotions with others (Rimé 2009; Rimé et al. 1998). Consumers
tend to write reviews when they have a strong desire to express themselves, and
dissatisfaction appears to increase the likelihood of writing a review (Anderson
1998).

In addition to being a stronger motivator for writing a review than
satisfaction, dissatisfaction may also lead to longer reviews. Researchers have
studied the relationship between review length and review score for various
products and services, and have found that longer reviews are often associated
with lower scores in the hotel industry (Zhao et al. 2019). This pattern has
also been observed in Amazon products (Eslami et al. 2018; Korfiatis et al.
2012), services reviewed on Yelp and Google (Agarwal et al. 2020; Hossain and
Rahman 2022; Pashchenko et al. 2022), and other services such as insurance,
financial services, and specialized drug treatment facilities (Agarwal et al.
2020; Ghasemaghaei et al. 2018; Hossain and Rahman 2022).

The widespread adoption of smartphones has significantly increased the
accessibility of online review systems, which has contributed to the growth in
the volume of online reviews (Burtch and Hong 2014). However, while rating on a
smartphone may be more convenient and user-friendly than on a desktop computer,
typing lengthy texts may not be as convenient, which can impact the quality and
quantity of text-based reviews submitted through mobile devices. This is
confirmed by a study using London hotel reviews, finding that the share of
text-less reviews is higher for smartphone than desktop devices. Also, when they
contain text, it is usually less extensive and with limited analytical thinking
(Park et al. 2022). This deterioration in the quality of reviews when written
with mobile devices has also been identified in other studies (Mariani et al.
2019; Mudambi and Schuff 2010; Park 2018). Interestingly, Mariani et al. (2019)
found a significant increase in the percentage of reviews submitted through
mobile devices. At the beginning of 2015, mobile and desktop reviews were
similar in percentage, but by the end of 2016, mobile reviews nearly doubled
that of desktop reviews.

ReviewTrackers’ recent research (2022) analyzed review length on Facebook,
Google, TripAdvisor, and Yelp from 2010 to 2020. The study found that the
increasing use of mobile devices is linked to a 65% decrease in review length
over the decade. This trend towards mobile devices is impacting review length on
prominent platforms, including Booking.com. Yoon et al.‘s (2019) study on
TripAdvisor data also supports these findings, indicating that reviews submitted
through mobile devices tend to be shorter and of lower quality compared to those
submitted through desktop computers in the tourism sector. However, the study
also sought to confirm the relationship between review length and score,
disaggregated by device type. Interestingly, the results showed a significant
difference between desktop and mobile reviews. While mobile reviews exhibited an
inverse relationship between review length and score, with shorter reviews
tending to have higher scores, this was not the case for desktop reviews. In
other words, there was no apparent correlation between review length and score
for reviews submitted through desktop computers.

Platforms typically display reviews in a default order, which can be modified by
applying different criteria, such as date and language. The length of textual
comments also plays a significant role in review appearance on platforms. In the
case of Google Reviews, that default order reward reviews that contain text and
photographs or that are more recent. Reviews without text or photos often rank
lower in online review systems because they provide minimal information and are
less helpful to potential users who are trying to make informed decisions.
TripAdvisor seems to take into account solely the language and date, that is to
say, in the first positions the reviews of the user’s language ordered from most
recent to oldest. Booking.com also has default review order criteria, which it
explains on a section of its website that it first shows the reviews in the
user’s language and “Anonymous reviews and those with scores but no comments
will appear lower down the page” (Booking.com 2023). This means that if a review
submitted recently does not contain any text or photos, it will not be displayed
on the first pages of the list of reviews that are shown by default.

The order in which reviews are presented on booking platforms can have a
significant impact on consumer behavior. The appearance order can also sway an
individual’s opinion as prior ratings may act as a reference point. Research has
demonstrated how prior ratings can impact people’s perceptions and their
intention to rate (Cicognani et al. 2022). The importance of considering the
impact of others’ opinions on individual actions is supported by both
well-grounded economic and psychological theories, such as the anchoring effect
and social bias (Cicognani et al. 2022; Book et al. 2016; Muchnik et al. 2013).
These theories suggest that individuals are influenced by the opinions and
actions of those around them, and that this influence can significantly shape
their own behaviour. Blank reviews play a crucial role in this context. One
might argue that if textless reviews are consistently relegated to the bottom of
the list and given low scores, they may be overlooked by consumers, leading to
uninformed decisions. On the other hand, if blank reviews receive high scores,
their placement at the bottom of the list could potentially harm business
results, as less satisfied customers’ reviews will be the first to catch
consumers’ attention. Therefore, it is crucial to conduct a thorough analysis to
determine whether blank reviews are consistently assigned by extremely satisfied
or dissatisfied customers, or if they are not specifically associated with
either group. This analysis will provide valuable insights into the distribution
of blank reviews and their potential impact on consumer behavior.


3 DATA AND METHODOLOGY

The following subsections provide a description of the dataset used in the
analysis and the empirical strategy adopted to inspect the correlation between
the review text length and the overall score.


3.1 DATA

We use data collected on four European cities: Milan, Madrid, Zurich and
Brussels. The data has been gathered from the Booking.com website, using a
custom-designed web crawler which enables us to retrieve information about
reviews both at the hotel level and at the individual levelFootnote 1. From a
geographical standpoint, analyzing multiple destinations improves the generality
of our results and mitigate the risk of any singular destination’s idiosyncratic
effects influencing the results. Moreover, the set of cities considered in the
current study displays a good balance between leisure and business tourism. The
selection of Booking.com as platform for gathering online reviews is driven by
its numerous advantageous features. Firstly, the platform utilizes a scale of
1–10 for rating, as opposed to the commonly used scale of 1–5, which allows for
greater variability in the data collected (Mellinas and Martin-Fuentes 2021;
Leoni and Boto-Garcia 2023). Secondly, it features two distinct sections for
positive and negative feedback, providing a more accurate representation of the
overall experience. This is particularly useful as current sentiment analysis
software, while advanced, may not be able to accurately distinguish between
positive and negative aspects of a review. Thirdly, Booking.com also provides
some information on the guests’ characteristics, such as the name, the type of
travel party, the nationality, the length and the time of the stay. This allows
for a more comprehensive analysis as it provides insights into the demographic
profile of the guests, which can also be used to control for preferences and
expectations of different groups of travellers. Lastly, as opposed to other
platforms, Booking.com is a verified online review platform which prevent the
risk of fake reviews, ensuring higher reliability and accurateness of the
collected data (Figini et al. 2020). Booking.com currently requests customers to
evaluate their hotel experience through an email sent after they check out,
which ensure legitimacy of reviews. The evaluation process entails guests rating
their overall experience on a scale of 1 to 10. Guests can also provide a
non-mandatory textual comment about the positives and negatives of their stay in
separate sections.

The dataset comprises approximately 458,000 individual reviews, contributed by
guests who stayed in one of the 423 hotels, condo hotels, and guesthouses
included in our analysis. We deliberately excluded other types of
accommodations, such as private apartments, to ensure comparability in terms of
critical mass of reviews. About half of the accommodation is in Milan (48%),
followed by Madrid (29%), Brussels (13%) and Zurich (10%). However, in terms of
individual records, Madrid has the highest number of reviews (47%), followed by
Brussel (23%), Milan (21%) and Zurich (9%). Each observation has three main
pieces of information: (i) travelled- related characteristics, including the
guest’s name (which could also be displayed as anonymous), the type of travel
party, the length of stay in the accommodation, the nationality, the travel
period (month and year), and the review date; (ii) an overall numeric score, on
a scale from 1 to 10; (iii) a textual review composed by pros and cons sections.
It is important to note that Booking.com does not required to provide a textual
review in order to provide a numeric evaluation of the received service. This
implies that some reviews in the dataset have no textual content. Reviews are
all in their original language.

As per the temporal frame covered, we considered all reviews for overnight stays
in 2021 and 2022. This allows to exclude from the analysis the hotel lockdowns
periods of 2020 due to Covid-19 pandemic (Leoni and Moretti 2023), and also, to
some extent, allow for a higher homogeneity by avoiding the effects of changes
in the Booking.com score system, implemented between late 2019 and the beginning
of 2020 (Mellinas and Martin-Fuentes 2021). This approach helps us avoid the
complications of mixing reviews calculated using different scales and
calculation systems within the same study.

Table 1 displays the descriptive statistics of the considered sample and a brief
description of the variables used in the econometric model. The overall score,
which acts as our dependent variable, has been transformed into a natural
logarithm to handle with its strong asymmetry, characterized by a highly left
skewed distribution (Mariani and Borghi 2018). About 3,6% of reviews are left
guests who decided not to reveal their identity. Such share is consistent across
cities. The average percentage of reviews from domestic guests is 35%, but this
distribution varies across different cities due to the different weights of
domestic travel in the analyzed countries. Madrid displays the highest share of
domestic travel (44%), followed by Milan and Zurich (around 23%), and Brussels
(13%). On average, people stayed 2.19 days in the accommodation, again with a
high degree of variation (min.1, max 39). However, the 99th percentile of the
length of stay variable is less than 8 days, such distribution being common to
all cities. The temporal distribution of reviews shows the classic seasonality
of European destinations, with a peak of reviews during summer months. By
contrast, 2022 is over represented with respect to 2021, accounting or the 65%
of the sample. This is most likely linked to the fact that, during part of 2021,
despite the end of lockdowns, the ongoing COVID-19 pandemic resulted in fewer
guests staying in accommodations, thus leading to fewer reviews being posted.
43% of reviews belong to trips made in couple, followed by family (25%),
solo-traveler (18%,) and group (14%). We include two metrics per hotel over
time: the average score and average review length (word count) up to the week
before the individual review. This approach considers prior ratings and the
hotel’s reputation and feedback evolution in determining guests’ expectations.

Table 1 Descriptive statistics of review scores and guests’ profile
Full size table

Table 2 presents a collection of hotel time-invariant characteristics. Based on
the descriptive statistics, less than 1% of hotels are classified as one-star
properties, while approximately 5% are categorized as two-star properties. The
majority fall into the four-star category, accounting for 53% of the total.
Furthermore, 34% of hotels are classified as four-star properties, while roughly
6% of hotels are five-star establishments. Notably, a small percentage of hotels
on the Booking.com website lack a star classification. For traditional
accommodations, the star rating is assigned by an official organization and
transmitted to Booking.com by the hotel. In contrast, alternative accommodations
are rated by the platform itself, based on various characteristics.

Table 2 Descriptive statistics of providers’ characteristics
Full size table

In terms of the type of accommodation property, hotels make up 93% of the total,
followed by 7% of Condo Hotels (apartments and aparthotels). The remainder of
the accommodations are represented by guesthouses. Location-wise, the distance
from the city center is considered for each city and ranges from 5 m to 150 km,
with an average distance of 800 m.

Regarding hotel amenities, approximately 54% of hotels offer private parking,
while 97% provide free Wi-Fi. Moreover, 82% of hotels offer an all-day reception
service, and 18% have a laundry service. In terms of room cleaning, 24% of
hotels provide daily cleaning services. Additionally, around 58% of hotels have
a lift, and 28% of hotels have a gym area.

As per the key variables of the study, almost half of the reviews have no
textual comment. Such share is constant over the two-year considered (No
statistically significant difference in the average values for the two years
under consideration (Pr(|T| > |t|) = 0.753). The pros sections tend to be longer
(on average 8 words) than the cons section (on average 2 words), such variables
display a very pronounced standard deviations, especially for the pros section
(St.Dv. 19.97). On average, reviews were one word shorter in 2022 compared to
2021, based on a simple comparison of means (Ha: diff != 0 Pr(|T| >
|t|) = 0.0000). Table 3 displays the percentile distribution of the review’s
length -related variables.

Table 3 Percentile distribution sample of the review’s length
Full size table

Moreover, in Table 4, we display the average number of words for the pros and
cons sections and the share of blank reviews, for different intervals of the
dependent variable. We offer a descriptive analysis of the joint distribution of
the overall score and review length for positive and negative aspects. This
analysis provides an understanding of how review length relates to overall
score. Blank reviews shed light on how often guests choose not to leave a
review. This analysis can serve as a starting point for more detailed
statistical analysis of the variables’ correlation.

Table 4 Average number of words and share of blank reviews, by score level
Full size table


3.2 EMPIRICAL STRATEGY

In this subsection we use the dataset described above to explore the correlation
between the score evaluation and the reviews’ length. For sake of clarity, it is
important to underline that the analysis, and the following interpretation of
results, does not allow for a causal discourse. As a matter of fact, the
analytical and textual reviews are simultaneous and the correlation between them
does not necessarily imply causality. On these premises, we estimate the
following baseline log-linear model:

$$\:{Y}_{iht}=\:\alpha\:\:+\beta\:{\prime\:}{X}_{i\:\:}++{\mu\:{\prime\:}W}_{h\:\:}+\:\phi\:{\prime\:}{ReviewStock}_{ht\:\:}+\:\gamma\:{TextLenght}_{iht\:\:}+{TimeFE}_{t\:\:}+{\epsilon\:}_{iht}$$
(1)

where \(\:{Y}_{iht}\) is the natural logarithm of the score left by guest i and
for the hotel h at the time t (with t being the exact date of the review)
\(\:{X}_{i\:\:}\) a vector of guests’ controls, \(\:{W}_{h\:\:}\)is a vector of
hotel time-invariant characteristics (including amenities, stars, and location),
while \(\:{ReviewStock}_{ht\:\:}\)account for the characteristics of the
pre-existing stock of reviews (average score and text length) which varies
overtime. We control also for time fixed effects. To keep an easy notation, we
use a generic time t. However, it should be noted that while the time of the
review is daily, the aggregation level of the review stock is weekly, while
fixed effects are on a monthly basis. \(\:\gamma\:\) is the main parameter to be
estimated, which express the correlation between the text length and the
numerical score. \(\:{\epsilon\:}_{ih}\) is a normally distributed error term.
To account for auto-correlated error terms for the hotel over time, we cluster
standard errors at the hotel level. The models are estimated using classic OLS
regression.

In addition to the baseline specifications, we explore other aspects of the
relationship between score and text length. These include:

 1. (i)
    
    Analyzing the length of pros and cons sections separately;

 2. (ii)
    
    Examining the linearity of the relationship between the number of words and
    score by adding quadratic terms to the regression analysis;

 3. (iii)
    
    Considering the association between blank reviews and the attributed score;

 4. (iv)
    
    Proposing an alternative model specification by excluding Wh and
    incorporating hotel fixed effects for better control of unobserved
    heterogeneity;

 5. (v)
    
    Exploring potential geographical heterogeneity.


4 RESULTS

This section reports the estimates of the model in (1) as well as of the
extended specifications described in the methodology section.

In Table 5, Column 1 displays a positive association between the length of the
full review (wordcount total) and the numerical score (lnscore). Ceteris
paribus, additional words link to a 0.5% decrease in score. However, this
measure overlooks the sentiment of the text and fails to account for the
difference between blank reviews and those with a positive word count. When the
analysis includes a dummy variable for text-less reviews (blank review), the
relationship is initially positive, but reaches a turning point. This aligns
with previous research indicating that dissatisfied customers tend to write
longer reviews (Zhao et al. 2019). The quadratic term (TotalWordCount Squared)
significance indicates a non-linear relationship, with a negative correlation
between variables as word count increases. These findings emphasize the
importance of separating blank reviews to avoid biased estimates. Figure 1
provides a visual representation of the relationship between score and word
length under both specifications.

Table 5 Regression estimates
Full size table
Fig. 1

Marginal effects of the model in Column 1 and Column 2 (Table 4)

Full size image

Our results reveal interesting insights regarding the association between the
length of positive and negative comments and the overall score of the stay. In
Column 2 we show that negative comments (wordcount cons) tend to be associated
with lower scores, with the lowest score occurring at the 149th word. On the
other hand, in Column 3, we see that lengthy positive comments (wordcount pros)
are associated with higher scores, but this effect is also nonlinear. We
observed an inverted U-shape relationship with a negative interaction parameter
(Wordcount_pros Squared), indicating that the gain in score decreases after a
certain number of words. Specifically, we identified the maximum of the study
function to be at the 72th word, which we have graphically represented in
Fig. 1.

In line with Lind and Mehlum (2010), we provide further evidence of a
statistically significant non-linear relationship for both pros and cons. To
verify this, we check two conditions: (i) the first and second derivatives have
the correct sign, and (ii) the extreme points fall within the data range but not
too close to the minimum and maximum values. Additionally, we calculated
confidence intervals for the maximum and minimum points using the
Fieller-interval method corrected for finite samples. U-test results indicate
that positive comments demonstrate an inverted U-shaped relationship. However,
we found insufficient evidence to suggest that negative comments have a U-shaped
relationship. More specifically, the test confirms that the relationship is
significant for positive comments (t-value = 14.92; 95%; Fieller interval for
the extreme point: [67.246489; 76.936343]). Conversely, the minimum of the
relationship between cons and the score falls outside the sample range (minimum
in 421 words). These results are further confirmed by visual inspection of the
two relationships, as represented in Fig. 2.

Fig. 2

Prediction margins (with 95% confidence intervals) for negative (panel a) and
positive (panel b) reviews

Full size image

The current study explores the correlation between analytical score and blank
reviews, which is a novel aspect. Results indicate that more satisfied consumers
tend to provide blank reviews, all else being equal. On average, text-less
reviews (Blank Review) display 5.76% higher scores (Marginal effect calculated
using Halvorsen and Palmquist correction for log-linear model interpretation
(Halvorsen and Palmquist 1980),compared with wordy reviews, regardless of the
length and sentiment. Given that the great majority of reviews have a limited
number of words, as previously displayed in Table 2, in Column 4 of Table 5 we
have limited our analysis to the third quartile of the distribution, which
includes reviews with maximum 12 words. The results show that blank reviews
(Blank Review) still result in higher scores. To understand the impact of each
word, we analyzed the effect of adding each additional word with a maximum of 12
words. We found a significant decrease in average scores as the number of words
increased. On average, scores with one-word text are 17% lower compared to blank
reviews, while two-word and three-word reviews are 11% and 8% lower,
respectively. Figure 3 illustrates the marginal effects of word count on the
score.

Fig. 3

Marginal effects (with 95% confidence intervals) on each additional word on the
(ln) score, with respect to blank reviews

Full size image

Overall, the analysis and corresponding graphical representation suggest that a
quadratic effect alone may not adequately capture the complex relationship
between the number of words and the numerical. To better represent this
phenomenon, we propose a more flexible approach. Firstly, we introduce a new
dummy variable to distinguish blank reviews (Blank Review) from those with a
positive count of words. Secondly, we differentiate between positive (wordcount
pros) and negative reviews (wordcount cons), which significantly improves the
model’s performance. Our findings highlight the importance of separately
considering blank reviews to enhance the explanatory and predictive power of the
model.

Although not the primary focus, we provide a brief analysis of controls,
including guest profiles and time-invariant hotel characteristics. Anonymous
guests report lower scores, while guests who stay longer (LengthOfStay) at the
hotel report higher scores. Families display the highest level of satisfaction,
followed by Groups and Couples, compared to Solo travelers. Three, four, and
five-star hotels received higher scores than unrated properties, but there was
no significant difference for lower-end accommodations. Location plays a
significant role, with hotels farther from the city center (DistanceKm)
receiving lower scores. Interestingly, hotel amenities did not have a
significant effect on the final evaluation, as guests were likely aware of them
at the time of booking. Lastly, Condo tend to receive higher scores than Hotels
and Guesthouses.


4.1 ROBUSTNESS CHECKS

In this section, we present a series of robustness checks that we conducted to
test the validity of our results. We provide the detailed results of these tests
in the Online Appendix Section.

As explained in the methodological section, we included a set of hotel
characteristics in our model specification that we believe may also impact
evaluations. However, we acknowledge that there may still be unobserved
heterogeneity due to unknown attributes. Therefore, we conducted additional
analyses, which are reported in Table A1 (Appendix). This time, we included
hotel fixed effects instead of the vector \(\:{W}_{h}\). Overall, we found that
our results are robust to the main specification.

The study’s inference initially used clustered standard errors at the provider
level but additional analyses with standard errors clustered at the postal code
level showed no change in the significance level, except for the number of total
words, which showed no relationship with the numerical score. Table A2 displays
the results.

To conclude, we attempt to investigate the heterogeneity across various
geographical regions, using data from four cities in four European countries.
However, our analysis, as detailed in Table A3 of the Appendix section, did not
yield any conclusive evidence of significant differences between the four
countries, except for Madrid which showed a higher level of satisfaction
compared to the reference category (Brussels). As an additional check, we
explored whether there were significant differences in the frequency of blank
reviews based on guests’ nationality. In investigating how geographical location
influences guest reviews, we introduced an interaction term between the ‘blank
reviews’ variable and a dummy variable indicating whether the guest is from
Europe (Europeans comprise approximately 57% of the sample). Intriguingly,
results (Column5, Table A3) reveals that, on average, European guests tend to
give lower scores compared to guests from other continents. However, Europeans
also demonstrate a higher propensity to submit blank reviews.


5 DISCUSSION AND CONCLUSIONS

In this paper we offer an empirical analysis on the relationship between the
review length and the numerical score of rated posted on Booking.com platform,
for hotels located in four European destinations: Milan, Madrid, Zurich and
Brussels. Our findings confirm a statistically significant relationship between
the number of words and the overall score rating, with longer reviews displaying
average lower scores (Zhao et al. 2019; Hossain and Rahman 2022; Pashchenko et
al. 2022). Besides confirming existing results, our work takes a step forward by
addressing two significant gaps in the existing research on this subject.

First, we offer a deeper understanding on the above discussed relationship by
analyzing positive and negative comments separately. This represents a novelty
with respect with existing studies, which looked at a more generic review
length. Moreover, we allow for non-linearity in the relation. We find that
longer positive comments are associated with higher score, such effect
displaying diminishing returns. That implies that the correlation of “more
words, lower score” does not hold true if the review content is positive.
However, there positive relationship between length (of pros) and score displays
diminishing returns (maximum point at the 79th word). By contrast, longer
negative comments are associated with lower score, such effect increasing for
very long reviews, in accordance with what is described in previous literature.

Second, we expand our understanding of a largely neglected aspect of online user
generated content: blank reviews. Previous studies have overlooked this area of
research, systematically excluding blank reviews from empirical analysis, but
our results reveal valuable insights. We not only confirm the positive
correlation between blank reviews and higher scores, which has been neglected so
far, but also delve deeper into their implications. In fact, our current work
precisely quantifies the differences in scores between text-with and text-less
reviews. The score difference between a blank review and review with a positive
number of words (regardless of the text length) is, on average, 6%.
Interestingly, when restricting the analysis to short reviews, we found that a
single-word review is significantly lower scored (− 17%) than a blank review.

The systematic tendency for blank reviews to display better scores has
non-trivial implications. In most review platforms, blank reviews are typically
positioned at the bottom of the review list, and while they still contribute to
the overall score calculation, they might not carry the same weight (in terms of
the value attached by prospective buyers) as reviews with text. By default,
platforms display reviews with text first, while blank reviews tend to appear in
the last positions. This implies that on average, the highest scores, which
belong to blank reviews, are hidden. The rationale for ranking text-less reviews
last links to their lower informative value. This approach can make sense when
the goal of the platform is to provide users with useful and high-quality
information. Nonetheless, our research indicates potential ramifications for
providers. Specifically, our findings suggest a systematic “over visibility” of
lower-scored reviews. Essentially, due to the placement of blank reviews at the
bottom of the list, they receive less visibility to customers. Coupled with
their correlation with higher scores, this may mean guests are primarily exposed
to lower scores. This can have some sort of impact on future consumer purchasing
and rating behaviors, as predicted by anchoring and social bias theories
(Cicognani et al. 2022; Book et al. 2016; Muchnik et al. 2013). If users pay
attention to the review scores displayed on the first page, influencing in a
similar way as the overall score does, that way of ordering the reviews could
have some impact on future purchase behaviors and evaluation of the users
(Cicognani et al. 2022). In any case, the impact of ordering on the extent of
social bias is beyond the scope of the current work. However, future studies
could attempt to evaluate this effect, ideally within an experimental framework.

Another significant issue is related to the device used to provide reviews.
Since the advent of smartphones, the percentage of reviews written on these
devices has been gradually increasing (ReviewTrackers 2022), to the point of
outweighing those written on conventional keyboard devices, especially for
platforms adopting more lenient reviewing policies (Mariani et al. 2023). It is
indeed true that the ease and speed of providing a review through a smartphone
increases the likelihood of actually submitting one, due to the reduced
opportunity cost. However, it must also be noted that writing on a smartphone
may make it more challenging to draft longer and more detailed comments. There
is hence a trade-off between the promptness of writing the review, which can
also improve the accuracy of remembered details (Chen and Lurie 2013; Yang and
Yang 2018; Healey et al. 2019), and the reduction in precision due to the use of
a mobile device. In light of this, our works complement a recent work by Mariani
et al. (2023) which explore the heterogeneity in the use of mobile devices in
platforms displaying different review policies. The authors find that, compared
to TripAdvisor, which requires a minimum number of characters, Booking.com,
which allow text-free reviews, records a higher share of comments made using
mobile devices. Given that, since short and blank reviews tend to have higher
ratings, which are most probably from mobile devices, the increase in smartphone
usage might lead to a disproportioned increase in ratings that does not
necessarily correspond to an improvement in services.

We could interpret this phenomenon under the lens of Social Exchange and other
related theories, which explain how the motivations for sharing information also
depend on the degree of effort required. In other words, the more the time (or
commitment) required to write a review, the lower will be the willingness to
participate in feedback sharing. Platform characterized by an easier reviewing
process, will therefore register a higher number of reviews. One way to make the
process easier is to allow for contributions with little or no text, as for the
case of Booking.com platform. This allows certain users, who would be willing to
collaborate but lack sufficient motivation to write a substantial amount of
text, to also be included in the process.

The results of our study, which are consistent with those previously published,
indicate that the motivation to provide highly detailed feedbacks (longer
reviews) is stronger for more dissatisfied customers. Therefore, more lenient
review would help counterbalancing the lower motivation of satisfied consumers.
That is, given the well-known negativity bias in world-of-mouth (Herr et al.
1991) and the higher motivations in sharing unsatisfaction, an establishment
evaluated on a platform that requires a minimum number of characters would
receive a lower overall rating compared to that received on a text-free policy
platform as suggested by Mariani et al. (2023).

However, the higher participation in review provision, which is attributed to
lower opportunity costs, could also lead to an increase in impulsive scoring,
which reflects the hotel quality with less accuracy. It is then worth
considering the trade-off between having more reviews, which provides
prospective buyers with more opinions, and the loss of detail associated with
quickly written reviews. In other words, we believe that a higher number of
reviews does not necessarily translate into higher informational value for
consumers (Acemoglu et al. 2022), due to the lack of effort displayed in blank
reviews. In this sense, sometimes less is more.

Our work contributes to the existing literature is four ways. First, it
contributes to the extant literature on the relationship between review length
and score, using a larger and more complete dataset than previous studies.
Second, it prompts a rethinking of the previously established consensus
regarding the correlation between review length and score by emphasising the
distinction between pros and cons. Third, to the best of our knowledge, our
study is the first of its kind to examine blank reviews, which, on platforms
like Booking.com, make up nearly half of all reviews. Specifically, our study
identifies and quantifies the difference in score associated with blank reviews.
Fourth and last, offers a critical discussion on the under-explored area of the
review ordering, even though is not crucial aspect of the empirical setting.

From a methodological standpoint, our work sheds light on an important aspect
that should be taken into account in future studies. Specifically, our analysis
and graphical representation reveal that a quadratic effect may not adequately
capture the complex relationship between the number of words and numerical
score. To address this issue, we suggest relaxing this rigid structure by (i)
adding a new dummy variable to the model to distinguish between blank reviews
and those with a positive word count, and (ii) differentiating between positive
and negative reviews. This approach significantly improves the model’s
performance and underscores the importance of considering blank reviews
separately to improve the model’s explanatory and predictive capabilities.

Our findings could be extended also to other similar websites that collect
online reviews of products and services, such as Google Places or Amazon. This
opens up opportunities for a critical discussion and further research about the
ideal length of text that platforms should allow, both in terms of maximum and
minimum character requirements, also beyond the tourism industry. Given that
these decisions will be made by private entities, which currently control the
entire online review ecosystem, it is likely that commercial considerations will
take precedence over the goal of capturing the most accurate information
possible.

Limitations of this study include the inability to differentiate between mobile
and desktop review. Although the results are very similar for hotels in four
cities from 4 different European countries, we cannot guarantee external
validity of the results to other cities (with different city sizes and on
different continents), other non-urban destinations (beach, rural, ski, etc.) or
types of tourist accommodations (apartments, villas, B&B, etc.). While
Booking.com was optimal, exploring other platforms with similar features is
useful. A follow-up study on consumer behavior and psychology is valuable to
determine if ordering, especially for blank reviews, affects consumer
perceptions and rating attitudes. That follow-up study could also delve into
whether the obtained results have an explanation extending beyond the realm of
social exchange theory.


DATA AVAILABILITY

Replication files are available upon request to the authors.


NOTES

 1. We employed Octoparse software (https://www.octoparse.com/).


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FUNDING

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer
Nature. This study was funded by the Spanish Ministry of Science and Innovation
Spanish MCIN/AEI/10.13039/501100011033/ FEDER, UE within the RevTour project
[Grant Id. PID2022-138564OA-I00] “Use of online reviews for tourism intelligence
and for the establishment of transparent and reliable evaluation standards”.


AUTHOR INFORMATION


AUTHORS AND AFFILIATIONS

 1. Departamento de Comercialización e Investigación de Mercados, Universidad de
    Murcia, Murcia, Spain
    
    Juan Pedro Mellinas

 2. Department of Applied Economics, University of the Balearic Islands,
    Carretera de Valldemossa km 7.5, 07122, Palma de Mallorca, Spain
    
    Veronica Leoni

 3. Center for Advanced Studies in Tourism, University of Bologna, Via Angherá,
    Rimini, 22 47921, Italy
    
    Veronica Leoni

Authors
 1. Juan Pedro Mellinas
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 2. Veronica Leoni
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Correspondence to Veronica Leoni.


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Mellinas, J.P., Leoni, V. Beyond words: unveiling the implications of blank
reviews in online rating systems. Inf Technol Tourism (2024).
https://doi.org/10.1007/s40558-024-00300-4

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 * Received: 12 February 2024

 * Revised: 24 June 2024

 * Accepted: 03 September 2024

 * Published: 18 September 2024

 * DOI: https://doi.org/10.1007/s40558-024-00300-4


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