Online Reviews: A Literature Review and Roadmap for
Future Research
Martina Pocchiari∗
Davide Proserpio†
Yaniv Dover‡
August 13, 2023
∗ NUS
Business School, National University of Singapore, Singapore 119245.
Email:
pmartina@nus.edu.sg.
† University of Southern California Marshall School of Business, Los Angeles 90089. Email:
proserpi@marshall.usc.edu.
‡ Jerusalem Business School at the Hebrew University of Jerusalem, and The Federmann Center
for the Study of Rationality, Jerusalem 91905. Email: yaniv.dover@mail.huji.ac.il.
Electronic copy available at: https://ssrn.com/abstract=4565563
Abstract
Online marketplaces have emerged as a popular innovation in the dynamic landscape of
internet-based applications. To meet the challenge of building trust and reputation among
their buyers, these platforms employ feedback systems, allowing buyers (and sometimes sellers) to share what we know as “online reviews”. Over nearly three decades, online reviews
have become the go-to source for informed purchasing decisions across various platforms,
captivating the interest of researchers across computer science, economics, marketing, and
information science. Interdisciplinary attention, together with fast-paced changes in online
review platforms and their dynamics, have resulted in a fragmented landscape of knowledge, and created challenges in comprehending, generalizing, and effectively communicating
insights around online reviews. Consequently, in spite of the significant amount of extant
research on the topic, it seems there are several fundamental questions in the field that still
remain largely unanswered. This paper reviews and synthesizes existing research on online
reviews, addresses the knowledge gaps, and proposes directions for future research. Taking
an interdisciplinary approach, we delve into the stages of the online review process, including
review creation, exposure, and evaluation, and discuss the role of behavioral biases, review
formats, reviewer identity, and platform design across these stages. We discuss the significant
business value of online reviews, which is not limited to boosting sales, but also to impacting
the reputation of a firm, and providing essential information in uncertain contexts. We also
discuss the flaws inherent to the online review process, including promotional motivations,
self-selection biases, discrimination, and context effects, which might compromise the reliability and helpfulness of the reviews. Finally, we identify research gaps in the online review
literature, and propose a roadmap for future research to address them. As technology advances and consumer needs evolve, the complexity of online reviews must be further explored
to unleash novel insights, guide decisions, and better shape the future landscape of digital
platforms.
Electronic copy available at: https://ssrn.com/abstract=4565563
1
Introduction
The past few decades have been characterized by the rapid proliferation of internet-based
applications and digital platforms. Perhaps not surprisingly, one of the most popular innovations in this domain are online marketplaces. Unlike traditional brick-and-mortar stores,
these marketplaces allow transactions to happen among a diverse set of often anonymous
and geographically dispersed consumers.
One of the main challenges online platforms face in order to grow and prosper is creating
trust and reputation among consumers (Dellarocas 2003). To address the trust and reputation challenges, these platforms have implemented feedback systems that enable buyers (and
sometimes sellers) to provide their opinions and evaluations of the transactions and products
or services exchanged (Ante 2009). This customer feedback typically takes the form of written text that describes the consumer’s experience and/or the characteristics of the product
or service, photos complementing the text, and a numerical rating that assesses the overall
quality of the transaction. Together, these consumer-generated texts, photos, and ratings
constitute what we generally refer to as “online reviews”. Amazon pioneered the introduction
of online reviews, with its first customer review being posted in 1995. Nearly 30 years later,
online reviews have become ubiquitous in virtually all online platforms that sell products
or services, making them the primary source of information for consumers seeking to make
informed purchasing decisions across a wide range of products and services (Hennig-Thurau
et al. 2004).
The surge in the adoption and popularity of online reviews across digital platforms has
attracted substantial and diverse attention from computer science, economics, marketing,
and information science researchers. Across these disciplines, researchers have studied which
users are most prone to providing feedback, what motivates them to seek exposure to other
people’s feedback, what properties of the online reviews (and review systems) make them
more helpful and more trustworthy, and how online reviews impact a vast array of outcomes—
from sales to product demand to economic welfare. This scholarly focus has significantly
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expanded our understanding of online reviews such that today we know much more about
online reviews than we did 30 years ago. For example, thanks to research in marketing, we
now recognize that higher review ratings lead to higher sales (Chevalier and Mayzlin 2006a),
and we acknowledge that consumers are more inclined to provide reviews for exceptionally
good or bad experiences, compared to moderate ones (Hu et al. 2009). From research
in economics, we know that businesses have incentives to produce fraudulent user reviews
(Mayzlin et al. 2014), and from information science, we gained a sense of what platform
design features are more conducive to the seamless provision of online reviews (Gutt et al.
2019).
While exploring various facets of online reviews is beneficial, this multidisciplinary approach resulted in a fragmented knowledge landscape, and created challenges in comprehending, generalizing, integrating, and effectively communicating insights around online reviews.
Furthermore, the diverse range of studies and methodological approaches focused on investigating various aspects of online reviews leaves researchers across fields with an array of
important, yet unanswered questions.
In this paper, we address these challenges by organizing, summarizing, and synthesizing
the existing multidisciplinary research on online reviews. This allows us (1) to highlight key
insights discovered over the last 20 years, and (2) to identify prominent knowledge gaps and
emerging areas for future research.
To this end, we conduct a comprehensive literature search using scientific databases (e.g.,
Google Scholar, Scopus). We adopt an interdisciplinary approach by collecting information
from about 2,500 papers encompassing scientific fields, that include marketing, management,
economics, information science, computer science, and medicine.
With this data in hand, we first employ a data-driven unsupervised approach to classify,
categorize, and identify the topics studied by extant papers, and to describe the evolution
of topics studied over time. Using this approach, we present the state of the literature
and its dynamic evolution over nearly 20 years. For example, at first glance, the field
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appears dominated by studies of product reviews. Upon further inspection, clusters of related
topics emerge from the analysis, revealing multidisciplinary attention to substantive topics in
healthcare, entertainment, technology, and hospitality, along with a stream of investigation
on inherent review biases and flaws, and a branch of literature focusing on methodologies
and on online reviews as sources of text-as-data.
We then organize the literature according to the electronic word-of-mouth (eWOM) process framework and discuss the drivers and dynamics of review creation, exposure, and
evaluation (Babić Rosario et al. 2020). In addition, we describe the methodological and datarelated challenges stemming from the massive use of online reviews for marketing research
purposes by firms and organizations for policy- and decision-making. Following the eWOM
process framework, we distinguish the extant literature across two main areas, depending
on the design of the review system studied: one related to one-sided review platforms (i.e.,
platforms where buyers review sellers), and another related to two-sided review platforms
(i.e., platforms where buyers and sellers review each other). This distinction is important
because these platforms, and the research investigating them, exhibit substantial differences
and generate distinct sets of insights. We also discuss, wherever relevant, the moderating influences of different product and user characteristics as recommended in the eWOM process
framework.
This organization of the literature allows us to identify key insights about online reviews
and areas of future research. Starting with key insights, we identify three main themes.
First, online reviews have significant (and often quantifiable) business value, going above
and beyond positive effects on sales and demand. These reviews create consumer surplus,
affect firms’ reputation, and play a crucial role in providing complementary information in
contexts where information is lacking, uncertainty is high, or alternative sources of information are needed. However, and most importantly, it is evident that online reviews are subject
to various biases, including selection biases, differential attrition, discrimination, and susceptibility to context effects. These inherent “flaws” may affect their reliability as sources
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of information, their helpfulness for decision-making, and their ability to generate business
value. Additionally, the presence of fake reviews poses a significant risk to the integrity of
review platforms and can damage economic systems in unexpected ways. The system-level
consequences of these present and potential problems are still unclear, and a systematic
solution is currently unavailable.
Second, there is emerging evidence that the reviews format, reviewers’ identity, and
platform design have a complex impact on the effectiveness and influence of reviews, with
implications for economic systems.
Third, the literature suggests that online reviews can be used as a valuable source of
quantitative insight into consumer behavior and market trends, as indeed is the case in the
real world, i.e., firms use online reviews for market research purposes. Consequently, the use
of online reviews for market research likely influences economic decisions on a large scale.
This insight is particularly crucial given the emergence of new technologies, such as large
language models, which present new opportunities for using online review text as data, and
virtual and augmented reality tools, which could affect consumption experiences and how
reviews are generated and consumed.
Turning to future research, we identify five areas where we suggest more research is
needed: (1) the overall effects of online reviews on economic systems; (2) diversity, equity,
and inclusion (DEI); (3) platform design; (4) new technologies; and (5) factors related to
review content and review content creators.
Regarding the first area, a major open question is the general effect the existence of online
review systems has on economic systems and society. It is evident that reviews generate
benefits, but it is also clear that the effects of online reviews on economic systems can be
complex and can lead to unexpected drawbacks. All in all, are review systems good or bad
for business ecosystems? Future research should clarify and quantify, perhaps through largescale field or natural experiments, the trade-offs of including reviews in economics systems,
and delineate the conditions under which they solve inherent problems (e.g., asymmetric
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information), rather than creating or aggravating other issues.
Regarding DEI aspects, there is a need to better understand and address underlying biases
in online reviews, with a focus on discrimination, and DEI considerations. Given that online
reviews represent a large (if not the largest) dataset of attitudes and opinions, it is crucial
to understand the impact of biases and flaws in reviews on various aspects of the world,
including policy-making and business decision-making. Further research should examine
how biases in online reviews affect factors related to products, services, pricing strategies,
market dynamics, and their implications for policy development and implementation. For
example, do reviews increase the quality of products, and therefore, consumer surplus? Do
they aggravate or improve discrimination phenomena based on gender, ethnicity, or socioeconomic conditions?
Another important and critical topic that calls for more fundamental research is platform
design. Review formats are relatively homogeneous across platforms and markets. A typical
review usually includes text, some form of media, numerical ratings on 5-point Likert scales,
and sometimes a separate numerical rating for different aspects of the reviewed experience.
However, it is unclear whether this format is optimal. While the literature suggests that
review formats matter, little research explores optimal formats for different purposes and in
specific contexts. For instance, is it best to use numerical ratings, or other types of evaluation
scales? Which numerical rating range is optimal to better capture underlying experiences?
What is the most efficient way to solicit review text? Furthermore, how review information
is sorted, aggregated, and presented to consumers may be crucial in how it affects consumer
decision-making. Are current review exposure practices optimal in terms of the goals of
platforms, businesses, and consumers?
New technologies such as Large Language Models (LLMs) will potentially change how
consumers search for information. How can review platforms continue to stay relevant in
this new status quo? As LLMs are also decreasing the costs of creating reviews, what does
this mean for the type of content created, and its value for consumers and firms alike?
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Finally, turning to review content creation and creators, we have seen the rise of different
types of content creators (micro and mega influencers, experts, amateurs, etc.) and content
creation modalities (video, photos, product demonstrations, etc). How do these novel formats, sources of social influence, and content creation modalities affect consumer perceptions
of online reviews, and consequently, relevant economic outcomes?
These questions are among the most urgent and crucial open issues in the field, and additional questions will certainly arise in the near future. Technologies evolve, platforms grow
and become more complex, and the need for consumer-to-consumer information increases
daily. Therefore, further exploration of online reviews’ complex antecedents and effects is
necessary to unlock new insights, inform decision-making, explain the dynamics of digital
and real-world economic systems, and shape the landscape of information exchange in the
future.
2
Methodology
We conducted a comprehensive literature search using scientific databases (e.g., Google
Scholar, Scopus) to understand, describe, and discuss the state of the scientific literature
on online reviews. Our search was limited to articles published between 2000 and 2023,
and included specific keywords related to online reviews (i.e., “online consumer reviews”,
“ocrs”, “online reviews”, “product reviews”, and “consumer reviews”). We adopted an interdisciplinary approach, encompassing scientific fields including marketing, management,
economics, information science, computer science, and medicine.
Each identified article included an array of relevant information, including the title, authors, abstract, publication year, and publication source, as well as the number of citations
at the time of data collection. Finally, to ensure that the review encompassed the most
influential works in the field, we only included articles that received a minimum of 10 citations at the time of data collection. The final database includes 2,492 papers authored by
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2,336 distinct authors, that were published in 1,381 journals or conference proceedings (see
Appendix Table 2 for descriptive statistics).
Online reviews have attracted significant academic attention across diverse disciplines.
The database reflects the extent of this attention, and comprises sources in the fields of
management, marketing, economics, computer science, and information science, as well as
a variety of multidisciplinary conference proceedings. Nonetheless, it is worth noting that
most papers in the database are in the field of business and management.
2.1
A Data-Driven History of the Online Review Literature
We relied on topic models to uncover latent topics from the abstracts of the documents in
our database. This descriptive analysis aims at gaining insight into the state of the scientific
literature on online reviews between 2000 and 2023, and at understanding the dynamic
emergence of topics over the past two decades.
We relied on a transformer-based BERTopic model to generate easily interpretable topics (Grootendorst 2022; Appendix B provides technical details about the procedure). We
selected a 17-topic solution that encompasses both substantive and methodological topics
(Table 1).
Table 1: BERTopic 17-Topic Solution Based on 2000-2023 Research on Online Reviews
Topic
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
# Documents
873
210
120
111
87
76
72
66
48
24
17
13
11
10
7
6
5
Label
Online Product Reviews
Tourism Satisfaction
Movie Recommendation Systems
Fake Reviews Detection
Mobile App Development
Health Reviews
Review Sentiment Analyses
Restaurant Reviews
Product Review Summarization
Cultural Influences on Consumers
Video Games
Review Sponsorship Disclosure
Employer Reviews
Review Attitudes
Apparel Reviews
Student Reviews
Third-Party Reviews
Top 4 Words
Product online consumers consumer
Travel tourism satisfaction analysis
Movies recommendation movie recommender
Fake detection spam deceptive
Apps app mobile software
Patient patients care physician
Aspect aspects sentiment model
Restaurant restaurants food yelp
Summarization product intertextuality discourse
Cultural culture country consumers
Game games video players
Sponsorship disclosure sponsored bloggers
Workers company bias employer
Ambivalence conflicting impoliteness attitude
Fit apparel ordinal fit product
Students comments sentiment universities
Impulse buying impulsiveness “third-party product reviews”
Figure 1 presents a visualization of the 17 topics in a 2-dimensional space, where topics
that share similarities appear closer together. In the visualization, “Online Product Reviews”
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(Topic 0) emerges as a dominant theme, encompassing and extending horizontally across the
latent space. The quadrants also loosely delineate several, latent research areas in the online
review literature. By quadrant, Quadrant I groups literature focusing on online reviews
as instrumental for sentiment and intertextual analyses, and for recommendation systems
(Topics 2, 6, 8, 15). Quadrant II primarily includes literature on fake, promotional, and
biased reviews (Topics 3, 11, and 12). Quadrant III includes literature on cultural aspects of
online reviews (Topic 9), impoliteness (Topic 13), impulse buying (Topic 16), and fashion and
apparel (Topic 14). Finally, Quadrant IV predominantly frames online reviews in the context
of mobile settings and devices (Topic 4) and video games (Topic 10). Interestingly, literature
examining online reviews created by patients in healthcare contexts (Topic 5) appears in close
proximity to the literature on mobile apps, software, and video game reviews. This proximity
may be attributed to the shared use of review platforms or the emergence of telemedicine
apps over the past decade.
Figure 1: BERTopic 17-Topic Solution, Using Sentence Embeddings to Visualize Documents
After providing a cross-sectional representation, Figure 2 documents the emergence of the
17 main topics over the years. Topic 2 (Movie Recommendation Systems) was the first topic
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to appear in the literature in 2000, including studies on online reviews in the context of movies
and movie recommendations. Research on online reviews in the domains of tourism, services,
and entertainment (Topics 1–3) emerged early on, and has remained active throughout the
past two decades. Conversely, Topics 11 to 16, which focus on biases, sponsorships, sentiment
analyses, and cultural aspects of online reviews, emerged relatively late, with some topics
not appearing until as late as 2014 (e.g., Topic 12 – Workers, companies, bias, employees).
Additionally, research exploring intertextual properties of online reviews and research on
online reviews in the context of movies and recommendations garnered significant attention
over the past two decades. However, the proportion of attention from the field decreased in
the last five years. Appendix B provides additional insight into the emergence of substantive
topics over 5-year intervals.
Figure 2: Emergence of Topics in the Literature, Years 2000–2023
2.2
Organizing Framework
The data-driven analysis revealed that the field of online reviews emerged about 20 years
ago with the study of reviews in entertainment, and quickly grew in breadth, width, and
complexity. Over the years, the field touched upon the topics of technology, biases, and
promotional motives, made use of methodological advancements from machine learning and
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text analysis, and extended across disciplines. We propose an overarching framework to
organize, discuss, and review this multi-disciplinary literature in a systematic way.
To structure our discussion, we adopt the definition of online reviews as electronic wordof-mouth (eWOM), which refers to information about goods, services, brands, or companies
shared by consumers with other consumers (Babić Rosario et al. 2016). Specifically, we rely
on the creation-exposure-evaluation framework, which conceptualizes the three key stages in
the eWOM process (Babić Rosario et al. 2020). The framework emphasizes the importance
of accounting for platform characteristics influencing the eWOM process. Therefore, in
applying this framework, we make a distinction between one-sided (Section 3.1) and twosided review platforms (Section 3.2). Finally, following the recommendation of Babić Rosario
et al. (2016) and whenever relevant, we discuss how online review creation, exposure, and
evaluation differ depending on product characteristics.
In Stage 1 (Sections 3.1.1 and 3.2.1), we explore the factors influencing online review
creation, and provide a summary of relevant research on who writes online reviews (i.e.,
consumer characteristics) and why they do so (i.e., consumer motivations, biased motivations
in review creation, and the effectiveness of incentives and solicitations). For this stage, we
discuss several substantive topics that emerged in Section 2.1, including the inherent biases
of online review generation and the biases underlying review ratings, as well as the cluster
of studies on fake reviews.
In Stage 2 (Sections 3.1.2 and 3.2.2), we discuss the mechanisms through which consumers
gain or seek exposure to online reviews. We discuss the characteristics of online reviews that
make them more or less visible, salient, and effective (e.g., content valence and sentiment,
and the role of textual or visual elements), the temporal dynamics in online review exposure
(e.g., timing and order effects), and the influence of context on exposure to online reviews
(e.g., platform characteristics and design). Among other things, in this stage, we touch
upon the substantive topics of review recommendation systems, and management responses
to online reviews.
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In Stage 3 (Sections 3.1.3 and 3.2.3), we address how consumers evaluate online reviews
to inform their decision-making process, and examine the effects of online reviews on various
economic and managerial outcomes. In this final stage, we explore substantive topics including the impact of online reviews as a source of information on demand, sales, and other
business indicators, and the helpfulness and credibility of online reviews.
To offer a comprehensive account of the state of the literature and to identify additional
conceptual gaps, we also discuss how online reviews contribute to market research efforts
through methodological advancements, such as the use of review text as data to mine consumer opinions (Section 3.3). This discussion encompasses the cluster of papers that focused
on analytical methods for collecting and analyzing online review data. Finally, in Section 4,
we provide overarching conclusions, and draft a roadmap for addressing important research
gaps and for encouraging future research in the field of online reviews. Figure 3 provides a
visual summary of the organizing framework.
Figure 3: Organizing Framework
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3
Looking Back: A Systematic Summary of the Extant
Literature and Emerging Trends
In this section, we systematically review, summarize, and discuss the extant literature on
online reviews, and synthesize any emerging trends. In doing so, we follow the eWOM
process framework (Babić Rosario et al. 2020). The framework emphasizes that it is crucial
for managers to recognize and manage the eWOM platform characteristics where people
create, seek exposure to, and evaluate online reviews. Therefore, this section makes a clear
distinction between literature focusing on one-sided versus two-sided online review platforms,
as this distinction is important in understanding how consumers navigate and interpret online
reviews.
3.1
Online Reviews on One-Sided Platforms
One-sided platforms, such as Yelp, TripAdvisor, and Amazon, primarily enable buyers to post
reviews and ratings for products, services, or their interactions with sellers, while restricting
sellers from reviewing their customers. Although sellers are unable to generate customer
reviews on these platforms, they sometimes have the ability to engage with the platform and
their consumers through other means. For example, sellers can respond to reviews or address
customer inquiries. In most cases, one-sided platforms serve as intermediaries facilitating
the provision of online reviews, and they play a vital role in the entire eWOM process, from
creation to evaluation.
3.1.1
Review Creation: Motivations and Incentives to Provide Online Reviews
Understanding the motivations and incentives behind the provision of online reviews is crucial, considering their impact on economic outcomes, marketing objectives, and information
systems. In this section, we categorize motivations to provide reviews found in the literature,
including altruism, social impact, emotion regulation, impression management, extrinsic in12
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centives, and promotional motives. We also examine the influence of reviewer identity and
personality on review provision, along with the moderating role of product characteristics in
the review provision stage.
Altruism and Social Factors Intrinsic and social factors are important drivers of review
creation. Some reviewers write reviews as an altruistic act, aiming to assist other consumers
in making informed and better decisions (Qiao et al. 2020, Berger 2014). Other studies reveal that reviewers seek to have a positive impact on consumers and businesses, particularly
when managers actively read and respond to reviews (Chevalier et al. 2018, Proserpio and
Zervas 2016, Wang and Chaudhry 2018). Anderson and Simester (2014) suggest that consumers write reviews to influence brand decisions and are motivated by managerial attention.
For example, some brand enthusiasts review products they haven’t purchased, to indirectly
impact the performance of disapproved items. The well-documented polarity bias, characterized by a tendency to share more extreme reviews, may also reflect the desire to assist
other consumers, given the perception that extreme reviews may be more informative than
moderate ones (Schoenmueller et al. 2020, Brandes et al. 2022). Moreover, consumers often
choose to write reviews for less popular products, likely hoping to enhance the information
available on review platforms (Dellarocas et al. 2010b).
People write reviews also for impression management, and to cultivate a social status in
digital platforms. Popularity and exposure play a role in motivating reviewing activity, with
popular reviewers generating more objective, detailed, negative, and varied reviews (Goes et
al. 2014, Ma et al. 2022). Consumers also write more reviews for popular products, even if
these products already have a substantial number of reviews, likely because they appreciate
the larger audience (Dellarocas et al. 2010a). Furthermore, integrating reviews into users’
social networks, thereby increasing their exposure, also motivates reviewing behavior (Huang
et al. 2017). Higher social exposure was also associated with more positive reviews, which,
in some cases, could raise concerns about the diminished informativeness of reviews on the
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relevant platforms (Huang et al. 2017, Ma et al. 2022).
Homeostase Utility and Emotion Regulation Consumers engage in online review
creation to restore emotional balance following a highly positive or negative consumption
experience (Hennig-Thurau et al. 2004). After a successful experience, they may express
positive emotions through online reviews, as a way to share their satisfaction. Conversely,
dissatisfying experiences prompt consumers to write reviews to vent negative feelings, alleviating frustration and anxiety associated with the negative encounter (Cheung and Lee
2012, De Matos and Rossi 2008, Hennig-Thurau et al. 2004). This phenomenon, known as
“homeostase utility” in Cheung and Lee (2012) and Hennig-Thurau et al. (2004), refers to
the use of online review creation for regulating internal emotional equilibrium. The fact that
users use online reviews to regulate emotion may have implications on how brands strategize
their service and marketing efforts.
The intensity of emotions, in addition to their valence, significantly impacts the dynamics
of review creation. Memory network theories suggest that people think more frequently about
events associated with strong positive or negative emotions, compared to neutral events, as
well as extremely emotional experiences compared to moderately emotional ones (Walker
et al. 2009). Building on the premise that highly valenced and extreme emotional experiences activate memory, Brandes et al. (2022) demonstrate that the valence and extremity of
consumption experiences explain variations in review provision rates, and contribute to the
emergence of extreme, J-shaped distributions of review ratings.
Extrinsic Incentives Empirical and experimental evidence supports the hypothesis that
offering extrinsic incentives, which include monetary compensation and reminders, increases
the volume and changes the nature of online reviews. Karaman (2021) conducted an experiment demonstrating the effectiveness of unincentivized extrinsic solicitations in increasing
review rates. Woolley and Sharif (2021) found that incentivized reviews contained a higher
proportion of positive emotions compared to unincentivized reviews, and receiving incen14
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tives also enhanced the enjoyment of the review-writing process. Burtch et al. (2018) found
that financial incentives were more effective than simple review requests in generating new
reviews. Additionally, information about other customers’ review activities was found to
increase review length more effectively than financial incentives or review requests. Furthermore, combining types of incentives, such as offering both monetary rewards and cues about
social norms, may yield even more substantial outcomes Burtch et al. (2016). However,
Khern-am Nuai et al. (2018) demonstrated that, while monetary incentives enhance review
volume, they may also lead to inflated star ratings, and negatively impact the helpfulness of
the reviews.
Extrinsic incentives can partially address the issue of selection bias, where certain customers systematically self-select into or out of creating online reviews. Brandes et al. (2022)
and Chevalier and Mayzlin (2006b) found that extrinsic incentives encourage the creation
of eWOM from reviewers with less extreme and more moderate experiences. This finding
has important implications for the representativeness of online opinions. Karaman (2021)
suggested that extrinsic incentives may not only increase review rates, but also positively
impact the representativeness of the reviews. In sum, the findings in the literature point
towards a trade-off between a positive effect of extrinsic incentives on reviews, on the one
hand, but a mixed effect on review quality and representativeness.
Promotional Motives and Fake Reviews Online reviews are also created for promotional motives. These promotional drives motivate deceptive practices by businesses, posing
as consumers and creating fraudulent reviews, which can have significant repercussions for
review platforms, businesses, and consumer decision-making. Injecting promotional reviews
undermines platform credibility, generates noise and deception, and results in sub-optimal
purchase decisions (Mayzlin et al. 2014, He et al. 2022, Luca and Zervas 2016).
Promotional motives are not uniform across businesses. Luca and Zervas (2016) identified
certain conditions, such as business reputation, reputation shocks, and changes in economic
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incentives due to increased competition, that can either suppress or enhance the production
of promotional reviews. Mayzlin et al. (2014) found that economic incentives and business environments affect the extent of review manipulation. Specifically, hotels with nearby
competitors are more susceptible to fake negative reviews, and smaller owners, independent
hotels, and small management teams are more likely to engage in fraudulent review creation
compared to larger holdings, branded chain hotels, and larger management teams.
The economics of fake reviews encompass not only systematic responsiveness to incentives
and economic environments, but also the existence of an active online market for fraudulent reviews (He et al. 2022). In this market, businesses compensate customers to purchase
selected products and leave positive (fake) reviews. Smaller brands tend to be the main
purchasers, implying that fake reviews can be perceived as a substitute for reputation (Hollenbeck 2018, Luca and Zervas 2016). The purchase of fake reviews may temporarily boost
the number of reviews posted per week, average ratings, and the proportion of five-star reviews. However, these artificial improvements diminish within two to four weeks, and then
quickly return to pre-purchase levels. These findings suggest that fake reviewing is likely
rooted in online review platforms, and is viewed as an effective strategic tool, but it is still
unclear to what extent it actually damages markets.
Reviewer Characteristics and Self-Selection Biases The characteristics of reviewers
can play a role in their likelihood to create reviews, but the literature on this moderator is
limited. Importantly, only a small percentage (estimated at 5% to 11%) of consumers actually
provide reviews after making a purchase (Gao et al. 2015, Weise 2017, Brandes and Dover
2022). Therefore, it is important to investigate the characteristics of online reviewers and to
determine whether this minority is representative of the broader population of consumers.
Hu et al. (2017) suggest that online reviewers exhibit two self-selection biases. The first
is an acquisition bias, where consumers who already have a favorable predisposition towards
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a product are more likely to acquire it and subsequently write a review.1 The second is
an under-reporting bias, whereby ratings and reviews are more likely to be generated by
consumers with extreme rather than moderate experiences. Brandes et al. (2022) attribute
this bias to differential attrition, wherein potential reviewers with moderate experiences are
more likely to disengage from the pool of active reviewers, compared to those with extreme
experiences. The combined effect of the acquisition and under-reporting/differential attrition
biases results in the well-documented J-shaped distribution of online reviews, characterized
by positive skewness, asymmetry, and bimodality.
Even among active reviewers, research has identified differential effects of reviewer characteristics on review provision patterns. Goes et al. (2014) demonstrated that the extent
to which reviewers gain status in their online social networks influences their rate of review
provision. Sunder et al. (2019) suggested that as the reviewer experience increases, the
relative influence of a social crowd versus the reviewer’s immediate friends changes, with
implications for the emergence of herding effects in online rating environments. Lastly, Hu
and Kim (2018) suggest that Big Five personality traits moderate the relationship between
self-enhancement and enjoyment motivations for reviewing, and actual review behavior. In
sum, it seems that the identity and characteristics of reviewers can be a crucial factor in how
they review and whether reviews could be biased, or not.
Product Characteristics The propensity of consumers to create online reviews can be
influenced by the properties of products and product communications. According to Dellarocas et al. (2010b), consumers are more likely to write online reviews for niche products
that are less available and less successful in the market. Additionally, product popularity
affects the tendency of reviewers to exhibit herding behavior based on previous ratings (Lee
et al. 2015). The characteristics of the specific eWOM channel where reviews are generated
may also impact the likelihood of more “interesting products” receiving a higher proportion
1
It is worth noting that the acquisition bias proposition assumes that only people who acquire a product
will review it, although platforms like Google and TripAdvisor allow non-buyers to review any product or
service of their choice.
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of online reviews (Berger and Iyengar 2012). Furthermore, the extent to which products
are publicly consumed and consumers’ perceived need for uniqueness are factors that influence online review sharing behavior (Cheema and Kaikati 2010). Product involvement, or
the perceived importance or relevance of a product, also plays a role in the production of
eWOM and online review creation. High product involvement can generate greater excitement, leading consumers to express positive feelings through online reviews (Sundaram et
al. 1998).
In summary, the creation of online reviews is regulated by a range of complex mechanisms. These mechanisms encompass intrinsic motivations (such as altruism, social factors,
and emotion regulation) as well as extrinsic drivers (such as monetary incentives and promotional motives). They are further influenced by reviewer characteristics (such as personality,
social network properties, and tie strength) and product characteristics (including product
popularity and involvement).
3.1.2
Review Exposure: Dynamics of Online Review Display and Consumption
Having examined the mechanisms and dynamics of review provision, we now turn our attention to the literature concerning consumers’ seeking and receiving of exposure to online
reviews. The process of exposure is a crucial prerequisite for online reviews to exert an
impact on economic and marketing outcomes.
Information-Seeking and Passive Exposure Consumers actively seek information and
recommendations before making a purchase, engaging in a search process that involves
screening and comparing products and services online (Goldsmith and Horowitz 2006). This
is particularly important for decisions involving highly involving products. By seeking information from online reviews, consumers can mitigate purchase risks, save time, and validate
their assessment of product quality through seemingly unbiased information (Hennig-Thurau
et al. 2004). Online reviews also facilitate information-seeking and advice-gathering for fu-
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ture decisions based on the experiences of others (Berger 2014).
In addition to pre-purchase information, online reviews serve as guides for product use
after purchase (Hennig-Thurau et al. 2004), and consumers may turn to them to address
post-purchase issues (Berger 2014). Finally, exposure to online reviews can occur passively
or accidentally, with some consumers reporting that the information “finds them” (Goldsmith
and Horowitz 2006).
Social Drivers of Exposure Exposure to online reviews is influenced by social drivers.
Consumers generally trust eWOM generated by fellow consumers more than marketergenerated content, considering online reviews as a valuable and trustworthy source of information with social influence (Cheong and Morrison 2008). Tie strength moderates the
relationship between information-seeking motivations and the likelihood of exposure to online reviews. While consumers may rely on recommendations from immediate social contacts,
they may also seek more information from weaker ties, such as online reviews, due to higher
interaction frequency (Berger 2014, Steffes and Burgee 2009).
Peer influence can also motivate exposure to online reviews. For instance, consumers
may seek exposure to online reviews in response to peer recommendations and word-ofmouth communication (Goldsmith and Horowitz 2006). Online reviewers themselves can
be perceived as opinion leaders by peers seeking information, given the volume of eWOM
available, and the increased perception of finding product experts among online reviewers
(Cheong and Morrison 2008, Lee and Youn 2009).
The social aspects of review exposure have drawbacks too. For instance, consumers may
find it challenging to assess the quality and credibility of product recommendations when
they come from online strangers, rather than immediate social contacts (Lee and Youn 2009).
Platform Design, Rating Environments, Recommendation Systems The design
and functionality of review platforms, rating environments, and recommendation systems are
influential in shaping consumer exposure to online reviews. Various elements within review
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system design, such as review order, presentation, ranking options, and filtering mechanisms,
as well as the presence of recommendation systems, impact exposure dynamics (Gutt et al.
2019).
The characteristics of review environments also affect exposure. Factors like rating distribution and the presentation and aggregation of opinions determine the likelihood of encountering a larger volume of reviews, and their influence on consumers’ belief formation.
For instance, environments with predominantly positive ratings encourage eWOM production and increase exposure to online reviews (Moe and Schweidel 2012). Furthermore, initial
product beliefs are often based on summary rating statistics on the product page, which can
lead to confirmation bias when evaluating subsequent reviews (Yin et al. 2016).
Platforms that facilitate the formation of social networks among reviewers shape review
provision and exposure patterns. Allowing users to follow each other on a review platform
leads to more objective reviews and increases exposure for future consumers (Goes et al.
2014). The presence of ratings by strangers (the “crowd”) compared to friends influences
consumers’ herding or differentiation behavior, impacting exposure to a representative distribution of opinions on online review platforms (Lee et al. 2015).
Finally, recommendation systems play a role in driving the extent and intensity of exposure to specific products, and consequently, their online reviews. Depending on product
characteristics and existing review ratings, recommendation systems can either complement
or substitute exposure to online reviews (Lee and Hosanagar 2021). The type of product
(hedonic or utilitarian) and the level of existing review ratings can also determine how recommendation systems and online reviews interact to influence consumer exposure.
Timing and Order Effects The timing and order of online reviews significantly impact
the ease and frequency of exposure. For instance, Godes and Silva (2012) demonstrate that
review ratings tend to increase over time, but decrease as the sequential order of reviews
progresses, even when considering time, reviewer characteristics, and product idiosyncrasies.
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This phenomenon has implications for review exposure, as previous reviews become less
influential when the reviewer composition changes over time. While exposure to more reviews
may increase, it can lead to more purchase errors and generate new reviews with lower ratings
(Godes and Silva 2012).
Moreover, the distribution of opinions expressed in online reviews is not static, but may
systematically change over time. Social dynamics and the emergence of a core group of
active contributors who generate a significant volume of eWOM drive this evolution. Over
time, negative and active reviewers tend to dominate the posting population, altering the
exposure to consumer opinions around the activities of these core and negative contributors
(Moe and Trusov 2011, Moe and Schweidel 2012). These timing effects can negatively impact
consumer surplus, especially if early buyers have different preferences regarding product
quality compared to later consumers (Li and Hitt 2008).
Finally, Park et al. (2021) discuss the lasting effects of placing a negative or positive first
review on an online product page. For instance, products with a negative first review tend
to experience a decrease in both the valence and volume of subsequent reviews, and these
effects can persist for several years. This has implications for exposure to varying volumes
and valence of reviews following the initial review.
Managerial Responses One-sided platforms may not allow businesses to create online
reviews about their consumers, but managers can still engage with consumer reviews through
responses, comments, and public feedback. Platforms like Google and Tripadvisor enable
businesses to publicly respond to consumer reviews, allowing managers to address criticism,
provide additional context to customer complaints, or express gratitude for positive feedback.
The exposure to management responses affects the likelihood of exposure to subsequent
consumer opinions online, and the formation of opinions. Proserpio and Zervas (2016) revealed that hotel management responded more frequently during periods of negative ratings,
and that exposure to management responses resulted in a 0.12-star increase in review rat-
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ings, and a 12% increase in review volume. However, the impact on review volume varies
depending on the sentiment of the reviews. Hotels that employed management responses
received fewer negative reviews, but these negative reviews were longer in length. Similar
findings were reported by Chevalier et al. (2018), suggesting that management responses can
stimulate exposure to more negative reviews, particularly due to more frequent and detailed
responses to negative reviews.
Managerial responses, especially in the context of negative reviews, significantly influence
the formation of customer opinions. Wang and Chaudhry (2018) suggest that, when reviewers
are exposed to managerial responses to negative reviews, these have a positive impact on
their subsequent opinion formation. Wang et al. (2020) propose an optimal approach to
company responses, suggesting that providing highly tailored responses to every negative
review and selectively responding with less tailored responses to positive reviews can be
effective in the presence of budget constraints. Supporting these arguments, Kumar et al.
(2018) suggest that implementing management response features on digital platforms can
benefit businesses’ bottom line.
Lastly, Proserpio et al. (2021) examine how management responses differentially affect
reviewing behavior based on gender. They find that after managers begin responding to
reviews on Tripadvisor, female reviewers tend to write fewer negative reviews. This disparity is attributed to the higher likelihood of receiving contentious responses, which can be
confrontational, aggressive, or aimed at discrediting the female reviewers. These findings
indicate that while management responses can stimulate reviewing behavior, they may also
impact the composition of future reviewers.
Product Characteristics Consumers’ exposure to online reviews can vary depending on
the properties of the product or service they intend to consume. For example, in Dai et
al. (2020), people rely less on consumer reviews for experiential purchases (“events to live
through”) compared to material purchases (“objects to keep”). This discrepancy may arise
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because online reviews are more effective at objectively describing the quality of material
goods than experiential ones.
Product characteristics can also influence the likelihood of exposure to different types of
reviews. Moore (2015) demonstrates that review writers’ reporting of actions and reactions
differs across utilitarian versus hedonic product reviews. Additionally, Ullah et al. (2016)
suggest that the emotional content of reviews varies systematically between search goods
(which allow for pre-purchase evaluation due to their assessable attributes) and experience
goods (whose attributes remain unknown until purchase), particularly during the initial
stages of product launch. However, as time progresses and more online reviews accumulate,
experience goods may be evaluated similarly to search goods in online environments, enabling
pre-purchase evaluation of their experiential attributes. Collectively, these findings indicate
that exposure to specific narrative styles, content, and sentiment in online reviews is more
likely when searching for, considering, and evaluating, products with distinct characteristics.
In summary, the factors influencing people’s exposure to online reviews stem from the
motivations of consumers, their social and physical environments, the nature of the products
and services, and the format and design of the platform itself, including the ability to interact
with firms through it.
3.1.3
Review Evaluation: Impact of Reviews on Economic Outcomes and Moderating Factors
Having examined review creation and exposure, we now turn our attention to the impacts
of online reviews on a range of economically significant outcomes, such as sales, demand,
financial performance, brand strength, and the reputation of reviewed businesses and organizations. Furthermore, we investigate the factors that shape the perceived helpfulness,
persuasiveness, and credibility of online reviews.
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Sales and Revenues Numerous studies establish a strong connection between online reviews and sales. Notably, even a single review can have a significant economic impact (Vana
and Lambrecht 2021). Chevalier and Mayzlin (2006b) reveal that higher book ratings on
Amazon.com lead to increased sales, with negative reviews exerting a stronger influence than
positive ones. Similarly, Reimers and Waldfogel (2021) find that online reviews have a significantly greater impact on book sales compared to professional critics’ reviews. The effects
of reviews on sales are also evident in the domain of hotels (Ye et al. 2009), movies (Duan et
al. 2008b), restaurants (Wu et al. 2015), and, more generally, across 600 product categories
(Liu et al. 2019).
Beyond sales, Huang (2018) and Chen et al. (2012) demonstrate a connection between
online review ratings and the stock market value of firms. Also, as reviews impact consumer
decision-making and financial bottom-lines, they may be employed as substitutes or complements to traditional marketing strategies (Hollenbeck et al. 2019, Lu et al. 2013). The
relationship between online reviews and the effectiveness of concurrent marketing efforts is
also evident in conjoint studies, such as Kostyra et al. (2016), which reveal that the presence
of online reviews may diminish the importance of the brand reputation itself.
The presentation context of online reviews plays a crucial role in their effectiveness and
impact. Liu et al. (2019) reveal that reviews have a stronger influence on sales in more
competitive markets, immature markets, or situations where brand information is scarce. In
the movie industry, Kim et al. (2023) emphasize the interaction between online reviews and
critics’ reviews, demonstrating that topic consistency between the two influences product
demand and shapes consumers’ memories and perceptions.
While the relationship between online reviews, sales, revenues, and financial performance
is well-established, the effect of reviews on demand and consumer preferences is not always straightforward. Ben Liu and Karahanna (2017) demonstrate that reviews can shape
consumer preferences in ways that may not align with their actual needs. Finally, Liu et
al. (2017) highlight the interplay between online reviews and pricing strategies, leading to
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scenarios where products may be overpriced, negatively affecting both consumers and firms.
Business Reputation and Consumer Welfare Alongside economic outcomes, online
reviews have the potential to influence consumer perceptions of brands, businesses, and
quality, as well as to contribute to consumer welfare. Hollenbeck (2018) suggests that the
revenue premium once enjoyed by franchising chain hotels, compared to independent hotels, has diminished in recent years primarily due to the rise of online review platforms.
As these platforms offer consumers more information, independent hotels experienced substantial revenue growth surpassing that of chain hotels from 2000 to 2015. Furthermore,
Ananthakrishnan et al. (2023) propose that online reviews and ratings enable consumers
to differentiate more precisely between high- and low-quality businesses, while also aiding
businesses in improving their critical operations. Specifically, hotels that pay attention to
reviews utilize the additional information to address frequently mentioned issues, resulting
in improved subsequent ratings and enhanced consumer welfare.
Helpfulness, Persuasiveness, and Marketing Effectiveness Common metrics used
to measure the impact of online reviews are their “helpfulness”, “persuasiveness”, and “credibility”. Helpfulness is typically determined through crowd-sourcing, where users can express
public helpfulness ratings. The literature suggests that users make an effort to write helpful reviews (Moore 2015), and certain review characteristics predict their helpfulness, such
as some aspects of content, media, and level of information (Ceylan et al. 2023, Singh et
al. 2017). Factors like review length (Susan and David 2010), readability, informativeness
(Srivastava and Kalro 2019), linguistic correctness, and a mix of subjective and objective
statements (Ghose and Ipeirotis 2010, Huang et al. 2015) contribute to perceived helpfulness. Mobile device reviews are perceived as more helpful due to the assumed effort required
(Grewal and Stephen 2019). Interestingly, the association between review valence and helpfulness remains inconclusive (Hong et al. 2017). Regarding persuasiveness and credibility,
Van Laer et al. (2019) show that reviews with engaging narratives and storytelling are more
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persuasive, while Cheung et al. (2012) and Lee and Shin (2014) emphasize the importance
of review and platform credibility in impacting the economic significance of online reviews
as information sources.
Psychological and Cultural Factors Online review evaluation is influenced by various
psychological factors. Although research in this area is limited, Huang et al. (2016) and Kim
et al. (2008) show that the physical and temporal distances of the product experience impact
how consumers evaluate reviewed products. Consumers tend to prioritize low-level, practical
aspects when the experience is recent and close, then shift to higher-level, abstract aspects
over time. This implies that psychological factors related to perceived experiences influence
the evaluation of reviews. Additionally, De Langhe et al. (2016) find that consumers tend to
overestimate the validity of online reviews as quality signals and struggle to make accurate
statistical inferences, leading to inaccurate evaluations.
Cultural factors also play a role in review evaluation. For instance, Kim et al. (2018)
demonstrate that consumers perceive reviews from reviewers belonging to the same culture
as more useful compared to reviews from foreign cultures. Furthermore, the social popularity
of reviewers influences evaluation. According to Cheng and Ho (2015), reviews from more
popular reviewers are generally perceived as better.
Review Content: Text, Visuals, Sentiment The characteristics of textual, media, and
visual content of online reviews can moderate their impact on economic outcomes. In particular, several studies demonstrated the role and importance of review valence (Chintagunta et
al. 2010, Chevalier and Mayzlin 2006b, Kostyra et al. 2016, Liu 2006, Duan et al. 2008a). For
instance, negative reviews can positively impact sales by increasing product awareness and
are generally perceived as more useful, while reviews containing both positive and negative
aspects are found to be more effective (Berger et al. 2010, Schlosser 2011, Park and Nicolau
2015).
Distinct emotion in reviews also plays a moderating role. Reviews expressing anxiety or
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high emotional arousal increase perceived helpfulness (Yin et al. 2014, 2017), while general
negative emotion in reviews may decrease perceived usefulness by reducing credibility (Kim
and Gupta 2012). Anger does not contribute to helpfulness, but can enhance persuasion
(Yin et al. 2020). Moreover, perceived unfairness in reviews increases empathy towards the
reviewed firm (Allard et al. 2020). Finally, other properties of review text, including writing
quality, informativeness, readability, subjectivity, and content similarity between photos and
text, can influence review effectiveness (Ceylan et al. 2023, Lee and Shin 2014, Ghose and
Ipeirotis 2010, Susan and David 2010).
Reviewer Identity Research suggests that reviewer identity plays a moderating role in
the effectiveness of reviews. Studies by Forman et al. (2008) and Dou et al. (2012) demonstrate that disclosing aspects of the reviewer’s identity increases the impact of the review on
consumers. The perceived experience of the reviewer also positively influences the influence
of their reviews (Zhu and Zhang 2010, Ghose and Ipeirotis 2010). Moreover, the personality
traits of the reviewer affect the perceived helpfulness of reviews. Text mining algorithms
reveal that higher levels of openness, agreeableness, extraversion, and conscientiousness contribute to perceived helpfulness (Xia Liu et al. 2021). Interestingly, when the identity of the
reviewer is undisclosed, consumers tend to make inferences and assume that the reviewer’s
identity and preferences align with their own (Naylor et al. 2011). This tendency to extrapolate personal similarity may introduce a systematic bias in consumers’ interpretation
of anonymized reviews.
Product Characteristics Product characteristics play a moderating role in the effects
of online reviews on economic and business outcomes. Specifically, product information
availability, pricing, market conditions, and competition can moderate the impact of reviews
on sales (Liu et al. 2019, Zhu and Zhang 2010). The type of purchase, whether material or
experiential, can also influence consumers’ reliance on online reviews, with material purchases
being more dependent on reviews (Dai et al. 2020). Additionally, the nature of the reviewed
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product, whether it is an experience good or a search good, can affect the susceptibility to
negative reviews (Park and Lee 2009).
The effectiveness of different types of reviews can be influenced by the characteristics of
the reviewed product. Positive affect in review texts has been shown to increase sales, but
its impact may vary depending on whether the product is hedonic or utilitarian (Ludwig
et al. 2013, Rocklage and Fazio 2020). Positive affect tends to have a positive effect on
sales for hedonic products, while the effect can be negative for utilitarian products in certain
cases. Moreover, the impact of online reviews differs for weaker and stronger brands, with
weaker brands benefiting more from reviews compared to strong brands with established
brand equity (Ho-Dac et al. 2013). These findings highlight the importance of considering
product characteristics and market dynamics when assessing the influence of online reviews
and identifying the conditions under which they are most influential.
In summary, the available evidence underscores the importance of online reviews in business and economic systems, as they have a significant impact on various business outcomes
and the functioning of information systems. These influences are moderated by psychological factors, cultural aspects, and product characteristics. As a result, theories and models
of economic systems should incorporate online reviews, along with their moderating factors
and contextual circumstances.
3.2
Online Reviews on Two-sided Platforms
In this section, we discuss the literature related to two-sided online platforms. A crucial
difference between one- and two-sided platforms is that, on the latter, buyers and sellers
review each other. This feature generates new challenges for platform designers and creates
new types of incentives for both buyers and sellers.
Most of the literature studying two-sided platforms focuses on service platforms that fall
under the umbrella of the “sharing economy” including popular platforms such as Airbnb,
Uber, Upwork, or TaskRabbit. The most common platforms when it comes to products are
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eBay and Taobao. eBay, in particular, was the first platform to implement a bilateral review
system, and therefore was the focus of a considerable amount of early work that studied
two-sided review systems using data from this platform.
3.2.1
Review Creation: Motivations and Incentives to Provide Online Reviews
We start this section by discussing why consumers write reviews on two-sided platforms,
and to what extent the mechanisms of online review provision on two-sided platforms can
be substantially different (or similar) compared to the provision mechanisms in one-sided
platforms.
Biases as Motivators One of the main drivers of review provision on two-sided platforms
is a “reporting bias”. Dellarocas and Wood (2008) were the first to describe “reporting bias”
regarding eBay. The authors show that, on eBay, a common practice among buyers and sellers was selectively choosing to report certain types of outcomes and not others. Nosko and
Tadelis (2015) directly shows how biased reputation measures really are. Using internal eBay
data, Nosko and Tadelis (2015) establish that the percent positive measure has a mean of
99.3% and a median of 100% In a series of related papers about eBay, researchers show that,
on this platform, feedback was remarkably positive and that this was partially due to “reciprocity”, a strategic reviewing behavior where positive (negative) feedback from one party
is likely to be reciprocated with positive (negative) feedback from the other party (Resnick
and Zeckhauser 2002, Jian et al. 2010). Bolton et al. (2013) also analyze eBay’s reputation
system and discuss an additional concern with two-sided reputation systems, i.e., “fear of
retaliation”. The authors show that on eBay, sellers would wait to receive feedback from
the buyers before giving feedback to buyers, and reciprocated with whatever feedback they
received. In doing so, they created a situation in which buyers became afraid of leaving
negative feedback. These early studies demonstrating the flaws of eBay’s reputation system
led the company to change its feedback system in 2008. Before 2008, buyers and sellers on
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eBay could leave each other positive, negative, or neutral feedback with comments. In 2008,
eBay changed the feedback system so that sellers would be limited to leaving either positive
feedback or no feedback at all.
More recent work studying service platforms like Airbnb reaches similar conclusions. Zervas et al. (2021) show that on Airbnb guests leave mostly positive reviews, and over 95% of
listings on the platform have an average rating of 4.5 or higher. Fradkin et al. (2021) shows
that reciprocity as documented in the papers studying eBay, as mentioned above, could provide at least a partial explanation for this distribution. To reduce this bias, in 2014, Airbnb
changed its reputation system to reduce reciprocation and retaliation by simultaneously revealing host and guest feedback. Fradkin et al. (2021) studied this change and found that
it lead to more reviews in total and less retaliation and reciprocation. However, positive
ratings continued to be the most prevalent outcome on the platform.
To explain why the distribution of ratings is extremely positive on Airbnb, Proserpio et al.
(2016) propose a different and new mechanism based on reciprocal behavior—the tendency
to increase (decrease) effort in response to others’ increased (decreased) effort. The authors
argue that reciprocal behavior plays a crucial role on platforms like Airbnb where personto-person interactions are an important transaction component. Using a theoretical model
and data from Airbnb, the authors show that Airbnb hosts who are more reciprocal receive
higher ratings.
Finally, Filippas et al. (2018) find similar patterns when looking at data from several
online marketplaces using a bilateral reputation system. In addition, the authors show how
that feedback has become more positive over time. The authors then focus on an online
labor marketplace to show that this is due to “reputation inflation”, a phenomenon where
raters give higher ratings without being more satisfied about the product or service they
buy.
Taken together, these findings point to the fact that leaving negative reviews on two-sided
platforms is costlier than leaving positive reviews, and this asymmetry leads to reviews being
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extremely positive on platforms using two-sided reputation systems. Despite several attempts
to mitigate this bias, two-sided platforms continue to suffer from this problem today.
Extrinsic Incentives Because reviews are necessary to create trust and reputation among
buyers and sellers, online markets have devised approaches to facilitate the creation of online
reviews. Among these approaches, a common one is offering rewards or rebates in exchange
for a review.
Li (2010) proposes a mechanism designed to solve the problem of lower review rates
and positive bias on platforms using two-sided reputation systems. The mechanism gives
the sellers an option for giving discounts in exchange for feedback. Li and Xiao (2014)
conduct a lab experiment to test the effect of this mechanism. The lab results show that,
compared with a system without rebates, a system with rebates can generate more efficient
transactions even when the rebate does not cover the full cost of leaving feedback. Similarly,
Cabral and Li (2015), through a set of controlled experiments where buyers are rewarded
by sellers for providing feedback, find that feedback barely increases as rewards increase.
More interestingly, despite the fact that rewards are independent of the feedback left, they
lead to more biased feedback as the feedback becomes more positive because the reward
increases. An important caveat of these results is that in the experiment, all sellers (low
and high quality) offered the rebate. When the platform lets sellers decide when to offer
rebates, the consequences of offering rebates are quite different. Li et al. (2020) use Taobao’s
reward-for-feedback mechanism and show that high-quality products are more likely to be
chosen for feedback rewards, which causes sales to increase by 36%. These results suggest
that marketplaces and consumers can, therefore, benefit from allowing sellers to buy feedback
and signal their high-quality products in the process.
Fradkin and Holtz (2023) discuss the results of an experiment in which Airbnb offered
a coupon to guests in exchange for a review in order to reduce rating bias and improve
market outcomes. The authors find that the coupon led to more and more negative reviews;
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however, these reviews did not affect the number of nights sold or total revenue. Moreover,
in contrast to the results discussed in Li et al. (2020) in the context of eBay, the coupon
caused transaction quality for treated sellers to fall, and Fradkin and Holtz (2023) conclude
that this is due to incentivized reviews being less correlated with the true transaction quality.
Review Fraud In addition to incentivized reviews, online marketplaces have struggled
with review fraud since the beginning. Since reviews and ratings affect economic outcomes
(see Section 3.2.3), sellers have strong incentives to manipulate online marketplaces by writing fake reviews to increase sales or harm the competition. Brown and Morgan (2006) show
some examples of this practice on eBay.
3.2.2
Review Exposure: Dynamics of Online Review Display and Consumption
Platform Design and Rating Environments Platform design aims to reduce some
of the biases we outlined above. As we discussed, to reduce retaliation and reciprocation,
eBay decided to limit sellers’ feedback to either positive or no feedback. As an example
for the importance of platform design, Airbnb, instead, took a different approach which led
to somewhat different outcomes. In 2014, the company changed its reputation system to a
simultaneous-reveal system in which host and guest can review each other during a period
of 14 days after the stay. At the end of the 14-day window, reviews are revealed. Fradkin
et al. (2021) shows that this approach partially eliminated reciprocal behavior, but did not
completely solve it.
3.2.3
Review Evaluation: Impact of Reviews on Economic Outcomes and Other
Benefits
As in the case of one-sided platforms, online reviews on two-sided platforms can have a
significant impact not only on an array of outcomes, including sales, revenue, and pricing
strategies, but also on the incidence of discriminatory practices and local gentrification.
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Economic Outcomes In a controlled randomized experiment, Resnick et al. (2006) show
that, on eBay, sellers with a high reputation can sell their products for higher prices relative
to sellers without or with a negative reputation. Lucking-Reiley (2000), using data from eBay
coin auctions find that negative feedback has a statistically significant effect on price, but
that positive feedback does not. Houser and Wooders (2006) studied the Pentium III 500
processors market on eBay and show that both negative and positive feedback affect price.
Specifically, they reveal that a 10% increase in positive comments will increase the winning
price by about 0.17%, while a 10% increase in neutral or negative comments will decrease
the price by 0.24%. Cabral and Hortacsu (2010), again using data from eBay, show that
when a seller receives the first negative feedback, weekly sales drop from a positive 5% to a
negative 8%. Moreover, subsequent negative feedback ratings arrive 25% more rapidly than
the first negative review, and do not have nearly as much impact as the first one. They also
find that the lower a seller’s reputation is, the more likely they are to exit the market.
Individual and Social Benefits A differentiating aspect of many two-sided platforms
is that sellers can decide whether to provide a service or sell a product to buyers. For
example, while hotels cannot deny a reservation request if there is availability, on Airbnb,
hosts can deny a reservation request for any reason through the platform, as is the case on
Uber. Recent research has shown that due to this feature, discrimination is not uncommon
on platforms like Airbnb or Uber. Edelman et al. (2017) find that African-American guests
are 16% less likely to be accepted relative to identical guests with distinctively white names.
Similarly, Ge et al. (2016) show that the probability that an Uber driver accepts a ride, sees
the name, and then cancels doubles when passengers used the account attached to an African
American-sounding name. While some researchers argue that these platforms should remove
the possibility to deny a reservation request or reduce the amount of information disclosed
(e.g., by hiding names and photos) to reduce discrimination, Cui et al. (2020) finds that the
presence of reviews alone can help reduce discrimination. Using a randomized experiment,
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the authors find that when guest accounts receive a positive review, the acceptance rates
of accounts with white-sounding and African-American-sounding names are statistically indistinguishable. This result highlights that verifiability and credibility of a review (i.e., a
review is linked to a valid transaction on the platform) is crucial for reducing discrimination.
Review Content: Text, Visuals, Sentiment Review content (text and images) provides information beyond that of ratings that can affect consumer choices. However, the
challenge for researchers is to extract and analyze this unstructured information. Recent advances in machine learning allow researchers to parse unstructured data and answer research
questions that until recently were prohibitive.
The review content on two-sided platforms, for example, seems to differ substantially
from that of one-side platforms. Two-sided platform reviews provide additional information
that does not only reveal information about the service or product, but also about the
experience itself in relatively more detail. For example, there is a growing literature that
shows that there are substantial differences between what Airbnb hosts care about and what
hotel customers care about (Cheng and Jin 2019, Gao et al. 2022).
On Airbnb, guests often discuss the safety of the neighborhood where the listing is located.
While very helpful for consumers, these reviews may be harmful for Airbnb. This perhaps
explains why the company recently implemented a policy that discourages and gives it the
ability to remove these “safety reviews”. Culotta et al. (2022) use a lexicon approach to
study the effect of this policy by categorizing reviews as safety reviews, which could reveal
important information that may affect consumer choices. As expected, Culotta et al. (2022)
find that having any location safety review is associated with a 1.01% reduction in the listing’s
monthly occupancy rate and a 1.33% reduction in its average paid price per night. Turning
to the effect of the Airbnb policy, the authors show, using counterfactual simulations, that
a complete removal of vicinity safety reviews would hurt guests, but increase revenues from
reservations on Airbnb. Conversely, highlighting vicinity safety would generate opposite
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effects.
Besides being helpful to consumers, the content of Airbnb reviews reveals important
information about the neighborhood where the listing is located, and this information can
be used to measure changes in neighborhood characteristics. Jain et al. (2021) shows that
the context of the reviews (among other pieces of data) can help “nowcast” gentrification by
using changes in housing affordability and demographics.
In summary, online reviews on two-sided platforms show similar traits to those on onesided platforms. Both types are shown to be important for sales and demand, and both
show systematic biases and sensitivity to platform design factors. A major difference is the
ability of sellers to reciprocate on two-sided platforms which has been shown to be used as
a means to affect buyer reviews and seems to be the cause for an extreme form of review
positiveness inflation.
3.3
Online Reviews for Market Research
Online reviews aim at facilitating communication of market-oriented information among consumers, but they also have indirect benefits for firms, organizations, and researchers. Online
review data is a public source of consumer preferences and opinions, which can be utilized as
a predictor of sales, a source of marketing insights, and a tool for studying economic markets
and human behavior. In addition to their impact on sales, early studies have shown that
incorporating online reviews into sales prediction models improves forecasting accuracy, such
as in the case of movie sales (Dellarocas et al. 2007) and various other domains (Decker and
Trusov 2010, Chong et al. 2017, Yu et al. 2010, Chong et al. 2016).
Online reviews can also be utilized to identify and visualize market structures based on
consumer feedback (Lee and Bradlow 2011), and to extract meaningful consumer preference
dimensions through topic analysis (Tirunillai and Tellis 2014). A growing body of literature
focuses on developing and surveying technical methods for extracting important information
from review texts, including sentiment analysis approaches (Humphreys and Wang 2018,
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Jia 2018, Berger et al. 2020, Büschken and Allenby 2016). These studies demonstrate that
sentiment measures can predict satisfaction levels (Qazi et al. 2017), and the use of figurative
language in reviews can influence attitudes towards hedonic consumption (Kronrod and
Danziger 2013). Overall, sentiment analysis techniques are widely employed in both research
and practical applications for extracting insights from online reviews (Rambocas and Pacheco
2018).
A major downside of using online reviews for market research purposes is that any bias
or distortion that exists in online reviewing has the potential to distort the outcomes of the
research and, consequently, the decision-making process. It is unclear how much this fact is
acknowledged by marketers, firms, and the public. For example, it may be unclear to what
extent the reviewer population is representative of the researched target markets. Representativeness is not easy to measure in the case of online reviews. Misalignment between
the reviewer population and the purpose of the research may be misleading. Minorities or
specific groups that are susceptible to bias and exclusion, may not be represented in public platforms. Therefore, researchers should be aware of the potential risks of using online
reviews to inform decisions.
3.4
Summary and Insights
Our data-driven analysis of research articles studying online reviews published between 2000
and 2023 reveals the emergence of 17 topics, encompassing both substantive and methodological areas of research. The topic analyses show distinct clusters within the online review
literature covering different research areas, including the provision of fake reviews, methodological advancement in the summarization and intertextual analyses of reviews for business
insights, the study of cultural aspects of online reviews including food, travel, and impulsive
buying, and the study of online reviews generated or evaluated in the domain of mobile
apps, games, and video games. The topic analysis also characterizes the evolution of topics
over time, showing that research on online reviews initially focused on the impact of these
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reviews as a source of information on various economic outcomes, and on their properties
as user-generated content. Between 2010 and 2015, the field experienced rapid growth, with
the study of analytical methods for collecting and analyzing online review data, and with
the rapid proliferation of research on the helpfulness and acceptance of online reviews, on
their role in upstream competition among retailers, and on inherent biases of online review
ratings, including the provision of fake reviews. Finally, more recent research in the field is
mostly building and expanding on the substantive topics that emerged in previous years.
We organized these extant studies based on the eWOM process of Babić Rosario et al.
(2020), and discussed the research insights according to whether they explained mechanisms
of creation, exposure, and/or evaluation of online reviews. Several important insights emerg
from the framework-driven discussion. First, the processes regulating online review provision reflect complex mechanisms explained by social and consumer psychology, span across
one- and two-sided platforms, and include altruism, impression management, emotion regulation, social network embeddedness, reciprocity, and retaliation. Of course, review provision
also responds to economic incentives, which include monetary compensation and review reminders. However, incentives provided by companies can become a threat to both the quality
and trustworthiness of review systems, if the motivations to provide the reviews are mostly
extrinsic. The process of review provision is also highly dependent on reviewer-specific factors, which our analysis reveals remain relatively unexplored. This has serious implications
on the (in)ability of online reviews to be an unbiased, representative source of experiences
and opinions, as only people with certain characteristics provide reviews, and their reviews
represent mostly extreme experiences.
Second, people’s exposure to online reviews is not solely regulated by the functioning
and design of the platforms that host them. Again, individual-level factors determine which
people seek exposure to online reviews for their decision-making processes, and to what extent they rely on this exposure to make decisions. Social ties, peer influence, and individual
perceptions, once more, play a big role in seeing exposure to online reviews. Clearly, plat-
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forms can facilitate (or hinder) access to online reviews through design choices. For example,
platforms can influence which people are exposed to online reviews through timing and order effects so that the first few reviews become essential in shaping the subsequent rating
distribution. Other platform-design choices include changing the way reviews are presented
and opinions aggregated, enabling access to social networking functions, and implementing
product recommendation systems that could either complement or substitute exposure to
online reviews. Beyond platforms, businesses can participate in the review dynamics in two
ways. On one-sided platforms, they can respond to reviews (however, firms should be aware
of the positive and negative externalities associated with this practice). On two-sided platforms, they can engage in the production of their own online reviews, while being mindful
of reciprocity and retaliation concerns.
Third, the impact of online reviews reaches far beyond sales and revenue, and even
one single review can have significant economic effects. The consequences of consumers
incorporating online reviews in their day-to-day evaluations can be both microeconomic
(i.e., impacting individual perceptions of helpfulness, credibility, and trustworthiness of single
pieces of information) and macroeconomic (i.e., impacting entire economic systems through
market competition, discrimination, gentrification, public safety, and consumer welfare).
Once again, individual characteristics and social factors can amplify or reduce these effects.
4
Looking Ahead: A Roadmap for Future Research
As we have discussed, the literature demonstrates that the introduction of online reviews on
digital platforms gives rise to a multitude of benefits and challenges for consumers, platforms,
and businesses. This motivated the study of various aspects of online reviews across a diverse
set of disciplines. While the multidisciplinary approach to studying online reviews has shed
light on all the stages of the eWOM process, the vast and fragmented nature of research in
this field has made it challenging to synthesize and communicate insights effectively. Effective
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insight is required to understand the underlying mechanisms that govern review systems and
help policymakers design platforms that could be optimal for economic systems. To address
this shortcoming, in the introduction, we characterized and discussed the current literature
on online reviews, and provided a comprehensive overview of the research progress over two
decades.
The systematic survey of existing research reveals three main insights. First, the creation, exposure, and evaluation of online reviews by consumers have numerous benefits for
businesses, information systems, and consumer welfare, but they can also harbor many unexpected drawbacks. Second, the review format, the reviewers’ identity, and the platforms’
design characteristics have a complex impact on all the stages of the review process, and the
implications of this complexity for economic systems remain largely unclear. Third, online
reviews can be used as a valuable source of quantitative insight into consumer behavior and
market trends, potentially influencing economic decisions on a large scale.
Our survey, therefore, reveals the inherent complexity of this field of research. We find
there is a need for models and frameworks that can take into account both the advantages
and disadvantages of reviews, the fast-paced evolution of technologies, and the increasing
sophistication of platforms and consumers. Due to these evolving and dynamic factors,
numerous open questions and knowledge gaps exist across multiple disciplines that, we argue,
should be studied. In this section, we summarize what we think are the most important
unresolved gaps, and pinpoint what we believe are the most urgent opportunities for future
research (synthesized in Figure 4, according to the eWOM framework). We conclude with a
few closing remarks.
The Overall Effect of Online Reviews on Economic Systems We believe one of the
most fundamental questions in the field, spanning across types of platforms and across stages
of the eWOM process, has not yet been fully answered: What are the main, system-wide,
robust effects of online reviews on economic systems? Further, how do such effects depend
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on the context of the system and on its design? Ultimately, research should aid policymakers
in deciding whether online reviews are “good” or “bad” for economic systems, and how to
optimize their design. This includes examining the impact of reviews on platform dynamics
such as inequality, market efficiency, growth, and consumer preferences. Ideally, to explore
these questions, researchers would need some relevant exogenous variation, or implement
field experiments, that will help compare outcomes across a system with online reviews and
a system without them. Even though it may seem like a daunting task, similar work has
been done in other fields (e.g., Salganik et al. 2006, and Stephen and Toubia 2010).
Diversity, Equity, and Inclusion We reviewed several studies that investigate behavioral
biases, unfairness, and in some cases, episodes of systematic discrimination arising on online
review platforms. However, it is unclear whether all forms of discrimination have been
identified, and the field also still struggles to propose potential solutions to these problems.
Future research should urgently address this gap. For example, do online reviews increase
or decrease overall fairness in judgment and decision-making? How can platforms create
reputation systems that are not only minimally biased, but are also fair towards their users,
their stakeholders, and society in general? How can platforms and businesses effectively
prevent the manipulation of ratings and reviews? What are the implications of platformwide policies that allow for more privacy (e.g., hiding username, location, photo) in terms
of fairness and discrimination? Finally, research needs to more deeply understand what
trade-offs, if any, arise when promoting the DEI aspects of online review platforms.
In regard to the review creation stage, the state of the literature calls for further research
on questions exploring the representativeness of review writers, and the influence of factors
like gender, diversity, personality, and values on the patterns of review creation. Specifically,
research studying the characteristics of the people who create reviews is needed. How well
do reviewers represent the typical distribution of the consumers who purchase a product
or engage with a service? How do individual-level characteristics, and in particular gen-
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der, identity, diversity, and equity concerns, values, and personality traits interact with the
mechanisms regulating review creation?
Platform Design The above open questions suggest that platform design remains a central topic for future research. Platform design principles have fundamental implications for
all the stages of the eWOM process, and numerous issues remain open to future investigations. For example, what is the optimal way to aggregate review ratings, text, and visuals,
knowing that consumers are sensitive to order, context, and timing effects? Should reviews
be displayed chronologically, by helpfulness, or by topic, to maximize their informativeness
and value in the review evaluation stage? How should the system weigh verified reviews versus non-verified reviews? As platforms rely on sponsored content and advertising to sustain
their existence and growth, what is the optimal approach to combining sponsored content,
advertising, and organic reviews? Which content strategies and design principles allow platforms to maximize both consumer and firm welfare with minimal redundancies? Moreover,
to what extent should platforms be transparent about their behind-the-scenes algorithmic
design?
Another important aspect of platform design in the review creation stage is related to
the seemingly inherent trade-off between privacy concerns and review informativeness. This
trade-off is becoming more and more relevant in a world in which data concerns and privacy
are becoming a salient topic. A consumer who does not care about privacy, and is willing to
reveal rich information about themselves and their experience, may be more willing to write
a review than a consumer concerned about privacy. This privacy-insensitive consumer can
potentially create a more informative review. In contrast, a reviewer who is highly sensitive
to privacy may not even write a review, and if they do it may be much more generic, and
lack details. These considerations may affect representativeness and incentives for review
generation. We believe future research should explore the boundaries of this trade-off, how
consumer preferences are structured in relation to it, and to what extent it affects platforms
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and the usefulness of reviews. Additionally, research could explore the characteristics of
a privacy-aware online review system, that allows users to provide valuable information
without disclosing sensitive personal details.
The remaining open questions in this domain have a methodological focus. In particular,
questions about the appropriateness of Likert scales, whether multi-dimensional or singledimensional ratings are more effective towards truthfully evaluating an experience, and which
is the optimal format in which the review text should be solicited. We believe that these
questions still require further and deeper investigation.
New Technologies New technologies with a disruptive potential towards review generation, exposure, and evaluation are rapidly emerging, including augmented and virtual reality
(AR/VR), large language models (LLMs), and artificial intelligence agents (AI). These new
tools generate a lot of interesting new questions for review platforms, spanning all the stages
of the online review process. For instance, can LLMs and AI agents benefit customers by
helping them process and aggregate the information in reviews? Will such technology induce
bias or reduce it?
In terms of review creation, consumers (and perhaps firms) might use AI tools to generate
reviews, with potentially significant implications. What are the implications of the use of
AI tools on the quantity and quality of the reviews? Does the introduction of these tools in
the review process cause review content to become more homogeneous and less informative?
What are the implications of the introduction of AI tools for review platform economies and
consumer welfare? Will platform manipulation increase due to the availability of these tools?
Can and should platforms identify or flag manipulated reviews created by LLMs?
AI tools could also impact the mechanisms of exposure and evaluation of online reviews.
Google with BARD and Microsoft with ChatGPT are already incorporating LLM features in
their search engines (Hsiao 2023). This means that LLM-generated responses to queries can
already summarize reviews for consumers. This has several implications: in the exposure
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stage, search engines might determine the type and characteristics of the online reviews
consumers will be exposed to. Additionally, consumers may choose to reduce their visits to
review platforms, like Yelp or Tripadvisor, with significant consequences for consumer ability
to evaluate and act upon online reviews. What are the consequences of these phenomena for
the future of online review platforms?
Technological advancements also enable novel multimedia formats for reviews, such as
long-form video reviews on YouTube, and short-form or live-streaming review videos on social
media platforms like Snapchat and TikTok. At the review creation and evaluation stages, is
video review content more informative, persuasive, and memorable than traditional online
review formats? Or does it overwhelm consumers with stimuli and information? Do these
effects change depending on synchronicity (for instance, when the review is presented in
a live stream versus video format)? What are the rich media features that make video
review content successful? Examining the psychological mechanisms, and determining the
appropriate circumstances for using different media formats, can provide valuable and timely
insights.
Content Creators and Creation Novel review formats also allow for the rise of novel
social influences playing a role in the online review process. This phenomenon also interests
sponsored and promotional reviews, which increasingly take the form of “informal” opinion
videos, try-on of gifted or supplied products, and sponsored hauls of PR packages, especially
among online influencers. In this domain, what is the role of influencers in creating online
reviews, relative to non-influencers? Do consumers more or less trust influencers’ review
content, compared to an "organic" review? What is the boundary of credibility of influencer
reviews, given the perception that influencers are frequently paid to showcase products and
services? Are the platforms hosting (disclosed or undisclosed) sponsored influencer reviews
negatively impacted in terms of trustworthiness and credibility, in line with the negative
impact of traditional promotional reviews?
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In addition to social factors, increasing our understanding of timing, context, and order
effects on the online review process is another important area requiring more investigation.
This area is related to the issue of (non)-representativeness of the distribution of online
opinions with respect to the customer population. What is the right timing and context to
solicit review writing from different customers? Would timing considerations be affected by
the possibility that the opinion of reviewers may change over time? Does the willingness to
create reviews change depending on the requested review format (e.g., picture, video, text)?
Finally, even though considerable literature shows that human decision-making is highly
related to emotions, there is only limited research that explores the role of emotional content
in the evaluation of online reviews, and the role of the emotional dynamics that occur during
exposure to reviews. For example, extant research suggests that negative reviews hurt firms’
sales and consumer demand. But what are the behavioral drivers of these effects? Several
aspects of emotional content can drive these results, such as valence or arousal. Consumers’
emotional process as they write or read a review likely plays an important role, but until
now, remains under-researched.
Figure 4: Main Gaps in Online Review Research
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5
Concluding Remarks
Digital platforms have attracted a massive number of consumers in the last few decades and
are increasingly playing an important role in their lives and decision-making. Yet, these
platforms are complex ecospheres with sometimes conflicting interests between firms and
consumers, and even between consumers themselves. Online reviews emerged as a solution
for publicly transferring information between consumers to reduce information asymmetry in
markets and increase the push for efficiency and fairness. As we have shown here, this picture
is extremely complex. We argue that studying online reviews is important because online
reviews are the best example of how user-generated content on digital platforms has economic
and societal effects. Online reviews are easier to study than other forms of digital interactions
because they include quantitative measures and have a well-defined purpose. Studying such
reviews can also help us gain insight into digital platform mechanisms in general, and their
effect on our lives. Given the increasingly large and dominant role these platforms play in
our lives, we believe this type of research is an opportunity to gain invaluable insight and
knowledge that can help platform designers to better understand and design digital platforms
to improve the welfare of consumers, firms, and platforms.
To achieve this goal, this paper discussed and analyzed the state of the literature and
offered several areas that can benefit from additional research. We hope to see these questions
addressed in the near future.
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Declaration of generative AI and AI-assisted technologies
in the writing process
During the preparation of this work, the authors used ChatGPT in order to proofread some
of the text in the article. After using this tool/service, the authors reviewed and edited the
content as needed, and take full responsibility for the content of the publication.
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Appendix
A
Descriptive Statistics
Table 2 provides descriptive statistics for the final database of articles on online reviews,
which includes 2,492 papers authored by 2,336 distinct authors, published in 1,381 journals
or conferences.
Table 2: 2000–2023 Literature on Online Reviews: Descriptive Statistics
Citations
Citations per Year
Citations per Author
# Authors
Years since Publication (in 2023)
Abstract Length (# Characters)
Abstract Length (# Words)
Mean
134.34
15.46
58.76
2.91
7.28
1272.66
185.88
SD
481.01
32.88
294.61
1.42
4.05
691.13
101.7
Min
10
0.53
2
1
1
172
27
Max
9671
794.45
8739
7
23
12385
1822
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B
BERTopic Modeling and 5-Year Interval Analyses
For the analysis of research topics detailed in Section 2.1, we focused on abstracts containing
a minimum of 250 characters, since BERTopic’s performance improves with the amount of
data available. To prevent the emerge of non-substantive topics, we excluded a subset of
frequently occurring words that do not contribute to the identification of meaningful research
areas (i.e., “ocrs”, “online reviews”, “methodology”, “scoping”, “study”, and “user”).
In terms of technical criteria, we required BERTopic to include at least 10 papers per
topic, and we fine-tuned the dimension reduction parameters to capture local structures while
performing dimension reduction. This involved limiting the UMAP procedure to a maximum
of 5 neighbors for estimating topics, and a maximum of 6 neighbors for the embeddings
reduction procedure to enhance result visualization (McInnes et al. n.d.).
We ran several iterations with automatic topic number selection. Eventually, we selected
the 17-topic solution described in the main text, based both on the orthogonality and the
interpretability of the topics. The model also identified an 18th topic, labeled “Topic -1”,
which consists solely of noise words, and is not included in the main analyses.
B.1
5-Year Interval Analyses
To provide more detailed insights into the emergence of different topics over time, we reestimated the 2-dimensional BERTopic models on abstracts by year of publication in three
5-year intervals: 2006–2010, 2011–2015, and 2016–2020 (Appendix Figure 5). Due to an
insufficient number of documents, we excluded the periods 2000–2005 and 2021–2023 from
this analysis.
The analysis suggests that literature on online reviews published between 2005 and 2010
converged on two substantive topics: the impact of online reviews as a source of information
on a variety of outcomes, and the properties of online reviews as user-generated content.
Between 2010 and 2015, the field experienced rapid growth, and a wide range of substantive
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and technical topics emerged. A dense cluster of papers explored analytical methods for
collecting and analyzing online review data, aimed at summarizing opinions through the
analysis of sentences, mining customer opinions and sentiment in online reviews, and classifying review sentiment using Support Vector Machines (SVMs). A set of smaller, but distinct
clusters indicated a rapid proliferation of various related substantive research topics, including the helpfulness and acceptance of online reviews, the role of online reviews in upstream
competition among retailers, and the inherent biases of online review ratings. Furthermore,
the substantive cluster of studies on fake reviews made its initial appearance, occupying a
relatively distinct and isolated space in the 2-dimensional representation of research topics.
Finally, research produced between 2016 and 2020 built upon (and provided further nuances)
to the substantive topics that emerged in previous years. For example, the macro-cluster of
studies on fake and promotional reviews began addressing the issue of sponsorship disclosure,
while the macro-cluster of culture and entertainment studies branched into distinct streams
focusing on travel, museums, and movie reviews. Furthermore, a research topic examining
management responses to online reviews, and linked to studies on tourism, services, and entertainment, emerged between 2016 and 2020. This connection may be due to the empirical
contexts of the studies investigating managerial responses.
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Figure 5: BERTopic Analyses by 5-Year Intervals.
(a) 2005–2010
(b) 2011–2015
(c) 2016–2020
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