Examining factors affecting online review helpfulness in the context of Vietnam: The role of review characteristic
Tram Thi Le Dinh, My Giang Chu/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Examining Factors Affecting Online Review Helpfulness in
the Context of Vietnam: The Role of Review Characteristic
Tram Thi Le Dinha*, My Giang Chub
aMarketing Faculty - University of Economics - The University of Danang, Vietnam
A B S T R A C T
This paper explores a kind of electronic word-of-mouth (e-WOM), the online consumer review (OCR). It has
increasingly become important sources of information that help consumers in their decision-making since
flourished e-commerce. In the recent years, OCRs have become an important information source that allow
consumers to search for detailed and reliable information by sharing past consumption experiences (Gretzel,
2011; Fesenmaier et al., 2008; Yoo & Gretzel, 2008). In a sense, E. Brynjolfsson et al. (2003) has found that
64% of the online shoppers spend 10 min or more reading reviews and 68% of the online shoppers read at
least four product reviews before purchasing. It has caused information overload, making it difficult for
consumers to choose reliable reviews. J.R. Bettman et al., (1998) have indicated that 78% of participants in
26,000 participants trust recommendations from other consumers. Consumers seek more detailed product
information from OCRs written by others because they find it difficult to make purchase decisions based on
information provided by sellers when buying products from an online retail market. To succeed it is important
for an online retail market to lead product reviewers to write more helpful reviews, and for consumers to get
helpful reviews more easily by figuring out the factors determining the helpfulness of online reviews (Hyunmi
Baek et al., 2012). By conceptualizing the online review helpfulness regards the peer-generated product
evaluation that facilitates the consumer’s purchase decision process. Online review helpfulness plays an
increasingly important role in consumers' online shopping behavior. The aim of this study is to explore the
factors that influence online review helpfulness while individuals gained review characteristics including
readability, length rating, content, and sentiment in the context of Vietnam with social and cultural
specificities that make a difference.
The second-data will be collected from Vietnam e-commerce sites such as Lazada.vn and Tiki.vn sites. A total
of 30000 reviews expect to receive for data analysis. The results of the data analysis will support several
findings: the review characteristics including rating, sentiment, length, readability, and content may
significantly influence online review helpfulness. These findings provide insights for both academics and
practitioners regarding the potential implications, which serve as new perspectives for researchers and online
market owners to understand what determinants factors on e-commerce sites may affect online review
helpfulness and leads to customer decision-making.
Keywords: Electronic word of mouth (e-WOM); online consumer reviews (OCRs); online review helpfulness
(ORH); review rating; review length; review sentiment; review readability; review content.
1. Introduction
1.1. Research motivations
Word-of-mouth (WOM) also called word-of-mouth advertising in trade marketing, differs from naturally
occurring WOM in the context of digital and online marketing, electronic word-of-mouth (e-WOM) is any
* Corresponding author. E-mail address: dtletram@gmail.com
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positive or negative statement made by potential, actual or a former customer which is available to multitude of
people via the internet (Hennig-Thurau et al., 2004) while in traditional WOM, the message disappears as soon
as it is spoken. In case of e-WOM, the message remains over period. In recent years, e-WOM have flourished
and played the important role on online customer behavior. Mudambi & Schuff (2010) have indicated that e-
WOM (e.g. blogs, online reviews, forum, retailing websites, social media posts, and messages posted on online
groups) rapidly impacted on business at any time and from anywhere. According to the Vietnam e-commerce
indicator report 2018, e-WOM has an annual growth rate of 35% and along with that online marketing attempt to
attract more and more consumers. Digital in 2017, the percentage of consumers who regularly search for product
information before buying is 48%, and access to retail pages is 43%. These trends are creating the inevitable
development of e-WOM in Vietnam, whereby consumer-generated online reviews will become increasingly
important for both consumers and businesses.
Customer buying behavior has greatly influenced by online reviews (Singh et al., 2017). Walther et al. (2012)
has pointed out that consumers often access consumer-generated sources when shopping online. A website
usually provides at least three reference sources including (1) consumers’ opinion or experience, positive or
negative feedbacks, and recommendation; (2) Information of consumer’s thank you for reviewing and rating
helpfulness; (3) ‘‘Review the reviews’’ (Walther et al., 2012). In Vietnam, businesses and especially online
retailers today are very interested in CORs because of their influence on customer's purchase decision-making.
In a sense, Risselada et al. (2018) has shown that consumers trust online reviews provided by the virtual
community over commercial information. Therefore, COR attracts the attention of many marketers, researcher,
and practitioners. CORs created by consumers are the source of consumers when shopping online. What factors
determinants in ORH and the effects of ORH on consumers’ attitudes and behaviors haven’t well understood in
the previous studies (Hong et al., 2017; Wu, 2017). On the retail sites such as lazada.vn, tiki.vn, so on, customer-
generated review sources day by day.
The above discussions highlight the importance of review characteristics on ORH which leads to customer's
purchase decision-making. However, there have been few attempts to address how helpfulness score evaluates
on Vietnam e-commerce sites. To fill these gaps, the aim of this study is to explore ORH while individuals
gained review characteristics including readability, length rating, content, and sentiment. This study may
contribute to the online review literature in several ways. First, from the perspective of rating, this study
demonstrates how rating from review rating enables online review helpfulness score. Second, from the
perspective of sentiment, this study explains how rating facilitates online review helpfulness score. Third, the
concepts of review length and readability, content were employed to explain online review helpfulness score. In
short, this study integrates the six different theoretical perspectives outlined above. It provides a richer model to
better examine the online review helpfulness score in the context of Vietnam. Such the framework building may
provide a complete understanding of how rating, length, readability, content, and sentiment may occur in turn
lead to online review helpfulness score.
1.2. Research questions
From the discussions above, it is important to understand what factors effect on ORH and consequently leads
to consumers’ decision-making. To fill the gaps in the previous research on ORH, rating, length, sentiment,
content, and readability associated with ORH, this study addresses the following research question:
What are the determinants of the online review helpfulness on Vietnam e-commerce sites?
2. Conceptual background
Ghose and Ipeirotis (2006) have indicated two methods which aim to evaluate ORH: (1) customers’
helpfulness expectation for evaluating individual’s ORH; (2) business’s sales expectation for companies. Most of
previous studies attempted to research on perspective of customers’ helpfulness expectation.
How do consumers evaluate online reviews? According to Risselada et al. (2018), consumers' motivation,
opportunity and ability influenced how consumers process information. Motivation is defined as goal-directed
behavior, and opportunity is an extent to which the consumer is distracted from processing or has limited time.
Ability is whether the consumer has sufficient skills to be able to process information. Online reviews are
considered helpful when the customer reads and finds it helpful after reviewing and evaluating information for
customer decision-making (Lee et al., 2018). In 2014, Risselad et al. (2018) has found that there are about 140
million product reviews and a product with thousands of reviews on Amazon.com site. Consumers could not
read all the available reviews, they just picked up some reviews to evaluate, and so the concept of review
helpfulness appears and is considered as an inevitable phenomenon. Consumers voluntarily vote for the online
review helpfulness score and then this vote again guides consumer in their decision-making process and have a
larger impact on the formation of consumes’ attitudes toward the reviewed product.
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Electronic word-of-mouth (e-WOM)
Different from traditional WOM, WOM on the Internet, called electronic word-of-mouth (e-WOM), is
measurable since comments on a product are written and available in the websites (D. Godes, D. Mayzlin, 2004).
e-WOM is defined as any positive or negative statement made by potential, actual, or former customers about a
product or company, which is made available to a multitude of people and institutions via the Internet (Hennig-
Thurau et al., 2004). e-WOM allows consumers to socially interact with one another, exchange product-related
information, and make informed purchase decisions via computer-mediated conversations (Blazevic et al., 2013;
Hoffman and Novak, 1996). One type of e-WOM, OCRs, marketers can decide whether to allow OCRs to be
shown or not, and if they are shown marketers can offer a specific review format in order to guide consumers to
post their opinions in the way they want.
2.1. Online customer reviews (OCRs)
Online customer reviews (OCRs) can be defined aspeer-generated product evaluations posted on company or
thirdparty web sites (Mudambi and Schuff, 2010). It has been shown to improve customer perception of the
helpfulness and social presence of the websites (Kumar and Benbasat 2006). In the recent years, OCRs have
become an important information source that allow consumers to search for detailed and reliable information by
sharing past consumption experiences (Gretzel, 2008; Fesenmaier, 2011; Lee, & Tussyadiah, 2011; Yoo &
Gretzel, 2008). The difference between the information sellers and buyers important in purchasing experiential
goods because people find it difficult to assess the quality of the intangible products before consumption OCRs
listing on a shopping website, they may not have easy access to information about the true quality of the product
and therefore may not be able to judge precisely a product's quality prior to its purchase (Fung & Lee, 1999).
Hence, consumers tend to rely on OCRs that allow them to obtain sufficient information and have indirect
purchasing experiences so as to reduce their level of perceived uncertainty (Ye, Law, Gu, & Chen, 2011).
2.2. Online review helpfulness (ORH)
Helpfulness of online product comments reveals how consumers evaluate a review. Based on information
economics theories, Mudambi and Schuff (2010) defined a helpful customer review as peer-generated product
evaluation that facilitates the consumer’s purchase decision process and online review is helpful when
consumers perceive value of online reviews while shopping online. Cheung, Sia, & Kuan have shown that
(2012) some online review sites allow readers to ‘‘review the reviews’’ to maintain the value of online reviews
and to address concerns about their credibility and quality. The most common approach is to rate a review as
‘‘Helpful’’ or ‘‘Not Helpful’’ (Baek et al., 2012; Li et al., 2013). A helpfulness score is then calculated as the
percentage of ‘‘Helpful’’ votes among all votes. Helpfulness has also been referred to as the value of the review
(Schindler and Bickart 2012). It is measured by dividing the number of people who find a review helpful by the
total number of people who voted for that review (Mudambi and Schuff 2010; Sen and Lerman 2007). Reviews
with a higher number of helpfulness votes were found to have a higher correlation with sales (Chen, 2013; Chen
et al., 2007). Review helpfulness represents the number of helpful votes that the review has received out of the
total number of votes that have been given regarding the helpfulness of the review. The helpfulness of reviews is
also determined by review readability, review length, content, and sentiment. In addition to being a quality
assurance tool, helpfulness can also be regarded as a subjective measurement of the potential value of the
information contained in a review. A review that influences potential customers could logically lead to a
purchase.
2.3. Factors influencing online review helpfulness
In the recent years, the researchers begin to explore the role of online reviews and have emphasized the
impact of on review online. According to Agnihotri & Bhattacharya (2016), these factors can be grouped into
two groups of quantitative factors and qualitative factors. The investigated qualitative factors such as content
readability and associated sentiments in text and concluded that these are two important qualitative cues
influenced on online review helpfulness. Hong et al. (2017) categorize the determinants of review helpfulness
into two categories: (1) Review related factors that are derived from review ratings, contents, including review
depth, review readability, linear review rating, quadratic review rating, and review age; (2) Reviewer related
factors that are derived from reviewers' background and self-description, including reviewer information
disclosure, reviewer expertise, reviewer expert label, reviewer friend number, and reviewer fan number. Thus,
this study introduces review characteristics that affect ORH in the following factors:
2.3.1. Review length
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Length of a review is an important predictor of its performance (Mudambi and Schuff, 2010; Schindler &
Bickart, 2012). Reading longer reviews may decrease consumer’s search costs through increased information
diagnosticity (Johnson and Payne 1985). The average length of a sentence determines the readability of writing
as much as any other quality (Garner, 2001). Short reviews are more likely to be shallow and lack the
comprehensive evaluation of product features. In contrast, longer reviews contain more information and are
more likely to contain deep analysis of the product, its features, and the context in which it was used. Longer
reviews are more likely to receive attention from users (Garner, 2001). According to Huang et al. (2015),
message length should not just be taken at the face value. Review length was indeed a significant predictor of
review helpfulness.
2.3.2. Review readability
In general terms, the concept of readability describes the effort and the educational level required for a person
to understand and comprehend a piece of text (DuBay 2004, Zakaluk and Samuels 1988). Online reviews are
information resources that consumers utilize to gain knowledge about products and services. Zakaluk and
Samuels (1988) stated that the extent to which an individual requires to comprehend the product information can
present the level of readability. According to the linguistic characteristics, the method to calculate readability is
considered as a scale-based indication of how difficult a piece of text is for readers to comprehend (Korfiatis et
al., 2012). A communication is difficult to understand, the reader is likely to make negative inferences about the
communicator (Schindler & Bickar, 2012). Thus, it is expected that the occurrence of style variables that reduce
the readers’ ability to comprehend a review will be associated with less valuable reviews. Such variables include
misspellings, bad grammar, the use of inexpressive slang, the use of qualifications, and repetition.
2.3.3. Review sentiment
The sentiment of a message can be effectively communicated through the text and significantly influences the
perceptions of the reader (Harris and Paradice, 2007; Riordan and Kreuz, 2010; Walther and D’Addario,
2001).Different people have different experiences with the same product. While some studies use human
subjects to extract the sentiment of online consumer review, others use automated sentiment mining to extract
sentiment from the text of reviews (Bai, 2011; Schindler and Bickart, 2012; Sen and Lerman, 2007). Sentiment is
the vehicle for people to convey their emotions to others through text or a binary variable indicates whether a
review conveys a mixture of positive and negative attitudes towards the product features (Schindler and Bickart,
2012). According to Harris and Paradice (2007), the emotions contained in a message transferred significantly
influence how the message is processed and interpreted by the receiver (Riordan & Kreuz, 2010; Walther and
D’Addario, 2001). One can argue that the sentiment contained in the review is the driver of the perceptions
regarding its helpfulness rather than just the numerical rating. The receiver of a message can detect the sender’s
emotions through verbal cues such as emotion words (Harris and Paradice, 2007). According to Agnihotri &
Bhattacharya (2016), online reviews with positive comment about firm’s products and services can bring
positive attitudinal changes in consumers’ perceptions and negative feedback can bring about the reverse.
2.3.4. Review rating
People tend to find reviews with extreme numerical ratings more helpful (Mudambi and Schuff 2010). As
consumers can expect that reviews with extreme ratings also contain more sentiment because the author is either
very satisfied or very unsatisfied. The extreme levels of satisfaction or dissatisfaction are very likely to turn into
strong emotions and consequently strong sentiment. Mudambi and Schuff (2010) attempted to analyze the
relationship between review rating and review helpfulness.They indicated that review helpfulness increases
when the rating is low or high for search goods and moderate for experience goods.
2.3.5. Review content
Review content, in this study, based on the qualify and functions of product, service (e.g. shipping, after-
sales) and others. As for the consumer, review content based on the product quality represents a valuable
information and can form consumer attitudes and shape buying intentions (Ghose & Ipeiroti, 2006; Glemet &
Mira, 1993). Reviews that list product features may close information gaps for readers by disclosing product
information. Based on product characteristics, functionalities and features, consumers make assessments about
actual product quality. In a sense, Zeithaml (1988) has pointed out that product quality as a “consumer's
judgment about a product's overall excellence or superiority”. Product quality relatedness, the retailer focus
readers on particularly determinant features such as the level of quality, functions or e-commerce services. A
review providing such information helps to reduce the information asymmetry between the actual user of a
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product and the reader of the review.
3. Proposed conceptual framework and hypothesis development
This essay pays much attention to demonstrate the relationship between the determinant review
characteristics and online review helpfulness without exploring the reviewer's characteristics because
Vietnamese seldom give many opinions and personal information due to cultural characteristic limits. This study
highlights the role of online review helpfulness on search products instead of experience products. As for
research products, online reviews play the important part in customer decision-making. Hence, negative reviews
are considered to be more helpfulness. Experience products; in contrast, require customers’ attention on
evaluating quality so that negative reviews are less helpfulness than neutral reviews.
Fig1. Research proposal framework
3.1. Review rating and online review helpfulness
Rating score of review is an important factor predicting helpfulness of online review (O’Mahony & Smyth,
2010; Lee et al., 2018). It also is an important reference for quality products and services. Consumers tend to
focus on different information sources of reviews. Specifically, peripheral cues such as star ratings of the
reviews are helpful in the information search stage whereas the number of total words in a review and the
number of negative words are influential in the evaluated stage. Hu, Liu, and Zhang (2008) concluded that online
consumer reviews infer product quality and reduce product uncertainty, in turn aiding the purchase decision.
This essay supposed that consumers get review rating, they are likely to be easy in the decision-making process.
The following hypothesis is proposed.
Hypothesis 1: Review rating is positively related to online review helpfulness
3.2. Review length and online review helpfulness
According to Mudambi & Schuff (2010), the review length influences online review helpfulness and depends
on the type of product. As for search products, the length has positively influenced on online review helpfulness.
Longer reviews are more likely to be perceived helpful (Johnson and Payne, 1985). An individual’s argument is
more persuasive when it provides the larger amount of information (Schwenk, 1986). Increased number of
reasons for a choice escalates the decision maker’s confidence (Tversky and Kahneman, 1974). As review length
gaining via e-commerce sites, consumers enable to get detailed and reliable information by sharing past
consumption experiences. Thus, it became the important sources of information that help consumers in their
decision-making. Accordingly, the following hypothesis is proposed to examine the effects of review length on
ORH.
Hypothesis 2: Review length is positively related to online review helpfulness
3.3. Review readability and online review helpfulness
Readability, the quality characteristics, is considered as an important factor determining the ORH (Schindler
& Bickar, 2012). According to the linguistic characteristics, readability is considered as a scale-based indication
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of how difficult a piece of text is for readers to comprehend (Korfiatis et al., 2012). Reviews are widely read,
understood and helpfulness once reviews are clearly written, easy to understand, good style (Agnihotri &
Bhattacharya, 2016). High-quality information may be characterized as accurate, reliable, current, concise, fair,
easy to understand, organized, and many other things (Huang et al., 2015). While review readability reached,
consumers are likely to feel easy to understand and get detailed information, they are able to review the reviews
to maintain the value of online reviews or rate a review as helpfulness. This essay took a specific interest in the
work that review readability including speeling errors, style, Vietnamses from perspectives of qualitative
characteristics plays on ORH on Vietnam e-commerce sites. We suggest that if an individual gained review
readability provided by experience consumers, they are able to get helpful in their decision-making. Thus,
review readability perceived by individuals may influence the ORH on e-commerce sites. Hence, the following
hypothesis is proposed.
Hypothesis 3: Review readability is positively related to online review helpfulness
H3a: Spelling errors are negatively related to online review helpfulness
H3b: Review written in Vietnamese is positively related to online review helpfulness
3.4. Review content and online review helpfulness
According to Siering & Muntermann (2013), information reviews of product quality is judged to be more
helpful than other information. The study of Weathers et al. (2012) examined information characteristics such as
the description of situation, listing product feature, and confirmed that review content affected helpfulness. The
review content, which is displayed in the body of the text along with review meta-characteristics such as quality,
function, e-commerce services. We suggest that if individual gained the content of reviews, they are able to get
reliable information. Thus, review content perceived by individuals may influence the ORH on e-commerce
sites. The following hypothesis is proposed.
Hypothesis 4: Review content is positively related to online review helpfulness
3.5. Review sentiment and online review helpfulness
The reviews with positive and negative evaluation are associated with a high value review (Schindler et
Bickart, 2012). Negative reviews often have a higher reference for consumers than positive ones. Sometimes
positive reviews are not trusted because of the sales phenomenon and review consultants promoting their
products. We suggest that review sentiment perceived by individuals may influence on ORH. Accordingly, the
following hypothesis is proposed to examine the influence of review sentiment on ORH.
Hypothesis 5: Review sentiment is positively related to online review helpfulness
4. Methodology
The second-data conducted in the context of Vietnam e-commerce sites to measure the following six
constructs proposed: (1) online review helpfulness, (2) length, (3) sentiment, (4) rating, (5) readability, (6)
review content. Online review helpfulness studies commonly are conducted by two types of data sources (Hong
et al., 2017): (1) First-hand data collected using surveys or questionnaires; (2) Second-hand data scraped from
online review systems provided by e-commerce practitioners. Collecting first-hand data is often time-consuming
and subject to common method bias. Second-hand data has the advantage of quickly collecting a large number of
reviews and has been commonly used in online customer review studies. This study will use second-hand data to
its advantage of collecting a large number of reviews.
Data collection
In order to collect the data and test our theoretical model, we will develop a web crawler to capture the
contents of the high-involvement product reviews such as smartphone, laptop, tablet, reading books device,
Product
This article collected second-data from the reviews of high-involvement product reviews such as smartphone,
laptop, tablet, reading books device, camera, IT product, TV. As for high-involvement products, online reviews
are an important information source in the purchasing process (Mudambi & Schuff, 2010).
Online retailer websites
Lazada Vietnam (http://www.lazada.vn) and Tiki (http://www.tiki.vn) are choosing in this study as the e-
commerce platform for the data source. Lazada.vn and Tiki.vn are online retail markets and have extensive
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consumer review systems which have been developed and improved comprehensively. In Vietnam marketplace,
Lazada.vn and Tiki.vn are the top to provide the helpfulness vote systems which create great values. Based on
the helpfulness votes, Lazada.vn and Tiki.vn rank all reviews and reviewers. Therefore, we will collect actual
consumer review data from these websites.
Coding of reviews
We operationalize the attribute “helpfulness”, rating score, review length, review readability, review
sentiment, review content with the description in the following table:
Attributes
Measurement
Source
Huang et al. (2015); Walther et al.
(2012); Hong et al. (2017);
Seiring et al. (2018)
The ratio of number of helpful
votes to total votes
Helpfulness
Rating
O’Mahony & Smyth, 2010; Lee et
al., 2018
Rating score
Huang et al. (2015); Otterbacher
(2009)
Review length
Spelling Errors
Word count
Ghose and Ipeirotis (2011);
Krishnamoorthy (2015)
Spelling error count
0: No
Vietnamese
1: Yes
-
5-Point Likert scale:
1: Negative
2: Quite negative
3: Neutral
Review sentiment
Schindler & Bickart (2012)
4: Quite positive
5: Positive
1. Product: Quality, functions
2. Service: Shipping, after-sales
3. Others
Weathers et al. (2012)
Review content
Siering & Muntermann (2013)
5. Limitations and future research
This essay explains the relationships between review rating, length, readability, sentiment, review content
and ORH and provides some findings. Although this study had taken an initial step in exploring ORH on
Vietnam e-commerce sites. Nonetheless, several limitations remained and are worth to be explored in the future
research.
First, the data will be collected from Lazada.vn and Tiki.vn with seven groups of product. It may not claim
that the results can be generalized to all reviews in the context of Vietnam e-commerce sites. This essay will
collect 30000 reviews from Lazada.vn and Tiki.vn. To increase generalizability, the future research needs to
consider sampling reviews from more e-commerce sites.
Second, data used for this study will collect from product type including Smart Phone, Laptop, Reading
Books Device, Camera, IT Product, Tablet, TV. Generalizing the study conclusions to all products including
eBook, clothes, shoes, and perspectives of reviewer characteristics should be well scrutinized with cautions. The
future research may investigate the reviews from various product categories.
Third, the review characteristics in this study focused only on rating, sentiment, length, readability, content
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gained from reviews of experience consumers. The future study should incorporate other types of review
characteristics such as review aspects, content abstractness of product review, and authorship of product review
into the research to widen the comprehensive understanding of ORH. Finally, this essay only investigated the
review characteristics via reviews on e-commerce sites. Future research may examine both the effect of reviewer
and review characteristics.
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