Các đặc điểm của đánh giá trực tuyến hữu ích đến mức nào? Quan sát thông qua các kênh OTA
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HOW USEFULARE ONLINE REVIEW’S CHARACTERISTICS?
OBSERVATION THROUGH OTA CHANNELS
CÁC ĐẶC ĐIỂM CỦA ĐÁNH GIÁ TRỰC TUYẾN HỮU ÍCH
ĐẾN MỨC NÀO? QUAN SÁT THÔNG QUA CÁC KÊNH OTA
MA, Nguyen Cao Lien Phuoc - MA, Nguyen Minh Tam - MA, Chu My Giang
University of Economics, The University of Danang, Vietnam
Abstract
The purpose of this paper is to discuss the effect of online reviews ‘characteristics including
content, review rating, valence, timeliness, length and form on hotel online booking decision
among OTA channels. A multivariate regression approach was utilized to analyze data from 421
responses of tourists who stay in small and medium-sized hotels in Da Nang and online popula-
tions from August 2019 to December 2019. The findings indicate that the content of online reviews
and review rating have the greatest impact on potential tourists’booking decisions while timeli-
ness and form of online reviews show a lower-level influence. Especially, the length of online re-
views fails to gain substantial attraction of readers when looking for accommodation for their
future trips. In addition, the result witnesses the positive relationship between valence of online
reviews and booking intentions towards a hotel. This study provides a theoretical insight into the
influence level of six common characteristics of online reviews.
Keywords: online reviews, hotel online booking decision.
Tóm tắt
Bài báo tập trung thảo luận về mức độ tác động của các đặc điểm của đánh giá trực tuyến
bao gồm nội dung, xếp hạng đánh giá, giá trị, tính kịp thời, độ dài và hình thức lên quyết định
đặt phòng trực tuyến của khách sạn giữa các kênh OTA. Phương pháp hồi quy đa biến được sử
dụng để phân tích dữ liệu từ 421 phản hồi của khách du lịch lưu trú tại các khách sạn vừa và
nhỏ tại Đà Nẵng và qua khảo sát trực tuyến những dã từng đọc đánh giá trực tuyến trên các
trang OTA từ tháng 8 năm 2019 đến tháng 12 năm 2019. Kết quả cho thấy nội dung đánh giá
trực tuyến và đánh giá xếp hạng có mức độ tác động lớn nhất đến quyết định đặt phòng của
khách du lịch tiềm năng trong khi tính kịp thời và hình thức đánh giá trực tuyến cho thấy mức độ
ảnh hưởng thấp hơn. Đặc biệt, độ dài của các bài đánh giá trực tuyến không thu hút được sự
quan tâm của độc giả khi tìm kiếm chỗ ở cho những chuyến đi của họ. Ngoài ra, kết quả cho thấy
mối quan hệ tích cực giữa giá trị của đánh giá trực tuyến và ý định đặt phòng đối với một khách
sạn. Nghiên cứu này cung cấp một cái nhìn lý thuyết về mức độ ảnh hưởng của sáu đặc điểm
chung của các bài đánh giá trực tuyến.
Từ khoá: đánh giá trực tuyến, quyết định đặt phòng khách sạn
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1. Introduction
In the ever-increasing development of individual non-mandatory tours compared to a
guided tour, travelers tend to look carefully for information about their future trips (Kim et al.,
2017). Especially, because of the intangible characteristics of travel products, User-generated
content or online reviews are becoming popular when finding advice about local foods, accom-
modation or must-see destinations (Chan et al., 2017). Online reviews show a significant role in
making customers’ decisions and even in hotel booking field. Therefore, Online travel agencies
(OTAs) have recently emerged as a useful and trustworthy channel where tourists able to get all
the needed information Hernández-Méndez et al. (2015). Customers can search; compare accom-
modation’s’quality and price; and make reservations directly through some well-known platforms
like Agoda, Booking.com. According to the study, two-thirds of online bookings have particularly
come from OTA channels. As a result, marketing budget for OTA should be raised if hotel man-
agement officers want to boost selling revenue. One of the important factors influencing the
choice of one accommodation among a list of hotels listed on OTA channels is the online reviews
of previous tourists who are used to stay or experience services at that hotel. Although there are
a number of works investigated which factors of online reviews influence hotel online booking
decision ( Chan et al., 2017; El-Said, 2020), a lack of studies have focused on the influence level
of online reviews on hotel booking intention. To fill this gap, this study is designed to measure
how online reviews influence booking decisions as well as propose solutions for accommodation
businesses.
2. Literature review
2.1. Content of online reviews
Online user-generated content (UGC) and online reviews had a significant impact on travel
behaviour (Ye et al., 2011). The content of the online reviews can be seen as one of the most im-
portant aspects of the online reviews.. Although there are a variety of terms to describe its defi-
nition, it is easier to call it as the depth and the width of information provided by previous
customers (Schindler & Bickart, 2012; Racherla & Friske, 2012). Marcirio (2012) presented a
detailed analysis of users ‘online reviews of small-sized and medium-sized hotels (SMH). The
findings showed that rooms and services are two concepts gaining more attention of potential
visitors. In addition, the knowledge of hotels ‘staff which are foreign language skill, local famous
destination-related understanding; and their attitude, including the friendliness and the usefulness
have also considered by the majority of comments. As a result, the content of reviews is an im-
portant factor to help reviews attract consumers (Ghose & Ipeirotis, 2006). Cao et al. (2011)
pointed out that the content of the reviews also positively or negatively affects the purchase
process of the customers, thus influencing the decision to book a hotel. Therefore, the following
hypothesis is proposed as
H1: Content of online reviews influences hotel online booking decision.
2.2. Review rating
After actually consuming or experiencing the tangible products or intangible services, cus-
tomers tend to express their feeling by evaluating or scoring a given product. Review rating is
measured by the range from one star (poor), which shows extremely negative view of the product,
to five stars (excellence). There was a great deal of works that coined how customer ratings (stars
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and points) have a great influence on their buying decision (Zhu & Zhang, 2010). Major of re-
searchers agreed the influence of. Rating on the choice of customers (Chen & Xie, (2008). Espe-
cially, star rating is on the first stage of the decision-making process (Liu, 2013), creating initial
attraction and becoming an important way to increase the patience of readers to review online.
Therefore, the following hypothesis is proposed as:
H2: Review rating influences hotel online booking decision.
2.3. Valence of online reviews
Valence of online reviews could be formed in two different aspects of the negative (lost)
and the positive ( benefits) (Zhao et al., 2015). In 2009, Ye and her team agreed the important
role of positive comments in the online booking process. However, after that, A number of tests
found out that both positive and negative information are able to impact customers ‘trust and
product evaluation, leading to the increase in the ability of booking intentions (Browning &
Sparks, 2013). Therefore, the following hypothesis is proposed as:
H3: valence of online reviews influences hotel online booking decision
2.4. Timeliness of online reviews
While searching for information, customers may face some difficulties regarding timeliness
of information. It is measured by the date of the online reviews published (Gretzel et al., (2007).
This aspect refers to the currency, timeliness and up-to-date characteristic of information (Cheung
et al., 2008). Despite this general importance, the characteristics of information as timeliness is
often neglected in the online review- related works. Research on online feedback hypothesized
that currency of information is important in defining its quality (Gretzel et al., 2007). Customers
who intend to purchase in a near future depends more on current information. When customers
read old feedback, they may think that such information may be outdated, thus, not reliable to
base upon in making decision (McKinney et al., 2002). Similarly, some researchers also found
out that in e-commerce environment, the feedback on products which is more current tends to
draw more attention from customers (Cheung & Thadiani, 2012). It evidenced that 59.3% of to-
tally surveyed-customers claimed their higher dependence of more current feedback (Gretzel et
al., 2007). The influence can be improved by highlighting the updated features, and displaying
the more current feedback before the older ones (Chen,2008) as usefulness of information reduces
over time (Liu, 2006). Therefore, the following hypothesis is proposed as:
H4: Timeliness of online reviews influences hotel online booking decision
2.5. Length of online reviews
Another important aspect of the online review is the length of the review. The length of
the review is the total number of characters entered (Chevalier & Mayzlin, 2006). Some works
confirmed that the expansive and contested reviews will provide enough information for con-
sumers to rate the quality of a product review (De Ascaniis & Morasso, 2011). Long assessments
can contain more information (Pan & Zhang, 2011), increasing the convinced and reliable argu-
ments (Filieri, 2015) than short ones. In addition, consumers who spend more time in writing a
long review are more likely to be trustworthy than someone who only writes a few lines (Pan &
Zhang, 2011). Therefore, the following hypothesis is proposed as:
H5: Length of online reviews influences hotel online booking decision.
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2.6. Form of review
An image can be seen as a form of an attractive review that usually appears in the first sec-
tion of a review or an additional review. In a normal way, online reviews may contain vivid
images that reflect the true quality of the goods, such as color or inconsistencies of technical in-
formation. The visual reviews can reduce the consumer’s risk in the buying process and provide
additional experience for the reader. In the process of perceiving consumer information, image
evaluation has a great influence on the actual buying behavior. This is even more important for
experience services like invisible hotel industry, however, imaging allows us to visualize the serv-
ice context and helps consumers to infer about its level of quality before purchasing. Scholars
believe that images are always more persuasive than words because readers can get more objective
information about the destination from photos of Fang et al., (2016). Therefore, the following
hypothesis is proposed as:
H6: Form of online reviews influences hotel online booking decision.
3. Theoretical Framework and hypothesis
3.1. Proposed model
Although there is a list of previous studies that have discussed online reviews and the effects
of its on travelers’ choice of accommodation, they have mostly focused on studying the effects
of an online assessment in general or a specific aspect of the online evaluation. Most studies fo-
cused on how online product evaluation of product choice impact on product choice and buyer
experience (Chua and Banerjee, 2016; Mudambi and Schuff, 2010; Racherla and Friske, 2012).
From earlier domestic and international studies on online reviews and the impact of online reviews
on travel accommodation decisions, research model to explain more specifically was launched.
- Independent variables: content of review, review rating, valence of review, recentness of
review, length of review, form of review.
- Dependent variable: Hotel booking decision
Figure 1 Proposed research model
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3.2. Methodology
By using convenience sampling method, paper-based surveys were distributed to random
travelers who already stayed in some small-sized and medium-sized hotels in Da Nang City, Viet-
nam from September 2019 to November 2019; and online surveys for those who read online re-
views on the OTA site from September 2019 to November 2019. The questionnaire has two
sperate parts including questions about demographic profile of respondents and construct items.
In particular, content of online reviews (CR) includes four items (CR1-CR4) adopted from Mar-
cirio Silveira Chaves (2014) such as hotel services, room, location of hotel, staff respectively;
five items of review rating (RR) adopted from Forman et al (2008), five items of valence of online
reviews (VR) adopted from Osman Ahmed El-Said. (2019), four items of timeliness of online
reviews were applied from Cheung & Thadani (2012). In addition, three items of length of
online review were adopted and modifed from Pan & Zhang, (2011); De Ascaniis & Morasso,
(2011); Filieri, (2016). Form of review were measured by four items from Fang et al., (2016);
Papathanassis & Knolle, (2011); Peck & Childers, (2003). Three items of hotel online booking
decision were adopted from Osman Ahmed El-Said. (2019). The five-point Likert scale with “1”
(totally disagree) and “5” (totally agree) was used to measure all of items.
3.3. Sample
The main online survey gained 421 valid respondents. Table 1 summarize the demographic
of the respondents. While just under 48 percent were male; approximately 52% were female. The
data also shows that about 93 percent of the respondents are in the age of 18-35.
Table1. Descriptive statistics
Demographic Variables
Male
Number
Frequency (%)
201
220
421
6
47.7
52.3
100%
1.4
Sex
Female
otal
<18
18-25
221
170
24
52.5
40.4
5.7
Age
26-35
>36
Total
421
99
100%
23.5
33.5
16.2
19.7
7.1
Office
Student
House work
Freelancer
Other
141
68
Occupation
83
30
Total
421
100%
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4. Results and discussion
4.1 Cronbach’s alpha test
Table 2. Cronbach-alpha results
Corrected
Item-Total
Correlation
Cronbach’s
Alpha if
deleted
Cronbach’s
Alpha
Item
Mean
1. Content of review (CR)
CR1
CR2
CR3
CR4
.855
.852
.724
.611
.820
.822
.870
.911
4.33
4.34
4.30
4.27
.889
.837
4.3112
4.4152
2. Review rating (RR)
RR1
RR2
RR3
RR4
RR5
.624
.807
.809
.813
.810
.778
4.35
4.40
4.48
4.44
4.40
.618
.604
.617
.729
3. Valence of review (VR)
VR1
VR2
VR3
VR4
VR5
.658
.689
.714
.675
.681
.841
.833
.827
.837
.835
4.14
4.07
4.13
4.08
4.08
.863
.861
4.0979
3.7785
4. Timeliness of Review
TR1
TR2
TR3
TR4
.689
.750
.757
.638
.830
.805
.803
.852
3.84
3.79
3.76
3.73
5. Length of review
LR1
LR2
LR3
.250
.767
.752
.924
.359
.381
3.87
3.58
3.65
.735
.820
6. Form of review
FR1
FR2
FR3
FR4
.888
.614
.641
.493
.683
.787
.775
.841
4.23
4.21
4.24
4.14
4.2031
4.1101
7. Hotel booking decision
BD1
BD2
BD3
.735
.803
.718
.832
.770
.847
4.12
4.12
4.09
.870
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In order to be able to use the survey results in further evaluations, the reliability of the data
was tested by using the Cronbach-alpha test. According to the table 2, LR1 and FR4 had Cron-
bach’s Alpha if deleted index was 0,924 and 0.841, being greater than 0.735 and 0.820 respec-
tively. This led to the rejection of LR1 and FR4 to guarantee the reliability of the variables in
factor analysis.
4.2. EFA analysis
Table 3 EFA result
Rotated Component Matrix
Component
1
2
3
4
5
6
VR2
VR5
VR3
VR4
VR1
RR5
RR1
RR2
RR4
RR3
CR1
CR2
CR3
CR4
TR2
TR3
TR1
TR4
FR1
FR2
FR3
LR2
LR3
.809
.801
.779
.762
.745
.846
.772
.767
.748
.739
.931
.929
.833
.738
.856
.855
.812
.789
.931
.840
.785
.961
.960
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Bartlett’s Test of Sphericity
Approx. Chi-Square =6055.750
Df =253
Sig.= 0. 000
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After testing exploratory factor analysis (EFA) for 6 independent factors, KMO and
Bartlett’s Test is 0.5 ≤ KMO = 0.740 ≤ 1 and Sig. = 0.000 <0.05, so the analysis for these 6 factors
is appropriate. The rotation matrix results show that 25 observed variables are grouped into 6
factors. In addition, EFA for dependent factor (hotel online booking decision) show the value of
KMO (0.5 ≤ KMO = 0.723 ≤ 1), Sig. 0.000 <0.05, Average variance extracted 79.476%. As a re-
sult, the EFA model is appropriate
4.3. Model test
Table 4 ANOVA result
Model Summary
Model
R
R Square
.543
Adjusted R
Square
Std. Error of Durbin-Watson
the Estimate
1
.737a
.536
.31396
1.814
a. Predictors: (Constant), L_LoaiL1, RE, C, RA, F_LoaiF4, V
b. Dependent Variable: BD
ANOVA
Model
Regression
Sum of
Squares
df
Mean Square
F
81.879
Sig.
1
48.426
40.809
89.234
6
8.071
.099
.000b
Residual
Total
414
420
a. Dependent Variable: BD
b. Predictors: (Constant), L_LoaiL1, RE, C, RA, F_LoaiF4, V
From the multivariate regression results, the adjusted R-squared is 0.536. This means that
53.6% of the variation of dependent variable (hotel online booking decision - BD) is explained
by 6 independent factors. Durbin-Watson index, at 1.814, has variable value to meet data require-
ments. Sig value of F-test in ANOVA result was 0.05, confirming the fit of linear regression
model.
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Table 5 Regression result
Coefficients
Unstandardized Standardized
Collinearity
Statistics
Model
Coefficients
Coefficients
t
Sig.
B
Std. Error
Beta
Tolerance VIF
1
(Constant)
TR
.207
.166
.245
.165
.158
.205
.021
.026
.024
.033
1.008
8.002
9.375
6.761
4.842
.314
.000
.000
.000
.000
.284
.345
.233
.163
.878
.814
.934
.972
1.140
1.229
1.071
1.029
FR
CR
RR
VR
LR
.187
.017
.027
.015
.268
.039
6.957
1.149
.000
.251
.746
.984
1.341
1.016
a. Dependent Variable: BD
First of all, the VIF index of all variables has index <2, so there is no multicollinearity phe-
nomenon. The Coefficients results in table 5 show that the value of Sig in tests of individual
variables CR, RR, TR, VR, FR is lower than 0.05, leading to the significance of the regression
model. In other words, these 5 independent variables have an impact on the dependent variable-
BD. However, LR is rejected because of the higher p-value (0.251> 0.05). From the Beta indica-
tors, a standardized regression equation is
BD = 0.284*TR + 0.345*FR + 0.233*CR + 0.163*RR + 0.268*VR
From the regression equation, the variable FR (the form of the online reviews) has the
greatest influence on the hotel online booking decision.
Hypothesis
Conclusion
Beta
P-value
Influence level
H1: Content of online reviews
influence hotel online booking
decision.
Accepted
0.233
***
Significant effect
H2: Review rating influence
hotel online booking decision.
Accepted
Accepted
Accepted
0.163
0.268
0.284
***
***
***
H3: valence of review influence
hotel online booking decision
Moderate effect
Little effect
H4: Timeliness of review influ-
ence hotel online booking deci-
sion
H5: Length of online reviews in-
fluence hotel online booking de-
cision.
Rejected
Accepted
-
0.251
***
H6: Form of online reviews in-
fluence hotel online booking de-
cision.
0.345
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Through the linear regression analysis, hypothesis H5 is rejected, the test results are shown
in the table below:
BD = 0.284*TR + 0.345*FR + 0.233*CR + 0.163*RR + 0.268*VR
According to the findings of Market Metrix (2013), there is a list of factors influencing
travelers’ booking decisions in which online reviews show great influence. In the same line with
this phenomenon, the results of this paper fully support the conclusions of Market Metrix (2013);
andAyesha and Jarot (2018) when explaining the elements of impacting on 53.6 % online booking
intentions.
After finishing the testing phase of the hypotheses, it can be seen that most of the attribute
factors of online reviews such as CR, RR, TR, VR, FR all influence the tourist’s decision when
booking a hotel. Especially, FR (the form of online reviews) is the most influential element. The
majority of respondents (mean index at 4.23) said “they are more convinced by online reviews
with videos and images than online reviews with the only text”. At the same time, almost agreed
that “Online reviews in the form of videos and images help them to be more confident in evalu-
ating and choosing hotels.” with mean index, at 4.24. These results are similar to the conclusion
of Raffaele Filieri and team (2018) Zan Mo, Yan-Fei Li, Peng Fan (2015) when the authors indi-
cated the positive relationship between online booking decisions and online reviews containing
images or videos.
In terms of the content of online reviews, the research results are completely consistent
with the conclusions of Marcirio Silveira Chaves, (2012). When looking for hotel information,
travelers tend to be interested in factors related to the room, service, staff and location of the
hotel which affect the guests’booking decision. Review rating or the score is considered the gen-
eral rating and is the initial attention point of the readers. Zan Mo et al. (2015) argued that rating
can be of paramount important for purchases. The results of this study coincide with the results
of Zan Mo et al. (2015) and Sangwon Park, Juan L. Nicolau (2014).
From the research results show that the valence of reviews has a relatively large impact on
the booking decision. While the rating is the first factor for readers to start the interest in an online
review, the valence of online reviews is considered a detailed review, further explaining the overall
value of what customer experience is. In addition, negative information tends to spread more
quickly than positive ones because dissatisfied customers were more likely to manifest themselves
than satisfied ones who told relatives and friends about their experience (Richins, 1983). This
conclusion is also supported by Lee et al., (2008), and in the series of studies by Maheswaran
and Meyers-Levy, (1990).
What outstanding finding in this paper is the rejection of the positive link between LR -
the length of online reviews- and booking decisions. It can be concluded that while the amount
of words’ comments fails to impact on the buying decision, factors related to intrinsic value and
appearance show a great influence on booking behaviour.
5. Conclusions
Although online reviews are one of the word-of-mouth topics that has gained huge attention
of researchers, it may be ignored in Vietnam. Therefore, the implementation of the research has
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contributed to build up the concept of online reviews, contributing more theoretical basis for later
studies in Vietnam. Research has focused on clarifying the theoretical aspect of online reviews
on OTA sites as well as the urgency of its in today’s fiercely competitive environment. The pos-
itive relationship between online reviews’ characteristics including CR (Content of online re-
views), RR (Review Rating), VR (Valence of online reviews), TR (Timeliness of online reviews),
FR (Form of online reviews) and hotel online booking decision. In particular, the form of online
reviews has the strongest impact when almost the respondents said that online reviews using pic-
tures or videos would persuade readers and strongly influence their booking decisions. Therefore,
Hotel managers can encourage customers to write detailed reviews about location, staff, rooms,
services with vivid photos or videos taken by themselves. Customer images are considered more
trustworthy than company photos and are also very useful because of the performance of actual
appearance of products / services (Filieri, 2016). However, the length of online reviews (LR)
does not influence the booking decision. Long assessments can contain more information (Pan
& Zhang, 2011) and the arguments are more convincing (De Ascaniis & Morasso, 2011) than
short ones. However, it can only be concluded that a long or short review only temporarily attracts
customers to read and get information, and the content of the review, its value or content can in-
fluence the buying decision.
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