Các đặc điểm của đánh giá trực tuyến hữu ích đến mức nào? Auan sát thông qua các kênh OTA

HOW USEFUL ARE 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
tamnm@due.edu.vn
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; R ...  .752 .381 3.65
6. Form of review
FR1 .888
.820
.683 4.23
4.2031FR2 .614 .787 4.21
FR3 .641 .775 4.24
FR4 .493 .841 4.14
7. Hotel booking decision
BD1 .735
.870
.832 4.12
4.1101BD2 .803 .770 4.12
BD3 .718 .847 4.09
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
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Rotated Component Matrix
Component
1 2 3 4 5 6
VR2 .809
VR5 .801
VR3 .779
VR4 .762
VR1 .745
RR5 .846
RR1 .772
RR2 .767
RR4 .748
RR3 .739
CR1 .931
CR2 .929
CR3 .833
CR4 .738
TR2 .856
TR3 .855
TR1 .812
TR4 .789
FR1 .931
FR2 .840
FR3 .785
LR2 .961
LR3 .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
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
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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Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-Watson
1 .737a .543 .536 .31396 1.814
a. Predictors: (Constant), L_LoaiL1, RE, C, RA, F_LoaiF4, V
b. Dependent Variable: BD
ANOVA
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 48.426 6 8.071 81.879 .000b
Residual 40.809 414 .099
Total 89.234 420
a. Dependent Variable: BD
b. Predictors: (Constant), L_LoaiL1, RE, C, RA, F_LoaiF4, V
Table 5 Regression result
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.
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Coefficients
Model
Unstandardized 
Coefficients
Standardized
Coefficients
t Sig.
Collinearity 
Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) .207 .205 1.008 .314
TR .166 .021 .284 8.002 .000 .878 1.140
FR .245 .026 .345 9.375 .000 .814 1.229
CR .165 .024 .233 6.761 .000 .934 1.071
RR .158 .033 .163 4.842 .000 .972 1.029
VR .187 .027 .268 6.957 .000 .746 1.341
LR .017 .015 .039 1.149 .251 .984 1.016
a. Dependent Variable: BD
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 0.163 ***
H3: valence of review influence
hotel online booking decision
Accepted 0.268 *** Moderate effect 
H4: Timeliness of review influ-
ence hotel online booking deci-
sion
Accepted 0.284 *** Little effect 
H5: Length of online reviews in-
fluence hotel online booking de-
cision.
Rejected - 0.251
H6: Form of online reviews in-
fluence hotel online booking de-
cision.
Accepted 0.345 ***
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);
and Ayesha 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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