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

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Bài viết 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. Mời các bạn cùng tham khảo!

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Nội dung Text: Các đặc điểm của đánh giá trực tuyến hữu ích đến mức nào. Quan sát thông qua các kênh OTA

  1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 795
  2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 796
  3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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. 797
  4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 798
  5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 Number Frequency (%) Male 201 47.7 Sex Female 220 52.3 otal 421 100% 36 24 5.7 Total 421 100% Office 99 23.5 Student 141 33.5 Occupation House work 68 16.2 Freelancer 83 19.7 Other 30 7.1 Total 421 100% 799
  6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 4. Results and discussion 4.1 Cronbach’s alpha test Table 2. Cronbach-alpha results Corrected Cronbach’s Item Item-Total Cronbach’s Alpha if Mean Correlation Alpha deleted 1. Content of review (CR) CR1 .855 .820 4.33 CR2 .852 .889 .822 4.34 4.3112 CR3 .724 .870 4.30 CR4 .611 .911 4.27 2. Review rating (RR) RR1 .624 .807 4.35 RR2 .618 .809 4.40 RR3 .604 .837 .813 4.48 4.4152 RR4 .617 .810 4.44 RR5 .729 .778 4.40 3. Valence of review (VR) VR1 .658 .841 4.14 VR2 .689 .833 4.07 VR3 .714 .863 .827 4.13 4.0979 VR4 .675 .837 4.08 VR5 .681 .835 4.08 4. Timeliness of Review TR1 .689 .830 3.84 TR2 .750 .861 .805 3.79 3.7785 TR3 .757 .803 3.76 TR4 .638 .852 3.73 5. Length of review LR1 .250 .924 3.87 LR2 .767 .735 .359 3.58 LR3 .752 .381 3.65 6. Form of review FR1 .888 .683 4.23 FR2 .614 .820 .787 4.21 4.2031 FR3 .641 .775 4.24 FR4 .493 .841 4.14 7. Hotel booking decision BD1 .735 .832 4.12 BD2 .803 .870 .770 4.12 4.1101 BD3 .718 .847 4.09 800
  7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 .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. 000 801
  8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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
  9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 5 Regression result Coefficients Unstandardized Standardized Collinearity Model Coefficients Coefficients Statistics B Std. Error Beta t Sig. 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 First of all, the VIF index of all variables has index 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 Accepted 0.233 *** Significant effect influence hotel online booking decision. H2: Review rating influence Accepted 0.163 *** hotel online booking decision. H3: valence of review influence Accepted 0.268 *** Moderate effect hotel online booking decision H4: Timeliness of review influ- Accepted 0.284 *** Little effect ence hotel online booking deci- sion H5: Length of online reviews in- Rejected - 0.251 fluence hotel online booking de- cision. H6: Form of online reviews in- Accepted 0.345 *** fluence hotel online booking de- cision. 803
  10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 804
  11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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. REFERENCES Browning, V., So, K. K. F., & Sparks, B. (2013). The influence of online reviews on con- sumers’ attributions of service quality and control for service standards in hotels. Journal of Travel & Tourism Marketing, 30(1-2), 23-40. Cao, Q., Duan, W., Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50 (2), 511–521. Chan, I. C. C., Lam, L. W., Chow, C. W. C., Fong, L. H. N., & Law, R. (2017). The effect of online reviews on hotel booking intention: The role of reader-reviewer similarity. International Journal of Hospitality Management, 66, 54–65. https://doi.org/10.1016/j.ijhm.2017.06.007 Chen, H., Zhang, M., Qu, Z., & Xie, B. (2008). Antioxidant activities of different fractions of polysaccharide conjugates from green tea (Camellia Sinensis). Food Chemistry, 106(2), 559-563. Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of- mouth: The adoption of online opinions in online customer communities. Internet research, 18(3), 229-247 Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth com- munication: A literature analysis and integrative model. Decision support systems, 54(1), 461-470. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of marketing research, 43(3), 345-354. Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of re- 805
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  13. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human behavior, 27(2), 634-639. Zhao, X. (Roy), Wang, L., Guo, X., & Law, R. (2015). The influence of online reviews to online hotel booking intentions. International Journal of Contemporary Hospitality Management, 27(6), 1343–1364. https://doi.org/10.1108/IJCHM-12-2013-0542 Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of marketing, 74(2), 133-148. 807
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