1. Introduction
As the development of information technology is changing the world towards global digitization, various sectors are starting to carry out various operational activities by involving the role of technology in them. Online meetings between sellers and buyers are becoming easier in the trade and tourism sectors. According to a survey from the Association of Indonesian Internet Providers in 2020, there was an increase of 25.5 million new Internet users in Indonesia. In the second quarter of 2020, Internet users in Indonesia rose to 73.7 percent of the population, equivalent to 196.7 million users. The significant increase in Internet users has proven that Indonesia is more technologically literate. Various sectors are starting to utilize technology in their operations (apjii.or.id, 2021).
Technological developments and digitization in all aspects of human life have also affected tourism, particularly in the accommodation sub-sector. At least 11 sites and applications provide online accommodation booking services in Indonesia (99.co). Minimal interaction and direct contact when booking accommodation through the accommodation booking application are one of the solutions for booking accommodation during the current pandemic. The review component is one of the components that any accommodation booking application provides in the accommodation selection view. Online reviews can be a source for tourists to evaluate the quality of hotel services before making reservations; travellers also tend to trust reviews from visitors compared to the information provided by business owners through promotions (Wee et al., 2018). According to data from Bright Local in its report about Local Consumer Reviews in a 2016 survey, about 84 percent of people trusted reviews from other customers. Seven out of 10 people will also leave a review if asked. (tirto.id). Online reviews displayed on the accommodation booking application mainly discuss the facilities and quality of accommodation which are also indirectly the responsibility of the accommodation booking application.
This research is a replication of research conducted by Zhao et al. (2015). Based on the regression analysis results, six of the seven hypotheses are significant. Variables that are not significant are the variables from the Valence of Online Reviews, namely Positive Online Reviews. The regression coefficient (beta value) of the positive variable online reviews on purchase intention in this study was 0.112. This shows that positive online Review’s impact on hotel booking intention was not statistically significant. To respond to this inconsistency, this study proposes a mediating variable in the form of “Trust” for the positive variable of online reviews.
2. Literature Review and Hypotheses
2.1. Accommodation Booking Intentions
Purchase intention or order intention is often used to analyze consumer behavior. According to Sumarwan (2016), the intention is a strong tendency or desire for an individual to perform a behavior. Kotler and Keller (2016) define purchase intention as a consumer decision regarding brand preferences in a choice set. Schiffman and Wisenblit (2015) defined purchase intention as a conative component of attitudes related to the possibility of consumers buying certain products.
2.2. Online Reviews Online
Reviews often become the center of attention for consumers when observing a product before making a purchase or order. Online reviews are considered the most important form of eWOM communication as they are an important source of information that facilitates consumer purchasing decisions (Ruiz-Mafe et al., 2020). Hong et al. (2018) defined online reviews as user-generated product evaluations posted on corporate or third-party websites. El-Said (2020) defined online reviews as “numerical ratings and descriptive comments” provided by current and former consumers to express satisfaction or dissatisfaction, often with opinions or recommendations revolving around experiences with a particular product or service.
2.3. The valence of Online Reviews
Zhao et al. (2015) defined the Valence of Online Review as a value contained in a review. This value can be divided into 2 categories: messages that focus on positive things (benefit gain) and vice versa, which contain negative things (benefits )—lost). The valence of Online Review is information that contains positive and negative reviews on a product (Cheong et al., 2020).
H1: Positive Online Reviews positively affect Hotel Booking Intention in accommodation booking application users.
H2: Negative Online Reviews have a negative effect on Hotel Booking Intention in accommodation booking application users.
2.4. Usefulness of Online Reviews
Filieri et al. (2021) defined the usefulness of online reviews as a measure of consumer knowledge about a product/service in a review. Siering et al. (2018) defined the usefulness of online reviews as a measure of the value perceived by readers of reviews in the decision-making process. The usefulness of online reviews helps consumers deal with information overload and facilitate consumer decision-making (Li et al., 2019). Park and Lee (2009) defined the usefulness of online reviews as a measure of the degree to which online reviews can facilitate the consumer’s purchasing decision-making process. The usefulness of Online Reviews is defined as consumers’ subjective perception of whether online reviews are useful for their purchasing decisions (Mudambi & Schuff, 2010). One of the main reasons users pay attention to reviews before making an accommodation booking is to find reviews from previous users, which are expected to meet the user’s expectations for accommodation (Zhao et al., 2015).
H3: Usefulness of Online Reviews positively affects Hotel Booking Intention in accommodation booking application users.
2.5. Reviewer Expertise
Han (2021) defined Reviewer Expertise as a form of the depiction of the reviewer’s knowledge and ability to provide high-quality and useful opinions about products/services for Review by readers. Reviewer Expertise is the reviewer’s ability to understand a product’s attributes. Reviewer Expertise is used to process and display product information according to reviewers (Wang & Cole, 2016). Yang et al. (2019) defined Reviewer’s Expertise as a reflection of a reviewer’s dedication to the online review platform, degree of experience in writing help online product reviews, or ability to provide high-quality reviews.
H4: Reviewer Expertise positively affects Hotel Booking Intention in accommodation booking application users.
2.6. Timeliness of Online Reviews
Consumers face much relevant information associated with a specific period in the information search process. Fu et al. (2011) defined the Timeliness of eWOM as the length of the interval between the review upload and the Review’s viewing. Cheung et al. (2018) defined the Timeliness of Online Reviews as a measure of an online review based on time.
H5: Volume of Online Reviews positively affects Hotel Booking Intention in accommodation booking application users.
2.7. Volume of Online Reviews
Kordrostami et al. (2021) defined the Volume of Online Reviews as the number of online reviews for a product. The volume of Online Reviews is also defined as the total number of reviews on a product (Baek & Choe, 2020). Zhao et al. (2015) defined the Volume of Online Reviews as the number of comments or testimonials from a reviewer about a product or service that is more specific. Danish et al. (2019) stated the Volume of Online Consumer Reviews as the total number of review interactions. From the definition above, it can be concluded that the Volume of Online Reviews is a measurement of online reviews based on the total or the number of reviews on an item or service.
H6: The Timeliness of Online Reviews positively affects Hotel Booking Intention in accommodation booking application users.
2.8. Comprehensiveness of Online Reviews
Zhao et al. (2015) defined the comprehensiveness of online reviews as a measure of how detailed and complete a review is on a product. The comprehensiveness of online reviews is also defined as a condition in which completeness in an online review will lead to a person’s desire to own or buy the product or service reviewed (Fitriana et al., 2020). Cheung et al. (2018) defined the comprehensiveness of online reviews as a measure of how detailed and complete the reviewer’s message is conveyed in a review.
H7: Comprehensiveness of Online Reviews positively affects Hotel Booking Intention in accommodation booking application applications.
2.9. Trust
Kotler and Keller (2016) defined customer trust as a person’s trust in a business partner. Customer trust is also defined as consumers’ knowledge and conclusions about objects, attributes, and benefits (Mowen & Minor, 2013). Meanwhile, Indarjo (2011) defined customer trust as the willingness of customers to depend on partners in a transaction relationship.
H8: Positive Online Review positively affects Hotel Booking Intention in accommodation booking application users.
2.10. Framework
Based on the theoretical basis obtained through previous research, a framework can be developed as presented in the figure below (Figure 1). The model consists of Independent Variables, namely Positive Online Reviews (X1), Negative Online Reviews (X2), Usefulness of Online Reviews (X3), Reviewers’ Expertise (X4), Timeliness of Online Reviews (X5), Volume of Online Reviews (X6), Comprehensiveness of Online Review (X7), the Dependent Variable is Intention to Book Accommodation or Hotel Booking Intention (Y) and the Mediation Variable namely Trust (Z).
Figure 1: Research Model
3. Research Methodology
This type of research used is quantitative research. The sampling technique used is non-probability sampling, namely purposive sampling. The sample of this study is users of the accommodation booking application in Yogyakarta who read online reviews before making accommodation reservations and are over 18 years old. The sample is 135 people, and data collection is done by distributing questionnaires through Google Forms. This study uses a Likert scale on the questionnaire distributed for the purposes of quantitative analysis.
Data processing techniques in this study were carried out using Structural techniques Partial based Equation Model (SEM). Least Square (PLS) (Ghozali & Latan, 2015) using software SmartPLS version 3.0. The first step in this research is to test the validity (convergence validity and discriminants validity ), and reliability of the data of the first 30 respondents. 2 indicators were eliminated (TOR 3 and VOR 1), which were then removed from this research model.
A two-step approach carries out quantitative analysis testing; it can also be called a two-step approach. The steps taken are measuring the outer model to test the instrument and the inner model to test the hypothesis from the path analysis. Evaluation assessment criteria internal model used to calculate R-Square is If the result is R2 ≥ 0.67, the model is categorized as strong. If the results R2 are between 0.33–0.67, the model is categorized as moderate. If the results R2 < 0.33, the model is categorized as weak. While Q-Square is If Q2 > 0, the model has predictive relevance. If Q2 < 0, the model has no predictive relevance. And the path significance test or t-test is indicated by the t-statistic value at (α) 5% must be above 1.64 (one-tailed). The hypothesis testing criteria uses a p-value with a critical value (α) = 5%. If the p-value > 0.05, then H0 accepted, and if the p-value < 0.05, then H0 rejected.
4. Data Analysis and Results
4.1. Respondent Profile
Researchers distributed 135 questionnaires to users of the accommodation booking application in Yogyakarta online via Google Forms. The average respondent is dominated by the male gender, has an age range of 18–22 years, works as a student, and on average, has accessed the accommodation booking application 1–3 times.
4.2. Inner Model Testing
After testing the outer model, test the inner model, which is summarized in the following table (Table 1):
Table 1: Inner Model Test
4.3. Hypothesis Testing
This study tested the indirect hypothesis on one variable - Positive Online Review of Hotel Booking Intention mediated by Trust. The following is the result of these variables’ indirect effect and direct relationship table (Tables 2 and 3).
Table 2: Indirect Effect Results
Table 3: Path Results Coefficient (Direct Relations)
5. Discussion and Implications
Online reviews are a useful source of information for most travelers to generate their intentions and make travel decisions (Gretzel & Yoo, 2008). Previous research by Zhao et al. (2015) researched the relationship between 7 dimensions of online reviews on accommodation booking intentions by travelers in Mainland China. The regression analysis results show six of the seven hypotheses are significant.
In this study, based on the path table coefficient, Positive Online Review influences the Hotel Booking Intention of –0.083, t-statistics 0.912 < 1.64, and a p-value of 0.181 > 0.05, thus Positive Online Reviews are directly declared not significant to the Hotel Booking Intention of Booking application users in Yogyakarta. Based on the path table coefficient, Negative Online Reviews influence the Hotel Booking Intention of –0.178, t-statistics 1.849 > 1.64, and a p-value of 0.033 < 0.05, thus Negative Online Review is directly stated to have a negative and significant effect on Hotel Booking Intentions of Booking application users in Yogyakarta. Based on the path table coefficient, the Usefulness of Online Reviews influences Hotel Booking Intentions of 0.205, t-statistics 2.210 > 1.64 and 0.05. Thus, Usefulness of Online Reviews is directly stated to positively and significantly influence the users of Hotel Booking Intention of accommodation booking application. Based on the path table coefficient, Reviewer Expertise influences the Hotel Booking Intention of –0.034, t-statistics 0.437 < 1.64, and a p-value of 0.331 > 0.05. Thus, the Reviewer’s Expertise is directly stated to have a negative and insignificant effect on users of the Hotel Booking Intention of accommodation booking application. Based on the path table coefficient, the Timeliness of Online Reviews influences the Hotel Booking Intention of –0.159, t-statistics 1.952 > 1.64 and a p-value of 0.026 < 0.05, thus Timeliness of Online Review is directly stated to have a negative and significant influence on Hotel Booking Intention of accommodation booking application users. Based on the path table coefficient, the Volume of Online Reviews has an impact on the Hotel Booking Intention of 0.307, t-statistics 2.389 > 1.64, and a p-value of 0.009 < 0.05, thus the Volume of Online Reviews is directly stated to have a positive and significant influence on Accommodation Booking Intentions of users of the Booking application in Yogyakarta. Based on the path table coefficient, the Comprehensiveness of Online Reviews influences the Hotel Booking Intention of 0.181, t-statistics 1.826 > 1.64 and a p-value of 0.034 < 0.05, thus Comprehensiveness of Online Review is directly stated to have a positive and significant influence on Hotel Booking Intention of accommodation booking application users.
Positive Online Review indirectly has a positive influence on Intention to Book Accommodation through Trust of 0.228 and is significant due to t-statistics 3.939 > 1.64 and p-value of 0.000 < 0.05, thus Positive Online Review indirectly has a positive and significant influence on Hotel Booking Intention through Trust Booking application users in Yogyakarta. Positive Online Review has a direct negative and insignificant effect on Hotel Booking Intentions because it influences –0.083, a p-value of 0.165 > 0.05, and a t-statistics of 0.976 <1.64. A comparison of the two results shows that the mediating variable in this study has a full relationship because the independent variable cannot significantly influence the dependent variable without going through the mediating variable.
6. Conclusion and Limitations
Based on the results of data analysis and discussion in this study, it can be concluded that the Usefulness of Online Reviews, Volume of Online Reviews, and Comprehensiveness of Online Review has a direct positive and significant effect on Hotel Booking Intention of accommodation booking application users. Negative Online Review Variables and Timeliness of Online Reviews negatively and significantly influence the Hotel Booking Intention of accommodation booking application users. Positive Online Review and Reviewer Variables Expertise is not significant in this study. Meanwhile, the Trust variable has a fully mediated indirect relationship between the Positive Online Review variable and Hotel Booking Intention of accommodation booking application users.
This study uses data to observe the effect of online reviews on hotel booking intention by accommodation booking applications. Online customer reviews are needed in today’s tourism sector. Potential customers read online reviews before deciding to book accommodations. In online customer reviews, the advantages and drawbacks alignreal customers’customersch is wheelospectivers need. This study also analyzes the factors from online reviews that influence accommodation booking decisions. Respondents in this study were people who booked accommodation through the accommodation booking application. However, this research does not examine the factors that influence buying interest.
Further research should further analyze other factors that influence accommodation bookings. Thus, the data obtained will be more focused. Further research can also explore how online reviews influences the intention to book accommodations.
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