Research Article: 2022 Vol: 21 Issue: 3
Effect of Brand Image, Perceived Price, Perceived Trust, and Online Review on Consumers Intention of Online Hotel Booking in Thailand
Pornnapa Thanapotivirat, Rajamangala University of Technology Thanyaburi
Tharnupat Jithpakdeepornrat, Rajamangala University of Technology Thanyaburi
Citation Information: Thanapotivirat, P., & Jithpakdeepornrat. T. (2022). Effect of brand image, perceived price, perceived trust, and online review on consumers’ intention of online hotel booking in Thailand. Academy of Strategic Management Journal, 21(3), 1-17.
Abstract
The information technology for online hotel booking was rapidly developed, and it is necessary for hotel operation. The models that were used in this study include the developments of brand image, perceived price, perceived trust, and online review on consumers’ intention of online hotel booking in Thailand. The data were collected by the researchers from 400 participants who have experienced online hotel booking for at least 12 months. The research methodology was quantitative research and use Structural Equation Modeling (SEM) statistics to analyse the data. Based on our findings, it demonstrated that the brand image factor had a positive effect on perceived trust and an indirect effect on purchase intention. Regarding to online review, the researchers found that it had an indirect effect on perceived trust, perceived value, and purchase intention. Moreover, the perceived value had a positive effect on purchase intention.
Keywords
Purchase Intention, Hotel Booking, Online Reviews, Brand Image, Perceived Price, Perceived Trust.
Introduction
The hotel industry significantly relies on the use of the Internet, which has been an important channel of distribution especially for electronics booking and transactions. It has become a part of hotel operations (Kuo et al., 2014; Che et al., 2015; Damayanti & Andes, 2017). According to Lee & Morrison (2010), the internet is essential for the hospitality industry to operate their business such as room reservation, expanding channel of distribution, and replacing for travel agents from traditional booking methods. Therefore, online booking is more beneficial to consumers including hotel information, location, photos, videos, animations, special pricing, and reservation tariffs (O’Connor & Frew, 2004; Sparks & Browning, 2011). For that reason, consumers prefer to use online booking instead of the traditional style.
According to Electronic Transactions Development Agency (ETDA) survey in 2019 found that 47.5 million Thai citizens become internet users and has rapidly rising 81.5% each year, the proportion of hotel booking presented the highest rate at 94.1% and online transaction for hotel accommodation at 85.2% (ETDA, 2020; Adelia et al., 2016).
Most of the studies focus on the impact of brand image on consumer behavior in the hospitality industry (Aghekyan et al., 2012; Che et al., 2015; Bai et al., 2008). As intangible factors for service such as a hotel’s name, hotel’s logo, and hotel’s facilities are important to influence customer behavior including building a hotel brand, developing a distinctive image of a business to differentiate itself from the competitors, and communicate services to their target customers (Aghekyan et al., 2012).
Previous researchers have found that perceived value is a key factor in anticipating an individual's purchasing behavior. Perception value might be an important role for customers who have to make a reservation through the website (Chen et al., 2014; Dong & Ling, 2015). Perceived value is affected by consumers’ decision in hotel choice and booking intention (Krasna, 2008; Chun & Soo Cheong, 2007). The value is related to the price and quality of service. It is known that both of them could be the main factors for individuals' perceived value before decision making (Zeithaml, 1988). This is consistent with other studies that consumers favor comparing room rates from other websites instead of hotel websites (Krasna, 2008; Denizci & Law, 2010; Chevalier & Mayzlin, 2006). Similarly, other studies have found that perceived prices as part of the impact on consumers making online reservations. This illustrated that consumer prefer to compare room prices from travel websites to the hotel's website. Otherwise, to develop an online marketplace, trust is an important factor in online marketing than traditional marketing. According to risk perception and uncertainties in online transactions distrust can lead consumers to avoid online purchase (Efraim et al., 2008; Jyh & Yong, 2005). Therefore, trust is the most important factor for consumers' decision-making to purchase products or services.
Therefore, consumers trust online websites, consumers are likely to purchase services from them. The trust in online reservations has a profound effect on customers' buying intentions from online stores (Che et al., 2015; David et al., 2003; Andrea & Dennis, 2011). On the other hand, consumers required information from online booking site. Nevertheless, they have to trust online booking sites offering hotel information and room rates for their reservations. Therefore, it is an essential process for building a strong relationship between consumers and vendors (Chen, 2012).
Moreover, it is stated that consumers book their destination through a third-party website in order to get special prices and save travel costs (Dabas & Manaktola, 2007). Since online reviews are an important quality source and service for travelers (Dickinger, 2010) because it plays a majority role in helping consumers' decisions making more easily. Hence, online reviews will be studied as they affect consumers' booking intentions. According to Chen (2012), it was demonstrated that the suggestions from other consumers influenced the product instated of an expert review. Hence, online customers have to deal with big data, search engines, social media, and influencer for decision making (Diana et al., 2018; Che et al., 2015). Currently, in the hospitality industry offer and maintain customer satisfaction and loyalty as it is important in marketing activity which link to purchasing and consumption process (Raouf & Jyoti, 2016; Hossain et al., 2019a). This is an important concept for the entrepreneur to improve trust, loyalty, and customer satisfaction to the internet, and e-business (Kuo et al., 2014; Raouf & Jyoti, 2016).
To fill the gap, this study will focus on four main effects which are the brand image, perceived price, perceived trust, and online review that led to consumers’ online hotel booking intention. This model was inspired by consumers' purchase intention model by Jaafar et al. (2012).
Literature Review
Brand Image
The Brand image influences customers' perception processes and their behavior and it shows that consumers evaluate products or services before purchase (Ryu et al., 2008). According to the action theory, consumers will behave considering the outcome of their choices before engaging (Bang et al., 2000; Ajzen & Fishbein, 1980). The determination from the customer was delivered from personal attitude and individual standards (Bang et al., 2000). The brand image can be explained that the brand perception was reflected by brand association with consumers' recognition (Keller, 1993; Hossain et al., 2020). In relation with a brand’s strength level and a brand is powerful when it was based on consumers' experience (Aaker, 1991). Brand associations can be divided into three categories based on their features, interests, and personal attitudes. It depends on preferences, strength, and uniqueness (Keller, 1993). In particular, brand image is a factor that can draw consumers, influencing the product attitude and their characteristics (Aghekyan et al., 2012, Che et al., 2015). Moreover, CSR activities are positively effect on brand equity to customer (Hossain et al., 2019b). Consequently, brand image supports consumers' perception of their needs and differentiates them from competitors (Anwar et al., 2011). Furthermore, it is an important aspect of reflection by the associations that the consumer held. It is beneficial and important for marketers who want to differentiate between the lower levels that are related to consumer perception. With performances, features and benefits of specific and higher levels such as felling and correlation (Kevin & Vanitha, 2020).
Perceived Price
According to consumer perception, the price represents amount, and the consumer has received the product and services. Consumers use price as a quality indicator, consideration to the traditional understanding of “You get what you pay for” (Erickson & Johansson, 1985; Zheng et al., 2010). Most consumers cannot perceive the price of the product, rather it encodes the prices in a manner that is meaningful for them (Zeithaml, 1988). For online shopping, the customer compares the price offered by the seller against the reference price from others then they will generate price perception (Kim et al., 2012). Price is important than service quality (Sukki et al., 2014). Under the circumstances for competition, a lower price or reasonable price gives hotels an advantage over their products or services rather than setting a single price. The consumer will have an acceptable price for the purchase intention (Bojanic, 1996).
Perceived Trust
Trust demonstrated a customer's sense of safety and willingness to depend on someone or something (Chung & Kwon, 2009). It is the main qualities of the relationship between consumers and sellers. The functional role of trust in society for exchange relationships and interest to researchers can be understood depending on the person or object (Che et al., 2015; Everard & Galletta, 2005). Trust occurred when customers have confidence in vendors' credibility and honesty (Kim et al., 2009). The consumer will trust in service provider and reduce perceived risks or insecurities, therefore maintain long-term relationships (Gefen, 2000). More customers trust in the website, online transactions have lower risk, and have more purchase intention through websites (Mansour et al., 2014). Making hotel reservations online, customers are exposed to the service model and the customers expects to receive the services which were committed on the website. Customers’ expectations are based on trust that customers perceived from the hotel. In addition, the relationship between perceived value and trust from customer can build trust after the purchase of a product (Hossain et al., 2019a). Hence, hotels can use trust to create brand loyalty, which is effective for marketing strategies (Kim et al., 2009).
Online Review
Online review refers to the ratings as positive, negative, neutral, or none (Adelia et al., 2016). In some cases, positive reviews have been found to improve customer attitudes and the likelihood of purchasing a product or service, whereas negative reviews have been found that is a drawback of customers' purchase intention (Dellarocas et al., 2007; Floyd et al., 2014). Based on a numerous research of reviews in the marketing field have found that negative effects and negative reviews have strong impact which cannot be opposed (Luis et al., 2015; Cui et al., 2012; Alexis & Friederike, 2011). Furthermore, it also has a great influence on consumer decision and response in negative or positive to marketing strategies. Also, the positive review it can help business to maintain a good relationship with their customer and rapidly increase revenue (Zheng et al., 2010; Khalil et al., 2020). The study has shown that positive reviews were least effective or none. Therefore, resources should have essential qualities such as attractive, accessibility, safety etc. (Akhundova et al., 2021).
Purchase Intention
Customer’s purchase intention is an expectation or possibility that customers might purchase a specific product or service. It can be predicted that customers purchasing behavior and relationship are empirically validated in the hospitality service (Bai et al., 2008; Dodds et al., 1991; Sparks & Browning, 2011). As purchase intention through online booking affect consumers who booked from hotel website. The studies clarify that brand image, price, trust, and value can convince customers to purchase online (Chen & Dubinsky, 2003; Chiang & Jang, 2007; Everard & Galletta, 2005). Moreover, it can measure brand image attitudes closely considered from purchase intention focusing on buying the brand or other brand. These are most likely to predict when there is correspondence in the following dimensions as action, target, context, and time (Kevin & Vanitha, 2020; Kobra et al., 2019).
Hypotheses
Influence in brand image, perceived price, perceived trust, and purchase intention
Ryu et al. (2008) stated that brand image has a positive effect on consumer perceived value and readiness for purchase. Chen et al. (2014) study in consumer consumption in restaurants can clarify that food images have beneficial effect on consumer consumption. Brand image increases consumers' trust as they can be confident in their purchase intention (Chen & Chen, 2010; Chian & Jang, 2007; Chen, 2010; Chen et al., 2010). A reasonable price can increase satisfaction and trust, which leads to consumer purchasing product as their intention (Dodds et al., 1991; Kim et al., 2012; Hossain et al., 2020).
According to the previous discussions, even though the direct impact on perceived value, perceived trust, and online reviews have been explained in service marketing theory, but very few researches have studied about intention of online hotel booking. This leads to the following hypotheses (Hossain et al., 2019a).
H1: Brand image has a positive effect on perceived price.
H2: Brand image has a positive effect on perceived trust.
H3: Online review has a positive effect on perceived trust.
H4: Online review has a positive effect on perceived value.
H5: Perceived price has a positive effect on purchase intention.
Relationships among perceived price, perceived trust, and purchase intention
According to the hospitality sector, the reasonable price can affect customer’s value perception and contribute to the consumer purchase (Chiang & Jang, 2007; Lee & Morrison, 2010; Kobra et al., 2018). Duman & Mattila (2005) mentioned that price is an important factor considering perceived value for the service sector. Hence, the more acceptable the price range is the lower price specified than qualities. These lead to high-value awareness and result in more purchase intention. Similarly, Faryabi et al. (2012) found that online shopping, reasonable price has positive effect on customers' purchase intention. According to retail and discount studies, promotions can increase sales volume rapidly (Sukki et al., 2004). Besides, empirical research by Everard & Galletta (2005) showed that trust perception in online stores has positive effect on online purchase intentions. According to Mansour et al. (2014) had been study the results of online reliability regarding purchase intention and investigation have shown that online purchase intention has been affected by trust. Ling et al. (2011) stated that online purchase had a positive relationship between trust and willingness, and it is also encouraged. Johnson’s (2007) showed that both onsite and online service have a positive impact on perceived trust. Chong et al. (2003) mentioned the function of value between trust and purchase intention. Therefore, our findings from the theoretical basis for hypothesis had direct impact and price confidence, and recognition of trust on purchase intention (Hossain et al., 2019b).
H6: Perceived trust has a positive effect on purchase intention.
H7: Perceived value has a positive effect on purchase intention.
Relationships among online review, perceived trust, and purchase intention
Normally, most consumers read reviews online before booking from third-party website, in which other customers have shared their hotel experiences. The potential consumers will be provided with information concerning with hotel, gradually increase their expectations before the decision- making. Online reviews are neutrally updated and easily accessible and more reliable than content posted by the service provider (Ulrike & Kyung, 2008). Positive reviews have transformed a positive attitude towards the hotel (Ivar & Daphne, 2009). Positive reviews can increase the amount of hotel booking. Meanwhile, previously studies (Chevalier & Mayzlin, 2006; Sylvian & Jacques, 2004) have explored that online reviews provide beneficial information to prospective customers before making a purchase; consumers have increased significantly depending on online reviews from customers. Elwalda et al. (2016) stated that the reviews affect the customer's intentions and trust in e-vendor, especially customers using information from online reviews before decision making for purchase. Consumers who trust an online website will spend less time searching for information concerning the website or the seller and spend less time doing transactions on the website (Kim et al., 2012). Whereas, a lack of trust has resulted in consumers avoid buying online (Turban et al., 2002; Jyh & Yong, 2005). Trust is important for consumers planning for travel (Lewis & Semeijn, 1998). When consumers trust online websites, they prefer to purchase from them. According to a study of online shopping, trust in online stores has a positive effect to purchase intention from online stores (Lien et al., 2005; Gefen et al., 2003; Everard & Galletta, 2005).
While consumers rely on information from hotel reservation websites, they also need to trust the information on the website which offer information and rating and required to make a reservation. Chen & Dibb (2010) suggested that websites affecting consumers' online booking intention, as the website provides information for making decisions (Lien et al., 2005; Manhas, 2012; Kim et al., 2010).
Based on the reviews, we can posit the following hypothesis: Hence, the proposed hypotheses are:
H8: Brand image has a positive indirect effect on purchase intention.
H9: Online review has a positive indirect effect on purchase intention.
Research Methodology
This research is applied to quantitative research to validate the research framework and collect quantitative data through questionnaires addressing different levels. The study sample consisted of all consumers who made online hotel reservations. However, since there is no list of online hotel reservations in Thailand. Therefore, it is difficult to collect sample directly from the population. Hence, convenient sampling is used to collect data (San & Herrero, 2012).
Participants
Participants consist of customers who make hotel reservations online. Data of 400 participants were collected by researchers which have found all the questionnaires useful and further analyzed by Bartlett and Barclay (Bartlett et al., 2010).
As a complex regression model, it involves five paths to build trust. As a rule, 50 answers were required to make minimum samples for study. As 1,431 cases were collected and the samples and it better for study. The model was performed by the equation suggest by Westland n ≥ 50r2-450r+100, and n is the sample size, r is the ratio of a variable. Due to the current sample cases were collected, the research sample size is lower than the criterion of sample size for building structural equations model (Westland, 2010).
Questionnaire
For this study, the questionnaire was divided into two sections. The first section is about individual structures based on existing measures or adapted from similar scales. It can be noticed that all structure has reflectance measurements. Another section contains questions about the demographic of the participants as gender, age, marital status, education level, occupation, monthly income, frequency of online hotel reservation, making online reservation in advance, length of stay, an average room rates for online reservation, and website that was used to make online reservation. To prevent duplicate responses, IP number was used to register. The model has 11 structures, each measured on a rating scale 1-5 (Hossain et al., 2019b).
Trust and obligation were developed by researchers such as Kim et al. (2010) and Roman & Ruiz (2005). There are three items for trust and two items for the obligation, it has been enhanced and revised based on consumer interviews and testing. Hotel booking intention has been determined by purchase intention and ongoing response. Purchase intention for online hotel reservations can be measured from three items (Bigne et al., 2010; Kim et al., 2010). According to the measurements that were built related to Morgan & Hunt (1994) study, those two scales were used as appropriate. The scale of behavior was established from the relevant study (Morgan & Hunt, 1994; Riquelme & Román, 2014).
Due to the two items were taken by Morgan & Hunt (1994), it is suitable for online reservation. The privacy and security were developed and revised by the consumer's feedback (Kim et al., 2009). The perception parameter of user friendly and benefit, perceptions were conducted in this study, with three recommended items (Cheng et al., 2006).
Perception will reflect on consumers' trust in online hotel reservations which contribute to customer planning. User friendly perception and convenience will reflect in online hotel reservations. Attitudes towards online hotel reservations were performed by two applied items. The final item used to measure familiarity was applied (Ajazen & Fishbein, 1980; Chiu et al., 2010; Limayem et al., 2007). Five items were further used to measure online hotel bookings (HOR) was used for study. Ten items used to measure positive e-WOM (PEW) were improved by Tseng and Hsu (Fang & Fang, 2010). Next, ten items were used to measure negative (new) negative e-WOM from Law and Chung (Gretzel & Yoo, 2008). The next ten items were used to Hotel Brand Measure (HBR), adapting from the latest Oh (Sparks & Browning, 2011). Five items of price measurement (PRC) were used, adapted from Oh's work (Sparks & Browning, 2011). All elements are measured by using a Likert Scale 5 point.
Data collection
Firstly, the participants are those who have booked rooms from the online website at least in the past 12 months. Secondly, the participants should be at least 18 years old and able to make online payments via cards. According to a report from the Ministry of the Interior, Thailand in 2011, the population of those aged 18 and above separated by region such as North, Central, and South of Thailand are 15,797,000 (47.94%), 9,059,659 (27.49%), and 8,093,910 (24.57%), respectively.
The samples were collected online from Pantip (www.pantip.com) which is an online community network with more than 100,000 members in Thailand. It was collected by stratified and the questionnaires were sent to the members; 600 in the North, 300 in Central and 300 in the South of Thailand. Six hundred members were reserving rooms from the hotel website. Responses counted as 32.5% after removing the sample with suspicious answers. (e.g., Participants who answered “Strongly Disagree” to “Strongly Agree”, all the questions were shown in the second section). The valid observations in the North, Central, and South of Thailand were 189 (47.3%), 99 (24.9%), and 111 (27.9%), respectively. Representatives in each region yielded insignificant results (p-value ¼ 0.747), suggesting that there were no significant difference between populations and samples in the regions. The stratified sample was a different probability sampling method as the following two steps. First, populations are divided into two or more, preferably with each other and a complete subsection. Second, the simplified sampling method is used to select a sample from each subsection. Due to our research, it was found that all samples were divided into subsampling with three different regions in Thailand (North, Central, and South) and stratified sampling from Northern, Central, and Southern regions of Thailand.
Results and Discussion
Participants’ profiles are described using the distribution of social demographic information. Structural equation modeling (SEM) was used to analyze variables relationship according to the data that has been collected. In this model, it is hoped that the optimal model or variable variations can be found in this data analysis. SEM can predict the magnitude which contributed to brand image factor, perceived price, perceived trust, and reviews on social media related to hotel bookings (Chevalier & Mayzlin, 2006).
According to Table 1, most of the participants were female (275 persons, 68.75%), aged between 21–30 years old (136 persons, 34.0%), were single (317 persons, 79.25%), were in bachelor’s degree (255 persons, 63.75%), were students (195 persons, 48.75%), had monthly income lower than 10,000 baht.
| Table 1 Frequency and Percentage of Participants’ General Information | ||
| Personal Factors | Frequency | Percentage |
| Gender | ||
| Male | 125 | 31.25 |
| Female | 275 | 68.75 |
| Age | ||
| Less than or equal to 20 years old | 107 | 26.75 |
| 21 - 30 years old | 136 | 34.00 |
| 31 - 40 years old | 84 | 21.00 |
| 41 - 50 years old | 66 | 16.50 |
| More than 51 years old | 7 | 1.75 |
| Marital Status | ||
| Single | 317 | 79.25 |
| Married | 78 | 19.50 |
| Widowed or divorced | 5 | 1.25 |
| Education Level | ||
| Lower than high school | 30 | 7.50 |
| Under graduation | 255 | 63.75 |
| Graduated | 81 | 20.25 |
| Higher education | 34 | 8.50 |
| Occupation | ||
| Student | 195 | 48.75 |
| Public employee | 76 | 19.00 |
| Private employee | 68 | 17.00 |
| Business owner | 42 | 10.50 |
| Others (housewife, tourist guide, flight attendant) | 19 | 4.75 |
| Monthly Income | ||
| Lower than 10,000 baht | 141 | 35.25 |
| 10,001 - 20,000 baht | 97 | 24.25 |
| 20,001 – 30,000 baht | 46 | 11.50 |
| 30,001 – 40,000 baht | 58 | 14.50 |
| Over than 40,001 baht | 58 | 14.50 |
| Total | 400 | 100 |
Table 2 demonstrated reservation behavior, and consumer's trust in online hotel reservation contributes to customer’s planning. The results have shown that most of the customer reserved hotel online 1-2 times per year (59.50%), reserved the room in advance 3-7 days (38.25%), stayed in the hotel 1-2 nights (84.00%), an average room rate was 1,000 – 3,000 baht per night (63.75%), and customers made online reservation through Agoda.com (58.75%).
| Table 2 Reservation Behavior, Consumer's Trust in Online Hotel Reservation Contributes to Customer’s Planning | ||
| Details | Frequency | Percentage |
| Frequency of online hotel reservation | ||
| 1 - 2 times per year | 238 | 59.50 |
| 3 - 4 times per year | 109 | 27.25 |
| 5 - 6 times per year | 34 | 8.50 |
| 7 - 8 times per year | 8 | 2.00 |
| More than 8 times per year | 11 | 2.75 |
| Making online reservation in advance | ||
| 1 - 2 days in advance | 68 | 17.00 |
| 3 – 7 days in advance | 153 | 38.25 |
| 1 month in advance | 135 | 33.75 |
| 2 – 3 months in advance | 44 | 11.00 |
| Length of stay | ||
| 1 - 2 nights | 336 | 84.00 |
| 3 - 4 nights | 60 | 15.00 |
| 5 - 6 nights | 4 | 1.00 |
| An average room rates for online reservation | ||
| Less than 1,000 baht per night | 80 | 20.00 |
| 1,000 – 3,000 baht per night | 255 | 63.75 |
| 3,001 – 6,000 baht per night | 52 | 13.00 |
| 6,001 – 9,000 baht per night | 4 | 1.00 |
| 9,001 – 11,000 baht per night | 5 | 1.25 |
| More than 11,001 baht per night | 4 | 1.00 |
| Website that was used to make online reservation | ||
| Agoda.com | 233 | 58.75 |
| Airasiago.com | 2 | 0.5 |
| Booking.com | 111 | 27.25 |
| Expedia.co.th | 9 | 2.25 |
| Hotels.com | 6 | 1.50 |
| Hotelsthailand.com | 5 | 1.25 |
| Tooktrip.com | 2 | 0.50 |
| Thaitravelcenter.com | 6 | 1.50 |
| Trivago.co.th | 18 | 4.50 |
| Other (Traveloka) | 8 | 2.00 |
| Total | 400 | 100 |
From Table 3, it demonstrated that perceived price and brand image were at an extremely high level, the mean score at 4.32 and 4.30, respectively. Meanwhile, perceived value, online review, perceived trust, and purchase intention were at a high level, the mean scores were at 4.16, 4.12, 4.09, and 3.70, respectively. Also, skewness and kurtosis values were ranging from -0.297 to -0.939, and from -0.246 to 0.926, which are in a range between -3 and +3, meaning that all data were distributed normally and appropriately for constructing the structure. Lastly, the study investigated the correlation of the variables to avoid multicollinearity and revealed that the coefficient (r) of the variables were between 0.276 and 0.786, which were lower than the recommended amount at 0.90.
| Table 3 Skewness, Kurtosis, Mean, S.D., and Interpretation of the Variables | |||||
| Items | Skewness | Kurtosis | Mean | S.D. | Interpretation |
| Brand image | -0.879 | 0.926 | 4.30 | 0.650 | Extremely high |
| Online review | -0.646 | 0.471 | 4.12 | 0.690 | High |
| Perceived price | -0.939 | 0.918 | 4.32 | 0.680 | Extremely high |
| Perceived value | -0.589 | 0.733 | 4.16 | 0.630 | High |
| Perceived trust | -0.536 | 0.429 | 4.09 | 0.651 | High |
| Purchase intention | -0.297 | -0.246 | 3.70 | 0.779 | High |
From Figure 1, it shows the adjusted model with the acceptable good-fit model indices and its regression weights. This model has been adjusted due to the consideration of the modification indices. The detail of the model both before and after adjustment was portrayed in the following sections.
Figure 1 Research Model
From Table 4, it revealed that the model-fit indices of a non-adjusted model including Cmin/df, p-value, GFI, AGFI, RMR, RMRSEA, TLI, CFI, and NFI were not acceptable because their value was not in the recommended model-fit indices range. However, after the model adjustment, the adjusted model-fit indices including Cmin/df, p-value, GFI, AGFI, RMR, RMRSEA, TLI, CFI, and NFI were acceptable, which the value was at 1.129, 0.072, 0.948, 0.972, 0.029, 0.018, 0.994, 0.995 and 0.959, respectively. Due to this accepted value, the model can then be used to investigate the hypotheses.
| Table 4 Good-Fit Model Analysis and Modification | ||
| Good-fit model indices | Non -Adjusted | Adjusted |
| Cmin/df | 4.579 | 1.129 |
| df | 317 | 268 |
| P - Value | 0.000 | 0.072 |
| GFI | 0.780 | 0.948 |
| AGFI | 0.738 | 0.972 |
| RMR | 0.165 | 0.029 |
| RMRSEA | 0.095 | 0.018 |
| TLI | 0.821 | 0.994 |
| CFI | 0.838 | 0.995 |
| NFI | 0.803 | 0.959 |
From Table 5, the study showed the standardized estimate, standard error, and critical value (t) of the variables that were in a statistically significant positive direction and standardized estimates with p-value is lower than 0.000. However, two directions affecting perceived price and perceived trust on purchase intention were not significant due to the p-value was higher than 0.000, they were at 0.328 and 0.655, respectively.
| Table 5 Standardized Estimate, Standard Error, and Critical Value | ||||||
| Model | Standardized Estimate | S.E | C.R | P | ||
| Brand image | → | Perceived price | 0.856 | 0.053 | 15.743 | *** |
| Brand image | → | Perceived trust | 0.413 | 0.067 | 5.632 | *** |
| Online review | → | Perceived trust | 0.391 | 0.074 | 4.815 | *** |
| Online review | → | Perceived value | 0.819 | 0.056 | 10.214 | *** |
| Perceived price | → | Purchase intention | 0.035 | 0.054 | 0.977 | 0.328 |
| Perceived trust | → | Purchase intention | 0.026 | 0.094 | 0.447 | 0.655 |
| Perceived value | → | Purchase intention | 0.342 | 0.177 | 4.090 | *** |
From Table 6, the study found that BI had a total effect on PP, PT, and PI with regression weight as of 0.856, 0.413, and 0.041; had a direct effect on PP and PT with regression weight as of 0.856 and 0.413 and had an indirect effect on PI with regression weight as of 0.041 at the statistical significance at 0.000. Meanwhile, OR had a total effect on PT, PV, and PI with regression weight as of 0.391, 0.819, and 0.290; had a direct effect on PT and PV with regression weight as of 0.391 and 0.819 and had a direct effect on PI with regression weight as of 0.290 at the statistical significance at 0.000. Lastly, PV had a direct effect on PI with regression weight as of 0.342 and had a direct effect on PI with regression weight as of 0.342 at the statistical significance as of 0.000. Nevertheless, the effect of PP and PT on PI cannot be stated since there is no significance.
| Table 6 Total Effect, Direct Effect, and Indirect Effect | ||||||||||||
| Standardized total effect | Standardized direct effect | Standardized indirect effect | ||||||||||
| PP | PT | PV | PI | PP | PT | PV | PI | PP | PT | PV | PI | |
| BI | 0.856 | 0.413 | - | 0.041 | 0.856 | 0.413 | - | - | - | - | - | 0.041 |
| OR | - | 0.391 | 0.819 | 0.290 | - | 0.391 | 0.819 | - | - | - | - | 10.290 |
| PP | - | - | - | - | - | - | - | - | - | - | - | - |
| PT | - | - | - | - | - | - | - | - | - | - | - | - |
| PV | - | - | - | 0.342 | - | - | - | 0.342 | - | - | - | - |
Regarding Hypothesis Testing
Hypothesis 1 Brand image has a positive effect on perceived price. It was found that brand image has a positive effect on the perceived price at the statically significant level at 0.001 (t-test=15.743, standard error=0.053).
Hypothesis 2 Brand image has a positive effect on perceived trust. It was found that brand image has a positive effect on perceived trust at the statically significant level at 0.001 (t-test=5.632, standard error=0.413).
Hypothesis 3 Online review has a positive effect on perceived trust. It was found that online review has a positive effect on perceived trust at the statically significant level as of 0.001 (t-test= 4.815, standard error=0.074).
Hypothesis 4 Online review has a positive effect on perceived value. It was found that online review has a positive effect on perceived value at the statically significant level as of 0.001 (t-test= 10.214, standard error=0.819).
Hypothesis 5 Perceived price has a positive effect on purchase intention. It was found that perceived price did not have a positive effect on purchase intention since the p-value was higher than 0.05, it was 0.328 (t-test=0.977, standard error=0.054).
Hypothesis 6 Perceived trust has a positive effect on purchase intention. It was found that perceived trust did not have a positive effect on purchase intention since the p-value was higher than 0.05, it was 0.655 (t-test= 0.447, standard error=0.094).
Hypothesis 7 Perceived value has a positive effect on purchase intention. It was found that perceived value has a positive effect on purchase intention at the statically significant level as of 0.001(t-test=4.090, standard error=0.177).
Hypothesis 8 Brand image has a positive indirect effect on purchase intention. It was found that brand image has a positive indirect effect on purchase intention at the statically significant level as of 0.001 (standardized estimate=0.041).
Hypothesis 9 Online review has a positive indirect effect purchase intention. It was found that online review has a positive indirect effect on purchase intention at the statically significant level as of 0.001 (standardized estimate=0.290).
The results show that brand image has a positive effect on perceived trust and has an indirect effect on purchase intention. Brand image influences customers' perception processes and their behavior and it shows that consumers are evaluating products or services before making a purchase. Normally, most of the brand image that consumers like, the more influence the product will have towards attitude and its characteristics on consumers. Consequently, the brand image also helps consumers perceive their needs. Moreover, it can differentiate them from other competitors. The most important aspect of a brand is reflecting by the associations that consumers held. It is beneficial and important for marketers who want to differentiate between the lower levels and that are related to consumer perception. The performance and features and benefits of specific and higher levels such as felling and correlation.
For online review, it was found that online review has a positive effect on perceived trust, and perceived value which is indirect effect of purchase intention. According to the result in relation with the content from online reviews refers to ratings such as positive, negative, and neutral review. Based on a review of numerous researches in marketing field, it was found that negative effects and negative reviews have strong impact and difficult to interrupt.
Conclusion
According to the results, it was found that perceived value can be a positive effect on purchase intention. Customers tend to compare the price which was offered by the seller against the reference price from others, and then they will generate price perception. Price is more important than quality. Under the circumstance of a competition, a reasonable price gives hotels an advantage over their products or services rather than setting a single price. Consumers will have their own acceptable price for purchase intention. Consumers’ trust in service providers can help reduce cognitive risks and insecurities, hence maintain long-term relationships. Most customers trust in the website, online transactions without risk, and have more purchase intention through the website.
In hospitality industry, a reasonable price affecting the perceived value and contribute to consumers' purchase intention. In 2005, Duman & Mattila stated that price is a key factor to define the perceived value in the service sector. Hence, the reasonable price range or the lower price specified than qualities (i.e., perceived price is affordable). These lead to high-value awareness and result in more purchase intentions.
Furthermore, purchase intention is able to predict the true purchasing behavior of the customer, and the relationship is empirically validated in the hospitality service. As online hotel bookings, purchase intention is affecting consumers' booking accommodations through the hotel website. The research describes that Brand Image, Online review, Perceived value are able to convince customers to purchase online. These can measures closely related to attitudes towards brand image and consideration from purchase intentions and focus on buying the brand or other brand. These are most likely to be predicted when there is correspondence between the two in the following dimensions; action, target, context, and time.
Therefore, the results from perceived value, brand image, and online review have a positive effect on customers' purchase intention, related to personal attitude, norm, and customer memory which are based on consumers' experience or communication. Most of the brand image that consumers like, the more influence the product will have towards attitude and its characteristics on consumers. Consequently, brand image will help consumers recognize the brand demand and differentiate themselves from competitors Moreover, online content refers to a positive review that have been found to improve customer attitudes and the likelihood of purchasing a product or service whereas, negative review which have been found that is a drawback of customers purchase intention, and both are able to influence consumers’ purchase decision.
Hence, an online review is affecting the customer's purchase intention which was closely related to brand image, attitudes, and considered from purchase intention and focus on buying the brand or other brand.
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