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Article

The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention

by
Seo Yeon Kim
1,
Jong Uk Kim
2,* and
Sang Cheol Park
3
1
Department of ITS Business Planning, SK Holdings, 26, Jong-ro, Jongro-gu, Seoul 03188, Korea
2
School of Business, Sungkyunkwan University, Sungkyunkwan-ro, 25-2, Jongro-gu, Seoul 03063, Korea
3
Department of Business, Daegu University, 201 Daegu-Ro, Gyeongsan-si, Gyeongbuk 38453, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(12), 2262; https://doi.org/10.3390/su9122262
Submission received: 27 October 2017 / Revised: 28 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
(This article belongs to the Special Issue Mobile Technology and Smart Tourism Development)
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Abstract

:
With the rapid development of information technology in hotel booking context, it is no doubt that many hotels consequently enhance the needs of integrating information technologies into their overall business operations. In this study, we developed a research model which consists of perceived value, trust toward a third party online booking site, and trust toward hotels, and tested it by using partial least square techniques. Survey data were collected from 307 individuals who have prior experiences on making a reservation using third-party online booking sites. Based upon our findings, we found that the perceived value, which was affected by both price and quality, was positively related to individuals’ intention to book. We also found that both trust toward third-party online booking sites and trust toward hotels, which was influenced by online review, have positive impacts on individuals’ intention to book. The implications of these findings for both research and practice are discussed.

1. Introduction

The development of information technology enables most hotels to change their business operations rapidly. Nowadays, it is no doubt that many hotels consequently enhances the needs of integrating information technologies (hereafter, IT) into their overall business operations. A good example of combination of IT and business operations in overall hotel industry would be third-party online hotel booking sites [1]. Prior to the emergence of third-party online hotel booking sites, consumers would have to directly contact a department of hotel reservation by phone. However, the advent of third-party booking sites (e.g., Hotels.com, Booking.com, TripAdvisor, and HotelsCombined) give consumers various benefits before making a reservation. As third-party online booking sites have become sophisticated, they have been transformed into a variety of mobile platforms such as on smart phones (Android, iPhone, Blackberry, etc.) or tablet PC (iPad, etc.). For example, consumers can use mobile devices to book accommodation using mobile apps via a smart phone [2]. The mobile platform has made hotel booking much easier, since the booking is completed on third-party online booking sites itself and users are not redirected to a hotel’s website [3]. Thus, a better mobile experience for hotel booking enhances value for the consumers and increases online bookings [4].
In prior work, the perceived value has been considered as a salient factor to predict individuals’ purchase behavior; perceived value could play an important role for those who want to make hotel reservations using third-party online booking sites [5,6]. For example, according to Krasna’s work [7], the perceived value influences consumers’ decision on both hotel choice and booking intention [8], since the value was closely related to price and quality. It is well known that both price and quality could be salient factors for calculating individuals’ perceived value before they make final decisions [9]. Consistent with prior work [10], this study has also considered individuals’ perceived value as one of the determinants that could affect their intention to book a hotel. This study further attempts to examine how both price and quality could influence individuals’ perceived value in our context.
Otherwise, as a consequence of online market development, trust also plays a more important role in the online market than in traditional offline markets, due to the perceived risk and uncertainty present in online transactions [11]. While lack of trust can be the reason consumers avoid purchasing online [12,13], trust is the main concern for many consumers who make a purchase [14]. When a consumer trusts an online website, they are more likely to purchase from the website. Previous studies on online purchase have argued that trust in the online store positively influences the consumers’ intentions to purchase from the online store [15,16,17]. While consumers need to rely on the information provided by a third-party online booking site to book, they then need to trust the online booking site with their offering of hotels’ information and hotel rates needed for any booking. In this study, we further pay attention to trust toward third-party online booking sites, which should greatly influence intention to book.
In this research area, some research has revealed that the main reason individuals were reluctant to purchase online was related to the lack of trust toward online channels [12,13,14]. The role of trust toward third-party online booking sites could be similar to the findings from previous work (e.g., [12,13,14]). For instance, individuals usually use third-party booking sites when they believe those sites are trustworthy. Once third-party booking sites guarantee their own competences, integrity and reliability toward consumers, consumers could feel that the sites have excellent reputations to offer reliable service and integrity [18,19]. Therefore, we also consider the trust toward third-party online booking sites as one of key factors affecting individuals’ intention to book.
In general, consumers are more likely to trust a hotel that makes its services and policies available and informs them about new offerings [20]. Trust toward the hotel could ultimately lead to consumers’ booking intention [21]. Therefore, trust toward hotels could be an important factor in predicting individuals’ intention to book. In addition, trust toward hotels would be established based on the level of feedback from individuals who had prior experiences staying in those hotels [22,23] Individuals’ feedback could be represented as online reviews. Especially, positive online reviews make individuals trust a specific hotel [24]. In this study, we further examine the relationship between online reviews and trust toward hotels.
In summary, this study aims to better understand consumers’ intention to book in the third-party online booking site context. In doing so, we seek to answer the following research question:
To what extent do perceived value, trust toward the third-party online booking sites, and trust toward hotels help us to predict consumers’ intention to book?
While prior research has examined the impact of trust and value on individuals’ intention to book [21,24], we know of no research that has examined the effects of perceived value and two different types of trust (i.e., trust toward third-party websites and hotels) on consumers’ intention to book. By addressing the above research question, this study contributes to the current body of knowledge regarding consumer behavior in hotel reservation context. From the standpoint of research implications, this study has presented integrative theoretical views of perceived value and two types of trust in a single research model that may contribute to individuals’ intention to book. In practical standpoint, ours could offer some hotels, which connected with the third-party online booking sites, some guidance to establish operational strategies for themselves.

2. Literature Review and Hypotheses

2.1. Price

From the consumer’s perspective, price was seen as the amount of money consumers must give up to get a product and service [9]. Several prior studies have long presented that price is regarded as either a monetary sacrifice for obtaining a product or a quality signal of a product [9,25,26]. As a financial sacrifice, price has long been found to have a negative effect on a product’s value for money [9,27,28]. Price is one of the major considerations in deciding to purchase as evaluating value. Consumers compare prices offered by online websites with reference prices, and then find the cheapest alternative possible [29]. Prior research found that a lower price for a given quality (i.e., perceived price is reasonable) strongly affects consumer’s perceived value [27,30]. If a price is perceived as reasonable, consumers could find high value with financial transaction in their purchase [31]. Under competitive circumstances, reasonable prices help hotels allow consumers to perceive a relative advantage [32]. Price is one of the most important aspects in hotels, and influences accommodation selection decision [33]. According to prior studies, price has been proposed as an effective way to enhance consumers’ value [34] and to positively influence perceived value of services [35], as well as hotel booking. Although price is considered as a consumer’s perception that their selected hotels’ prices represent the best dollar-for-dollar value, consumers do not really expect the hotels to meet the price of an inferior product [36]. Therefore, in the acceptable price range, a given more reasonable price consequently leads to a higher perceived value [15,27]. Therefore, we can present the following hypothesis:
Hypothesis 1.
Reasonable price positively influences perceived value.

2.2. Quality

In general, perceived quality has been used as a measure of how well the service provided by hotels matches consumers’ expectations [37]. Previous research has defined perceived quality as the consumer’s judgment about a product or a service’s overall excellence [9,15]. Quality in hotels was also related to increase consumer’s expectations with core service offering of hotels [38] as well as developing a significant relationship between consumers and hotels [39]. Since consumers generally tend to find value by balancing reasonable price and quality [9], quality could be one of key drivers in terms of formulating the perceived value. Sweeney and Soutar [40] have also suggested that quality could be one of the functional sub-factors that contribute to perceived value. In addition, several prior studies have examined the role of quality in individuals’ intention to book a hotel [41,42,43]. Specifically, the quality of hotels contains evaluation of room cleanliness, convenience of location, value for money and friendliness of employees [44,45]. Based upon assessing quality of hotels, consumers could perceive their value by figuring out the quality [41,46]. Therefore, this study proposes the following hypothesis:
Hypothesis 2.
Quality positively influences perceived value.

2.3. Perceived Value

The perceived value has been widely defined as the trade-off between price and quality, with a concept of value-for-money [28,40]. It has been regarded as a salient factor that has a great influence on the decision making process of customers, having a significant role in determining customer satisfaction, decision making and purchase behavior [47,48]. For example, Zeithaml [9] has presented the value act as a mediator between quality and purchase in purchase decision. Most studies on perceived value have found a relationship between value and intention to purchase in online [16,25,27]. In online hotel booking context, we also predict that the relationship between individuals’ perceived value and intention to book would be positively related. Therefore, we could state the following research hypothesis:
Hypothesis 3.
Perceived value positively influences intention of booking a hotel.

2.4. Trust toward a Third-Party Online Booking Sites

Trust has been defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to trustor, irrespective of the ability to monitor or control that other party” ([49], p. 712). It is one of the central features of buyer–seller relationships [15], and has long been an important factor in the decision to buy or not to buy a certain product in a certain store [16,50]. In marketing, trust has been considered as a psychological state comprising intention to accept vulnerability based on positive expectation of the consumers’ intention [51]. Bijlsma-Frankema and Woolthuis [52] argued that consumers do indeed buy more from a trusted than a distrusted salesperson.
Previous research discussed the challenges which consumers had to face in traditional ways of booking a hotel, such as travel agents, broadcast media, printed advertisement, etc. [53,54]. With the growth of the internet, many online platforms enable online consumers to make more considered decisions with detailed information of the hotels, such as individual comments or experiences about the hotels as well as online reviews from past consumers on hotel booking websites [54]. While they easily obtain information about the hotels, building trust toward websites is a key determinant in their booking intentions.
When consumers trust the online website, they tend to expend less effort searching for information about the website or vendor and less effort when carrying out transactions with the website [25]. While lack of trust can lead to the reason consumers avoid purchase online [12,13], trust is the main concern for many consumers for those who plan to travel [14]. When a consumer trusts an online website, they are more likely to purchase from the website. Previous studies on online purchase have argued that trust in the online store positively influences the consumers’ intentions to purchase from the online store [15,16,17]. While consumers need to rely on the information provided by hotel booking website to get information, they also need to trust the website. Online hotel booking websites offer hotels’ information and ratings, which are needed for any booking. Chen and Dibb [55] further suggest the importance of the websites which influence consumer’s online intention, as the websites provide consumers information to proceed with their online hotel booking decision [15,54,56]. Hence, the following hypothesis is developed.
Hypothesis 4.
Trust toward a third-party online booking sites positively influences intention to book.

2.5. Online Reviews

Most people generally read online reviews before making a reservation. While previous consumers share their hotel experiences in online reviews, a potential consumer receives more detailed information about a target hotel, which gradually increases expectation before making a decision. Online reviews provide easily accessible up-to-date information on hotels, which is more reliable than content posted by service providers themselves [57]. A positive review especially induces a positive change of attitude toward the hotel [24]. Ye et al. [58] have presented that positive online reviews can significantly increase the number of bookings in a hotel. While prior studies [22,23] found that online reviews provide useful references for potential consumers before purchase, consumers increasingly depend on online customer reviews. For example, Elwalda et al. [59] examines that online reviews significantly affect customer intention and trust in e-vendors, particularly for customers who frequently use online reviews before making a purchase. Based upon the above, we can posit the following hypothesis:
Hypothesis 5.
Online reviews positively influence trust toward hotels.

2.6. Trust toward Hotels

While lack of trust toward a hotel might be a major obstacle to book a room [60], consumers normally have a more cautious behavior to book a hotel. Before making a reservation, consumers tried to collect all information for reducing uncertainty regarding inexperienced product or service. To avoid from this uncertainty, hotels need to present a trustworthy image on booking websites by providing detailed information, which should be consistent with the website’s promises [9,54]. Previous studies on trust toward hotel domains have suggested that consumers believe there is less risk associated with online purchase when they have greater trust toward hotels [61], and have eventually more positive towards intention to book [62]. While Jarvenpaa et al. [63] found that trust is still a critical challenge even when purchasing from a known online vendor, such as a hotel booking website in hotel contexts, the level of trust toward hotels affects online hotel booking intentions. Therefore, we propose the following hypothesis:
Hypothesis 6.
Trust toward hotels positively influences intention to book.
Figure 1 illustrates our proposed research model. As noted in Figure 1, perceived value, trust toward third-party online booking sites and trust toward hotels all have a direct influence on customers’ intention to book a hotel. We also examine the relationships among price, quality and perceived value as well as test the relationship between online review and trust toward hotels.

3. Data Collection

This study performed an online survey as a methodology to test the proposed hypotheses. The online survey instrument consisted of questions from prior studies which have proven their validity and reliability. Empirical data to support the proposed model were collected from Korean-speaking respondents who have online hotel booking experiences.
A pilot test with 30 part-time MBA students at Korean private university was conducted to assure the content validity and reliability of the questions translated from English to Korean because meaning could be affected by the particular context of the actual survey. Through the pilot test, we examined whether the survey questions work correctly, and confirmed the format, readability, and clarity. The final survey contained 24 questions.
The online survey was available between 16 April and 15 May 2016. By voluntarily participating in the study, respondents were notified via mobile or email message that contained participation instruction and a hyperlink to access the online survey.
All questions were anchored on a five-point Likert-type format, and respondents rated from 1 (strongly disagree) to 5 (strongly agree). Table 1 presents our measurement items based on previous studies. Each of four items were borrowed from scales previously used to measure price and quality [31,40]. Four of the perceived value items were adapted from Chiang [31]. The variables of trust in websites in this study use three items to access the compatibility, which were borrowed from Bilgihan et al. [64]. Trust toward hotels were measured by six items developed by Sparks and Browning [21]. Online review was operationalized with three items, as proposed by Zhao et al. [65]. Finally, consumers’ intention to book was measured with four items developed by Chiang [31].
A total of 313 respondents participated, and this study finally obtained 307 valid questionnaires after eliminating problematic answers. The collected data consisted of 59.3% male and 40.7% female respondents aged 20–59 years old, with average age of 30 (55.4%). Table 2 shows the demographic profile of respondents. As shown, respondents had had more than one online booking experience, and they reported using Hotels.com (30.6%), HotelsCombined (15.3%), Agoda (14.3%), and booking.com (12.1%), which are the top four online third-party hotel booking sites around the world.

4. Data Analysis and Results

Since all constructs adopted reflective indicators, multiple items were used to measure the constructs for this study. For evaluating the research model, the measurement model was tested by examining the reliability and validity of the measures used to represent each construct [66].
Reliability was examined by internal consistency for each block of measures. Thus, Cronbach’s alpha, composite reliability, and average variance extracted (AVE) for each construct and cross loadings of all items were examined to show internal consistency and discriminant validity for establishing measurement reliability (Table 3). The Cronbach’s alpha coefficients for all constructs are higher than threshold value of 0.7. All of the constructs in our measurement model are higher than Cronbach’s alpha of 0.723 composite reliabilities of 0.821 in this measurement model. The average variance extracted (AVE) for all constructs exceeded its threshold value of 0.5, meaning that 50% or more variance of the indicators is accounted for [67]. Thus, all of the constructs in the measurement model exceeded the threshold to be acceptable for all the measures of construct reliability.
Individual item reliability was determined by examining the cross-loading of items. As shown in Table 4, all items resulted in loadings greater than 0.7, i.e., all constructs indicated adequate internal consistency reliability. The measurement model exhibited significant discriminant validity, while the loading of each item also had a higher loading with its construct than a cross-loading with all the other constructs.
Discriminant validity can be also assessed comparing the variances between all constructs with the average variance from each construct. As shown in Table 5, the square root of a construct’s AVE is higher than the correlations between all constructs within the model.
Since the evaluation of the measurement model examined the reliability and validity of the measures used to represent each construct, the structural model was tested to evaluate the hypothesized relationships among the constructs in the research model [68]. The structural model of this study was evaluated using partial least squares (PLS) analysis to examine all of the paths simultaneously in this research framework.
Given the sample of 307 used in this study, a strict significance level of 0.05 was used for all statistical tests. As shown in Figure 2, the results of the path analysis indicate that all four paths were significant. The path between reasonable price and perceived value (β = 0.414, t = 9.178); the path between quality and perceived value (β = 0.316, t = 7.027); the path between perceived value and intention to book (β = 0.353, t = 6.999); the path between trust toward a third-party online booking sites and intention to book (β = 0.259, t = 5.535); the path between online review and trust toward hotels(β = 0.244, t = 4.331); and the path between trust toward hotels and intention to book (β = 0.252, t = 4.495) were all significant at p < 0.05.
While the R2 value presents the variances explained, it was examined for meaningful interpretation of path coefficients in this study. The final dependent construct explained 50.2% of intention to book. The intermediate variable was 36.0% of perceived value and 6.0% of trust toward hotels for the total sample of data. Although the R2 value for trust toward hotels is relatively low for online reviews, the p-value still indicates a significant relationship between online reviews and trust toward hotels. There may be other factors which influence the trust toward hotels and still need to be determined.

5. Discussions and Future Research

While investigating the consumer experience in online hotel booking is an emerging area of research, online hotel booking is a useful source of potential consumers for planning a trip to generate their hotel booking intentions. To achieve this objective, this study proposed a research model that addresses the influential factors to intention to book a hotel on online booking sites. To test the proposed hypotheses, this study performed an online survey as a methodology and collected valid data from 307 respondents. The results via partial least squares (PLS) analysis were based on the analysis of research framework and six research hypotheses were proposed. The research model of this study addresses that perceived value, trust toward third-party online booking sites and trust toward hotels all have a direct influence on customers’ intention to book a hotel. In addition to the direct relationships, while both price and quality influences directly the perceived value, online reviews have direct effects on trust toward hotels. Additionally, while the perceived value is significantly derived from reasonable price and quality, the price and quality are important antecedents of value in an online hotel booking context. At the same time, the online review is a relevant antecedent to have trust toward in online hotel booking.
Based on our findings, this study has the following research contributions. First, according to our results, we have found that the perceived value plays the most significant role in online hotel booking context since they may book a hotel online when they perceive benefits of more reasonable price and higher quality of hotel. Compared to results from previous work [9,15,26], this study has also obtained consistent patterns of results in terms of examining the relationships among price, quality and perceived value. Second, we have also found that both trust toward third-party online booking sites and trust toward hotels have significant effects on individuals’ intention to book. In this study, we considered two types of trust (i.e., trust related to the third-party websites and trust related to a hotel itself) as major determinants of affecting an individual’s intention to book. In online booking a hotel context, two types of trust could play critical roles in predicting individuals’ intention. Unless individuals overcome online uncertainty and transactional risk by increasing trust toward two objects (i.e., websites and hotels), they will be reluctant to make a reservation. Based on our findings, which related to online review on trust toward hotels, we further found that the effect of online review on trust toward hotels was significant. The results we obtained were consistent with those reported by Elwalda et al. [59] in examining the relationship between online review and trust toward hotel booking context.
Besides the above research implications, our study also holds several implications for practice. First, the study emphasizes that hoteliers and managers in the hotel industry should focus their efforts on the perceived value of their offering, since value proves to have decisive impact on consumers with benefits of more reasonable price and higher quality of the hotel. The understanding of consumers’ perceived value allows the managers to better design their offerings of price and quality corresponding operational strategies around online hotel booking environment. Hence, both hoteliers and managers should keep in mind how perceived value can be fostered by adding the benefits of price and quality consumers emphasize. Second, it also highlights that consumers’ intention to book is consistent with trust toward a third-party online booking sites and trust toward hotels. Therefore, both hotels and third-party websites should exert more efforts to encourage customers to build up trust through online hotel booking process. Consequently, it is indeed possible to build up the hotel trust by enhancing and promoting online reviews on websites. The reason behind this may be found in this study that consumers prefer to rely on online reviews to build trust as reducing the online uncertainty associated with intangibility about a hotel. Therefore, hotel companies should be aware that gathering online reviews is an important determinant for gaining customers’ trust toward hotels. Thus, the present study’s findings have also revealed some important implications for academic researchers, as well as making a relevant practical implication for the hotel industry and online hotel booking websites to improve their business strategies. While value and trust appear to be particularly important determinants of online hotel booking, it is meaningful for hotel related industry to understand the factors that promote consumers’ booking intentions.
Despite having both research and practical implications in our study, it is appropriate to point out the limitations associated with our study. First, our data in this study was collected in South Korea. This reflects the intention of South Korean hotel contexts, but not corresponding cross-cultural consumer behavior, while any comparable study and data analysis from different cultures. Hence, future research needs to collect a larger dataset with broader participation of different demographic segments to obtain generalization. Second, due to the parsimonious model in predicting individuals’ intention to book, we did not examine how individuals’ intention to book on third-party online booking sites may lead to hotels’ performance [69]. We believe that additional research is warranted to address these limitations. Third, different types of consumers, hotels and third-party sites might be considered for future research. The respondents of the conducted survey may not be reflective of many different types of “consumers” in this study and broad generalization should not be made. In addition, the information provided by the conducted survey may not be accurate, while most of the consumers were not interested in letting the researcher conduct the survey on premise, which leads to their limited hotel booking experiences. There should be more detailed information on data collection and detailed characteristics of respondents for the future study. Consequently, all hotels and third-party sites are also assumed identical in this study. Hence, different hotels, third-party sites or price may also be effective in intention to book for future study.

Supplementary Files

Supplementary File 1

Author Contributions

Seo Yeon Kim had developed the proposed research model by reviewing relevant literature, analysed the data, and wrote the paper; Jong Uk Kim and Sang Cheol Park designed the research, analysed the data, contributed to develop both research and practical implications in the paper, and revised the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 09 02262 g001 550
Figure 1. Research Model.
Figure 1. Research Model.
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Sustainability 09 02262 g002 550
Figure 2. PLS results for the proposed research model (n = 307).
Figure 2. PLS results for the proposed research model (n = 307).
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Table
Table 1. Measurement Scales.
Table 1. Measurement Scales.
FactorQuestionnaireReferences
Reasonable PricePrice1Price is reasonably priced for the hotel.[31,40]
Price2Price offers value for money.
Price3Price is a good for the price
Price4Price would be economical.
QualityQuality1The hotel would be excellent.[64]
Quality2The hotel would be of high quality.
Quality3The hotel would be superior.
Quality4The hotel would be favorable.
Trust toward a third-party online booking sitesTrust-W1The hotel booking site will always be honest with me.[64]
Trust-W2I believe in the information that this website provides.
Trust-W3This hotel booking website is genuinely concerned about its customers.
Trust toward hotelsTrust-H1I believe this hotel would be trustworthy.[21]
Trust-H2I believe this hotel would be reliable.
Trust-H3I believe this hotel would be responsible.
Trust-H4I would have confidence in this hotel.
Trust-H5This seems like a good quality hotel.
Trust-H6If I was to discuss this hotel with others, I would probably say positive things.
Perceived ValueValue1The hotel offers good value for the price.[31]
Value2It is worth to book the hotel.
Value3It is a very good bargain to book the hotel at this price shown.
Value4The overall expected value of staying at the hotel is very high.
Online reviewsReview1The online reviews are mostly negative (R).[65]
Review2After having read online reviews, I would not book this hotel.
Review3After having read online reviews, I cannot say I like this hotel.
Intention to bookIntention1If I am going to book this hotel, I would consider booking this hotel at the price shown.[31]
Intention2The probability that I would book this hotel is very high.
Intention3My willingness to book this hotel is very high.
Intention4I would book the hotel from this booking site.
Table
Table 2. Demographic Profiles.
Table 2. Demographic Profiles.
ItemsCategoryFrequencyPercept
GenderMale18259.3
Female12540.7
Age20–2911738.1
30–3917055.4
40–49175.5
50–5931.0
Area of EmploymentClerical20969.4
Retail134.3
Professional237.6
Technical155.0
Edu. and Public Admin.134.3
Student268.6
Homemaker51.7
Other31.0
Number of online booking experience1–26320.5
3–49530.9
5–64815.6
7–8247.8
Over 97725.1
Number of hotel visits1–2247.8
3–44013.0
5–64314.0
7–83210.4
Over 916854.7
Table
Table 3. Results of composite reliability and convergent validity.
Table 3. Results of composite reliability and convergent validity.
VariablesCronbach’s AlphaComposite ReliabilityAVE
Total Sample (n = 307)
Intention to book0.8270.8860.661
Reasonable Price0.7620.8450.581
Quality0.8270.8830.654
Online Review0.7880.8700.691
Trust toward hotels0.8810.9100.629
Trust toward a third-party online booking sites0.7580.8590.670
Perceived Value0.8020.8710.631
Table
Table 4. Loadings and cross-loadings of measurement.
Table 4. Loadings and cross-loadings of measurement.
ConstructsTotal Sample (n = 307)
ItemsIntenPriceQualReviewThotelTwebValue
Intention to bookInten10.7510.3200.3630.2310.3720.4070.483
Inten20.8910.3570.4740.1640.5730.4120.548
Inten30.8730.3250.5070.1790.5470.5020.550
Inten40.7250.2920.3240.0210.3180.4170.432
Reasonable pricePrice10.3620.8380.252−0.0080.2760.2990.454
Price20.3510.8210.3320.1830.3980.2110.444
Price30.3210.7960.3090.0700.2680.2380.427
Price40.0850.5610.056−0.1300.0620.0200.186
QualityQual10.4080.2830.8420.1640.5030.3440.392
Qual20.3960.3020.8650.2090.5460.3260.350
Qual30.3250.1320.7390.0990.4450.2000.237
Qual40.5100.3270.7830.2150.4930.3150.440
Online ReviewReview10.1300.0740.1640.7790.1500.1650.210
Review20.1500.0000.1560.7940.1280.0870.076
Review30.1840.0970.2360.9140.2700.1210.232
Trust toward hotelsThotel10.5210.3190.5460.2040.8840.3380.542
Thotel20.4940.3010.5250.1630.8640.2980.530
Thotel30.4230.3090.4360.1780.8070.3490.437
Thotel40.3720.2350.4420.0310.7340.2670.339
Thotel50.3440.2850.4450.1550.6890.3830.379
Thotel60.5100.2650.5240.3020.7620.4270.499
Trust toward a third party online sitesTweb10.3990.2950.3320.1370.3840.8280.370
Tweb20.5270.1350.3520.1340.3970.8510.400
Tweb30.3570.3030.2150.0790.2630.7750.386
Perceived ValueValue10.4580.5820.3490.1530.4520.3640.799
Value20.5660.4250.4000.2140.5180.3650.878
Value30.5200.4300.3290.0930.3970.3880.824
Value40.4270.1580.3870.2310.5070.3970.661
Table
Table 5. Squared pairwise correlations and assessment of discriminant validity.
Table 5. Squared pairwise correlations and assessment of discriminant validity.
Total sample (n = 307) IntenPriceQualReviewThotelTwebValue
Inten0.813
Price0.3980.762
Qual0.5210.3400.809
Review0.1900.0800.2320.831
Thotel0.5710.3610.6180.2440.793
Tweb0.5350.2810.3780.1480.4360.818
Value0.6210.5210.4560.2160.5850.4700.794
Note: Leading diagonal shows the squared root of AVE of each construct; Legend: Inten, intention to book; Price, Reasonable price; Qual, Quality; Thotel, Trust toward hotels; Tweb, Trust toward a third party online sites; Value, Perceived value.

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MDPI and ACS Style

Kim, S.Y.; Kim, J.U.; Park, S.C. The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention. Sustainability 2017, 9, 2262. https://doi.org/10.3390/su9122262

AMA Style

Kim SY, Kim JU, Park SC. The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention. Sustainability. 2017; 9(12):2262. https://doi.org/10.3390/su9122262

Chicago/Turabian Style

Kim, Seo Yeon, Jong Uk Kim, and Sang Cheol Park. 2017. "The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention" Sustainability 9, no. 12: 2262. https://doi.org/10.3390/su9122262

APA Style

Kim, S. Y., Kim, J. U., & Park, S. C. (2017). The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention. Sustainability, 9(12), 2262. https://doi.org/10.3390/su9122262

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