1. Introduction
Vietnam currently has nearly 44 million smartphone users, more than 67 million internet users, approximately 66 million social media users, and 145 million mobile subscribers (Nhan Dan Newspaper, 2020). 48% of tourists use their smartphones for booking rooms and 42% for searching tourism information (Vietnamplus, 2017). Specialized providers of hotels and private accommodation booked online are Hotels.com, Booking.com, and Airbnb (Statista, 2021). The market share of online room booking has increased sharply in recent years, up to 30 – 40 percent of total bookings (Vietnamplus, 2017). Linton and Kwortnik (2015) claimed that the individual tourists preferred to use websites rather than apps for hotel room reservations, as 63 percent of users chose to view the hotel’s website, and only 32 percent used the hotel’s app.
Tour operators and travel agents are still the most popular way for guests to book hotel rooms, accounting for 30.4 percent of all reservations. Online travel agencies (OTAs) are in second place, accounting for 25.6 percent of all reservations, and direct booking with the hotel is in third place, accounting for 17.6 percent (Grant Thornton, 2020). Punnasuparom and Choibamroong (2020) claimed that guests get their first impression of a hotel from the website or by searching for hotel information on the Internet and that many tourists prefer to search for hotel information from OTAs (online travel agents) before making a room reservation through the hotel website. Thus, to cope with the competition from international, domestic OTAs, hotels should invest in technology, design a convenient and make great efforts to attract travelers to book a room online via the hotel-owned website. According to Abdullah et al. (2017), travelers often utilize online booking tools such as hotel websites, online travel agency websites as well as airline websites. Hence, proper deployment of individual hotel websites is very crucial to each hotel business. Hoteliers need to know how to better deploy their booking websites successfully. Hotel websites are more and more important for increasingly significant channels for hotel businesses (Punnasuparom & Choibamroong, 2020). Moreover, leisure travelers mostly try to minimize and reduce their traveling costs, especially from the aspect of lodging expenses (Ismail et al., 2014). By understanding consumer choices, the managers can effectively develop and position service offerings to better suit their market’s needs (Verma, 2015).
Thus, this study will base on the new model known as the E-P-TAM-TPB model as the underpinning theory by adding two external factors: eWOM, price value to test the exploratory power of the framework in prediction online booking intention toward actual behavior is hotel selection behavior of domestic traveler in Ho Chi Minh City
2. Literature Review
2.1. Technology Acceptance Model (TAM)
Davis (1986) and Davis et al. (1989) proposed the technology acceptance model (TAM) and assumed that there are two major beliefs such as perceived ease of use and perceived usefulness which are significant indicators that have positively significantly impacted the adoption of information technology. Davis et al. (1989) also found that perceived ease of use positively affects perceived usefulness. The relative importance of perceived ease of use and perceived usefulness in influencing tourist attitude as well as TAM indicates that PU has a direct effect on tourist behavior intention and attitude (Davis et al., 1989). This model can explain the determinants of user acceptance of information systems and technology by measuring individuals’ intentions and explaining their intentions by their attitudes, subjective norms, perception of usefulness, perceived ease of use, and external variables (Wang, 2016). In summary, perceived ease of use, perception of usefulness, attitude, and intention has been theorized to be the prime influence for usage and acceptance attitude (Agag & El-Masry, 2016a, 100).
Although the TAM is considered a very important model, it has also been widely criticized for its limited explanatory power (Bagozzi, 2007). TAM needs to include the influences of the consumer’s intention via online interaction as eWOM (Tseng & Hsu, 2010) to analyze the influence when customers decide/intend to book a hotel online. However, in the context of eWOM, the TAM might not offer a sufficient understanding of users’ attitudes and intentions (Ayeh, 2015, 3; Erkan, 2016). TAM needs to be integrated with other variables to make it a stronger model (Abdullah et al., 2017). Thus, the interaction of TAM-TPB was proposed by Taylor and Todd (1995) indicating that the TAM-TPB fully explained behavioral intention (Boripunt, 2015). The integration between TAM and other related variables will allow for an understanding of why internet users plan to use or not to use online booking technology such as hotel booking websites. For these reasons, hotel booking websites need to be more interactive than before (Abdullah et al., 2017). Therefore, the study examines the important role of the combined TAM-TPB, eWOM, and price value which is named E-P-TAM-TPB to analyze the influence when customers decide/intend to book the room via the hotel website.
2.2. Theory of Planned Behavior (TPB)
The TPB was proposed by Ajzen (1991) and is used to explain two elements contributing to the likelihood of individuals performing a specific action. These two aspects comprise volitional and non-volitional elements. The volitional elements are attitude toward behavior and subjective norm, and the non-volitional factor is perceived behavioral control (PBC), which together influence behavioral intentions (Anantamongkolkul & Kongma, 2020, 128). Thus, the inclusion of PBC is necessary to improve the predicting power of intention (Armitage & Conner, 2001). This model claims that the attitude toward behavior, subjective norm, and perceived behavioral control can affect the intentions and further affect the actual behaviors (Wang, 2016).
Both TPB and TAM were developed based on the theory of reasoned action to explain online tourist behavior, which argues that both behavioral attitude and subjective norm affect behavioral intention, which in turn affects the actual behavior (Lu et al., 2009). The combined these two theories hold advantages and help to improve previous models (Sahli et al., 2015; Joo, 2017) and to explain the factors that influenced the online behavior for hotel booking intention (Lin et al., 2010) as well as to provide a better explanation or prediction of tourist behavioral intention (Sahli et al., 2015).
This current research tries to insert two new variables consist eWOM, price value, into the combined TAM-TPB proposed by Taylor and Todd (1995) to serve as the underlying theory for the present study. Therefore, the aim of this study was to evaluate and validate the influence of domestic tourists’ perceptions on online hotel booking intention via hotel website based on the new E-P-TAM-TPB and to explore the issues related to hotels in Ho Chi Minh City.
2.3. Perceived Ease of Use (EOU)
Perceived ease of use is an indicator that is used in TAM, and EOU is defined as “the extent to which a user perceives that using the target system will be free of physical and mental effort.” (Davis, 1986). In this study, perceived ease of use for online purchasing refers to the degree to which the prospective consumer expects the online purchases to be free of effort (Chiu et al., 2005); perceived ease of use affects both perceived usefulness (Casaló et al., 2010; Mohamad et al., 2021) and attitude (Lin, 2007). Hotel guests will develop positive attitudes toward using hotel websites if such systems are perceived as useful and easy to use (Morosan, 2010). Therefore, the following hypotheses are proposed for the current study:
H1: Perceived ease of use has a positive impact on Attitude toward booking hotel.
H2: Perceived ease of use has a positive impact on Perceived usefulness.
2.4. Perceived Usefulness (PU)
Perceived usefulness is defined as “the degree to which an individual believes that using a particular system enhances performance” (Davis, 1986). Perceived usefulness for online purchases can be defined as the prospective consumer’s subjective probability that using the internet will efficiently facilitate their purchasing (Chiu et al., 2005). Perceived usefulness can be one of the determinants to predict whether or not they will use online booking (Agag & El-Masry, 2016a); perceived usefulness also directly influences attitude (Lin, 2007). Perceived usefulness is a fundamental component of technology adoption in the hotel industry (Morosan, 2010). Davis et al. (1989) found that usefulness is a highly significant predictor of attitudes as well as behavioral intentions. Therefore, the following hypotheses are proposed for the current study:
H3: Perceived usefulness has a positive intention to book hotel accommodation online. impact on
H4: Perceived usefulness has a positive impact attitude toward booking hotel.
2.5. Electronic Word of Mouth (eWOM)
Traditional WOM is seen to play a vital role in consumer purchasing decisions, and the evolution of word of mouth has allowed the emergence of electronic word of mouth (Dada, 2021). Therefore, eWOM is defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau et al., 2003, 39). eWOM can also be defined as all informal communications directed at consumers through Internet-based technology related to the usage or characteristics of particular goods and services or their sellers (Litvin et al., 2008).
On the internet, guests can post their opinions, comments, and reviews of products on weblogs, discussion forums, review websites, social networking sites (Cheung & Thadani, 2012, 462); They will also assess the net perceived utility, gained from online booking a hotel (function of the expected utility, actual price, and additional utility perceived from available online reviews) (Rianthong et al., 2016). Moreover, eWOM has the potential to reduce the risk, uncertainty, and ambiguity associated with a product or service (Virgilio & Antonelli, 2017). Thus, customers always seek information from various sources to minimize uncertainty in the decision making process; the information-intensive, intangible, and impersonal characteristics of eWOM environments can reduce uncertainty when an individual tries to make a decision (Kavitha et al., 2020).
Attitude refers to “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Ajzen, 1991). Goh et al. (2015) show that e-consumers’ attitude towards a given e-seller will positively influence their buying intention from the given e-seller, and behavioral intention is positively influenced by attitude. Dada (2021) claimed that the attitude is positive and significantly influenced by eWOM and intention. Therefore, the following hypothesis was proposed:
H5: eWOM has a positive influence on attitude to book hotels online.
H6: Attitudes toward booking hotel has a positive impact on intention to book hotel accommodation online.
The second determination of intention in the TPB model is subjective norms. Subjective norms can be described as an individual perception of social pressures on conducting or not conducting certain behavior (Ajzen, 1991). Venkatesh et al. (2012) found that subjective norm is crucial to ascertain users’ intention towards the acceptance and usage of technology. Dada (2021) found that eWOM positively influenced subjective norms. In addition, subjective norms also influence consumers’ online purchase intention (Pavlou & Fygenson 2006). Therefore, the following hypothesis was proposed:
H7: eWOM has a positive influence on Subjective norm.
H8: Subjective norm has a positive impact on intention to book hotel accommodation online.
The third component of TPB is known as perceived behavioral control. Ajzen (1991) defined perceived behavioral control as “the perceived ease or difficulty of performing the behavior”. Goh et al. (2015) suggested that perceived behavioral control directly impacts the intention to perform a behavior and also the actual performance of that behavior as compared to the extent of the actual behavioral control. In addition, Dada (2021) claimed that eWOM was related positively to perceived behavioral control. Boripunt (2015) also found that the online booking behavior intention of tourists has a positive influence on the real online hotel selection behavior. Therefore, the following hypothesis was proposed:
H9: eWOM has a positive influence on perceived behavioral control.
H10: Perceived behavioral control has a positive impact on intention to book hotel accommodation online.
H11: Intention to book hotel accommodation online has a positive impact on actual hotel selection behavior.
In addition, tourists are now willing to rely on eWOM as a key source of information about specific products/ services. Thus, understanding eWOM is especially important for those products/services whereby consumers potentially obtain information (search), book, or buy online, such as hotels, airlines, and restaurants (Sparks et al., 2011). Tourists are likely to use eWOM if the online information presented is useful and easy (Elwalda et al., 2016). Tseng and Hsu (2010) indicated that eWOM is a key antecedent of perceived ease of use perceived usefulness. Thus, Boripunt (2015) and Lin et al. (2019) confirmed that eWOM has a positive impact on perceived usefulness, perceived ease of use. Therefore, the following hypothesis was proposed:
H12: eWOM has a positive influence on perceived usefulness.
H13: eWOM has a positive impact on perceived ease of use.
2.6. Price Value
Dodds et al. (1991) defined price value as consumers’ cognitive trade-off between perceptions of quality and sacrifice results in perceptions of value. The price value is frequently defined together with the quality of products or services to determine the perceived value of products or services (Mohamad et al., 2021). The price value is positive when the benefits of using technology are perceived to be greater than the monetary cost, and such price value has a positive impact on intention. Thus, the following hypothesis was proposed:
H14: Price value has a positive influence on intentions to book hotel accommodation online.
2.7. Online Hotel Booking Intention
Online booking means doing a room reservation service via the internet (Abdullah et al., 2017). Casaló et al. (2015, 2010) defined booking intentions as the traveler’s willingness to book the hotel accommodation. Hoteliers deploy online booking channels to minimize costs and to provide more convenient service to hotel guests (Abdullah et al., 2019, 46). Online hotel booking intention is defined as the desire of consumers to book hotel rooms through a hotel website (Lien et al., 2015) or the condition in which customers are willing and have the intention to participate in an online transaction for booking a hotel room (Saw et al., 2015). Lin (2007) confirmed that the online booking behavior intention of tourists has a positive influence on the real/ actual online booking behavior. Real tourist behaviors are influenced not only by stimulations from the significant environment but also by the behavioral intention of the tourist (Lin et al., 2010). Thus, the following hypothesis was proposed:
H15: Online hotel booking intention has a positive influence on hotel selection behavior.
2.8. Hotel Selection Behavior
The main dependent variable of this model is hotel selection behavior, as the TPB postulates that actual behavior is determined by behavioral intention and perceived behavioral control. Boripunt (2015) defined actual online behavior as a consumer who has placed an order using the website and completed the transaction for the order. It means that guests may have chosen the hotel because of its favorable room rate, its brand name, quality rating, features and amenities, reviews posted by past guests on social media sites (Verma, 2015). In conclusion, a better understanding of the influence of the factors involved in the guest selection process allows managers to better utilize their resources and to develop their facilities for guests (Soulidou et al., 2018).
The study is organized as follows: In the first section, the study represents the variables and theories as well as the hypotheses development. Then the study focuses on research design with data collection and measures operationalization. Finally, the study focuses on the results, discussion, and managerial implications as well as the limitations and future research.
3. Research Methodology
3.1. Research Framework
In the new E-P-TAM-TPB model, the actual behavior is positively and significantly influenced by behavioral intention, which, in turn, is influenced by attitude, subjective norm, perceived behavioral control, and perception of usefulness; In addition, perceived behavioral control will have a direct effect on actual behavior. Perceived usefulness and perceived ease of use are determinants of attitude, whereas perceived ease of use directly affects perceived usefulness (Al-Mamary et al., 2916, 152). To explore the intentions to book hotels online, this study focuses on the new E-P-TAM-TPB model by inserting the two external variables as Price value by Dodds et al. (1991) and eWOM by Elwalda (2016), which are expected to have influenced the intention to book a hotel online and in turn, hotel selection behavior. (see Figure 1).
Figure 1: The Research Framework
3.2. Scales and Instruments
This study consists of two phases. The first phase was qualitative research in which the author interviewed 6 lecturers and 4 experts, after explaining the purpose of the study, who had experiences in online booking at the hotels in Ho Chi Minh City to validate the scales. After completing the scales of measurement, the pretest was conducted with a discussion group of 10 guests to ensure that the respondents understood the scales as well as to complete the survey questionnaire. The final survey contained 36 questions. The variables used in the study consist of items, as shown in Table 1. The complete questionnaire survey was designed to collect data for this empirical research, which comprises of two sections: The first section includes general demographic information such as gender, age, and education level. The second section includes the main variables are measured by 36 questions/items based on a five-point Likert scale ranging from 1 with strongly disagree to 5 with strongly agree. All scale items were adopted from previous papers in tourism and hospitality literature but slightly modified for this research context.
Table 1: Scales of the Study
In the second phase, the new proposed research model is tested by using the partial least squares structural equation modeling (PLS-SEM) approach because this study is prediction-oriented research that aims to predict hotel selection behavior in the online hotel booking context. The PLS-SEM approach was used to analyze the proposed model, and to prove the hypothesis, positive influence of constructs.
3.3. Sampling
According to Hair et al. (1995), to have a reliable representation of the population, the sample size must be at least p × 5, where p is the number of independent variables. As there are 33 independent variables used in this study, the sample size must be at least 165. The author, however, performed an official survey with 480 respondents to ensure high dependability. Individual domestic tourist respondents who have had online hotel booking experiences provided empirical data for this study. Following an explanation of the study’s aim and confidentiality of the information, the respondents were sent an offline questionnaire survey. There were 378 completed questionnaires collected (return rate: 78.75%), with 23 invalids among them. As a consequence, 355 valid responses, or 93.91 percent, are used in the data analysis.
4. Results
4.1. Descriptive Analysis
There are three questions asked about the customer’s demographic information, including gender, age, and education. Demographic data obtained from the survey are shown in Table 2.
Table 2: Respondent Characteristics and The ANOVA Result
Based on this survey, the sample size consists of 162 male respondents (45.6 percent of the total respondents) 193 female respondents (54.4 percent of total respondents). According to the result, it seems that female respondents are more interested in booking their hotel room online than males. In terms of the age group, the majority of respondents fall into the age of 18–30 years old (39.4 percent), 31–43 years old (29.6 percent), the age below 18 years old (14.9 percent), 44–56 years old (9.3 percent) and above 56 years old (6.8 percent). The average age group of 18–30 was the largest, indicating that the younger the generation, the more likely that they adopt technology for online booking purposes. In terms of graduation background, the majority of the respondents graduated from various universities with a bachelor’s degree (38.9 percent), followed by a college certificate (24.2 percent) and other degrees (7.9 percent), Intermediate Professional (19.2 percent), High School degree holders (9.9 percent). This study demonstrates that the majority (38.9%) are those with a bachelor’s degree - the highest among other education levels in the sample. This could indicate that this group is mostly busy and have less time to make an online booking.
In addition, this study evaluates the significant differences between the respondents’ demographic characteristics with online booking hotel intention. The results of the independent sample T-test, the ANOVA, and post-hoc multiple comparison tests on the demographic variables show that gender and education background did not have any significant effect on the online booking intention. Next, the result indicates that age has a significant effect on online booking intention. Furthermore, Dunnett t-tests revealed that people aged 18–30 were significantly more likely to make reservations online (Sig. = 0.031, 0.05). This suggests that those who booked hotels online were generally younger. These findings are similar to Teng et al. (2020).
4.2. Reliability and Validity Analysis
The current study examined the reliability, construct reliability, average variance extracted (AVE), and discriminant validity of the indicators of latent variables to assess the measurement model of reflective constructs. The composite reliability (CR) and average variance extracted (AVE) should be greater than 0.7, and 0.5, respectively to establish the reliability and convergent validity (Hair et al., 2019). Based on the assessment of the results in Table 3, The value of the reliability statistics using Cronbach’s alpha was above 0.872; all the calculated values of the composite reliability (more than 0.912) are acceptable (Henseler et al., 2016). The average variance extracted (AVE) values for all constructs were 0.708, above the minimum required level of 0.5 (Chin, 1998). The information provided in Table 3 is concluded that the model construct indicators have met the reliability test. Thus, all the measures of construct reliability in the proposed model are accepted
Table 3: Composite Reliability
It observed from Table 4 signifies the discriminant validity using the PLS approach. Fornell-Larcker criterion is commonly used to evaluate the degree of shared variance between latent variables of the model. For each construct, all the discriminant validity values are higher than 0.700. Also, to examine the discriminant validity using the PLS approach, the values of Heterotrait-Monotrait correlations less than 0.900 will be acceptable (Henseler et al., 2015). The calculated values are less than the Heterotrait-Monotrait correlations discriminant validity, so the discriminant validity has been fulfilled as a research model. Thus, the results proved that the measurement scales are declared reliable and valid. In addition, The results in Table 4 showed that all the hypotheses following the proposed model were found positive and significant as expected. The results also indicated that all three variables consist of Attitude, Subjective Norm, and Perceived behavioral control, were found to have positive effects on behavior intention, which is consistent with that of Pham et al. (2021) and Ha et al. (2021).
Table 4: Discriminant Validity
4.3. Structural Model
Figure 2 shows that all path coefficients are positive. This result supports the hypotheses that this research model has proposed. Figure 2 represents the R2 value for the estimated equation is 0.602, which is significant at a 1 percent level of probability. The R2 adjusted shows that 0.601 (60.1%) percent of the variation in hotel selection behavior is described by the E-P-TAM-TPB model.
Figure 2: The Results of the Research Model
The result revealed in Table 5 signify the results of 5000 re-sampling bootstrapping for the testing of the hypothesis. The results indicate that the E-P-TAM-TPB model has a positive significant influence on online hotel selection behavior (p < 0.05). The results revealed that all proposed hypotheses were declared supported
Table 5: Hypothesis Testing
Multicollinearity is calculated by variance inflation factors (VIF) and tolerance. If the values of VIF exceeds 4.0 or less than 0.2 reflects the problems with multicollinearity (Hair et al., 2014, 197). The Collinearity Statistics (inner VIF values) revealed in table 6 were less than 4.0. All the VIF values are less than 4 (the highest value is 1.912) (Hair et al., 2014). Thus, the result shows that there is no multicollinearity effect among the variables.
Table 6: The Collinearity Statistics
5. Discussion and Implications
This study aimed to propose a new conceptual framework to understand the hotel selection behavior for holiday vacations. The conceptual framework was developed based on a review of past literature related to online hotel booking intentions behavior. The study also contributes to the literature in examining the effects of the new E-P-TAM- TPB model on the online hotel selection behavior in HCMC. The results indicate that the new E-P-TAM-TPB model significantly and positively influences the online hotel booking intention, in turn, toward selection behavior evident by its highest unstandardized coefficient beta value of 0.547, which is consistent with that of Boripunt (2015). Based on these empirical results, theoretical and practical implications for enhancing the efficiency of the hotel website.
In closing, this paper proposed a new conceptual framework to enhance online hotel booking selection behavior. The authors offered support for the potential effects of website interactivity on perceptions of ease of use, perception of usefulness, and finally, online booking intention. Future studies could extend the framework by integrating it with potential moderators to better explain tourist’s hotel selection behavior in the online context.
First, the results of the study found that the perception of users is the most important factor determining domestics tourist online hotel booking intention, evident by its highest unstandardized coefficient beta value of 0.243, which is consistent with that of Oliveira et al. (2019). Also, the results indicated that perceived usefulness and perceived ease of use related to the direct impact on the attitude variable with path coefficient values of 0.392 and 0.168, which is consistent with that of Ashour and Al-Qirem (2021).
Second, eWOM has a significant effect on subjective norm (β = 0.574, p = 0.000), perceived behavioral control (β = 0.549, p = 0.000), perception of ease of use (β = 0.503, p = 0.000), perception of useful (β = 0.501, p = 0.000), and attitude toward the hotel (β = 0.203, p = 0.000). Interaction effects were confirmed in the case of the hotel selection behavior. Thus, the results confirm that eWOM was statistically significant, which is consistent with that of Ladhari & Michaud (2015). In addition, the result finds that the attitude toward the hotel is the second important factor determining domestics tourist online hotel booking intention, evident by its highest unstandardized coefficient beta value of 0.214, which is consistent with that of Agag and El-Masry (2016b).
Third, the result found that price value or price is a third important factor determining consumers’ online hotel booking intention, evident by its path coefficient value of 0.205, which is consistent with that of Dodds et al. (1991) and Kim et al. (2017). Finally, the result found that PBC and Subjective Norm are these important factors determining consumers’ online hotel booking intention, evident by its path coefficient value of 0.209 and 0.131, which is consistent with that of Kim et al. (2017).
These major findings open an opportunity for hotel websites not only to create a simple booking process but also to show price value information to attract or impress customers, as well as tourists, who can compare the rate value with other hotel websites and OTA. Secondly, this study also found that online hotel booking intention significantly impacts hotel selection behavior. Therefore, hotel managers should make a great effort to improve the quality of hotel websites in terms of color, graphics, and design. eWOM is perceived as a powerful tool that assists tourists in deciding what to book by referencing the other positive online comments on the online platform or social media. Thirdly, the results of the demographic analysis show that domestic tourist who is between 18 and 30 years old and holds a bachelor’s degree were the majority to prefer to use the Internet for making online hotel reservations. This result is useful for the hotel managers who may consider selecting this targeted group for the hotel performance. In conclusion, the hotel manager should increase online hotel booking intention for potential future tourists to select hotels via the hotel website.
6. Limitations
The current study includes several limitations that offer opportunities for future research. First, this study employed an offline survey to collect data from randomly domestic travelers who selected hotels via the website in Ho Chi Minh City. For further research, the study could extend the framework by integrating it with potential moderators in relation to different groups of customers who may have online behaviors in the different segments of the hotel industry to understand customers’ online behavior in various types of hotel settings.
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