In this study, UTAUT2 was employed to examine online hotel booking impact factors. First, the background of UTAUT2 model is described, followed by the related constructs and hypotheses.
2.1. Background of UTAUT2 Model
Venkatesh, Morris, Davis, and Davis [10
] integrated eight technology acceptance models and proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Under UTAUT model, there are four independent variables, namely, performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). The four variables can influence consumers’ use intention and use behavior. The four other variables, namely, gender, age, experience, and voluntariness play the moderating role, which moderate the relationship between the independent variables and dependent variables. Venkatesh et al. [10
] explained the relevant variables under UTAUT model as follows:
PE: the degree to which using a technology will provide benefits to consumers.
EE: the degree of ease associate with consumers’ use of technology.
SI: the extent to which consumers perceive that important others (family and friends) believe they should use a particular technology.
FC: consumers’ perceptions of the resources and support available to perform a behavior.
According to UTAUT, PE, EE, SI, and FC are theorized to influence the behavioral intention to use a technology, while behavioral intention and FC determines technology use.
Basically, UTAUT takes an approach that emphasizes the importance of utilitarian value and was developed for an employee acceptance and use setting. Therefore, Venkatesh, Thong, Xu [12
] incorporated four other variables—hedonic motivation (HM), price vale (PV), experience, and habit—with the UTAUT model and extended UTAUT into UTAUT2 model to be used for individual use of technology. They explained the variables as follows:
HM: the fun or pleasure derived from using a technology.
PV: consumers’ cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them.
Experience: an opportunity to use a target technology.
Habit: the extent to which people tend to perform behaviors automatically because of learning.
In UTAUT2, PE, EE, SI, FC HM, PV and habit influence behavioral intention, while behavioral intention, FC, PV and habit influence use behavior. Age, gender, and experience play as moderators. (See Figure 1
for more detailed relations).
Since this study focuses on consumers’ technology acceptance and use context, the UTAUT2 model is adapted as the fundamental prototype of our research framework. The constructs used in the model are explored and our hypotheses based on the model are tested to identify the key factors affecting consumers’ online hotel booking use intention and behavior.
On the basis of the UTAUT2 model, this paper proposed the hypotheses in the following sections.
2.3. Effort Expectancy (EE) and Behavioral Intention (BI)
According to Sun, Lou, Chao, and Wu [16
], users were more likely to accept and use a new technology when it not only was user-friendly but also provided an easy-t-use interface and learning guidance. Similarly, Tsao, Shieh, and Jan [17
] indicated when customers compared a new technology with what they had previously used, and found that new technology was easier to use and did not require much training, they were more likely to increase their intention to use the new technology. Past studies showed that perceived ease-to-use has been a determinant factor which drove users to use a new technology, and efforts required to use a new technology directly influenced users’ acceptance and implementation of the technology [10
]. In consistent with prior research, Lai, Huang, Lu, and Chang [15
] stated that perceived ease-to-use had impacts on online booking. However, while website trust was added to the model for further analysis, no significant difference was found to exist between perceived ease-to-use and online booking. Effort expectancy is considered a prominent factor that has much influence on behavioral intention within the UTAUT2 model. Therefore, the following hypothesis is proposed based on prior research:
H2: Effort expectancy will positively influence consumers’ behavioral intention to make online hotel reservation.
2.10. Moderating Effects of Gender, Age, Experience within UTAUT2
Gefen and Straub [35
] found women rated perceived usefulness to be more than men do. Mikkelsen [36
] reported women tended to express more computer anxiety than men. Tsao, Shieh, and Jan [17
] also indicated that gender seemed to have moderating effects on the influence of performance expectancy on behavioral intention. Venkatesh et al. [10
] stated the influence of performance expectancy on behavioral intention was moderated by age and the effect was stronger for younger men. Similarly, Tsao, Shieh, and Jan [17
] discovered that the influence of performance expectancy on behavioral intention was moderated by gender. Research showed a significant relationship between performance expectancy and behavioral intention in UTAUT, and based on prior research findings, the following hypothesis was created:
H9: The relationship between performance expectancy and behavioral intention is moderated by gender and age.
Davis, Bagozzi, and Warshaw [37
] studied 107 MBA students using new word processing software, and discovered that the influence of effort expectancy on behavioral intention was moderated by experience. Moreover, Gefen and Straub [35
] stated that the relationship between effort expectancy and behavioral intention was moderated by gender, and men were reported to rate perceived ease-of-use more than women. Therefore, the following hypothesis was proposed:
H10: The relationship between effort expectancy and behavioral intention is moderated by gender, age, and experience.
Thompson, Haggins, and Howell [38
] studied individual experiences on personal computer utilization, and discovered a moderating effect of personal experience on the relationship between social influence and behavioral intention. In addition, Morris and Venkatesh [39
] reported significant moderating effects of experience on the relationship between social influence and behavioral intention. In a different study, they also found a significant moderating effect of gender on the relationship between social influence and behavioral intention when studying acceptance of information system by financial organization employees, and research results showed that women had more influence on the relationship than men. Similarly, Venkatesh et al. [10
] reported the influence of social influence on behavioral intention was moderated by age, and the influence was more significant for older workers. Thus, the following hypothesis was created:
H11: The relationship between social influence and behavioral intention is moderated by gender, age, and experience.
Venkatesh et al. [12
] highlighted the importance of UTAUT2. It is not difficult to understand that consumers have established a consumption pattern long before their actual consumption behavior. However, what attracts consumers to accept and use a new technology now may have to depend on technology design which increases consumer interests. A variety of research showed that age, gender, and experience of consumers have impacts on their hedonic motivation and behavioral intention, which in turn, influence the use behavior. In addition, price value and behavioral intention are affected by age and gender; on the other hand, individual difference in use behavior and behavioral intention are determined by age, gender, and experience.
Binde and Fuksa [40
] studied mobile Internet usage in Latvia, Russia and incorporated several constructs in their UTAUT2: performance expectancy, effort expectancy, social influence, facilitating conditions, and price value. They also added technological support and Internet experience as new constructs. They hypothesized technological support and Internet experience would have impacts on behavioral intention, and use behavior might be affected by facilitating conditions, technological support, and Internet experience. They hypothesized age, gender, and experience would have moderating effects on UTAUT2. Their study subjects included 2000 Latvia citizens, and study results showed that mobile Internet usage was affected by performance expectancy, effort expectancy, social influence, facilitating conditions, price value, technological support, and Internet experience. The impacts of these constructs on behavioral intention and use behavior were moderated by age, gender, and experience. As a result, following hypotheses were developed:
H12a: The impact of facilitating conditions on behavioral intention is moderated by gender, age, and experience.
H12b: The impact of facilitating conditions on use behavior is moderated by age and experience.
H13: The impact of hedonic motivation on behavioral intention is moderated by gender and age
H14: The impact of price value on behavioral intention is moderated by gender, age, and experience.
H15a: The impact of habit behavior on behavioral intention is moderated by gender, age, and experience.
H15b: The impact of habit behavior on use behavior is moderated by gender, age, and experience.
H16: The impact of behavioral intention on use behavior is moderated by experience.
The following figure illustrates the 16 hypotheses postulated above.
The research model consists of 16 hypotheses and is shown in Figure 1