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Article

Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model

by
Yuzong Zhao
1,*,
Hui Wang
1,
Zhen Guo
2,*,
Mingli Huang
3,
Yongtao Pan
1 and
Yongrui Guo
1
1
School of Tourism and Geography Science, Qingdao University, Qingdao 266071, China
2
Shenzhen Tourism College, Jinan University, Shenzhen 518053, China
3
College of Environmental Science and Engineering, Qingdao University, Qingdao 266071, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(16), 10395; https://doi.org/10.3390/su141610395
Submission received: 26 July 2022 / Revised: 11 August 2022 / Accepted: 18 August 2022 / Published: 21 August 2022
(This article belongs to the Section Tourism, Culture, and Heritage)
Browse Figures
Versions Notes

Abstract

:
Travel reservation is an important way to improve tourist experiences and digitally manage tourist attractions in the COVID-19 context. However, few studies have focused on the online reservation intentions of tourist attractions and its influencing factors. Based on the theory of the technology acceptance model (TAM), two variables (perceived risk and government policy) are introduced to expand on the theoretical model. This study investigates the influence of subjective norms, government policy, perceived usefulness, perceived ease of use, and perceived risk on reservation intentions of tourist attractions. An online survey was conducted in China, and 255 questionnaires were collected. The data were analysed using SPSS 26.0 and AMOS 28.0 to construct a structural equation modelling and analyse the path. The findings show that (1) subjective norms have no significant impact on reservation behaviours under voluntary situations; (2) perceived usefulness positively affects tourists’ reservation intention; and (3) perceived risk has a significant negative impact on reservation intention, and government policy is the main factor affecting tourists’ reservation intentions. These findings enhance the understanding of tourists’ reservation intentions and extend the TAM theory. From the practice perspective, tourist attraction operators should continue to strengthen the construction of the reservation system, improve tourists’ experiences, reduce the perceived risk of tourists, and other stakeholders such as the government should strengthen cooperation, promote the reservation system, and create a good reservation atmosphere.

1. Introduction

Travel reservation systems have been one of the most important tourism marketing channels [1,2]. From the perspective of tourists, and they can complete tour booking (a process that includes information search, booking, and paying) in the most convenient way, which helps realize personalized alternative selections and greater efficacy and efficiency [3]. Furthermore, reservation behaviours can also ensure tourists’ satisfaction with their travel experiences [4]. From the perspective of tourism and hospitality industry stakeholders, tourists’ reservation preferences can be understood, thus laying a foundation for the construction of marketing channels [5]. In addition, an understanding of reservation can help managers develop more reasonable pricing strategies [6]. Finally, online reviews have become an important source of information reflecting the tourists’ reservation experiences, which provides a reference for managers in improving the quality of travel services [7].
Previous research on online travel reservation mainly focused on the hotel industry [8,9], lacking a focus on tourist attractions [10,11]. Tourists are the main users of reservation services for tourist attractions, so it is necessary to study their reservation intentions. On the one hand, it provides a wider perspective for travel reservation theories; on the other hand, it can help improve the reservation system of tourist attractions, and thus, enhance tourist satisfaction and safety.
Previous research on online travel reservations have used theories such as trust theory [12], maturity theory [13], information search theory [14], self-efficacy theory [15], etc. Among them, the TAM theory is widely applied to investigate online reservation intentions [16,17]. A well-designed, user-friendly reservation website enhances the online travel booking experience [18]. It is clear that information technology not only facilitates the booking process but is also an important factor influencing reservation behaviour [19]. In the actual reservation process for tourist attractions, tourists need to face a complex and diverse network and technological environment. At the same time, the COVID-19 pandemic has also promoted the digital transformation of tourist attractions [20]. Tourist attractions in different tourism destinations have gradually reopened during the COVID-19 pandemic. Tourists prefer destinations close to home, especially short distances, and local attractions appear to be dominant in the recovery phase [21]. On the premise of ensuring tourist safety, the government and tourism industry sectors have implemented a travel reservation and booking policy (i.e., ticket reservation, time-segment tour reservation, and visitor interval entry) to promote domestic tourism markets in China. According to statistics from the Ministry of Culture and Tourism of the People’s Republic of China, by the end of 2021, more than 6000 A-level attractions in China offered online reservation services. “No reservations, no travel” has been integrated into the travel life of residents. A total of 58.7 percent of respondents expressed that they often use online travel reservation platforms based on a special survey on tourist behaviour by the China Tourism Academy in 2021. Normalized and high-frequency reservations for tourist attractions have become the mainstream mode.
There exists the need to consider whether and how tourists perceive risks and external variables of government policy, which affect tourist reservation intentions within the COVID-19 context. Overall, considering the importance of tourists’ perspectives and the role of technology in the tourist attraction reservation process, the study introduced the technology acceptance model (TAM), which studies people’s willingness to use new technologies and explores the influencing factors of tourists’ reservation intentions of tourist attractions.
This study contributes to the body of knowledge about tourists’ reservation intentions in the COVID-19 context in two ways. First, this study reveals the antecedents that affect tourists’ reservation intentions. Second, this study extends TAM based on comprehensive insight into understanding tourists’ reservation intentions. The findings shed light on the theoretical investigation and sustainable development of reservation services.

2. Literature Review

2.1. Travel Reservations

Reservation services were first proposed in the study of medical outpatient service in the 1950s [22] and later were widely applied to the transportation industry such as railway [23] and aviation [24], as well as other industries such as tourism [25], in particular, the hotel industry [26]. In recent years, travel reservations have developed rapidly with the progression of information technology [9]. Currently, tourists can directly book a hotel room/tourist attraction ticket through the official website or application provided by a third party [27].
Zhang and Yuan [28] believe that travel reservations are a form of tourism spending in which individual travel plans are arranged in advance of a predetermined time and place, so that discounted prices can be taken advantage of. There is no doubt that the aim of travel reservation services is to achieve a more economical and efficient mode to benefit both management and tourists by rationally allocating tourism resources [29]. Using the Internet as a new reservation method has become increasingly popular [30]. Some researchers emphasized the technical attributes of travel reservations [19,31]. For example, Elhaj [32] pointed out that online travel reservation was a type of booking method made by tourists based on a network and booking platform. Later, scholars explored the online consumption behaviour of tourists from consumer behaviour theories such as the theory of planned behaviour (TPB) and theory of reasoned action (TRA) to study the factors affecting tourist behaviours [33]. To the best of our knowledge, the factors that affect online tourist attraction reservation intentions have not yet been investigated.
The COVID-19 pandemic has changed the environment for travel reservation. On the one hand, there has been an opportunity for potential development on the supply side of the tourism industry [34]. Initial indications are that the current crisis is accelerating the digital transformation of the tourism sector [35]. On the other hand, there is a change on the demand side of the tourism industry. The COVID-19 outbreak may have dramatically changed tourists’ demand for visiting tourist destinations [36]. It is clear that travel reservation is becoming a regular part of the traveling process. In addition, there is the role of policy guidance. At the institutional level, the government has implemented many policies to encourage the reservation system of tourist attractions in China. Therefore, travel reservation services have developed rapidly under the dual role of policy promotion and market demand [29].
With the advance of research, more attention is being paid to the reasons behind tourists’ reservation intentions, including system quality and functional attributes [19,37], the quality of reservation websites [38,39], information quality [40], and e-service quality [41]. Obviously, high-quality, useful, and reliable information and convenient and advanced technology are the basal stimulants encouraging tourists to book. At the same time, perceived risk is commonly examined as one of the various determinants of travel reservation intentions that were affected by the pandemic [42].

2.2. The TAM

One of the most important theories to explore the acceptance and use of technology by individuals is TAM, which provides a theoretical framework for the study of new technologies and systems, such as internet technology and e-commerce. Davis (1989) proposed the technology acceptance model through previous research on the TRA [33]. This model believes that perceived usefulness and perceived ease of use are the main measures of attitudes, which can affect behavioural intentions and have a profound impact on users. At the same time, external variables affect perceived usefulness and perceived ease of use. Perceived ease of use affects perceived usefulness.
With the booming tourism industry and the breakthrough of internet technology, scholars have applied TAM to tourism research. Wober and Gretzel [43] tracked and investigated the use of marketing decision support systems by tourism managers in many European countries to study the factors that affect users of information systems. With the emergence of new technologies, scholars have begun to shift from focusing on travel websites and mobile devices to new technologies, such as virtual tourism and artificial intelligence. For example, Kaplanidou and Vogt [44] studied the influence of the characteristics of the tourist destination website on the tourists’ perceived usefulness of the website. Later, scholars were more interested in the research on tourists’ trust in websites [45]. Agag and El-Masry [46] used a structural equation model to explore tourists’ trust in online travel websites and influencing factors. With the increased use of smartphones by tourists, Lew et al. [47] expanded the mobile technology acceptance model by introducing self-efficacy theory, critical mass theory, and mobility theory. El-Said and Aziz [48] explored the acceptance of virtual tourism by tourists in the context of the COVID-19 pandemic.
With the emergence of new technologies and changing objects of use, scholars have been enriching and expanding TAM and have formed two main branches. One is to test the robustness of the technology acceptance model and the credibility of the relationship between internal variables [49]. The other is to integrate or expand the TAM by introducing new theories and improving the explanatory power of the TAM in specific objects and specific technology situations by adding external variables [50,51] and tourism industries [36]. Agag and El-Masry [52] introduced commitment-trust theory to study the determinants of customers’ online hotel reservations. Disztinger et al. [53] expanded the TAM by adding the variable of perceived immersion. It has been found that mobile Internet technology not only brings convenience, but also security risks [54]. Therefore, scholars have introduced variables such as perceived risk within the TAM model [55,56].
Information technology has changed constantly, and tourist attraction has become increasingly intellectualized and digitalised. Meanwhile, tourists are becoming increasingly diversified and personalized. Therefore, it is necessary to explore the acceptance behaviour of tourists towards new things, such as tourists’ reservations, according to changes in the technology and characteristics of tourists. In summary, based on the TAM, this paper comprehensively considers the risk of COVID-19 and the guiding role of policies in China’s research scenarios and introduces two new variables, namely, perceived risk and government policy, to extend the TAM. The structural equation model (SEM) was used to study the behavioural willingness of tourists to make reservations in the post-epidemic era and provide more research angles in terms of travel reservation and the TAM.

3. Hypothesis Development and Conceptual Model

3.1. Variable Definitions and Research Hypotheses

3.1.1. Perceived Usefulness, Perceived Ease of Use, and Reservation Intention

In the TAM, perceived usefulness and perceived ease of use can be regarded as the cognitive level of individual attitudes and the performance of consumers’ perceived value. Perceived usefulness refers to the individual’s belief that the use of a new system or technology can improve work efficiency [33]. Perceived ease of use refers to the individual’s belief that a certain system or technology is easy to implement [33]. In tourism activities, perceived usefulness refers to the role that making reservations plays for tourist attractions, and perceived ease of use is the convenience that tourists feel when using the reservation system, including the official website of a tourist attraction or application provided by a third party. Studies have shown that both perceived usefulness and perceived ease of use have positive effects on individuals’ reservation intention, and there is a positive correlation between perceived ease of use and perceived usefulness [57]. Perceived ease of use indirectly affects reservation intention through perceived usefulness [58]. If the reservation system cannot provide convenient and effective services for tourists, this perceived difficulty will inevitably make tourists question the usefulness of reservation services and then affect their reservation intentions. According to the above theoretical and actual situation analysis, the following hypotheses are proposed:
Hypothesis 1 (H1).
Tourists’ perceived usefulness has a positive impact on their reservation intentions for tourist attractions.
Hypothesis 2 (H2).
Tourists’ perceived ease of use has a positive impact on their perceived usefulness in tourist attraction.

3.1.2. Perceived Risk and Reservation Intention

Since the 1960s, the theory of perceived risk has been used to explain consumer behaviours [59]. Perceived risk is not objective risk in the real world but remains a subjectively determined expectation of loss by consumers [60]. According to the protective action decision model and perceived risk model [59], perceived risk has a significant impact on consumer behaviour when customers receive new information. Given the immateriality of tourism services, perceived risk can become more important [60]. In particular, the perceived risk of online communication and shopping behaviours in tourism has been studied [25,61]. When tourists’ perception of risk increases, they have been shown to change or cancel their booking behaviours. Lin et al. [62] argued that perceived risk negatively affects online travel booking intention. After the outbreak of COVID-19, Nazneen et al. [63] found that COVID-19 altered travel risk perceptions in China, which negatively affected travel decisions. Bae and Chang [64] also found that both cognitive risk perception and emotional risk perception have an impact on reservation intention. Generally, the risks faced by tourists include inconsistency between expectations and actual products or services, leakage of personal privacy information, and the unnecessary waste of time and energy. These risks further hinder the reservation willingness to use the reservation system. Both before and after the outbreak of COVID-19, perceived risk is consistently a determinant of reservation intention [42]. Therefore, the following hypothesis is proposed:
Hypothesis 3 (H3).
Tourists’ perceived risk has a negative impact on their reservation intention for tourist attractions.

3.1.3. Subjective Norms and Reservation Intention

The TPB uses the variable “subjective norms”, which are social pressures individuals perceive when making adoption decisions and are defined as “an individual’s view of the importance of others in his or her social environment when they expect him or her to act in a certain way” [65,66]. The beliefs behind subjective norms are called normative beliefs, and generally speaking, if a person believes that most people think he should behave that way, he will feel social pressure and then comply. Accordingly, existing research mainly focuses on exploring the relationship between subjective norm and motivation, intention and behaviour. Furthermore, previous studies have shown that subjective norms positively influence the formation of reservation intention [67,68]. For example, Venkatesh and Davis [69] found that perceived usefulness was affected by subjective norms. In addition to indirect effects, subjective norms also have an impact on intention in involuntary situations. Similarly, Bhatiasevi and Yoopetch [70] found that subjective norms can influence tourists’ willingness to use e-booking. That is, subjective norms can predict reservation intention. According to the above theoretical and actual situational analysis, the following assumption is put forward:
Hypothesis 4 (H4).
Tourists’ subjective norms have a positive impact on their reservation intention for tourist attractions.

3.1.4. Government Policy and Reservation Intention

The development of China’s tourism economy has been gradually promoted under the government-led model. That is, the local and central government are considered critical stakeholders for the sustainable development of tourism [71]. With the COVID-19 pandemic, government support is critical for businesses [72], especially those in the tourism and hospitality sectors [73]. As Wright [72] stated, the tourism industry needs government stimulus packages and interventions to reduce the harmful impact COVID-19 has had. Therefore, to control the spread of infectious disease and protect tourists’ health and safety, collaboration between public health, tourism authorities, and the tourism industry is necessary [74,75]. Moreover, Ritchie and Jiang [76] also affirmed that a greater understanding of the effectiveness and efficiency of government policies was needed in times of crisis. Tourist attraction reservation was one of the government policies implemented to deal with the impact of the epidemic in China.
Diversified booking channels, including instant messaging tools, mobile phone clients, the official website of tourist attractions, telephone booking, etc., can provide tourists with a good travel experience and humanized service. Therefore, the government has promoted online reservation through the public media, highlighting the convenience it brings to consumers in the context of the COVID-19 pandemic. According to the agenda setting theory [77], when the news media emphasizes certain attributes of an object, people’s understanding of the object is necessarily affected by the agenda of those attributes. Tourism consumers accept the agenda set by the government and the media, and then actively participate in tourist attraction reservations, that is, an active travel reservation policy can also encourage tourists to experience the usefulness of tourist attraction reservations in the post-pandemic era. In addition, the publicity of the government and public media on tourist reservations will also directly affect the intention of tourists to make reservations. According to the above theoretical and actual situational analysis, the following assumptions are put forward:
Hypothesis 5 (H5).
Government policy has a positive impact on tourists’ perceived usefulness of reservation services.
Hypothesis 6 (H6).
Government policy has a positive impact on tourists’ reservation intentions.

3.2. Conceptual Model

According to the above assumptions, combined with the TAM, the conceptual model is provided in Figure 1.

4. Methodology

4.1. Measurement of Variables

All constructs were assessed by a 5 point Likert scale (1 = completely disagree to 5 = completely agree). Perceived usefulness was measured by 4 items, and perceived ease of use was measured by 3 items adapted from Davis et al. [78] and Yen et al. [79]. A 5 item scale to measure perceived risk was applied and modified from the study by Jacoby and Kaplan [80], and Peter and Tarpey [81]. The scale, related to subjective norms, consisted of 3 items adapted from Wang [82] and Verma [83]. Government policy was assessed by 5 items adapted from Wang and Shou [84]. Reservation intention was also measured via a 4 item scale derived from Davis et al. [78].

4.2. Data Collection and Sample Profile

The formal questionnaire was conducted online on Questionnaire Star on 28 March 2022. As of 5 April 2022, a total of 305 online questionnaires were collected, 255 of which were valid, which met the sample size standard of AMOS [85]. The data show that the proportions of males and females were 32.5% and 67.5%, respectively. The imbalance between males and females may be related to the respondents of the online questionnaire. From the perspective of age, the number of people aged 21–30 is the largest, with 202 people, accounting for 79.2% of the sample. In terms of education level, there were 244 people with junior college or bachelor’s and master’s degrees or above, accounting for 95.7% of the population; 12.2% of the respondents were employees of enterprises, civil servants, or employed by public institutions. In addition, 59.6% of the respondents were students.

4.3. Data Analysis

This paper uses SPSS 26.0 to obtain the descriptive properties of the respondents. The reliability analysis was measured by Cronbach’s alpha values, and the validity analysis was performed by principal component analysis to reduce the dimension. Finally, AMOS 28.0 was used for SEM construction and path analysis.

5. Results

5.1. Nonresponse Bias and Common Method Bias

According to Armstrong and Overton’s suggestion [86], SPSS 26.0 was used in this paper to conduct a non-response bias test of the questionnaire. First, the questionnaire was divided into two parts according to the time sequence of return: early responders (the first 25% of the questionnaires) and late responders (the last 25% of the returned questionnaires). Second, the two groups were compared by the chi-square test. The results showed that there were no significant differences in the control variables of gender between the two groups at the 5% confidence interval. Therefore, this study excluded the possibility of nonresponse bias.
In addition, Harman’s single-factor test was used to evaluate potential common method bias. All items are loaded into an exploratory factor analysis, the results of the non-rotating factor analysis are checked, and the minimum number of factors required to explain the variance of the variables is determined. When only one factor is extracted or it has strong explanatory power, it must be considered that there is a serious common method bias. According to the results of this study, the contribution rate of the general factors is not more than 50%, the first factor accounted for 27.8%, and the total contribution rate of the six factors is 65.7%. It can be seen that there is no common method bias.

5.2. Measurement Model

Measurement model evaluation usually included testing for reliability, convergent, and discriminant validity. Reliability assessment depends on Cronbach’s α and the composite reliability (CR) (see Table 1), values of 0.7 to 0.9 are considered as satisfactory [87].
The data show that the Cronbach’s α values of the six variables are all above 0.74. After each item was deleted, there was no significant improvement in the reliability of each scale. At the same time, all CR values are higher than 0.7. Therefore, the reliability of the questionnaire is very good, and the internal stability and consistency are high. These results suggest that the measurement model is reliable and valid.
Convergent validity is assessed using the average variance extracted (AVE) for each construct (see Table 1). The AVE values of all constructs are between 0.37 and 0.612 in this study. Although the AVE value of perceived risk is less than 0.5, the composite reliability is higher than 0.6 and in the acceptable range of 0.36 to 0.5 [88].
This study presents the results of discriminant validity assessment using Fornell-Larcker criterion [88] and the heterotrait-monotrait ratio of correlations (HTMT) [89]. The square root of each construct’s AVE is higher than the correlations with other constructs, so the Fornell-Larcker criterion was fulfilled (see Table 2). HTMT is the ratio of the mean of indicator correlation between different constructs to the mean of indicator correlation between same constructs. As shown in Table 3, the values of HTMT do not exceed the required threshold value of 0.90 by Gold et al. [90]. These results suggest that discriminant validity is achieved.
Validity is the basis for measuring whether the item design is reasonable (see Table 4). The overall KMO value was 0.859, greater than 0.6, and the χ2 statistic test value was 2631.589 (p < 0.001), which met the conditions of exploratory factor analysis. After the maximum variance orthogonal rotation of principal component analysis, it was found that there were 6 common factors with the eigenvalue of the questionnaire greater than 1, and the cumulative variance contribution rate was 65.66%, which was greater than 60%, which met the research requirements. The factor loadings of the 24 measurement items were all greater than 0.5, and they belonged to different dimensions, which were in line with the expected assumptions, indicating that the questionnaire design was reasonable.

5.3. Structural Model

Before hypothesis testing, the model fit indices’ ability to meet the requirements needs to be examined. In this paper, 11 indices such as χ2/df, absolute fit indices (GFI, AGFI, RMSEA) and value added fit indices (CFI, IFI, NFI, TLI), and parsimony corrected fit indices (PCFI, PNFI, PGFI) were selected to test the model fit (see Table 5). The data show that, except for the three indices of GFI, AGFI, and NFI, all other indices have reached the reference standard in the initial structural model. To enhance the degree of fit between the theoretical model and the actual model of the sample, the initial model needs further modification [91].
The modified model I takes the method of adding and subtracting observed variables. The normalized factor loading value should be greater than 0.50 and not greater than 0.95. Referring to this criterion, the observed variable PR4 should be deleted. After deletion, the χ2/df value of the model was changed, and other fit indices improved. The modified model II involved the method of revising the covariance of the residuals of the variables. Since the correlation between the variable residuals was not considered when constructing the theoretical model, the model fitting effect will be affected by the strongly correlated variable residuals. Referring to this standard and combining the correction indices provided by AMOS, the study established a correlation between the variable residuals with correlation and, after many operations, until the variable residuals were uncorrelated. The modified models’ fit indices were as follows in Table 5. The data show that the χ2/df value of the model has changed, and all fit indices meet the reference standard.
According to the results, each model modification can reduce the χ2/df value, and other fit indices can be significantly improved. Therefore, these modifications are feasible in theory, and the model modification results are accepted. When another attempt was made to establish the connection between observed variables of different dimensions, it was found that the new structural relationship was not as ideal as the modified model II, so the modified model II was finally selected in this study (see Figure 2).
The results of hypothesis testing in this study are shown in Table 6.
Among the cognitive variables, perceived usefulness has a significantly positive effect on tourists’ reservation intentions for tourist attractions (β = 0.16, p = 0.03), while tourists’ perceived ease of use has a positive effect on perceived usefulness (β = 0.46, p < 0.001). Tourists’ reservation intention was also significantly affected by perceived risk (β = −0.32, p < 0.001). Thus, H1~H3 were supported.
Among the external variables, subjective norms have no significant effect on tourists’ reservation intentions (β = 0.07, p = 0.355). Government policy has a significantly positive effect on both perceived usefulness (β = 0.24, p = 0.003) and tourists’ reservation intentions (β = 0.47, p < 0.001). Thus, H4 was not supported, and H5~H6 were supported.

6. Discussion and Conclusions

6.1. Conclusions

This study takes the TAM as the theoretical basis to investigate the online reservation intentions for tourist attractions and its influencing factors. Two variables (perceived risk and government policy) were introduced to expand the theoretical model in the COVID-19 context.
An online survey was conducted in China and derived from a sample of 255 through the Questionnaire Star platform, the data for this research were analysed using SPSS 26.0 and AMOS 28.0. Then, this study analysed the influence of subjective norms, government policy, perceived usefulness, perceived ease of use, and perceived risk on reservation intention for tourist attractions.
Based on the above research, this paper draws the following conclusions: (1) subjective norms have no significant impact on reservation behaviour under voluntary situations; (2) perceived usefulness positively affects tourists’ reservation intentions; and (3) perceived risk has a significant negative impact on reservation intentions, and government policy is the main factor affecting tourists’ reservation intentions. Compared with perceived risk, the external variable of government policy has a greater impact on tourists’ reservation intentions.

6.2. Theoretical Implications

The primary objectives of this study are to identify tourists’ reservation intentions for tourist attractions in the COVID-19 context and to measure the influencing factors via the extended TAM. Specifically, the findings advance reservation services research in the following three ways.
First, this study contributes to the understanding of tourists’ intentions to reserve tourist attractions on theoretical grounds. While the development of hotel online booking is gaining popularity [8,26], previous studies on tourist attraction reservation is insufficient. This research fills this gap in the literature regarding which important aspects tourists consider when booking tourist attractions. Based on the TPB, the TAM is introduced in this study to analyse tourists’ intentions to reserve tourist attractions. Obviously, the development of information technology has become the basic support for the implementation of reservation systems. Some prior studies have also analysed the effects of technological factors on tourists’ reservation preferences [19,29]. In the same way, these results show that tourists’ perceived usefulness of the reservation systems positively affects their reservation intention. Tourists’ perceived ease of use positively affects perceived usefulness. These findings revalidate the value of the TAM in the study of reservation intention and further support the research of Li and Zhang [58].
Second, according to the rapid development of travel reservations in China and the change in tourists’ travel behaviour since COVID-19, the variables of perceived risk and government policy are integrated into the TAM. The extended model not only confirms the predictive role of risk perception on reservation intentions, but also effectively improves the explanatory ability of the model, and helps deepen the understanding of reservation intention. The significant impact of the risk variable on reservation intention has been verified. Perceived risk is commonly examined as one of the various determinants of travel reservation intentions that were affected by the pandemic [42]. In the face of complex information, virtual networks, and the spread of the COVID-19 epidemic, tourists will inevitably feel the risks, such as personal information, time, money, public health, and other aspects. These situations lead to tourists’ concerns about the safety of tourist attractions and reservation services, which in turn, affects their intention to make reservations. Tourist attraction reservation is the management measure advocated by the Chinese government in the context of COVID-19. To a certain extent, tourists’ reservation has significantly promoted. Therefore, it is reasonable to introduce risk and policy variables in this study, and the results of the study also show that it is necessary to expand TAM.
Finally, this study found that the influence path of subjective norms is not supported, i.e., tourists’ subjective norms do not significantly affect their reservation intentions. Tourists’ reservation intentions are generated in a situation of voluntary use, so social pressure from surrounding people may not directly affect individual reservation intention. This result validates Venkatesh and Davis’s [69] view that “subjective norms have no significant effect on intentions in voluntary situations”. Although in TPB [92], subjective norms are factors that directly affect behavioural intentions, Davis [33] did not use subjective norms in the original TAM. Mathieson’s [93] study also showed that subjective norms do not have a significant impact on reservation intention.

6.3. Practical Implications

These results have important implications for tourist attraction managers. Through online reservation systems, the query and traceability of tourist information can be realized, which is an indispensable means for tourist attractions to ensure safe operation under the normalization of epidemic prevention and control. At the same time, the operators and managers of tourist attractions should continuously strengthen the functional construction of the reservation system to improve the perceived usefulness of tourists, so that tourists can reserve and purchase tickets reasonably according to the bookable volume of the destination before departure, accurately plan the route, and arrange the itinerary reasonably. In addition to providing online ticket reservation and time segment tour reservation services, tourist attractions also need to actively develop digital experience products and popularize intelligent services (such as electronic maps, route recommendations, voice guides, information inquiry, feedback, etc.). It is necessary to constantly optimize the interaction between tourist attractions and users, which improves tourists’ perceived ease of use. Measures that can be taken include improving the timeliness of information provision, attaching great importance to the personal experience of users, and reducing the cost of information search for tourists.
The findings suggest that perceived risk is a negative determinant of booking intentions. Tourist attraction managers should pay attention to the security of reservation systems to reduce the perceived risks for tourists. The personal privacy information of tourists should be guaranteed to eliminate the possible disclosure risk. On the tourism destination level, the government should continue to promote the convenience of the “reservation system” and formulate reservation regulations. Using big data, cloud computing, the Internet of Things, and other means to build a smart tourism system can promote reservation services ability for tourist attractions. At the same time, the government needs to expand channels for booking and cooperate with stakeholders, such as tourism enterprises, tourist attractions, tour leaders, and communities, to create a good reservation environment [94,95].
According to the findings, reservation systems have an impact on tourist decision making and behavioural intention which would aid in destination marketing. This study shows that destination marketing organisations (DMOs) and tourist attraction marketers should improve promotional materials and content of online reservation platforms to meet market expectations [96]. Thus, during the time of the COVID-19 pandemic, tourists can use online reservation applications that allow them to easily and securely obtain destination information and compare products and prices, etc. [97,98].

6.4. Limitations and Future Research

Several limitations of this study should be acknowledged, which may provide guidance for future research. Firstly, due to the impact of COVID-19, this study used a convenience sample of tourists through the Questionnaire Star platform. It is necessary to conduct a face to face survey. Secondly, the respondents in this study were Chinese tourists only. However, the differences in perceived risk might be influenced by cultural context. Thus, future research could investigate tourists’ reservation intentions for tourist attractions in different cultural backgrounds.

Author Contributions

Conceptualization, Y.Z.; data curation, Z.G. and H.W.; formal analysis, Z.G., H.W. and Y.Z.; funding acquisition, Y.Z.; investigation, Z.G. and M.H.; methodology, Y.Z., Z.G. and Y.G.; project administration, Y.P. and Y.G.; writing—original draft, Z.G., Y.P. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China (Grant No. 19BJY215).

Institutional Review Board Statement

Approval for the study was not required in accordance with local/national legislation.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the author. The data are not publicly available due to potential copyright problems.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ert, E.; Fleischer, A. Mere position effect in booking hotels online. J. Travel Res. 2016, 55, 311–321. [Google Scholar] [CrossRef]
  2. San-Martín, S.; Jiménez, N.; Liébana-Cabanillas, F. Tourism value VS barriers to booking trips online. J. Retail. Consum. Serv. 2020, 53, 101957. [Google Scholar] [CrossRef]
  3. Kim, M.-J.; Chung, N.; Lee, C.-K. The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tour. Manag. 2011, 32, 256–265. [Google Scholar] [CrossRef]
  4. Fernández-Herrero, M.; Hernández-Maestro, R.M.; González-Benito, Ó. Autonomy in trip planning and overall satisfaction. J. Travel Tour. Mark. 2017, 35, 119–129. [Google Scholar] [CrossRef]
  5. Murphy, H.C.; Chen, M.-M.; Cossutta, M. An investigation of multiple devices and information sources used in the hotel booing process. Tour. Manag. 2016, 52, 44–51. [Google Scholar] [CrossRef]
  6. Wen, J.; Lin, Z.; Liu, X.; Xiao, S.H.; Li, Y. The interaction effects of online reviews, brand, and price on consumer hotel booking decision making. J. Travel Res. 2020, 60, 846–859. [Google Scholar] [CrossRef] [Green Version]
  7. Sparks, B.A.; Browning, V. The impact of online reviews on hotel booking intentions and perception of trust. Tour. Manag. 2011, 32, 1310–1323. [Google Scholar] [CrossRef] [Green Version]
  8. Masiero, L.; Viglia, G.; Nieto-Garcia, M. Strategic consumer behavior in online hotel booking. Ann. Tour. Res. 2020, 83, 102947. [Google Scholar] [CrossRef]
  9. Boto-García, D.; Zapico, E.; Escalonilla, M.; Baños Pino, J.F. Tourists’ preferences for hotel booking. Int. J. Hosp. Manag. 2021, 92, 102726. [Google Scholar] [CrossRef]
  10. Wang, X.; Li, X.R.; Zhen, F.; Zhang, J. How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tour. Manag. 2016, 54, 309–320. [Google Scholar] [CrossRef]
  11. Srivastava, P.; Kinshuk, S.; Ajay, K.; Baidyanath, B.; Alessio, I. Post-epidemic factors influencing customer's booking intent for a hotel or leisure spot: An empirical study. J. Enterp. Inf. Manag. 2021, 35, 78–99. [Google Scholar] [CrossRef]
  12. Burgess, S.; Sellitto, C.; Cox, C.; Buultjens, J. Trust perceptions of online travel information by different content creators: Some social and legal implications. Serv. Ind. J. 2011, 13, 221–235. [Google Scholar] [CrossRef] [Green Version]
  13. Jiang, J.; Liang, F. A mechanism study on the impact of tourism e-commerce maturity on the e-travel booking intention: As a case with ctrip. Tour. Trib. 2014, 29, 75–83. (In Chinese) [Google Scholar]
  14. Gursoy, D.; Chen, J.S. Competitive analysis of cross cultural information search behavior. Tour. Manag. 1997, 24, 503–523. [Google Scholar] [CrossRef]
  15. Vijayasarathy, L.R. Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Inform. Manag. 2004, 41, 747–762. [Google Scholar] [CrossRef]
  16. Sevim, N.; Yüncü, D.; HALL, E.E. Analysis of the extended technology acceptance model in online travel products. J. Internet Appl. Manag. 2017, 8, 45–61. [Google Scholar] [CrossRef] [Green Version]
  17. Wicaksono, A.; Maharani, A. The effect of perceived usefulness and perceived ease of use on the technology acceptance model to use online travel agency. J. Acad. Manag. Rev. 2020, 1, 313–328. [Google Scholar] [CrossRef]
  18. Lu, J. Development, distribution and evaluation of online tourism services in China. Electron. Commer. Res. 2004, 4, 39–221. [Google Scholar] [CrossRef]
  19. Elci, A.; Abubakar, A.M.; Ilkan, M.; Kolawole, E.K.; Lasisi, T.T. The impact of travel 2.0 on travelers booking and reservation behaviors. Bus. Perspect. Res. 2017, 5, 124–136. [Google Scholar] [CrossRef]
  20. Ndou, V.; Mele, G.; Hysa, E.; Manta, O. Exploiting technology to deal with the covid-19 challenges in travel & tourism: A bibliometric analysis. Sustainability 2022, 14, 5917. [Google Scholar]
  21. Li, X.; Gong, J.; Gao, B.; Yuan, P. Impacts of COVID-19 on tourists’ destination preferences: Evidence from China. Ann. Tour. Res. 2021, 90, 103258. [Google Scholar] [CrossRef] [PubMed]
  22. Bailey, N.T.J. A study of queues and appointment systems in hospital out-patient departments, with special reference to waiting-times. J. R. Stat. Soc. B 1952, 14, 185–199. [Google Scholar] [CrossRef]
  23. Tsai, T.-H. A self-learning advanced booking model for railway arrival forecasting. Transp. Res. Part C Emerg. Technol. 2014, 39, 80–93. [Google Scholar] [CrossRef]
  24. Haerian, L.; Homem-de-Mello, T.; Mount-Campbell, C.A. Modeling revenue yield of reservation systems that use nested capacity protection strategies. Int. J. Prod. Econ. 2006, 104, 340–353. [Google Scholar] [CrossRef]
  25. Park, S.; Tussyadiah, I.P. Multidimensional facets of perceived risk in mobile travel booking. J. Travel Res. 2016, 56, 854–867. [Google Scholar] [CrossRef] [Green Version]
  26. Gao, G.-X.; Bi, J.-W. Hotel booking through online travel agency: Optimal Stackelberg strategies under customer-centric payment service. Ann. Tour. Res. 2021, 86, 103074. [Google Scholar] [CrossRef]
  27. Ahmad, W.; Kim, W.G.; Choi, H.-M.; Haq, J.U. Modeling behavioral intention to use travel reservation apps: A cross-cultural examination between US and China. J. Retail. Consum. Serv. 2021, 63, 102689. [Google Scholar] [CrossRef]
  28. Zhang, A.; Yuan, J. Reservation tourism: Exploration of new forms of tourism consumption. Tour. Surv. 2011, 4, 176–177. (In Chinese) [Google Scholar]
  29. Han, Y.; Zhang, T.; Wang, M. Holiday travel behavior analysis and empirical study with integrated travel reservation information usage. Transp. Res. A Policy Pract. 2020, 134, 130–151. [Google Scholar] [CrossRef]
  30. Kim, W.G.; Kim, D.J. Factors affecting online hotel reservation intention between online and non-online customers. Int. J. Hosp. Manag. 2004, 23, 381–395. [Google Scholar] [CrossRef]
  31. Schaarschmidt, M.; Höber, B. Digital booking services: Comparing online with phone reservation services. J. Serv. Mark. 2017, 31, 704–719. [Google Scholar] [CrossRef]
  32. Elhaj, M. Factors that contribute to consumers’ perceptions of online and traditional travel reservation systems. Anatolia 2012, 23, 118–122. [Google Scholar] [CrossRef]
  33. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  34. Brouder, P. Reset redux: Possible evolutionary pathways towards the transformation of tourism in a COVID-19 world. Tour. Geogr. 2020, 22, 484–490. [Google Scholar] [CrossRef]
  35. Cuomo, M.T.; Tortora, D.; Foroudi, P.; Giordano, A.; Festa, G.; Metallo, G. Digital transformation and tourist experience co-design: Big social data for planning cultural tourism. Technol. Forecast. Soc. 2021, 162, 120345. [Google Scholar] [CrossRef]
  36. Seraphin, H. COVID-19: An opportunity to review existing grounded theories in event studies. J. Conv. Event Tour. 2020, 22, 3–35. [Google Scholar] [CrossRef]
  37. Law, R.; Bai, B. How do the preferences of online buyers and browsers differ on the design and content of travel websites? Int. J. Contemp. Hosp. Manag. 2008, 20, 388–400. [Google Scholar] [CrossRef]
  38. Breitenbach, C.; Van Doren, D. Value-added marketing in the digital domain: Enhancing the utility of the Internet. J. Int. Consum. Mark. 1998, 15, 558–575. [Google Scholar] [CrossRef]
  39. Wang, L.; Law, R.; Guillet, B.D.; Hung, K.; Fong, D.K.C. Impact of hotel website quality on online booking intentions: ETrust as a mediator. Int. J. Hosp. Manag. 2015, 47, 108–115. [Google Scholar] [CrossRef]
  40. Lin, J.C.; Lu, H. Towards an understanding of the behavioral intention to use a website. Int. J. Inform. Manag. 2000, 20, 197–208. [Google Scholar]
  41. Yi, Y.; Gong, T. The electronic service quality model: The moderating effect of customer self-efficacy. Psychol. Mark. 2008, 25, 587–601. [Google Scholar] [CrossRef] [Green Version]
  42. Pham Minh, Q.; Ngoc Mai, N. Perceived risk and booking intention in the crisis of Covid-19: Comparison of tourist hotels and love hotels. Tour. Recreat. Res. 2021, 46, 1–13. [Google Scholar] [CrossRef]
  43. Wöber, K.; Gretzel, U. Tourism managers' adoption of marketing decision support systems. J. Travel Res. 2000, 39, 172–181. [Google Scholar] [CrossRef]
  44. Kaplanidou, K.; Vogt, C. A structural analysis of destination travel intentions as a function of web site features. J. Travel Res. 2006, 45, 204–216. [Google Scholar] [CrossRef]
  45. Amin, M.; Ryu, K.; Cobanoglu, C.; Nizam, A. Determinants of online hotel booking intentions: Website quality, social presence, affective commitment, and e-trust. J. Hosp. Mark. Manag. 2021, 30, 845–870. [Google Scholar] [CrossRef]
  46. Agag, G.M.; El-Masry, A.A. Why do consumers trust online travel websites? Drivers and outcomes of consumer trust toward online travel websites. J. Travel Res. 2017, 56, 347–369. [Google Scholar] [CrossRef]
  47. Lew, S.; Tan, G.W.-H.; Loh, X.-M.; Hew, J.-J.; Ooi, K.-B. The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technol. Soc. 2020, 63, 101430. [Google Scholar] [CrossRef]
  48. El-Said, O.; Aziz, H. Virtual tours a means to an end: An analysis of virtual tours' role in tourism recovery post COVID-19. J. Travel Res. 2022, 61, 528–548. [Google Scholar] [CrossRef]
  49. Jin, C.-H. Adoption of e-book among college students: The perspective of an integrated TAM. Comput. Hum. Behav. 2014, 41, 471–477. [Google Scholar] [CrossRef]
  50. Pavlou, P.A. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 2003, 7, 101–134. [Google Scholar]
  51. Pikkarainen, T.; Pikkarainen, K.; Karjaluoto, H.; Pahnila, S. Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Res. 2004, 14, 224–235. [Google Scholar] [CrossRef] [Green Version]
  52. Agag, G.; El-Masry, A.A. Understanding the determinants of hotel booking intentions and moderating role of habit. Int. J. Hosp. Manag. 2016, 54, 52–67. [Google Scholar] [CrossRef] [Green Version]
  53. Disztinger, P.; Schlögl, S.; Aleksander, G. Technology acceptance of virtual reality for travel planning. In Information and Communication Technologies in Tourism; Schegg, R., Stangl, B., Eds.; Springer: Cham, Switzerland, 2017; pp. 255–268. [Google Scholar]
  54. Cunningham, L.F.; Gerlach, J.; Harper, M.D. Perceived risk and e-banking services: An analysis from the perspective of the consumer. J. Financ. Serv. Mark. 2005, 10, 165–178. [Google Scholar] [CrossRef]
  55. Featherman, M.; Pavlou, P.A.; Zhang, Y.T. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum. Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef] [Green Version]
  56. Nunkoo, R.; Ramkissoon, H. Travelers' E-Purchase Intent of Tourism Products and Services. J. Hosp. Mark. Manag. 2013, 22, 505–529. [Google Scholar] [CrossRef]
  57. Li, Y.; Qi, J.; Shu, H. Review of relationship among variables in TAM. Tsinghua Sci. Technol. 2008, 13, 273–278. (In Chinese) [Google Scholar] [CrossRef]
  58. Li, D.H.; Zhang, L.X. Model of influential factors for downloading and using tourism Apps based on a technology acceptance model. Tour. Trib. 2015, 30, 26–34. (In Chinese) [Google Scholar]
  59. Dowling, G.R.; Staelin, R. A model of perceived risk and intended risk-handling activity. J. Consum. Res. 1994, 21, 119–134. [Google Scholar] [CrossRef]
  60. Ruiz-Mafé, C.; Sanz-Blas, S.; Aldás-Manzano, J. Drivers and barriers to online airline ticket purchasing. J. Air Transp. Manag. 2009, 15, 294–298. [Google Scholar] [CrossRef]
  61. Kim, L.H.; Qu, H.; Kim, D.J. A study of perceived risk and risk reduction of purchasing air-tickets online. J. Travel Tour. Mark. 2009, 26, 203–224. [Google Scholar] [CrossRef]
  62. Lin, P.J.; Jones, E.; Westwood, S. Perceived risk and risk-Relievers in online travel purchase intentions. J. Hosp. Mark. Manag. 2009, 18, 782–810. [Google Scholar] [CrossRef]
  63. Nazneen, S.; Hong, X.; Din, N. COVID-19 Crises and Tourist Travel Risk Perceptions. 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3592321 (accessed on 25 July 2022).
  64. Bae, S.Y.; Chang, P. The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘untact’ tourism in South Korea during the first wave of the pandemic (March 2020). Curr. Issues Tour. 2020, 24, 1017–1035. [Google Scholar] [CrossRef]
  65. Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar]
  66. Bilgihan, A.; Barreda, A.; Okumus, F.; Nusair, K. Consumer perception of knowledge-sharing in travel-related online social networks. Tour. Manag. 2016, 52, 287–296. [Google Scholar] [CrossRef]
  67. Hsieh, C.M.; Park, S.H.; McNally, R. Application of the extended theory of planned behavior to intention to travel to Japan among Taiwanese youth: Investigating the moderating effect of past visit experience. J. Travel Tour. Mark. 2016, 33, 717–729. [Google Scholar] [CrossRef]
  68. Choe, J.Y.J.; Kim, J.J.; Hwang, J. The environmentally friendly role of edible insect restaurants in the tourism industry: Applying an extended theory of planned behavior. Int. J. Contemp. Hosp. Manag. 2020, 32, 3581–3600. [Google Scholar] [CrossRef]
  69. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  70. Bhatiasevi, V.; Yoopetch, C. The determinants of intention to use electronic booking among young users in Thailand. J. Hosp. Tour. Manag. 2015, 23, 1–11. [Google Scholar] [CrossRef]
  71. Wang, R.; Liu, G.; Zhou, J.; Wang, J. Identifying the critical stakeholders for the sustainable development of architectural heritage of tourism: From the perspective of China. Sustainability 2019, 11, 1671. [Google Scholar] [CrossRef] [Green Version]
  72. Wright, C. Local government fighting Covid-19. Round Table 2020, 109, 338–339. [Google Scholar] [CrossRef]
  73. Salem, I.E.; Elbaz, A.M.; Elkhwesky, Z.; Ghazi, K.M. The COVID-19 pandemic: The mitigating role of government and hotel support of hotel employees in Egypt. Tour. Manag. 2021, 85, 104305. [Google Scholar] [CrossRef]
  74. Maphanga, P.M.; Henama, U.S. The tourism impact of Ebola in Africa: Lessons on crisis management. Afr. J. Hosp. Tour. Leis. 2019, 8, 1–13. [Google Scholar]
  75. Jamal, T.; Budke, C. Tourism in a world with pandemics: Local-global responsibility and action. J. Tour. Futures 2020, 6, 181–188. [Google Scholar] [CrossRef]
  76. Ritchie, B.W.; Jiang, Y. A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management. Ann. Tour. Res. 2019, 79, 102812. [Google Scholar] [CrossRef]
  77. Schweinsberg, S.; Darcy, S.; Cheng, M. The agenda setting power of news media in framing the future role of tourism in protected areas. Tour. Manag. 2017, 62, 241–252. [Google Scholar] [CrossRef]
  78. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
  79. Yen, D.C.; Wu, C.S.; Cheng, F.F. Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Comput. Hum. Behav. 2010, 26, 906–915. [Google Scholar] [CrossRef]
  80. Jacoby, J.; Kaplan, L.B. The components of perceived risk. In SV-Proceedings of the 3rd Annual Conference of the Association for Consumer Research; Venkatesan, M., Ed.; Association for Consumer Research: Chicago, IL, USA, 1972; pp. 382–393. [Google Scholar]
  81. Peter, J.P.; Tarpey, L.X., Sr. A Comparative analysis of three consumer decision strategies. J. Consum. Res. 1975, 2, 29–37. [Google Scholar] [CrossRef]
  82. Wang, C.; Zhang, J.H.; Yu, P.; Hu, H. The theory of planned behavior as a model for understanding tourists’ responsible environmental behaviors: The moderating role of environmental interpretations. J. Clean. Prod. 2018, 194, 425–434. [Google Scholar] [CrossRef]
  83. Verma, V.K.; Chandra, B. An application of the theory of planned behavior to predict young Indian consumers’ green hotel visit intention. J. Clean. Prod. 2018, 172, 1152–1162. [Google Scholar] [CrossRef]
  84. Wang, Z.; Shou, M. Research on the influence mechanism of consumers’ environmental knowledge on green consumption intentions: Analysis of mediating effect based on perceived usefulness. Zhejiang Trib. 2022, 52, 123–132. (In Chinese) [Google Scholar]
  85. Mueller, R.O. Structural equation modeling: Back to basics. Struct. Equ. Modeling Multidiscip. J. 1997, 4, 353–369. [Google Scholar] [CrossRef]
  86. Armstrong, J.S.; Overton, T.S. Estimating nonresponse bias in mail surveys. J. Mark. Res. 1977, 14, 396–402. [Google Scholar] [CrossRef] [Green Version]
  87. Urbach, N.; Ahlemann, F. Structural equation modeling in information systems research using partial least squares. J. Inf. Technol. Theory Appl. (JITTA) 2010, 11, 2. [Google Scholar]
  88. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  89. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  90. Gold, A.H.; Malhotra, A.; Segars, A.H. Knowledge management: An organizational capabilities perspective. J. Manag. Inform. Syst. 2001, 18, 185–214. [Google Scholar] [CrossRef]
  91. Hou, J.; Wen, Z.; Cheng, Z. Structural Equation Model and Its Application; Education Science Press: Beijing, China, 2004. [Google Scholar]
  92. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Dec. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  93. Mathieson, K. Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Inform. Syst. Res. 1991, 2, 173–191. [Google Scholar] [CrossRef]
  94. Zhao, Y.; Cui, X.; Guo, Y. Residents’ Engagement Behavior in Destination Branding. Sustainability 2022, 14, 5852. [Google Scholar] [CrossRef]
  95. Guo, Y.; Hou, X. The effects of job crafting on tour leaders' work engagement: The mediating role of person-job fit and meaningfulness of work. Int. J. Contemp. Hosp. Manag. 2022, 34, 1649–1667. [Google Scholar] [CrossRef]
  96. Wang, T.Y.; Park, J. Destination Information Search in Social Media and Travel Intention of Generation Z University Students. J. China Tour. Res. 2022, 18, 1–19. [Google Scholar] [CrossRef]
  97. Garcı’a-Milon, A.; Olarte-Pascual, C.; Juaneda-Ayensa, E. Assessing the moderating effect of COVID-19 on intention to use smartphones on the tourist shopping journey. Tour. Manag. 2021, 87, 104361. [Google Scholar] [CrossRef]
  98. Florido-Benitez, L. The impact of tourism promotion in tourist destinations: A bibliometric study. Int. J. Tour. Cities 2022. ahead-of-print. [Google Scholar] [CrossRef]
Sustainability 14 10395 g001 550
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. The SEM of online reservation intentions for tourist attractions.
Figure 2. The SEM of online reservation intentions for tourist attractions.
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Table
Table 1. Descriptive statistics, reliability, and validity of the constructs.
Table 1. Descriptive statistics, reliability, and validity of the constructs.
Constructs and ItemsMeanSDLoadingAVECRCronbach’s α
Perceived Usefulness 0.5040.8020.798
PU1: Making a tourist attraction reservation would improve the travel experience3.850.7410.663
PU2: Making a tourist attraction reservation would reduce the information search cost3.980.8200.706
PU3: Making a tourist attraction reservation would enhance the tour efficiency4.010.7960.790
PU4: Making a tourist attraction reservation would promote the security in the travel3.920.8680.675
Perceived Ease of Use 0.5610.7930.792
PEOU1: I found it is easy to employ the reservation system in practice3.730.8470.749
PEOU2: I found it is easy to operate the reservation system expertly3.720.9080.770
PEOU3: I found the reservation system easy to understand3.820.8750.728
Perceived Risk 0.3700.7430.742
PR1: I think that tourist attraction reservation would be risky2.900.9540.612
PR2: I think that the products or services booked for the tourist attraction would not be consistent with reality3.290.8760.630
PR3: I think that tourist attraction reservation would lead to a financial loss2.891.0290.702
PR4: I think that tourist attraction reservation would lead to personal information leakage3.390.9490.466
PR5: I think that tourist attraction reservation would lead to a loss of convenience2.801.0780.605
Subjective Norms 0.5160.7610.758
SN1: People I am familiar with would make reservation when they visited a tourist attraction3.270.9970.683
SN2: People whose opinions I value would prefer that I make a tourist attraction reservation3.271.0350.788
SN3: Most people who are important to me think that I should make a tourist attraction reservation3.610.9110.678
Government Policy 0.6120.8860.885
GP1: The government has encouraged everyone to make a tourist attraction reservation3.740.7670.675
GP2: The government has supported the construction of tourist attractions reservation system3.690.7810.789
GP3: The government has created a good social atmosphere for tourist attraction reservation3.710.7950.793
GP4: The government has developed reservation policy to facilitate the travel during the pandemic3.730.8080.810
GP5: The government has developed a reservation policy to ensure the travel safety during the pandemic3.780.8010.831
Reservation Intentions 0.5590.8350.832
RI 1: I would like to use the tourist attraction reservation system4.070.6810.780
RI 2: I would make a tourist attraction reservation in the future4.140.6960.699
RI 3: I would recommend others to make a tourist attraction reservation3.820.7880.715
RI 4: If there is a plan to visit a tourist attraction, I will give priority to make a reservation4.080.7460.793
Table
Table 2. Fornell-Larcker criterion results.
Table 2. Fornell-Larcker criterion results.
123456
1. Perceived Usefulness0.71
2. Perceived Ease of Use0.590.75
3. Perceived Risk−0.08−0.160.61
4. Subjective Norms0.210.270.250.72
5. Government Policy0.480.52−0.020.360.78
6. Reservation Intention0.490.44−0.330.170.580.75
Note: Values on the bolded diagonal are the square root of the AVE.
Table
Table 3. HTMT results.
Table 3. HTMT results.
123456
1. Perceived Usefulness
2. Perceived Ease of Use0.61
3. Perceived Risk0.180.19
4. Subjective Norms0.250.280.26
5. Government Policy0.490.510.090.37
6. Reservation Intention0.500.480.320.230.60
Table
Table 4. Rotated component matrix.
Table 4. Rotated component matrix.
Measurement ItemsRotated Factor Loading Values
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6
GP 30.824
GP 20.804
GP 50.802
GP 40.760
GP 10.649
RI 1 0.783
RI 4 0.753
RI 3 0.730
RI 2 0.704
PU 2 0.794
PU 3 0.788
PU 1 0.685
PU 4 0.665
PR 5 0.731
PR 2 0.713
PR 4 0.704
PR 3 0.674
PR 1 0.613
PEOU 2 0.789
PEOU 3 0.775
PEOU 1 0.725
SN 2 0.845
SN 1 0.817
SN 3 0.709
Eigenvalues3.5712.8122.5962.5112.1772.091
Variance explained rate after rotation (%)14.87711.71710.81710.4629.0708.711
Cumulative variance explained rate after rotation (%)14.87726.59437.41147.87356.94365.654
Table
Table 5. Model fit indices.
Table 5. Model fit indices.
χ2/dfGFIAGFICFIIFINFITLIRMSEAPCFIPNFIPGFI
Ideal value1~3>0.9>0.9>0.9>0.9>0.9>0.9<0.08>0.5>0.5>0.5
Initial model1.8450.8750.8430.9170.9180.8380.9050.0580.7980.7280.700
Modified model I1.7840.8850.8550.9280.9290.8520.9170.0560.8000.7340.699
Modified model II1.1800.9290.9010.9850.9850.9120.9810.0270.7630.7070.660
Table
Table 6. Results of hypothesis testing.
Table 6. Results of hypothesis testing.
Hypotheses/PathEstimated Valuet-Valuep-ValueResults
H1. Perceived Usefulness →Reservation Intention0.162.1650.030Supported
H2. Perceived Ease of Use →Perceived Usefulness0.464.9890.000Supported
H3. Perceived Risk →Reservation Intention−0.32−4.0920.000Supported
H4. Subjective Norms →Reservation Intention0.070.9240.355Not supported
H5. Government Policy →Perceived Usefulness0.242.9320.003Supported
H6. Government Policy →Reservation Intention0.475.5890.000Supported
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Zhao, Y.; Wang, H.; Guo, Z.; Huang, M.; Pan, Y.; Guo, Y. Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model. Sustainability 2022, 14, 10395. https://doi.org/10.3390/su141610395

AMA Style

Zhao Y, Wang H, Guo Z, Huang M, Pan Y, Guo Y. Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model. Sustainability. 2022; 14(16):10395. https://doi.org/10.3390/su141610395

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Zhao, Yuzong, Hui Wang, Zhen Guo, Mingli Huang, Yongtao Pan, and Yongrui Guo. 2022. "Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model" Sustainability 14, no. 16: 10395. https://doi.org/10.3390/su141610395

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