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Article

COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator

by
Chih-Ching Teng
,
Ya-Jen Cheng
,
Wen-Shen Yen
* and
Ping-Yu Shih
Department of Restaurant, Hotel and Institutional Management, Fu Jen Catholic University, New Taipei City 242, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 13048; https://doi.org/10.3390/su151713048
Submission received: 24 July 2023 / Revised: 17 August 2023 / Accepted: 21 August 2023 / Published: 30 August 2023
(This article belongs to the Special Issue Meeting the Challenges and New Opportunities: Sustainable Development of Tourism and Hospitality Industry Following the COVID-19 Pandemic)
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Abstract

:
The global hotel and tourism business has been significantly affected by the COVID-19 pandemic, prompting governments and researchers to explore ways for mitigation. Within this particular setting, this study investigation centers on Taiwan at a crucial phase of the pandemic. It utilizes the uncertainty avoidance theory and the protection motive theory to analyze the impact of perceived risk associated with COVID-19, perceptions of travel risk, and hotel hygiene and safety practices on the intention to stay in those hotels. A total of 500 valid survey responses were obtained from travelers in Taiwan during the nationwide level 3 alert for COVID-19 in 2021, via the cluster sampling technique. Using SPSS 25.0 and AMOS 22.0, the reliability and validity test as well as structure equation analysis were used to examine the proposed model. Analytical results indicate that perceived risk of COVID-19 positively affects travel risk perceptions, which in turn negatively affects hotel staying intention. Moreover, travel risk perceptions mediate the relationship between perceived risk of COVID-19 and hotel staying intention. The findings of this study indicate that there is a strong moderating effect of hotel hygiene and safety practices on the link between travel risk perceptions and hotel staying intention, highlighting the importance of these practices in influencing individuals’ decisions to remain at a hotel. The results of this study provide valuable insights into the management of tourism crises, specifically emphasizing the need for prioritizing safety and hygiene measures throughout the post-COVID era. These findings underline the importance of effectively managing perceptions of travel risks and maintaining high standards of hotel hygiene in order to enhance the likelihood of tourists’ intent to stay at a hotel. Furthermore, this study presents an in-depth strategy for hoteliers and practitioners to navigate the road to recovery, consequently facilitating the revitalization of the sector and offering valuable perspectives for future research investigations.

1. Introduction

Is the hotel business well equipped to address the worldwide pandemic resulting from the outbreak of COVID-19? The COVID-19 pandemic, which originated in late 2019, has caused significant disruptions to individuals’ lives. Moreover, the subsequent economic downturn has had far-reaching consequences, particularly affecting the hotel and tourism sectors to a great extent [1]. The COVID-19 pandemic has been considered the most serious epidemic of infectious disease that affected the world in 2020, causing a global health crisis, economic and social upheaval, and devastating the tourism and travel industries in various countries [2]. In Taiwan, the Tourism Bureau of the Ministry of Transportation and Communications (Tourism Bureau, M.O.T.C.) [3] reported a significant decline in the average occupancy rate of tourist hotels from 66.69% in 2019 to only 38.83% in 2020. This unpresented drop reflects a substantial decrease in travelers’ intentions to stay in tourist accommodation in Taiwan due to their heightened concerns about the severity of the COVID-19 pandemic. Travelers are now more aware of COVID-19 induced risk and are taking safety considerations into account when planning their trips and related activities [4].
Although some literature has explored the outcomes of public health crises on tourism, including COVID-19 [4,5], there is still much to be learned about the specific influences of the pandemic. Recognizing the influence of COVID-19 on travel behavior is crucial for the recovery of the tourism and hospitality industry [4,6,7]. Protection motivation theory (PMT) [8,9] is a commonly adopted framework for exploring how health threats influence tourism behavior [10,11,12]. Recent research has extended the application of PMT to understand the impact of the COVID-19 risk on general travel plans and inclinations [4,5]. However, few studies have explored travel risk perception’s serving as a bridge in understanding the impact of COVID-19 risk perceptions on hotel staying intention [13], and previous research has not adequately addressed the travel risk perception components in travelers’ intentions. This highlights a need to explore how perceptions of risk influence intentions to stay in hotels.
Furthermore, hygiene and safety have been identified as crucial elements of health-protective behaviors in the tourism literature [14,15], but the potential effect of moderators, such as hotel hygiene and safety practices, on the relationship between risk perceptions and hotel staying intention has not been sufficiently explored. This indicates a dearth of studies on the possible effects of cleanliness and safety requirements on guests’ views and travel plans. The study, therefore, integrated Hofstede’s uncertainty avoidance (UA) [15] to more closely examine the factors that guide the potential effect of moderators on tourists’ revisit intentions [16].
In order to fill the existing gaps in the literature, this study proposes a theoretical framework that combines the Protection Motivation Theory (PMT) with Hofstede’s uncertainty avoidance theory [15]. The objective is to assess the influence of the COVID-19 pandemic on individuals’ perceptions of travel risks and their inclination to stay in hotels. The research model further examines the moderating influence of hotel hygiene and safety practices. This study focuses specifically on travelers who have experienced hotel stays during the COVID-19 pandemic. These travelers have a better understanding of the processes of hygiene and safety practices provided by hotels during COVID-19. The results of this study could provide insights into potential changes in travelers’ intentions and offer recommendations to hotel managers to benefit the industry in the future [13].
Building on the arguments outlined above, the current study aims to propose and examine a research model concerning COVID-19 perceived risks and hotel staying intention. Although previous studies have explored the mechanisms underlying tourist decision-making behaviors (e.g., booking intentions of full board hotels [4]; travel intentions: [2,13,17,18]), there are still unexplored areas in evaluating a systematic theoretical framework that explains the effect of COVID-19 perceived risk on hotel staying intention. As a result, this study proposes a research model based on the UA and PMT theories that takes into account the components of COVID-19 perceived risk, travel risk perceptions, hotel hygiene and safety practices, and hotel staying intention. The anticipated outcomes of our research are expected to make significant advances to both theoretical understanding and practical applications. From a theoretical perspective, this study would support the integration of PMT and UA for conceptualizing tourists’ intentions to stay in hotels. Moreover, the empirical results derived from this inquiry have the capacity to enhance the knowledge base within the field of hospitality. In practice, the analytical findings may shed light on the mental dynamics that drive traveler intents and actions during times of turbulence, providing insight into the resilience of visitors’ behaviors in the face of crises. This understanding is critical in promoting the tourist and hospitality industry’s recovery in the aftermath of the pandemic.

2. Theoretical Framework and Hypotheses

The mechanism between perceived risk of COVID-19 and hotel staying intention is examined through the lens of PMT [9]. This study adapted Rogers’ PMT [9], explaining that travel intent related to perceived risk during COVID-19 in the travel contextual inquiry. The motivation for protection comes from a three-component fear appeal: (1) the management of the noxiousness environment, (2) the probability of the event’s occurrence, and (3) a protective efficacy response, corresponding to the “cognitive assessment process“ that affects attitude change [9]. Travelers cognitively assess the severity and likelihood of exposure to the environment and evaluate their ability to cope with the environment. In this way, protective motivation is stimulated, followed by changes in behavioral intentions.

2.1. Perceived Risk of COVID-19 and Travel Risk Perceptions

Covid-19, originally discovered as a new pandemic in December 2019, has rapidly spread around the world since it was first detected [19]. Beginning in 2020, many countries imposed short-term travel restrictions to halt the spread of the virus, heightening concerns about the COVID-19 pandemic’s impact on the tourism and hospitality industries [8]. Previous research has suggested that researchers should draw lessons from the 2003 SARS outbreak on how to recover from and manage crisis situations [20,21].
As past studies have shown, during the SARS period, the number of travelers dropped dramatically, indicating that the perceived danger of SARS had caused a sharp decline in people’s desire to stay in Taiwan. According to data from the M.O.T.C. [3], from the outbreak of the SARS epidemic on March 19 to May 18, the number of people coming to Taiwan decreased by 49.46%, and the number of people going abroad decreased by 60.94%. In April 2003, the number of visitors to Taiwan’s 279 tourist recreation areas had grown by 15.26%, whereas the number of visitors to private recreation areas had decreased by 60% to 80%, and the occupancy rate of tourist hotels was only about 37%. Important tourism activities led by the Ministry of Transportation and Communications were postponed or canceled, causing the leisure and tourism industry to stagnate and retreat. However, recent research has found the impact of the COVID-19 outbreak is more severe than that of SARS, with a higher level of perceived threat [4]. Since the Great Depression of 1933, the occupancy rate was historically low (38%) in 2020, and nearly 50% of US hotel revenues were expected to decline [22].
PMT was created to learn how people develop protective actions in the face of health concerns. It implies that, when presented with danger, the decision to respond is impacted by a cognitive evaluation of both the perceived severity and likelihood of occurrence [23]. Because COVID-19 is highly infectious and has a high fatality rate, it is deemed a scenario with both high threat severity and high probability [4]. PMT evaluates how an individual appears behaviorally as a protective strategy when confronted with such a danger [24]. As a result, individuals who believe they are more likely to be infected may be more motivated to take precautionary measures, such as avoiding public spaces or participating in social distancing, if they believe COVID-19 is a serious disease, in order to limit or lessen the risk [4].
In tourism literature, risk perception is a highly discussed topic [25,26,27]. With the growing impact of COVID-19 on the tourism and hospitality industry, researchers have explored the issue of perceived risk associated with the pandemic [4,14,28]. COVID-19 perceived risk refers to an individual’s estimate of their own likelihood to catch the disease as well as their perception of the severity of the illness [23,29]. Susceptibility refers to an individual’s evaluation of the possibility of contracting an illness, whereas perceived severity refers to their sense of how severe or deadly the disease is [30].
Individuals are more likely to make efforts to reduce the danger of catching an illness when their knowledge of the likelihood and seriousness of the condition develops [31], such as avoiding travel when they feel at risk of contracting a disease [32]. As a result, the impact of the COVID-19 pandemic has a significant impact on visitors’ perceptions of travel risk [19]. According to Neuburger and Egger [33], multiple types of risks affect perceived travel risk, which is defined as a person’s assessment of the threat level depending on the features and intensity of the scenario [34,35]. Once analyzed, this risk perception can influence how a person acts [36]. Individual characteristics, societal constructs, and cultural values may all influence risk perception [35,37]. Thus, the following hypotheses are proposed:
Hypothesis 1 (H1).
COVID-19 Perceived Risk (severity) has a positive effect on travel risk perceptions.
Hypothesis 2 (H2).
COVID-19 Perceived Risk (susceptibility) has a positive effect on travel risk perceptions.

2.2. Travel Risk Perceptions and Hotel Staying Intention

Travel risk perception is a key factor in keeping tourists away from a destination that does not provide a safe environment [38]. When the destination country is considered a high-risk country, it discourages tourists [39]. The substantial health hazards linked with travel have created apprehension regarding travel during the COVID-19 pandemic and in its aftermath. Tourist choices and decisions are heavily influenced by their perception of health risks. When tourists perceive a high level of health risk at a site, they are less likely to visit [40,41]. When travelers perceive high risk and have greater confidence in their ability to manage it, they will take precautionary measures [33]. Conversely, if the perceived travel risk outweighs their belief in their ability to handle it, they will not engage in preventative actions [33].
According to a Longwoods International study performed in April 2020, over half of U.S. tourists canceled their trips entirely, while 43% reduced their travel plans in reaction to the COVID-19 pandemic. Furthermore, 66% of U.S. tourists said the pandemic will have a significant influence on their travel plans in the next six months [42]. This observation indicates that COVID-19 poses a significant health risk to travelers, making the reduction of these risks a crucial strategy in attracting hotel guests. Individual and contextual factors impact how people perceive threats and travel behavior [43]. For COVID-19, it has been established that the perceived threat is related to a person’s perception of the possibility of contracting the disease at a hotel where contamination may occur [4]. The greater the perceived threat, the less likely people are to book full-board hotel stays [44].
Thus, the following hypotheses are proposed:
Hypothesis 3 (H3).
Travel risk perceptions have a negative effect on hotel staying intention.

2.3. Mediating Effects of Travel Risk Perceptions

Previous research has suggested that travelers tend to take precautionary measures to avoid travel-related behaviors when their perceptions of travel risk increase [45]. However, the COVID-19 pandemic has demonstrated a nature and impact that surpasses all previous health crises, causing a shift in travelers’ risk perception and behavior [14,29]. Travelers now prioritize complete tourism packages, safety and security when traveling to popular destinations [19]. They seek to avoid risks and crowded tourist destinations and may choose not to visit destinations if their well-being is compromised due to the outbreak [19].
The decision-making process of a traveler when confronted with a risk is influenced by cognitive evaluations of the perceived severity and probability of occurrence, as shown by the PMT framework [44]. This process involves evaluation of both how well a coping strategy tackles the threat and the individual’s belief in their ability to carry out that strategy effectively [23]. Following this appraisal, travelers may opt for either constructive or counterproductive behaviors [4]. Adaptive behaviors aim to protect a traveler from the threat, while maladaptive responses are associated with actions where the individual facing the threat refrains from taking the advised course of action [23,46].
Based on previous studies, tourist risk perception has been widely analyzed after health crises [25,47,48]. However, the influence of COVID-19 on risk perception and travel behavior surpasses all prior health emergencies [14,49]. The recovery of the post-COVID-19 tourism industry will heavily rely on the risk perceptions of travelers, even after actions have been taken to control the spread and the industry has reopened [39]. Thus, the following hypotheses are proposed:
Hypothesis 4.1 (H4.1).
Travel risk perceptions have a mediating effect between COVID-19 perceived risk (severity) and hotel staying intention.
Hypothesis 4.2 (H4.2).
Travel risk perceptions have a mediating effect between COVID-19 perceived risk (susceptibility) and hotel staying intention.

2.4. Moderating Effects of Hotel Hygiene and Safety Practices

One can contemplate culture in the context of business without invoking Hofstede’s work [16,50]. However, including all dimensions of culture can create bias and result in prejudice when constructing typologies of visitor behavior [51]. In the context of travel, UA is particularly applicable to an individual’s cultural values [16]. UA refers to the extent to which individuals in a society are uneasy with uncertainty and ambiguity [15]. Yang et al. [16] found that travelers with low UA are more inclusive and tolerant, can handle uncertainties more readily, and are less likely to experience stress and anxiety. In contrast, travelers with high UA may prefer structured situations and experience distress when faced with the unknown [16].
Previous research has applied the concept of UA to predict travelers’ behavior in marketing and tourism environments [16,52,53]. Although UA has been used as a moderator influencing traveler behavior in past studies [54,55], recent research found that UA did not exert a moderating effect on the relationship between self-congruity (whether actual or ideal) and the intention to return to a destination [16]. Furthermore, travel behavior has changed after the COVID-19 pandemic [49]. Travelers now prioritize hotel hygiene and cleanliness for their health and safety [14,56,57]. Tourists who prefer structured situations with high levels of hotel hygiene and safety practices may have a higher hotel staying intention. Specifically, they may not feel uncertainty and ambiguity and are more likely to display a hotel staying intention if the destination aligns with their concept of hygiene and safety (including hotel hygiene and safety practices). Hence, the following hypotheses are proposed:
Hypothesis 5 (H5).
Hotel hygiene and safety practices have a moderating effect on the relationships between travel risk perceptions and hotel staying intention.
Drawing from the existing theoretical framework and empirical evaluations, this study presents a hypothesized model as depicted in Figure 1.

3. Methods

3.1. Data Collection and Participants

In the present study, data was collected via an online survey conducted by an experienced company specializing in online data gathering in Taiwan. The population of this study consisted of Taiwanese customers aged 18 and above who had stayed in tourist hotels during the COVID-19 period. Two screening questions (“Are you older than 18?” and “Have you stayed in tourist hotels during the COVID-19 period in the past 12 months?”) were used to sample participants. The questionnaire was distributed from June 1 to 8 June 2021, during the nationwide level-three alert for COVID-19 in Taiwan. The data collection company distributed the online survey for our research to residents across various regions of Taiwan, selecting participants randomly in accordance with the population distribution in each area. The online survey was randomly distributed to participants in different regions, ensuring that the collected samples accurately represented the gender, age, and education of the entire population.
Cluster sampling was utilized in this study to ensure the representation of the population in each region of Taiwan. The regions were classified as Northern Taiwan, Central Taiwan, Southern Taiwan, and Eastern Taiwan, and participants were sampled based on these clusters. The sample population consisted of Taiwanese adult customers who had stayed in tourist hotels during the COVID-19 period. Participants voluntarily joined the online survey, and those who finished the survey were rewarded with a $2 gift, sent by the research team. Out of the 500 surveys that were distributed, 500 valid responses were gathered, achieving a response rate of 100%. The demographics of the participants are detailed in Table 1. Among the respondents, 50.6% were female, 53.2% were married, and 88.4% had a degree from a college or university. In terms of age, 36.2% were 51–60, 20.4% were 18–30, 19.4% were 41–50, 18.4% were 31–40, and 5.6% were 61–80. Moreover, 33.2% worked in service industry, and 28.6% worked in manufacturing industry. Regarding monthly household income, 27.8% reported earning NTD 20,001–40,000 and 24.5% reported earning NTD 40,001–60,000. Regarding the geographical location of the participants’ residences, 45.4% were in Northern Taiwan, 27.2% were in Southern Taiwan, 24.4% were in Central Taiwan, and 3.0% were in Eastern and Outlying Islands. Within the past 12 months, 33.2% had stayed in tourist hotels 7–8 times and 28.6% had stayed in tourist hotels 5–6 times.

3.2. Survey Instrument and Measures

The questionnaire consisted of scales selected from literature that had demonstrated good reliability and validity. The researchers adjusted the wording of the chosen scales to align with the specific characteristics of the hotel industry. Since the survey region was Taiwan, the adjusted questionnaire items were translated into Chinese by professionals for questionnaire distribution afterward. Then, two experts each from academia and industry were asked to review the appropriateness of the questionnaire items. Finally, the translated and reviewed questionnaire was back-translated into English by bilingual staff familiar with both Chinese and English. Experts were then consulted to confirm that the adjusted questionnaire items still maintained the original intent of the questions.
The perceived risk of COVID-19 was assessed, considering both severity and susceptibility, following the framework proposed by Nazione et al. [33]. Severity was measured using a scale of 4 items, such as “Coronavirus (COVID-19) is a serious risk” and “Coronavirus (COVID-19) is a serious threat” (α = 0.94). Susceptibility, on the other hand, was evaluated using another set of 4 items, including “I am at risk of getting coronavirus (COVID-19)” and “I am susceptible to getting coronavirus (COVID-19)” (α = 0.96).
To measure travel risk perceptions, the scale developed by Neuburger and Egger [29] was employed. This scale consisted of seven items, for instance, “Tourism will be massively affected by coronavirus” and “Staying in a hotel is a risk, as there are many people from different countries, who could carry the virus” (α = 0.87).
The assessment of hotel hygiene and safety practices was based on Yu et al.’s [58] scale, with modifications made to align with the health and safety measures implemented by hotels in Taiwan. This scale comprised seven items, including “This hotel cleans guest rooms and public areas (i.e., toilets and washroom floors) using disinfectants” and “This hotel washes its laundry using antibacterial products and practices (i.e., towels, bed covers, blankets, and pillows”) (α = 0.93).
To evaluate hotel staying intention, a modified Tussyadiah’s [59] scale was adopted, resulting in a three-item measurement. An example item from this scale is “I expect to continue using this accommodation in the future.” Every item within the scales was assessed using a 7-point Likert scale, with scores extending from 1, representing “strongly disagree,” to 7, signifying “strongly agree (α = 0.96).

3.3. Analysis

The data of the study was analyzed by using two statistical tools, SPSS 25.0 and AMOS 22.0. Specifically, SPSS 25.0 was employed for a range of analyses, including descriptive statistics, one-way ANOVA, reliability assessment, and correlation analysis. AMOS 22.0, on the other hand, was applied for confirmatory factor analysis (CFA) and structural equation modeling (SEM). These analyses provided a rigorous assessment of the measurement model’s fit and allowed for the examination of the structural relationships among variables, providing a comprehensive understanding of the theoretical framework.
One should take into account that the data were gathered through individual survey responses, via a cross-sectional design. To tackle the possible concern of common method variance (CMV), the Harman’s single-factor test was employed in the study, following the procedure outlined by Tehseen et al. [60]. The result of the test indicated that less than 50% of the covariance was accounted for by any single factor, indicating that CMV was not a major concern in our study, in line with the findings reported by Tehseen et al. [60].

4. Results and Discussion

4.1. CFA Results, Validity, and Reliability of Measurements

This section presents the results of the CFA, as well as the validity and reliability of the measures used in this study. The factor structure of the modified uncertainty scale was examined through exploratory factor analysis to establish its suitability for subsequent statistical analysis. Prior to conducting the factor analysis, the Kaiser–Meyer–Olkin (KMO) and Bartlett spherical tests were employed to assess the adequacy of the data for factor analysis. A KMO value above 0.6 is generally considered acceptable, while a value above 0.8 is excellent [61]. In this study, the KMO was found to be satisfactory, indicating a good correlation between the questions and suitability for factor analysis. Additionally, all factor loadings for the questions were above 0.7, leading to the inclusion of a total of seven questions in the subsequent analysis. These results demonstrated good discriminant validity for the modified scale.
Table 2 shows the results of the validity and reliability analyses. CFA using AMOS was conducted to assess the reliability and validity of the constructs. The analysis yielded appropriate results based on established criteria [62,63]: χ2 = 941.73; df = 265; χ2/df = 3.55; CFI = 0.94; GFI = 0.86; AGFI = 0.83; NFI = 0.92; TLI = 0.93; SRMR = 0.05; RMSEA = 0.07. These indices indicated a good fit between the measurement model and the data, meeting the recommended criteria [62].
Following the guidelines provided by Hair et al. [64], factor loadings equal to or above 0.5 were considered preferable. The Cronbach’s alpha values ranged from 0.87 to 0.96, all surpassing the recommended threshold of 0.7, indicating good internal consistency. The values of composite reliability (CR), which is conceptually similar to Cronbach’s alpha, ranged from 0.88 and 0.96, exceeding the acceptable threshold [64]. The average variance extracted (AVE) ranged from 0.53 and 0.88, all above the threshold [65].
The findings of the discriminant validity analysis are presented in Table 3. The squares of correlation coefficients between the constructs were compared to the AVE values, following the approach proposed by Fornell and Larcker [65]. In this study, all the squared correlation coefficient values were smaller than the AVE values, indicating good discriminant validity.
Furthermore, the AVE values (0.53–0.88) of all factors are greater than the square of the correlation coefficient (0.26–0.63). Therefore, the measurement model of this study is considered to be of discriminant validity. The confirmation of discriminant validity is evident as the square roots of AVE for the majority of constructs surpassed their correlation coefficients with other constructs [62].

4.2. Results of Hypotheses

This section presents the results of hypothesis testing for the proposed main effects (H1–3), mediating effects (H4), and moderating effects (H5). The fit indices of the structural model were deemed acceptable based on the established criteria (χ2 = 544.43; df = 131; χ2/df = 4.16; CFI = 0.95; GFI = 0.88; AGFI = 0.85; NFI = 0.94; TLI = 0.94; SRMR = 0.06; RMSEA = 0.08.) (Kline, 2011).
Table 4 displays the results of SEM, encompassing both direct (H1–H3) and indirect paths (H4). COVID-19 perceived risk (severity) (β = −0.19; p < 0.001) and COVID-19 perceived risk (susceptibility) (β = −0.02; p < 0.05) were found to have a positive and significant relationship with travel risk perceptions. Additionally, travel risk perceptions (β = −0.27; p < 0.001) exhibited a negative and significant relationship with hotel staying intention, thereby supporting H1, H2, and H3.
In the examination of the mediating effects suggested in H4, three distinct methods were employed: the product of coefficients approach, which assessed the effect of the intervening variable [66]; bootstrapping, which provided estimations for the standard errors of both direct and indirect consequences [66]; and PRODCLIN2, which conducted individual tests for mediation effects [66]. Consistent findings were obtained across these methods, indicating that perceived risk of COVID-19 (severity) (β = −0.19, p < 0.001) and perceived risk of COVID-19 (susceptibility) (β = −0.02, p < 0.05) had significant indirect relationships with hotel staying intention. In the mediation tests, the direct effects of perceived risk of COVID-19 (severity) and perceived risk of COVID-19 (susceptibility) on hotel staying intention were also significant, indicating partial mediation for H4.1 and H4.2. The results are presented in Table 4. The bias-corrected and PRODCLIN2 testing results, within a 95% confidence interval (CI), indicated that travel risk perceptions had a mediating effect in the research model.

4.3. Moderating Effect of Hypothesis 5

The moderating effects of hotel hygiene and safety practices were investigated through a multi-group analysis, carried out with the aid of AMOS 22.0 software. First, the average scores for hotel hygiene and safety practices were calculated for the top 27% and bottom 27% of the sample. Participants were divided into two groups based on their scores. Those with scores exceeding the average of the top 27% were classified into the high-score group, whereas those with scores falling below the average of the bottom 27% were assigned to the low-score group. This resulted in a low score group of 130 samples and a high score group of 144 samples, which were used for the invariance and moderation tests.
Table 5 displays the findings from both the invariance test and the moderation test. Significant differences were found in the effects from travel risk perceptions (ΔX2 (1) = 6.998, p < 0.01), indicating that the effects varied across the groups. However, no difference was found in the direct effect. Specifically, regarding hotel hygiene and safety practices, it was found that the negative relationship between travel risk perceptions and hotel staying intention was weakened when hygiene and safety practices were high (standardized coefficients: −0.07; p < 0.01) compared to when they were low (standardized coefficients: −0.28; p < 0.01). Therefore, H5, which posited that hotel hygiene and safety practices would moderate the relationship between travel risk perceptions and hotel staying intention, was supported.

5. Discussion

The COVID-19 pandemic has caused significant economic and social disasters worldwide, particularly within the field of hospitality and tourism. The study aims to examine the mechanism between perceived risk of COVID-19 and hotel staying intention based on the theoretical support of UA theory [15] and PMT [9]. Antecedents of hotel staying intention have become an emergent issue in tourism research during the COVID-19 pandemic, particularly pertaining to mental processes and the interactions of moderating variables. A more detailed insight into this subject may improve comprehension regarding how individuals perceive the risk of COVID-19 in relation to staying in hotels. To fulfill the objectives of this research, data were collected from tourists to investigate an overarching framework with nationwide level 3 alert for COVID-19 in Taiwan in 2021. The current findings are consistent with those of previous studies.
Despite the abundance of research on the direct effects of perceived risk on intention [19,67,68], the mediating mechanisms between perceived risk of COVID-19 and hotel staying intention remain underexplored. Investigating the role of travel risk perceptions as a mediator is vital for comprehending how the perceived risk of COVID-19 influences the intention to stay in a hotel. This study revealed that perceived risk of COVID-19 indirectly affected hotel staying intention through travel risk perceptions. That is, tourists’ perceived risk of COVID-19 appears to be more congruent with their travel risk perceptions. In terms of risk perceptions, previous studies have applied UA theory [15] in the dining setting [54] and the tourism industry [69]. This study applied UA theory [15] to both perceived risk of COVID-19 and travel risk perceptions and demonstrated the predictive power of hotel staying intention. Regarding travel risk perceptions, travelers may be much more involved in their hotel decisions during travel, considering anxiety, perceived risk, and uncertainty [16]. The testing of indirect effects of travel risk perceptions between perceived risk of COVID-19 and hotel staying intention suggested that travel risk perceptions have an indirect effect (see H4).
The main objective of this study was to investigate the potential moderating roles of hotel hygiene and safety practices on the relationship between travel risk perceptions and hotel staying intention. The findings indicate that hotel hygiene and safety practices did moderate the relationship between travel risk perceptions and hotel staying intention (see H5), which is in line with previous research in the hotel industry [70]. To control infectious diseases, hotels can implement health screening of staff, emergency standard operation procedures (SOP), safety and well-being of employees, as well as maintaining rigorous cleanliness and disinfection practices [71,72]. Restaurants can enhance their ability to withstand the challenges of the COVID-19 pandemic by adhering to hygiene regulations and implementing food safety practice [73].
Based on Rogers’ (1975) protection motivation theory [9], travelers can protect themselves, manage the hazardous environment, confirm the probability of the event’s occurrence, and respond with a protective efficacy response. The cognitive assessment process that would affect their attitudes and intentions is related to travelers’ protection motivation. In other words, hotels must ensure that both employees and guests are supplied with necessary personal protective equipment, adhere to proper hygiene standards, and carry out comprehensive cleaning procedures [74,75]. When hotel hygiene and safety practices align closely with travelers’ travel risk perceptions, it can reduce the gap in risk perceptions during travel. Consequently, in contrast to other offerings within the hotel industry, the subtle differences in travel experiences related to hotel hygiene and safety practices may elucidate these consistent findings. Tourists may experience an enhanced sense of security when engaging in activities separate from traditional hotel hygiene and safety protocols. Furthermore, travel risk perceptions, which can induce anxiety, perceived danger, and uncertainty, could be lessened or altogether eliminated. The robust connection between travel risk perceptions and intentions to stay in hotels thus appears to be contingent on the implementation and perception of specific hotel hygiene and safety measures. Specifically, the alignment of hotel hygiene and safety practices with travelers’ perceptions of travel risk leads to a diminished gap in risk perceptions during travel. Findings from the multi-group analysis in this study did uncover distinct influences concerning the role of hotel hygiene and safety practices as a moderating variable in the nexus between travel risk perceptions and intentions to stay at hotels. The intention of travelers to stay in hotels, as confirmed in this study, demonstrates the critical role of hotel hygiene and safety procedures in determining their views on travel risk.

5.1. Theoretical and Practical Implications

Our empirical study was conducted within the context of a nationwide level-three alert for COVID-19 in Taiwan during the year 2021 to investigate the impact of hotel hygiene and safety practices on travelers’ hotel staying intentions. Grounded in uncertainty avoidance theory [15] and protection motivation theory [9], this study contributes to the hospitality management literature by highlighting how travelers’ hotel-staying intentions may be influenced by hotel operations, practices, and initiatives during the COVID-19 pandemic. Specifically, it adds to the body of work exploring hotel hygiene and safety practices during this critical period. Accordingly, this contextual study provides valuable insights into traveler’s intention during the COVID-19 pandemic in Taiwan, which can serve as a useful guideline for the hotel industry in other nations facing similar situations. Furthermore, the study advances the field of tourism and hospitality crisis literature in several significant ways.
Firstly, it offers a contextual analysis, supplying key source information regarding travelers’ intentions and behaviors during Taiwan’s Nationwide Level 3 alert for COVID-19. Such information serves as an essential guideline for the hotel industry in other nations, suggesting initiatives that could be adopted both during and following the COVID-19 pandemic, as well as for other health crises that may arise in the future. Secondly, the study elucidates the underlying mechanisms linking travelers’ perceived risk of COVID-19 to their intentions to stay in hotels. This adds a new dimension to our understanding of consumer behavior in the context of health emergencies. Thirdly, by examining 500 responses obtained from participants who completed an online survey, the study offers valuable insights into the perceptions and practices of hotels in response to the pandemic. These findings can guide hotels in enhancing their safety and hygiene protocols. Fourthly, through rigorous testing of the research model, this investigation provides empirical evidence that enriches our understanding of how hotels can adapt their operations and strategies during a crisis. This substantial contribution can serve as a roadmap for other hotels seeking to navigate similar challenges in the future.
As the world progresses towards a new normalcy, the emerging economic recovery and return of travelers appear to herald a dawn for the tourism industry. However, the exact timing for a return to the pre-pandemic prosperity of 2019 remains uncertain. Findings from this study suggest that a prerequisite to enhancing guests’ willingness to stay in hotels or reducing their perception of travel risks is to mitigate public perception of COVID-19 risks. Thus, the study calls for a nationwide approach, specifically recommending governments to promptly update national epidemic prevention and travel and hotel-related policies during outbreaks. This ensures that all relevant bodies and tourism and hotel operators can effectively safeguard the health and safety of their customers, offering a reassuring consumption experience [1]. In conjunction, hotel managers should comply with government interventions, mitigating the impacts of pandemics (such as COVID-19) on the travel and hotel industry through initiatives, policies, and practices concerning finance, health and hygiene, labor and training, marketing, and domestic tourism targeted at customers [76]. To reduce travel risk perceptions and motivate travelers to change their intentions, hotel managers should utilize hotel hygiene and safety practices strategically. Moreover, hotel personnel must establish their capabilities to cope with COVID-19 and future crises, increasing the frequency of cleaning and disinfection, thereby enhancing customer safety. Specifically, hotel managers should strategically reduce travel risk perception or enhance the attractiveness of hotel accommodation. For worried travelers, hotel managers should offer emergency standard operation procedures (SOP) and guide travelers to stay safely in the hotel. To improve tourists’ confidence in the hotel organization and reduce the influence of anxiety on booking intentions, promotional efforts must emphasize hygiene and cleanliness [4]. In other words, this research discovered how individuals view the risk associated with COVID-19 and travel risk perceptions have strong direct effects on hotel staying intention. Hence, hotel managers should primarily focus on strategies and programs related to hotel hygiene and safety practices.
To strengthen travelers’ intention to avoid uncertainty, hotel managers can strategically reduce perceived risks related to safety prevention. Hotels can enhance their image of hygiene and safety by utilizing visual elements such as signs, posters, or videos that communicate their commitment to cleanliness and safe practices. Hotel hygiene and safety practices could also incorporate suggestions to keep the tables and chairs in restaurants and public areas at a social distance to prevent aerosol infections [76]. Hotels aiming to create a safe environment should seek professional collaboration, establishing effective cleaning and hygiene measures, such as utilizing specialized air purifiers or vacuum cleaners equipped with HEPA filters or ozone machines. Amid the pandemic, hotel managers may consider employing technological support, such as robots or artificial intelligence, to provide contactless services or regular assistance in cleaning and hygiene execution [29]. Hotel managers should consider other feasible approaches to predict hotel staying intention by measuring perceived risk beyond COVID-19, such as travelers’ feedback and suggestions. This will enable staff to focus on creating a warmer, more affable atmosphere to meet customers’ post-pandemic needs for comfort, joy, and experiential services [38]. All preventative measures should be emphasized and highlighted to effectively encourage potential travelers and evoke their intentions to stay in hotels.

5.2. Limitations and Suggestions for Future Studies

While this study offers valuable implications, it also has limitations that are worth considering for future research. Firstly, this study did not differentiate the hotel segment levels for the participants. Future research could explore this issue by classifying hotels according to various types of accommodations like chain versus independent establishments, small and medium-sized locations, family-owned and operated businesses, as well as differences between upscale and budget-friendly options [69,77]. Therefore, future studies should investigate the impact of different hotel segments on travelers’ staying intentions and behaviors. Secondly, the research for this study was gathered at one specific point in time using one survey, rather than over an extended period or through multiple methods. To reduce common method biases and gain insights into travelers’ staying intentions during the COVID-19 pandemic, subsequent research could benefit from gathering data over an extended period or from various origins, allowing for a more comprehensive and dynamic understanding of the subject [78]. Experimental design is another approach that can examine the causal relationships between antecedents and travelers’ intentions. Additionally, tourists’ travel behavior may change with the different levels of the outbreak pandemic. Thus, future studies could ask about participants’ longitudinal actual travel behavior [79] via the integration of online surveys and smartphone apps [80,81]. Thirdly, this study only examined travelers’ hotel staying intentions. However, during the COVID-19 pandemic, travelers have different purposes such as leisure/holiday or shopping purposes, education/conference, healthcare, business, and other purposes [19]. Future studies could examine the differences between potential intentions and travel outside in person at the hotel [19]. Fourthly, technology, such as AI temperature scans or self-service options, plays a crucial role in the hygiene perspective of the hospitality industry [4,14]. Therefore, future studies could integrate the perspective of these technologies into the framework to provide new insights into their influence on travelers’ hotel staying intentions. Fifth, this research model is based on the uncertainty avoidance theory and protection motivation theory. Future studies could consider other frameworks to enrich the theoretical understanding of the complex mechanisms of travelers’ hotel staying intention.

Author Contributions

Conceptualization, methodology writing—review and editing and Supervision, C.-C.T.; formal analysis, P.-Y.S.; writing—original draft preparation, Y.-J.C.; Visualization and writing—review and editing, W.-S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Science and Technology Council (NSTC), Taiwan. Project number: MOST110-2511-H-030-002-MY3.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hadi, D.M.; Naeem, M.A.; Karim, S. Impact of COVID-19 on the connectedness across global hospitality stocks. Int. J. Hosp. Manag. 2022, 104, 103243. [Google Scholar] [CrossRef]
  2. Fan, X.; Lu, J.; Qiu, M.; Xiao, X. Changes in travel behaviors and intentions during the COVID-19 pandemic and recovery period: A case study of China. J. Outdoor Recreat. Tour. 2022; in press. [Google Scholar] [CrossRef]
  3. Tourism Bureau, Ministry of Transportation and Communications. The Entry and Exit Statistics. Available online: https://admin.taiwan.net.tw/fapi/AttFile?type=AttFile&id=24758 (accessed on 15 November 2022).
  4. Apaolaza, V.; Paredes, M.R.; Hartmann, P.; García-merino, J.D.; Marcos, A. The effect of threat and fear of COVID-19 on booking intentions of full board hotels: The roles of perceived coping efficacy and present-hedonism orientation. Int. J. Hosp. Manag. 2022, 105, 103255. [Google Scholar] [CrossRef]
  5. Zheng, D.; Luo, Q.; Ritchie, B.W. Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear. Tour. Manag. 2021, 83, 104261. [Google Scholar] [CrossRef]
  6. Abbas, M.; Malik, M.; Sarwat, N. Consequences of job insecurity for hospitality workers amid COVID-19 pandemic: Does social support help? J. Hosp. Mark. Manag. 2021, 30, 957–981. [Google Scholar] [CrossRef]
  7. Garrido-Moreno, A.; Garcia-Morales, V.J.; Martin-Rojas, R. Going beyond the curve: Strategic measures to recover hotel activity in times of COVID-19. Int. J. Hosp. Manag. 2021, 96, 102928. [Google Scholar] [CrossRef]
  8. Maddux, J.E.; Rogers, R.W. Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. J. Exp. Soc. Psychol. 1983, 19, 469–479. [Google Scholar] [CrossRef]
  9. Rogers, R.W. A protection motivation theory of fear appeals and attitude change. J. Psychol. Interdiscip. Appl. 1975, 91, 93–114. [Google Scholar] [CrossRef] [PubMed]
  10. Fisher, J.J.; Almanza, B.A.; Behnke, C.; Nelson, D.C.; Neal, J. Norovirus on cruise ships: Motivation for handwashing? Int. J. Hosp. Manag. 2018, 75, 10–17. [Google Scholar] [CrossRef]
  11. Wang, J.; Liu-Lastres, B.; Ritchie, B.W.; Mills, D.J. Travellers’ self-protections against health risks: An application of the full Protection Motivation Theory. Ann. Tour. Res. 2019, 78, 102743. [Google Scholar] [CrossRef]
  12. Wong, J.Y.; Yeh, C. Tourist hesitation in destination decision making. Ann. Tour. Res. 2009, 36, 6–23. [Google Scholar] [CrossRef]
  13. Meng, Y.; Khan, A.; Bibi, S.; Zhao, W.; Chen, W.; Lee, Y. The effects of COVID-19 risk perception on travel intention: Evidence from Chinese travelers. Front. Psychol. 2021, 12, 655860. [Google Scholar] [CrossRef]
  14. Pillai, S.G.; Haldorai, K.; Seo, W.S.; Kim, W.G. COVID-19 and hospitality 5.0: Redefining hospitality operations. Int. J. Hosp. Manag. 2021, 94, 102869. [Google Scholar] [CrossRef]
  15. Hofstede, G. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations, 2nd ed.; Sage: Thousand Oaks, CA, USA, 2001. [Google Scholar]
  16. Yang, S.; Isa, S.M.; Ramayah, T. Does uncertainty avoidance moderate the effect of self-congruity on revisit intention? A two-city (Auckland and Glasgow) investigation. J. Destin. Mark. Manag. 2022, 24, 100703. [Google Scholar] [CrossRef]
  17. Golets, A.; Farias, J.; Pilati, R.; Helena, C. COVID-19 pandemic and tourism: The impact of health risk perception and intolerance of uncertainty on travel intentions. Curr. Psychol. 2021, 1, 13–14. [Google Scholar] [CrossRef]
  18. Yoo, J.W.; Park, J.; Lee, J.H.; Park, H. Recovering from the COVID-19 shock: The role of risk perception and perceived effectiveness of protective measures on travel intention during the pandemic. Serv. Bus. 2022, 16, 557–580. [Google Scholar] [CrossRef]
  19. Rahman, M.K.; Gazi, M.A.I.; Bhuiyan, M.A.; Rahaman, M.A. Effect of Covid-19 pandemic on tourist travel risk and management perceptions. PLoS ONE 2021, 16, e0256486. [Google Scholar] [CrossRef]
  20. Mao, C.; Ding, C.; Lee, H. Post-SARS tourist arrival recovery patterns: An analysis based on a catastrophe theory. Tour. Manag. 2010, 31, 855–861. [Google Scholar] [CrossRef]
  21. Wen, J.; Huang, S. Chinese tourists visiting volatile destinations: Integrating cultural values into motivation-based segmentation. Int. J. Tour. Res. 2019, 15, 520–540. [Google Scholar] [CrossRef]
  22. AHLA, Leisure and Hospitality Industry Proves Hardest Hit by COVID-19. Available online: https://www.ahla.com/covid-19s-impact-hotel-industry (accessed on 23 June 2022).
  23. Floyd, D.L.; Prentice-Dunn, S.; Rogers, R.W. A meta-analysis of research on protection motivation theory. J. Appl. Soc. Psychol. 2000, 30, 407–429. [Google Scholar] [CrossRef]
  24. Ryu, K.; Jarumaneerat, T.; Promsivapallop, P.; Kim, M. What influences restaurant dining out and diners’ self-protective intention during the COVID-19 pandemic: Applying the Protection Motivation Theory. Int. J. Hosp. Manag. 2023, 109, 103400. [Google Scholar] [CrossRef] [PubMed]
  25. Pine, R.; McKercher, B. The impact of SARS on Hong Kong’s tourism industry. Int. J. Contemp. Hosp. Manag. 2004, 16, 139–143. [Google Scholar] [CrossRef]
  26. Schroeder, A.; Pennington-Gray, L.; Kaplanidou, K.; Zhan, F. Destination risk perceptions among US residents for London as the host city of the 2012 Summer Olympic Games. Tour. Manag. 2013, 38, 107–119. [Google Scholar] [CrossRef]
  27. Yavas, U. Correlates of vacation travel: Some impirical evidence. J. Serv. Mark. 1990, 5, 3–18. [Google Scholar] [CrossRef]
  28. Neuburger, L.; Egger, R. Travel risk perception and travel behavior during the Covid-19 pandemic 2020: A case study of the DACH region. Curr. Issues Tour. 2021, 24, 1003–1016. [Google Scholar] [CrossRef]
  29. Park, E.; Kim, W.H.; Kim, S.B. How does COVID-19 differ from previous crises? A comparative study of health-related crisis research in the tourism and hospitality context. Int. J. Hosp. Manag. 2022, 103, 103199. [Google Scholar] [CrossRef]
  30. Brewer, N.T.; Fazekas, K.I. Predictors of HPV vaccine acceptability: A theory-informed, systematic review. Prev. Med. 2007, 45, 107–114. [Google Scholar] [CrossRef]
  31. Glanz, K.; Rimer, B.K.; Viswanath, K. Health Behavior and Health Education: Theory, Research, and Practice, 4th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
  32. Brewer, N.T.; Chapman, G.B.; Gibbons, F.X.; Gerrard, M.; McCaul, K.D.; Weinstein, N.D. Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination. Health Psychol. 2007, 26, 136. [Google Scholar] [CrossRef]
  33. Nazione, S.; Perrault, E.; Pace, K. Impact of information exposure on perceived risk, efficacy, and preventative behaviors at the beginning of the COVID-19 pandemic in the United States. Health Commun. 2021, 36, 23–31. [Google Scholar] [CrossRef]
  34. Moreira, P. Stealth risks and catastrophic risks: On risk perception and crisis recovery strategies. J. Travel. Tour. Mark. 2008, 23, 15–27. [Google Scholar] [CrossRef]
  35. Sjöberg, L.; Moen, B.E.; Rundmo, T. Explaining risk perception: An evaluation of the psychometric paradigm in risk perception research. Rotunde Publ. Rotunde 2004, 84, 55–76. [Google Scholar]
  36. Weinstein, N.D. The precaution adoption process. Health Psychol. 1988, 7, 355–386. [Google Scholar] [CrossRef] [PubMed]
  37. Boholm, A. Comparative studies of risk perception: A review of twenty years of research. J. Risk Res. 1998, 1, 135–163. [Google Scholar] [CrossRef]
  38. Bonfanti, A.; Vigolo, V.; Yfantidou, G.; Gutuleac, R. Customer experience management strategies in upscale restaurants: Lessons from the Covid-19 pandemic. Int. J. Hosp. Manag. 2023, 109, 103416. [Google Scholar] [CrossRef]
  39. Godovykh, M.; Pizam, A.; Bahja, F. Antecedents and outcomes of health risk perceptions in tourism, following the COVID-19 pandemic. Tour. Rev. 2021, 76, 737–748. [Google Scholar] [CrossRef]
  40. Lu, Q.; Atadil, H.A. Do you dare to travel to China? An examination of China’s destination image amid the COVID-19. Tour. Manag. Perspect. 2021, 40, 100881. [Google Scholar] [CrossRef]
  41. Williams, A.M.; Baláž, V. Tourism, risk tolerance and competences: Travel organization and tourism hazards. Tour. Manag. 2013, 35, 209–221. [Google Scholar] [CrossRef]
  42. Longwoods International. COVID-19 Travel Sentiment Study-Wave 5. 2020. Available online: https://longwoods-intl.com/news-press-release/covid-19-travel-sentiment-study-wave-5 (accessed on 23 June 2022).
  43. Tsaur, S.H.; Tzeng, G.H.; Wang, K.C. Evaluating tourist risks from fuzzy perspectives. Ann. Tour. Res. 1997, 24, 796–812. [Google Scholar]
  44. Ju, Y.; Jang, S.S. The Effect of COVID-19 on hotel booking intentions: Investigating the roles of message appeal type and brand loyalty. Int. J. Hosp. Manag. 2023, 108, 103357. [Google Scholar] [CrossRef]
  45. Chi, C.G.; Ekinci, Y.; Ramkissoon, H.; Thorpe, A. Evolving effects of COVID-19 safety precaution expectations, risk avoidance, and socio-demographics factors on customer hesitation toward patronizing restaurants and hotels. J. Hosp. Mark. Manag. 2022, 31, 396–412. [Google Scholar] [CrossRef]
  46. Prentice-Dunn, S.; Rogers, R.W. Protection motivation theory and preventive health: Beyond the health belief model. Health Educ. Res. 1986, 1, 153–161. [Google Scholar] [CrossRef]
  47. Cahyanto, I.; Wiblishauser, M.; Pennington-Gray, L.; Schroeder, A. The dynamics of travel avoidance: The case of Ebola in the US. Tour. Manag. Perspect. 2016, 20, 195–203. [Google Scholar] [CrossRef] [PubMed]
  48. Leggat, P.A.; Brown, L.H.; Aitken, P.; Speare, R. Level of concern and precaution taking among Australians regarding travel during pandemic (H1N1) 2009: Results from the 2009 Queensland Social Survey. J. Travel. Med. 2010, 17, 291–295. [Google Scholar] [CrossRef]
  49. Ivanova, M.; Ivanov, I.K.; Ivanov, S. Travel behaviour after the pandemic: The case of Bulgaria. Anatolia 2020, 32, 1–11. [Google Scholar] [CrossRef]
  50. Litvin, S.W.; Crotts, J.C.; Hefner, F.L. Cross-cultural tourist behaviour: A replication and extension involving Hofstede’s uncertainty avoidance dimension. Int. J. Tour. Res. 2004, 6, 29–37. [Google Scholar] [CrossRef]
  51. Huang, S.S.; Crotts, J. Relationships between Hofstede’s cultural dimensions and tourist satisfaction: A cross-country cross-sample examination. Tour. Manag. 2019, 72, 232–241. [Google Scholar] [CrossRef]
  52. Jung, J.M.; Kellaris, J.J. Cross-national differences in proneness to scarcity effects: The moderating roles of familiarity, uncertainty avoidance, and need for cognitive closure. Psychol. Mark. 2004, 21, 739–753. [Google Scholar] [CrossRef]
  53. Qian, J.; Law, R.; Wei, J. Effect of cultural distance on tourism: A study of pleasure visitors in Hong Kong. J. Qual. Assur. Hosp. Tour. 2018, 19, 269–284. [Google Scholar] [CrossRef]
  54. Seo, S.; Kim, K.; Jang, J. Uncertainty avoidance as a moderator for influences on foreign resident dining out behaviors. Int. J. Contemp. Hosp. Manag. 2018, 30, 900–918. [Google Scholar] [CrossRef]
  55. Srite, M.; Karahanna, E.; Evaristo, J.R. Levels of culture and individual behaviour: An integrative perspective. Inf. Manag. 2006, 5, 30–50. [Google Scholar]
  56. Hao, F.; Xiao, Q.; Chon, K. COVID-19 and China’s hotel industry: Impacts, a disaster management framework, and post-pandemic agenda. Int. J. Hosp. Manag. 2020, 90, 102636. [Google Scholar] [CrossRef] [PubMed]
  57. Jiang, Y.; Wen, J. Effects of COVID-19 on hotel marketing and management: A perspective article. Int. J. Contemp. Hosp. Manag. 2020, 32, 2563–2573. [Google Scholar] [CrossRef]
  58. Yu, J.; Seo, J.; Hyun, S.S. Perceived hygiene attributes in the hotel industry: Customer retention amid the COVID-19 crisis. Int. J. Hosp. Manag. 2021, 93, 102768. [Google Scholar] [CrossRef]
  59. Tussyadiah, I. Factors of satisfaction and intention to use peer-to-peer accommodation. Int. J. Hosp. Manag. 2016, 55, 70–80. [Google Scholar] [CrossRef]
  60. Tehseen, S.; Ramayah, T.; Sajilan, S. Testing and controlling for common method variance: A review of available methods. Manag. Sci. 2017, 4, 142–168. [Google Scholar] [CrossRef]
  61. Kaiser, H.F. An index of factorial simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
  62. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Pearson: London, UK; Prentice Hall: Upper Saddle River, NJ, USA, 2019. [Google Scholar]
  63. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model 1999, 6, 1–55. [Google Scholar] [CrossRef]
  64. Hair, J.; Anderson, R.; Tatham, R.; Black, W. Multivariate Data Analysis, 5th ed.; Pearson: London, UK; Prentice Hall: Upper Saddle River, NJ, USA, 1998. [Google Scholar]
  65. Fornell, C.; Larcker, D.F. Evaluating structural models with unobservable variables and measurement error. J. Mark. Res. 1981, 28, 39–50. [Google Scholar] [CrossRef]
  66. MacKinnon, D.P.; Lockwood, C.M.; Hoffman, J.M.; West, S.G.; Sheets, V. A comparison of methods to test mediation and other intervening variable effects. Psychol. Methods 2002, 7, 83–104. [Google Scholar] [CrossRef]
  67. Falahuddin, A.F.; Tergu, C.T.; Brollo, R.; Nanda, R.O. Post COVID-19 Pandemic international travel: Does risk perception and stress-level affect future travel intention? J. Ilmu Sosial. Ilmu Politik. 2020, 24, 1–14. [Google Scholar] [CrossRef]
  68. Yang, Y.; Zhang, H.; Chen, X. Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Ann. Tour. Res. 2020, 83, 102913. [Google Scholar] [CrossRef] [PubMed]
  69. De La Hoz-Correa, A.; Muñoz-Leiva, F. The role of information sources and image on the intention to visit a medical tourism destination: A cross-cultural analysis. J. Travel. Tour. Mark. 2019, 36, 204–219. [Google Scholar] [CrossRef]
  70. Salem, I.E.; Elkhwesky, Z.; Ramkissoon, H. A content analysis for government’s and hotels’ response to COVID-19 pandemic in Egypt. J. Hosp. Tour. Res. 2022, 22, 42–59. [Google Scholar] [CrossRef]
  71. Henderson, J.C.; Ng, A. Responding to crisis: Severe acute respiratory syndrome (SARS) and hotels in Singapore. Int. J. Tour. Res. 2004, 6, 411–419. [Google Scholar] [CrossRef]
  72. Hung, K.K.; Mark, C.K.; Yeung, M.P.; Chan, E.; Graham, C. The role of the hotel industry in the response to emerging epidemics: A case study of SARS in 2003 and H1N1 swine flu in 2009 in Hong Kong. Glob. Health 2018, 14, 117–123. [Google Scholar] [CrossRef]
  73. De Freitas, R.S.G.; Stedefeldt, E. COVID-19 pandemic underlines the need to build resilience in commercial restaurants’ food safety. Food Res. Int. 2020, 136, 109472. [Google Scholar] [CrossRef]
  74. Hoefer, A.; Pampaka, D.; Wagner, E.R.; Herrera, A.A.; Alonso, E.G.; López-Perea, N.; Portero, R.C.; Herrera-León, L.; Herrera-León, S.; Gallo, D.N. Management of a COVID-19 outbreak in a hotel in Tenerife, Spain. Int. J. Infect. Dis. 2020, 96, 384–386. [Google Scholar] [CrossRef]
  75. Rosemberg, M.A.S. Health and safety considerations for hotel cleaners during Covid-19. Occup. Med. 2020, 70, 382–383. [Google Scholar] [CrossRef]
  76. Duan, L.; Zhu, G. Psychological interventions for people affected by the COVID-19 epidemic. Lancet Psychiatry 2020, 7, 300–302. [Google Scholar] [CrossRef]
  77. Le, D.; Phi, G. Strategic responses of the hotel sector to COVID-19: Toward a refined pandemic crisis management framework. Int. J. Hosp. Manag. 2020, 94, 102808. [Google Scholar] [CrossRef]
  78. Wen, J.; Kozak, M.; Yang, S.; Liu, F. COVID-19: Potential effects on Chinese citizens’ lifestyle and travel. Tour. Rev. 2021, 76, 74–87. [Google Scholar] [CrossRef]
  79. Hu, X.; Yan, H.; Casey, T.; Wu, C.H. Creating a safe haven during the crisis: How organizations can achieve deep compliance with COVID-19 safety measures in the hospitality industry. Int. J. Hosp. Manag. 2021, 92, 102662. [Google Scholar] [CrossRef] [PubMed]
  80. Lin, P.M.; Peng, K.L.; Ren, L.; Lin, C.W. Hospitality co-creation with mobility impaired people. Int. J. Hosp. Manag. 2019, 77, 492–503. [Google Scholar] [CrossRef]
  81. Sobaih, A.E.; Elshaer, I.; Hasanein, A.M.; Abdelaziz, A.S. Responses to COVID-19: The role of performance in the relationship between small hospitality enterprises’ resilience and sustainable tourism development. Int. J. Hosp. Manag. 2021, 94, 102824. [Google Scholar] [CrossRef]
Sustainability 15 13048 g001
Figure 1. The hypothesized model.
Figure 1. The hypothesized model.
Sustainability 15 13048 g001
Table 1. Demographic profiles of participants.
Table 1. Demographic profiles of participants.
VariablesFrequency%VariablesFrequency%
1. Gender 7. Location
Male24749.4Northern Taiwan22745.4
Female25350.6Central Taiwan12224.4
Southern Taiwan13627.2
2. Age Eastern and Outlying Islands153.0
18–2020.4
21–3010020.08. Frequency of staying out
31–409218.41–2 times193.8
41–509719.43–4 times6713.4
51–6018136.25–6 times14328.6
61–70224.47–8 times16633.2
71–8061.29–10 times489.6
11 times or more214.2
3. Education
Under junior high school91.89. County of residence
High school499.8Taipei City7615.2
College or University34869.6New Taipei City10521.0
Above graduate9418.8Keelung City51.0
Yilan County61.2
4. Marriage Taoyuan City265.2
Unmarried21843.6Hsinchu City51.0
Married no kid418.2Hsinchu County40.8
Married with kids22545.0Miaoli County71.4
Divorced or widowed163.2Taichung City8016.0
Nantou County40.8
5. Occupation Changhua County163.2
Student193.8Yunlin County153.0
Government agencies6713.4Chiayi City81.6
Manufacture14328.6Tainan City91.8
Service16633.2Kaohsiung City397.8
Freelance489.6Pingtung County7414.8
Homemaker214.2Taitung County61.2
Retired112.2Kinmen County61.2
Others255.0Lienchiang County61.2
6. Monthly household income (NTD)
$20,000 or less5712.5
$20,001–$40,00012727.8
$40,001–$60,00011224.5
$60,001–$80,0006714.7
$80,001–$100,000378.1
More than $100,001153.3
USD 1 is about $30 NTD (New Taiwan Dollar).
Table 2. Results of the confirmatory factor analysis.
Table 2. Results of the confirmatory factor analysis.
Scales and ItemsLoadingst-ValueαCRAVE
Perceived risk of COVID-19 (severity) (PCe) 0.940.940.81
PCe1Coronavirus (COVID-19) is a serious risk.0.89
PCe2Coronavirus (COVID-19) is a serious threat.0.9433.11
PCe3Coronavirus (COVID-19) is harmful.0.9231.60
PCe4Coronavirus (COVID-19) is deadly.0.8526.60
Perceived risk of COVID-19 (susceptibility) (PCu) 0.960.960.85
PCu1I am at risk for getting with coronavirus (COVID-19)0.92
PCu2I am susceptible to getting with coronavirus (COVID-19)0.9439.06
PCu3I may possibly get with coronavirus (COVID-19)0.9439.13
PCu4I may be diagnosed with coronavirus (COVID-19)0.8932.68
Travel risk perceptions (TR) 0.870.880.53
TR1Tourism is mainly responsible for the spread of the coronavirus0.50
TR2Tourism will be massively affected by coronavirus0.6510.18
TR3Staying in a hotel is a risk, as there are many people from different countries, who could carry the virus0.8011.22
TR4I fear that the virus will be carried by tourists to my near surroundings0.8011.26
TR5Traveling should be prohibited to avoid a wider spread of the virus0.7811.142
TR6Currently, it is irresponsible to be sent on business trips to countries with a high number of cases0.7310.77
TR7Currently, it is irresponsible to travel to destinations with cases of coronavirus0.7711.02
Hotel hygiene and safety practices (HH) 0.930.940.69
HH1This hotel cleans guest rooms and public areas (i.e., toilets and washroom floors) using disinfectants.0.90
HH2This hotel washes its laundry using antibacterial products and practices (i.e., towels, bed covers, blankets, and pillows).0.8325.68
HH3This hotel cleans in-room facilities (i.e., desks, chairs, sofas, beds, mirrors, and closets) using disinfectants.0.9233.04
HH4The rooms in this hotel are equipped with special air cleaners to prevent aerosol infections.0.7923.43
HH5This hotel conducts hot water sterilization (heating for more than 30 s in boiling water) of utensils used in its restaurants (i.e., cutlery, crockery, and cutting boards).0.8527.41
HH6This hotel cleans restaurant facilities (i.e., tables and chairs) using disinfectants0.9131.55
HH7This hotel keeps the tables and chairs in restaurants and public areas at a social distance to avoid group gatherings0.5513.49
Hotel staying intentions (HSI) 0.960.960.88
HSI1I expect to continue using this accommodation in the future.0.91
HSI2I can see myself using this accommodation in the future0.9638.38
HSI3It is likely that I will use this accommodation in the future.0.9638.59
Note: Loading = Factor Loading; α = Cronbach’s α; CR = Composite Reliability; AVE = Average Variance Extracted. All t-values were significant at the p < 0.001 level. Goodness of fit indices: χ2 = 941.73; df = 265; χ2/df = 3.55; CFI = 0.94; GFI = 0.86; AGFI = 0.83; NFI = 0.92; TLI = 0.93; SRMR = 0.05; RMSEA = 0.07.
Table 3. Means, standard deviations, and correlations.
Table 3. Means, standard deviations, and correlations.
VariablesMeanSD12345
1. Perceived risk of COVID-19 (risk)1.700.78(0.81)
2. Perceived risk of COVID-19 (threat)3.361.350.26 **(0.85)
3. Travel risk perceptions1.960.760.63 **0.25 **(0.53)
4. Hotel hygiene and safety practices5.640.87−0.33 **−0.16 **−0.37 **(0.69)
5. Hotel staying intensions5.450.97−0.28 **−0.16 **−0.24 **0.47 **(0.88)
** p < 0.01. SD = Standard Deviation. The square roots of AVE for discriminant validity are in parentheses along the diagonal.
Table 4. Results of effect coefficients and significance levels.
Table 4. Results of effect coefficients and significance levels.
EffectPoint
Estimation		SignificanceHypotheses
Testing Results
p-ValueBootstrappingPRODCLIN2
Bias-Corrected
95% CI		95% CI
LowerUpperLower Upper
Direct effect
PRC (severity)→TRP0.680.001 ***0.6040.759---H1 supported
PRC (susceptibility)→TRP0.080.050 *0.0000.165---H2 supported
TRP→HSI−0.270.001 ***−0.356−0.191---H3 supported
Indirect effect
PC (severity)→TRP→HSI−0.190.001 ***−0.225−0.124−0.336−0.148H4-1 supported (partial mediation)
PC (susceptibility)→TRP→HSI−0.020.041 *−0.050−0.001−0.033−0.001H4-2 supported (partial mediation)
*** p < 0.001, * p < 0.05. PC (severity): Perceived Risk of COVID-19 (severity); PC (risk): Perceived Risk of COVID-19 (susceptibility); TRP: Travel risk perceptions; HIS: Hotel Staying Intentions. Goodness of fit indices: χ2 = 544.43; df = 131; χ2/df = 4.16; CFI = 0.95; GFI = 0.88; AGFI = 0.85; NFI = 0.94; TLI = 0.94; SRMR = 0.06; RMSEA = 0.08.
Table 5. Summary of invariance test and moderation test.
Table 5. Summary of invariance test and moderation test.
PathsLow HHSP
(N = 130)		High HHSP
(N = 144)		Unconstrained Model
Chi-Square		Constrained Model
Chi-Square		ΔX2
(Δdf = 1)
Standardized CoefficientsZ-ValueStandardized CoefficientsZ-Value
H5: Travel risk perceptions → Hotel staying intentions−0.28−2.99 **−0.07−2.99 **571.080
(df = 116)		578.078
(df = 115)		6.998 **
(p = 0.01)
Moderator: Hotel hygiene and safety practices (HHSP). ** p < 0.01.
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Teng, C.-C.; Cheng, Y.-J.; Yen, W.-S.; Shih, P.-Y. COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator. Sustainability 2023, 15, 13048. https://doi.org/10.3390/su151713048

AMA Style

Teng C-C, Cheng Y-J, Yen W-S, Shih P-Y. COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator. Sustainability. 2023; 15(17):13048. https://doi.org/10.3390/su151713048

Chicago/Turabian Style

Teng, Chih-Ching, Ya-Jen Cheng, Wen-Shen Yen, and Ping-Yu Shih. 2023. "COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator" Sustainability 15, no. 17: 13048. https://doi.org/10.3390/su151713048

APA Style

Teng, C. -C., Cheng, Y. -J., Yen, W. -S., & Shih, P. -Y. (2023). COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator. Sustainability, 15(17), 13048. https://doi.org/10.3390/su151713048

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