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. 2022 Dec 16:14673584221119379. Online ahead of print. doi: 10.1177/14673584221119379

Understanding Tourists’ Attitude Toward Online Travel Health Information During and Post-COVID-19: A Health Belief Model Application

Arej Alhemimah 1,✉
PMCID: PMC9760503  PMID: 40479417

Abstract

Due to people’s anxieties about COVID-19, it may take years before international tourism returns to pre-pandemic levels. Thus, it is crucial to understand how tourists’ health beliefs influence their travel decision-making processes during and after the SARS-COV2 pandemic, and to develop new strategies to support and meet tourists’ current needs and concerns. The current study employs a Health Belief Model (HBM) perspective to examine the influence on tourists’ health risk prevention – and subsequently on their travel intention – of reading travel health information online, while considering tourists’ perceptions of threat susceptibility and severity, and usefulness of travel health information. As risk perception is influenced by individual differences such as gender and previous experience, the study model includes the demographic factors of age, gender, and health status. The model was tested using a survey questionnaire completed by 261 respondents in Saudi Arabia who were considering travelling abroad for tourism. Results were analyzed using PLS-SEM. The study found that perceived susceptibility and perceived usefulness each significantly and positively influenced the perception of importance of reading health information, and the perception of importance of reading travel health information online significantly and positively influenced travel intention. The discussion includes additional findings as well as implications for industry practice and policy regarding online pandemic-related information, in order to improve protection efficacy and enhance information content and style to adequately serve the needs of tourists from a health belief perspective.

Keywords: Health belief, e-marketing, perceived risk, online information, travel intention

Introduction

As a result of the COVID-19 pandemic, in 2020 international tourism suffered its worst year ever recorded. Travel and tourism decreased by 74% worldwide, and airline passenger traffic dropped by 60% (UNWTO, 2021). This sharp decline has resulted in “an estimated loss of 1.3 trillion USD in global inbound tourism expenditure” compared to 2019 (UNWTO, 2021: 28). As the pandemic recedes, tourism is expected to rebound, but a serious challenge is the imperative to assure that tourists feel safe and ready to resume travel. Due to fears and anxieties about the serious health threat posed by COVID-19, people’s social behaviors have changed and may not return to pre-pandemic norms in the near future: this may be even more true for tourists (Bae and Chang, 2020). In fact, UNWTO has estimated at least 2.5–4 years before international tourism returns to pre-pandemic levels (UNWTO, 2021). Thus, it is crucial to understand how tourists’ health beliefs influence their travel decision-making processes during and after the pandemic, and to develop new strategies to support and meet tourists’ current needs and concerns.

Studies have shown that the decision to visit a destination involves a reasoned cost–benefit analysis based on information taken from various sources (Abubakar and Ilkan, 2016; Chen et al., 2014). Moreover, a 2017 study showed that perceived risk preceded increased user traffic on a travel health website (Petersen et al., 2017). Thus, reading travel health information about COVID-19 on travel websites and from official sources such as government public health and industry platforms may influence travel decision-making (Huang et al., 2020).

Currently, travelers go through multiple stages (e.g. checking updated travel restrictions for travel routes and destinations, pre-travel COVID-19 screening and certificates, etc.), and as tourists mostly travel to seek relaxation, or for pleasurable experiences, the long and complicated travel process has become a challenge for the tourism industry. Therefore, tourism marketers and travel managers are urged to adapt by offering adequate tourism marketing strategies and travel services that suit these evolving requirements. One of the main aspects that tourism marketers and managers should consider is how health risks would influence their customers’ travel health beliefs, and what tourists’ behavior would be to protect themselves.

To date, few studies have considered the influence of reading online information on tourists’ health risk preventive behavior, and subsequently on their travel intention, from a health behavior perspective. The current study employs a Health Belief Model (HBM; Rosenstock, 1974) perspective to examine the influence on tourists’ health risk prevention and subsequently on their travel intention, of reading online travel health information, considering tourists’ perceptions of threat susceptibility and severity as well as barriers, benefits, and usefulness of that information.

Health risks in tourism

Tourism is an intangible yet high-involvement product (Park et al., 2007), and as such presents various sorts of risk to the tourism consumer, which may be financial, psychological, social, and/or physical in nature (Loda et al., 2009). Health risks may be among the most consequential of risks because they can affect a person not only physically, but financially, psychologically, and socially as well (Sturgeon et al., 2016).

It is important to distinguish between actual risk and perceived risk. While actual risk can be empirically measured and shown to be factual, a person’s perception of that risk remains subjective and is a combination of “facts and feelings” (Ropeik, 2011, paragraph 4). For example, people will perceive a risk that seems new and that they know little about to be more worrisome than a risk that they are more familiar with and that they believe they understand (Godovykh et al., 2021), even if the former is a smaller risk to them than the latter. This study is concerned with perceived, rather than actual risk.

Perceived risks to personal health, both during a journey and at the tourism destination, impact tourists’ travel decisions (Huang et al., 2020; Petersen et al., 2017). For example, they will look for ways to reduce risk, or to weigh the perceived risks against the perceived benefits. These decisions, when multiplied over hundreds and thousands of individual tourists, can have a substantial and existential impact on tourism providers and related industries, as has been seen during the recent regional epidemics and global pandemics, such as H1N1, SARS, Ebola, Zika, and of course the current COVID-19 pandemic. For these reasons, tourism research on tourists’ decision-making has often included risk perception (Cahyanto et al., 2016; Godovykh et al., 2021; Huang et al., 2020; Petersen et al., 2017). The way in which beliefs about health risk influence risk prevention behavior is not fully understood (Huang et al., 2020). Protection motivation theory (PMT; Maddux and Rogers, 1983) treats health beliefs related to risk (e.g. perceived susceptibility or severity) as multidimensional variables.

Bae and Chang (2020) employed HBM in their study of perceptions of COVID-19 risks on tourists’ behavioral intentions in South Korea. Recently, the theory of planned behavior (TPB; Ajzen, 1991) has been combined with HBM to describe the associations between health beliefs, attitudes, self-efficacy, and preventive behaviors of tourists (Huang et al., 2020), and to explore the relationship between health beliefs and preventive behavior during the COVID-19 pandemic (Yastica et al., 2020). In their study, Huang et al. (2020) omitted the fourth HBM construct, perceived barriers.’ On the other hand, they included self-efficacy, which Bandura (1998) explained as “beliefs in one’s capabilities to organize and execute the courses of action required to produce given levels of attainments” (p. 624). Ajzen (1991) argued that the TPB construct of perceived behavioral control was interchangeable with self-efficacy.

The current study involves tourists who already use online travel websites. A travel website provides brief details on destinations and their associated activities/attractions, and often includes travel reviews, hotel recommendations, booking options, and fares. The information of interest in the current study is travel health information that is posted in travel websites due to the ongoing pandemic. Thus, reading this information is now part of the process of using travel websites, and therefore, the participants in the current study are probably familiar with seeking/reading online travel information. The construct perceived barriers, is included in the current study because it assesses tourists’ levels of confidence and familiarity, whether reading the information is time consuming, and whether it requires effort/starting a new habit. Thus, the study focuses on constructs that encapsulate tourists’ health perceptions; hence, self-efficacy/perceived behavioral control is not included in the current study.

The literature on online information seeking contains multiple theoretical frameworks that examine the determination of risk information seeking behavior. The most commonly cited models are Risk Information Seeking and Processing model (RISP: Griffin et al., 1999), Planned Risk Information Seeking Model (PRISM: Kahlor, 2010), and the Framework for Risk Information Seeking (FRIS: ter Huurne, 2008).These models focus on understanding individuals’ risk information seeking behavior by assessing perceived risk, subjective norms, perceived information (in)sufficiency, response efficacy, and issue engagement (ter Huurne, 2008; Kahlor, 2010). As mentioned above, the current study intends to cover the gaps between the influence of tourists’ health belief perceptions and their health protective behavior within the online information context. Thus, the current study model incorporates elements from the above-mentioned models, namely, risk perception in the form of perceived susceptibility and perceived severity.

Moreover, a meta-analysis by Yang et al. (2014), which updated Griffin et al.’s (2012) review, examined all RISP key variables: information insufficiency, information subjective norms, risk judgment, worry, and perceived information gathering capacity. Yang et al. (2014) found that both current knowledge and information subjective norms are key determinants of information seeking and processing. In addition, their study states that the RISP model is more useful when it assesses a risk that respondents are familiar with.

The current study focused on tourists who were planning to travel during/after the COVID-19 pandemic, while the perceived health risk was relatively high and unpredictable, mainly due to the continually evolving knowledge of the disease. Therefore, these tourists would spend time looking for facts and information related to their tourism trip. Taking this into consideration, subjective norms would have a minimal impact on the information seeking behavior process under such circumstances. Huang et al. (2020) combined HBM and TPB in their investigation of tourists’ health risk preventive behavior; they excluded subjective norms from their model on this basis. Thus, subjective norms are not incorporated in the present study model.

To date, few studies, if any, have combined the HBM and TPB frameworks to examine tourists’ travel intentions during high travel threat situations such as the COVID-19 pandemic. Even fewer studies have assessed individuals’ technology behavior while considering health preventive behavior and their influence on behavioral intention. This study aims to address these gaps by investigating the relationships between travelers’ general health beliefs, health risk preventing behaviors, perceptions of information usefulness, and intention to travel.

Reading online health information as health risk-preventing behavior

People tend to perceive risks that are new and not well understood as more concerning than risks that are more familiar and better understood. During the COVID-19 pandemic, fears about the unknown virus and its new variants, compounded by ever-changing travel advisories and restrictions, negatively impacted tourists’ perceptions, attitudes, and behavioral intentions regarding travel health risks (Godovykh et al., 2021). Accessing health information, and in particular, actively seeking that information, has long been linked with healthier behaviors (Anker et al., 2011). This association occurs via mediating factors such as new information acquisition and normative reinforcement (Hornik et al., 2013). Reading health information also affects a person’s decision-making process by eliminating perceived barriers and strengthening behavioral intention (Shen et al., 2018). Thus, the higher the perceived health risk, the greater the perceived importance of reading health information. Travel health information posted on travel websites, providing information related to the COVID-19 pandemic, for example, provides prospective tourists with the latest travel restrictions by destinations. Moreover, these websites often offer links to official sites of authorities at those destinations that explain their restrictions in detail. According to the World Health Organization (WHO), travel agents and tour operators should encourage travelers to seek information from such official websites at destination on a regular basis (WHO, 2020).

Research on the influence of online information on tourists’ health perceptions (Mills and Todorova, 2016; Zhang et al., 2018), and on tourists’ intention to travel (Abubakar and Ilkan, 2016; Alhemimah, 2019) are not new; however, to date no research has investigated the influence of online travel information, together with the mediating impact of attitude, on tourists’ health perceptions and intention to travel. Reviewing tourism and online information literature highlights the need for a study with the aforementioned factors.

Theoretical framework and hypothesis development

Health-related behavior is known as “health behavior” in the literature, and it refers to the “combination of knowledge, practices, and attitudes that together contribute to motivate the actions we take regarding health” (Dorland, “health behavior,” 2012).

To further examine the influence of tourists’ travel health beliefs on their preventive health risk behavior, and subsequently on their travel intentions, the current study model includes constructs from HBM, namely, perceived severity, perceived susceptibility, perceived benefits and perceived barriers.

In the field of tourism and hospitality, it is crucial to understand tourists’ health beliefs and how they relate to perceptions of risk (both susceptibility and severity), benefits and barriers, and importance of reading travel health information (Cahyanto et al., 2016). According to HBM, perceived risk is accounted for in the form of susceptibility to and severity of the health risk (Champion and Skinner, 2008).

Risk perception is influenced by individual differences such as gender and previous experience (Godovykh, 2021). Thus, the research model (Figure 1) includes the following demographic factors: age, gender, and health status. The study model is intended to examine the relationships among these factors: four constructs from HBM (perceived susceptibility, perceived severity, perceived benefits, perceived barriers), perceived usefulness, attitude toward the importance of reading travel health information online, and travel intention. The information of interest in this research is information, posted on travel websites, that was obtained from local/international health authorities and/or WOT, which are considered reliable sources.

Figure 1.

Figure 1.

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Tourist response to online travel health information (TROTHI) model.

Health belief model

Since its introduction in the mid-twentieth century, the Health Belief Model (Rosenstock, 1974) has become broadly accepted for use as a framework to explain and predict health behavior (Carpenter, 2010; Champion, 1984; Champion and Skinner, 2008; Janz and Becker, 1984; Yastica et al., 2020). In addition to its well-established use in health and healthcare research (Carpenter, 2010), HBM has been employed in a number of travel and tourism studies to explain and predict tourists’ travel-health behaviors (Bae and Chang, 2020; Ban and Kim, 2020; Cahyanto et al., 2016; Huang et al., 2020).

Health belief model remains the predominant framework commonly used to explain and predict health-related behavior (Huang et al., 2020; Janz and Becker, 1984). HBM has been used to explore and explain travelers’ preventive health behaviors, and it is the framework that forms the basis of the current study. The current study examines the influence of tourists’ health perceptions on their attitude toward the importance of reading travel-health information, and then on their travel intention. Thus, HBM offers a suitable theoretical framework for this research. The constructs of the TROTHI model are defined and their relationships within the model are hypothesized as follows:

Perceived threat susceptibility and preventive behavior

Perceived susceptibility refers to a person’s subjective evaluation of their personal vulnerability and exposure to risk. Perceived susceptibility has been found to correlate positively with preventive health behavior (Janz and Becker, 1984). This relationship can be explained as a push-and-pull effect. That is, a perceived risk of being susceptible to an existing health threat may decrease positive attitudes toward behaviors or actions that carry that risk, and may increase positive attitudes toward behaviors that prevent or mitigate the health risk (Amuta et al., 2016). In the tourism context as well, high perceived susceptibility is associated with risk mitigation other and preventive behaviors (Huang et al., 2020). Thus,

  • H1. Perceived susceptibility positively influences attitude toward the importance of reading.

Perceived threat severity and preventive behavior

Perceived severity can be defined as an individual’s perception of the level of potential harm, including illness. Perceived severity correlates positively with preventive health behavior (Janz and Becker, 1984). Perceived severity, like perceived susceptibility, involves the push-and-pull effect described above. Thus, the perceived severity of an existing health threat would decrease positive attitudes toward behaviors that carry that risk, and increase positive attitudes toward behaviors that prevent or mitigate the health risk (Amuta et al., 2016). In the tourism context, travelers with a higher perception of perceived severity of an infectious disease are more likely to take preventive measures (Huang et al., 2020). Hence,

  • H2. Perceived severity positively influences attitude toward the importance of reading.

Perceived information benefits and preventive behavior

Perceived benefit refers to an individual’s perception of how effectively protective measures would reduce a particular risk (Janz and Becker, 1984). Perceived benefit correlates positively with preventive health behavior (Cahyanto et al., 2016). According to TPB, attitude toward refers to an individual’s evaluation (positive, neutral, or negative) regarding carrying out a behavior, and a person’s beliefs determine his/her attitudes (Ajzen, 1991). Hence, an individual who believes that a particular action leads to reduced health risk would hold a favorable attitude toward that preventive health behavior. Thus,

  • H3. Perceived benefits positively influence attitude toward the importance of reading.

Perceived information barriers and preventive behavior

Perceived barriers are perceptions regarding factors preventing them from protective health behaviors. A perceived barrier to an action is whatever may prevent or discourage an individual from that behavior, even while the individual believes in the benefits of that action. People typically weigh perceived barriers against perceived benefits of a behavior, and if barriers are judged to be greater than the benefits, they would be less likely to engage in that behavior (Cahyanto et al., 2016; Janz and Becker, 1984). A meta-analysis of HBM variables found that benefits and barriers were stronger predictors of adoption of preventive health behaviors than any other factor (Carpenter, 2010).

In the current study, the primary perceived barriers would be confidence in using websites, unfamiliarity with searching online health information, and that reading it would be time consuming, or require effort or starting a new habit. These perceived barriers may hinder prospective tourists from performing the recommended preventive health behavior. Hence,

  • H4. Perceived barriers negatively influence attitude toward the importance of reading.

Perceived information usefulness and preventive behavior

Perceived usefulness is a construct from the Technology Acceptance Model (TAM; Davis, 1989), which is used to predict users’ attitudes toward accepting a particular technology. TAM proposes that perceived usefulness influences behavioral intention. In research on health preventive behavior, many studies have reported that perceived usefulness was positively associated with consumer/patient intention to use technologies related to health services (e.g. Mills and Todorova, 2016; Qi et al., 2021; Schnall et al., 2015).

TAM has been employed to investigate the effects of information adoption (Alhemimah, 2019). Perceived usefulness of information provided has been found to have a positive influence on preventive health behavior (Domínguez-Salas et al., 2020). Thus,

  • H5. Perceived usefulness positively influences attitude toward the importance of reading.

Risk-preventive behavior and travel intention

Studies have found that high perceived risk influences decision-making regarding travel destinations (Cahyanto et al., 2016), as well as an association between tourists’ risk prevention behavior and overall travel satisfaction (Huang et al., 2020). As mentioned, according to TPB, attitude mediates the relationship between beliefs and behavioral intention (Ajzen, 1991). This relationship has been demonstrated in studies looking at perceived risk and travel intention (Bae and Chang, 2020; Cahyanto et al., 2016; Godovykh et al., 2020).

Favorable attitude toward a behavior positively influences performance of that behavior (Ajzen, 1991). Risk perception is a belief, so it influences attitude toward a behavior that carries risk, which in turn influences the intention to perform that behavior (Anker et al., 2011). Thus,

  • H6. Attitude toward the importance of reading positively influences travel intention.

The above six hypotheses pertain to the influence of five perceived factors (susceptibility, severity, benefits, barriers, and usefulness) on tourists’ preventive health behavior (in this case, tourists' attitude toward the importance of reading health travel information) and its influence on travel intention. Regarding the demographic factors in the TROTHI model, the current study does not test their relationships to the other variables in the model. Rather, these demographic data were collected from the survey respondents and considered in light of their responses to the construct items, as discussed below.

Methods

In line with previous social-psychological research on information seeking behavior, a quantitative approach has been adopted. An online survey was conducted from August 1 to 12, 2021. The current study sample were individuals aged 18+ years. A total of 261 completed questionnaires were received from respondents in Saudi Arabia who were considering travelling abroad for tourism, and/or who had traveled for tourism purposes within the previous 12 months. For the purposes of this study, an appropriate sample size was determined using Cohen et al.’s (2007) rule of thumb for survey research; that is, at least 30 participants for each variable in the model (p. 101). There are seven variables in the current study model, thus, the minimum number of participants in the sample should be 210. Therefore, the number of 261 valid questionnaires is deemed sufficient.

Due to the high risk of COVID-19 transmission during the data collection period, a contactless tool in the form of a survey barcode was made for participants to access the online survey. The study participants were travelers in Jeddah airport, who were asked to fill out the questionnaire and to share it among them. At the start of the questionnaire, respondents were given clarification of what was meant by COVID-19 travel instructions and travel websites. The participants were asked if they used travel websites to book their tourism trip. If “never” was selected the survey was ended. Then, multiple questions related to the respondents’ personal experiences of travelling and internet using were asked (e.g. the extent to which they look for COVID-19 travel instructions before their tourism flight; which travel website they mostly refer to for booking their tourism flight, etc.). These questions were intended to enable respondents to recall their situation in the pre-travel stage so they would more easily be able to respond to the subsequent items in the questionnaire. The remaining respondents (N = 261) then were asked if they looked for COVID-19 instructions before their tourism flight. They were also asked about the number of times they had traveled within the last year.

The study model is based on HBM, to explain the influence of tourists’ heath perceptions on their travel intention. Four independent variables were based on HBM: perceived severity, perceived susceptibility, perceived benefit, and perceived barriers. Additionally, the model incorporated perceived usefulness as an independent variable and perceived important of reading (attitude) as a mediating variable; the model’s dependent variable is travel intention. The demographics characteristics of the survey sample are presented in Table 1.

Table 1.

Demographic characteristics of study sample.

Characteristics Group Overall sample N = 261
n %
Gender Male 145 55.5
Female 116 44.4
Age 18–24 24 9.2
25–34 75 28.7
35–44 68 26.1
45–54 63 24.1
55–64 31 11.9
Education level Diploma 21 8.1
High school 21 8.1
Bachelor’s degree 149 57.1
Master’s degree 45 17.2
PhD 25 9.5
Marital status Single 49 18.7
Married 183 70.1
Divorced 29 11.11
Health status Excellent 45 17.2
Very good 20 7.6
Good 111 42.5
Fair 50 19.2
Poor 35 13.4
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Measures

Perceived susceptibility was measured with 3 items adapted from Becker (1974) and Champion and Skinner (2008); perceived severity was measured with 3 items adapted from Becker (1974) and Champion and Skinner (2008). Perceived benefits were measured with 2 items adapted from Becker (1974) and Champion and Skinner (2008) and perceived barriers were measured with 5 items adapted from Champion (1984), Korobili et al. (2011), Pan and Fesenmaier (2006), and Woon et al. (2005). The scale of perceived usefulness was adapted from Sussman and Siegal (2003) and included 4 items; the attitude scale was adapted from Lee et al. (2012), using 4 items. Finally, travel intention was measured with 3 items adapted from Zhang et al. (2016).

Control variables

The survey respondents skewed slightly more male (55.4%) than female (44.8%). The average age of female participants was 25–34 years and average age of males was 35–44 years. A substantial proportion (36.5%) of participants had traveled 1–3 times during the previous year. Four in ten participants (42.7%) listed their health status as “good.”

Analysis

The current research examines the influence of tourists’ health perceptions on their attitude toward the importance of reading, and the on their travel intention. The study expands the HBM model by including perceived usefulness, and linking health risk-preventive behavior to travel intention, thus developing theory. Partial least squares structural equation modelling (PLS-SEM) is an analytical tool that is well suited for developing and testing theories (Hair et al., 2016). Previous studies in tourism have made use of PLS-SEM (Alhemimah, 2019; Rasoolimanesh and Ali, 2018).

Through the iterative use of factor analysis, multivariate regression, and other techniques, PLS-SEM can analyze a complex model involving multiple relationships. Hair et al. (2016) have recommended PLS-SEM for use in studies that seek to uncover salient or predictive factors. Additional advantages of PLS-SEM are that it works with small sample sizes and non-normal data.

In this study, WarpPLS 7.0 PLS-SEM software was used to test the hypothesized relationships between the model constructs, establishing the maximum R2 values. Stone-Geisser Q2 values serve to indicate the presence of endogenous latent variables (Hair et al., 2016). This study seeks to explain how attitude toward the importance of reading travel-health information moderates between tourists’ perceptions of health threat severity, susceptibility, benefits and barriers to reading information, information usefulness, and their travel intention. The Model of Tourists’ Response to Online Travel Health Information (TROTHI) comprises 7 constructs in some complex relationships; thus PLS-SEM is a suitable choice as an analytical tool.

Measurement model

Reliability and validity of the model constructs were evaluated: the constructs were examined for convergent validity, and tests were run to determine the constructs’ composite reliability (CR) and Cronbach’s alpha. The factor loadings (correlation coefficients) were assessed. Table 2 displays the results of these tests. For all constructs, the Cronbach’s alpha values lay between 0.724 and 0.869. These values exceed the recommended minimum threshold for convergent validity, meaning that the constructs can be deemed to be reliable. CR values of all constructs were larger than 0.85 and average variance extracted (AVE) values for all constructs were above the recommended value, thus establishing convergent validity of the reflective constructs (Fornell and Larcker, 1981).

Table 2.

Indicator loadings, composite reliability, average variance extracted values, and Cronbach’s alpha.

Construct CR AVE Cronbach’s alpha VIF
Perceived susceptibility 0.884 0.718 0.803 1.601
Perceived severity 0.851 0.656 0.736 1.472
Perceived benefits 0.913 0.840 0.810 1.158
Perceived barriers 0.819 0.477 0.724 1.190
Perceived usefulness 0.867 0.622 0.794 1.830
Attitude toward importance of reading 0.852 0.591 0.768 1.756
Travel intention 0.920 0.793 0.869 1.089
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According to Kock (2015) the way survey instruments are designed can often lead to common method bias in PLS-SEM. A remedy for this problem is to employ the full variance inflation factor (VIF) for each predictor variable. This should bring to light any common method bias. Using VIF also works to measure full collinearity (Kock, 2015). As shown in Table 2, all VIFs are lower than 2, which is well below the threshold of 3 (Kock, 2015: 7); thus, common method bias and multicollinearity between the constructs can be ruled out. In order to establish each construct’s validity, item loadings were assessed (see Table 7 in Appendix). Discriminant validity was also evaluated, to ensure that no measurement reflected another. In Table 3, it can be seen that the square root of AVE for each construct, which is used to measure correlation between constructs. As can be seen from Table 3, the square root of AVE for each construct is greater than any other correlations for that same construct, hence establishing discriminant validity.

Table 3.

Correlations among constructs with square roots of average variance extracteds.

PSUS PSE PBEN PBAR PUSE ATTID TINT
PSUS (0.847) 0.560 0.160 0.066 0.370 0.320 0.122
PSE 0.560 (0.810) 0.089 0.038 0.277 0.239 0.081
PBEN 0.160 0.089 (0.917) 0.307 0.201 0.212 0.187
PBAR 0.066 0.038 0.307 (0.691) 0.269 0.231 0.191
PUSE 0.370 0.277 0.201 0.269 (0.789) 0.634 0.122
ATTID 0.320 0.239 0.212 0.231 0.634 (0.769) 0.205
TINT 0.122 0.081 0.187 0.191 0.122 0.205 (0.891)
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Based on Table 4, the structural model appears to be an appropriate fit to the data: the model has a good fit with indices, i.e. APC, ARS, AARS, AVIF, AFVIF, GoF, SPR, and RSCR, and has met the criteria.

Table 4.

Model fit and quality indices.

Item analysis result
Average path coefficient (APC) = 0.167, p = .002
Average R-squared (ARS) = 0.250, p < .001
Average adjusted R-squared (AARS) = 0.243, p < .001
Average block VIF (AVIF) = 1.425, acceptable if ≤ 5, ideally ≤ 3.3
Average full collinearity VIF (AFVIF) = 1.442, acceptable if ≤ 5, ideally ≤ 3.3
Tenenhaus goodness of fit (GoF) = 0.410, small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36
Sympson’s paradox ratio (SPR) = 1.000, acceptable if ≥ 0.7, ideally = 1
R-squared contribution ratio (RSCR) = 1.000, acceptable if ≥ 0.9, ideally = 1
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Table 5, shows the Heterotrait–Monotrait ratio (HTMT) values, as can be seen all HTMT values fell far below the more stringent threshold of 0.85. (Henseler et al., 2015). Thus, the HTMT ratio supported the discriminant validity of all constructs.

Table 5.

Heterotrait–monotrait ratio.

PSUS PSE PBEN PBAR PUSE ATTID TINT
PSUS
PSE 0.730
PBEN 0.197 0.117
PBAR 0.193 0.129 0.399
PUSE 0.458 0.361 0.258 0.429
ATTID 0.402 0.316 0.269 0.317 0.807
TINT 0.149 0.108 0.223 0.251 0.155 0.248
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Having determined that the study constructs are reliable and valid, the structural model is analyzed in the next section.

Structural model

The current section provides the analysis of the structural model. All the constructs’ validity and reliability are confirmed in the current model. The relationships hypothesized and tested in this study are represented by the path coefficients (β) in Figure 2.

Figure 2.

Figure 2.

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Results of hypothesis testing for 6 relationships in TROTHI model.

As can be seen in Figure 2, perceived information usefulness had the strongest impact (β = 0.59), followed by the relatively lower influence of perceived susceptibility (β = 0.10). In contrast, the influence of perceived severity, perceived benefits, and perceived barriers were nonsignificant. Attitude toward the importance of reading information had a significant effect on travel intention (β = 0.21). Thus, H1, H5, and H6 are accepted, whereas H2, H3, and H4 are rejected. The results of the analysis indicate that perceived severity and perceived usefulness together explain 45% of the importance of reading information attitude; the importance of reading information attitude explains 5% of travel intention.

Specifically, perceived severity and perceived usefulness were found to influence attitude toward the importance of reading online travel-health information, while perceptions of susceptibility, benefits, and barriers were found to have no influence on this attitude. Moreover, attitude toward the importance of reading travel health information was found to influence travel intention. Table 6 summarizes the results for each hypothesis tested in this study.

Table 6.

Summary of results for each hypothesis tested.

Hypothesis Test
H1: Perceived susceptibility positively influences attitude toward the importance of reading information. Accepted
H2: Perceived severity positively influences attitude toward the importance of reading information. Rejected
H3: Perceived benefits positively influence attitude toward the importance of reading information. Rejected
H4: Perceived barriers positively influence attitude toward the importance of reading information. Rejected
H5: Perceived usefulness positively influences attitude toward the importance of reading information. Accepted
H6: Attitude toward the importance of reading information positively influences travel intention. Accepted
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Regarding the mediating effect, the results indicate that only perceived usefulness has significant indirect relationship on visit intention (p = .002), in addition to the significant direct relationship between perceived usefulness and attitude. If the indirect relationship is significant, then there is a mediating effect preset (Kock, 2015; Hair et al., 2016). Thus, perceived information usefulness had a positive significant effect on travel intention, without the mediation of attitude toward reading information.

Discussion and conclusion

The proposed model has important implications for policy and industry practice regarding not only online pandemic-related information, but also online travel-related information within the tourism context, by exploring travel health risks from a health belief perspective. It also contributes to the e-marketing literature by pointing out the importance of improving and enhancing online information content and style to adequately serve the needs and demands of tourists from a health risk-prevention perspective.

Perceived usefulness was found to have the strongest influence on tourists’ attitudes toward the importance of reading travel health information. This result indicates that tourists evaluate the usefulness of the information, and it accords with previous research findings that information usefulness influences information adoption (Cheung et al., 2008; Cheung and Thadani, 2012) and decision making (Park et al., 2007; Lee and Youn, 2009). This result also indicates that the more useful the travel health-information is found to be, the stronger the tourist’s intention to travel. Therefore, when this information is useful and interesting, tourists would think reading this information is valuable for them, which in turn influences their travel intentions positively.

Perceived severity was found to have significant influence on tourists’ attitudes toward the importance of reading travel health information. This result is in line with previous research indicating that greater perceived severity positively influences attitude toward prevention behavior (Amuta et al., 2016). This result shows that when tourists perceive severity is high, they are more likely to seek more travel health information. Thus, providing travel health information via travel websites, especially during and after a health pandemic, is crucial to keep up with tourists’ needs and desires.

Additionally, attitude toward reading travel-health information was found to have significant influence on tourists’ travel intentions. Therefore, if tourists have a more positive attitude toward reading this information, the stronger their intention to travel. In sum, disease severity, along with usefulness of travel-health information, are the key motivators of tourists to engage with reading this information before their upcoming tourism trip. This highlights the need for marketers and managers at destinations to provide access to this information, not only on booking websites and apps, but also via commercial and/or government websites; because by providing travel-health information through different channels, marketers make sure they reach broader segments of tourists.

Perceptions of susceptibility, benefits, and barriers, on the other hand, were found to have non-significant influence on tourists’ attitudes toward the importance of reading travel health information. Regarding this finding for perceived susceptibility, it is possible that people’s perceptions of susceptibility to COVID-19 may decrease as vaccination rates increase around the globe – both among tourists and at destination locations. This is in contrast to the perceived severity of COVID-19, which is less likely to decrease as quickly.

Regarding the non-significant influence of perceived benefits on tourists’ attitude toward the importance of reading travel health information, this may be related to the types of risks and the related prevention behavior. In the current study, the risk is catching COVID-19, and the risk preventive behavior is reading travel health information, whereas tourists might consider preventing COVID-19 through other behaviors such as hand washing, wearing masks, getting vaccinated, and so on.

For the non-significant influence of perceived barriers, an explanation could be that the current information channels need to be improved. As discussed above, offering accessible travel health information on various online travel platforms would enhance tourists’ information seeking experience. Based on the RISP model, individual perception of information channel usefulness is a determinant of information seeking behavior (Griffin et al., 2005); in turn, tourists’ lack of capacity to choose the information channels that are valuable for them should be considered, as this component would impact their information seeking behavioral process (Griffin et al., 2012).

This study uncovered some interesting findings with regard to gender. More females (58%) than males (43%) searched for and read travel health information before their tourism trip. An explanation could be that women, who are typically the caregiver in their household, may feel more responsibility for their own and their family members’ health and safety, and therefore they would be more likely to seek to read travel health information than men would. In what seems like a contradiction to this, however, 30% of men agreed that reading travel health information decreased their chance of catching COVID-19, while only 23% of women agreed with that. One explanation could be that although women are more likely than men are to seek this information, women may not necessarily perceive the risk as high. Indeed, 42% of males agreed that the virus (SARS-COV2) was highly contagious, whereas only 22% of females agreed with this statement. Interestingly, male participants (30%) showed more positive perceptions of information usefulness than female participants (19%) did. This finding is worthy of further investigation in the context of how travel health information is presented to females versus males.

Females and males did not differ substantially in their self-reported health status (“good”), and most of the participants who reported their health status as good agreed that reading online travel health instructions decreased the chance of catching COVID-19. Both genders also responded similarly in terms of their positive attitude toward the importance of reading travel health information. Men and women also agreed that “reading health travel-information is time-consuming” (63% and 65% respectively). However, males (58%) and females (46%) differed in their level of confidence in using travel websites.

These results may mark current gaps in knowledge for future efforts to address by investigating differences in attitudes and health perceptions between genders from a health risk-preventing perspective, and how these would impact tourists’ travel intentions.

Implications and limitations

The current study findings provide significant theoretical and practical implications. Regarding the theoretical implications, the current research found that not all Health Belief Model factors are relevant in the Saudi tourism context, unlike other researchers’ findings. This finding could be due to the role of culture; thus, the study encourages further research to examine the role of culture in the relationship between tourists’ health perceptions and travel intention.

Additionally, the significant influence of perceived information usefulness implies that tourists would have a more positive attitude toward reading travel-health information if they found it useful. Attitude toward the importance of reading travel health-information found to mediate the relationship between tourists’ severity perceptions and their travel intentions. Thus, the positive influence of tourists’ severity perceptions could be conditional to their attitudes toward the importance of reading. This finding encourages further research to clarify the aforementioned relationship.

In terms of the practical implications, the study results provide significant implications to travel managers and marketers in Saudi Arabia. Tourists’ severity perceptions had a significant influence on their attitudes toward reading travel-health information, which in turn influenced their travel intentions. Thus, travel managers and marketers in Saudi Arabia are encouraged to enhance their online information platforms to attract more tourists. According to the General Authorities of Statistics in Saudi Arabia (GASTAT), in 2018, around 64.8% of tourists in Saudi Arabia used air transportation, and 100% of tourism activities (e.g. air and railway passenger transport) used social media as a main channel of communication (GASTAT, 2018).

Travel and tourism industries are among the most affected industries from health threats. Therefore, providing the relevant online information on travel websites is effective within travel and tourism industries (Abubakar and Ilkan, 2016; Chen et al., 2014).

In addition, this result highlights the need to offer accessible, informative travel health information on travel websites and other online platforms (e.g. social media, government websites, etc.). To be specific, tourism marketers would benefit from offering marketing strategies that keep up with their customers' demands and desires. As nowadays travelers have to comply with multiple requirements before their trip, it would be wise if travel websites offered accessibility to most of these requirements on the same webpage (i.e. links for updated World Health Organization travels advisories, travel advice for popular destinations in every continent/region, online portal for COVID-19 and/or PCR certificate, etc.), that would facilitate tourists’ travel planning process.

The current study assessed the variable “attitude toward reading travel-health information” in order to examine a new behavioral pattern that has started to be more urgent among tourists during the Coronavirus pandemic. Tourists travel-health information seeking behavior requires more research in the post-pandemic phase, as it is expected that tourists would not simply resume their to the pre-pandemic behavior, but rather would keep changing and adapting to new perceived threats. Thus, destination marketers are encouraged to consider the most updated features within the e-information context, such as offering a link on destination websites that provides customized information based on tourists’ needs. This would enhance tourists’ information seeking experiences, which in turn would improve their travel decision-making.

Among the limitations of this study are that it examines the influence of health perceptions on the reading attitudes of tourists in Saudi Arabia; thus, generalizing the study findings would be immature. Therefore, further qualitative research considering cultural factors is recommended. In addition, the study controlled for some demographics, including health status; however, the current study does not test the relationships of these factors to the other variables in the model. Thus, more research considering the demographics within their relationships testing is recommended.

Author Biography

Arej Alhemimah, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia aalhemaimah@kau.edu.sa. Arej Alhemimah is an Assistant Professor at King Abdul-Aziz University, Jeddah, Saudi Arabia, aalhemaimah@kau.edu.sa Arej Alhemimah received her PhD in tourism and hospitality from University of Plymouth, UK, and her master degree in Tourism and hospitality management from Rochester Institute of Technology, NY, USA. Her main research interests lie in the areas of tourism marketing, destination management, sustainable tourism, travel behavior and tourist experience.

AppendixTable 7.

Confirmatory factor analysis (Partial least squares approach).

Constructs Loading Mean SD Confidence Interval (CI)
2.5% 97%
Perceived susceptibility
 Worried about the likelihood of getting COVID-19 (0.853) 3.221 0.908 0.858 0.873
 Chance of getting COVID-19 in the next few months is great (0.884) 4.162 0.922 0.850 1.010
 Getting COVID-19 is possible (0.803) 4.111 0.972 0.813 1.017
Perceived severity
 Covid-19 Complications are serious (0.821) 4.223 0.804 0.656 0.913
 I’m afraid of getting COVID-19 (0.847) 3.211 1.050 0.734 0.940
 I’ll be very sick if I get COVID-19 (0.760) 3.143 0.943 0.707 0.914
Perceived benefits
 Reading online travel health-regulations decreases my chance of catching COVID-19 (0.917) 3.264 1.082 0.788 0.977
 Reading online travel health-regulations make me feel less worried (0.917) 4.134 1.053 0.811 1.004
Perceived barriers
 I’m not confident with using travel websites for my tourism trip (0.615) 3.433 1.672 0.879 0.905
 I’m not familiar with searching travel health-instructions online (0.744) 3.933 1.098 0.944 0.988
 Reading online health travel-instructions is time-consuming (0.737) 4.245 1.056 0.805 0.901
 Reading online health travel-instructions would require considerable investment of effort other than time
 Reading online health travel-instructions would require starting a new habit, which is difficult (0.721) 3.266 1.113 0.821 0.946
Perceived usefulness
 In general, I think travel health-instructions are valuable. (0.675) 4.164 1.007 0.935 1.039
 In general, I think travel health-instructions are informative. (0.834) 3.341 1.067 0.822 1.005
 In general, I think travel health-instructions are helpful. (0.852) 4.132
 In general, I think travel health-instructions are instructive. (0.781) 4.044 1.032 0.745 1.055
Attitude
 I think reading online travel health-instructions is a positive behavior. (0.785) 3.268 0.982 0.754 0.970
 I think reading online travel health-instructions is a valuable behavior. (0.810) 4.177 0.859 0.809 0.932
 I think reading online travel health-instructions is a beneficial behavior. (0.788) 3.153 0.900 0.654 0.888
 I think reading online travel health-instructions is a necessa behavior. (0.688) 4.055 0.955 0.832 0.800
Travel intention
 I intend to travel to a tourist destination in the future (0.852) 4.088 0.963 0.848 0.943
 I predict that I should travel to a tourist destination in the future (0.931) 3.169 0.944 0.711 1.021
 I am willing to visit a tourist destination in the future (0.888) 4.094 0.921 0.880 0.895
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Footnotes

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Arej Alhemimah https://orcid.org/0000-0002-4522-8645

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