Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2021 Mar 1;95:102905. doi: 10.1016/j.ijhm.2021.102905

The motivations of visiting upscale restaurants during the COVID-19 pandemic: The role of risk perception and trust in government

Bekir Bora Dedeoğlu a,*, Erhan Boğan b
PMCID: PMC9756826  PMID: 36540678

Abstract

Despite the massive impacts of COVID-19 pandemic on hospitality industry, only limited papers empirically examined consumer reactions to current pandemic in the context of restaurants. To fill this gap, the primary aim of this paper is to reveal how individuals’ intentions to visiting upscale restaurants are affected by dining out motivations under the COVID-19 pandemic. The second aim is to investigate the moderating role of risk perceptions of COVID-19 and trust in government in building relationships. The research was conducted in Istanbul, a city deeply affected by the coronavirus. Data gathered from 681 people living Istanbul were analyzed via structural equation modeling and multi-group analysis. Findings indicated that two motivations—namely, sociability and affect regulation—have positive impacts on visit intention toward an upscale restaurant. Consumers’ COVID-19 risk perception and their trust in government moderate the relationship between some motivational factors and visit intention. The study makes a significant contribution to the literature in terms of both managing the risk perceptions of consumers and building trust in government.

Keywords: COVID-19 risk perception, Trust in government, Motivations, Visit intention, Upscale restaurant

1. Introduction

After emerging in Wuhan, China in December 2019, the outbreak of the novel coronavirus (COVID-19) quickly turned into a pandemic, and was officially declared as such by the World Health Organization on March 11, 2020 (WHO, 2020a). The epicenter of the outbreak shifted from China to Europe, then from Europe to America. As of February 13, 2021, over 100 million people worldwide have been infected with the virus, resulting in over two million deaths. The countries currently listed as having the highest number of cases are the United States, India, Brazil, Russia, and UK. The countries currently listed as having the highest number of deaths are United States, Brazil, Mexico, India, and UK (Worldometer, 2021).

Increases in the number of cases have brought about strict restrictions and measures worldwide (Gössling et al., 2020). These restrictions and measures have shaped people’s attitudes and behaviors to a remarkable extent. Tourism and leisure are among the fundamental activities most affected by the COVID-19 pandemic. Various measures such as lockdowns and curfews, closings of restaurants and bars, and suspensions of international travels have brought tourism and leisure activities to a standstill (Atsız, 2021; Brouder, 2020; Gössling et al., 2020; Naumov et al., 2020; Sobaih et al., 2021). For instance, France and the United Kingdom announced that restaurants, cafés, gyms, cinemas, and nightclubs will be closed across the country to limit the spread of COVID-19 (BBC, 2020; Stewart and Walker, 2020). Similarly, due to growing numbers of cases across the country, Turkey temporarily ceased operations of restaurants, cafés, coffee houses, and cafeterias. On the other hand, as in the United Kingdom, delivery services and takeaway were allowed in Turkey. Accordingly, restaurants and cafés were provided with the opportunity to receive orders online or via phone for delivery (Byrd et al., 2021; Nadler, 2020; Hurriyet, 2020). Over time, many countries, including Turkey, Germany, and France, loosened certain strict measures and restrictions owing to the slowdown in the growth rate of new cases and to relative control over the spread of virus, as well as for economic and social reasons. Nevertheless, flexible measures and restrictions in place do not diminish the fact that the COVID-19 pandemic is ongoing, which highlights that people have no other choice than getting used to living in a world with COVID-19 (Bradbury-Jones and Isham, 2020; Kim and Su, 2020).

As one of the rare countries in the world to have managed the pandemic in a successful way, Turkey has undergone similar processes and experiences. The first confirmed COVID-19 case was reported on March 9, 2020 and the first death on March 17, 2020 (Demirbilek et al., 2020). An official statement by Ministry of Health on April 1 announced that the virus has spread to all provinces. As of February 12, 2021, the number of people who contracted the virus was over 2,5 million, the number of deaths was 27,284 and the number of those who recovered was over 2,5 million (Ministry of Health, 2021). In light of the decreasing number of cases and deaths in England, Germany and France, Turkey took the first step on June 1, 2020 toward limited reopening. Thus, restaurants, cafés, patisseries, coffee houses, tea gardens, and spas were allowed to serve until 10 p.m. under specific rules. According to a statement by the President on June 9, the closing time of these enterprises was extended to midnight. The rules to be followed were: measurement of customers’ body temperature at the entrance to the restaurant, hand sanitation, wearing of masks and face visors by staff, and setting a distance of 70 cm between the tables.

Before the COVID-19 pandemic, dining out motivations included hunger, social image, health, hedonic value, atmospheric, subjective well-being, celebration, socialization, convenience, natural concerns, traditional eating, price, affect regulation, take-away, and habits (Kwun et al., 2013; Ponnam and Balaji, 2014; Renner et al., 2012). However, the new way of life brought about by restrictions and precautions applied to contain the virus may alter the motivational elements in question. Visiting intention to upscale restaurants deserves special attention in the post-pandemic period. Compared to other types of restaurants, consumers expect to eat high-quality, hygienic, healthy, and delicious meals in upscale restaurants (Dubois and Laurent, 1994; Kim et al., 2006; Lee and Hwang, 2011). Given that precautions for hygiene and health have become much more important during the pandemic, it is of special necessity to investigate and explore the relationship between dining out motivations and visit intention to upscale restaurants.

As a result of the reopening of restaurants and cafés in Turkey, the number of customers visiting restaurants is increasing. For those who have been distant from social life for a long time, restaurants provide an opportunity to meet and socialize with acquaintances. Aside from the social benefits, individuals may visit restaurants due to the convenience of eating out, visual appeal of dishes, desire to feel a sense of pleasure, affect regulation, and image (Renner et al., 2012). On the other hand, it is expected that individuals acutely aware of COVID-19 would have lower intention to visit restaurants, even though they may have intense dining out motivations, as visiting restaurants may increase the risk of being infected with the virus. While consumers visit restaurants to meet different expectations or needs, they venture some risks in their actions (Kim et al., 2008). The fact that the risk in question is related to the pandemic, which is contagious and has no standard treatment in the face of rising death tolls, will significantly alter consumers’ behavioral intentions (Gössling et al., 2020). Previous hospitality studies indicated that consumers’ risk perception largely shapes their attitude and behavioral intention (Choi et al., 2013; Harris et al., 2018).

Another important matter that shapes consumers’ attitudes and behaviors during periods of uncertainty is trust. Trust encompasses positive expectations that may arise in case of uncertainty (Hosmer, 1995). Furthermore, it is suggested that trust reduces complexity or uncertainty through acceptance of risk (Chen, 2013; Luhman, 1979). Mishra (1996, p. 265) defines trust as “one party’s willingness to be vulnerable to another party based on the belief that the latter party is competent, open, concerned, and reliable.” Willingness to be vulnerable means that the person is ready to take risk. Trust does not refer to the action of risk-taking but to the willingness to take a risk. In the current study, it is argued that consumers’ trust in the government regarding the COVID-19 pandemic will have an impact on their intention to visit restaurants. When transitioning to controlled social life, governments take a series of measures to reduce the spread of the virus. That these measures are embraced and effectively implemented by citizens depends on citizens’ trust in the government (Nakayachi and Cvetkovich, 2010). In the event that citizens lack trust in their government, they might feel suspicious about whether the measures in question are sound and sufficient (Rudolph, 2009). When trust in the government is lacking, it can be difficult for consumers to prefer restaurants even if they are motivated to do so. At this juncture, governments may have the power to control consumer perceptions with the help of sound measures they adopt (Nakayachi and Cvetkovich, 2010). De Jonge et al. (2007) indicated that trust in government is strongly related to consumer confidence in the safety of food. Grayson et al. (2008) and Chen (2013) found that trust in government is positively linked to the customer trust in firms. Beyond that, Chen (2013) indicated that trust in food manufacturers is positively related to perceptions of food safety.

The current study provides some significant contributions to the hospitality literature. Thus far, a great number of tourism and hospitality studies published are composed of literature reviews, projections, and perspective articles (Gössling et al., 2020; Iaquinto, 2020; Sigala, 2020; Wen et al., 2020; Williams and Kayaoglu, 2020; Zheng et al., 2020). Only limited papers empirically examined consumer reactions to current pandemic in the context of restaurants (e.g., Kim and Lee, 2020; Luo and Xu, 2021; Brewer and Sebby, 2021; Byrd et al., 2021). Among these limited papers, for instance Kim and Lee (2020) investigated the impact of perceived threat of COVID-19 on customers’ preference for restaurants. Luo and Xu (2021) examined customer online reviews during the COVID-19 pandemic using deep learning methods. Since the old ways are expected to change dramatically due to this pandemic, it is of vital importance to conduct empirical studies on COVID-19’s possible impacts on consumer behavior in hospitality industry. Previous studies indicated that consumers’ risk perceptions of pandemics shaped their attitudes and behaviors (Brewer and Sebby, 2021; Harris et al., 2018; Cahyanto et al., 2016; Tavitiyaman and Qu, 2013). To the best of our knowledge, no study has been conducted yet to investigate the effect of dining out motivations on consumers’ intention to visit restaurants during the COVID-19 pandemic and reveal how COVID-19 risk perceptions would shape the relationship in question. In this respect, the current study is the first one to examine the effect of COVID-19 risk perceptions in the relationship between dining out motivations and intention to visit upscale restaurants. Another important contribution offered by this study is that it explores reflections of consumers’ trust in measures and precautions implemented by the government against the pandemic on their behavioral intentions. Considered as a whole, this study is one of the first empirical studies examining the effect of COVID-19 on consumer behavior in the context of upscale restaurants. Accordingly, the current study aims to investigate the moderating role of consumers’ COVID-19 risk perceptions and trust in government in the relationship between dining out motivations and intention to visit upscale restaurants (see Fig. 1 ). Thus, it reveals the importance of the management of COVID-19 risk perceptions and of building trust in public toward governmental practices implemented against COVID-19.

Fig. 1.

Fig. 1

Open in a new tab

Research Model.

2. Literature review and hypothesis development

2.1. Dining out motivations and visit intention to upscale restaurant

Schiffman and Kanuk (2004) defined motives as “the driving forces within the individuals that impel them to action” (p. 87). Motives play a critical role in diners’ expectations, choice process, and product or service evaluation (Ponnam and Balaji, 2014). According to Renner et al. (2012) “eating behavior is a complex function of biological, learned, sociocultural, and material-economic factors” (p. 118). Many studies in the literature explore the factors that motivate people to dining out (e.g., Jackson et al., 2003; Jeong et al., 2019; Ponnam and Balaji, 2014; Renner et al., 2012; Steptoe et al., 1995). Renner et al. (2012) developed a comprehensive survey measuring eating motivation which includes fifteen dimensions: liking, habits, need/hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image. Among these motives, the present study examined six of them namely sociability, visual appeal, pleasure, affect regulation, convenience, and social image.

Pleasure refers to a person’s feelings of delight and joy over the food s/he has consumed. Affect regulation reflects a person’s tendency to choose specific food in response to the negative mood s/he is in. Sociability refers, on the other hand, to a person’s choosing of specific food for social reasons. Social image concerns the consumption of foods to create positive impressions in a social environment. Visual appeal reflects a person’s preference for specific foods due to their high appeal. Finally, convenience is a motivation for food consumption that is easily and quickly accessible with little effort (Renner et al., 2012). Pleasure, affect regulation, and sociability may be the most prominent motives in the current pandemic, as the coronavirus pandemic is an important stressor that impacts individuals’ daily lives on psychological and social levels (Bilge and Bilge, 2020). Consequently, consumers forced to refrain from their normal daily routines for a long period of time due to the pandemic have trouble meeting their psychological and social needs (Klaus and Manthiou, 2020). Individuals report that staying at home due to the pandemic has affected not only themselves but also their relationships with those they live with. Thus, the situation is bothersome for many (Bilge and Bilge, 2020). In this respect, restaurants are places that meet individuals’ need for food consumption while offering them social and psychological satisfaction (Andersson and Mossberg, 2004). By adopting necessary health and safety measures, upscale restaurants can persuade consumers to meet their needs in terms of socialization, affect regulation, and social image in this time of pandemic. On the other hand, Ponnam and Balaji (2014) indicated that food presentation and upscale image impact customer evaluation of casual dining restaurants. Luxury consumption provides an upscale image that encourages diners to see themselves as being in a good position in their social environment (Yang and Mattila, 2014). As a whole, motives provide a strong predictive power with regard to a person’s intention to visit food providers (Dowd and Burke, 2013; Lockie et al., 2004).

Some previous studies provide strong evidence about the link between motivations and intention (Ahmad et al., 2020; Dowd and Burke, 2013; Kim et al., 2009). Among these studies, Ahmad et al. (2020) investigated the mediating role of attitude in the impact of food choice motives on purchase intention of Pakistani ethnic food. Their findings supported the mediating role of attitude in the relationship between motives and purchase intention. Dowd and Burke (2013) investigated the impact of food choice motivations on intentions to purchase sustainably sourced foods. Their findings indicated that, among the food motives studied, only ethical values and health had positive impact on purchase intention. Kim et al. (2009) used grounded theory to examine the factors influencing consumption of local food and beverages in destinations. Motivational factors such as escape from routine, togetherness, prestige, sensory appeal, and concern for health were found to influence local food consumption. Based on these theoretical arguments and previous empirical findings, we propose the following hypothesis:

Hypothesis 1a

Sociability positively affects visit intention to upscale restaurants.

Hypothesis 1b

Convenience positively affects visit intention to upscale restaurants.

Hypothesis 1c

Visual appeal positively affects visit intention to upscale restaurants.

Hypothesis 1d

Pleasure positively affects visit intention to upscale restaurants.

Hypothesis 1e

Affect regulation positively affects visit intention to upscale restaurants.

Hypothesis 1f

Social image positively affects visit intention to upscale restaurants.

2.2. Moderating role of COVID-19 risk perception

Feelings of anxiety continue to escalate owing to the growing number of cases and deaths resulting from the COVID-19 outbreak all over the world (Bavel et al., 2020). An empirical study conducted by Dryhurst et al. (2020) indicated that people’s COVID-19 risk perceptions, which encompass perceived vulnerability and perceived severity (de Zwart et al., 2009), are fairly high across much of Europe, Asia, and North America. At this point, consumers’ risk perceptions can be expected to shape their consumption behavior as they transition into controlled social life (Mainous, 2020; Williams and Noyes, 2007). Risk perception refers to “the way in which individuals intuitively see and judge the level of risk associated with a particular exposure or hazard” (Thomas et al., 2003, p. 394). In this context, COVID-19 risk perception expresses the subjective evaluations of a person regarding the possibility of contracting COVID-19. Intensive discussions about the pandemic and information of varying reliability shared via both mass media and social media may drive individuals to engage in excessive reactions to the pandemic, increasing fear, panic, and anxiety (Huynh, 2020).

The protection motivation theory (Rogers, 1975) suggests that an individual takes protective measures not to catch the disease when the individual’s risk perception of the pandemic gets higher. As known, close contact with people facilitates the transmission of the virus. In this respect, people strive to isolate themselves from their social circles (Farooq et al., 2020). Individuals perceiving high probability of contracting the virus and transmitting it to others in their social environment or feeling anxious about this matter have high risk perceptions. Therefore, individuals with highrisk perception do not go outside or use public transportation unless necessary, despite having the motivation to do so. In addition, they endeavor not to spend time in social places where people gather (Laato et al., 2020).

The findings of Laato et al. (2020) supported the positive impact of perceived severity on intention to self-isolate. Cahyanto et al. (2016) indicated that perceived risk influenced Americans’ avoidance of domestic travel due to Ebola cases in the United States. Tavitiyaman and Qu (2013) concluded that travelers’ risk perception of traveling to Thailand as it was struggling with both SARS and a tsunami had a moderating role in the relationship of image, satisfaction, and behavioral intention. Harris et al. (2018) found that perceived severity and perceived vulnerability predict consumers’ intention to patronize a restaurant that experienced a foodborne illness outbreak. In the current study, it is considered that increased risk perception will increase consumers’ avoidance of dining out for self-protection purposes even though they have motivation to do so. Based on these explanations, we propose the following hypothesis:

Hypothesis 2a-2f

The level of risk perception of COVID-19 moderates the relationship between dining out motivations (H2a sociability; H2b convenience; H2c visual appeal; H2d pleasure; H2e affect regulation; H2f social image) and visit intention to upscale restaurants.

2.3. Moderating role of trust in government

It is known that governments face difficulties when trying to build public trust in the early periods of a pandemics for which a cure is yet uncertain (Balog-Way and McComas, 2020). Informing citizens about the pandemic and being open and transparent throughout the whole process—especially with regard to the number of available beds in intensive care units, the competency of health personnel, and the adequacy of equipment such as masks and respiratory equipment—is one effective way to earn public trust (MacDonald, 2006; Wong and Jensen, 2020). Furthermore, when government officials act on the best advice of reliable scientists, this can be quite effective in building public trust (Balog-Way and McComas, 2020). In fact, an Advisory Board composed of medical doctors working as academicians in different universities was formed by the Turkish Ministry of Health on January 10, 2020 with the purpose of combatting the COVID-19 pandemic. Besides taking necessary steps to reduce the effects of the pandemic, the Board has undertaken the task of conveying accurate information to the public through social media, television, and newspapers, thus raising awareness on misleading information and advice communicated through those very channels (Demirbilek et al., 2020; Yılmaz, 2020).

Another important issue in building trust is benevolence (Mayer et al., 1995). According to an official statement made by Ministry of Foreign Affairs on April 16, Turkey has aimed to highlight the importance of global cooperation in fighting against the pandemic, provided 44 countries (including Spain, Italy, the United States, the United Kingdom, Iran, Somalia, Colombia, the Balkan states, and Afghanistan) with necessary protective equipment, respiratory equipment, and so on. It can be asserted that these initiatives are effective in earning the trust of citizens under pandemic conditions. Citizens’ compliance with such measures is directly related to their trust in government (McComas and Trumbo, 2001).

Consumers with a high sense of trust in government are more likely to think that their government’s decisions regarding the transition to controlled social life are sound and appropriate (Nakayachi and Cvetkovich, 2010). They are therefore more likely to follow the instructions of government officials to limit the spread of disease (Rubin et al., 2009). In the event that individuals are motivated enough, they may intend to visit restaurants, relying on measures adopted by the government to protect citizens. Nevertheless, the fact that some individuals lack trust in their government may lead them to believe that sound and adequate measures have not been taken (Rudolph, 2009). As a result, lack of trust in the government may make it more difficult for consumers to visit restaurants even though they have sufficient motivation to do so. At this point, the government may have the power to control consumer perceptions with the help of sound measures it adopts (Nakayachi and Cvetkovich, 2010). At this point, it can be expected that the relationship between dining out motivations and intention to visit upscale restaurants is stronger in people with high trust in the government. Based on these explanations, we propose the following hypothesis:

Hypothesis 3a-3f

The level of trust in government moderates the relationship between dining out motivations (H3a sociability; H3b convenience; H3c visual appeal; H3d pleasure; H3e affect regulation; H3f social image) and visit intention to upscale restaurants.

3. Methodology

3.1. Instrument

In this study, data were collected via questionnaire. The first section of the questionnaire includes statements related to dining out motivations, intention to visit upscale restaurants, risk perception of COVID-19, and trust in government. To measure risk perception of COVID-19, the scale of Dryhurst et al. (2020) was adapted. Risk perception of COVID-19 was measured with six items. Although the literature encompasses various measurement items concerning trust in government, the items in question do not measure trust directly but rather factors such as reliability of information provided by the state and adequacy of the actions taken by the state (e.g., Chen, 2013; De Jonge et al., 2007). Although Robinson and Rousseaou (1994) measured trust in management, we were able to directly adapt their measurement items to the context of national governments. Trust in government was measured with seven items. Intention to visit upscale restaurants was measured with three items as adapted from the study of Verma et al. (2019). Renner et al.’s (2012) study was used to measure dining out motivations. Pleasure, sociability, convenience, and social image were measured with five items, affect regulation dimension with six items, and visual appeal dimension with four items. A 7-point Likert scale was used for all items. The second part of the questionnaire gathered demographic information such as age, gender, health history (chronic disease), and education.

3.2. Sampling and data collection

It is officially declared that the most COVID-19 cases in Turkey appeared in İstanbul (Koca, 2020). At this point, it can be stated that individuals living in Istanbul experience the COVID-19 pandemic more than the rest of Turkey and are there the difficulties of the COVID-19 pandemic in their lives. Research conducted in Istanbul by the Istanbul Statistical Office (ISO) on COVID-19 revealed that 37.5 % of participants feel restricted in movement and that 35.1 % feel socially restricted (ISO, 2020). For this reason, individuals living in Istanbul were the preferred population of the present study.

Following Westland (2012), this study uses an a priori sample size calculator for structural equation models (Soper, 2020). Accordingly, the sample size used for the study was tested given the number of observed variables (46), latent variables (9), anticipated effect size (0.30), desired statistical power level (0.95), and probability level (0.05). The results imply that 264 responses/cases were required as a minimum sample size. In this case, the convenience sampling method was preferred. First, the measurement scales were adapted into online survey form. Then, the online survey form was shared on various social media platforms at different intervals. Users were invited to participate in the survey with their consent. Users or groups sharing posts related to Istanbul on Facebook and Instagram were preferred. Every three days, within a two-week span, the same steps were repeated for content generated by different social media groups and users. Thus, we aimed to reach individuals residing in Istanbul. On the other hand, the fact that there are many people not living in Istanbul but using social media was taken into account. And so, in order to prevent people living outside of Istanbul from participating in the survey, a mandatory screening question was added. Only those who answered affirmatively to “I live in Istanbul” were used for analysis. A total of 803 people participated in the survey. However, 122 of these people stated that they did not live in Istanbul. Therefore, these people who stated that did not live in Istanbul were not included in the analysis. Thus, data obtained from 681 people who stated that live in Istanbul were used for data analysis. The minimum sample according to Westland (2012) should be 264. The present study’s sample size of 681 well exceeded that requirement.

3.3. Data analysis

Structural equation modelling (SEM) and multiple group analysis (MGA) were used for testing the hypotheses in the present study. When implementing SEM, the two-step approach suggested by Anderson and Gerbing (1988) was adopted. Because maximum likelihood was used as an estimation method for the analysis of measurement and structural models, normal distribution assumption of data was checked. However, before these stages, a data screening process was followed in order translate data into eligible format. In this regard, the mean substitution method was used in order to replace missing data (Hair et al., 2013). Next, Mahalanobis distance was checked to see whether there were any outliers (Hair et al., 2013). Taking into consideration the cut-off values proposed by Hair et al. (2013), no outliers were found (Mahalanobis D (46)>182,96700, p < .001). After these steps, normal distribution assumption was checked (Ali et al., 2018). Shapiro-Wilk, Kolmogorov-Smirnov, values of skewness and kurtosis can be used to statistically control the normal distribution assumption (Elliott and Woodward, 2007; Mishra et al., 2019). However, the sample size is critical for the selection of these techniques (Field, 2013; Kim, 2013). In this study, data were obtained from a total of 681 participants. Accordingly, as the Shapiro-Wilk and Kolmogorov-Smirnov tests were not suitable in terms of this sample size, the absolute values of skewness and kurtosis were examined and normality assumption was checked (Kim, 2013). For the examining of skewness and kurtosis values, the cut-off values (Skewness ±3, Kurtosis ±7) proposed by Curran et al. (1996) were taken into account. Since the kurtosis value (7.149) of one item from the affect regulation dimension and the kurtosis value (10.168) of one item from the trust in government dimension exceed the recommended values (±7), these items were removed. The remaining statements’ skewness and kurtosis values did not violate the recommended values. Therefore, normal distribution assumption was met.

4. Findings

4.1. Demographic findings

As seen in Table 1 , while 50.2 % of the participants are men, 49.8 % are women. The percentage of married participants is 53.5 %. While the percentage of participants under the age of 25 is 25.3 %, the percentage of participants between the ages of 26–43 is 31 %. The percentage of those in the 44–55 age group is 31.4 %. While the percentage of high school graduates is 31.9 %, the percentage of undergraduates is 21.7 %. While 38.5 % of the participants have a monthly income of between 5001−7500 TL, 36.4 % earn an income over 7500 TL. While 49.6 % of the participants stated that they visited upscale restaurants once a week, 31.7 % stated that they visited upscale restaurants twice a week.

Table 1.

Result of measurement model.

Dimensions Items Std. Fac. Load. λ t values Construct Reliability AVE
Convenience because it is quick to prepare .93 *Fixed .86 .61
because it is easy to prepare .85 27.358
because it is easy and convenient to purchase .64 18.445
because it is readily available (e.g. at hand or being offered by someone) .68 20.266
Sociability because it is social .89 *Fixed .94 .75
so that I can spend time with other people .84 29.816
because it makes social gatherings more comfortable .92 36.073
because it is pleasant to eat with others .87 31.975
because it makes a social gathering more enjoyable .83 29.649
Visual because the presentation is appealing (e.g. packaging) .71 *Fixed .87 .71
because it spontaneously appeals to me (e.g. situated at eye level, appealing colors) because it is nicely presented .88 21.073
because it looks appealing .92 21.124
Affect regulation because I am sad .74 *Fixed .92 .71
because I feel lonely .92 25.004
as a distraction .89 24.101
because I feel stressed .93 25.258
because it cheers me up .69 18.034
Social Image because it is trendy .75 *Fixed .89 .63
because it makes me look good in front of others .79 20.511
because others like it .88 22.814
to stand out from the crowd .74 19.157
because it is considered to be special .79 20.455
Pleasure because I enjoy it .97 *Fixed .98 .93
in order to indulge myself .96 73.282
because it puts me in a good mood .97 70.893
in order to reward myself .98 56.466
because it is fun to eat .94 55.942
Risk The coronavirus/COVID-19 will NOT affect very many people in the country I’m currently living in. .99 *Fixed .99 .97
I will probably get sick with the coronavirus/COVID-19. .98 96.462
Getting sick with the coronavirus/COVID-19 can be serious. .96 104.391
How likely do you think it is that your travel will be affected by COVID-19 after 6 months? .99 146.786
How likely do you think it is that your friends and family in the country you are currently living in will be directly affected by COVID-19 after 6 months? .97 101.028
How worried are you personally about the COVID-19? .98 100.333
Trust Turkish government is open and upfront with me about COVID-19 restrictions. .99 *Fixed .98 .94
I believe Turkish government has high integrity about COVID-19 restrictions. .98 92.363
In general, I believe government motives and intentions are good about COVID-19 restrictions. .95 70.943
Turkish government is always honest and truthful about COVID-19 restrictions. .97 87.276
I think government treats me fairly about COVID-19 restrictions. .98 95.828
Visit intention I’m willing to visit an upscale restaurant, when I eat out. .92 *Fixed .92 .79
I plan to visit an upscale restaurant for eating, when I eat out. .98 44.052
I will make an effort to visit an upscale restaurant when I eat out. .76 26.451
Open in a new tab

Goodness-of-fit statistics, χ2/df=4.538; CFI=.94; TLI=.93; RMSEA=.072.

4.2. Structural model

Since the two-step approach recommended by Anderson and Gerbing (1988) was used for applying the structural model, the measurement model was examined at first. Goodness-of-fit indices regarding the analyzed measurement model were found acceptable (χ2/df = 4.634; CFI = .92; TLI = .91; RMSEA = .073) (Schermelleh-Engel et al., 2003). However, factor loadings of some items (one item from trust in government is -.004, one item from convenience is .366, and one item from visual is .397) were below .50. Therefore, any items that caused problems in terms of providing convergent validity were removed from the measurement model and the analysis was reapplied. The goodness-of-fit statistic values obtained by reapplying the measurement model after removing these items were acceptable (χ2/df = 4,538; CFI = .94; TLI = .93; RMSEA = .072) (Schermelleh-Engel et al., 2003).

Moreover, convergent and discriminant validities and composite reliability were examined. As all standard factor loadings were significant and AVE values exceeded .50, convergent validity was met. Because the composite reliability values examined for internal consistency were above the minimum recommended value, internal consistency was met (Bagozzi and Yi, 1988). Lastly, discriminant validity was checked in accordance with Fornell and Larcker’s (1981) method. Table 2 shows the AVE square root on the diagonal (bold) and the correlations estimated for each pair of constructs in the elements outside the diagonal. This, in turn, confirms discriminant validity (Fornell and Larcker, 1981).

Table 2.

Results of discriminant validity.

Discriminant validity Visit Risk Convenience Sociability Visual Affect regulation Social Image Pleasure Trust
Visit .889
Risk −.479 .985
Convenience .052 .049 .781
Sociability .240 −.253 .046 .869
Visual .033 −.114 .034 .007 .84
Affect regulation .097 −.078 .066 .085 −.024 .841
Social Image .065 .021 .023 .041 .090 .065 .791
Pleasure .040 −.055 .018 .127 .125 −.06 −.033 .965
Trust .338 −.104 −.013 .152 .056 .138 .072 .106 .973
Open in a new tab

After verifying the measurement model, the structural model was examined. The goodness-of-fit of structural model was deemed acceptable (χ2/df = 4.869; CFI = .92; TLI = .91; RMSEA = .075), and sociability (β = .23; t = 5.702; p < .001) and affect regulation (β = .07; t = 1.863; p < .10) had a positive impact on visit intention to upscale restaurants. Therefore, H1a and H1e are accepted; however, H1b, H1c, H1d, and H1f are rejected.

4.3. Moderating effect of risk perception

To test the moderating effect of risk perception, respondents were first divided into two groups based on their responses to the items discerning risk perception using K-means clustering. Groups were divided into low- (405) and high- (276) risk perception groups. Next, the configural invariance model was examined. As goodness-of-fit statistics of the configural invariance model were acceptable (χ2/df = 3.126; CFI = .92; TLI = .90; RMSEA = .056), this model was supported. Secondly, the configural invariance model was compared with metric invariance model using a chi-square difference test. No significant difference was found between the two models (Δχ2(21)-25.20; p = .239). Therefore, metric invariance was partially supported. After confirming that metric invariance was supported, the moderating effect of risk perception on the relationship between dining out motivations and visit intention was examined.

Table 3 presents the results of the multi-group analysis. The result of the chi-square test confirmed that significant differences were found in the relationships between dining out motivation factors (sociability, visual appeal, pleasure, affect regulation, and social image) and visit intention to upscale restaurants, according to low- and high-risk perception groups. In other words, risk perception had a moderating effect on the relationship between dining out motivation factors and visit intention to upscale restaurants. The effect of sociability, pleasure, and social image on visit intention to upscale restaurants was more determinative for the low-risk perception group. On the other hand, visual appeal and pleasure negatively affected visit intention to upscale restaurants for the high-risk perception group. Therefore, H2a, H2c, H2d, H2e, and H2f are accepted; however, H2b is rejected.

Table 3.

Result of moderating effect of risk perception.

Hypothesis Relations Std. Factor Loadings
 Δχ2 Support
High risk Low risk
H2a Sociability→Visit −.030 .385*** Δχ2(1)-31.00*** Yes
H2b Convenience→Visit .083 .022 Δχ2(1)-0.90 No
H2c Visual appeal→Visit −.138* .077 Δχ2(1)-7.20** Yes
H2d Pleasure→Visit −.147* .181*** Δχ2(1)-19.00*** Yes
H2e Affect regulation→Visit −.019 .152** Δχ2(1)-4.60* Yes
H2f Social image→Visit −.018 .170*** Δχ2(1)-4.10* Yes
Open in a new tab
*

p < .050.

**

p < .010.

***

p < .001.

4.4. Moderating effect of trust in government

To test the moderating effect of trust in government, first, the respondents were divided into two groups based on their responses to the items discerning trust in government using K-means clustering. The groups were named the low (389) and high (292) trust in government groups. In order to perform the MGA, the metric invariance is required to be met for the classified groups (Hair et al., 2009). Therefore, first, the configural invariance model, without constraining any factor loadings among groups, was examined. As the goodness-of-fit statistics obtained from the configural invariance model are at an acceptable level (χ2/df = 3.072; CFI = .92; TLI = .91; RMSEA = .055), this model was supported. Second, the full-metric invariance model, in which all factor loadings were equally constrained across groups was investigated. The chi-square difference test between the configural and metric invariance model was found to be not significant for each group (Hair et al., 2009). Therefore, the metric invariance was met partially for low-high groups of trust in government (Δχ2(20)-28.30; p = .102). After confirming that metric invariance was supported, the moderating effect of trust in government on the relationship between dining out motivations and visit intention was examined.

Table 4 presents the results of the multi-group analysis. The result of the chi-square test confirmed that significant differences were found in the relationships between dining out motivation factors (sociability and social image) and visit intention to upscale restaurants, according to the low and high trust in government groups. In other words, trust in government had a moderating effect on the relationship between dining out motivation factors and visit intention to upscale restaurants. The effect of sociability and social image on visit intention to upscale restaurants is more determinative for the group with high trust in government. When individuals trust the actions taken by the government with regard to COVID-19, the impact of sociability and social image on visit intention to upscale restaurants increase. However, it seems that the effect disappears for the group with low trust. Therefore, H3a and H3f are accepted; however, H3b, H3c, H3d, and H3e are rejected.

Table 4.

Result of moderating effect of trust in government.

Hypothesis Relations Std. Factor Loadings
 Δχ2 Support
High trust Low trust
H3a Sociability→Visit .355*** .017 Δχ2(1)-22.0*** Yes
H3b Convenience→Visit .069 .011 Δχ2(1)-0.70 No
H3c Visual appeal→Visit .006 .010 Δχ2(1)-0.00 No
H3d Pleasure→Visit −.008 .004 Δχ2(1)-0.00 No
H3e Affect regulation→Visit .051 .065 Δχ2(1)-0.00 No
H3f Social image→Visit .154** −.079 Δχ2(1)-8.80** Yes
Open in a new tab
**

p < .010.

***

p < .001.

5. Discussion and implications

It is beyond any doubt that one of the sectors experiencing the greatest impacts of COVID-19 is the tourism and hospitality industry (Knight et al., 2020; Yang et al., 2020). In this period of absolute uncertainty, countries have had no other choice than initiating the transition to controlled social life in order to avoid an economic dead end in the future. Based on the decreasing number of cases and deaths, and with the purpose of ensuring the sustainability of the economy, Turkey has allowed the reopening of hotel enterprises, restaurants, and cafés on the condition that all necessary measures and precautions are taken. As a result, the number of consumers visiting food and beverage enterprises has started to grow. However, just as in other pandemics (Cahyanto et al., 2016; Lobb et al., 2006; Tavitiyaman and Qu, 2013), it is foreseen that COVID-19 risk perceptions of consumers will greatly shape the magnitude of this mobilization. In the context of Turkey, which has given a good account of itself in management of the pandemic so far, it is considered that individuals’ trust in the measures implemented by government officials with the aim of reducing the perceived risk will also shape consumer behavior. Hence, the current study investigates moderating effects of consumers’ COVID-19 risk perceptions and trust in government in the relationship between dining out motivations and restaurant visit intention.

Within the framework of these findings, only socialization and affect regulation among various other motivation factors have significant positive effect on visit intention to upscale restaurants. Due to the curfew, as well as the prohibition of gathering, people have remained distant from their usual social environments (Berg-Weger and Morley, 2020; Bavel et al., 2020). With the reopening of restaurants, people might have perceived an opportunity to meet up and spend time with people in their social circles. Furthermore, within the scope of the transition to controlled social life, consumers (Stieger et al., 2020; Voitsidis et al., 2020) who are emotionally worn out due to the increasing number of cases and deaths in the pandemic process may have the intention to visit restaurants for emotional recovery. Examining the moderating effect of consumers’ risk perception, it is indicated that those with low-risk perceptions have stronger intention to visit upscale restaurants with the purpose of socialization, pleasure, affect regulation, and social image. This state of affairs points to the escalating severity of individuals’ needs for socialization. With the slow downward tendency of the pandemic, consumers have endeavored to recover from emotional and psychological exhaustion. Consumers may have thought that they could feel happy and treat themselves well by visiting upscale restaurants. Taking into account that social image was a dominant motive in the pre-pandemic period (Kang and Park, 2016; Yang and Mattila, 2014, 2017), it is considered that individuals will have the intention to visit upscale restaurants with the purpose of maintaining or upgrading their social image on the condition that the perceived risk is low. Another important finding concluded in the present study is that visual appeal of food generates a negative effect on restaurant visit intention in the event that the perceived risk is high. Having the presumption in the mind that visually appealing food would attract a lot of people, consumers might perceive upscale restaurants to be risky.

Examining the moderating effect of trust in measures implemented by government officials against the COVID-19 pandemic, it is observed that the positive effect of socialization and social image on intention to visit upscale restaurants comes to the forefront in consumers with high sense of trust in the government. On the other hand, the relationship in question is insignificant in consumer groups with low trust in the government. This finding highlights a vital need for people as social beings to meet with those in their social environment in such places as restaurants where leisure time can be used effectively. However, this necessitates building a sense of trust in the government.

Research findings provide some significant contributions to the hospitality literature. First, this study is one of the leading empirical studies investigating consumers’ attitude and behaviors toward restaurant enterprises in the transition to controlled social life. The study reveals that consumers’ motivation to visit upscale restaurants has changed during the pandemic. People who have been away from social environments for a long time owing to the curfew and social distancing rules consider restaurant establishments as a place for socialization in this period when the first steps toward social life have been taken. In addition, individuals who are emotionally exhausted by the growing number of deaths and new cases think that they will recover by spending time in restaurants. Another important contribution is that consumers’ COVID-19 risk perceptions have a moderating effect on the relationship between dining out motivations and visit intention. It is observed that motivation factors having no direct significant effect on visit intention generate significant effects as the moderating effect of risk perception is incorporated. This finding reveals the fact that risk perception as a factor shapes consumer behavior and attitudes to a remarkable extent in the pandemic process. Last but not least, taking into notice that government officials take on great responsibility in containing the pandemic, it is revealed that consumers’ overall trust in government about the measures has a significant impact on their behavior.

Within the framework of the findings acquired, several suggestions can be offered to destination management organizations and government officials. Despite the fact that the COVID-19 is a very challenging risk factor to control (Carballo et al., 2017), destination management organizations can inform consumers through various channels so as to reduce consumers’ COVID-19 risk perceptions (Jonas and Mansfeld, 2017) in such a period when a lot of fake news/information about the pandemic appears on social media (Bavel et al., 2020). Communication channels such as social media, corporate web pages, local TV channels, radio, and newspapers, can be used to emphasize that all protective measures enforced by the government (e.g., ensuring particular distance between tables, requiring all personnel to wear protective equipment, not offering menus in the restaurant, etc.) are complied with at the maximum level. So as to ensure credibility, information must be presented to consumers in an integrated or consistent manner. Particularly considering the fact that restaurants are to be preferred for socialization, consumers can be instilled with a certain level of awareness by way of reminding them at the entrance the necessity of complying with the rules in place. Acting as a driving force behind the implementation of central government decisions in enterprises, local government officials can reduce consumers’ risk perceptions. In addition, imposing severe sanctions on the enterprises which violate the rules could encourage other enterprises to follow suit.

Current research findings indicated that trust in government moderates the relationship between some of dining out motivations and visit intention to upscale restaurants. Accordingly, the research provides some practical implications for governmental officials. To create and reinforce public trust in government, governmental officials may consider the OECD’s (2017) framework on determinants of trust that is driven basic two components as competence (includes responsiveness, reliability) and values (includes openness, integrity and fairness). Providing quality public services through listening and responding citizens expectations and concerns about Covid-19 represent responsiveness of governmental institutions (Brezzi et al., 2020). For instance, as practiced in Turkey, all citizens with some symptoms should be able to reach the test kit comfortably and free of charge in the health institutions closest to where they live. Building a regular, transparent and unambiguous two-way communication channel with citizens, especially those affected by the pandemic and those in the risk groups, is highly influential on trust in government (WHO, 2020b). This not only build trust in the government, but also may help mitigate the impact of fake news and misinformation through social media. Therefore, it may be stated the responses given from different governmental institutions should be consistent to build and maintaining trust (Guglielmi et al., 2020). Moreover, all the citizens should gain a fair access to the governmental services and benefits that put into practice to mitigate the economic consequences of Covid-19. Han et al. (2020) revealed that governments perceived as well organized during the pandemic, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively linked with trust in government. Last but not least, creating a consistency between what is said and what is done, keeping the promises during and after Covid-19 pandemic are key determinants of integrity that is also one of the key dimensions of values that result in trust in government (Boğan and Dedeoğlu, 2017).

The measures have taken before and during the Covid-19 pandemic in Turkey may provide some good lessons for other destinations to create and foster citizens’ trust in government and to reduce their risk perceptions. In a recent cross-national study, Dryhurst et al. (2020) indicated that risk perception is negatively related to trust in government. When people are more trusting in their government, risk perceptions of coronavirus are lower. Thus, it is utmost importance to ensure the trust of citizens toward government. Firstly, although the first Covid-19 case in Turkey reported in 11 March 2020, the Ministry of Health had already set up the advisory board to fight and be ready against Covid-19. Following the recommendations of this board, government closed the borders and stopped international flights for some counties including China, Italy, South Korea and Iraq. Afterwards, it announced a total curfew initially for those who are over age of 65 and then for those are twenty and younger. Setting the board before the first Covid-19 case occurred and generally placing restrictions by listening to the recommendations of the board may have contributed to citizens’ trust in government. Therefore, other countries and destinations can create the perception that the steps taken are made in line with scientific data.

Secondly, the government started sending masks to all the citizens for free to promote face-mask use. Besides that, many countries (e.g., USA, Germany, Portugal) have attempted to evacuate their citizens abroad due to international travel restrictions. Similarly, Turkey as one of the leading countries brought about 25 thousand citizens from 59 countries by special flights. Adopting the social state understanding and acting in an embracing and protective manner of its citizens can also play a role in increasing citizens' perceptions of trust. All these proactive and fast reactive measures reflect the responsiveness of government which is one of the determinants of trust in government. In accordance with this, other countries and destinations can act similarly, at least during the pandemic, by adopting the social state understanding.

Lastly, Turkey host almost four million refugees (UNHCR, 2020). The government offers free Covid-19 test kits and treatment to anyone living in the country, without discrimination. Almost 12.288 refugees house reached one-off Covid-19 immediate cash benefits (UNHRC, 2020). The government stated that it is planned to provide cash assistance to families in need within the scope of the social assistance program, consisting of 3 phases. Within this scope, in first phase 2,1 million, second phase 2,3 million house reached 1000 Turkish Liras within this program. As in most countries, many workers and businesses throughout the pandemic in Turkey have also experienced economic difficulties. In these difficult processes, the efforts of the state to help its citizens within the scope of its economic power (as much as it can) may have created the perception that they are not left alone during the pandemic. All these benevolent, fair and responsive actions would indeed result in ensuring and fostering governmental trust. Therefore, it should be suggested that other countries and destinations should pay attention to these similar issues.

6. Limitations and further research directions

One of the greatest limitations employed by the current study is that the research data were only collected in the context of Istanbul, Turkey. Although Istanbul is the number one province where the outbreak has been experienced, comparing the findings in the context of other provinces where the number of cases is not so intense could yield important results. Consumers’ risk perceptions may differ in provinces with lower numbers of cases. The other important limitation in the study is that only six dimensions were analyzed within the scope of consumer dining out motivations. In future research, factors such as price, celebration, and subjective well-being can be included in the research model, thus paving the way for acquiring more comprehensive results. Another limitation is that no control variable was used while testing the model. Factors such as age, family members’ composition, underlying health condition, income level, and type of job may affect consumers’ intention to dine out during the pandemic period. In future research, the moderating effects of these factors on the model could be tested.

Additionally, future studies may apply protection motivation theory (Rogers, 1975) through investigating the role of some psychological constructs such as perceived severity, perceived vulnerability, self-efficacy, and maladaptive perceptions that could affect restaurant visit intention. Although some previous tourism and hospitality papers have used protection motivation theory in various research contexts (Harris et al., 2018; Ruan et al., 2020; Wang et al., 2019), to best our knowledge, to date no empirical studies have applied the theory while examining diners’ behavioral intentions in the current COVID-19 pandemic. Such studies may provide deeper knowledge about understanding customers’ attitudes and behaviors under the current pandemic. It should be remembered that Turkey is among the countries which have successfully managed the pandemic process, leading to a relatively low number of national cases. Accordingly, comparative findings could be presented by testing a similar model in the countries where the number of cases is relatively high.

Footnotes

Appendix A

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijhm.2021.102905.

Appendix A. Supplementary data

The following is Supplementary data to this article:

mmc1.zip (589B, zip)

Data availability

Data will be made available on request.

References

  1. Ahmad M.S., Jamil A., Latif K.F., Ramayah T., Leen J.Y.A., Memon M., Ullah R. Using food choice motives to model Pakistani ethnic food purchase intention among tourists. Br. Food J. 2020;122(6):1731–1753. [Google Scholar]
  2. Ali F., Rasoolimanesh S.M., Sarstedt M., Ringle C.M., Ryu K. An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int. J. Contemp. Hosp. Manage. 2018;30(1):514–538. [Google Scholar]
  3. Anderson J.C., Gerbing D.W. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 1988;103(3):411–423. [Google Scholar]
  4. Andersson T.D., Mossberg L. The dining experience: do restaurants satisfy customer needs? Food Serv. Technol. 2004;4(4):171–177. [Google Scholar]
  5. Atsız O. Virtual reality technology and physical distancing: a review on limiting human interaction in tourism. J. Multidisc. Acad. Tour. 2021;6(1):27–35. [Google Scholar]
  6. Bagozzi R.P., Yi Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988;16(1):74–94. [Google Scholar]
  7. Balog-Way D.H., McComas K.A. COVID-19: reflections on trust, tradeoffs, and preparedness. J. Risk Res. 2020:1–11. [Google Scholar]
  8. Bavel J.J.V., Baicker K., Boggio P.S., Capraro V., Cichocka A., Cikara M., et al. Using social and behavioural science to support COVID-19 pandemic response. Nat. Hum. Behav. 2020;4:460–471. doi: 10.1038/s41562-020-0884-z. [DOI] [PubMed] [Google Scholar]
  9. BBC . 2020. Coronavirus: Paris to Shut Bars and Raise Alert to Maximum.https://www.bbc.com/news/world-europe-54413563 Retrieved from. [Google Scholar]
  10. Berg-Weger M., Morley J.E. Loneliness and social isolation in older adults during the Covid-19 pandemic: implications for gerontological social work. J. Nutr. Health Aging. 2020;24(5):456–458. doi: 10.1007/s12603-020-1366-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bilge Y., Bilge Y. Investigation of the effects of coronavirus and social isolation on psychological symptoms in terms of psychological resilience and coping styles. Turk. J. Clin. Psychiatry. 2020;23:38–51. [Google Scholar]
  12. Boğan E., Dedeoğlu B.B. The effects of perceived behavioral integrity of supervisors on employee outcomes: moderating effects of tenure. J. Hosp. Mark. Manage. 2017;26(5):511–531. [Google Scholar]
  13. Bradbury-Jones C., Isham L. The pandemic paradox: the consequences of COVID‐19 on domestic violence. J. Clin. Nurs. 2020;29:2047–2049. doi: 10.1111/jocn.15296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brewer P., Sebby A.G. The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. Int. J. Hosp. Manag. 2021;94 doi: 10.1016/j.ijhm.2020.102777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Brezzi M., González S., Prats M. The OECD Statistics Newsletter; 2020. All you need is trust: Informing the role of government in the Covid-19 context.http://www.oecd.org/gov/all-you-need-is-trust-statistics-newsletter-12-2020.pdf Issue No. 73. Retrieved from. [Google Scholar]
  16. Brouder P. Reset redux: possible evolutionary pathways towards the transformation of tourism in a COVID-19 world. Tour. Geogr. 2020:1–7. [Google Scholar]
  17. Byrd K., Her E., Fan A., Almanza B., Liu Y., Leitch S. Restaurants and COVID-19: What are consumers’ risk perceptions about restaurant food and its packaging during the pandemic? Int. J. Hosp. Manag. 2021;94 doi: 10.1016/j.ijhm.2020.102821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cahyanto I., Wiblishauser M., Pennington-Gray L., Schroeder A. The dynamics of travel avoidance: the case of Ebola in the US. Tour. Manage. Perspect. 2016;20:195–203. doi: 10.1016/j.tmp.2016.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Carballo R.R., León C.J., Carballo M.M. The perception of risk by international travellers. Worldw. Hosp. Tour. Themes. 2017;9(5):534–542. [Google Scholar]
  20. Chen W. The effects of different types of trust on consumer perceptions of food safety. China Agric. Econ. Rev. 2013;5(1):43–65. [Google Scholar]
  21. Choi J., Lee A., Ok C. The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: a study of street food. J. Travel Tour. Mark. 2013;30(3):222–237. [Google Scholar]
  22. Curran P.J., West S.G., Finch J.F. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol. Methods. 1996;1(1):16–29. [Google Scholar]
  23. de Jonge J., Van Trijp H., Jan Renes R., Frewer L. Understanding consumer confidence in the safety of food: its two‐dimensional structure and determinants. Risk Anal.: Int. J. 2007;27(3):729–740. doi: 10.1111/j.1539-6924.2007.00917.x. [DOI] [PubMed] [Google Scholar]
  24. de Zwart O., Veldhuijzen I.K., Elam G., Aro A.R., Abraham T., Bishop G.D., Voeten H.A.C.M., Richardus J.H., Brug J. Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: results of an international survey. Int. J. Behav. Med. 2009;16(1):30–40. doi: 10.1007/s12529-008-9008-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Demirbilek Y., Pehlivantürk G., Özgüler Z.Ö., Meşe E.A. COVID-19 outbreak control, example of ministry of health of Turkey. Turk. J. Med. Sci. 2020;50:489–494. doi: 10.3906/sag-2004-187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Dowd K., Burke K.J. The influence of ethical values and food choice motivations on intentions to purchase sustainably sourced foods. Appetite. 2013;69:137–144. doi: 10.1016/j.appet.2013.05.024. [DOI] [PubMed] [Google Scholar]
  27. Dryhurst S., Schneider C.R., Kerr J., Freeman A.L.J., Recchia G., van der Bles A.M., Spiegelhalter D., van der Linden S. Risk perceptions of COVID-19 around the world. J. Risk Res. 2020:1–13. [Google Scholar]
  28. Dubois B., Laurent G. Attitudes toward the concept of luxury: an exploratory analysis. Asia-Pacific Adv. Consumer Res. 1994;1:273–278. [Google Scholar]
  29. Elliott A.C., Woodward W.A. Sage; 2007. Statistical Analysis Quick Reference Guidebook: With SPSS Examples. [Google Scholar]
  30. Farooq A., Laato S., Islam A.N. Impact of online information on self-isolation intention during the COVID-19 pandemic: cross-sectional study. J. Med. Internet Res. 2020;22(5) doi: 10.2196/19128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Field A. Sage; 2013. Discovering Statistics Using IBM SPSS Statistics. [Google Scholar]
  32. Fornell C., Larcker D.F. Evaluating structural models with unobservable variables and measurement error. J. Mark. Res. 1981;18(1):39–50. [Google Scholar]
  33. Gössling S., Scott D., Hall C.M. Pandemics, tourism and global change: a rapid assessment of COVID-19. J. Sustain. Tour. 2020:1–20. [Google Scholar]
  34. Grayson K., Johnson D., Chen D.F.R. Is firm trust essential in a trusted environment? How trust in the business context influences customers. J. Mark. Res. 2008;45(2):241–256. [Google Scholar]
  35. Guglielmi S., Dotti Sani G.M., Molteni F., Biolcati F., Chiesi A.M., Ladini R., Maraffi M., Pedrazzani A., Vezzoni C. Public acceptability of containment measures during the COVID-19 pandemic in Italy: how institutional confidence and specific political support matter. Int. J. Sociol. Soc. Policy. 2020;40(9/10):1069–1085. [Google Scholar]
  36. Hair J.F., Black W.C., Babin B.J., Anderson R.E. 7th ed. Prentice Hall; 2009. Multivariate Data Analysis. [Google Scholar]
  37. Hair J.F., Black W.C., Babin B.J., Anderson R.E. 7th ed. Pearson Higher.; Harlow: 2013. Multivariate Data Analysis (Pearson New International Edition. [Google Scholar]
  38. Han Q., Zheng B., Cristea M., Agostini M., Belanger J., Gutzkow B., Kreienkamp J., Leander P. 2020. Trust in Government and its Associations with Health Behaviour and Prosocial Behaviour During the Covid-19 Pandemic. PsyArXiv Preprints. [Google Scholar]
  39. Harris K.J., Ali F., Ryu K. Foodborne illness outbreaks in restaurants and patrons’ propensity to return. Int. J. Contemp. Hosp. Manage. 2018;30(3):1273–1292. [Google Scholar]
  40. Hosmer L.T. Trust: the connecting link between organizational theory and philosophical ethics. Acad. Manag. Rev. 1995;20(2):379–403. [Google Scholar]
  41. Hurriyet . 2020. İçişleri Bakanlığı’ndan restoranlar ve kafeler için yeni genelge! Detaylar belli oldu.https://www.hurriyet.com.tr/gundem/icisleri-bakanligindan-restoranlar-ve-kafeler-icin-yeni-genelge-detaylar-belli-oldu-41529299 Retrieved from. [Google Scholar]
  42. Huynh T.L. The COVID-19 risk perception: a survey on socioeconomics and media attention. Econ. Bull. 2020;40(1):758–764. [Google Scholar]
  43. Iaquinto B.L. Tourist as vector: viral mobilities of COVID-19. Dialogues Hum. Geogr. 2020;10(2):174–177. [Google Scholar]
  44. ISO . İstanbul Statistic Office; 2020. COVID-19’un günlük hayatı etkileme durumu.https://www.verikaynagi.com/grafik/covid-19un-gunluk-hayati-etkileme-durumu/ [Google Scholar]
  45. Jackson B., Cooper M.L., Mintz L., Albino A. Motivations to eat: scale development and validation. J. Res. Pers. 2003;37(4):297–318. [Google Scholar]
  46. Jeong E., Jang S.S., Behnke C., Anderson J., Day J. A scale for restaurant customers’ healthy menu choices: individual and environmental factors. Int. J. Contemp. Hosp. Manage. 2019;31(1):217–246. [Google Scholar]
  47. Jonas A., Mansfeld Y. Exploring the interplay between the use of risk-related information, risk perception formation, and the stages of travel product consumption. Curr. Issues Tour. 2017;20(14):1470–1488. [Google Scholar]
  48. Kang Y.J., Park S.Y. The perfection of the narcissistic self: a qualitative study on luxury consumption and customer equity. J. Bus. Res. 2016;69(9):3813–3819. [Google Scholar]
  49. Kim H.Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor. Dent. Endod. 2013;38(1):52–54. doi: 10.5395/rde.2013.38.1.52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Kim J., Lee J.C. Effects of COVID-19 on preferences for private dining facilities in restaurants. J. Hosp. Tour. Manag. 2020;45:67–70. [Google Scholar]
  51. Kim S.W., Su K.P. Using psychoneuroimmunity against COVID-19. Brain Behav. Immun. 2020;87:4–5. doi: 10.1016/j.bbi.2020.03.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kim W.G., Lee Y.K., Yoo Y.J. Predictors of relationship quality and relationship outcomes in luxury restaurants. J. Hosp. Tour. Res. 2006;30(2):143–169. [Google Scholar]
  53. Kim D.J., Ferrin D.L., Rao H.R. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 2008;44(2):544–564. [Google Scholar]
  54. Kim Y.G., Eves A., Scarles C. Building a model of local food consumption on trips and holidays: a grounded theory approach. Int. J. Hosp. Manag. 2009;28(3):423–431. [Google Scholar]
  55. Klaus P., Manthiou K. Applying the EEE customer mindset in Luxury: reevaluating customer experience research and practice during and after Corona. J. Serv. Manag. 2020;31(6):1175–1183. [Google Scholar]
  56. Knight D.W., Xiong L., Lan W., Gong J. Impact of COVID-19: research note on tourism and hospitality sectors in the epicenter of Wuhan and Hubei Province, China. Int. J. Contemp. Hosp. Manage. 2020;32(12):3705–3719. [Google Scholar]
  57. Koca F. Ministry of Health; 2020. Dışarı Çıkmayalım, Virüse Fırsat Tanımayalım.https://www.saglik.gov.tr/TR,64846/bakan-koca-disari-cikmayalim-viruse-firsat-tanimayalim.html [Google Scholar]
  58. Kwun D.J.W., Hwang J.H., Kim T.H. Eating-out motivations and variety-seeking behavior: an exploratory approach on loyalty behavior. J. Hosp. Mark. Manage. 2013;22(3):289–312. [Google Scholar]
  59. Laato S., Islam A.N., Farooq A., Dhir A. Unusual purchasing behavior during the early stages of the COVID-19 pandemic: the stimulus-organism-response approach. J. Retail. Consum. Serv. 2020;57 [Google Scholar]
  60. Lee J.H., Hwang J. Luxury marketing: the influences of psychological and demographic characteristics on attitudes toward luxury restaurants. Int. J. Hosp. Manag. 2011;30(3):658–669. [Google Scholar]
  61. Lobb A.E., Mazzocchi M., Traill W.B. Risk perception and chicken consumption in the avian flu age – a consumer behaviour study on food safety information. Selected Paper Presented at the American Agricultural Economics Annual Meeting; Long Beach, CA, 23–26 July; 2006. [Google Scholar]
  62. Lockie S., Lyons K., Lawrence G., Grice J. Choosing organics: a path analysis of factors underlying the selection of organic food among Australian consumers. Appetite. 2004;43(2):135–146. doi: 10.1016/j.appet.2004.02.004. [DOI] [PubMed] [Google Scholar]
  63. Luhman N. John Wiley; New York: 1979. Trust and Power. [Google Scholar]
  64. Luo Y., Xu X. Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. Int. J. Hosp. Manag. 2021;94 doi: 10.1016/j.ijhm.2020.102849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. MacDonald A. In: Transparency: The Key to Better Governance? Hood C., Heald D., editors. Oxford University Press; Oxford: 2006. What hope for freedom of information in the UK? [Google Scholar]
  66. Mainous A. A towering babel of risk information in the COVID-19 pandemic: trust and credibility in risk perception and positive public health behaviors. Fam. Med. 2020;52(5):317–319. doi: 10.22454/FamMed.2020.530121. [DOI] [PubMed] [Google Scholar]
  67. Mayer R.C., Davis J.H., Schoorman F.D. An integrative model of organizational trust. Acad. Manag. Rev. 1995;20(3):709–734. [Google Scholar]
  68. McComas K.A., Trumbo C.W. Source credibility in environmental health–risk controversies: application of meyer’s credibility index. Risk Anal. 2001;21(3):467–480. doi: 10.1111/0272-4332.213126. [DOI] [PubMed] [Google Scholar]
  69. Ministry of Health . 2021. General Coronavirus Table.https://covid19.saglik.gov.tr/TR-66935/genel-koronavirus-tablosu.html Available at. [Google Scholar]
  70. Mishra A.K. In: Trust in Organizations. Kramer R.M., Tyler T., editors. Sage; Newbury Park, CA: 1996. Organizational responses to crisis: the centrality of trust; pp. 261–287. [Google Scholar]
  71. Mishra P., Pandey C.M., Singh U., Gupta A., Sahu C., Keshri A. Descriptive statistics and normality tests for statistical data. Ann. Card. Anaesth. 2019;22(1):67. doi: 10.4103/aca.ACA_157_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Nadler S. 2020. COVID-19: Information for Fast Food Outlets, Cafes and Restaurants in the UK Operating Takeaway and Delivery Services.https://www.twobirds.com/en/news/articles/2020/uk/covid-19-information-for-fast-food-outlets-cafes-and-restaurants-in-the-uk Retrieved from. [Google Scholar]
  73. Nakayachi K., Cvetkovich G. Public trust in government concerning tobacco control in Japan. Risk Anal.: Int. J. 2010;30(1):143–152. doi: 10.1111/j.1539-6924.2009.01306.x. [DOI] [PubMed] [Google Scholar]
  74. Naumov N., Varadzhakova D., Naydenov A. Sanitation and hygiene as factors for choosing a place to stay: perceptions of the Bulgarian tourists. Anatolia. 2020:1–4. [Google Scholar]
  75. OECD . OECD Publishing; Paris: 2017. Trust and Public Policy: How Better Governance Can Help Rebuild Public Trust. [DOI] [Google Scholar]
  76. Ponnam A., Balaji M.S. Matching visitation-motives and restaurant attributes in casual dining restaurants. Int. J. Hosp. Manag. 2014;37:47–57. [Google Scholar]
  77. Renner B., Sproesser G., Strohbach S., Schupp H.T. Why we eat what we eat. The Eating Motivation Survey (TEMS) Appetite. 2012;59(1):117–128. doi: 10.1016/j.appet.2012.04.004. [DOI] [PubMed] [Google Scholar]
  78. Robinson S.L., Rousseau D.M. Violating the psychological contract: not the exception but the norm. J. Organ. Behav. 1994;15(3):245–259. [Google Scholar]
  79. Rogers R.W. A protection motivation theory of fear appeals and attitude change1. J. Psychol. 1975;91(1):93–114. doi: 10.1080/00223980.1975.9915803. [DOI] [PubMed] [Google Scholar]
  80. Ruan W., Kang S., Song H. Applying protection motivation theory to understand international tourists’ behavioural intentions under the threat of air pollution: A case of Beijing, China. Curr. Issues Tour. 2020;23(16):2027–2041. [Google Scholar]
  81. Rubin G.J., Amlôt R., Page L., Wessely S. Public perceptions, anxiety, and behaviour change in relation to the swine flu outbreak: cross sectional telephone survey. BMJ. 2009;339:b2651. doi: 10.1136/bmj.b2651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Rudolph T.J. Political trust, ideology, and public support for tax cuts. Public Opin. Q. 2009;73(1):144–158. [Google Scholar]
  83. Schermelleh-Engel K., Moosbrugger H., Müller H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Methods Psychol. Res. Online. 2003;8(2):23–74. [Google Scholar]
  84. Schiffman L., Kanuk L. 8th ed. Pearson Prentice Hall; Upper Saddle River: 2004. Consumer Behaviour. [Google Scholar]
  85. Sigala M. Tourism and COVID-19: impacts and implications for advancing and resetting industry and research. J. Bus. Res. 2020;117:312–321. doi: 10.1016/j.jbusres.2020.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Sobaih A.E.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. doi: 10.1016/j.ijhm.2020.102824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Soper D. 2020. A-priori Sample Size Calculator for Structural Equation Models [Software]https://www.danielsoper.com/statcalc/calculator.aspx?id=89 Available at: www.Danielsoper.Com/Statcalc. [Google Scholar]
  88. Steptoe A., Pollard T.M., Wardle J. Development of a measure of the motives underlying the selection of food: the food choice questionnaire. Appetite. 1995;25(3):267–284. doi: 10.1006/appe.1995.0061. [DOI] [PubMed] [Google Scholar]
  89. Stewart H., Walker P. 2020. Coronavirus UK: Boris Johnson Announces Closure of All UK Pubs and Restaurants.https://www.theguardian.com/world/2020/mar/20/london-pubs-cinemas-and-gyms-may-close-in-covid-19-clampdown Retrieved from. [Google Scholar]
  90. Stieger S., Lewetz D., Swami V. 2020. Psychological Well-being Under Conditions of Lockdown: An Experience Sampling Study in Austria During the COVID-19 Pandemic. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Tavitiyaman P., Qu H. Destination image and behavior intention of travelers to Thailand: the moderating effect of perceived risk. J. Travel Tour. Mark. 2013;30(3):169–185. [Google Scholar]
  92. Thomas A.V., Kalidindi S.N., Ananthanarayanan K.A.B.T. Risk perception analysis of BOT road project participants in India. Constr. Manag. Econ. 2003;21(4):393–407. [Google Scholar]
  93. UNHCR . 2020. UNHCR Turkey Stats.https://www.unhcr.org/tr/en/unhcr-turkey-stats Retrieved from. [Google Scholar]
  94. Verma V.K., Chandra B., Kumar S. Values and ascribed responsibility to predict consumers’ attitude and concern towards green hotel visit intention. J. Bus. Res. 2019;96:206–216. [Google Scholar]
  95. Voitsidis P., Gliatas I., Bairachtari V., Papadopoulou K., Papageorgiou G., Parlapani E., Syngelakis M., Holeva V., Diakogiannis I. Insomnia during the COVID-19 pandemic in a Greek population. Psychiatry Res. 2020 doi: 10.1016/j.psychres.2020.113076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. 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 [Google Scholar]
  97. Wen J., Kozak M., Yang S., Liu F. COVID-19: potential effects on Chinese citizens’ lifestyle and travel. Tour. Rev. 2020;76(1):74–87. [Google Scholar]
  98. Westland J.C. Vol. 11. Elsevier; 2012. Erratum: Lower bounds on sample size in structural equation modeling (electronic commerce research and applications (2010) 9:6 (476-487)) (Electronic Commerce Research and Applications). Issue 4, p. 445. [DOI] [Google Scholar]
  99. WHO . 2020. WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19–11 March.https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 Accessed 13 April 2020. [Google Scholar]
  100. WHO . 2020. COVID-19 Strategy Update–14 April 2020.https://www.who.int/publications/i/item/covid-19-strategy-update---14-april-2020 Retrieved from: [Google Scholar]
  101. Williams C.C., Kayaoglu A. COVID-19 and undeclared work: impacts and policy responses in Europe. Serv. Ind. J. 2020:1–18. [Google Scholar]
  102. Williams D.J., Noyes J.M. How does our perception of risk influence decision-making? Implications for the design of risk information. Theor. Issues Ergon. Sci. 2007;8(1):1–35. [Google Scholar]
  103. Wong C.M.L., Jensen O. The paradox of trust: perceived risk and public compliance during the COVID-19 pandemic in Singapore. J. Risk Res. 2020:1–10. [Google Scholar]
  104. Worldometer . 2021. COVID-19 Coronavirus Pandemic.https://www.worldometers.info/coronavirus/ Retrieved from. [Google Scholar]
  105. Yang W., Mattila A.S. Do affluent customers care when luxury brands go mass? Int. J. Contemp. Hosp. Manage. 2014;26(4):526–543. [Google Scholar]
  106. Yang W., Mattila A.S. The impact of status seeking on consumers’ word of mouth and product preference—a comparison between luxury hospitality services and luxury goods. J. Hosp. Tour. Res. 2017;41(1):3–22. [Google Scholar]
  107. Yang Y., Liu H., Chen X. COVID-19 and restaurant demand: early effects of the pandemic and stay-at-home orders. Int. J. Contemp. Hosp. Manage. 2020;32(12):3809–3834. [Google Scholar]
  108. Yılmaz A. Milliyet; 2020. İşte Kovid-19 savaşçısı bilim insanları. Accessed 28 February 2020. [Google Scholar]
  109. Zheng Y., Goh E., Wen J. The effects of misleading media reports about COVID-19 on Chinese tourists’ mental health: a perspective article. Anatolia. 2020;31(2):337–340. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.zip (589B, zip)

Data Availability Statement

Data will be made available on request.

Articles from International Journal of Hospitality Management are provided here courtesy of Elsevier

RESOURCES