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Article

Pandemic Dining Dilemmas: Exploring the Determinants of Korean Consumer Dining-Out Behavior during COVID-19

by
Unji Baek
1 and
Seul Ki Lee
1,2,*
1
Tourism Industry Data Analytics Lab (TIDAL), Sejong University, Seoul 05006, Republic of Korea
2
Department of Hospitality and Tourism Management, Sejong University, Seoul 05006, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(10), 8323; https://doi.org/10.3390/su15108323
Submission received: 12 April 2023 / Revised: 17 May 2023 / Accepted: 17 May 2023 / Published: 19 May 2023
(This article belongs to the Special Issue Meeting the Challenges and New Opportunities: Sustainable Development of Tourism and Hospitality Industry Following the COVID-19 Pandemic)
Browse Figure
Review Reports Versions Notes

Abstract

:
Amid the COVID-19 pandemic’s significant impact on the dining-out industry, this study examined factors influencing consumers’ dining-out behavior changes using a unified theoretical framework based on the theory of planned behavior (TPB) and select components of the risk information seeking and processing (RISP) model. A quantitative research method was employed, analyzing 536 valid survey responses collected in South Korea in early 2021 using partial least squares structural equation modeling (PLS-SEM). Findings showed that consumer attitude, perceived control, and subjective norm positively influenced dining-out intention, supporting the TPB. However, risk information-seeking behavior discourages dining-out behavior without significantly affecting intention. Fear emerged as a determinant of dining-out intention, risk information-seeking behavior, and dining-out behavior, highlighting the importance of emotions over rational thinking. This study contributes to existing literature by incorporating dining-out intention, COVID-19-related information-seeking behavior, and fear as key antecedents of dining-out behavior during the pandemic, while validating formative indicators that constitute risk information-seeking behavior and dining-out behavior in the research model.

1. Introduction

The transition from COVID-19 being a pandemic to an endemic state led to the restoration of a semblance of normalcy and stability worldwide. However, the virus undoubtedly thrust the international community into an unprecedented situation [1,2]. During the initial phase of the novel coronavirus (SARS-CoV-2) outbreak in December 2019, there were no available vaccines or effective treatments. As a result, non-pharmaceutical interventions (NPIs) were implemented globally to reduce the spread of COVID-19 through physical means. In South Korea, stringent NPI measures were enforced, including mask-wearing, self-isolation, social distancing, quarantine, and contact tracing. Additionally, restrictions on operating hours were imposed for specific businesses, including restaurants and bars. These measures inevitably led to social and economic changes, with the hospitality and tourism industry facing a significant crisis due to its reliance on mobility.
COVID-19 is the third global pandemic declared by the World Health Organization (WHO), following the Hong Kong flu in 1968 and the H1N1 influenza in 2009. While the COVID-19 crisis shares similarities with previous epidemics such as SARS and MERS, it also presents distinct characteristics. Despite prior experiences with coronaviruses during the SARS and MERS outbreaks, people’s perception of risk and fear associated with COVID-19 proved substantially higher [3]. The lower case fatality rate of COVID-19 (4.4%) compared to MERS (34.3%) and SARS (9.6%) [4] did not mitigate the global sense of crisis [5].
The imposition of limited operating hours and the shuttering of businesses amid the COVID-19 pandemic led to a substantial decline in the industry [6]. This situation, however, created opportunities for scholarly research to enhance the comprehension of the foodservice sector and its capacity to withstand unparalleled challenges [7,8]. Although the rise in delivery and take-away services may appear as an expansion of the dining-out concept, it cannot fully replace the authentic experience of dining-out [6,9]. Dining-out offers individuals a brief respite and an opportunity to recharge from their daily routines [10]. It provides more than just a simple solution to hunger and household chores, as it enables social and cultural activities to take place with minimal time and cost investment. While delivery and take-away services may partially fulfill this function, they cannot entirely replicate the social and cultural experience of dining-out.
The COVID-19 pandemic prompted a marked increase in risk-related research within the realm of hospitality literature. These findings chiefly underscore the significance of managerial endeavors in maintaining hygiene and implementing preventive measures [11,12,13,14], as well as the communication strategies employed to convey these efforts [15,16,17,18]. Nevertheless, effective communication entails not only adept operational management and execution at the individual establishment level but also a comprehension of how risk information-seeking behavior and fear associated with the pandemic can impact intentions and behaviors. A thorough investigation of the underlying mechanisms governing these relationships is crucial for devising more efficacious interventions. This research aims to investigate the factors influencing consumers’ dining-out behavior change in response to uncertainties and fear posed by COVID-19, using a combined theoretical framework based of the theory of planned behavior (TPB) and the select components of the risk information seeking and processing (RISP) model. To this end, the study seeks to identify the antecedents affecting consumers’ dining-out behavior and establish a model elucidating the relationships among these factors, considering cognitive, emotional, and social aspects. Specifically, the objectives of the study are to (1) investigate the impact of behavioral attitude, perceived control, and subjective norm towards dining-out during the COVID-19 pandemic on dining-out intention, (2) assess the influence of risk information-seeking behavior and fear of COVID-19 over dining-out intention and behavior, (3) evaluate the relationship among dining-out intention, information-seeking behavior, fear of COVID-19, and dining-out behavior change, and (4) analyze the significance of formative indicators constituting risk information-seeking behavior and dining-out behavior in the context of the COVID-19 pandemic.

2. Literature Review

2.1. Dining-Out Intention and Behavior

Attitude can be described as a favorable or unfavorable assessment of a specific object, closely associated with an enduring predisposition that encompasses cognition, emotion, and behavioral intention. The relationship between attitude and behavior is intricate, as it involves not only the attitude towards the object but also the attitude and efficacy of the behavior itself as a social position in the context of interpersonal interactions. The theory of reasoned action (TRA) [19,20] significantly advanced the understanding of this relationship by differentiating between attitudes towards the object and attitudes towards behavior, thereby enhancing the predictive power of attitude in determining behavioral intention and behavior. This broader perspective, which regards attitudes towards behavior as a motivational force stemming from a micro-level preference and evaluation of the “object”, constructs individual attitudes towards behavior and societal normative perceptions, thereby augmenting the connection between behavioral intention and actual behavior [21]. The TRA posits that an individual’s attitude and subjective norms pertaining to a specific behavior are the primary antecedents of behavioral intention, which, in turn, impacts their behavior and potential behavioral modifications [20]. The TPB extends the TRA by integrating the perceived behavioral control variable, accounting for situational constraints and individual efficacy [22]. As a result, the TPB provides a comprehensive theoretical framework for examining human behavior within the domain of social science.
Recent studies that adopted the TPB extended the framework by incorporating additional factors. A multitude of investigations within the hospitality domain utilized the TPB to elucidate an individual’s decision-making process and predict their propensity for engaging in particular behaviors. These behaviors include organic food and menu selection [23], landscape restaurant visits [24], visiting green restaurants [25], and employing mobile food delivery applications [26]. Based on the TPB framework and the interplay between the constructs, we propose the following hypotheses:
Hypothesis 1.
Dining-out attitude positively influences dining-out intention during the COVID-19 pandemic.
Hypothesis 2.
Perceived dining-out control positively influences dining-out intention during the COVID-19 pandemic.
Hypothesis 3.
Subjective dining-out norm positively influences dining-out intention during the COVID-19 pandemic.
Hypothesis 4.
Dining-out intention positively influences dining-out behavior during the COVID-19 pandemic.

2.2. Risk Information-Seeking Behavior and Dining-Out Behavior

Behavior cannot be solely attributed to internal motivation or intention. Contextual barriers may prevent motivation or intention from consistently resulting in actual behavior [27,28,29]. The intention–behavior gap can also be ascribed to social influence or situational interaction, as converting behavioral intention into action necessitates individual effort and collective agreement. In the realm of health-related behaviors, social influence was demonstrated to play a particularly crucial role [30,31].
During crises, public perception is fluid and closely tied to communication and interventions in response to the crisis. The cultivation theory [32,33] underscores the impact of media messages on shaping individuals’ perceptions of their surroundings, including attitudes, values, beliefs, and expectations. Media exposure progressively molds an individual’s worldview, culminating in a perception of reality akin to that depicted by the media. The social amplification of risk theory [34] contends that risk perception can be magnified or diminished through social communication, with risks being formed through societal or media interactions rather than being solely defined by their physical characteristics. According to this theory, groups exposed to higher frequencies or intensities of risk-related communication display greater risk sensitivity than those who are not. Risk perception and cognition emerge through individual beliefs during information communication, fostering engagement in information communication in a cyclical fashion [35,36]. Especially in hazardous situations, humans tend to be more proactive in the communication process [35,36,37]. Consequently, risk information acquired through active information behavior encourages preventive measures [35,36,38]. Based on this line of reasoning, we expect the following relationships to hold:
Hypothesis 5.
COVID-19 related risk information-seeking behavior negatively influences dining-out intention during the COVID-19 pandemic.
Hypothesis 6.
COVID-19 related risk information-seeking behavior negatively influences dining-out behavior during the COVID-19 pandemic.
In situations where the consequences of specific behavior are uncertain, individuals tend to take into account more intricate aspects, such as social implications and potential outcomes of their actions [39,40,41]. Consequently, individuals acquire external information through communication and integrate it into their decision-making processes. Griffin and colleagues introduced the RISP model, arguing that risk information behavior significantly influences an individual’s engagement in preventive behaviors, in conjunction with their motivation and competence. The RISP model differentiates information behavior into information-seeking behaviors and processing strategies, suggesting that these factors can ultimately impact an individual’s adoption of preventive measures [36]. Moreover, the RISP model recognizes sociodemographic and cultural characteristics, subjective norms, risk perception, emotional responses, information sufficiency, and information-gathering abilities as influential elements in risk information behavior. Expanding upon the realm of social norms affecting risk information-seeking behavior, we hypothesize:
Hypothesis 7.
Subjective dining-out norm positively influences COVID-19 related risk information-seeking behavior during the COVID-19 pandemic.

2.3. Pandemic Fear and Dining-Out Behavior

Intangible assets, characterized by inherent uncertainty, are particularly susceptible to risk. As a result, risk emerged as a pressing concern within the hospitality industry [42]. In risky situations, cognition and emotion often display an asymmetry, with emotions such as fear exerting a more substantial influence. People perceive risk through various factors beyond actual probability, and emotional responses to risk play a significant role [43]. Emotions are crucial in decision-making processes, working alongside reason rather than opposing it [44].
Fear, one of the most fundamental emotions, can serve as a potent catalyst for changes in perception or behavior [45,46]. In the face of unfamiliar or challenging circumstances, humans frequently experience a sense of helplessness, with fear representing one of the most basic manifestations of this feeling [9]. To mitigate negative emotional arousal, humans pursue various coping mechanisms, thereby fostering exploratory behavior [47]. Neuroscience research indicates that individuals seek information to establish control and maintain homeostasis, and such behavior reduces input fear (e.g., foraging behavior) [48,49]. In essence, information-seeking behavior becomes an endeavor to manage subjective experiences that run counter to safety. Based on this understanding, we propose the following hypothesis:
Hypothesis 8.
Fear of COVID-19 positively influences COVID-19 related risk information-seeking behavior.
Conversely, individuals may employ defensive preventive strategies such as avoidance (e.g., refraining from going out or minimizing exposure). The health belief model posits that perceived threats prompt preventive actions within the scope of personal health motivation [50,51]. Additionally, the protection motivation theory elucidates why people assess specific threats and engage in preventive measures [52]. Protection motivation pertains to the intention to undertake preventive actions to safeguard oneself from health threats [53]. Negative emotions, including fear, anger, and sadness, arise in threatening situations, with fear being the predominant emotion in infectious disease contexts [9,54]. Based on this understanding, we tested the below causal relationships:
Hypothesis 9.
Fear of COVID-19 negatively influences dining-out intention during the COVID-19 pandemic.
Hypothesis 10.
Fear of COVID-19 negatively influences dining-out behavior during the COVID-19 pandemic.

3. Methodology

3.1. Construct Measures

This study employed a quantitative research method to empirically test the hypotheses. Latent variable measurement items were obtained through a literature review. In this study, internal states—dining-out attitude, perceived control, subjective norm, dining-out intention, and fear of COVID-19—were assessed using reflective indicators. In contrast, risk information-seeking behavior and dining-out behavior change were measured using formative indicators, as they facilitated a more comprehensive assessment of multiple facets of the behavior. Reflective indicators are employed when items stem from the underlying construct and are interchangeable and highly correlated. On the other hand, formative indicators are used when items contribute to the formation of a construct and may not necessarily be correlated, capturing distinct aspects or dimensions of behavior [55,56]. TPB constructs—attitude, perceived control, subjective norm, and intention—were each measured by three questions [21,57]. Fear of COVID-19 was assessed using three validated items [58,59,60]. All the reflective indicators were anchored on a 5-point Likert-type scale of 1 (not at all) to 5 (very much).
To investigate the factors that influence and, subsequently, change aspects of dining-out behavior, we employed representative items such as frequency and time spent as resources to measure behavior [56,61]. Risk information-seeking behavior was operationalized using three items: frequency, effort, and time spent. Both frequency and effort were assessed using a 5-point Likert scale, ranging from 1 (not at all) to 5 (very much), while time was measured in absolute units. Dining-out behavior was assessed using three items measuring changes in dining-out frequency, travel time (i.e., time taken to reach the dining place), and stay time (i.e., time spent at the dining place) on a 5-point scale ranging from 1 (significantly decreased) to 5 (significantly increased). The decision to include absolute time spent as an indicator for risk information-seeking behavior, but not for dining-out behavior, was based on observed variability between the two constructs. While dining-out behaviors can vary significantly among individuals and households, risk information-seeking behavior during the COVID-19 pandemic tends to be relatively homogeneous. Korea’s advanced information and communication technology infrastructure further standardizes information-seeking behavior.

3.2. Sampling and Data Collection

Data collection took place from January 28 to 7 February 2021, in South Korea, where COVID-19 posed a significant threat. An online research specialist company administered the survey. Korea’s effective COVID-19 response and digital technology network made it a suitable location for data collection. Participants were provided with clear definitions of key concepts before answering the survey questions, to minimize measurement error and enhance the validity and reliability of the collected data. Specifically, participants were informed that dining-out referred to consuming meals at restaurants, cafes, or other food establishments where food was prepared and served on-site. To avoid confusion or overlap with dining formats such as delivery, take-away, or convenience foods, respondents were instructed to focus their answers on this particular dining-out behavior. In order to mitigate potential demographic bias, gender and age group ratios were proportionately distributed. Out of 604 responses, 68 were excluded from the analysis due to incompleteness or lack of sincerity. Therefore, a total of 536 samples of Korean consumers were ultimately analyzed. Table 1 displays the demographic characteristics.

3.3. Data Analysis

Partial least squares structural equation modeling (PLS-SEM) was employed to test the research hypotheses, analyze empirical validity, and understand paths and influence relationships. PLS-SEM is suitable for exploratory relationship analysis, emphasizing variable relationships rather than model sophistication [62,63,64]. PLS fits more for this study since it effectively handles both reflective and formative indicators in structural equation modeling [64,65]. The conceptual model evaluation involved two steps: assessing the measurement model to authenticate measurement quality, and implementing structural equation modeling to examine structural model soundness and test hypotheses. Path coefficient significance was evaluated using the PLS algorithm and bootstrapping with 5000 subsamples [66]. Figure 1 illustrates the results of structural model analysis.

4. Results

4.1. Measurement Model Assessment

First, the reliability and validity of the reflective measurement model were examined using PLS-SEM algorithm analysis, with SmartPLS 4.0 for data analysis. Outer loading values were set at 0.5 or higher, Cronbach’s alpha at 0.6 or higher, and composite reliability (CR) at 0.7 as a baseline [67,68] (see Table 2). The average variance extracted (AVE) was set at 0.5 or higher, and the Heterotrait and Monotrait Ratio (HTMT) was set with a cut-off point of 0.9 or lower (see Table 3). Discriminant validity was examined through cross-loadings, with results meeting the given threshold criteria [66] (see Table 4). Therefore, the reflective measurement model’s validity and reliability were satisfactory.
Formative indicators’ evaluation criteria typically include content validity, discriminant validity, collinearity, statistical significance, and the relationships among formative indicators constituting each variable [63,64,69]. It is generally advised that all indicators have statistically significant outer loadings, contributing to the construct formation. However, retaining an indicator with an outer loading below 0.5 might be warranted if it still demonstrates a significant contribution to the model, as its removal could result in losing certain construct aspects [64,69,70]. No strict rule of thumb exists for formative indicators’ outer weights, as these depend on the research context and assessed construct [71].
Table 5 presents the collinearity assessment results using the variance inflation factor (VIF), indicator reliability assessment using outer loadings, and construct validity assessment using indicator weights. All VIF values were below two, indicating no multicollinearity concerns. Outer loadings and outer weights were significant at a level of 0.05 or below, demonstrating acceptable indicator reliability and construct validity. Regarding risk information-seeking behavior items, the contributions ranked from highest to lowest were: search effort, frequency, and time. For dining-out behavior, the order of contribution was stay time, travel time, and visit frequency.

4.2. Structural Model Assessment

The structural model evaluation involved examining the coefficient of determination (R2) and PLS Predict (Q2). R2 and Q2 verify the explanatory power and predictive accuracy of latent variables when their values are 0 or higher [66]. Consequently, the R2 values for dining-out intention, risk information-seeking behavior, and dining-out behavior were 0.289, 0.151, and 0.115, respectively, while the Q2 values were 0.273, 0.136, and 0.049, respectively, indicating sufficient explanatory power and predictive accuracy (Table 5). The SRMR (standardized root mean square residual) value was 0.053, indicating a favorable model fit, as the SRMR criterion for a good fit is below 0.08 [67].
The f² measure evaluates the effect size based on the variance explained (R²) by the predictor variables, with benchmarks of 0.35 for high impact, 0.15 for medium impact, and 0.02 for low impact [63,64]. Table 6 represents the f² values, with the highest value of 0.161 observed in the relationship between risk information-seeking behavior and fear, and the lowest value of 0.012 from risk information-seeking behavior to dining-out behavior. No effect was found between risk information-seeking behavior and dining-out intention. The statistical robustness of the results is limited due to small Q2 and f2 values, which may be attributable to the fact that the internal variables examined in this study only partially explain actual behavior change. Nevertheless, when interpreting the effect size measures in PLS-SEM, it is important to note that the benchmarks are only general guidelines and considered within the specific context and objectives of the research [63,64].

4.3. Hypothesis Testing

The bootstrapping procedure was conducted to test the significance of the path coefficients in the structural model. Table 7 presents the modeled associations of projected path coefficients, standard deviation, p-value, and T statistics. The findings revealed that behavioral attitude (β = 0.354, p < 0.001), perceived control (β = 0.087, p < 0.05), and subjective norm (β = 0.233, p < 0.001) exhibited statistically positive relationships with dining-out intention. Perceived control demonstrated a relatively weak path coefficient at the 0.05 level. Dining-out intention was also found to be positively related to dining-out behavior (β = 0.240, p < 0.001). Hence, hypotheses H1, H2, H3, and H4 were accepted. Risk information-seeking behavior showed a significant negative relationship with dining-out behavior (β = −0.112, p < 0.05) but an insignificant relationship with dining-out intention. H6 was found to be statistically significant at the 0.05 level and was, thus, supported, whereas H5 was not. Subjective norm displayed a significant relationship with risk information-seeking behavior (β = 0.107, p < 0.01), thereby supporting H7. Fear demonstrated a significant positive relationship with risk information-seeking behavior (β = 0.370, p < 0.001) and negative relationships with dining-out intention (β = −0.113, p < 0.01) and dining-out behavior (β = −0.162, p < 0.01). Therefore, hypotheses H8, H9, and H10 were accepted.

5. Discussion

This study investigated the variables influencing dining-out behavior changes under pandemic risk. The findings confirmed several of the proposed hypotheses.
Hypotheses 1, 2, and 3, which posited that dining-out attitude, perceived control, and subjective norm would positively influence dining-out intention during the COVID-19 pandemic, respectively, were supported by the study. These findings reinforce the TPB. However, the relatively weaker effect of perceived behavioral control suggests that the pandemic context might have undermined individuals’ sense of control.
Although not all human behavior is premeditated, intentions predominantly serve as the driving force behind goal-directed actions [72,73]. In support of Hypothesis 4, the study found that dining-out intention positively influenced dining-out behavior. Conversely, risk information-seeking behavior and fear of COVID-19, as posited in Hypotheses 6 and 10, negatively affected dining-out behavior. Moreover, all formative indicators of dining-out behavior—travel time, stay time, and dining-out frequency—proved to be significant. These results indicate that while dining-out intention positively influences aspects such as travel time to a dining establishment, time spent there, and dining-out frequency, COVID-19-related information-seeking behavior and fear hindered these aspects.
Contrary to Hypothesis 5, risk information-seeking behavior did not significantly affect dining-out intention, although it did discourage dining-out behavior. The formative indicators of risk information-seeking behavior—frequency, effort, and time spent seeking information—were statistically significant, aligning with Hypothesis 6. These findings suggest that an increase in the frequency and extent of individuals’ search for COVID-19-related information corresponded with a decline in their dining-out behavior.
Support was also found for Hypothesis 7, which proposed that subjective dining-out norms positively influence COVID-19 related risk information-seeking behavior. Individuals with heightened subjective dining-out norms exhibited increased engagement in risk information-seeking behavior. While risk information-seeking behavior reduced dining-out behavior, it did not diminish dining-out intention, indicating a nuanced relationship between information processing and behavioral intention.
Finally, Hypotheses 8, 9, and 10 relating to the role of fear were validated. Fear was identified as a determinant of dining-out intention, risk information-seeking behavior, and dining-out behavior, emphasizing the power of emotions over rational thinking. Fear negatively affected both dining-out intention and behavior, exhibiting a partial mediation effect that could counterbalance the catalyst effect of dining-out intention leading to dining-out behavior. This does not necessarily stem from the causality between dining-out and disease transmission but rather due to the nature of dining-out as an activity that involves interaction with outsiders, particularly during meals when masks must be removed. Studies suggest that measures such as increasing spaces between tables, optimizing seat arrangement, improving ventilation, and using protective equipment can significantly decrease the infection risk, consumer risk perception, and behavior [11,13,14]. These findings highlight the importance of measures to mitigate fear among consumers and enhance their comfort when dining out.

6. Conclusions

This study explored factors influencing dining-out behavior among Korean consumers during the COVID-19 pandemic. Key findings include the positive impact of attitudes towards dining out, perceived control, and subjective norms on the intention to dine out. The intention to dine out positively drives dining-out behavior, while risk information-seeking behavior and fear of COVID-19 negatively affect it. Notably, while risk information-seeking behavior discourages actual dining-out behavior, it does not significantly impact the intention to dine out. Moreover, subjective norms regarding dining out positively impact COVID-19 related risk information-seeking behavior, and fear negatively affects both the intention and behavior of dining out.

6.1. Theoretical Implications

This study offers valuable contributions to existing literature particularly in aligning with the primary objectives outlined. The first objective was to investigate the impact of behavioral attitude, perceived control, and subjective norm towards dining-out during the COVID-19 pandemic on dining-out intention. The findings from this study enhance the understanding of consumer psychology and behavior by establishing a clear link between risk information-seeking behavior and changes in dining-out behavior.
The research also addressed the influence of risk information-seeking behavior and fear of COVID-19 on dining-out intention and behavior. In doing so, it examined the factors leading to alterations in dining-out behavior under pandemic situations considering cognitive, emotional, and social aspects. This contributes to a more nuanced understanding of the intention-behavior gap in the realm of dining-out. Additionally, this study aimed to evaluate the interconnectedness among dining-out intention, information-seeking behavior, fear of COVID-19, and changes in dining-out behavior. Previous studies on dining behavior primarily focused on the relationship between internal aspects such as preference, attitude, satisfaction, and intention [74]. In extraordinary contexts, however, external influences play a significant role in behavior and behavior change [28]. This study broadens the scope of existing dining literature by incorporating dining-out intention, COVID-19-related information-seeking behavior, and COVID-19-related fear as crucial antecedents of dining-out behavior during the pandemic. A notable contribution includes the exploration of the relationship between subjective dining-out norms and risk information-seeking behavior, representing a novel and valuable addition to the current literature, as this relationship was rarely investigated.
Lastly, the study sought to analyze the significance of formative indicators that constitute risk information-seeking behavior and dining-out behavior in the context of the COVID-19 pandemic. The findings affirmed the influence and validity of formative indicators that constitute risk information-seeking behavior and dining-out behavior in the research model through PLS-SEM. While CB-SEM is useful for verifying causality and validation among variables based on established theories [62,75], efforts to understand the interplays and progress the concepts in rapidly changing environments and situational dynamics can contribute to the expansion of academic horizons. Although not all loadings and significance levels were robust, all formative indicators of risk information-seeking and dining-out behavior exhibited statistical significance. These indicators can serve as a solid foundation for future studies.

6.2. Practical Implications

The COVID-19 pandemic presented considerable challenges to the hospitality industry, necessitating a focus on consumer perceptions and concerns beyond traditional domains such as food and hygiene management, customer relationship management, and operational management [12,18,76]. This study’s findings offer valuable insights for the food and beverage industry, as well as policymakers, in formulating strategies to mitigate the adverse impacts of the pandemic on dining-out behavior.
Understanding the relationship between fear of COVID-19 and dining-out behavior can help develop effective communication strategies to reduce fear and alleviate consumers’ concerns about dining out. Although the pandemic impacted dining-out behavior, individuals who must dine out tend to prefer closer venues and quicker meals, in addition to adopting delivery, take-away, and convenience food options. Implementing and promoting safety measures, such as physical distancing, increased sanitation, and improved ventilation in dining establishments, can help restore consumer confidence and encourage more frequent dining-out behavior [12,15].
Additionally, the study highlighted the importance of risk information-seeking behavior and its impact on dining-out behavior. Restaurant owners and managers can utilize this understanding to provide accurate, timely, and relevant information about their establishments’ safety measures, operating hours, and policies related to COVID-19. By making this information easily accessible through various channels such as social media, websites, and in-person communications, customers can be reassured of the safety of their dining experience.
Moreover, the results also emphasized the role of subjective norms in shaping risk information-seeking behavior. Policymakers and public health authorities can leverage this insight to design targeted public health campaigns that encourage responsible dining-out practices and foster a collective sense of responsibility among the public. By encouraging the adoption of safe dining behaviors, these campaigns can help to reduce the negative impacts of the pandemic on the food and beverage industry while maintaining a focus on public health [7,77].

6.3. Limitations and Future Research

Despite its contributions, this study had several limitations that should be considered when interpreting the results and could serve as potential avenues for future research. First, the study’s cross-sectional design limited its ability to capture the dynamic nature of the pandemic and its effects on dining-out behavior over time. Longitudinal research designs could provide a more comprehensive understanding of how these behaviors change as the pandemic progresses and as new information, policies, and guidelines are introduced.
Second, the study’s scope was confined to the COVID-19 pandemic, and the findings may not be generalizable to other types of public health crises or risks. Moreover, the study focused on the impact of fear, risk information-seeking behavior, and dining-out intention on dining-out behavior. Other factors, such as trust in information sources, risk perception, and personal experiences, might also influence dining-out behavior during a pandemic. Future research could explore the role of these additional factors in shaping consumer behavior in the context of health crises or high-risk situations, as well as how dining-out behavior adapts in response to various risks, such as foodborne illness outbreaks or natural disasters, to comprehend the generalizability of these relationships.
Third, this study did not adequately represent the behavior of the elderly population. Data collection was conducted via an online research company panel, with only a small proportion of individuals aged 60 and above included. As the elderly population is more susceptible to diseases and generally exhibits greater health involvement, they may have displayed distinct patterns or even reinforced the results. Given the increasing proportion of the elderly population and the persistence of dining-out consumption in various forms, conducting a similar study targeting this demographic in the future would be advantageous for applying and interpreting this research’s findings.

Author Contributions

Conceptualization, U.B. and S.K.L.; methodology, U.B. and S.K.L.; software, U.B. and S.K.L.; validation, U.B. and S.K.L.; formal analysis, U.B. and S.K.L.; investigation, U.B. and S.K.L.; resources, U.B. and S.K.L.; data curation, U.B.; writing—original draft preparation, U.B.; writing—review and editing, U.B. and S.K.L.; visualization, U.B. and S.K.L.; supervision, S.K.L.; project administration, U.B. and S.K.L.; funding acquisition, U.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5B5A07087715).

Institutional Review Board Statement

The study, being observational and not involving therapeutic medication, did not require formal approval from the local ethics committee’s institutional review board. Nonetheless, participants were fully informed about the study, and their participation was strictly voluntary. The study was conducted in accordance with the principles of the Helsinki Declaration.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 08323 g001 550
Figure 1. The estimated structural model.
Figure 1. The estimated structural model.
Sustainability 15 08323 g001
Table
Table 1. Demographic profile of respondents.
Table 1. Demographic profile of respondents.
VariableCharacteristicsFrequency (%)VariableCharacteristicsFrequency (%)
GenderFemale
Male		268 (50%)
268 (50%)		Marital statusUnmarried
Married
Others		227 (42.4%)
301 (56.2%)
8 (1.5%)
Age20 s
30 s
40 s
50 s or above		135 (25.2%)
133 (24.8%)
134 (25.0%)
134 (25.0%)		EducationHigh school
College/University
Graduate school		131 (24.4%)
358 (66.8%)
47(8.8%)
Table
Table 2. Reflective indicator loadings, construct reliability, and convergent validity.
Table 2. Reflective indicator loadings, construct reliability, and convergent validity.
VariableItemsMean (SD)LoadingCronbach’s αCR (rho_a)AVE
AttitudeI find dining-out to be a favorable experience.3.11 (0.757)0.8500.7720.7740.686
I find dining-out to be a pleasurable experience.3.18 (0.835)0.841
I find dining-out to be an enjoyable experience.3.90 (0.689)0.792
ControlI believe I have control over my decision to dine out in the future.3.45 (0.924)0.8670.6880.7260.620
I feel that I can dine out whenever I want.3.59 (0.909)0.937
I have the necessary resources (time, money) and ability to dine out.3.68 (0.808)0.911
NormPeople who are important to me would want me to dine out.2.86 (0.838)0.7120.8910.9090.820
People who are important to me would think I should dine out.2.77 (0.873)0.739
People who are important to me would consider dining out to be necessary.2.79 (0.903)0.898
IntentionI intend to dine out in the future.3.18 (1.056)0.9470.9270.9270.873
I plan to dine out in the future.3.05 (1.080)0.948
I intend to continue dining out regularly.3.22 (1.041)0.908
FearI feel anxious about the COVID-19 pandemic.4.25 (0.706)0.7490.7130.7210.634
The COVID-19 pandemic makes me feel nervous.4.32 (0.810)0.816
The COVID-19 pandemic frightens me.3.72 (0.904)0.823
Attitude = Dining-out attitude, Control = Perceived dining-out control, Norm = Subjective dining-out norm, Intention = Dining-out intention, Fear = Fear of COVID-19.
Table
Table 3. Reflective indicator discriminant validity (HTMT approach).
Table 3. Reflective indicator discriminant validity (HTMT approach).
AttitudeControlNormIntentionFear
Attitude
Control0.510
Norm0.4570.432
Intention0.5440.3550.426
Fear0.1360.1590.0750.09
Attitude = Behavioral attitude, Control = Perceived behavioral control, Norm = Subjective norm, Intention = Dining-out intention, Fear = Fear of COVID-19.
Table
Table 4. Reflective indicator discriminant validity (cross-loadings).
Table 4. Reflective indicator discriminant validity (cross-loadings).
AttitudeControlNormIntentionSeekingFearDine-Out
attitude10.8500.2870.3330.380−0.0310.0240.122
attitude20.8410.2770.3390.3410.0340.0850.097
attitude30.7920.3610.2710.4230.0160.1080.030
control10.3320.2960.8670.2950.0870.0340.000
control20.3510.3060.9370.3610.1080.0370.030
control30.3430.3070.9110.3960.1240.0200.034
norm10.2540.7120.2650.2020.1220.0400.029
norm20.2940.7390.2590.1960.0940.1310.028
norm30.3390.8980.2720.2700.0370.0830.039
intention10.4490.2860.3370.947−0.015−0.0750.230
intention20.4120.2780.3600.948−0.001−0.0750.260
intention30.4430.2380.4020.908−0.009−0.0380.215
fear10.0340.098−0.023−0.0600.2590.749−0.158
fear20.0870.1210.111−0.0110.3190.816−0.143
fear30.0860.041−0.011−0.0860.3110.823−0.218
Seeking = risk information-seeking behavior, Dine-out = dining-out behavior.
Table
Table 5. Formative indicator assessment.
Table 5. Formative indicator assessment.
ConstructItemDescriptionMean (SD)VIFOuter LoadingOuter Weight
Risk information-seeking behaviorfrequencySearch frequency4.00 (0.688)1.9020.937 ***0.637 ***
effortSearch effort3.68 (0.844)1.8620.838 ***0.393 **
timeTime spent on each search32.98 (41.69)1.0290.341 ***0.217 *
Dining-out behavior changefrequencyDining-out frequency1.68 (0.975)1.3870.696 ***0.271 *
traveltimetime taken to reach the dining place2.27 (0.956)1.5200.789 ***0.363 *
staytimetime spent at the dining place1.91 (0.923)1.8480.923 ***0.569 ***
*** p < 0.001. ** p < 0.01. * p < 0.05.
Table
Table 6. Coefficient determination.
Table 6. Coefficient determination.
VariableR2Adj. R2Q2
Dining-out intention0.2890.2820.273
COVID19 infoseeking0.1510.1470.136
Dining-out behavior0.1150.1100.049
Table
Table 7. Structural model assessment.
Table 7. Structural model assessment.
PathOriginalSample MeanStDevt-Statisticsf2Result
H10.3540.3530.0477.516 ***0.137S
H20.0870.0920.0511.702 *0.009S
H30.2330.2330.0484.855 ***0.062S
H40.2400.2420.0406.064 ***0.065S
H5−0.006−0.0080.0430.1380.000NS
H6−0.112−0.1150.0522.13 *0.012S
H70.1070.1080.0442.418 **0.013S
H80.3700.3750.0418.941 ***0.161S
H9−0.113−0.1130.0392.903 **0.015S
H10−0.162−0.1650.0542.991 **0.025S
Note. SRMR = 0.053. *** p < 0.001. ** p < 0.01. * p < 0.05. S = Support. NS = Not support. Original: The mean value of the construct as estimated in the population or based on theoretical assumptions. Sample Mean: The mean value of the construct calculated from the collected data within the sample used for analysis.
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Baek, U.; Lee, S.K. Pandemic Dining Dilemmas: Exploring the Determinants of Korean Consumer Dining-Out Behavior during COVID-19. Sustainability 2023, 15, 8323. https://doi.org/10.3390/su15108323

AMA Style

Baek U, Lee SK. Pandemic Dining Dilemmas: Exploring the Determinants of Korean Consumer Dining-Out Behavior during COVID-19. Sustainability. 2023; 15(10):8323. https://doi.org/10.3390/su15108323

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Baek, Unji, and Seul Ki Lee. 2023. "Pandemic Dining Dilemmas: Exploring the Determinants of Korean Consumer Dining-Out Behavior during COVID-19" Sustainability 15, no. 10: 8323. https://doi.org/10.3390/su15108323

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