Influencing Travelers’ Behavior in Thailand Comparing Situations of during and Post COVID-19

Abstract

Tourism is the primary source of income for many countries, particularly developing ones. However, due to the impact of the 2019 Coronavirus epidemic, the tourism sector has been significantly affected. This study aims to identify factors that influence motivation and travel frequency. Two scenarios were compared: during COVID-19 and post-COVID-19. The questionnaire was developed based on the Health Belief model. The data collection process involved distributing a comprehensive questionnaire throughout Thailand, with the aim of achieving a fair and balanced representation of respondents from six distinct regions: northern, central, eastern, western, north-eastern, and southern. The study included a total of 2100 participants. Twenty hypotheses were formulated to analyze the relationship between the latent constructs. Factor analysis and Structural Equation Modeling were utilized to analyze the data from the questionnaires. The results from SEM found that the model was consistent with the empirical data. The model of the during COVID-19 pandemic had three supported hypotheses, namely H2d, H5d, and H9d, which were the correlation between tourism motivation and perceived severity, self-efficacy, and outcome expectations, respectively. In the post-COVID-19 pandemic period, seven hypotheses, including H1p, H2p, H3p, H4p, H5p, H9p, and H10p, supported the correlation between intention and perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, outcome expectations, and frequency. The support for H10p indicated that stimulated motivation could lead to behavioral changes and increase people’s travel frequency. This study proposes policy recommendations and public relations guidelines to encourage more frequent travel.

1. Introduction

Tourism is considered a primary source of income for many countries, especially in developing countries like Thailand, where tourism revenue accounted for 17.79% of GDP in 2019, with an average growth rate of 0.04% [1]. However, the COVID-19 pandemic has significantly impacted the number of tourists [2] due to domestic and international tourism preventive measures, such as lockdowns, capacity limits in shops, and social distancing [3]. Wu et al. [4] have confirmed that city lockdowns have a significant impact on the economy.

Currently, it is evident that the coronavirus has not been eradicated; rather, mutations have emerged, causing a focus on the spread of infection rather than its severity [5]. Therefore, some countries may declare themselves endemic [6]. It is necessary to analyze the differences in tourist behavior between these two situations. The first situation refers to the epidemic period, starting from the official announcement of the emergency (since 25 March 2019), during which the agency responsible for controlling the coronavirus enforced measures such as city lockdowns and the regulation of shop and hotel operations. The post-epidemic period is measured by the relaxation of mask-wearing mandates and the cancellation of emergency public administration, which occurred on 1 October 2022 [7]. These two situations differ significantly and may affect tourism motivation and behavior. Dwivedi et al. [8] have found that during the COVID-19 pandemic, the emotional aspect of attitude strongly influenced travel decisions.

There have been several tourism research studies analyzing the situation during COVID. For example, Nguyen et al. [9] studied the role of trust in government performance on travel intentions in Vietnam. Vukomanovic et al. [10] analyzed the decision-making process regarding whether to travel or not during the COVID-19 situation in the USA. People who choose to travel have various factors to consider, such as government policies in each state and safety measures like social distancing and wearing masks, consistent with the study in Nepal by Devkota et al. [11], which identified health concerns among tourists during the pandemic.

Regarding post-epidemic tourism, Yu et al. [12] found an increasing trend in domestic tourism in Beijing. Sohn et al. [13] studied tourists in Korea and observed changes in their decisions different from pre-COVID-19. They discovered that in the post-COVID-19 pandemic, several factors influenced their motivation to travel, including social distancing measures, and noted that travel patterns often involve short-haul destinations. Additionally, Liu and Chong [14] found that social media content encourages more people to travel in the post-COVID era. Kim and Liu [15] revealed that social distancing affects purchase intentions in the restaurant and hotel sectors. Hassan et al. [16] examined the satisfaction of religious tourists in the post-COVID-19 situation.

For the studies comparing pandemic and post-pandemic tourism, Pappas [17] conducted a longitudinal study (2019, 2020, and 2021) and found that during the COVID epidemic year (2021), tourists had fewer travel intentions. Yang et al. [18] discovered that during the period of measures, the number of tourists was reduced. Shin et al. [19] studied the attitudes of Korean travelers, who discovered that there were different views on tourism in two situations, such as tourist trust, travel constraint, and the extended theory of planned behavior. Ali et al. [20] studied the intention to use smartphones on the tourist shopping journey to compare the period of COVID-19 and non-COVID-19

For a study in Thailand, Leelawat et al. [21] used machine learning to analyze Twitter data that could identify the tourists’ opinions on their interests during the COVID-19 pandemic. Wongmonta [22] studied post-COVID-19 tourism recovery. Chansuk et al. [23] studied post-pandemic travel behavior and found that Thai tourists prefer to initially travel in the country. In addition, there are various factors affecting travel motivation, such as perceived behavioral control, destination knowledge, etc. From the research on behavior and perceptions that influence travel decisions mentioned above, it is evident that most studies focus on a single situation. Only Shin et al. [19] provide a clear comparison between the during and post-COVID-19 periods, examining both the decision to travel during COVID-19 and future travel intentions after the pandemic.

To ensure the robustness of the causal model development, it was deemed essential to employ the Health Belief Model (HBM) as the foundational framework for constructing the HBM model in this study. This encompassed various facets pertaining to individuals’ health-related practices, incorporating multiple factors anticipated to exert an impact, such as perceived susceptibility, perceived severity, and cues to action, among others. Various studies have applied the HBM in decision-making in different scenarios. For example, Champahom et al. [24] examined decision-making regarding child restraint use, while Jomnonkwao et al. [25] focused on the motivation for wearing helmets. Suess et al. [26] analyzed the benefits of a COVID-19 vaccine based on the HBM. The COVID-19 pandemic and tourism are evidently considered the decision-makings regarding health. Furthermore, a frequently employed method for causal modeling is Structural Equation Modeling (SEM). SEM enables the construction of a comprehensive model to examine causal relationships between latent variables, encompassing both latent and observed variables [24].

This academic study delves into the exploration of factors, grounded in the Health Belief Model (HBM), and their influence on travel intention and frequency in developing countries. The research seeks to augment the existing literature by conducting a comparative examination of travel behavior between two distinct situations. Specifically, the study categorizes the two situations based on the Royal Gazette announcements, wherein the “during the epidemic” period encompasses the time since the declaration of the emergency decree (25 March 2019), and the “post-epidemic” period represents the time subsequent to the repeal of the Emergency Decree (1 October 2022). Notably, significant differences emerge between these two situations, as the post-epidemic period allows for unrestricted travel for tourists, characterized by the absence of lockdown measures and limitations on the number of tourists in specific locations. Consequently, such differences may potentially influence the attitudes of tourists during these distinct periods.

Moreover, this investigation adopts a psychological modeling theory as its analytical foundation and incorporates various modifying factors to enhance the realism of the model. By addressing the gap in understanding attitudes and perceptions that shape travel behavior in developing countries, particularly during the epidemic and post-epidemic periods, this research aims to furnish valuable insights that can aid in formulating effective post-COVID-19 travel policies and recommendations. This study contributes to the broader discourse on travel behavior during times of crises and holds potential implications for policymakers, tourism authorities, and other stakeholders concerned with promoting sustainable and resilient travel practices in developing countries.

This paper is structured as follows: (1) Introduction: the primary objective of this study is to investigate the impact of various factors on travel intention and frequency in developing countries. (2) The Health Belief Model (HBM): in this section, we provide a comprehensive overview of the HBM and the hypotheses. (3) Materials and Methods: this section presents the research design, including the development of the questionnaire used to collect data. (4) Results: The findings of the study are presented in this section, which includes descriptive statistics and the results of the statistical models. (5) Discussion: In this section, a comprehensive discussion of the results is provided. Each hypothesis is analyzed in detail, evaluating their significance and comparing them with previous research. (6) Conclusion: the final section summarizes the key findings of the research and offers guidelines for implementation.

2. Health Belief Model (HBM)

The Health Belief Model (HBM) is a psychological model used for behavior modification that can explain and predict behavior according to health belief [27]. The HBM illustrates that people who believe in their health problem and perceived benefits, as well as perceived obstacles, including external factors like cues to action, can influence behavior [28] (Fundamental theories can be further studied from Champion and Skinner [29]). The elements of the HBM involve several aspects that contribute to a conceptual framework and hypotheses development (Figure 1), outlined as follows: Action refers to expressed behavior. In this study, it is interpreted as the frequency of travel both during and after the pandemic.

Perceived susceptibility involves assessing the risk of experiencing problems when deciding to travel, particularly the chance of being infected with COVID-19. For example, questions could include, “You know whenever you travel for tourism, you must always follow preventive measures against the COVID-19 pandemic, such as wearing masks, checking the temperature, washing hands with sanitizer gel or alcohol, or social distancing in queueing for getting service, etc.” Cahyanto et al. [30] reached a definitive conclusion that the risk factors have undergone extensive examination and have consistently been shown to heighten the level of perceived destination risk among visitors. Consequently, these perceived risks diminish travel incentives, leading to the formulation of:

Hypothesis 1. 

Perceived susceptibility exhibits a statistically significant negative impact on travel intention.

Perceived severity means the assessment of the severe impact on health resulting from the consequences of the decision. In this case, it refers to the decision to travel and being infected afterwards. This may include the duration of recovery, which requires treatment until it can affect both work and family. An example of question items includes “You know that being infected with the epidemic may result in disabilities or death.” Su et al. [31] found a substantial positive correlation between the perceived severity and health risk preventive behavior. In other words, if individuals perceive COVID-19 as a highly severe threat, it is likely that travel incentives will diminish. Consequently, we hypothesize the following:

Hypothesis 2. 

The perceived severity significantly and negatively influences travel intention.

Perceived benefits involve behaviors that result from perceived actions. In this case, it refers to the decision to comply with regulations or measures aiming to prevent the epidemic. This can be assessed by asking questions like, “You think that following the preventive measures against the COVID-19 pandemic makes you feel worthwhile and enjoy traveling for tourism.” Erul et al. [32] have revealed that there exists a noteworthy positive correlation between oppositional attitudes towards tourism and passive opposition to tourism. This implies that individuals are less inclined to make travel decisions when they hold a negative perception of tourism. Conversely, a positive perception of the advantages of traveling is likely to be associated with a heightened propensity to be encouraged or motivated to travel. As a result, we propose:

Hypothesis 3. 

Perceived benefits exert a statistically significant positive influence on travel intention.

Perceived barriers refer to the obstacles that arise in decision-making. In this case, it is seen that there are some obstacles that make it uncomfortable to travel without the spread of COVID-19. Question items such as “You feel that the internal facilities in places for tourists, such as restrooms, waiting seats, under the unstrict preventive measures may make you feel uncomfortable.” Devkota et al. [11] have demonstrated that there is a strong positive correlation between hygiene and safety and the decision to travel. In other words, when hygiene and safety standards are low, it leads to a decrease in the intention to travel. Furthermore, considering the prevalence of COVID-19 as a major obstacle, the probability of reduced motivation to travel increases. Therefore, we propose:

Hypothesis 4. 

Posits that Perceived barriers exert a statistically significant negative impact on travel intention.

Self-efficacy refers to behavior caused by self-control. In this case, it is confidence in oneself while traveling that will not be infected with COVID by asking question items such as “You feel that the chance of getting infected by the COVID-19 pandemic depends on yourself.” Kim et al. [15] discovered that self-efficacy can amplify the influence of social distancing measures on trust, implying that individuals who possess a high level of confidence are more likely to refrain from bringing infections back to their homes. Consequently, such individuals are more strongly inclined to engage in travel. Therefore, we propose:

Hypothesis 5. 

Self-efficacy has a statistically significant positive impact on travel intention.

Cues to action refer to external stimuli that prompt such behaviors. In this case, the government sector plays a crucial role in stimulating tourism by either implementing measures such as mask mandates or store-related regulation, such as limiting the number of people entering inside. These issues can impact travel behavior. Question item is “You think that the government helps stimulate and support Thai tourism in the context of the COVID epidemic.” Pham, et al. [33], Okafor, et al. [34] have demonstrated that robust government support for the tourism industry contributes significantly to its recovery. Undoubtedly, governmental encouragement serves as a catalyst, motivating people to embark on more frequent travels and leading to changes in their travel behaviors. This observation indicates the potential for an increase in travel frequency, thereby supporting the following hypotheses:

Hypothesis 6. 

The provision of cues to action exerts a statistically significant positive impact on travel intention.

Hypothesis 7. 

The provision of cues to action exerts a statistically significant positive impact on travel frequency.

HBM defines modifying variables which are variables adjusting based on situations. According to the previous research related to tourism during and after the COVID-19 pandemic, these factors, which have been added into decision-making analysis, consisted of health motivation and outcome expectation.

Health Motivation refers to individuals’ attitudes towards their own health, such as their awareness of risks in different situations. For example, traveling to places where there are strict preventive measures or not. Question items such as “You think that traveling for tourism in places where the number of service users is limited is the safest.” Sohn et al. [13] have posited that when individuals embrace social distancing, they can still embark on journeys with relative safety. Hence, prioritizing one’s well-being and that of their families may prompt a realization that traveling could potentially result in infections that could be transmitted to their households. Consequently, individuals with such concerns often exhibit diminished motivation to travel. As a result, we hypothesize the following:

Hypothesis 8. 

Health motivation exerts a statistically significant negative impact on travel motivation.

Outcome expectation relates to a variety of motivation resulting in travel decisions. For instance, statements like “You feel happy whenever you travel for tourism in every situation.” Dwivedi et al. [8] have highlighted that human responses to psychological emotions or logical thinking often lead to specific patterns of behavior, particularly in the context of travel behavior. Additionally, when individuals harbor elevated expectations, it invariably triggers a compelling inclination towards travel. Hence, we posit the following hypothesis:

Hypothesis 9. 

Outcome expectation exerts a statistically significant positive influence on travel motivation.

Intention refers to the motivation for travel decisions. Many studies use intention as dependent variables by measuring how many expected trips to travel in the future and the reasons for traveling, a question item such as “You need to have some rest and relaxation.” Chen et al. [35] conducted a comprehensive analysis of travel intention and behavior in their research. Similarly, Durmaz et al. [36] discovered a significant positive correlation between the perception of gastronomy tourism and behavioral intentions, indicating its potential to predict consumer behavior. In simpler terms, when individuals exhibit high levels of motivation or favorable perceptions towards travel, there is a subsequent increase in travel behavior and frequency. Consequently, we can propose the following hypothesis:

Hypothesis 10. 

States that intention exerts a statistically significant positive effect on travel frequency.

Figure 1. Conceptual Frameworks and Hypotheses. Note: d and p in a bucket indicate the hypothesis of during pandemic model and post-pandemic model, respectively. Variable codes are indicated in Table 1.

Figure 1. Conceptual Frameworks and Hypotheses. Note: d and p in a bucket indicate the hypothesis of during pandemic model and post-pandemic model, respectively. Variable codes are indicated in Table 1.

Table 1. Variable Description and Descriptive Statistics.

Variable (Code)	Description	During COVID-19		Post-COVID-19
		M	S.D.	M	S.D.
Frequency (FREQ)	0 is less than 1 time/month, 1 is 1 time/month, 2 is 2 time/month, 3 is more than 2 time/month	1.79	0.94	2.12	0.87
Intention (INT)
INT1 a	You travel to change the atmosphere or create inspiration.	3.02	0.94	4.57	0.61
INT2 a	You like having privacy.	3.05	0.90	4.56	0.61
INT3 a	You need to have some rest and relaxation.	3.04	0.93	4.57	0.61
INT4 a	You want to spend time with your family/ beloved people.	3.00	0.86	4.53	0.62
Perceived Susceptibility (PSU)
PSU1 b	You think that when traveling for tourism, if you protect yourself very well, you will not be infected with the disease.	2.35	1.17	4.52	0.66
PSU2 b	You think that in traveling for tourism by public transportation system, you take a risk of be infected with COVID-19 disease.	4.49	0.63	4.56	0.60
PSU3 b	You think that by traveling for tourism by public transportation, you take a risk of being infected with COVID-19.	4.25	0.86	4.52	0.64
PSU4 b	You know whenever you travel for tourism, you must always follow preventive measures against COVID-19 pandemic, such as wearing masks, checking the temperature, washing hands with sanitizer gel or alcohol, or social distancing in queueing for getting service, etc.	4.52	0.62	4.49	0.65
Perceived Severity (PSE)
PSE1 c	You know that if you travel for tourism and you are infected, it will make you sick.	4.53	0.61	4.53	0.61
PSE2 c	You know that being infected with the epidemic may result in disabilities or death.	4.46	0.66	4.52	0.64
PSE3 c	You know that sickness from infection greatly affects your studies or your work.	4.53	0.59	4.55	0.58
PSE4 c	Each sickness or death makes you waste your time and your family’s income.	4.59	0.56	4.53	0.59
Perceived Benefits (PBE)
PBE1 e	You think that during the COVID-19 pandemic, traveling by a private vehicle is safer than traveling by public transportation system.	4.51	0.65	4.60	0.54
PBE2 f	You think that following the preventive measures against the COVID-19 pandemic makes you feel worthwhile and enjoy traveling for tourism.	4.32	0.86	4.54	0.58
PBE3 f	You think that having responsibilities for yourself and society can reduce the risks of COVID-19.	4.54	0.58	4.50	0.61
PBE4 f	You think that traveling for tourism in open places with a limited number of service users makes you feel at ease.	4.55	0.62	4.55	0.60
Perceived Barriers (PBA)
PBA1 b	You feel that traveling for tourism by public transportation system during the COVID-19 pandemic makes you feel uncomfortable.	4.53	0.65	4.54	0.58
PBA2 b	You feel that during the COVID-19 pandemic, if tourist places are still open, you will continue using the service.	3.01	0.90	4.55	0.57
PBA3 b	You feel that there is no need to spend more money on purchasing safety in the situation of the COVID-19 pandemic if you can protect yourself on your own.	3.06	0.90	4.50	0.63
PBA4 c	You feel that the internal facilities in places for tourists, such as restrooms, waiting seats, under the unstrict preventive measures may make you feel uncomfortable.	4.57	0.56	4.53	0.61
Self-efficacy (SEF)
SEF1 a	Each decision is based on your own. It does not depend on anyone.	3.04	0.88	4.43	0.71
SEF2 h	You feel that the chance of getting infected by the COVID-19 pandemic depends on you.	2.99	0.92	4.49	0.69
SEF3 h	You think that if you get infected with the disease, the cause is not yours.	2.99	0.90	4.48	0.73
SEF4 h	You think that the chance to get infected from COVID-19 pandemic depends on tourists’ behavior.	4.42	0.74	4.49	0.66
Cues to action (CTO)
CTO1 b	You think that the government attaches importance to the safety of traveling by public transport in a COVID-19 pandemic situation.	2.92	0.87	3.03	0.92
CTO2 g	You think that the government helps stimulate and support Thai tourism in the context of the COVID epidemic.	3.01	0.87	2.93	0.87
CTO3 g	You think that the policy to promote tourism makes you feel like traveling in the context of the COVID-19 epidemic.	2.97	0.95	3.03	0.85
CTO4 g	You think that the government has allocated budget for managing public health and safety in tourist attractions appropriately	3.01	0.87	3.00	0.92
Health Motivation (HMO)
HMO1 b	You think that traveling by a private vehicle is the best travel mode.	4.53	0.62	4.52	0.67
HMO2 b	You think that traveling for tourism in the places where the number of service users are limited is the safest.	4.52	0.62	4.54	0.60
HMO3 a	You think that health and safety are the most important.	4.56	0.58	4.52	0.61
HMO4 b	You think that being infected from traveling is the worst issue.	3.05	0.89	4.47	0.71
Outcome Expectation (OCE)
OCE1 d	You feel happy whenever you travel for tourism in every situation.	3.02	0.94	4.47	0.70
OCE2 d	You feel worthwhile spending money on travelling for tourism in every situation.	2.95	0.90	4.47	0.70
OCE3 d	You have no opinion on stopping traveling for tourism with the reason stating that “In any situation, you will get what you expect from traveling for tourism”	4.31	0.87	4.50	0.67
OCE4 d	You feel happy whenever you travel for tourism in every situation.	4.52	0.62	4.43	0.77

Note: M denotes mean, S.D. denotes standard deviation. Questionnaire items was adapted from ^a. Chansuk et al. [23]; ^b. Devkota et al. [11]; ^c. Kock et al. [37]; ^d. Dwivedi et al. [8]; ^e. Hassan et al. [16]; ^f. Morales-Hernández et al. [38]; ^g. Nguyen et al. [9]; ^h. Erul et al. [32].

Table 1. Variable Description and Descriptive Statistics.

Variable (Code)	Description	During COVID-19		Post-COVID-19
		M	S.D.	M	S.D.
Frequency (FREQ)	0 is less than 1 time/month, 1 is 1 time/month, 2 is 2 time/month, 3 is more than 2 time/month	1.79	0.94	2.12	0.87
Intention (INT)
INT1 a	You travel to change the atmosphere or create inspiration.	3.02	0.94	4.57	0.61
INT2 a	You like having privacy.	3.05	0.90	4.56	0.61
INT3 a	You need to have some rest and relaxation.	3.04	0.93	4.57	0.61
INT4 a	You want to spend time with your family/ beloved people.	3.00	0.86	4.53	0.62
Perceived Susceptibility (PSU)
PSU1 b	You think that when traveling for tourism, if you protect yourself very well, you will not be infected with the disease.	2.35	1.17	4.52	0.66
PSU2 b	You think that in traveling for tourism by public transportation system, you take a risk of be infected with COVID-19 disease.	4.49	0.63	4.56	0.60
PSU3 b	You think that by traveling for tourism by public transportation, you take a risk of being infected with COVID-19.	4.25	0.86	4.52	0.64
PSU4 b	You know whenever you travel for tourism, you must always follow preventive measures against COVID-19 pandemic, such as wearing masks, checking the temperature, washing hands with sanitizer gel or alcohol, or social distancing in queueing for getting service, etc.	4.52	0.62	4.49	0.65
Perceived Severity (PSE)
PSE1 c	You know that if you travel for tourism and you are infected, it will make you sick.	4.53	0.61	4.53	0.61
PSE2 c	You know that being infected with the epidemic may result in disabilities or death.	4.46	0.66	4.52	0.64
PSE3 c	You know that sickness from infection greatly affects your studies or your work.	4.53	0.59	4.55	0.58
PSE4 c	Each sickness or death makes you waste your time and your family’s income.	4.59	0.56	4.53	0.59
Perceived Benefits (PBE)
PBE1 e	You think that during the COVID-19 pandemic, traveling by a private vehicle is safer than traveling by public transportation system.	4.51	0.65	4.60	0.54
PBE2 f	You think that following the preventive measures against the COVID-19 pandemic makes you feel worthwhile and enjoy traveling for tourism.	4.32	0.86	4.54	0.58
PBE3 f	You think that having responsibilities for yourself and society can reduce the risks of COVID-19.	4.54	0.58	4.50	0.61
PBE4 f	You think that traveling for tourism in open places with a limited number of service users makes you feel at ease.	4.55	0.62	4.55	0.60
Perceived Barriers (PBA)
PBA1 b	You feel that traveling for tourism by public transportation system during the COVID-19 pandemic makes you feel uncomfortable.	4.53	0.65	4.54	0.58
PBA2 b	You feel that during the COVID-19 pandemic, if tourist places are still open, you will continue using the service.	3.01	0.90	4.55	0.57
PBA3 b	You feel that there is no need to spend more money on purchasing safety in the situation of the COVID-19 pandemic if you can protect yourself on your own.	3.06	0.90	4.50	0.63
PBA4 c	You feel that the internal facilities in places for tourists, such as restrooms, waiting seats, under the unstrict preventive measures may make you feel uncomfortable.	4.57	0.56	4.53	0.61
Self-efficacy (SEF)
SEF1 a	Each decision is based on your own. It does not depend on anyone.	3.04	0.88	4.43	0.71
SEF2 h	You feel that the chance of getting infected by the COVID-19 pandemic depends on you.	2.99	0.92	4.49	0.69
SEF3 h	You think that if you get infected with the disease, the cause is not yours.	2.99	0.90	4.48	0.73
SEF4 h	You think that the chance to get infected from COVID-19 pandemic depends on tourists’ behavior.	4.42	0.74	4.49	0.66
Cues to action (CTO)
CTO1 b	You think that the government attaches importance to the safety of traveling by public transport in a COVID-19 pandemic situation.	2.92	0.87	3.03	0.92
CTO2 g	You think that the government helps stimulate and support Thai tourism in the context of the COVID epidemic.	3.01	0.87	2.93	0.87
CTO3 g	You think that the policy to promote tourism makes you feel like traveling in the context of the COVID-19 epidemic.	2.97	0.95	3.03	0.85
CTO4 g	You think that the government has allocated budget for managing public health and safety in tourist attractions appropriately	3.01	0.87	3.00	0.92
Health Motivation (HMO)
HMO1 b	You think that traveling by a private vehicle is the best travel mode.	4.53	0.62	4.52	0.67
HMO2 b	You think that traveling for tourism in the places where the number of service users are limited is the safest.	4.52	0.62	4.54	0.60
HMO3 a	You think that health and safety are the most important.	4.56	0.58	4.52	0.61
HMO4 b	You think that being infected from traveling is the worst issue.	3.05	0.89	4.47	0.71
Outcome Expectation (OCE)
OCE1 d	You feel happy whenever you travel for tourism in every situation.	3.02	0.94	4.47	0.70
OCE2 d	You feel worthwhile spending money on travelling for tourism in every situation.	2.95	0.90	4.47	0.70
OCE3 d	You have no opinion on stopping traveling for tourism with the reason stating that “In any situation, you will get what you expect from traveling for tourism”	4.31	0.87	4.50	0.67
OCE4 d	You feel happy whenever you travel for tourism in every situation.	4.52	0.62	4.43	0.77

Note: M denotes mean, S.D. denotes standard deviation. Questionnaire items was adapted from ^a. Chansuk et al. [23]; ^b. Devkota et al. [11]; ^c. Kock et al. [37]; ^d. Dwivedi et al. [8]; ^e. Hassan et al. [16]; ^f. Morales-Hernández et al. [38]; ^g. Nguyen et al. [9]; ^h. Erul et al. [32].

3. Materials and Method

3.1. Questionnaire and Data Collection

The questionnaire consisted of two parts. The first part collected personal information from the respondents, including gender, age, income, occupation, and purpose of the trip etc., The second part focused on various perceived perspectives, which were derived from existing literature, such as perceived susceptibility and perceived severity. This part comprised sub-questions, and a Likert-type scale ranging from 1 to 5 for indicators (1 indicating the lowest level of agreement and 5 indicating the highest) [8]. The data in this part were divided into two sides: the left-side contained tourists’ attitudes during the pandemic, and the other side was the attitudes of those in the post-pandemic. Additionally, post-pandemic attitudes were assessed separately. The detailed questions are presented in Table 1.

The surveyed provinces were determined by firstly dividing the region of Thailand into six regions (

Table 2. Description of the investigated.

Region	Province	No. of Tourist a (Person)	Percentage of Tourist in a Province of Each Region	No. of Participants b
Central	Bangkok	22,900,082	74.48%	261
	Phra Nakhon Si Ayutthaya	4,957,358	16.12%	56
	Chachoengsao	2,889,277	9.40%	33
Western	Kanchanaburi	7,420,719	38.87%	136
	Prachuap Khiri Khan	5,947,987	31.15%	109
	Phetchaburi	5,724,793	29.98%	105
Eastern	Chon Buri	8,088,281	76.89%	269
	Rayong	1,470,276	13.98%	49
	Nakhon Nayok	960,351	9.13%	32
Southern	Phuket	2,480,830	42.55%	149
	Surat Thani	2,163,323	37.11%	130
	Nakhon Si Thammarat	1,185,689	20.34%	71
Northern	Chiang Mai	5,317,089	53.26%	186
	Chiang Rai	3,016,867	30.22%	106
	Phitsanulok	1,648,930	16.52%	58
Northeastern	Nakhon Ratchasima	3,887,548	48.30%	169
	Khon Kaen	2,190,857	27.22%	95
	Udon Thani	1,970,869	24.49%	86
Total				2100

Note: ^a cumulative number of tourists from January to August 2022. ^b The number of participants in each province is derived by multiplying the ‘Percentage of Tourists in a Province of Each Region’ by 350, where 350 represents the standard number of participants in each region.

). Then 350 sets will be determined for each region, which selected the provinces in the top 3 to the number of tourists. For example, the central region surveyed 261 sets in Bangkok, 56 sets in Phra Nakhon Si Ayutthaya, and 33 sets in Chachoengsao. Therefore, the total number of questionnaires was 2100. The areas for survey were tourist attractions, such as natural sources, cultural sources, cafes, shopping malls, etc. Before administrating questionnaires and asking for information, the research team informed the respondents of the questionnaire details first, such as their operating agency, project objectives, etc. If the respondents are willing to provide information, the survey was conducted. The survey was operated from December 2022 to January 2023.

The characteristics of the respondents are shown in

Table 3. Respondents’ characteristics.

Characteristics		Frequency	Percentages
Gender	Male	1270	60.48
	Female	830	39.52
Age	<20 years	37	1.76
	20–30 years	598	28.48
	31–40 years	803	38.24
	41–50 years	438	20.86
	>51 years	224	10.67
Marital status	Single	734	34.95
	Married	1060	50.48
	Divorce	306	14.57
Highest education	Elementary School	198	9.43
	Junior High School	299	14.24
	Senior High School/Vocational Certificate	299	14.24
	High Vocational Certificate	284	13.52
	Bachelor’s degree	929	44.24
	Master’s degree	77	3.67
	Doctor of Philosophy.	14	0.67
Income per month	<10,000 Baht	1100	52.38
	10,001–30,000 Baht	893	42.52
	30,001–50,000 Baht	102	4.86
	>50,001 Baht	5	0.24
Number of persons in family	2–3 persons	1168	55.62
	4–5 persons	843	40.14
	More than 5 persons	89	4.24
Occupation	Student	139	6.62
	Government	106	5.05
	Private Company	815	38.81
	Private business	524	24.95
	Agriculture	154	7.33
	General	314	14.95
	Maid	37	1.76
	Other	11	0.52
Resident zone	Urban	1187	56.52
	Suburban	500	23.81
	Rural	413	19.67

Note: The suburban areas are located within the same district as the urban regions, but the participant’s house is not situated in the central business district of Thailand, commonly referred to as the “Muang district.” On the other hand, the rural areas encompass households situated outside the Muang district.

. Most of them are male, aged 31–40 years old. Most of them are married. The average income of most respondents is less than 10,000 baht per month. The number of people in the family is mostly in the range of 2–3 people. Most of the occupations involve working for private companies, followed by self-employed and general contractors. Furthermore, most of the respondents reside in urban areas.

3.2. Factor Analysis

Factor analysis (FA) is an independent analysis. In other words, it is an analysis of the structure of the variables to develop a key indicator for each latent variable [38,39]. FA has two main steps: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is used to identify the correlation of complicated question items amid questions and groups of question items. In this study, EFA was used to classify the correct group for each attitude, while the CFA, which is more complicated, is a test of the established hypothesis (By virtue of the results from the EFA that have been grouped before). Once the completed CFA result is used as a measurement model of Structural Equation Modeling. Due to the widespread use of factor analysis, readers are recommended to read more at Hair et al. [40].

3.3. Structural Equation Modeling

Structural Equation Modeling (SEM) is a multivariate statistical analysis technique used to analyze structural relationships. This technique is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between the observed variable (in this study, each question item) and latent construct (in this study, a variety of perception such as Health Motivation and Outcome Expectation, etc.) since SEM is widely used. Readers can learn more at Kline [41].

The data analysis procedure shown in Figure 2 began by dividing the questionnaire into two phases: the epidemic phase and the post-epidemic phase. After that, a factor analysis was performed using EFA to determine the correlation between each question item whether it can be grouped according to the theory of Health Belief Model or not. CFA was then developed based on the EFA results, in which the observed variable of each latent variable was adapted by removal until the CFA yields results that passed the goodness of fit (GOF) criterion. The CFA’s GOF criterion consists of (1) the χ²/df value should be 2–5 for suitability; (2) the root mean square error of approximation (RMSEA) should be less than 0.07; (3) the CFI value should be ≥0.90; (4) the Tucker-Lewis Index (TLI) values should be equal to or more than 0.90 and (5) the standardized root mean square residual (SRMR) should be ≤0.70. Once the final measurement model has been obtained, SEM analysis can be initiated. GOF analysis for SEM uses the same values as CFA [40].

4. Results

4.1. Descriptive Statistics

Descriptive statistics of the question items are shown in Table 1. When considering the frequency of travel, it is evident that the average frequency during the COVID-19 period is lower compared to the after-COVID-19 period, indicating an increase in travel frequency among the respondents after COVID. Similarly, the travel intention follows a similar pattern; in other words, in the post-COVID period, the motivation to travel was greater than during the COVID pandemic. Regarding the mean values of the variables based on the Health Belief Model theory, it was found that both periods yielded similar responses. Among negative variables, the notable different question item in perceived risk was PSU 1: “You think that when traveling for tourism, if you protect yourself very well, you will not be infected with the disease.” The mean value during COVID-19 was only 2.35, while in the post-COVID period, the mean was 4.52, indicating that during the COVID-19 epidemic, people were highly aware that even with proper self-protection, infection could still occur. In terms of perceived severity and perceived benefits, the mean values of the two scenarios were not quite different. The other interesting result was perceived barriers, such as closed stores (PBA2) or increased spending money (PBA3). The results showed that the mean value of perception during COVID-19 was lower. Additionally, the group factor of self-efficacy demonstrated a reasonable difference, with lower self-confidence observed during COVID-19 when compared to the post-COVID period. When considering the modifying factors, namely health motivation and outcome expectation, they were not quite different between the two situations.

4.2. EFA Results

The results of Exploratory Factor Analysis (EFA) are shown in

Table 4. EFA Results.

	Component	During COVID	Post COVID-19
INTE1	1	0.754	0.567
INTE2		0.525	0.653
INTE3		0.640	0.682
INTE4		0.641	0.592
PSU1	2		0.474
PSU2		0.447	0.505
PSU3			0.519
PSU4		0.754	0.435
PSE1	3	0.535	0.693
PSE2		0.635	0.684
PSE3
PSE4
PBE1	4	0.551	0.678
PBE2		0.755	0.722
PBE3			0.670
PBE4			0.493
PBA1	5		0.579
PBA2			0.676
PBA3			0.633
PBA4			0.560
SEF1	6	0.658	0.687
SEF2		0.630	0.753
SEF3		0.529	0.729
SEF4
CTO1	7	0.721	0.898
CTO2		0.705	0.566
CTO3		0.625	0.423
CTO4		0.517	0.879
HMO1	8	0.632	0.720
HMO2		0.698	0.713
HMO3		0.559	0.573
HMO4
OCE1	9	0.673	0.671
OCE2		0.617	0.468
OCE3		0.739	0.443
OCE4			0.472

Note: Statistical model of during COVID-19 model (post-COVID-19 model): Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.603 (0.757); Bartlett’s Test of Sphericity Approx. Chi-Square 5045.534 (8528.149) df = 496 (496), p-value < 0.000. Percentage variance cumulative = 52.292 (58.182). Competent 1–9 denote Intention, Perceived Susceptibility, Perceived Severity, Perceived Benefits, Perceived Barriers, Self-efficacy, Cues to action, Health Motivation and Outcome Expectation, respectively. Cronbach’s alpha of element 1–9 during COVID-19: 0.758, 0.501, 0.697, 0.503, NA, 0.626, 0.754, 0.611 and 0.537, respectively. Cronbach’s alpha of element 1–9 of post COVID-19: 0.787, 0.789, 0.740, 0.705, 0.695, 0.896, 0.875, 0.628 and 0.878, respectively.

. The results of grouping nine variables of the latent construct found that the data during COVID-19 were not quite well grouped, but it was acceptable based on three indicators, including: (1) the factor loading must be greater than 0.4 [23], the lowest value was 0.447; (2) Kaiser-Meyer-Olkin (KMO) = 0.603, which was greater than 0.6 [11]; (3) Percentage variance cumulative of 52.292%, where the criterion must be greater than 50% [40] to be considered acceptable; and (4) Cronbach’s alpha coefficient is used to measures the internal consistency or reliability of a set of questionnaire items. Cronbach’s alpha ranges from 0.501 to 0.758. Although most recommend values greater than 0.7, values greater than 0.5 are considered acceptable [42] because it is a relatively new situation, and beliefs about the risk of infection during COVID are quite different. The grouping of observed variables is, therefore, difficult to classify. The results of the grouping showed that perceived barriers could not be grouped because the factor loading was less than 0.4. Most of the observed variables were 3–4, except for perceived severity, perceived benefits, and perceived barriers, which had only two. For the post-COVID-19 model, the grouping went smoothly. All four indicators met the criteria, which were KMO = 0.757, factor loading ranging from 0.443–0.879, Percentage variance cumulative being 58.182, and Cronbach’s alpha 0.628–0.896. As for the results of the grouping, only the Perceived Severity had two observed variables. Variables that could not be observed due to low correlation were PSE3, PSE4, SEF4, and HMO4.

4.3. CFA Results

The results of the Confirmatory Factor Analysis (CFA) are shown in

Table 5. CFA Results.

Variable	During COVID-19					Post-COVID-19
	Factor Loading	S.E	t-Stat	AVE	CR	Factor Loading	S.E	t-Stat	AVE	CR
Intention (INT)				0.392	0.908				0.356	0.977
INTE1						0.424	0.026	16.308
INTE2	0.300	0.042	7.143			0.529	0.025	21.160
INTE3	0.350	0.049	7.143			0.577	0.024	24.042
INTE4	0.981	0.177	5.542			0.525	0.024	21.875
Perceived Susceptibility (PSU)				0.327	0.884				0.472	0.975
PSU1
PSU2	0.441	0.046	7.413			0.605	0.024	25.208
PSU3						0.569	0.024	23.708
PSU4	0.678	0.118	5.746			0.504	0.024	21.000
Perceived Severity (PSE)				0.366	0.929				0.373	0.951
PSE1	0.461	0.043	10.721			0.571	0.036	15.861
PSE2	0.721	0.063	11.444			0.648	0.040	16.200
PSE3
PSE4
Perceived Benefits (PBE)				0.478	0.928				0.424	0.965
PBE1	0.304	0.029	10.483			0.473	0.029	16.310
PBE2	0.929	0.089	10.438			0.601	0.033	18.212
PBE3						0.513	0.030	17.100
PBE4
Perceived Barriers (PBA)									0.399	0.962
PBA1						0.432	0.032	13.500
PBA2						0.485	0.032	15.156
PBA3						0.457	0.032	14.281
PBA4						0.408	0.031	13.161
Self-efficacy (SEF)				0.342	0.962				0.436	0.986
SEF1	0.503	0.037	13.595			0.603	0.019	31.737
SEF2	0.688	0.042	16.381			0.656	0.018	36.444
SEF3	0.547	0.039	14.026			0.718	0.017	42.235
SEF4
Cues to action (CTO)				0.307	0.963				0.584	0.987
CTO1	0.663	0.037	17.919			0.995	0.022	45.227
CTO2	0.595	0.035	17.000			0.363	0.021	17.286
CTO3
CTO4	0.357	0.027	13.222			0.794	0.019	41.789
Health Motivation (HMO)				0.337	0.962				0.312	0.956
HMO1	0.544	0.037	14.703			0.619	0.054	11.463
HMO2	0.712	0.043	16.558			0.561	0.042	13.357
HMO3	0.455	0.036	12.639			0.488	0.033	14.788
HMO4
Outcome Expectation (OCE)				0.411	0.959				0.474	0.985
OCE1	0.397	0.028	14.179			0.473	0.029	22.524
OCE2	0.946	0.080	11.825			0.687	0.018	38.167
OCE3	0.426	0.027	15.778			0.649	0.018	36.056
OCE4						0.551	0.020	27.550

Note: S.E. denotes the standard error. AVE denotes Average Variance Extracted and CR denotes Construct Reliability. Model Statistical of during COVID-19: χ²(177) = 370.902, p-value < 0.0000, RMSEA = 0.023 [90% C.I = 0.020–0.026], CFI = 0.925, TLI = 0.902 and SRMR = 0.025. Model Statistical of post-COVID-19: χ²(370) = 936.886, p-value < 0.0000, RMSEA = 0.027 [90% C.I = 0.025–0.029], CFI = 0.936, TLI = 0.925 and SRMR = 0.028.

. For the goodness of fit using the same criterion for Structural Equation Modeling, both models are within acceptable criteria. Model Statistical of during COVID-19 model: χ²(177) = 370.902, p-value < 0.0000, RMSEA = 0.023 [90% C.I = 0.020–0.026], CFI = 0.925, TLI = 0.902, and SRMR = 0.025. during COVID-19, CFA was consistent with EFA. The Average Variance Extracted (AVE), which indicates the mean value of items loading on a construct, was found to be in the range of 0.307–0.478. Construct Reliability (CR), which considers factor loading and the sum of error variance, was quite high, ranging from 0.884 to 0.963. For recommended values of AVE and CR, values should be greater than 0.5 and 0.7, respectively [23]. Furthermore, when contemplating the factor loading of INT, notably low values of 0.300 and 0.350 are observed (with the majority being recommended at a threshold of 0.400 or higher). For instance, certain individuals possess a background in healthcare and are knowledgeable about managing illnesses should they arise. Conversely, those outside the healthcare domain solely rely on news or government announcements, potentially leading to excessive anxiety. These distinct circumstances give rise to varying attitudes, ultimately resulting in diverse interpretations for each question. However, Lam [43] accepts factor loading at 0.31 and Allen et al. [44] accept factor loading at 0.29. Regarding the AVE and CR, some studies using AVE > 0.3 and CR > 0.7 are acceptable. Due to the question items in the new situation, the factor loading may not be very high [43]. Convergent and discriminant validities were thoroughly investigated to effectively assess construct validity. Discriminant validity was evaluated by comparing the square roots of each construct’s AVE with the inter-construct correlations [32].

Table 6. Discriminant validity analysis results.

During COVID-19
	INT	PSU	PSE	PBE	PBA *	SEF	CTO	HMO	OCE
INT	0.626
PSU	−0.023	0.572
PSE	0.063	0.334	0.605
PBE	−0.037	−0.090	−0.028	0.691
PBA *
SEF	−0.077	0.049	0.088	0.025		0.554
CTO	0.027	0.017	−0.023	−0.043		−0.019	0.641
HMO	−0.107	0.105	0.213	−0.009		0.024	0.143	0.585
OCE	−0.057	−0.052	−0.010	0.516		−0.026	−0.026	0.039	0.580
Post-COVID-19
	INT	PSU	PSE	PBE	PBA	SEF	CTO	HMO	OCE
INT	0.597
PSU	0.501	0.687
PSE	0.293	0.433	0.611
PBE	−0.073	0.041	0.081	0.651
PBA	0.164	0.022	0.058	0.041	0.631
SEF	0.019	0.006	0.006	−0.009	0.089	0.661
CTO	−0.032	−0.054	−0.041	−0.104	0.004	−0.081	0.764
HMO	−0.059	−0.084	0.031	0.103	0.076	−0.079	−0.023	0.559
OCE	0.077	−0.037	0.011	−0.006	0.063	0.667	−0.050	−0.105	0.688

Note: * The Perceived Barrier (PBA) has been eliminated from the during COVID-19 model. The bold text elements represent the square root of the variance shared between the factors and their measures (average variance extracted). The remaining elements indicate the correlations between factors. Regarding discriminant validity, it is essential that the diagonal elements to be greater than any other corresponding row or column entry.

presents the discriminant validities for both the during and post-COVID-19 models, and the results of the discriminant validity tests are corroborated [45].

Model Statistical of post-COVID-19: χ²(370) = 936.886, p-value < 0.0000, RMSEA = 0.027 [90% C.I = 0.025–0.029, CFI = 0.936, TLI = 0.925, and SRMR = 0.028, indicating that it is within acceptable criteria. The grouping was found to be consistent with EFA. When considering the values of AVE and CR, they were between 0.356 and 0.584 and 0.951 and 0.987, respectively, which are within the acceptable range.

4.4. SEM Results

The results of Structural Equation Modeling (SEM) are shown in

Table 7. SEM Result.

Variable	During COVID-19				Post-COVID-19
	Standardized Coefficient	S.E.	t-Stat	p-Value	Standardized Coefficient	S.E.	t-Stat	p-Value
Measurement Model
Intention (INT)
INT1					0.426 ***	0.026	16.634	<0.000
INT2	0.199 ***	0.042	4.739	<0.000	0.529 ***	0.025	21.493	<0.000
INT3	0.250 ***	0.049	5.084	<0.000	0.575 ***	0.024	23.826	<0.000
INT4	0.990 ***	0.179	5.531	<0.000	0.523 ***	0.024	21.482	<0.000
Perceived Susceptibility (PSU)
PSU2	0.241 ***	0.046	5.193	<0.000	0.605 ***	0.024	25.069	<0.000
PSU3					0.569 ***	0.024	23.491	<0.000
PSU4	0.678 ***	0.118	5.763	<0.000	0.504 ***	0.024	20.665	<0.000
Perceived Severity (PSE)
PSE1	0.461 ***	0.043	10.62	<0.000	0.571 ***	0.036	15.792	<0.000
PSE2	0.721 ***	0.063	11.408	<0.000	0.648 ***	0.040	16.381	<0.000
Perceived Benefits (PBE)
PBE1	0.204 ***	0.029	7.132	<0.000	0.473 ***	0.029	16.572	<0.000
PBE2	0.929 ***	0.089	10.439	<0.000	0.601 ***	0.033	18.434	<0.000
PBE3					0.513 ***	0.030	16.876	<0.000
Perceived Barriers (PBA)
PBA1					0.432 ***	0.032	13.67	<0.000
PBA2					0.485 ***	0.032	15.218	<0.000
PBA3					0.457 ***	0.032	14.489	<0.000
PBA4					0.408 ***	0.031	12.964	<0.000
Self-efficacy (SEF)
SEF1	0.402 ***	0.037	10.91	<0.000	0.603 ***	0.019	31.998	<0.000
SEF2	0.488 ***	0.042	11.603	<0.000	0.656 ***	0.018	36.359	<0.000
SEF3	0.447 ***	0.039	11.323	<0.000	0.718 ***	0.017	41.301	<0.000
Cues to action (CTO)
CTO1	0.661 ***	0.037	17.89	<0.000	0.995 ***	0.022	45.645	<0.000
CTO2	0.596 ***	0.035	17.218	<0.000	0.363 ***	0.021	17.625	<0.000
CTO4	0.357 ***	0.027	13.43	<0.000	0.794 ***	0.019	41.472	<0.000
Health Motivation (HMO)
HMO1	0.444 ***	0.037	11.872	<0.000	0.618 ***	0.053	11.567	<0.000
HMO2	0.512 ***	0.043	11.853	<0.000	0.462 ***	0.042	10.968	<0.000
HMO3	0.355 ***	0.036	9.972	<0.000	0.288 ***	0.033	8.702	<0.000
Outcome Expectation (OCE)
OCE1	0.097 ***	0.028	3.541	<0.000	0.473 ***	0.021	22.344	<0.000
OCE2	0.946 ***	0.08	11.866	<0.000	0.687 ***	0.018	39.214	<0.000
OCE3	0.226 ***	0.027	8.421	<0.000	0.649 ***	0.018	35.971	<0.000
OCE4					0.551 ***	0.02	27.776	<0.000
Structural Model
PSU→INT	0.048	0.036	1.333	0.185	−0.467 ***	0.043	−10.94	<0.000
PSE→INT	−0.114 **	0.038	−3.000	0.003	−0.092 **	0.046	−1.999	0.046
PBE→INT	0.01	0.029	0.345	0.725	0.105 **	0.038	2.768	0.006
PBA→INT					−0.155 ***	0.04	−3.896	<0.000
SEF→INT	0.082 *	0.029	1.690	0.087	0.099 *	0.054	1.841	0.066
CTO→INT	−0.046	0.031	−1.484	0.146	0.015	0.029	0.532	0.595
HMO→INT	0.129 **	0.037	2.216	0.026	0.013	0.041	0.330	0.742
OCE→INT	0.049 **	0.042	−1.095	0.002	0.147 **	0.054	2.714	0.007
INT→FREQ	−0.008	0.022	−0.367	0.714	0.057 **	0.028	2.08	0.038
CTO→FREQ	0.028	0.028	0.996	0.319	−0.006	0.022	−0.26	0.795

Note: *** p-value < 0.001, ** p-value < 0.05 and * p-value < 0.1.

. During COVID-19, the model achieved a goodness-of-fit value consisting of χ²(175) = 369.801, a p-value < 0.0000, RMSEA = 0.023, [90% C.I = 0.020–0.026], CFI = 0.924, TLI = 0.900, and SRMR = 0.025. These values are within acceptable limits. Overall, the measurement model was consistent with CFA; that is, all observed variables had a significant p-value < 0.000. For the structural model (Figure 3), only four latent constructs were found to have a significant effect on intention, including Perceived Benefits were positively significant (H4d was supported); Self-efficacy was positively significant (H5d was supported); Health Motivation was negatively significant for intention, but H8d was identified as being negatively significant; and Outcome Expectation was significantly positive (H9d was supported).

SEM of Post-COVID-19, χ²(368) = 932.542, p-value < 0.0000, RMSEA = 0.027 [90% C.I = 0.025–0.029], CFI = 0.936, TLI = 0.925, and SRMR = 0.028; it is within acceptable criteria. The overall measurement model corresponds to the CFA results. For the structural model, several significant factors were found, and direction was consistent with the significant latent construct consisting of perceived Susceptibility (PSU), Perceived Severity, Perceived Benefits, Perceived Barriers, Self-efficacy, and outcome expectation; therefore, H1p, H2p, H3p, H4p, H5p, and H9p were supported. The conclusions of the hypothesis testing are shown in

Table 8. Results of hypotheses testing.

Relationship	Expected Correlation	During COVID-19		Post-COVID-19
		Hypotheses	Result	Hypotheses	Result
PSU → INT	−	H1d	Not supported	H1p	Supported
PSE → INT	−	H2d	Supported	H2p	Supported
PBE → INT	+	H3d	Not supported	H3p	Supported
PBA → INT	−	H4d	Not supported	H4p	Supported
SEF → INT	+	H5d	Supported	H5p	Supported
CTO → INT	+	H6d	Not supported	H6p	Not supported
CTO → FREQ	+	H7d	Not supported	H7p	Not supported
HMO → INT	−	H8d	Not supported *	H8p	Not supported
OCE → INT	+	H9d	Supported	H9p	Supported
INT → FREQ	+	H10d	Not supported	H10p	Supported

Note: * denotes opposite sign. d is a hypothesis in the during COVID-19 model. p is a hypothesis in the post-COVID-19 model.

.

5. Discussion

The results will be discussed based on the theoretical results from SEM, considering the significance of the latent constructs and direction.

Intention in the during COVID-19 group consists of three observed variables. The highest factor loading was INT4, suggesting that in case of during the epidemic, motivation often comes from getting out of the house and spending time with family even more. However, in the post-epidemic period, people have returned to work and may want to travel to relax from work. This effect is similar to the study by Cheng et al. [46], which noted that the issue of relaxation is very important when everyone is back to on-site work.

H1, perceived risk, has a significantly negative effect on intention. The analysis result revealed that only H1p was supported, potentially interpreting that during the epidemic, perceived risk may vary according to the received messages [47]. Thus, the relationship with intention is insignificant. While in the post-pandemic period, it was clear that when perceived risk was higher, there was less intention; the results rather make sense. The issue people were most concerned about in this group was PSU2. In this study, public transportation may also refer to public facilities, such as a group of public toilets, or things that must be in common contact with other people as well. This is very clear: People who are anxious do not often travel due to the fear of infection. Similar to Savadori, et al. [48], Joo, et al. [49], they discovered a negative correlation between risk perception and intention to tourism.

H2 is a significantly negative relationship between perceived severity and intention. Although the measurement model had only two observed variables, it was significant. The results from SEM indicated that H2d and H2p were supported, interpreting that the more you perceived risks, the less your motivation decreased. Considering the observed variables with high values of factor loadings, the two models were identical—PSE2. This item is very reasonable. It is in line with previous research, such as Su et al. [31], stating that if people are worried about their own health problems or perceived severity such as congenital diseases, especially respiratory diseases, their health risk preventative behavior is high; in other words, they do not want to go out due to the fear that contracting the disease will cause severe symptoms and fatalities.

H3 is the positive correlation of perceived benefits from tourism with motivation. The results found that in the during COVID model, the hypothesis H3d was not supported. The reason is that even if they follow the preventive measures, they will have a chance to be infected. Therefore, the opinions of the respondents are quite different, as well as the low intention from a lockdown condition and access limitation to various areas. Thus, the correlation between perceived benefits and intention was insignificant. As for H3p, it was supported, especially when considering the question with a high factor loading, PBE2. It is evident that after the epidemic, more knowledge or news makes people confident that compliance with measures will prevent the spread until the motivation of tourism increases. The discovery comprises Nguyen et al. [9] who proposed that a positive impact strategy is associated with tourist attitudes and product strategies capable of harnessing this imperceptible force to elevate travel experiences following the COVID-19 pandemic.

H4 is the correlation between perceived barriers and motivation. It is predicted to be significantly negative. In the During-COVID model, H4d was not supported, while H4p was supported. The results rather make sense, in other words, when people face obstacles or difficulty while traveling, such as wearing a mask, always wash your hands or cleaning with items that are shared with others, it results in decreased motivation for tourism. The reason may be that when comparing the situation after COVID (which still has an epidemic, but not much) with the situation before COVID when travelling used to be more comfortable. The loading factors of the observed variables were found to be close (0.408–0.485), which suggests that obstacles that arise include the need to spend more money for safety, the closure of tourist attractions, or the use of public facilities. This is consistent with the study of Devkota et al. [11], which states that expenses such as travel or hotels are very important in travel decisions.

H5 is related to self-efficacy, which has a positive effect on travel motivation. The results showed that both H5d and H5p were supported. Considering the observable variables of latent constructs were similar, in other words, the variable with a high loading factor is SEF2 and SEF3. Before being infected, people think that they could protect themselves, while in the post-pandemic period, the infection results from other people, or in other words, the confidence that their own practices can prevent infection with COVID-19 very well, such as washing hands with alcohol gel on a regular basis or avoiding crowded places. Such measures can prevent infection during travel [19].

H6 is the correlation between cue to action that has a significantly positive effect on motivation. Models of both situations were found to be unsupported. Cue to action is a collection of question items about assistance measures as well as measures used to stimulate tourism by the government, such as allocating budget and ensuring safety. Interestingly, various stimuli did not significantly increase motivation. The reason may be that people do not perceive concrete results from government measures, such as reducing the number of infected people. Similarly, the study of Nguyen et al. [9] stated that although the government plays an important role in stimulating tourism, the epidemic must be simultaneously controlled. These measures must be effective so that there will be stimulation and motivation for tourism. Furthermore, Abbas et al. [50] have posited the necessity of meticulously monitoring the ramifications and repercussions of the COVID-19 pandemic, encompassing critical stakeholders such as the tourism demand, tourism resources, tourism organizations, as well as government leaders, all of whom bear the potential to effect transformative changes and hold implications for the sustainable revitalization of the travel and leisure industry.

H7, which is a continuation of H6, is the correlation between various government measures that affect tourism behavior. As a result, H7d and H7p were not supported. The reason is similar to H6, which is that the results of the measures are no longer tangible. Therefore, they cannot stimulate an increase in tourism frequency.

H8: Health motivation issues have a significant negative effect on tourism motivation. Results from the during COVID-19 model are quite interesting because, in addition to not supporting H8d, the correlation effect was opposite to the hypothesis, potentially interpreting that even though there was quite a lot of health motivation, the travel motivation was high. Considering the question items in the latent construct, the loading factor results focused on HMO1 and HMO2, which are question items about the kinds of travel as well as the selection of attractions that will help reduce the chance of infection. This result is quite reasonable due to good travel planning in advance; even during the COVID epidemic, people wanted to travel [32], while the post-pandemic situation was insignificant (H8p was not supported). The cause may be that when they are no longer worried about COVID (they think if they are infected, it is not severe due to the less severe virus generation), there is no need to plan the trip in advance.

H9 is the correlation between outcome expectations and tourism motivation. It is predicted to be significantly positive. The results found that H9d and H9p were supported. The observed variables of outcome expectations involve question items about expectations when traveling, such as whether budgeting for infection is appropriate and whether the experience of traveling is worthwhile. These issues are all stimuli for tourism motivation. Considering the outstanding loading factor, the two models were identical, with OCE2 focusing on the incurred expenditure, which may result from protective devices such as alcohol gel or sanitary masks that can be purchased at a low price. This is consistent with the study of Zhang et al. [51], which found that the prediction of determined or limited tourism expenditures affects tourism in Hong Kong.

H10 is the correlation between motivation and travel behavior, which is indicated by the travel frequency. The result showed that the hypothesis was supported only for H10p. The result makes quite sense because, during the epidemic, there were fewer people traveling. So even though there is a lot of motivation, it cannot cause the travel behavior to increase. This may be caused by many factors, such as the severity of the disease, the strict measures of the government, etc., while the situation after the epidemic and stimulating motivation can increase the travel frequency. This is consistent with the study of Dwivedi et al. [8], which found that behavioral intention was positively correlated with travel behavior.

6. Conclusions

This study aims to find the relationship between the different factors affecting motivation and travel behavior by comparing the two situations during two periods, namely During the COVID-19 epidemic (timeframe under the country’s emergency management) and after the COVID-19 pandemic (Cancellation of Emergency Situation Management, 1 October 2022). The psychological model was developed based on the Health Belief Model and included a modifying factor. The questionnaire was surveyed among representatives of three provinces distributed across six regions of Thailand, amounting to 2100 sets. Factor analysis was used for grouping questions into the framework of Structural Equation Modeling (SEM), 10 hypotheses for each situation. The model results can be developed into policy recommendations to boost tourism in the present situation.

The results showed that the question items can be grouped according to the theory, and the SEM results were consistent with the empirical data in both situations. In the epidemic scenario model, three hypotheses were supported: H2d, H5d, and H9d, which are the relationships between tourism motivation and perceived severity, self-efficiency, and outcome expectations, respectively. H8d was significant, but in the opposite direction to the hypothesis, which is the relationship between health motivation and intention. For the post-COVID-19 situation, there are seven hypotheses that are supported: H1p, H2p, H3p, H4p, H5p, H9p, and H10p, which are the relationships between intention (INT) and perceived susceptibility (PSU), perceived severity (PSE), perceived benefits (PBE), perceived barriers (PBA), self-efficacy (SEF), outcome expectation (OCE), and frequency (FREQ).

The support of H10p shows that if motivation can be increased, it will be able to change behavior to make people turn to travel more frequently. Therefore, the policy recommendations were determined from the loading factor in SEM of both models. But most of them focus on the model. post-COVID-19 rather than because it is close to the current situation. The selection of a latent construct was considered from the structural model and the proposed subsections were considered from measurement models. The variable with the highest factor loading was the PSU, which had a negative effect on motivation. Therefore, policy recommendations to reduce perceived risk include taking preventive measures such as wearing a mask, temperature check, wash hands with alcohol gel, or spacing in queues for services, etc. These measures were seen as obstacles by some groups (this was supported by the effect of the relationship between perceived barriers and motivation, which was found to be of negative significance as well). The suggestion is that the safety of tourist attraction areas may need to be promoted, for example, by setting up well-spaced tables or providing open spaces. This is to give interested people more reason to come and visit [32,52]. The government may use benchmarks to assess the chances of infection with COVID-19. If any place is certified for proper spacing, it can use the results to promote its own attractions and increase tourists’ confidence [9]. The next suggestion results from the effect of the relationship between outcome expectation and intention (H9, which is significant in both models), which indicates that outcome expectation awareness should be increased. The outstanding question item in this group is “Traveling brings more new experiences.” Thus, the agencies involved in promoting tourist attractions should focus on motivating tourists to choose adventure attractions or natural attractions [36]. A further suggestion was the relationship between perceived benefits and self-determination with intention (H3 and H5), indicating that this should increase tourists’ awareness of benefits. Therefore, the proposal is that in addition to promoting the safety of tourist attractions, promotion should focus on tourists’ confidence. By following the measures, such as not neglecting to wear a mask and regularly washing hands with alcohol gel, tourists will not worry about infection. Agencies that can promote these measures include provinces, districts, or the owners of tourist attractions who can publicize them through websites or social media.

There are certain limitations to the findings of this study, particularly regarding the policy details set by the government sector. These details are still lacking, specifically in terms of the actions taken by the government, such as the development of a tourist application to alert visitors about risk areas, a public relations website aimed at promoting secure tourist destinations, or even contingency plans for tourists in the event of a coronavirus infection. Due to these limitations, the variable related to government actions in this study did not show a significant effect. To address this issue, future studies could delve into the specific details of various measures implemented by the government, assessing their effectiveness and gathering opinions from both Thai and foreign tourists. Moreover, the questionnaire could explore the potential features and functionalities that tourists would desire in such an application in the future.