A Study on the Intention of Shanghai Residents to Travel Abroad in the Post-Pandemic Era Based on the Theory of Planned Behavior

Abstract

This study has adopted the theory of planned behavior as a framework to examine the relationship among subjective norms, perceived behavioral control, perceived policy orientation, perceived pandemic response capabilities, attitudes, and behavioral intentions towards outbound travel in a post-pandemic society. Specifically, a total of 895 valid questionnaires were employed to test the 9 hypotheses proposed in this study. The results of the structural equation modeling (SEM) analysis show that subjective norms, perceived behavioral control, and perceived policy orientation positively impact residents’ intention to travel abroad. Furthermore, attitude partially mediates the relationship between subjective norms, perceived behavioral control, perceived policy orientation, and residents’ intention to travel abroad. However, the study indicates that the impact of perceived pandemic response capabilities on residents’ intention to travel abroad is not significant. This study’s conclusions emphasize that external policy factors can significantly influence tourists’ intention to travel abroad, contributing to the theoretical research on consumer outbound tourism behavior. Moreover, the study provides important implications for the policy formulation of outbound tourism enterprises and destination governments.

1. Introduction

Outbound tourism, holding its strong social impact and economic value, has long been a focal point of tourism studies [1,2]. The Chinese outbound tourism market has achieved remarkable growth over the past decade [3] partly due to the sustained improvement of China’s socio-economic conditions, disposable incomes of residents, convenience of transportation, and globalization. As a result, in 2019, China maintained its status as the world’s largest source of outbound tourists [4] with more than 155 million Chinese outbound tourists. However, this gratifying situation was abruptly interrupted at the outset of 2020. The COVID-19 pandemic broke out and quickly spread over the whole world, resulting in a sharp decline in the number of Chinese outbound tourists [5]. The figures plummeted to 20.33 million and 25.62 million in 2020 and 2021, respectively, a relatively low level compared to the pre-pandemic era. In addition to the heavy blow to domestic service industries such as catering, transportation, accommodation, exhibition, and tourism, COVID-19 also resulted in an almost entire suspension of China’s outbound travel due to the need for pandemic prevention and control.

As the pandemic situation stabilizes and domestic tourism begins to resume, the term “post-pandemic” has gained popularity in tourism studies [6,7,8]. Nevertheless, extant studies concerning the post-pandemic period predominantly center on delineating the recuperation of the tourism industry; subtle differences between pre-pandemic and post-pandemic tourism behavior mechanisms are somehow neglected, resulting in a lack of in-depth research on the social characteristics of the post-pandemic era and their impact on tourism behavior. Furthermore, empirical studies on outbound tourism intention direction primarily examine the impact of individual and community behavior on tourists’ intention with little consideration of national-level policies.

During the COVID-19 pandemic, various national administrative agencies introduced pandemic prevention and control policies aimed to control the flow of people; this, inevitably, had a significant negative impact on the tourism industry. In the post-pandemic era, due to the time lag of policies, the pandemic prevention and control policies implemented in many places may remain, thereby exerting a continued influence on foreign visitors to a given destination. Current policies implemented to prevent and control the novel coronavirus can be broadly grouped into two categories: either preventative measures to stop the import of foreign pandemics, such as reducing flights, stricter visa systems, and requiring negative nucleic acid test certificates, or pandemic response policies to prevent the spread of the disease, such as developing emergency plans and implementing isolation measures. Accordingly, this study seeks to extend the theory of planned behavior by introducing the components of “perceived policy orientation” and “perceived pandemic response capability”. By using the structural equation modeling (SEM), this empirical research was conducted to investigate the following two questions. (1) Do pandemic prevention and control policies and the response capabilities of tourism destinations affect outbound tourists’ intention to travel? (2) What are the specific impact mechanisms of pandemic prevention and control policies and the response capabilities of tourism destinations on outbound travel intentions? The component of “perceived policy orientation” measures the influence of destination countries’ relaxed entry policies on the intention of foreign tourists to travel. Meanwhile, the component of “perceived pandemic response capability” was employed to assess the impact of tourists’ confidence in the destination’s pandemic response ability on their travel intention.

Though the concept of the “post-pandemic era” has been widely adopted in tourism studies, its clear and concise definition is rarely delivered. Based on the current state of global pandemic prevention and control and various descriptions framed by social consensus, this study identifies three salient features characterizing this era. (1) Remarkable improvement has been achieved worldwide in pandemic prevention and control capabilities, rendering large-scale COVID-19 outbreaks unlikely to recur in the future. (2) Sporadic local outbreaks arising from new coronavirus variants may persist in the long-term, signifying a coexistence of the virus with humans. (3) Pandemic prevention and control measures are gradually transitioning towards normalized approaches. The research subject selected for this study is Shanghai residents who had demonstrated a strong inclination to travel abroad before the COVID-19 pandemic [9]. This study adopted the extended theory of planned behavior and empirically assessed their post-pandemic era travel intentions.

At the theoretical level, this study verifies the applicability of the theory of planned behavior within the distinct social context of the post-pandemic era and expounds upon the theoretical logic underpinning the outbound travel intentions of Shanghai residents during this era. At the practical level, the findings of this study provide valuable insights for outbound travel enterprises and destination’s government for formulating effective policies.

2. Literature Review

2.1. Theory of Planned Behavior

The Theory of Planned Behavior (TPB) is a widely adopted social psychology model for analyzing human behavior [10]. Developed by Ajzen, it builds on the Theory of Reasoned Action (TRA) and is commonly applied in empirical research to elucidate how belief factors influence behavioral intention [11]. Similarly to TRA, the TPB postulates that behavior results from a rational decision-making process [12], rendering it particularly apposite in explaining the intricate tourism decision-making process [13]; it has been widely used in studies of outbound travel [14,15]. TPB contends that behavioral intention can be explained by three distinct beliefs—behavioral beliefs, normative beliefs, and control beliefs—which do not operate independently but interact with each other [16,17,18]. Behavioral beliefs denote an individual’s attitude toward the target behavior [19], which in this study, corresponds to the component of attitude used to measure residents’ views on outbound tourism. Normative beliefs reflect people’s expectations of others and the influence of others’ behavior which correspond to the component of the subjective norm in this study, measuring residents’ views on others’ support for their travel. The concepts of control beliefs diverge into two classes: one defines control beliefs as the perception of behavioral barriers, leading to perceived behavioral control, while the other views control beliefs as a type of self-efficacy—the perception of one’s ability to overcome perceived obstacles and achieve specific pre-designed goals [20,21]. In this study, control beliefs correspond to the component of perceived behavioral control which measures residents’ perception of their ability to travel abroad. Therefore, this study proposes the following hypotheses:

Hypotheses 1 (H1). 

Subjective norms positively impact attitude.

Hypotheses 2 (H2). 

Subjective norms positively impact behavioral intention.

Hypotheses 3 (H3). 

Perceived behavioral control positively impacts attitude.

Hypotheses 4 (H4). 

Perceived behavioral control positively impacts behavioral intention.

Hypotheses 5 (H5). 

Attitude positively impacts behavioral intention.

2.2. Perceived Policy Orientation, Attitude, and Behavior Intention

TPB is a well-established theory that can be further improved by incorporating extended constructs to predict behavior in various backgrounds [10]. The formulation and implementation of tourism policies are considered fundamental tools for governments to intervene and regulate the tourism industry, particularly during economic and public health crises [22]. While there has yet to be a consensus regarding tourism policy types [23], many researchers have recognized tourism promotion policies as an indispensable part of tourism policies [24,25]. It is noticeable that tourism promotion policies do not necessarily mean providing more services to tourists. In fact, “to do or not to do” both count as tourism policy [26]. Empirical evidence from studies on Turkish visa policies suggests that stringent visa regulations may hinder tourists’ entry and exit, while visa-free policies are prone to attracting more visitors [27,28].

The outbreak of COVID-19 has brought about numerous travel restrictions for tourists worldwide [29], many of which may remain in the post-pandemic era due to the time lag of policies. However, the reevaluation of these policies becomes imperative in the post-pandemic era as the receding pandemic ceases to provide a convincing reason for their implementation, making them unnecessary scrutiny and exclusion measures for tourists. This is contrary to the theme of the tourism industry [30] and may evoke adverse emotional responses among tourists and, consequently, exert an inevitable influence on the attitudes and intentions of Chinese residents concerning outbound travel [31]. Therefore, this study proposes the following hypotheses:

Hypotheses 6 (H6). 

Perceived policy orientation positively impacts attitude.

Hypotheses 7 (H7). 

Perceived policy orientation positively impacts behavioral intentions.

2.3. Perceived Pandemic Response Capabilities, Attitude, and Behavior Intention

Trustworthiness is an individual’s positive expectation of the trusted recipient’s capability to fulfill commitments [32]. Among various forms of trustworthiness, competence trust, stemming from the belief in the trust recipient’s competence to provide good products and services, plays a crucial role [33]. In the tourism industry, the destination functions as the trusted recipient [34] and the destination’s government plays the role of a caretaker for foreign visitors by implementing relevant policies [35]. Preceding the pandemic, tourists’ trust in a particular destination primarily relied on their trust in the brand spokesperson and influential figures [36]. However, in the aftermath of this public health crisis, the safety capability of the local tourism industry has become inherently intertwined with the government’s competence in handling the sporadic local outbreaks arising from new coronavirus variants. Consequently, travelers will presently firstly evaluate the local pandemic prevention and control measures to decide their confidence in the destination’s capacity to safeguard their well-being and ensure their journey as perceived security is closely related to the trustworthiness of the authorities in a tourist destination [37].

When it comes to competence trust towards a destination, once a traveler doubts the destination’s ability to safeguard them, safety concerns may be raised [8], consequently affecting the traveler’s intention to visit. Health crises have been regarded as a critical perceived risk factor for tourists [38], significantly harming the local tourism industry [39,40]. During the post-pandemic era, the emergence of new strains and the possibility of sporadic outbreaks persisted as a formidable health threat. Under this circumstance, destination governments’ capability to ensure tourist safety and prevent disruptions to tourists’ travel plans is a critical factor influencing outbound travelers’ attitudes and intentions to travel abroad. Therefore, this study proposes the following hypotheses:

Hypotheses 8 (H8). 

Perceived pandemic response capabilities positively impact attitude.

Hypotheses 9 (H9). 

Perceived pandemic response capabilities positively impact behavioral intentions.

Based on the literature review and study hypothesis above, the conceptual framework of this research has been deduced as shown in Figure 1.

3. Methods

3.1. Survey Instrument

This paper proposes a model containing six components: attitudes, subjective norms, perceived behavioral control, perceived policy orientation, perceived pandemic response capabilities, and behavioral intentions. A 5-point Likert scale is adopted to examine these variables where 1 means strongly disagree and 5 means strongly agree. The measurement scales of subjective norms, behavioral intention, and perceived behavioral control were derived from multiple researchers’ work [41,42,43,44,45]. The perceived policy orientation scale was designed by combining the two scales developed by Liu Xiangyan and Yuan Xubo [46,47]. The perceived pandemic response capabilities scale was built up on Li Mei’s and Li Wen’s works [42,48]. The demographic characteristics section of the questionnaire was designed based on existing research on outbound tourism, including 11 items such as gender, age, education level, occupation, monthly income, and several other demographic characteristics.

A pre-survey was first conducted prior to the formal survey and a total of 111 samples were collected. The pre-survey results showed that Cronbach’s alpha coefficients for all variables were greater than 0.70, indicating that the questionnaire had good reliability.

3.2. Data Collection

This study aimed to survey Shanghai residents who lived in Shanghai during the survey period from 17 May 2022 to 24 May 2022. All data were collected by the researchers themselves. Researchers were allowed to post an online questionnaire link in a live broadcast room of a local influencer in Shanghai and every respondent would receive a reward of CNY 1 to CNY 2. Although the researchers made necessary statements on the homepage of the questionnaire, it was inevitable that there were non survey subjects who answered the questionnaire. To ensure the validity of the sample, two measures were utilized during the formal survey: (1) a participant guarantee that all data in this survey would only be used for this academic research, ensuring the objectivity and neutrality of the answers and (2) set questionnaire screening questions (e.g., “Time spent living or studying and working in Shanghai?”) and controlled IP sources to ensure that questionnaire respondents were preset survey subjects.

A total of 1126 questionnaires were collected in the formal survey among which 231 questionnaires with consistent answers, missed answers, or invalid IP addresses were excluded. Therefore, 895 valid questionnaires eventually remained, with an effective recovery rate of 79.48%.

3.3. Data Analysis

The present study employed a range of data analysis techniques, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), and descriptive analysis. SPSS 20.0 and AMOS 24.0 were used as statistical software tools. This study first tested common method bias (CMB) in the data, then implemented the two-step analysis method proposed by Anderson and Gerbing. Specifically, a CFA was initially conducted on a theoretical measurement model followed by testing hypothetical relationships after obtaining a satisfactory construct model. Additionally, the study evaluated the indirect effects of attitude through the bootstrapping method.

4. Results

4.1. Overview of the Sample Population

The sample structure is as follows (

Table 1. Socio-demographic characteristics of the main survey respondents (N = 895).

Variable		Frequency	(%)	Variable		Frequency	(%)
Gender	Male	427	47.7	Occupation	Ordinary employees of enterprises	392	43.8
	Female	468	52.3		Enterprise management	182	20.3
Age	≤18	1	0.1		artisan	81	9.1
	19–30	216	24.1		retired personnel	19	2.1
	31–55	659	73.6		student	42	4.7
	≥56	19	2.1		civil servant	32	3.6
Education level	Middle school or below	4	0.4		professional	70	7.8
	High school graduate	11	1.2		Individual operator	12	1.3
	Two-year college/Four-year university	540	60.3		other	65	7.3
	Master’s degree or above	340	38.0	Registered Residence	Non-local registered residence	317	35.4
Monthly Income (RMB)	≤4000	35	3.9		Local registered residence	578	64.6
	4001–9000	82	9.2	Previous Outbound Travel Experience (Times)	0	35	3.9
	9001–14,000	188	21.0		1–4	214	23.9
	14,001–20,000	191	21.3		≥5	646	72.2
	≥20,001	399	44.6	Travel Preferences	Group tourism	45	5.0
Single Outbound Travel Budget (RMB)	≤6000	76	8.5		Self-guided travel	831	92.8
	6001–20,000	582	65.0		other	19	2.1
	≥20,001	237	26.5	Travel Companion Preference	Traveling with family	352	39.3
Vaccination	Not vaccinated	122	13.6		Traveling with friends	425	47.5
	Vaccinated but not boosted	341	38.1		Traveling alone	118	13.2
	Vaccinated and boosted	432	48.3

Source: The author (2023).

). In terms of gender, 427 are males, accounting for 47.7%, and 478 are females, accounting for 52.3%, indicating a good gender balance of the data. When it comes to other key items, such as age, monthly income, and education level, all measurement variables basically follow a normal distribution. Thus, the sample is considered adequate and demonstrates good representativeness.

4.2. Validity and Reliability Tests for Measures

To estimate common methods’ variance, we performed a confirmatory factor analysis in line with Baldauf et al. and Patwardhan et al. [49,50]. The one-factor test showed that a single factor, assessed on all items, significantly worsened fit indices, with a significant chi-square difference (${\mathsf{\chi }}^2/\mathrm{d}\mathrm{f}$= 22.806, RMSEA = 0.156, NFI = 0.764, RFI = 0.742, IFI = 0.772, TLI = 0.751, CFI = 0.772, GFI = 0.577, AGFI = 0.501, and PGFI = 0.489). Thus, common methods’ variance did not appear to be an issue in this study [51]. In addition, referring to the verification method of Wen et al. [52], a new latent variable of common method bias was constructed as part of a seven-factor model and the model fitting index did not show significant improvement (

Table 2. Test results of common method deviation.

Fit Index	CFI	TLI	RMSEA	SRMR
Before control	0.977	0.974	0.043	0.035
After control	0.988	0.986	0.033	0.018
D-value	<0.1	<0.1	<0.05	<0.05

). Both the above verification methods prove that there is no significant common method bias in this study.

This study used exploratory factor analysis to test the componential structure of all 25 items (

Table 3. Results of exploratory factor analysis.

Constructs and Items	Factor Loading	Mean	SD
Attitude (Cronbach’s α = 0.961, CR = 0.963, AVE = 0.811)
In the post-epidemic era, I think outbound travel is a good way of leisure	0.741	4.527	0.965
In the post-epidemic era, I think choosing to travel abroad will make a unique experience	0.728	4.454	0.985
In the post-epidemic era, I feel very happy to be able to travel to my favorite country	0.773	4.708	0.800
In the post-epidemic era, I think traveling abroad can help me relax my mind	0.767	4.579	0.934
In the post-epidemic era, I think outbound travel can relieve the pressure of work and life	0.753	4.608	0.883
In the post-epidemic era, I look forward to traveling abroad frequently	0.711	4.569	0.949
Subjective Norms (Cronbach’s α = 0.937, CR = 0.940, AVE = 0.797)
If my family supports me to travel abroad in the post-epidemic era, I will be more willing to participate	0.641	4.524	0.933
If people I know choose to travel abroad in the post-epidemic era, I will be more willing to participate	0.614	4.540	0.908
If online information on outbound travel in the post-epidemic era is easily available, I would be more willing to consider traveling	0.641	4.551	0.883
I will be very interested if all the travel KOLs in the online media recommend outbound tourism products in the post-epidemic era	0.692	4.363	1.022
Perceived Behavioral Control (Cronbach’s α = 0.862, CR = 0.868, AVE = 0.686)
I think that in the post-epidemic era, I have enough budget to travel abroad	0.852	4.085	1.060
I think that in the post-epidemic era, I have enough time to travel abroad	0.843	3.923	1.064
I think that in the post-epidemic era, I’m healthy enough to travel abroad	0.629	4.456	0.851
Perceived Policy Orientation (Cronbach’s α = 0.960, CR = 0.962, AVE = 0.863)
From a policy perspective, if the relevant countries (regions) lift the quarantine restrictions on entry in the post-epidemic era, I would be more willing to consider traveling	0.684	4.651	0.807
From a policy perspective, if the relevant countries (regions) provide convenient visa services in the post-epidemic era, I would be more willing to consider traveling	0.693	4.663	0.774
From a policy perspective, if the relevant countries (regions) have looser requirements for nucleic acid testing in the post-epidemic era, I would be more willing to consider traveling	0.734	4.560	0.905
From a policy perspective, if the relevant countries (regions) resume (or increase) the frequency of international direct flights in the post-epidemic era, I would be more willing to consider traveling	0.682	4.642	0.809
Perceived Pandemic Response Capabilities (Cronbach’s α = 0.933, CR = 0.935, AVE = 0.782)
If the relevant countries (regions) have explicit measures to deal with the spread of the new coronavirus in the post-epidemic era, I will be more willing to consider traveling	0.849	4.351	1.020
If the relevant countries (regions) release timely and transparent information about the new coronavirus in the post-epidemic era, I will be more willing to consider traveling	0.873	4.485	0.914
If the relevant countries (regions) have enough experience in virus prevention and control in the post-epidemic era, I would be more willing to consider traveling	0.896	4.432	0.944
If the relevant country (region) has a high coverage of the new coronavirus vaccination in the post-epidemic era, I would be more willing to consider traveling	0.845	4.455	0.941
Behavioral Intention (Cronbach’s α = 0.931, CR = 0.934, AVE = 0.779)
In the post-epidemic era, I will choose to go and travel abroad	0.589	4.535	0.928
Compared with the past, even if the price of outbound travel in the post-epidemic era slightly increases, I will still choose to travel abroad	0.708	4.168	1.098
Compared with domestic travel, in the post-epidemic era I prefer to go and travel abroad	0.671	4.362	1.034
I hope to enter the post-epidemic era as soon as possible and that I can go and travel abroad as soon as possible	0.601	4.492	0.993

Note: CFA model fits: ${\mathsf{\chi }}^2/\mathrm{d}\mathrm{f}$ = 2.755, RMSEA = 0.044, NFI = 0.973, RFI = 0.969, IFI = 0.983, TLI = 0.980, CFI = 0.983, GFI = 0.937, AGFI = 0922, and PGFI = 0.750.

). First, the maximum variance rotation method was used to extract factors and then the factor loading coefficients of each item on the rotation component matrix were analyzed. The analysis results show that the KMO value is 0.973 (greater than 0.7) and the significance of Bartlett’s spherical test is 0.000 (less than 0.001), indicating that the recovered data are suitable for factor analysis. The exploratory factor analysis results are shown in Table 3. Among them, items of all six components fell into the preset principal component factors, showing no multiple load phenomenon. The cumulative variance contribution rate of factor analysis reached 84.83% (greater than 60%), indicating a solid questionnaire structure.

As is shown in Table 3, the Cronbach coefficient and CR value of the measured variable were both greater than 0.8, indicating the high reliability of the measurement scale. In addition, the standardized factor load of each item in the scale ranges from 0.805 to 0.955, all greater than 0.4, and is significant at a certain level of p-value, indicating that the measurement model has good aggregation validity [53].

Despite aggregate validity, the validity test of the scale also includes the differential validity test. The results of the differential validity test (

Table 4. The square root of AVE and correlation coefficient.

Constructs	1	2	3	4	5	6
1	0.901
2	0.886	0.893
3	0.658	0.649	0.828
4	0.874	0.871	0.638	0.929
5	0.494	0.536	0.49	0.534	0.885
6	0.881	0.849	0.741	0.845	0.504	0.883

Note: 1 = Attitude; 2 = Subjective Norms; 3 = Perceived Behavioral Control; 4 = Perceived Policy Orientation; 5 = Perceived Pandemic Response Capabilities; 6 = Behavioral Intention; Diagonal values (bold) are AVE values. Off-diagonal values (plain) were squared inter-construct correlations of the constructs.

) show that the AVE square root of each latent variable is greater than its correlation coefficient with other latent variables. This indicates that the measurement scale has good discriminative validity.

The Pearson correlation coefficient can reveal the strength of the correlation between variables [54]. The analysis results (

Table 5. Pearson correlation coefficient among main variables.

Constructs	1	2	3	4	5	6
1	1
2	0.850 **	1
3	0.612 **	0.590 **	1
4	0.842 **	0.823 **	0.582 **	1
5	0.481 **	0.506 **	0.448 **	0.503 **	1
6	0.833 **	0.789 **	0.679 **	0.792 **	0.473 **	1

Note: 1 = Attitude; 2 = Subjective Norms; 3 = Perceived Behavioral Control; 4 = Perceived Policy Orientation; 5 = Perceived Pandemic Response Capabilities; 6 =Behavioral Intention; ** p < 0.01.

) show that the correlation between each latent variable is significant at a p-value less than 0.01, providing a basis for further testing the potential causal relationship between variables.

4.3. Structure Model and Hypothesis Testing

In this study, the fit indices (${\mathsf{\chi }}^2/\mathrm{d}\mathrm{f}$ = 2.755, RMSEA = 0.044, NFI = 0.973, RFI = 0.969, IFI = 0.983, TLI = 0.980, CFI = 0.983, GFI = 0.937, AGFI = 0922, and PGFI = 0.750) suggested that the structural model fit well to the data [18].

Regarding the relationship between the variables in the model, as shown in Table 4, all hypotheses were supported except for H8 and H9. Specifically, subjective norms had a significant positive effect on attitude (β = 0.490, p < 0.001) and behavioral intention (β = 0.162, p < 0.001) while perceived behavioral control had a significant positive effect on attitude (β = 0.101, p < 0.001) and behavioral intention (β = 0.247, p < 0.001). Attitude also had a significant positive effect on behavioral intention (β = 0.413, p < 0.001) as did perceived policy orientation (β = 0.191, p < 0.001). However, perceived pandemic response capabilities did not have a significant effect on either attitude (β = −0.031, p = 0.127) or behavioral intention (β = −0.010, p = 0.641). The concrete results are shown in

Table 6. Standardization path coefficient and hypothesis testing results.

Hypothesis	β	SE	t-Value	p-Value	Result
H1: SN→AT	0.490	0.044	12.087	***	Supported
H2: SN→BI	0.162	0.052	3.431	***	Supported
H3: PB→AT	0.101	0.024	4.032	***	Supported
H4: PB→BI	0.247	0.026	9.092	***	Supported
H5: AT→BI	0.413	0.047	8.903	***	Supported
H6: PO→AT	0.399	0.043	10.444	***	Supported
H7: PO→BI	0.191	0.049	4.499	***	Supported
H8: PR→AT	−0.031	0.020	−1.527	0.127	Unsupported
H9: PR→BI	−0.010	0.021	−0.466	0.641	Unsupported

Note: AT = Attitude; SN = Subjective Norms; PB = Perceived Behavioral Control; PO = Perceived Policy Orientation; PR = Perceived Pandemic Response Capabilities; BI =Behavioral Intention; *** p < 0.001.

.

To further verify whether attitude plays a mediating role between subjective norms, perceived behavioral control, perceived policy orientation, and behavioral intention, this study used the Bootstrap method to conduct 5000 repeated sampling tests to examine the mediating effect of attitude. The test results show (

Table 7. Bootstrap test result of mediating effect (standardized coefficient).

Path	β	SE	Bias-Corrected 95%CI			Percentile 95%CI
			Lower	Upper	P	Lower	Upper	P
SN→AT→BI	0.202	0.047	0.134	0.286	***	0.134	0.286	***
PB→AT→BI	0.042	0.015	0.022	0.071	***	0.021	0.07	***
PO→AT→BI	0.165	0.043	0.104	0.243	***	0.105	0.246	***

Note: AT = Attitude; SN = Subjective Norms; PB = Perceived Behavioral Control; PO = Perceived Policy Orientation; BI =Behavioral Intention; *** p < 0.001.

) that the indirect effect size values of the three influence paths do not include zero in the 95% confidence intervals of bias corrected and percentile which means the mediating effect is significant [55]. Therefore, it indicates that subjective norms, perceived behavior control, and perceived policy orientation all have an impact on the behavioral intention of outbound tourists through influencing attitudes in which attitudes play a partial mediating role. The indirect effects of each variable on behavioral intention account for the total proportion of the effects, as is shown in

Table 8. The effects of each variable on behavioral intention.

Independent Variable	Direct Effect	Indirect Effect	Total Effect	Proportion
SN	0.162	0.202	0.364	55.49%
PB	0.247	0.042	0.289	14.53%
PO	0.191	0.165	0.356	43.82%

Note: SN = Subjective Norms; PB = Perceived Behavioral Control; PO = Perceived Policy Orientation.

.

In summary, the hypothesis testing results of this study’s theoretic framework are as follows (Figure 2):

5. Discussion and Conclusions

5.1. Discussion

This study investigates the formation mechanism of Shanghai residents’ outbound tourism intention during the post-pandemic era based on the theory of planned behavior and previous research on outbound tourism. Specifically, the study considers residents’ perspectives from the place of departure. An empirical investigation was conducted with 895 Shanghai residents as research subjects and drew three key conclusions: (1) subjective norms, perceived behavioral control, and perceived policy orientation positively impact residents’ intention to travel abroad; (2) attitude partially mediates the relationship between subjective norms, perceived behavioral control, perceived policy orientation, and residents’ intention to travel abroad; (3) perceived pandemic response capabilities do not have a significant impact on residents’ intention to travel abroad.

5.2. Theoretical Contributions

Firstly, this study identified subjective norms, perceived behavioral control, and perceived policy orientation positively impacting outbound tourism intention. This finding again confirms the extensive applicability of the planned behavior theory in tourism intention research. Additionally, the study reveals that individuals are not always free to act according to their own will [56]. Their behavioral intention is influenced by their sense of self-efficacy and people who have a close relationship with them [57]. Also, by adding the component of perceived policy orientation to extend the structure of the traditional TPB model, this study concluded that tourism policies inspire potential tourists’ visit intentions [58], further revealing the formation mechanism of residents’ outbound tourism intentions. These findings provide a valuable reference for future quantitative research on the impact of tourism policies on tourism intention.

Secondly, this study found that outbound tourists’ intention to travel is relatively unaffected by the destination’s pandemic response capability. This outcome may be attributed to two reasons. On the one hand, the destination was not specified in the survey questionnaire so the research participants may consequently possess divergent perspectives regarding outbound tourism. On the other hand, it is important to note that due to China’s stringent pandemic prevention and control policies, a destination’s strong pandemic response capability may be associated with the implementation of stringent measures, such as lockdowns and mandatory nucleic acid testing. These measures may raise concerns about potential travel restrictions and obstacles which could lower tourists’ motivation to travel. Perceived pandemic response capability, being a distinctive form of trustworthiness in the post-pandemic era, differs from general competence trust. It is not simply a recognition of the administrative management capability of the destination but rather a dual perception of the potential risks of the destination and its risk response capability. Moreover, perceived risk, according to previous research, can negatively influence tourists’ intention to travel. Therefore, this conclusion provides valuable insights into the impact of destination risk control capability on tourists’ intention to travel.

Thirdly, attitudes play a significant role as a partial mediator in the relationship between subjective norms, perceived behavioral control, perceived policy orientation, and outbound travel intention. Previous studies have identified that the beliefs of others can influence an individual’s attitudes and subsequently impact their behavioral intentions, which is reaffirmed by the present study. This result also emphasizes that attitudes and subjective norms are not independent of one another in the practical application of the theory of planned behavior [59]. Furthermore, perceived behavioral control and perceived policy orientation represent an individual’s beliefs in their ability to engage in outbound travel and the perceived facilitation or obstacles posed by destination tourism policies. These factors will further affect residents’ intention to travel abroad by influencing their attitude. When it comes to the path coefficient of the impact, the direct and indirect effects of subjective norms and perceived policy orientation on behavioral intention are similar, indicating that supportive relationships and tourism policies closely associated with individuals can not only directly stimulate their intention to travel but also enhance their attitudes towards outbound travel, ultimately affecting their outbound travel intention in both direct and indirect approaches. The perceived behavioral control mainly affects residents’ outbound travel intention forthright rather than affecting it indirectly via attitudes. This also suggests that self-efficacy, as an individual’s recognition of their behavioral capability, has a weaker effect on changing their views on specific behaviors but a stronger influence on their intention to execute specific behaviors as a behavioral subject.

5.3. Managerial Implications

This study presents three critical conclusions with significant implications for increasing residents’ intention to travel abroad, promoting outbound tourism market recovery and development after 2022, and guiding policy making for outbound tourism-related enterprises and destination administrative management agencies.

Firstly, it is crucial to improve the self-efficacy and perception of tourism policies of the residents of departure places. This study indicates that subjective norms, perceived behavioral control, and perceived policy orientation directly impact outbound travel intentions. Therefore, relevant service providers such as travel agencies and destination hotels should focus on reducing tourists’ costs of travel (such as time and money spent) by creating price advantages and convenient conditions. Destination administrative management agencies must expedite policy transitions and reinstate pre-pandemic tourism policies, including introducing tourist-friendly travel policies like visa-free and visa-on-arrival policies to reduce the inconvenience that tourists might face.

Secondly, this study suggests that destination administrative management agencies should adopt appropriate publicity strategies in the post-pandemic era. Results show that perceived pandemic response capabilities do not significantly add to outbound travelers’ travel intentions. Therefore, the publicity department of tourist destinations should avoid overemphasizing pandemic prevention and control capabilities while formulating outbound travel strategies.

Thirdly, this study emphasizes the need to enhance residents’ positive attitudes towards outbound travel. It concludes that subjective norms, perceived behavioral control, and perceived policy orientation can all indirectly influence residents’ outbound travel intentions through attitudes which emphasizes the significance of attitudes in the formation mechanism of residents’ outbound travel intentions. However, the Chinese domestic media coverage of foreign pandemic situations may harm residents’ attitudes toward outbound travel. Hence, outbound tourism operators should take vigorous measures to enhance residents’ positive attitudes towards outbound travel in the post-pandemic era. These include using diversified marketing channels, such as Weibo, Tiktok, and Facebook, to promote the value of outbound travel and highlight the destination’s exoticism by creating a sense of unfamiliarity and novelty about the destination among potential tourists.

5.4. Limitations and Further Research

This study exhibits limitations due to research conditions and other factors.

Firstly, the study chose Shanghai residents as the empirical research subjects for outbound travel intentions analysis. Despite their significant representativeness in critical indices such as outbound travel volume and total tourist consumption, the generalizability of the research findings still requires future validation.

Secondly, the decision-making process for tourism is a complex phenomenon shaped by a confluence of internal and external factors [60]. However, this study’s theoretical framework did not further extend the planned behavior theory due to certain limitations of the research conditions. Hence, future research endeavors should delve deeper into the underlying influencing factors of outbound tourists’ travel intentions and their complex behavioral mechanisms.

Thirdly, as this study focuses on the travel intentions of the residents in the place of departure, no specific overseas tourist destination was identified. However, the different destinations have varying pandemic prevention and control policies and capabilities, which will impact the research subjects’ responses. Despite employing standardized measurement items, the questionnaire may only partially reflect research subjects’ authentic perspectives.

Fourthly, “Approved Destination Status” is an important tourism policy factor that affects the intention of Chinese residents to travel abroad. However, since this study did not set a specific destination for overseas travel so as to examine the overall intention of Shanghai residents to travel abroad, it is difficult to examine the impact of this highly- targeted (to specific destinations) policy on the overall intention. Our future research on the impact of tourism policies will consider it as an important influencing factor to further improve the research framework.

Therefore, future research may broaden the research range, optimize the questionnaire, and advance the findings with a further enriched theoretical framework.