A new generation behavior intention to visit tourism destinations in perspective of post-COVID-19

Figures & data

Figure 1. A proposed conceptual model. SN: Subjective norm; ATT: Attitude; PBC: Perceived behavior control; VRSAT: VR Satisfaction; BIVD: Behavior intention to visit destinations. Source: Author’s own elaboration.

Table 1. Reliability and validity of the constructs.

Factor constructs	Cronbach’s α	AVE	CR	References	Items
Subjective norm (SN) (4)	.814	0.661	0.886	Sánchez-Cañizares et al. (2021); Ajzen (1991) and Sadiq et al. (2022).	My relatives who are important to me think that I should travel by VR application.
					My friends who are important to me think that I should travel by VR application.
					My seniors who are important to me think that I should travel by VR application.
					My colleagues who are important to me think that I should travel by VR application.
Attitude (ATT) (4)	.787	.558	.834	Sánchez-Cañizares et al. (2021), Ajzen (1991), and Sadiq et al. (2022).	I think traveling by VR application will be favorable
					I think traveling by VR application will be pleasant.
					I think traveling by VR application will release stress. I think traveling by VR application will make me get new experiences.
Perceived behavior control (PBC) (6)	.839	.616	.905	Sánchez-Cañizares et al. (2021), Ajzen (1991), and Sadiq et al. (2022).	I am confident, I can travel by VR application.
					I am able to travel by VR application.
					I have the resources to travel by VR application.
					I have the ability to travel by VR application.
					I have the knowledge to travel by VR application.
					Traveling by VR application is entirely within my control.
VR Satisfaction (VRSAT) (3)	.830	.680	.864	Chen and Rahman (2018); Jeong and Shin (2020); Rahimizhian et al. (2020); Torabi et al. (2022).	I am satisfied to travel by VR application.
					I will use traveling by VR application.
					I will share traveling by VR application with people.
Behavior intention to visit destinations (BIVD) (5)	.826	.769	.943	Chen and Rahman (2018); Jeong and Shin (2020); Torabi et al. (2022).	I intend to visit destinations. I predict I would visit destinations. I plan to visit destinations.
					I intend traveling to destinations soon after using VR applications
					I will travel to destinations.

* Scale: strongly disagree (1) to strongly agree (5).

Table 2. Respondent profile (n = 430). The construct means and (STD).

Variables	Categories	Frequency	Percentage
Sex (n = 430)	Male	128	29.8
	Female	302	70.2
Age (n = 430)	19–20 years old	381	88.6
	21–22 years old	38	8.8
	23–24 years old	11	2.6
Education (n = 430)	High school	51	11.9
	Bachelor degree	367	85.3
	other	12	2.8
Experience traveling by VR application (n = 430)	1–3 times	360	83.7
	4–6 times	59	13.7
	7–9 times	5	1.2
	More than 10 times	6	1.4
Time spent by VR application (n = 430)	Less than 1 hour	165	38.4
	1–2 hours	195	45.3
	More than 2 hours	70	16.3
Destinations (n = 430)	Domestic	247	57.4
	International	183	42.6

Table 3. Confirmatory Factor Analysis (CFA) new generation behavior on VR satisfaction and intention.

Item	Unstandardized estimates	Standardized estimates	Standard Error (S.E.)	P	AVE	CR
SN (α = 0.814)					.661	.886
SN1	.861	.713	.051	***
SN2	.982	.802	.050	***
SN3	1.051	.862	.048	***
SN4	1.000	.850		***
ATT (α = 0.787)					.558	.834
ATT1	1.064	.792	.076	***
ATT2	1.152	.848	.077	***
ATT3	.962	.717	.059	***
ATT4	1.000	.688		***
PBC (α = 0.839)					.616	.905
PBC1	1.310	.826	.082	***
PBC2	1.259	.811	.077	***
PBC3	1.212	.788	.076	***
PBC4	1.302	.864	.077	***
PBC5	1.000	.698		***
PBC6	1.051	.707	.067	***
VRSAT (α = 0.830)					.608	.864
VRSAT1	1.291	.876	.071
VRSAT2	1.134	.854	.064	***
VRSAT3	1.000	.728		***
BIVD (α = 0.826)					.769	.943
BIVD1	1.042	.861	.047
BIVD2	1.060	.890	.045	***
BIVD3	1.129	.885	.048	***
BIVD4	1.113	.871	.043	***
BIVD5	1.000	.822		***

Criteria for a suitable goodness fit are as follows; p > .05, χ²/df < 5.0, RMSEA < .08, CFI > .90, NFI > .90, TLI > .90, 0.05≤ RMR ≤ 0.08, GFI ≥ 0.85, AGFI ≥0.80.

The model fit statistics; χ² = 198.206, df = 142, P = 0.001, χ²/df = 1.396, RMSEA = 0.030, RMR= 0.024, GFI = 0.960, AGFI = 0.929, CFI = .993, NFI=.975, TLI=.988.

*** Significant at the .01 level; ** Significant at the .05 level; * Significant at the .10 level.

Figure 2. The result of the SEM. Model fit statistics; χ² = 217.557, df = 159, P = 0.001, χ²/df = 1.368, RMSEA = 0.029, RMR = 0.018 GFI = 0.958, AGFI = 0.932, CFI = .992, NFI=.973, TLI= 0.989. Source: Author’s own elaboration.

Table 4. Total, direct, and indirect effects.

Independent	Dependent	Standardized Direct Effects (significance)	Standardized Indirect Effects (significance)	Standardized Total Effects (significance)	Hypotheses
SN	SAT	.0677	.000	.067	H1 unaccepted
ATT	SAT	.402***	.000	.402***	H2 accepted
PBC	SAT	.510***	.000	.510***	H3 accepted
SAT	BIVD	.933***	.000	.933***	H4 accepted

*** Significant at the .01 level; ** Significant at the .05 level; * Significant at the .10 level.

Table 5. Path estimates.

Constructs		Unstandardized Estimates	Standardized Estimates	S.E	P
DV	IV
VRSAT	SN	.074	.067	.050	.135
VRSAT	ATT	.442	.402	.061	***
VRSAT	PBC	.676	.510	.075	***
BIVD	VRSAT	.960	.933	.047	***

*** Significant at the .01 level; ** Significant at the .05 level; * Significant at the .10 level.

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