Antecedents and Consequences of Flow Experience in Virtual Reality Tourism: A Path Analysis of Visit Intention

Abstract

This study examines the psychological mechanisms underlying virtual reality (VR) tourism experiences through an integrated theoretical framework centered on flow experience and visit destination intention. Drawing upon flow theory, the research investigates how interactivity, perceived vividness, and telepresence influence flow experience and subsequently affect hedonic motivation and perceived visual appeal in VR tourism contexts. Using partial least squares structural equation modeling (PLS-SEM) analysis of data collected from 255 VR tourism users across major Chinese metropolitan centers, the study reveals that perceived vividness and telepresence significantly impact flow experience, while interactivity shows no significant effect. Flow experience demonstrates significant positive relationships with hedonic motivation and perceived visual appeal. Furthermore, hedonic motivation and perceived visual appeal significantly positively affect visit destination intention. The findings advance the theoretical understanding of VR tourism by illuminating the psychological pathways through which technological characteristics influence behavioral intentions. These results offer practical implications for destination marketers and VR tourism developers in designing more effective virtual experiences that enhance destination visit intentions.

1. Introduction

Digital transformation has fundamentally altered the technological landscape, with virtual reality (VR) emerging as a revolutionary force central to modern information systems [1]. At its core, VR is an advanced computational platform that creates detailed synthetic environments, pushing beyond traditional digital limitations to reshape how users perceive and interact with digital spaces [2]. The technology excels at creating authentic human–computer interactions, leading to breakthrough applications in entertainment, education, and professional training [3]. The rapid adoption and continuous advancement of VR technology underscore its vital role in current digital transformation [4] strategies.

Within tourism management, VR has introduced innovative approaches that fundamentally alter established operational practices [5]. The platform’s sophisticated spatial modeling capabilities empower destination management organizations to execute multifaceted environmental analyses through diverse theoretical lenses, transcending conventional spatial perspectives. This advanced functionality enables rapid prototyping and visualization of proposed environmental modifications, facilitating comprehensive impact assessment protocols [6]. VR is essential for engaging stakeholders in tourism development, enabling collaborative planning and structured feedback processes. In cultural heritage, particularly museums and preservation efforts, VR’s interpretive features create engaging narratives that deepen public understanding of historical and cultural contexts [7]. The application of VR in tourism spans multiple domains, including sophisticated marketing paradigms, experiential recreation frameworks, educational methodologies, accessibility enhancement protocols, and cultural heritage preservation initiatives [8]. Research consistently demonstrates VR’s effectiveness in enhancing various aspects of visitor experiences [9]. VR’s transformative potential in tourism manifests through its capacity to revolutionize destination marketing strategies and experiential service delivery frameworks [10]. As VR capabilities advance, new opportunities emerge for creating customized, interactive, and widely accessible travel experiences in modern tourism [11].

Contemporary empirical research has established interactivity (INT) [12], perceived vividness (PERV) [13], and telepresence (TLP) [14] as distinct and significant antecedents of flow experience (FLE) in digital environments. INT, characterized by user control and reciprocal communication capabilities, directly influences users’ FLE during digital interactions [15,16]. Similarly, PERV, which reflects the richness and clarity of digital content presentation, exhibits substantial empirical support as a crucial determinant of FLE formation [17]. TLP, representing users’ psychological sense of being present in the digital environment, has also emerged as a significant predictor of FLE in virtual contexts [18]. Prior research has established the significance of each relationship, yet a comprehensive analysis examining the combined impact of these three precursors on FLE remains limited in scope. The need for such integrated analysis becomes increasingly critical as digital interactions evolve and the formation of flow experiences gains prominence in modern virtual environments [3]. To address this notable gap in current research, this study thoroughly examines how INT, PERV, and TLP work together to shape flow experience within digital settings.

The rapid expansion of virtual reality tourism has fundamentally transformed how tourists interact with destinations, creating a critical need to examine the psychological foundations that drive user engagement in these sophisticated digital environments [2]. Within this evolving theoretical landscape, hedonic motivation (HM)—conceptualized as the intrinsic psychological drive for experiential pleasure and satisfaction in virtual tourism encounters [19]—and perceived visual appeal (PVA)—the subjective aesthetic evaluation of virtual destination environments—have emerged as fundamental determinants in VR tourism efficacy [20]. FLE, marked by deep psychological absorption and altered time perception, emerges as a vital psychological factor that influences the HM aspects of virtual tourism through sophisticated cognitive and emotional channels [21]. As the tourism sector increasingly embraces virtual experiences, understanding these psychological dynamics becomes essential for developing and implementing effective VR tourism platforms [22]. While extant scholarly discourse has established the discrete significance of these theoretical constructs, the intricate interrelationships between FLE and these experiential outcomes within VR tourism contexts remain theoretically underdeveloped and empirically underexplored [23,24]. This investigation addresses these critical epistemological gaps by examining the distinctive pathways through which FLE shapes HM and PVA within VR tourism contexts.

Within the evolving paradigm of VR tourism, visit destination intention (VDI) emerges as a pivotal behavioral outcome that fundamentally determines the efficacy of virtual tourism experiences in catalyzing actualized travel behavior [25]. Despite the increasing integration of VR technologies in tourism marketing and destination promotion, a significant gap persists in comprehending the specific psychological mechanisms that facilitate converting virtual experiences into VDI. HM has demonstrated significant empirical influence on VDI by modulating tourists’ emotional connectivity and experiential expectations regarding prospective destinations [9,26]. Concurrently, PVA assumes a fundamental role in destination image formation and subsequent VDI development through its impact on visual information processing mechanisms and emotional arousal patterns [27,28]. The primary research issue examined in this study pertains to the inadequate understanding of how HM and PVA interact to influence VDI within VR tourism contexts. Although earlier research has investigated these relationships separately, comprehensive insight is lacking into their combined effects and the psychological mechanisms that link emotional and perceptual responses to VDI. Hence, this research investigates the theoretical mechanisms through which HM and PVA collectively influence VDI within VR tourism environments.

Although current research on VR tourism has focused on individual technological and psychological factors separately, a significant gap remains in understanding the integrated pathways through which these elements collectively shape tourist behavior. This study investigates novel edges in VR tourism by creating an integrated framework embedded in FLE theory [29]. The research design connects three key theoretical areas: first, investigating how INT [15], PERV [17], and TLP [18] serve as antecedents of FLE in VR. The investigation then delves into understanding how FLE triggers HM [19] and PVA [20]. Finally, the research examines the way these psychological elements shape VDI in tourism settings [27,30]. The innovation of this study is reflected in two key contributions. First, it creates the first comprehensive theoretical model that concurrently investigates the technological antecedents and psychological outcomes of FLE in VR tourism. Second, it empirically validates dual HM and PVA pathways through which FLE impacts behavioral intentions. This work fills a crucial research gap in VR tourism scholarship by bridging these theoretical components. The findings are valuable for tourism practitioners and VR developers, providing evidence-based guidelines for creating more compelling VR experiences. The research explains the specific guides through which VR technology influences tourist decision-making, offering practical insights for enhancing destination marketing strategies. Beyond practical applications, this work supplements the theoretical landscape of technology-mediated tourism experiences by planning the multifaceted connections between technological capabilities, psychological responses, and behavioral conclusions in VR tourism contexts.

To address these research challenges, this study poses three fundamental research questions:

RQ1: How do technological characteristics—such as INT, PERV, and TLP collectively influence FLE in VR tourism contexts?

RQ2: What psychological mechanisms enable FLE to trigger HM and PVA in virtual tourism environments simultaneously?

RQ3: How do these flow-induced psychological responses (HM and PVA) differentially impact VDI, and what is their relative contribution to behavioral outcomes?

2. Literature Review and Hypothesis Development

2.1. Flow Theory: Theoretical Foundation and Virtual Environment Applications

FLE theory, introduced by Csikszentmihalyi in 1975, is a pivotal framework for comprehending optimal human experiences defined by deep engagement and intrinsic motivation [31]. This theory outlines nine fundamental components of the FLE: the establishment of clear goals, the provision of immediate feedback, the balance between challenge and skill, the merging of action and awareness, intense concentration on the task, a sense of control, the absence of self-consciousness, a distorted perception of time, and the autotelic nature of the experience [32]. A key aspect of achieving FLE is the delicate balance between perceived challenges and an individual’s skill level; when challenges exceed skills, feelings of anxiety arise, whereas an excess of skill over challenges leads to boredom.

In virtual environments, FLE theory has gained considerable attention across various interdisciplinary fields. User experience (UX) design research indicates that immersive digital interfaces can promote FLE by offering clear navigation cues, responsive feedback systems, and adjustable difficulty levels [33]. Studies in cognitive psychology suggest that virtual environments can foster FLE by enhancing focus and reducing cognitive load, especially when elements of presence and immersion are maximized [18]. Additionally, research in educational technology supports the relevance of FLE in virtual learning contexts [34].

Tourism provides distinct opportunities for applying FLE, as virtual destination experiences can replicate the balance between challenge and skill through exploration tasks, deliver immediate visual and auditory feedback, and establish clear objectives for virtual journeys [35]. Recent studies in VR tourism suggest that FLE plays a vital role in technological features and behavioral outcomes [36]; however, fully integrated theoretical models encompassing both the antecedents and consequences of flow remain scarce. This gap in theoretical understanding underscores the need for a more detailed exploration of how specific VR characteristics can cultivate FLE and influence tourist behaviors.

2.2. Antecedents of Flow Experience of VR

The theoretical conceptualization of INT encompasses technological affordances and psychological dimensions shaped by communication environments, technological capabilities, and individual perceptual processes within virtual spaces [12]. INT is a foundational architectural element in digital tourism platforms that fundamentally modulates visitor experiential patterns and engagement trajectories [37]. In VR-enabled tourism environments, INT manifests through visitors’ capability to navigate, manipulate, and engage with three-dimensional representations of cultural and heritage sites [16]. Scholars across digital media studies have intensively investigated the intricate dynamics linking interactive capabilities and flow states within virtual domains. Empirical investigations on digital engagement platforms reveal compelling evidence: users who actively manipulate interactive elements consistently report heightened flow experiences [15,20]. Evidence indicates a strong positive relationship between the level of interactive capabilities in virtual environments and the depth of users’ flow experiences [38]. This relationship is significant in tourism-centered virtual reality platforms, where interactive design elements fundamentally shape experiential outcomes. Field observations indicate that carefully calibrated INT is a critical determinant of engagement quality, suggesting broader implications for the development of virtual tourism. Recent empirical evidence indicates that INT capabilities in virtual environments enhance FLE, facilitating more profound engagement with digital tourism content [15,39]. The following is postulated.

H1. 

INT significantly impacts FLE.

PERV represents technology’s inherent capability to create enriched assisted user environments, characterizing the system’s capacity to generate rich and detailed virtual representations [40]. Within virtual reality environments, PERV manifests through digital content’s visual appeal and representational richness, which can significantly impact users’ FLE through enhanced perceptual engagement [17]. Unlike INT, which primarily concerns user–system dialogue, PERV facilitates users’ ability to envision and anticipate their forthcoming experiences within virtual environments through enhanced sensory stimulation [29]. When users encounter highly vivid virtual representations characterized by rich environmental detail and sensory depth, they demonstrate an increased propensity to achieve and maintain FLE during their virtual experience [34]. Recent empirical investigations examining FLE across diverse technological contexts have emphasized PERV’s fundamental role in facilitating flow states within virtual platforms [40]. Comparative studies analyzing traditional versus technology-enhanced experiences have particularly illuminated how PERV’s sensory richness contributes significantly to users’ achievement and maintenance of FLE states during VR tourism experiences [13]. This relationship assumes particular salience in immersive VR tourism contexts, where PERV is a crucial determinant of FLE [17]. The following is postulated.

H2. 

PERV significantly impacts FLE.

The relationship between TLP and FLE in virtual environments has garnered substantial empirical support through a robust body of research [3]. Seminal work established the foundational evidence for TLP as a critical antecedent to FLE in digital environments [41]. This theoretical relationship received further validation through subsequent investigations that documented strong positive correlations between TLP and FLE [3,18]. While direct empirical examination of TLP’s relationship with specific FLE components remains somewhat limited, compelling theoretical frameworks suggest robust connections. Users experiencing heightened TLP consistently report enhanced experiential enjoyment and heightened pleasantness during virtual encounters [42]. The immersive qualities of TLP in VR environments facilitate diminished self-consciousness and increased escapism, thereby amplifying enjoyment dimensions [43]. TLP functions as a crucial mediator of concentration during VR-mediated tasks, with immersed individuals demonstrating elevated levels of cognitive absorption and sustained attentional focus [18,43]. Furthermore, TLP can potentially enhance multiple FLE constructs [18]. The following is postulated.

H3. 

TLP significantly impacts FLE.

2.3. Flow Experience of VR and Hedonic Motivation

Understanding how flow states influence hedonic responses during virtual reality engagement represents a critical area of psychological [36] research [39]. Usually, in optimal psychological and full immersion, users reveal HM when they find themselves in idealized virtual worlds [44]. The theoretical mechanism linking FLE to HM operates through several key processes. First, the autotelic nature of flow states inherently generates positive affect and intrinsic reward, naturally aligning with hedonic motivational systems. Second, flow’s temporal dissociation and focused attention characteristics create optimal conditions for sensory pleasure and experiential enjoyment. Third, the balance between challenge and skill during flow states generates a sense of mastery and accomplishment, contributing to hedonic satisfaction [45]. Empirical evidence from virtual environment research has consistently demonstrated strong associations between FLE and HM outcomes [24]. Similar patterns emerge in virtual learning environments, where FLE correlates strongly with pleasure-oriented engagement. These findings suggest that FLE is an influential antecedent to HM across various VR applications [46]. When users enter FLE states while engaging with virtual environments, their psychological engagement enhances both the functional value and hedonic aspects of the experience. FLE first appears to establish optimal psychological conditions that enable enhanced HM responses. The sustained nature of FLE during VR interaction creates extended periods of heightened receptivity to hedonic stimuli, potentially strengthening the overall relationship between these constructs [47]. The following is postulated.

H4. 

FLE significantly impacts HM.

2.4. Flow Experience of VR and Perceived Visual Appeal

The intricate connection between FLE and PVA takes on heightened relevance amid rapid advances in virtual reality [23] display technology [23]. Users experiencing FLE exhibit markedly increased focus and diminished susceptibility to distraction, suggesting enhanced visual content processing [48]. Such concentrated attention enables individuals to better recognize visual and aesthetic components within virtual spaces. FLE enhances perceptual processing capabilities, fostering heightened awareness of visual attributes, including chromatic elements, tonal variations, structural arrangements, and dimensional relationships. This enhanced perceptual state could lead to a more nuanced appreciation of visual elements [22]. When individuals experience FLE during virtual interactions, their capacity to process and appreciate visual information appears to be enhanced [49], suggesting a direct relationship between FLE and PVA assessment. Studies in human–computer interaction have shown that users in FLE demonstrate increased sensitivity to interface aesthetics and visual design elements [29]. The psychological mechanisms underlying this relationship suggest that flow states create optimal conditions for visual processing and aesthetic appreciation [48]. The reduced self-consciousness and enhanced focus characteristic of FLE may allow users to engage more fully with visual content, leading to more favorable evaluations of PVA [22]. The following is postulated.

H5. 

FLE significantly impacts PVA.

2.5. Hedonic Motivation and Visit Destination Intention

In virtual tourism, user engagement manifests through immersive interactions with destination content, where the entertainment value becomes a crucial experiential component. This interaction paradigm aligns with the foundational work emphasizing the distinctive characteristics of pleasure-oriented systems compared to utilitarian ones [25]. The empirical evidence supporting HM’s influence on behavioral intentions is substantial and multi-faceted [50]. Research demonstrated HM’s significant impact on technological system adoption, while subsequent studies have extended this understanding to virtual tourism contexts [26]. The relationship between HM elements and visit intentions gains particular significance in VDI, where the technology’s immersive nature creates a unique emotional engagement platform [30]. The pleasure derived from virtual tourism experiences has emerged as a critical antecedent to VDI, demonstrating its predictive power in destination choice modeling [9,50]. This relationship becomes particularly salient when considering the transformative potential of VR tourism in shaping travel decisions and VDI [25,26]. The integration of HM elements in virtual tourism platforms thus represents a strategic imperative for destination marketers seeking to influence VDI through immersive technologies. The following is postulated.

H6. 

HM significantly impacts VDI.

2.6. Perceived Visual Appeal and Visit Destination Intention

The psychological mechanisms underlying visual elements’ influence on decision-making reveal complex cognitive and affective pathways. When users encounter high-quality representations, these experiences trigger responses aligned with the Stimulus–Organism–Response (S-O-R) framework, where visual stimuli generate emotional and cognitive reactions that shape behavioral outcomes [51]. Contemporary tourism scholarship has established PVA elements as fundamental drivers in destination choice processes, particularly in digital contexts [52]. The PVA phenomenon extends beyond aesthetic appreciation, creating mental imagery and emotional connections that mitigate perceived travel risks. The process aligns with the elaboration likelihood model, facilitating both central and peripheral routes of persuasion in VDI [53]. Recent research in digital tourism contexts has revealed robust correlations between PVA and tourist VDI [54]. These findings suggest that PVA elements influence psychological processes beyond immediate aesthetic responses, affecting deeper VDI decision-making mechanisms [27,28]. This relationship becomes particularly salient when considering the transformative potential of immersive technologies in destination marketing strategies. The following is postulated.

H7. 

PVA significantly impacts VDI.

Figure 1 indicates the theoretical framework. Furthermore,

Table 1. Summary of Key Studies on Virtual Tourism Adoption and Experience.

Author(s) and Year	Context/Platform	Theoretical Foundation	Methodology/Sample	Key Findings	Relevant Variables/Constructs
Arghashi and Yuksel [15]	Retail AR Applications	Flow Theory, Consumer Engagement	Quantitative Study	AR apps’ interactivity enhances consumer engagement through flow experiences.	Interactivity, Flow, Consumer Engagement
Ruangkanjanases et al. [29]	E-commerce	ECM, TAM, Flow Theory	Empirical Study	Flow experience mediates between platform characteristics and consumer behavior.	Flow, User Experience, Platform Characteristics
Kim et al. [3]	VR Concerts	Flow Theory, Telepresence	Empirical Investigation	Telepresence significantly influences the flow experience in VR concerts	Flow, Telepresence, VR Experience
Oliveira et al. [44]	Gamified Learning	Flow Theory, Motivation Theory	Experimental Study	Personalization affects flow experience and motivation	Flow, Motivation, User Enjoyment
Sharma et al. [19]	VR in Education	Technology Adoption, Hedonic Motivation	Quantitative Study	Hedonic motivation mediates VR adoption in education	Hedonic Motivation, Technology Adoption
Pan et al. [20]	Museum Marketing	Visual Communication Theory	Empirical Study	Visual appeal and text interact to influence visit intention	Visual Appeal, Visit Intention
Wiangkham et al. [1]	Virtual Tourism	Behavioral Intention Theory	Spectral Clustering Analysis	Identified distinct patterns in VR tourism adoption behavior	Behavioral Intention, VR Adoption
Yu et al. [2]	VR/AR Tourism	Experience Quality Theory	Empirical Study with Senior Tourists	VR/AR enhances the experiential quality and reuse intention among seniors	Experiential Quality, Perceived Enjoyment, Reuse Intention

summarizes key studies on virtual tourism.

3. Methodology

3.1. Data Collection Strategy

This study employed a quantitative research design to examine the relationships between VR tourism experience factors and visit destination intention. The research framework was developed based on established theoretical foundations in technology acceptance and tourism behavior literature, utilizing structural equation modeling to test the hypothesized relationships.

The primary data collection utilized a sophisticated multi-platform approach, predominantly leveraging the Sojump (WJX.cn) survey system, supplemented by targeted distribution through specialized VR tourism channels. The data collection encompassed major VR tourism platforms across Chinese metropolitan centers, including the Jingdezhen Digital Cultural Heritage Experience Center, Beijing VR Culture Museum, Shanghai Digital Art Center, and the Guangzhou Virtual Heritage Hub. These institutions were selected based on advanced VR implementation protocols and substantial monthly visitor traffic (>5000 visitors/month). Additional collection points included the Yungang Grottoes Digital Museum, Xi’an Terra Cotta Warriors Virtual Experience Center, and the Digital Dunhuang Project, ensuring comprehensive geographical and cultural representation.

3.2. Sampling Framework

Researchers selected participants through purposive sampling, focusing on those engaged with VR tourism platforms. This non-random selection approach proved essential given the specialized requirements of studying virtual heritage experiences. Each participant needed to demonstrate exposure to VR tourism applications within a recent six-month window, ensuring their feedback reflected current technological implementations. Such careful participant selection supported the investigation’s core aim of examining how virtual tourism experiences influenced subsequent visit intentions among technology-aware users. The purposive sampling was executed through targeted recruitment at VR tourism venues, digital cultural heritage centers, and online VR tourism communities, where potential respondents meeting the specified criteria were most likely to be found.

3.3. Sample Characteristics

The final dataset comprised 255 valid responses from an initial pool of 378 participants (67.4% response rate). The sample demographics reflected diverse representation across various parameters. Age distribution analysis revealed a predominant concentration in the young to middle-aged adult segments, with 27.8% aged 18–25, 35.2% aged 26–35, 22.4% aged 36–45, 10.6% aged 46–55, and 4.0% above 55 years. Educational background showed a high proportion of tertiary education, with 52.7% holding undergraduate degrees and 32.0% possessing postgraduate qualifications, while 15.3% had completed high school education. Monthly income distribution indicated economic diversity: 18.4% earned below RMB 5000, 35.6% between RMB 5000 and 10,000, 28.7% between RMB 10,000 and 15,000, and 17.3% above RMB 15,000. The sample’s geographic distribution aligned with China’s urban development tiers, comprising 38.4% from first-tier cities, 42.3% from second-tier cities, and 19.3% from third-tier cities. Professional backgrounds demonstrated variety across sectors: technology (28.6%), education (22.4%), business (19.8%), creative industries (15.7%), and others (13.5%).

3.4. Data Quality Management

The study implemented comprehensive data quality control measures throughout the collection process. Initial validation efforts encompassed preliminary testing with thirty users across varied virtual reality expertise levels, leading to instrument modifications based on observed response trends. During-collection monitoring utilized real-time response validation algorithms, incorporating attention checks strategically positioned throughout the survey. Post-collection verification employed multi-stage data cleaning procedures, including response time analysis (excluding completions < 5 min or >45 min), pattern consistency verification, and missing data analysis using Little’s MCAR test.

3.5. Instrument Development

The survey instrument was developed based on established scales in VR tourism and technology acceptance literature. The measurement items underwent rigorous validation through an expert panel review (n = 8), including VR technology specialists, tourism researchers, and psychometric experts, ensuring content validity and construct clarity. The final instrument incorporated seven primary constructs measured through multiple indicators, utilizing a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). This scale choice was informed by empirical evidence suggesting optimal discriminatory power and reliability in technology acceptance studies [55].

The items from the study by Atzeni et al. [52] were used to measure VDI. TLP was measured using the items from Wei and Li’s [56] research. PERV was measured by the items adopted from Song et al.’s [57] research, while FLE was measured by the items suggested by Zhao and Khan [34]. INT was measured by the items adopted from Tawafak et al.’s [58] research. Finally, HM was measured using the items suggested by Anand et al. [59], and PVA by the items suggested by Feng and Zhao [27]

4. Data Analysis

4.1. Convergent Validity

The research utilized partial least squares structural equation modeling (PLS-SEM), implemented via SmartPLS 4.0, to evaluate theoretical relationships and test hypothesized connections. The analytical procedure adhered to established methodological protocols, encompassing comprehensive measurement and structural model validity assessments. The measurement model evaluation revealed robust psychometric properties across all constructs: INT, PERV, TLP, FLE, PVA, HM, and VDI.

The measurement model demonstrated strong statistical integrity across multiple validity indicators. Internal consistency reliability metrics exhibited robust values, with Cronbach’s alpha coefficients ranging from 0.717 to 0.886, substantially exceeding the conventional threshold, as shown in

Table 2. Convergent Validity.

Constructs	Cronbach Alpha	Rho_A	CR	AVE
FLE	0.717	0.732	0.840	0.637
HM	0.875	0.877	0.910	0.668
INT	0.886	0.894	0.921	0.745
PERV	0.725	0.730	0.845	0.645
PVA	0.816	0.833	0.876	0.639
TLP	0.806	0.834	0.870	0.628
VDI	0.886	0.891	0.921	0.745

Note: FLE = Flow Experience, HM = Hedonic Motivation, INT = Interactivity, PERV = Perceived Vividness, PVA = Perceived Visual Appeal, TLP = Telepresence, VDI = Visit Destination Intention.

. The measurement model’s convergent validity was established through average variance extracted (AVE) calculations, yielding values between 0.628 and 0.745, surpassing the critical benchmark of 0.5. Additionally, the composite reliability (CR) indices and Rho_A values consistently exceeded 0.7, reinforcing the constructs’ internal consistency reliability. These statistical indicators substantiate the measurement model’s psychometric soundness, establishing a robust foundation for subsequent structural analysis.

4.2. Discriminant Validity

The examination of discriminant validity incorporated dual analytical approaches: the established Fornell–Larcker criterion and the more rigorous Heterotrait–Monotrait (HTMT) ratio analysis. In the Fornell–Larcker assessment matrix, diagonal elements represent the extracted square roots of AVE for individual constructs, while off-diagonal elements denote inter-construct correlation coefficients, as shown in

Table 3. Fornell–Larcker criterion.

Constructs	FLE	HM	INT	PERV	PVA	TLP	VDI
FLE	0.798
HM	0.459	0.818
INT	0.443	0.719	0.863
PERV	0.616	0.431	0.400	0.803
PVA	0.406	0.481	0.443	0.446	0.799
TLP	0.438	0.624	0.585	0.362	0.390	0.792
VDI	0.392	0.569	0.503	0.353	0.438	0.474	0.863

Note: FLE = Flow Experience, HM = Hedonic Motivation, INT = Interactivity, PERV = Perceived Vividness, PVA = Perceived Visual Appeal, TLP = Telepresence, VDI = Visit Destination Intention.

. Discriminant validity assessment relies on comparing the square root of average variance extracted against inter-construct correlations. Analysis revealed higher AVE square root values for each construct relative to their correlations with other theoretical elements, thus meeting Fornell–Larcker requirements.

Complementing this analysis, the HTMT ratio examination provided additional validation through its more stringent methodological approach. The HTMT matrix demonstrated values consistently below the conservative threshold of 0.85, with all construct pairs exhibiting ratios beneath 0.90, as indicated in

Table 4. Heterotrait–Monotrait (HTMT).

Constructs	FLE	HM	INT	PERV	PVA	TLP	VDI
FLE
HM	0.552
INT	0.532	0.817
PERV	0.858	0.543	0.500
PVA	0.501	0.537	0.480	0.560
TLP	0.528	0.735	0.698	0.453	0.451
VDI	0.487	0.638	0.560	0.441	0.493	0.545

Note: FLE = Flow Experience, HM = Hedonic Motivation, INT = Interactivity, PERV = Perceived Vividness, PVA = Perceived Visual Appeal, TLP = Telepresence, VDI = Visit Destination Intention.

. This dual-method validation strategy provides robust evidence of construct distinctiveness and theoretical discrimination.

4.3. Hypotheses Results

The PLS-SEM structural analysis generated robust evidence regarding hypothesized model relationships.

Table 5. Hypotheses Results.

Hypotheses	Path Coefficient (β)	T Values	p Values	Results
H1: INT → FLE	0.148	1.896	0.058	Not Supported
H2: PERV → FLE	0.492	10.128	0.000	Supported
H3: TLP → FLE	0.180	2.525	0.012	Supported
H4: FLE → HM	0.460	6.075	0.000	Supported
H5: FLE → PVA	0.408	6.257	0.000	Supported
H6: HM → VDI	0.463	7.798	0.000	Supported
H7: PVA → VDI	0.218	3.390	0.001	Supported

Note: FLE = Flow Experience, HM = Hedonic Motivation, INT = Interactivity, PERV = Perceived Vividness, PVA = Perceived Visual Appeal, TLP = Telepresence, VDI = Visit Destination Intention.

and Figure 2 summarize inner model results, displaying path coefficients (β), T-statistics, and significance values (p-values) across seven tested theoretical propositions. This systematic evaluation of the structural relationships provides quantitative validation of the proposed theoretical framework through rigorous statistical examination.

Based on the results, in terms of antecedents, PERV (β value = 0.492; T value = 10.128) and TLP (β value = 0.180; T value = 2.525) significantly impacted FLE, while INT (β value = 0.148; T value = 1.896) was found to have no significant association with FLE. Furthermore, FLE significantly impacted HM (β value = 0.460; T value = 6.075) and PVA (β value = 0.408; T value = 6.257). Moreover, HM (β value = 0.463; T value = 7.798) and PVA (β value = 0.218; T value = 3.390) significantly impacted VDI.

Table 6. Indirect Effects.

Indirect Effects	Path Coefficient (β)	T Values	p Values	Results
FLE -> PVA -> VDI	0.089	2.881	0.004	Supported
FLE -> HM -> VDI	0.215	4.223	0.000	Supported
INT -> FLE -> HM -> VDI	0.033	1.498	0.134	Not Supported
PERV -> FLE -> HM -> VDI	0.105	4.015	0.000	Supported
TLP -> FLE -> HM -> VDI	0.039	2.071	0.038	Supported

Note: FLE = Flow Experience, HM = Hedonic Motivation, INT = Interactivity, PERV = Perceived Vividness, PVA = Perceived Visual Appeal, TLP = Telepresence, VDI = Visit Destination Intention.

indicates indirect relationships. According to the findings, FLE indirectly impacted VDI while having PVA (β value = 0.460; T value = 6.075) and HM (β value = 0.460; T value = 6.075). This study used chain mediating variables of FLE and HM regarding antecedents. The chain mediation results found that PERV (β value = 0.460; T value = 6.075) and TLP (β value = 0.460; T value = 6.075) indirectly impacted VDI, while INT (β value = 0.460; T value = 6.075) did not indirectly impact VDI.

5. Discussions

5.1. Comparison of Results

The findings reveal an unanticipated lack of significant correlation between INT and FLE, challenging established virtual reality theoretical models. This finding contrasts Arghashi and Yuksel’s [15] foundational augmented reality engagement patterns research. Their in-depth study, conducted through direct survey methodology, identified INT and inspirational components as key drivers of consumer FLE. Their work further demonstrated that FLE in augmented reality fosters positive attitudes and enhances trust development. The divergence between current findings and previous theoretical understanding suggests that unique moderating factors may exist within virtual tourism contexts, indicating a need to examine boundary conditions of INT’s role in FLE.

The empirical investigation demonstrated a statistically significant relationship between PERV and FLE, aligning with contemporary theoretical perspectives in digital interaction research. This finding corresponds with the recent theoretical advancement by Ruangkanjanases et al. [29], who developed an integrated conceptual framework synthesizing FLE antecedents with the expectation confirmation model and technology acceptance paradigms. Their research into online shopping behaviors revealed key connections between experiential states and digital purchase engagement. They uncovered three primary FLE drivers using systematic survey methods: feedback systems, enjoyment factors, and PERV elements. The current findings align with this theoretical model, supporting PERV’s central role in shaping digital immersion across technological platforms.

The current analysis revealed significant correlations between TLP and FLE, advancing this research’s understanding of virtual engagement patterns. This result partially aligns with Kim et al.’s [3] examination of psychological processes in VR concert experiences. Their flow-based framework illuminated relationships between technological immersion and experiential outcomes in virtual performances. Their findings established TLP as a key moderator linking FLE to positive emotional responses in virtual concert environments.

The statistical analysis demonstrated a significant positive relationship between FLE and HM. This finding exhibits theoretical alignment with the research conducted by Oliveira et al. [44], although their investigation revealed more nuanced relationships within educational gamification contexts. Their comprehensive study examined the moderating effects of gamer typologies on the relationship between personalization approaches and multiple outcome variables, including FLE, enjoyment dimensions, gamification perceptions, and HM. Through a sophisticated mixed factorial within-subject experimental design implemented in elementary educational settings, their research compared personalized and non-personalized gamified learning systems. Their analysis found no direct effects between FLE, gamification perceptions, and HM. Nevertheless, distinct patterns emerged regarding FLE, particularly noting how users favoring multiplayer interactions maintained strong FLE across personalized and standard system versions.

The empirical analysis revealed a statistically significant influence of PVA on FLE. This finding contrasts with Jeon et al.’s [60] examination of virtual event platform dynamics and attendee experiences. Their research, investigating FLE determinants through comprehensive surveys, identified platform information quality and communication features as key flow predictors. However, unlike the results of the present study, no significant connection between PVA and FLE was found. This discrepancy suggests potential contextual moderators affecting how aesthetic elements shape experiences across virtual environments.

The current analysis identified a strong connection between HM and VDI, revealing a significant pattern in virtual tourism behavior. This relationship aligns with Sharma et al.’s [19] investigation into VR adoption within educational contexts. Through methodologically rigorous research using custom-designed survey instruments, their study examined the psychological mechanisms driving educators’ acceptance of VR technology. Drawing data specifically from higher education faculty, their analysis revealed HM’s pivotal mediating role in shaping both attitudinal formations and VDI.

The statistical modeling revealed an interconnection between PVA and VDI. Pan et al.’s [20] research on museum marketing provides compelling comparative insights in a blended digital–physical context. Their study, focusing on the psychological effects of PVA in museum promotions, revealed how deliberately crafted PVA drives youth engagement. Their findings specifically demonstrated that PVA elicits two key responses from young potential visitors: enhanced curiosity and strengthened VDI.

5.2. Theoretical Implications

This research advances theoretical understanding through several significant contributions. First, this research advances FLE in virtual tourism by illuminating how technological features jointly foster immersive states [61]. The results demonstrate the synergistic rather than isolated effects of these antecedents—PERV [13] enhances sensory depth, INT [62] enables meaningful participation, and TLP creates psychological immersion, working together to facilitate FLE in digital tourism settings. This integrated understanding refines the conceptualization of FLE in virtual tourism contexts.

Second, the research enriches the theoretical understanding of the psychological dimensions shaping virtual tourism experiences. FLE represents a state of optimal engagement where users become deeply immersed in virtual environments, marked by a lost sense of time and reduced self-awareness. HM reflects the pursuit of pleasure in virtual tourism, highlighting how enjoyment and entertainment drive user actions [19]. PVA represents a distinct aesthetic element, showing how visual quality and artistic features shape the tourism experience [20]. Together, these three psychological constructs offer a complete framework for understanding the complex dimensions of virtual tourism experiences.

Third, this research advances tourism theory by connecting virtual and physical tourism experiences. The findings reveal that thoughtfully designed virtual environments can evoke genuine psychological responses paralleling physical tourism encounters [63]. This evidence challenges conventional views about virtual tourism authenticity, suggesting a more sophisticated theoretical framework where virtual and physical experiences exist along a spectrum rather than as separate categories.

Fourth, the study enhances the understanding of technology-mediated tourism by revealing the complex interaction between technological elements and psychological responses. Results show that technological features do not directly influence behavioral intentions but work through intricate psychological mechanisms that shape how users interpret and engage with virtual environments. This insight refines technology adoption models in tourism by highlighting psychological processes as crucial mediators between technological characteristics and user behavior [64].

5.3. Practical Implications

The study’s empirical findings offer valuable practical implications for key stakeholders in tourism. For destination managers and operators, results emphasize investing in advanced virtual tourism platforms. The strong effect of PERV highlights the need for high-resolution imagery, immersive audio, and rich multimedia content to enhance sensory engagement. Additionally, INT’s significant impact suggests that platforms should incorporate sophisticated features like multi-dimensional navigation controls, experience customization options, and dynamic environmental interactions. For virtual tourism application developers, the findings provide evidence-based guidelines. Results show that visual interface optimization alone is insufficient; development should prioritize facilitating flow states through streamlined navigation, intuitive controls, and engaging content progression. The proven importance of HM calls for entertainment elements like gamification and narrative structures while maintaining educational and informational value. Moreover, the empirical support for PVA’s significance demands careful attention to aesthetic elements, including color schemes, spatial layout, and overall visual coherence.

For policymakers, these findings present evidence-based frameworks for integrating virtual tourism into comprehensive destination marketing strategies. The empirical findings validate virtual tourism as a valuable complement to traditional tourism promotion, primarily when physical access is restricted or during crisis periods. The results suggest establishing standardized virtual tourism development protocols to ensure consistent quality standards, broad accessibility, and authentic destination portrayal. Furthermore, the findings justify strategic investment in digital infrastructure and professional training for virtual technology deployment.

The findings indicate that virtual experiences should transcend basic destination showcasing to foster affective connections through immersive narrative techniques and interactive functionalities. The empirical evidence suggests that virtual tours require careful calibration between information dissemination and entertainment value, ensuring sustained engagement while cultivating genuine interest in physical visitation. Furthermore, the findings support virtual tourism platforms incorporating social interaction capabilities and community development features to enhance user experience and strengthen destination attachment.

The practical implications span tourism education and professional development sectors. The findings indicate that educational institutions and training programs should incorporate virtual tourism technology into their teaching frameworks to prepare tourism professionals for effective technological deployment. Results suggest that training programs should extend beyond technical skills to include a deep understanding of psychological factors that drive successful virtual tourism experiences. This includes developing expertise in user experience design, digital storytelling, and strategic integration of virtual components within broader tourism marketing strategies.

6. Conclusions

This research effectively addressed the previously identified gap in the literature by formulating and validating a comprehensive theoretical framework that connects the technological attributes of VR to tourist VDI through the concept of FLE. The key findings indicate that PERV is the primary technological factor influencing FLE, thereby challenging traditional views regarding the preeminence of INT in virtual settings. The study reveals two pathways through which FLE affects the VDI, specifically through HM and PVA.

From a practical standpoint, these insights offer evidence-based recommendations for tourism professionals and VR developers aiming to enhance destination marketing strategies. By emphasizing visual fidelity over intricate interactive elements, practitioners can create more engaging VR tourism experiences that convert virtual interactions into actual visit intentions.

Nonetheless, the study has certain limitations. It did not account for potentially confounding variables that could significantly impact VR tourism experiences. For instance, prior familiarity with VR technology may influence the relationship between technological characteristics and FLE, as seasoned users may exhibit different engagement behaviors than those new to the technology. Additionally, participants’ existing interest in particular tourist destinations could skew their PERV and VDI, while varying levels of knowledge about virtual environments might influence their experiences of TLP and INT. Future research should consider incorporating these factors as covariates or moderators to isolate the genuine effects of the proposed theoretical pathways and strengthen the validity of findings in VR tourism.