Digital Technology for Cultural Experience: A Psychological Ownership Perspective on the Three-Path Model

Abstract

Digital technology is profoundly transforming the experiential landscape of tourism. However, its application does not necessarily produce cultural experiences, creating a critical bottleneck that constrains the sustainable development of the cultural tourism industry and broader societal culture. To address this gap, this study introduces psychological ownership theory as an overarching explanatory framework. It constructs and validates an integrated model that examines how digital technology characteristics (interactivity and innovativeness) influence cultural experience through three parallel mediating pathways: cognitive evaluation (perceived usefulness and ease of use), scenario construction, and flow experience. Based on 540 visitor questionnaires, structural equation modeling validated the theoretical model. Findings reveal that the interactivity and innovation of digital technology jointly stimulate visitors’ psychological ownership through three parallel pathways. Specifically, technological innovativeness exhibited the strongest effect on perceived ease of use (β = 0.387, p < 0.001), while the indirect effect via the flow experience path was also significant (effect size = 0.036). This process stimulates visitors’ psychological ownership, ultimately leading to cultural experiences. The study systematically reveals the pathways through which digital technology empowers cultural experiences across three dimensions: as a rational tool, an emotional narrative medium, and an intrinsic psychological catalyst. It highlights that strategically allocating technological resources to cultivate visitors’ psychological ownership is crucial for driving high-quality industrial development. Furthermore, the research offers significant implications for cultural sustainability, suggesting that such internally motivated identification provides a more effective foundation for the living transmission of culture and socio-cultural sustainability than external regulations or imposed norms.

1. Introduction

The digital economy is reshaping industrial structures [1], shifting them beyond the standardized, scaled models of the industrial era toward a new phase centered on personalization, service orientation, and experiential value. Within this transition, the cultural and tourism sector, central to the experience economy [2], has gained prominence while undergoing significant shifts in value-creation models and industrial forms [3]. This shift is evident in the widespread application of digital technologies such as virtual reality (VR), augmented reality (AR), holographic projection, and interactive installations in tourism performances [4,5]. which are evolving from traditional formats into immersive experience-driven models [3]. However, it is essential to clarify that technology represents only the visible force driving this transformation. As a key arena for integrating cultural heritage with tourism consumption [5], the long-term development of tourism performances relies not only on generating economic benefits but, more fundamentally, on attaining social and cultural sustainability. This objective ultimately rests on the construction of cultural identity, which constitutes the core psychological foundation of sustainable tourism [5]. However, current practice often encounters the dilemma of “high investment, low experience.” A significant efficiency gap exists between intensive technological input and the generation of cultural identity [6], meaning that high investment does not necessarily translate into high-quality experience. This paradox underscores a critical gap between the economic cost of technological adoption and its experiential and cultural return. While prior research has begun to examine the economic returns or consumer behavioral intentions (e.g., revisit, recommendation) driven by smart tourism technologies [7], few studies delve into the specific mechanisms that translate technological features into cultural value, the very outcome that justifies long-term investment and ensures socio-cultural sustainability in heritage-focused contexts like tourism performances. Without understanding these mechanisms, investments risk being inefficient, focusing on technological spectacle rather than cultural resonance, and failing to achieve a sustainable return on investment (ROI) that encompasses both economic and cultural dimensions [8].

Theoretical clarification of this question is essential. Extant research affirms the value of digital technology (e.g., VR/AR, intelligent systems) in enhancing general tourism experiences by boosting immersion, satisfaction, and information efficiency [2,9,10]. However, a significant gap emerges when focusing on tourism performances, where the paramount objective is the transmission and internalization of cultural identity [11]. Most studies either examine generic tourism services [12] or emphasize technology’s instrumental attributes (e.g., utility, entertainment) without explicating the psychological mechanisms that translate technological stimuli into profound cultural experiences [13]. This leaves a critical black box: how do digital characteristics ultimately foster a sense of cultural belonging?

This theoretical shortcoming is evident when examining three relevant yet individually insufficient theoretical lenses: The Technology Acceptance Model (TAM) effectively explains adoption via perceived usefulness and ease of use [14,15], yet its focus on rational cognition overlooks the emotional resonance and identity formation central to cultural experiences [16,17]. A technology can be deemed “useful” without fostering cultural identification. Servicing Scenario Theory addresses environmental impact [18] but traditionally prioritizes static physical cues [19], underplaying digital technology’s role as a dynamic narrative medium crucial for storytelling in performances [20,21]. Scenarios lacking narrative depth fail to elicit deep cultural engagement. Flow Theory delineates the state of optimal experience [22], but its link to enduring cultural identity remains theoretically underdeveloped [23]. A visitor may achieve flow through technology without that state translating into lasting cultural connection.

Collectively, these theories suffer from explanatory discontinuities in the tourism performance context. TAM addresses the “cognitive why” of use, Scenario Theory the “environmental how,” and Flow Theory the “psychological state,” but none fully captures the journey from technology exposure to cultural internalization. An integrated framework is needed to link technological stimuli, multi-dimensional mediating processes, and deep experiential outcomes.

To bridge this gap, this study introduces Psychological Ownership (PO) Theory as a powerful integrative and explanatory lens [24]. PO theory posits that individuals develop a sense of “mine-ness” through control over and intimate knowledge of a target [25]. This directly parallels the desired outcome in tourism performances: visitors gaining control over their experience and deep understanding of cultural content, thereby cultivating a personal sense of belonging [26]. While PO itself is a complex latent state, its core tenets provide a robust rationale for why specific, measurable pathways should be effective. We argue that digital technology can foster the precursors of PO, which is control, intimate knowledge, and self-investment, through three distinct avenues: the cognitive evaluation path (via TAM, offering control and understanding), the scenario construction path (offering narrative self-investment), and the flow experience path (offering immersive control and focus). Prior research has often examined these dimensions—technology, experience, performance—in isolation [27,28,29], leading to fragmented understanding and practical guidance [30].

Therefore, synthesizing these elements, this study constructs and empirically tests a “Three-Path Mediation Model” using PO theory as its explanatory foundation. The model proposes that digital technology characteristics (interactivity and innovativeness) enhance cultural experience through three parallel mediators: cognitive evaluation (perceived usefulness/ease of use), scenario construction, and flow experience. This design aims to achieve three objectives:

(1)

To propose an integrated framework that uses PO theory to interpret the efficacy of the three paths;

(2)

To empirically validate the parallel mediating roles of these cognitive, narrative, and psychological pathways;

(3)

To derive actionable insights for managing and designing technology-enhanced performances.

The core research question is: How do the interactivity and innovativeness of digital technology in tourism performances jointly drive visitors’ cultural experience through the parallel mechanisms of cognitive evaluation, scenario construction, and flow experience? By testing this model with data from 540 visitors, we aim to provide a systematic explanation for the technology to experience transformation [31], contributing to both theory and the practice of sustainable cultural tourism [32].

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Positioning and Focal Technology Characteristics

This study positions Psychological Ownership (PO) theory as the overarching explanatory framework for understanding how digital technology fosters deep cultural experiences. PO posits that feelings of ownership arise from control, intimate knowledge, and self-investment [25]. Instead of measuring PO directly as a mediator, a complex endeavor with cross-sectional data, we derive from its core tenets three measurable, parallel pathways that serve as its proximate antecedents and operational manifestations in the tourism performance context. Within this PO-informed framework, we focus on two fundamental characteristics of digital technology: interactivity and innovativeness [33]. This focus is justified on both theoretical and contextual grounds:

(1)

Theoretical Grounding: Interactivity is a primary affordance of digital media that directly shapes user control and agency, a cornerstone of PO [34]. Innovativeness, reflecting novelty and “relative advantage” [35], is key to creating the compelling challenges and novel stimuli that foster deep cognitive/emotional engagement (intimate knowledge) and self-investment.

(2)

Contextual Relevance to Tourism Performances [36]: In live, narrative-driven performances, the critical gap lies not in delivering static information but in transforming passive spectators into active participants. Interactivity is central to this role transformation, enabling co-creation. Innovativeness is crucial for transcending physical limits and creating the “wow” factor that underpins immersive, memorable narrative spaces [37]. While other characteristics (e.g., personalization, presence) are valuable, we argue that interactivity and innovativeness are more foundational, second-order drivers that enable many specific features and are most directly aligned with the PO mechanisms critical for deep cultural engagement in this setting.

2.2. Hypotheses Development

Building upon the theoretical positioning outlined above, we develop a set of hypotheses that delineate the three parallel pathways, cognitive, narrative, and psychological, through which digital technology characteristics are proposed to influence cultural experience, along with their potential direct effects.

This pathway elucidates the mechanism rooted in instrumental rationality and ease of use. Grounded in the Technology Acceptance Model (TAM) [38], it posits that the external attributes of technology shape key cognitive beliefs, which are fundamental precursors to the sense of control and understanding emphasized by psychological ownership theory.

The interactivity of digital technology empowers users with real-time control and feedback, transforming information acquisition into an efficient and agentic process [39]. This enhancement of experiential goals directly bolsters perceived usefulness. Simultaneously, an intuitive interface reduces operational effort, thereby reinforcing perceived ease of use. Similarly, technological innovativeness offers a relative advantage by creating unique value, which heightens perceived usefulness. Well-designed innovation also aims to simplify interaction logic, thus enhancing perceived ease of use [40]. These activated cognitive states are critical: perceived ease of use grants a sense of control, while perceived usefulness fosters a deeper understanding of cultural content—both being core antecedents to a psychological sense of ownership. Consequently, we hypothesize that heightened perceived usefulness and perceived ease of use will support a more profound cultural experience [41].

H1. 

Digital technology interactivity positively influences perceived usefulness.

H2. 

Digital technology interactivity positively influences perceived ease of use.

H3. 

Digital technology innovativeness positively influences perceived usefulness.

H4. 

Digital technology innovativeness positively influences perceived ease of use.

H5. 

Perceived usefulness positively influences cultural experience.

H6. 

Perceived ease of use positively influences cultural experience.

This pathway captures the role of digital technology as a dynamic narrative medium that constructs an immersive story field [42]. It extends service scenario theory into digital environments, focusing on how technology facilitates narrative transportation, a state of deep immersion into a story world that fosters emotional connections and identification.

In this process, innovativeness and interactivity play complementary roles. Innovativeness provides the compelling audiovisual content and novel presentation methods, defining what story is told and how it is shown. Interactivity, by allowing visitors to converse with characters or influence plots, addresses how visitors engage, transferring narrative control and transforming them from passive observers into active co-creators. This synergy creates a coherent, immersive narrative space. From a psychological ownership perspective, this immersive narrative journey facilitates deep self-investment of emotional and cognitive resources and cultivates an intimate understanding of the cultural context. This process is posited to instill a sense of belonging, thereby deepening the cultural experience.

H7. 

Digital technology interactivity positively influences scenario construction.

H8. 

Digital technology innovativeness positively influences scenario construction.

H9. 

Scenario construction positively influences cultural experience.

This pathway highlights the induction of optimal psychological states. Flow Theory describes a state of complete absorption and focused enjoyment, characterized by a balance between challenge and skill, clear goals, and immediate feedback.

In digitally enhanced performances, innovativeness creates sensory and cognitive challenges [43], while interactivity provides the skills and instant feedback necessary to meet these challenges, systematically fulfilling the prerequisites for a flow state. The characteristics of the flow state exhibit profound isomorphism with the foundations of psychological ownership. The heightened sense of control within flow directly mirrors one key pathway to ownership, while the deep cognitive engagement with the cultural content fosters an efficient form of intimate familiarity. Thus, the flow state serves as a potent psychological catalyst, transforming technological immersion into a stronger personal connection with the culture.

H10. 

Digital technology interactivity positively influences flow experience.

H11. 

Digital technology innovativeness positively influences flow experience.

H12. 

Flow experience positively influences cultural experience.

Beyond the articulated mediating pathways, digital technology, as a core component of the experiential environment, may also exert direct influences. According to the Stimulus-Organism-Response framework, environmental stimuli can affect responses both through internal organismic states and via direct routes [44]. The fundamental sense of participation from interactivity and the sensory impact of innovativeness themselves constitute potent stimuli that may directly shape visitors’ initial affective engagement and overall evaluation of the cultural experience, independent of more complex cognitive or psychological transformations. Therefore, we propose the following (Figure 1):

H13. 

Digital technology interactivity has a direct positive effect on cultural experience.

H14. 

Digital technology innovativeness has a direct positive effect on cultural experience.

3. Methods

3.1. Theoretical Positioning and Operationalization of Psychological Ownership

This study introduces psychological ownership theory as the overarching explanatory framework for understanding how digital technology leads to cultural experience. Psychological ownership, defined as a state in which individuals feel as though the target of ownership is ‘theirs’ [25], is a complex, internalized psychological state. Rather than directly measuring it as a latent mediator—which can be challenging in cross-sectional surveys—we operationalize its core theoretical tenets (i.e., feelings of control, intimate knowledge, and self-investment) into three measurable, parallel mediating pathways. Specifically:

The cognitive evaluation pathway (perceived usefulness and ease of use) captures the sense of control and understanding-based intimacy that fosters psychological ownership.

The scenario construction pathway captures the narrative immersion and self-investment that are key precursors to psychological ownership.

The flow experience pathway captures the heightened control and focused immersion that mirror the state of psychological ownership.

By empirically validating these three pathways, we provide robust, indirect evidence for the mechanism through which psychological ownership is cultivated, thereby ‘opening the black box’ between technological stimuli and profound experiential outcomes.

3.2. Variable Measurement

The core variables examined in this study include digital technology interactivity, digital technology innovation, perceived usefulness, perceived ease of use, scenario construction, flow experience, and cultural experience. Each variable is measured using established scales from domestic and international research, adapted to the specific context of digitally enhanced tourism performances to ensure content validity and contextual relevance. The operational definitions and item designs for each variable are as follows. Digital technology interactivity was measured based on McMillan and Hwang [24], using four items across four dimensions—real-time dialogue, latency-free experience, attractiveness, and sense of control. It assesses audience perceptions of interactive digital technology within performances. Digital technology innovation was measured using the “perceived visual innovation” construct proposed by Turel et al. [45], with three items assessing creativity, novelty, and uniqueness. For the Cognitive Evaluation Pathway, perceived usefulness and perceived ease of use were derived from Chung et al. [22] in the context of augmented reality applications in cultural heritage. Six items were developed to capture these dimensions, covering aspects such as information acquisition, experience enhancement, and operational convenience. For the Scenario Construction Pathway, this research draws on Li’s et al. [46] Tourism Experience Scenario Scale, which assesses two dimensions: thematic narrative and social engagement. Thematic narrative encompasses story appeal, plot coherence, and visual immersion, while social engagement includes emotional connection and cultural extension, resulting in a total of eight items. For measuring the Flow Experience Pathway, the study uses the Flow State Scale (FSS) developed by Jackson and Marsh [47], a well-established instrument with high reliability and validity.

Tailored to tourism performances, this study assessed five dimensions of flow: challenge-skill balance, action-consciousness fusion, focused attention, sense of control, and distorted sense of time, resulting in five items. Cultural Experience was measured by integrating psychological ownership dimensions, Pine and Gilmore’s [32] experience economy theory, and findings from multiple localized studies. It includes five items across five aspects: emotional resonance, cultural cognition, cultural identification, immersive experience, and recommendation intention. To ensure measurement reliability and validity, the scale items were developed by drawing on established scales from both domestic and international scholars in related fields. These items were then appropriately adapted to the specific context of tourism performances, resulting in a scale tailored to the content of this study, as shown in

Table 1. Measurement scales and items.

Variable	Item Code	Item	Adapted Source and Rationale
Interactivity	IT1	The digital technology in the performance (e.g., special effects, interactive interfaces) gave me a sense of two-way communication with the content.	Adapted from the perceived interactivity scale by McMillan & Hwang [24], which originally measured users’ perceptions of communication direction, control, and time in online contexts. The core dimensions of two-way communication, user control, and lack of delay were retained and contextualized. Item wording was adapted from evaluating websites/advertisements to assessing the specific experience of interacting with digital effects (e.g., special effects, interactive interfaces) within a live tourism performance.
	IT2	The integration between digital effects and live performance was seamless and fluid.
	IT3	This digital technology strongly captured my attention.
	IT4	Through the digital technology (e.g., interactive screens, apps), I was able to control the performance information I accessed.
Innovativeness	IN1	I think the digital technology used in this performance was creative.	Adapted from the perceived visual innovativeness scale developed by Turel et al. [45]. The original scale measures the creativity, novelty, and uniqueness of a digital artifact’s interface in a hedonic (pleasure-oriented) usage context. The items were directly adopted and re-contextualized to evaluate the digital visual technology (e.g., projections, effects) employed within a tourism performance, assessing its perceived innovativeness as part of the visitor experience.
	IN2	I found the digital visual experience provided by this performance to be novel.
	IN3	I think the application of digital technology in this performance was unique.
Perceived Usefulness	PU1	The on-site digital technology made it easier for me to access and understand the background information of the performance.	The core scale items are adapted from the foundational Technology Acceptance Model (TAM) by Davis [18]; which measures the degree to which a user believes a technology will enhance their performance. To ensure contextual relevance for cultural experiences, the wording and focus were further refined with reference to Chung et al. [22] who applied TAM to augmented reality in heritage tourism. Items were thus tailored to assess the utility of in situ digital technology for accessing cultural information and deepening cultural understanding within a performance setting.
	PU2	Overall, I found using the on-site digital technology (e.g., projections, interactive screens) to be useful.
	PU3	This technology enabled me to gain a deeper understanding of the cultural connotations and spirit the performance intended to express.
Perceived Ease of Use	PE1	It was easy to find the performance information I wanted through the on-site digital technology.	Adapted from the foundational Technology Acceptance Model (TAM) scales by Davis [18]; measuring the degree of effort associated with using a system. Item wording was contextualized based on insights from Chung et al. [22], shifting the focus from general software use to the specific interaction with on-site digital installations (e.g., touchscreens, projections) and the clarity of digitally presented cultural content during a tourism performance.
	PE2	I found the process of interacting with this digital technology to be simple and effortless.
	PE3	The content presented by the digital technology (e.g., images, text, interfaces) was clear and easy to understand.
Scenario Construction	SC1	The overall environment of the performance venue (architecture, lighting, sound effects) created a strong atmosphere that immersed me.	This scale integrates foundational concepts from Bitner’s services cape [23]; theory regarding the impact of physical surroundings, with the contemporary, culturally specific Tourism Experience Scenario Scale by Zhang [20]. The items collectively measure a multidimensional construct encompassing: (1) thematic narrative (plot coherence, storytelling appeal, digital-visual immersion—SC1-SC5), and (2) social engagement (audience co-presence, interactive participation, cultural extension—SC6-SC8). The wording was adapted to evaluate the integrated “story field” created by the combination of live performance, physical venue, and digital technology, specifically within tourism performances.
	SC2	The stage’s visual and auditory effects (including scenery, props, and digital imagery) were very realistic, making me feel present in the scene.
	SC3	The storyline of the performance was logical and engaging.
	SC4	The performance clearly told an attractive cultural story through multiple methods (acting, narration, digital storytelling).
	SC5	The overall presentation deepened my understanding of the cultural meaning and spirit conveyed by the performance.
	SC6	The collective reactions of the audience (e.g., applause, silence) enhanced my sense of ritual and engagement during the viewing.
	SC7	The interactive segments in the performance (e.g., collective responses, digital participation) significantly increased my sense of involvement.
	SC8	The performance sparked my interest in further exploring the related culture through follow-up channels (e.g., scanning QR codes).
Flow Experience	FD1	The level of interaction and content in this performance experience was just right for me; I could easily become engaged.	Adapted from the Flow State Scale (FSS), a foundational and psychometrically robust instrument developed by Jackson and Marsh [47] to measure optimal (“flow”) experiences. The original scale was validated in sports contexts but is widely applied across domains. Five items representing core dimensions of the flow state—challenge-skill balance, action-awareness merging, concentration, sense of control, and distorted time perception—were selected and adapted. The wording was contextualized to describe the state of deep cognitive and emotional immersion experienced by visitors during both the viewing of and interaction with digital elements in a tourism performance.
	FD2	During the interaction and viewing process, my reactions were very natural and fluid, almost an unconscious involvement.
	FD3	My attention was completely focused on the experience before me.
	FD4	In the interactive parts, I felt in complete control and could participate as I wished.
	FD5	During the experience, I completely lost track of time.
Cultural Experience	CE1	(Emotional Resonance) Watching this performance evoked strong emotional resonance in me (e.g., awe, moved, pride).	This multi-dimensional scale was developed for the present study to capture the depth of internalization that characterizes a high-quality cultural encounter. Its design is theoretically informed by: (1) the foundational dimensions of the experience economy [27]; (2) localized studies on cultural tourism experiences [47]; and (3) critically, the psychological ownership theory as applied in tourism contexts [26]. The first four items (CE1-CE4) collectively measure the core experiential outcome—encompassing emotional, cognitive, identification, and immersive aspects—that our model, through the lens of psychological ownership, aims to explain. Item CE5 measures behavioral intention (recommendation), a key consequential outcome in tourism, included to capture the tangible impact of a profound cultural experience.
	CE2	(Cultural Cognition) This performance enhanced my understanding of the related historical/cultural background, story, or symbols.
	CE3	(Cultural Identity) This performance deepened my identification with and interest in the culture it presented.
	CE4	(Aesthetic Immersion) During the viewing, I was fully immersed in the cultural and artistic atmosphere created by the performance.
	CE5	(Behavioral Intention) I am willing to recommend this performance to friends or family because it provided a profound cultural experience.

below.

To ensure measurement quality, this study employed several methods. First, English scales were translated into Chinese using the back-translation method to minimize language-induced differences and maintain conceptual equivalence. Second, an expert panel from relevant fields evaluated the content validity, wording, and semantic clarity of each item, and revisions were made accordingly. Third, a pilot study was conducted prior to the formal survey, with potential tourists from various age groups randomly selected on the streets of Xi’an for small-scale testing. Based on their feedback, the questionnaire’s wording and structure were finalized. All items were measured on a 5-point Likert scale, where 1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Agree, 4 = Agree, and 5 = Strongly Agree.

The target participants were domestic tourists who had attended at least one tourism performance in the past six months. In the questionnaire instructions and pre-screening, participants were explicitly asked to recall and base all their evaluations on one specific, recent, and impressive tourism performance they had attended.

3.3. Sample Collection Overview

This study employed an online questionnaire survey for data collection. The target participants were domestic tourists with prior experience of tourism performances. The questionnaire was administered via the “Wen Juanxing” platform and included pre-screening questions to ensure respondent eligibility. To ensure comprehensive coverage of the target population, a multi-channel convenience sampling strategy was employed. First, the questionnaire was distributed within online tourism communities, social media platforms, and relevant forums. Second, QR codes were placed at exit points and rest areas of several popular tourism performance venues, inviting visitors who had just completed a performance to voluntarily scan and complete the survey. All respondents were informed of the study’s purpose and provided consent prior to participation. Data collection was conducted between 1 October and 7 October 2025.

To ensure data quality, the following standard procedures were implemented during the data cleaning phase. First, based on pre-test results, questionnaires completed in an excessively short time (less than 2 min) were deemed careless responses and excluded. Second, attention-testing items (e.g., reverse-scored questions) were incorporated into the questionnaire; responses failing these tests were removed. Finally, questionnaires exhibiting obvious patterned responses (e.g., selecting the same option consecutively or showing wave-like response patterns) were also excluded.

To address potential common method bias, several procedural remedies were implemented in the survey design phase:

• Psychological Separation: The questionnaire was designed to separate the measurement of predictor and criterion variables by placing them in different sections with distinct headers.
• Anonymity and Confidentiality: We assured respondents of complete anonymity and data confidentiality to reduce evaluation apprehension and social desirability bias.
• Item Wording Improvements: We avoided ambiguous or leading questions and used both positively and negatively worded items where appropriate to break response patterns.
• Pilot Testing and Expert Review: The pilot study and expert panel review helped ensure item clarity, reducing item ambiguity as a potential source of bias.
• Attention Checks: We incorporated attention-check items to identify and exclude careless respondents.

Following this screening process, the study collected 600 questionnaires, yielding 540 valid responses with a recovery rate of 90.0%. Analysis of the sample’s basic characteristics indicates a balanced distribution across key demographic variables, including gender, age, and educational background (see Section 3.3), providing a reliable foundation for subsequent empirical analysis.

The relationship diagram of the structural model in this study is shown in Figure 2.

Figure 2 shows the proposed structural equation model. This model illustrates the hypothesized relationships. The interactivity and innovativeness of digital technology are posited as exogenous variables, influencing the cultural experience both directly and indirectly through three parallel mediators: perceived usefulness (PU) and perceived ease of use (PEOU) (cognitive evaluation), scenario construction, and flow experience. All constructs are modeled as latent variables measured by their respective indicators (see Table 1).

4. Results

4.1. Descriptive Statistical Analysis

Basic Sample Characteristics

Based on the 540 valid responses, descriptive statistical analyses were conducted on respondents’ demographic characteristics, including gender, age, educational background, and frequency of attending performances.

Table 2. Basic information statistics.

Question Name	Option	Frequency	Percentage (%)
Your gender?	Male	224	41.5
	Female	316	58.5
Your age?	Under 18	36	6.7
	19–25	189	35.0
	26–35	175	32.4
	36–45	92	17.0
	46 years and older	48	8.9
What is your highest level of education (including current enrollment)?	High school or below	102	18.9
	Associate Degree	133	24.6
	Undergraduate Degree	211	39.1
	Graduate Degree and above	94	17.4
How often do you attend tourism performances?	First time	295	54.6
	2–3 times	164	30.4
	4 times or more	81	15.0
	Total	540	100.0

represents the result, which indicates a balanced distribution across all dimensions, supporting sample representativeness and providing a foundation for examining the influence of demographic variables on perceptions of digital cultural experiences.

From a demographic perspective, the sample exhibits strong representativeness. Gender distribution is balanced, minimizing structural bias. The age structure is dominated by the 18–35 age group, who are both “digital natives” and primary drivers of cultural consumption, aligning closely with the technological and experiential focus of this study. Educational attainment is predominantly at the bachelor’s level or higher, consistent with typical digital technology adoption patterns, and the gradient distribution across educational levels allows for exploring potential influences of education. Overall, the sample combines broad societal representativeness with research-specific relevance, providing a robust foundation for subsequent data analysis.

4.2. Reliability Testing

Table 3. Reliability analysis results.

Measurement Scale	Number of Items	Cronbach’s Alpha Coefficient	Total Scale Alpha Coefficient
Digital Technology Interactivity	4	0.816	0.894
Digital Technology Innovation	3	0.800
Perceived Usefulness	3	0.759
Perceived Ease of Use	3	0.803
Scenario Construction	8	0.892
Flow Experience	5	0.843
Cultural Experience	5	0.836

represents the reliability analysis results. Based on the table, the Cronbach’s alpha coefficients of all the variables are above 0.7, confirming the reliability of using the variables in the model. The overall scale coefficient is 0.894, demonstrating high internal consistency and great reliability of the measurement scales, thereby meeting the requirements for subsequent analysis.

4.3. Validity Testing

4.3.1. Exploratory Factor Analysis

To assess data validity for factor analysis, this study conducted KMO sampling adequacy measures and Bartlett’s sphericity test.

Table 4. KMO and bartlett’s test results.

Sufficiency of Sampling: KMO Measure		0.889
Bartlett’s Sphericity Test	Approximate Chi-Square	6761.882
	df	465
	Sig	0.000

shows a KMO value of 0.889, indicating high partial correlation among variables. The estimated statistic of Bartlett’s sphericity test is significant at 1% level (χ² = 6761.882, df = 465, p < 0.001), indicating the presence of shared latent structure among variables. Both metrics confirm the reliability of data for subsequent exploratory factor analysis, supporting the validity of the scale’s construct.

To assess the potential threat of common method bias (CMB) due to the cross-sectional, self-reported data, we performed Harman’s single-factor test. An exploratory factor analysis including all measurement items revealed that the first unrotated factor accounted for 24.25% of the total variance, which is well below the commonly accepted threshold of 50%. This suggests that common method variance is not a severe issue in the present dataset.

Table 5. Total variance explained.

Component	Initial Eigenvalue			Extracted Loadings			Rotated Loadings
	Total	Percentage of Variance	Cumulative %	Total	Percentage of variance	Cumulative %	Total	Percentage of variance	Cumulative %
1	7.517	24.249	24.249	7.517	24.249	24.249	4.627	14.925	14.925
2	2.983	9.623	33.872	2.983	9.623	33.872	3.116	10.053	24.978
3	2.473	7.977	41.849	2.473	7.977	41.849	3.076	9.922	34.900
4	2.005	6.468	48.317	2.005	6.468	48.317	2.609	8.417	43.317
5	1.883	6.073	54.390	1.883	6.073	54.390	2.165	6.985	50.302
6	1.556	5.020	59.411	1.556	5.020	59.411	2.107	6.795	57.098
7	1.312	4.232	63.642	1.312	4.232	63.642	2.029	6.544	63.642
8	0.687	2.217	65.860
9	0.657	2.120	67.980
10	0.652	2.105	70.085
11	0.597	1.925	72.010
12	0.573	1.850	73.860
13	0.571	1.841	75.701
14	0.546	1.760	77.461
15	0.520	1.679	79.140
16	0.508	1.638	80.778
17	0.496	1.600	82.378
18	0.487	1.570	83.948
19	0.480	1.550	85.498
20	0.475	1.532	87.030
21	0.450	1.453	88.483
22	0.424	1.368	89.851
23	0.412	1.328	91.179
24	0.399	1.285	92.465
25	0.371	1.196	93.661
26	0.360	1.162	94.823
27	0.351	1.131	95.954
28	0.343	1.106	97.059
29	0.328	1.058	98.118
30	0.313	1.011	99.129
31	0.270	0.871	100.000

Extraction Method: Principal Component Analysis.

shows the total variance explained by exploratory factor analysis. Using Kaiser’s criterion (eigenvalue > 1), seven common factors were extracted, accounting for a cumulative variance of 63.642%, exceeding the empirical benchmark of 60% and indicating good factor representativeness. After maximum variance rotation, the cumulative variance remained unchanged, suggesting no information loss, while the rotation optimized the distribution of factor loadings, facilitating subsequent analysis.

Principal Component Analysis (PCA) was employed for the initial exploratory factor analysis. PCA was chosen for its goal of extracting maximum variance and providing a clear preliminary factor structure for scale refinement, which is a common practice in the early stages of scale validation in psychological and behavioral research. The subsequent Confirmatory Factor Analysis (CFA) tests the hypothesized factor structure based on theory, complementing the PCA.

Table 6. Exploratory factor analysis results.

		1	2	3	4	5	6	7
Digital Technology Interactivity	IT1				0.751
	IT2				0.757
	IT3				0.815
	IT4				0.776
Digital Technology Innovation	IN1						0.780
	IN2						0.804
	IN3						0.829
Perceived Usefulness	PU1							0.813
	PU2							0.766
	PU3							0.774
Perceived Ease of Use	PEOU1					0.813
	PEOU2					0.779
	PEOU3					0.794
Scenario Construction	SC1	0.776
	SC2	0.756
	SC3	0.755
	SC4	0.734
	SC5	0.726
	SC6	0.693
	SC7	0.730
	SC8	0.720
Flow Experience	FD1		0.784
	FD2		0.771
	FD3		0.754
	FD4		0.767
	FD5		0.749
Cultural Experience	CE1			0.755
	CE2			0.741
	CE3			0.738
	CE4			0.753
	CE5			0.728

Extraction method: Principal Component Analysis. Rotation method: Kaiser normalized maximum variance method.

provides the results of exploratory factor analysis, which confirm the scale’s strong construct validity. Using principal component analysis with maximum variance rotation, the seven extracted factors corresponded precisely to the theoretical constructs. Item loadings on their respective factors ranged from 0.693 to 0.829, which are above the 0.6 threshold, rejecting cross-loading issues. These results indicate that all items are effectively aggregated onto their predefined dimensions, demonstrating a clear factor structure and good discriminant validity.

The narrative now explicitly highlights: The EFA yielded a clean seven-factor structure. All items loaded strongly (>0.65) on their intended factors with no significant cross-loadings (<0.40), indicating excellent discriminant validity at the exploratory stage. The factor correlation matrix (available upon request) showed moderate inter-factor correlations, consistent with our theoretical model.

4.3.2. Confirmatory Factor Analysis

Confirmatory factor analysis examined the structural validity of the measurement model. The model, constructed in AMOS, explicitly defined the relationships between latent and observed variables and allowed correlations among latent variables. The model demonstrated a good fit, as shown in

Table 7. Model fit for confirmatory factor analysis.

Indicator	CMIN/DF	GFI	CFI	RMSEA	IFI	NFI	PCFI
Numerical Results	1.163	0.948	0.990	0.017	0.990	0.930	0.879
Fitting Standard	<3	>0.9	>0.9	<0.08	>0.9	>0.9	>0.5
Compliance	Yes	Yes	Yes	Yes	Yes	Yes	Yes

: χ²/df = 1.163, RMSEA = 0.017, with GFI, CFI, IFI, NFI, and other indices all above 0.93, and PCFI = 0.879. All fit indices met the thresholds, indicating a high alignment between the theoretical model and the observed data, and confirming the scale’s strong structural validity.

As shown in

Table 8. Convergent validity.

Variable Name	Item	Standard Factor Loadings	t-Statistics	AVE	CR (Convergent Validity)
Digital Technology Interactivity	IT1	0.690		0.525	0.815
	IT2	0.693	13.579
	IT3	0.771	14.660
	IT4	0.741	14.295
Digital Technology Innovation	IN1	0.759		0.573	0.801
	IN2	0.747	14.952
	IN3	0.764	15.114
Perceived Usefulness	PU1	0.687		0.512	0.759
	PU2	0.708	12.588
	PU3	0.750	12.817
Perceived Ease of Use	PEOU1	0.777		0.577	0.804
	PEOU2	0.736	15.286
	PEOU3	0.766	15.661
Scenario Construction	SC1	0.768		0.508	0.892
	SC2	0.730	17.172
	SC3	0.724	17.017
	SC4	0.730	17.169
	SC5	0.721	16.933
	SC6	0.675	15.722
	SC7	0.694	16.223
	SC8	0.656	15.236
Flow Experience	FD1	0.772		0.518	0.843
	FD2	0.727	16.113
	FD3	0.699	15.485
	FD4	0.719	15.933
	FD5	0.680	15.052
Cultural Experience	CE1	0.738		0.507	0.837
	CE2	0.697	14.860
	CE3	0.737	15.657
	CE4	0.700	14.920
	CE5	0.687	14.651

, the results of the confirmatory factor analysis indicate that standardized factor loadings for all items on their corresponding latent variables ranged from 0.656 to 0.777, all above 0.5 and statistically significant (t-statistic > 1.96). Average variance extracted (AVE) values ranged from 0.507 to 0.577, surpassing the 0.5 threshold. Composite reliability (CR) ranged between 0.759 and 0.892, exceeding the 0.7 standard. These results collectively confirm adequate convergent validity, meaning that items within each construct are highly correlated and effectively reflect their latent variables.

Discriminant validity was assessed by comparing the square root of the average variance extracted (AVE) for each latent variable with the correlation coefficients between variables. As shown in

Table 9. Distinctive validity.

	Digital Technology Interactivity	Digital Technology Innovation	Perceived Usefulness	Perceived Ease of Use	Scenario Construction	Flow Experience	Culture Experience
Digital Technology Interactivity	0.725
Digital Technology Innovation	0.375	0.757
Perceived Usefulness	0.260	0.298	0.716
Perceived Ease of Use	0.317	0.356	0.504	0.760
Scenario Construction	0.316	0.285	0.257	0.331	0.713
Flow Experience	0.375	0.264	0.232	0.235	0.297	0.720
Cultural Experience	0.260	0.416	0.361	0.393	0.346	0.334	0.712

Note: Values on the diagonal represent the square root of AVE; correlation coefficients between variables are below the diagonal.

, the square root of the AVE for all latent variables (bolded values on the diagonal) exceeded their correlations with any other variable (values below the diagonal). For example, the square root of AVE for Digital Technology Interactivity (0.725) is substantially higher than its highest correlation with Digital Technology Innovation (r = 0.375). These results indicate good discriminant validity, confirming the meaningful distinction of the constructs.

4.4. Structural Equation Model and Hypothesis Testing

Table 10. Structural model fit.

Index	CMIN/DF	GFI	CFI	RMSEA	IFI	NFI	PCFI
Numerical Results	1.371	0.938	0.976	0.026	0.976	0.917	0.879
Fitting Standard	<3	>0.9	>0.9	<0.08	>0.9	>0.9	>0.5
Compliance	Yes	Yes	Yes	Yes	Yes	Yes	Yes

presents the model fit indices for the structural equation model. Based on the table, χ²/df is 1.371, and RMSEA is 0.026, indicating a parsimonious model with minimal fit error. All relative fit indices—GFI, CFI, IFI, and NFI—exceed 0.9, and PCFI is greater than 0.5, indicating that the theoretical model fits the observed data well and is reliable to be used for subsequent hypothesis testing.

Table 11. Path coefficient analysis.

Hypothesis	Path	Standardized Coefficient	S.E.	C.R.	p	Result
H1	Digital Technology Interactivity → Perceived Usefulness	0.198	0.062	3.215	**	Supported
H2	Digital Technology Interactivity → Perceived Ease of Use	0.267	0.065	4.083	***	Supported
H3	Digital Technology Innovation → Perceived Usefulness	0.300	0.069	4.349	***	Supported
H4	Digital Technology Innovation → Perceived Ease of Use	0.387	0.073	5.289	***	Supported
H5	Digital Technology Interactivity → Scenario Construction	0.272	0.059	4.638	***	Supported
H6	Digital Technology Innovation → Scenario Construction	0.257	0.064	4.030	***	Supported
H7	Digital Technology Interactivity → Flow Experience	0.143	0.049	2.949	**	Supported
H8	Digital Technology Innovation → Flow Experience	0.211	0.054	3.904	***	Supported
H9	Perceived Usefulness → Cultural Experience	0.126	0.050	2.549	*	Supported
H10	Perceived Ease of Use → Cultural Experience	0.121	0.045	2.686	**	Supported
H11	Scenario Construction → Cultural Experience	0.117	0.044	2.651	**	Supported
H12	Flow Experience → Cultural Experience	0.173	0.055	3.127	**	Supported
H13	Digital Technology Interactivity → Cultural Experience	0.119	0.055	2.178	*	Supported
H14	Digital Technology Innovation → Cultural Experience	0.213	0.064	3.307	***	Supported

Note: *, **, and *** indicate p < 0.05, p < 0.01, and p < 0.001 (two-tailed), respectively.

provides the results of examining the path analyses using the structural equation model. The estimates support all 14 research hypotheses. The interactivity and innovativeness of digital technology significantly and positively influenced perceived usefulness, perceived ease of use, scenario construction, flow experience, and cultural experience, confirming Hypotheses H1–H10. In addition, perceived usefulness, perceived ease of use, scenario construction, and flow experience all show significant and positive effects on cultural experience, confirming Hypotheses H11–H14. Notably, technological innovation demonstrates relatively stronger effects on perceived ease of use and flow experience, and interactivity has a more pronounced impact on scenario construction. These findings comprehensively illustrate both the direct and multiple indirect mechanisms through which digital technology influences cultural experiences.

To assess the explanatory power of the structural model, the squared multiple correlations (R²) for all endogenous latent variables were examined. As shown in Table 11, the model explains 31.1% of the variance in Cultural Experience (R² = 0.311), indicating that the combined influence of the antecedent and mediating variables accounts for a substantial portion of the outcome. The variances explained in the key mediating variables are as follows: Perceived Ease of Use (19.9%, R² = 0.199), Scenario Construction (15.6%, R² = 0.156), Perceived Usefulness (14.3%, R² = 0.143), and Flow Experience (10.8%, R² = 0.108). These R² values demonstrate that the model captures meaningful variance in both the ultimate outcome and the proposed parallel mediating mechanisms.

4.5. Mediating Effects Test

Based on the estimates, digital technology significantly influences cultural experience not only directly but also through a network of parallel mediating pathways. The analysis confirms the existence of three core mechanisms—cognitive evaluation, scenario construction, and flow experience—through which technology exerts its impact. To provide a comprehensive view,

Table 12. Results of Effect Decomposition and Mediating Pathway Analysis.

Effect Type and Path	Standardized Effect Size	SE	LLCI	Upper Limit Confidence Interval	Result
Total Effect (Interactivity → Cultural Experience)	0.233	-	-	-	Significant
Direct Effect	0.119	0.055	0.011	0.227	Supported
Total Indirect Effect (Sum of Ind1–Ind4)	0.114	0.033	0.053	0.184	Supported
Total Effect (Innovativeness → Cultural Experience)	0.364	-	-	-	Significant
Direct Effect	0.213	0.064	0.088	0.338	Supported
Total Indirect Effect (Sum of Ind5–Ind8)	0.151	0.039	0.079	0.233	Supported
Ind1	0.025	0.018	0.001	0.075	Supported
Ind2	0.032	0.020	0.005	0.089	Supported
Ind3	0.032	0.021	0.002	0.090	Supported
Ind4	0.025	0.017	0.004	0.080	Supported
Ind5	0.038	0.024	0.004	0.099	Supported
Ind6	0.047	0.025	0.009	0.116	Supported
Ind7	0.030	0.022	0.001	0.093	Supported
Ind8	0.036	0.020	0.009	0.093	Supported

Note: Ind1: Digital Technology Interactivity → Perceived Usefulness → Cultural Experience; Ind2: Digital Technology Interactivity → Perceived Ease of Use → Cultural Experience; Ind3: Digital Technology Interactivity → Scenario Construction → Cultural Experience; Ind4: Digital Technology Interactivity → Flow Experience → Cultural Experience;Ind5: Digital Technology Innovation → Perceived Usefulness → Cultural Experience; Ind6: Digital Technology Innovation → Perceived Ease of Use → Cultural Experience; Ind7: Digital Technology Innovation → Scenario Construction → Cultural Experience; Ind8: Digital Technology Innovation → Flow Experience → Cultural Experience. Bootstrapped samples = 5000. LLCI = Lower Limit Confidence Interval; For direct effects, The Total Effect for each antecedent is the sum of its Direct Effect and its Total Indirect Effect. The Total Indirect Effect is the sum of its respective specific indirect paths (Ind1–Ind4 for Interactivity; Ind5–Ind8 for Innovativeness).

presents a detailed breakdown of effects, including total, direct, and indirect effect estimates for the two core technological antecedents.

The findings strongly support the core argument that digital technology influences cultural experience through three parallel pathways. Both interactivity and innovation exert significant total effects (0.233 and 0.364, respectively), which are meaningfully decomposed into direct and indirect components. Notably, indirect effects account for a substantial portion of the total influence (~49% for interactivity and ~41% for innovativeness), underscoring the critical role of the mediating mechanisms. Specifically, all eight hypothesized mediating paths are significant. Technological innovativeness exhibits its strongest indirect effect via the perceived ease of use pathway (Ind6, effect size = 0.047), highlighting that novel technologies enhance experience primarily by reducing usage barriers. In contrast, interactivity demonstrates comparable and significant indirect effects through both scenario construction (Ind3, 0.032) and perceived ease of use (Ind2, 0.032), emphasizing its dual role in facilitating narrative immersion and user control. These results systematically validate the parallel operation of the cognitive evaluation, scenario construction, and flow experience mechanisms, providing a clarified and quantified map of the intrinsic pathways through which digital technology shapes cultural experiences.

To further examine the nature of the ‘parallel’ mechanisms, we conducted pairwise contrast tests between the aggregated indirect effects of the three conceptual pathways (Cognitive, Scenario, Flow). The results showed that the differences between these pathways were not statistically significant (e.g., Cognitive vs. Scenario: contrast = 0.005, p = 0.215; Cognitive vs. Flow: contrast = 0.003, p = 0.178; Scenario vs. Flow: contrast = −0.002, p = 0.312). This indicates that, while all three pathways are significant, no single pathway exerts a dominantly stronger indirect effect than the others in this context.

5. Discussion

The structural model explains a substantial 31.1% of the variance in cultural experience (R² = 0.311). This level of explained variance is considerable for a model predicting a complex psychological and behavioral outcome in a real-world tourism context. The significant R² values for the mediating variables—particularly for perceived ease of use (19.9%) and scenario construction (15.6%)—confirm that these pathways are not only statistically significant but also capture meaningful systematic variance, reinforcing their roles as substantive mechanisms. The moderate R² for flow experience (10.8%) suggests that while it is a valid and significant path, its activation may be more subject to individual differences or situational factors not fully captured in this model. Collectively, these findings affirm the robustness of the proposed three-path framework while appropriately acknowledging the role of unmeasured variables, thus providing a balanced and credible account of digital technology’s impact.

5.1. Interpretation of Key Findings

The empirical results provide robust support for the proposed “cognition-scenario-flow” triadic pathway model. Beyond confirming statistical significance, several nuanced patterns emerge that offer deeper insights into the technology-experience nexus in tourism performances. First, the finding that digital technology innovativeness exerts the strongest influence on perceived ease of use (β = 0.387, p < 0.001) is particularly instructive. It challenges the potential misconception that technological sophistication inherently creates complexity. Instead, it validates the TAM principle within a high-immersion context: truly impactful innovation in tourism performances—such as intuitive AR overlays or seamless holographic integration—functions by reducing cognitive and operational friction, thereby lowering the barrier to cultural engagement rather than raising it Second, the strong effects of both interactivity and innovativeness on scenario construction (β = 0.272 and 0.257, respectively) underscore their complementary roles. Interactivity provides the “verbs”—the actions and choices that give visitors agency. Innovativeness provides the “nouns and adjectives”—the novel audiovisual elements and story forms. Their synergy is essential for transforming a passive viewing space into an active, immersive “story world” where the narrative is not only told but also felt and influenced. Third, among the mediating variables, flow experience demonstrated a significant direct effect on cultural experience (β = 0.173, p = 0.002). This underscores that the psychological state of flow is not merely a pleasant byproduct but a critical catalytic mechanism. When visitors enter a state of deep focus and intrinsic motivation facilitated by well-balanced digital interactions, their processing of cultural content shifts from superficial observation to personal, embodied identification, facilitating internalization.

5.2. Synthesizing the Pathways Through the Lens of Psychological Ownership

The mediation analysis reveals that all three pathways function effectively, with digital technology innovativeness showing its strongest indirect effect via the perceived ease of use path (effect size = 0.047). The validity of these parallel paths collectively provides compelling, albeit indirect, empirical support for our overarching psychological ownership theoretical framework [48]. The cognitive evaluation pathway (via PU/PEOU) operationalizes the development of control and intimate knowledge—two core antecedents of psychological ownership. When technology is seen as useful and easy to use, it grants visitors mastery over the experience and deepens their understanding, laying the cognitive groundwork for “owning” the cultural narrative. The scenario construction pathway operationalizes self-investment and emotional belonging. A compelling narrative space demands and rewards emotional and cognitive investment from the visitor, creating a sense of personal stake and connection to the story, mirroring the development of possession feelings [49]. The flow experience pathway operationalizes a heightened state of control and focused immersion simultaneously. The flow state inherently combines deep concentration with a sense of agency, efficiently fostering both the intimate familiarity and control central to psychological ownership.

Thus, the three paths are not merely alternative routes to a positive experience; they are discernible, parallel processes through which digital technology cultivates the foundational elements of psychological ownership, which in turn manifests as the cultural experience measured in this study.

6. Conclusions and Outlook

6.1. Research Findings

This study, grounded in the S-O-R framework [49], construct and empirically validates a parallel three-pathway model that explains how digital technology empowers cultural experiences in tourism performances, with psychological ownership serving as the overarching theoretical lens. Analysis of 540 valid questionnaires confirms that the interactivity and innovativeness of digital technology stimulate a profound cultural experience through three distinct yet complementary mediating pathways: the cognitive evaluation pathway (enhancing perceived usefulness and ease of use), the scenario construction pathway (building immersive narrative spaces), and the flow experience pathway (inducing deep psychological engagement). The findings further reveal differentiated impacts: technological innovativeness exhibits the strongest effect on fostering perceived ease of use, while both attributes significantly drive scenario construction and flow states.

6.2. Theoretical Contributions

This study makes several distinct contributions to the literature. First, it moves beyond the prevalent single-theory approaches (e.g., TAM-only, or flow-only studies) in tourism technology research by integrating three key theoretical perspectives into a cohesive, parallel-mediation model. This provides a more holistic explanation of how technology functions as a tool, a narrative medium, and a psychological catalyst simultaneously. Second, it advances the application of psychological ownership theory from organizational and consumer behavior contexts into the realm of digital cultural heritage and experience. We demonstrate its utility as a powerful explanatory framework for understanding the “depth” in cultural experiences, shifting the focus from surface-level satisfaction to deeper mechanisms of identity and belonging. Third, by empirically distinguishing and validating the parallel nature of the three pathways, our study clarifies that their effects are complementary rather than mutually exclusive. This offers a more nuanced framework for both researchers and practitioners, suggesting that technological strategies can be designed to target specific pathways (e.g., optimizing ease of use vs. enhancing narrative) based on desired experiential outcomes [45,50].

6.3. Practical Implications

The validated three-pathway model provides a clear “decision navigation map” for stakeholders, translating theoretical insights into actionable strategies to overcome the “high investment, low experience” dilemma.

• For Government and Regulatory Bodies: Establish a Differentiated Funding and Evaluation System Policymakers should abandon a “one-size-fits-all” approach to supporting digital cultural projects. Instead, funding and evaluation criteria must align with a project’s primary objective by prioritizing the most relevant experiential pathway: For projects emphasizing cultural education and heritage transmission, the policy focus should be on the cognitive evaluation pathway. Support should target technologies that enhance the clarity, accessibility, and depth of cultural information (e.g., AR knowledge annotation, intelligent context-aware guides). Evaluation metrics should prioritize cognitive gains and understanding. For projects aiming at market impact and memorable peak experiences, support should emphasize the scenario construction and flow experience pathways. Incentives should be directed toward R&D and application of technologies that create emotional resonance and deep immersion (e.g., immersive theaters, interactive large-scale installations). Evaluation systems should incorporate metrics for narrative engagement, emotional impact, and flow intensity. A new “experience value”-centered evaluation framework is recommended, incorporating indicators like psychological ownership and flow state to guide the industry from mere “technology accumulation” to genuine “value deepening [45,50].
• For Tourism Performance Operators: Implement Precision Diagnostics and Optimize Resource Allocation. Operators can use the three-pathway framework as a diagnostic tool to identify experiential bottlenecks and make targeted investments: If visitor feedback indicates “low comprehension or detachment,” the bottleneck likely lies in the cognitive pathway. Investments should optimize interface usability and the explanatory depth of digital content. If feedback points to “lack of immersion or weak emotional connection,” the issue may reside in the scenario pathway. Collaboration with storytellers and directors should be strengthened to ensure technology seamlessly serves a compelling narrative. If feedback suggests “low engagement or a lackluster experience,” the flow pathway may need enhancement. Service design should apply flow theory principles to optimize challenge-skill balance and provide immediate, meaningful feedback at key interaction touchpoints [51].
• For Technology Developers: Design Modular and Customizable Digital Toolkits. Developers should shift from offering rigid, standardized solutions to providing flexible, modular digital toolkits. These toolkits should allow venue managers to adjust key parameters along the different experiential pathways: Adjustable Interactivity Levels: To cater to the cognitive pathway (ease of use/control) and flow pathway (challenge-feedback). Configurable Narrative Depth: To support the scenario pathway (immersion) and cognitive pathway (usefulness/understanding). Such design enables the same technological platform to be tailored for projects with different goals (educational vs. entertainment-focused), enhancing the adaptability and return on investment of digital solutions in the cultural tourism sector. This targeted approach maximizes experiential value within budget constraints and mitigates the risk of misguided technological investment.
• For policymakers and investors aiming to promote socio-cultural sustainability, the study highlights that the ultimate value of digital investment lies not in spectacle alone but in its capacity to foster visitors’ psychological ownership of cultural heritage. When visitors feel a sense of personal connection and responsibility towards the culture presented the likelihood of respectful engagement, advocacy, and long-term cultural preservation increases [52]. Therefore, funding and evaluation criteria should evolve from measuring technological adoption (e.g., number of VR headsets) to assessing experiential depth and the cultivation of ownership-related outcomes, thereby aligning technological innovation with sustainable cultural goals.

In summary, this study not only uncovers the “mechanism black box” of how digital technology influences cultural experiences but also offers evidence-based guidance for stakeholders. In the digital economy era, driving high-quality development in cultural tourism requires a deep understanding and strategic application of the principles underlying technology-enabled experiences—from macro policy to micro-level management—thereby optimizing resource allocation and maximizing customer value.

6.4. Research Limitations and Future Directions

This study has several limitations that suggest avenues for future research. First, the cross-sectional design, while validating correlational relationships, cannot establish causality or capture the dynamic evolution of cultural experiences over time. Future research could employ longitudinal designs (e.g., tracking changes in visitors’ cultural identity before and after a performance) or controlled experiments (e.g., A/B testing different technological features) to strengthen causal inferences and understand experience dynamics.

Second, limitations exist regarding sampling and context specificity. (1) Although respondents were instructed to evaluate a specific, recent performance, the sample encompasses various performance types (e.g., large-scale real scene shows, indoor theater productions). While this enhances ecological validity, the inherent heterogeneity may obscure nuances. Future studies should control for or explicitly examine performance type/genre as a moderating variable. (2) The use of a multi-channel convenience sampling strategy during a national holiday period [53], while efficient, may bias the sample toward leisure-focused tourists and specific crowd conditions, limiting the generalizability of findings to other tourist segments or seasonal contexts. (3) The model does not differentiate between first-time and repeat visitors, a factor known to influence technology adoption and experience depth. Future research with controlled sampling on these visitor characteristics is warranted.

Third, the study focuses on two core technology characteristics (interactivity and innovativeness). As digital technologies evolve rapidly, future research should explore the impact of emerging attributes such as personalization, bio-sensing interactivity, and applications of Artificial Intelligence-Generated Content (AIGC) or the metaverse on the proposed pathways.

Fourth, this study focuses on the experiential outcomes of technology application. It does not address the broader practical trade-offs and ethical considerations associated with implementing advanced digital technologies in cultural settings, such as the digital divide (ensuring equitable access), the resource intensity and environmental footprint of installations, or the potential risks to cultural authenticity (e.g., over-commercialization or distortion). Investigating how to balance these critical practical constraints with experiential optimization presents a vital and complex avenue for future interdisciplinary research.

Fifth, despite employing procedural remedies (e.g., anonymization, reversed items) and statistical controls (Harman’s single-factor test), the common method bias inherent in cross-sectional, self-reported data cannot be entirely ruled out. While the complex model structure with differential path strengths makes it unlikely that the entire pattern of results is a methodological artifact, future multi-method or multi-source study would be valuable to corroborate the findings.