







































Y. Long et al. /Future Technology                                                                                      February 2026| Volume 05 | Issue 01 | Pages 
159-167 

159 

 

 

 

Article 

AI-enabled factors influencing cultural heritage 

conservation and tourism development towards 

tourist experience quality 
Ying Long1,2, Daranee Pimchangthong1*, Kang Li1,3 

1Institute of Science, Innovation and Culture, Rajamangala University of Technology Krungthep, Bangkok 10120, Thailand 
2Faculty of International Studies, Tongren Preschool Education College, Tongren ,554000, China 
3ELEE Display (Jiangsu) Co., Ltd., Nantong ,226081, China 

A R T I C L E   I N F O 
 

Article history: 
Received 22 August 2025  
Received in revised form 
17 October 2025 
Accepted 16 November 2025 
 
Keywords:  
Artificial intelligence, Cultural heritage tourism, 
Tourist experience quality,  
Structural equation modeling,  
Technology-Organization-Environment 
framework 
 
*Corresponding author 
Email address: 
daranee.p@mail.rmutk.ac.th 
 
DOI: 10.55670/fpll.futech.5.1.14 

A B S T R A C T 
 

This research examines the impact of AI technology on the quality of tourist 
experiences at cultural heritage sites, utilizing an integrated Technology-
Organization-Environment (TOE) framework. Analyzing 200 UNESCO World 
Heritage Sites with 52,847 reviews (2020-2024) using Structural Equation 
Modeling, we found AI creates dual value pathways: conservation technology 
enhances heritage value (β=0.45, p<0.001), which strongly influences 
experience quality (β=0.51, p<0.001), while tourism technology strengthens 
immersive experiences (β=0.58, p<0.001), which also enhance quality (β=0.36, 
p<0.001). Both paths significantly improve tourist experience quality, with 
direct effects of β=0.21 (p<0.01) and β=0.34 (p<0.001) respectively. The 
integrated model explains 59% of experience quality variance (R²=0.59), 
superior to alternative specifications. Multi-group analysis reveals technology 
readiness significantly moderates direct effects (Δβ=0.24-0.25), with 
sophisticated visitors showing 2-3 times stronger responses, while heritage 
value appreciation remains universal across digital literacy levels. Findings 
demonstrate AI enhances rather than diminishes authenticity, with cognitive-
emotional appreciation surpassing technological immersion in driving 
satisfaction. 

1. Introduction 

Multi-agent Heritage sites are challenged by the 
conflicting demands of conservation needs on the one hand, 
and the quality of the tourist experience on the other. 
Although AI provides solutions for managing heritage sites, a 
crucial question remains about the impact of AI-based 
conservation and tourism solutions on the quality of the 
tourist experience, specifically regarding the psychological 
processes mediated by these solutions. There are three 
crucial limitations in current studies on the topic. Firstly, 
there is fragmentation in the treatment of respective studies 
on the application of AI in conservation efforts [1,2] or 
tourism [3,4] without consideration of the cumulative value 
creation occurring in heritage locations in relation to AI 
application. Secondly, there is oversimplification on the effect 
of AI in the form of technology acceptance only [5,6], without 
consideration of the cognitive-emotional process of value 
appreciation/engagement with heritage locations. Lastly, 
there is a lack of empirical validation on the moderating effect 
of visitor technology readiness, without consideration of 
whether the application of AI in heritage locations 

exacerbates or mitigates the impact of digital inequality on 
the previously underserved populace due to disparities in 
technology readiness. Integration with AI is highly necessary 
because the increasing number of heritage sites that 
implement conservation technology, tourism technology, or 
both has yet to be studied, leading to the potential for 
suboptimal investment outcomes. The proposed work 
combines the Technology, Organization, Environment (TOE) 
Framework with the Service-Dominant Logic (SDL) paradigm 
[1,7]. The TOE Framework casts AI on a dual continuum: "AI 
for conservation" (surveillance, recording, predictive 
maintenance) on one side, and "AI for tourism" (VR/AR 
interpretation, AI-driven recommendations, visitor 
management) on the other, while SDL illuminates value co-
creation processes. We contribute to the advancement of 
knowledge in three ways: (1) exploring the intersection 
between conservation and visitor viewpoints by studying the 
two AI processes collaboratively [8], (2) uncovering 
underlying cognitive-affective value-creation processes, 
going beyond technology acceptance constructs [2], (3) 
exploring how meaning creation in heritage may be 

 

 Open Access Journal 

ISSN 2832-0379 

February 2026| Volume 05 | Issue 01 | Pages 159-167 

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Future Technology 

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Y. Long et al. /Future Technology                                                                                      February 2026| Volume 05 | Issue 01 | Pages 159-167 

160 

 

independent from digital literacy but require the complexity 
of technology for superior immersion experiences. 

We will evaluate five hypotheses: AI conservation 
variables positively affect experience quality (H1), AI tourism 
variables positively affect experience quality (H2), Heritage 
Value will mediate the conservation-experience association 
(H3), Immersive Experience will mediate the tourism-
experience association (H4), and Visitor Technology 
Readiness will moderate these relationships (H5). 
Conservation Technology is assumed to improve experience 
quality by secondarily enhancing the appreciation of Heritage 
Value, while Tourism Technology improves quality by 
secondarily enhancing the immersive experience. By applying 
Structural Equation Modeling to 200 UNESCO World Heritage 
Sites, with 52,847 visitor reviews collected between 2020 and 
2024, these relationships will be examined. While adjusting 
for different site attributes and time differences, the proposed 
TOE-SDL model is expected to provide a superior fit 
compared to other theoretical models for capturing 
differences in Experience Quality. Theoretical contributions 
include the validation of the integration of TOE-SDL, the 
identification of value paths, while the practical contributions 
include the application of the study in drawing insights on 
strategies for the integration of AI technology with the 
conservation, management, or even the protection of the 
heritage structures or units, depending on the context.  

2. Data and methods  

2.1 Research design 
This quantitative cross-sectional study examines the 

relationships between AI implementation and the quality of 
tourist experiences at cultural heritage sites. A cross-sectional 
design is appropriate given the recent emergence of AI 
deployment (post-2018), which precludes the availability of 
longitudinal data. We employed purposive sampling to select 
200 UNESCO World Heritage Sites with documented AI 
adoption based on four criteria: (1) verified technology 
implementation through official reports or management 
plans, (2) minimum 50 visitor reviews ensuring adequate 
statistical representation, (3) geographical diversity (40% 
Europe, 27% Asia/Pacific, 19% Americas, 14% Africa/Middle 
East) reflecting global distribution, and (4) heritage type 
diversity (67% cultural, 21% natural, 12% mixed) capturing 
varied conservation contexts.  

Data spanned 2020-2024 to capture AI adoption during 
the critical post-pandemic digital transformation period. 
Following SEM guidelines that require a minimum of 10 
observations per parameter, our sample (N = 200 sites, 
averaging 264 reviews each) provides power > 0.80 to detect 
small to medium effects (α = 0.05). TOE grounds the adoption 
of AI in technology aspects (conservation systems: 
monitoring, reporting, predictive maintenance, tourism 
systems: VR/AR explanation, customized recommendations, 
visitor management), organizational aspects (management 
competency), or environmental aspects (visitor technology 
readiness). Nonetheless, TOE observes adoption mostly 
instead of value realization post-adoption. SDL corrects the 
problem by reimagining the value role of AI through operant 
resources that support value creation on experiential paths, 
transforming the focus from the adoption of technology to the 
quality of outcomes from cognitive-emotional practices of 
heritage value realization. 

 

 

2.2 Data sources and collection 
This study integrated four complementary data sources 

spanning 200 UNESCO World Heritage Sites (2020-2024), 
selected to capture both technological implementation and 
experiential outcomes. 
Source 1: Site Characteristics and AI Implementation. 
UNESCO World Heritage Centre database provided site 
attributes (heritage type, coordinates, inscription year, 
conservation status). Two independent coders systematically 
reviewed monitoring reports and management plans using 
structured protocols to classify AI implementation across 
conservation monitoring, visitor management, and 
interpretation domains, achieving satisfactory inter-rater 
reliability (Cohen's κ>0.80). 
Source 2: Visitor Experience Data. Reviews (N=52,847) were 
collected from TripAdvisor and Google Reviews via web 
scraping compliant with Terms of Service, filtered for English-
language content, minimum 50 characters, and verified 
accounts. Extracted data included review text, numeric 
ratings (1-5), visit dates, and reviewer profiles. Site-level 
aggregation computed sentiment scores using VADER, 
experiential themes via BERT-based topic modeling 
(authenticity, educational value, immersion, satisfaction), and 
technology readiness proxies through linguistic complexity 
(Flesch-Kincaid Grade Level) and technology-term density. 
Source 3: Tourism Statistics. UNWTO and World Bank 
databases supplied visitor arrivals, tourism receipts, and 
infrastructure indices as control variables. 
Source 4: AI Specifications. Institutional records documented 
deployment dates, technology categories, and maturity levels 
for implementation validation. 
Quality assurance: Data triangulation across sources ensured 
validity. Multivariate outlier detection using the Mahalanobis 
distance (α = 0.001) removed extreme cases. Missing data 
analysis revealed <3% missingness, addressed through 
listwise deletion. This research utilized publicly available 
secondary data, which received an IRB exemption (Category 
4); all data were de-identified, and excerpts were 
paraphrased. 

2.3 Variable operationalization 
Variable selection was guided by established heritage 

tourism literature to capture both technological 
implementation and experiential outcomes. AI Conservation 
Factors measured deployment intensity across digital 
documentation (3D scanning, photogrammetry), intelligent 
monitoring (IoT sensors), predictive maintenance, and virtual 
restoration (0-1 scale; α=0.85). AI Tourism Factors assessed 
VR/AR interpretation, recommendations, chatbots, and 
visitor management (0-1 scale; α=0.82). Two mediating 
variables were extracted from visitor reviews through BERT 
thematic analysis: Perceived Heritage Value, which measured 
authenticity, educational value, and cultural significance (α = 
0.88); and Immersive Experience Quality, which captured 
presence, engagement, arousal, and memorability (α = 0.86). 
The dependent variable, Tourist Experience Quality, 
integrated VADER sentiment scores, z-standardized ratings, 
and behavioral intentions: TEQ=(sentiment_z + rating_z + 
behavioral_z)/3 (α=0.92). Technology Readiness, the 
moderator, combined linguistic complexity and technology-
term density, median-split into high-readiness (n=88, 
M=4.15, SD=0.49) and low-readiness groups (n=112, M=2.81, 
SD=0.54). Control variables included site type, location, log-
transformed visitor volume, site age, and year indicators. All 
continuous variables were z-standardized before analysis. 

 



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2.4 Analytical methods 
Analysis proceeded through four steps (Figure 1). 

Step 1: Preprocessing. NLP pipeline (Python 3.9 with NLTK 
and spaCy) performed tokenization, cleaning, and 
lemmatization. VADER computed sentiment scores, selected 
for its calibration on review text. BERT contextual 
embeddings extracted thematic content through transfer 
learning. Review-level measures were aggregated to site-level 
means. 
Step 2: Measurement Model. Confirmatory Factor Analysis 
(AMOS 26.0, maximum likelihood) evaluated fit using 
multiple indices (χ²/df<3.0, CFI/TLI>0.90, RMSEA<0.08, 
SRMR<0.08), as no single index is sufficient. Items with 
loadings <0.60 were removed following scale refinement 
guidelines. 
Step 3: Structural Model. SEM tested hypotheses using 
maximum likelihood with bootstrap standard errors (5,000 
resamples). Direct effects were assessed via path coefficients 
and 95% bias-corrected confidence intervals. Mediation was 
tested using Preacher-Hayes bootstrapping, which was 
selected for its higher statistical power compared to Baron-
Kenny causal steps. Moderation was employed using multi-
group SEM, comparing high/low technology readiness groups 
via invariance testing and χ² difference tests, chosen to 
examine whether the entire model structure differs across 
segments. 
Step 4: Robustness Checks. Alternative specifications (SDL-
only, TAM, direct-effects-only, full-mediation) were 
compared via information criteria. Temporal stability was 
assessed across the period from 2020 to 2024. Random forest 
regression with cross-validation validated feature 
importance rankings and predictive accuracy. 

STEP 1: PREPROCESSING
 NLP pipeline and data aggregation 

Sentiment and thematic extraction

STEP 2: MEASUREMENT MODEL               

Confirmatory factor analysis

Reliability and validity assessment

STEP 3: STRUCTURAL MODEL    

Direct and mediation effects

Multi-group moderation analysis          

STEP 4: ROBUSTNESS CHECKS
   Alternative model comparison

Temporal stability and validation

MODEL RESULTS

 

Figure 1. Four-step structural equation modeling analytical 
procedure 

2.5 Reliability and validity 

Measurement quality was assessed through multiple 
validation procedures. Reliability was evaluated using 
Cronbach's α and composite reliability (CR), with thresholds 

α, CR≥0.70 considered acceptable. Convergent validity 
required factor loadings exceeding 0.60 and average variance 

extracted (AVE) ≥0.50. Discriminant validity was assessed 
using the Fornell-Larcker criterion and the Heterotrait-
Monotrait ratio (HTMT<0.85), with HTMT providing a more 
conservative test. Common method variance was examined 
through Harman's single-factor test, common method factor 
analysis, and marker variable technique, as single-source 
review data require multiple diagnostic approaches. Inter-
rater reliability for AI implementation coding and NLP 
measures were validated against manual coding. 

3. Results 

3.1 Descriptive statistics and preliminary analysis 
Table 1 presents sample characteristics and descriptive 

statistics for 200 UNESCO World Heritage Sites. Panel A 
shows geographical distribution (40.5% Europe, 27.0% 
Asia/Pacific, 18.5% Americas, 14.0% Africa/Middle East) and 
heritage types (67% cultural, 21% natural, 12% mixed) 
consistent with UNESCO's global distribution, supporting 
sample representativeness. AI technology adoption varied: 
49.5% moderate implementation, 23.0% advanced, and 
27.5% limited, providing sufficient variation for hypothesis 
testing. Visitor volume distribution was balanced (34.5% 
high, 39.5% medium, 26.0% low). Panel B descriptive 
statistics confirm valid distributional assumptions. The 
quality of the tourist experience showed the highest mean 
(M=4.09, SD=0.79), indicating overall positive visitor 
experiences. AI Tourism Factors (M=3.64) exceeded AI 
Conservation Factors (M=3.38), suggesting tourism 
applications have greater implementation maturity. 
Predictive Analytics Applications showed the lowest mean 
(M=2.89, SD=1.08) and highest variance, reflecting nascent 
adoption. All variables exhibited acceptable skewness (±2) 

and kurtosis (±7), supporting the use of parametric analysis. 

Panel C correlation analysis revealed theoretically consistent 
patterns. AI Tourism Factors showed stronger correlation 
with Tourist Experience Quality (r=0.61, p<0.01) than AI 
Conservation Factors (r=0.52, p<0.01), suggesting differential 
experiential impacts. Mediating variables demonstrated 
strong correlations: Perceived Heritage Value (r=0.73, 
p<0.01) and Immersive Experience (r=0.69, p<0.01) with 
Tourist Experience Quality. All Variance Inflation Factors 
ranged between 1.18 and 2.87, which is well below the 
threshold of 3.0, indicating that multicollinearity is not a 
concern. 

3.2 Measurement model assessment 
Confirmatory factor analysis was used to evaluate the 

measurement model fit (Table 2). The initial 36-item model 
showed poor fit; modification indices identified four items 
with factor loadings ≤0.60, which were removed sequentially. 

The refined 32-item model demonstrated acceptable fit: χ²

(476)=982.54, p<0.001; χ²/df=2.06; CFI=0.92; TLI=0.91; 
RMSEA=0.058 (90% CI: 0.052-0.064); SRMR=0.062. Although 
chi-square was significant due to sample size (N=200) and 
model complexity, all incremental and absolute fit indices met 
recommended thresholds, supporting measurement model 
adequacy. Reliability and validity assessments confirmed 
measurement quality (Table 2). Cronbach's alpha (0.78-0.92) 
and composite reliability (0.80-0.93) exceeded the 0.70 
threshold across all constructs, demonstrating adequate 
internal consistency. The average variance extracted ranged 
from 0.50 to 0.71, exceeding the 0.50 threshold and thus 
establishing convergent validity. Factor loadings ranged from 
0.68 to 0.89 (all p<0.001), indicating strong item-construct 
relationships. 

 



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Table 1 Sample Characteristics and descriptive statistics (N = 200 heritage sites) 

Panel A: Sample characteristics 

Characteristic Category n % 

Site Typology Cultural heritage 134 67.0 

 Natural heritage 42 21.0 

 Mixed heritage 24 12.0 

Geographic Region Europe 81 40.5 

 Asia-Pacific 54 27.0 

 Americas 37 18.5 

 Africa & Middle East 28 14.0 

Visitor Volume (Annual) High (>1 million) 69 34.5 

 Medium (500k-1M) 79 39.5 

 Low (<500k) 52 26.0 

AI Technology Adoption Advanced implementation 46 23.0 

 Moderate implementation 99 49.5 

 Limited implementation 55 27.5 

Data Coverage Total reviews analyzed 52,847 — 

 Reviews per site (median) 248 — 

 Date range 2020-2024 — 

 

Panel B: Descriptive statistics 

Variable M SD Min Max Ske Kur 

1. AI Conservation Factors 3.38 0.91 1.20 5.00 -0.15 -0.58 

2. AI Tourism Factors 3.64 0.98 1.40 5.00 -0.31 -0.42 

3. Smart Tourism Infrastructure 3.31 0.86 1.30 5.00 -0.08 -0.67 

4. Predictive Analytics Applications 2.89 1.08 1.00 5.00 0.28 -0.89 

5. Tourist Experience Quality 4.09 0.79 2.10 5.00 -0.87 0.64 

6. Perceived Heritage Value 3.92 0.71 2.00 5.00 -0.53 0.18 

7. Immersive Experience Quality 3.69 0.88 1.70 5.00 -0.38 -0.31 

8. Visitor Volume (natural log) 13.19 1.22 10.65 16.12 0.11 -0.52 

 

Panel C: Correlation matrix and multicollinearity diagnostics 

Variable 1 2 3 4 5 6 7 8 VIF 

1. AI Conservation —        1.94 

2. AI Tourism 0.56** —       2.18 

3. Smart Infrastructure 0.67** 0.61** —      2.87 

4. Predictive Analytics 0.48** 0.51** 0.57** —     1.72 

5. TEQ 0.52** 0.61** 0.39** 0.34** —    — 

6. Heritage Value 0.49** 0.54** 0.37** 0.31** 0.73** —   2.06 

7. Immersive Experience 0.43** 0.64** 0.46** 0.38** 0.69** 0.58** —  1.98 

8. Visitor Volume (log) 0.19* 0.26** 0.34** 0.15* 0.32** 0.28** 0.24** — 1.18 

Note: N = 200 UNESCO World Heritage Sites based on aggregated data from 52,847 visitor reviews (2020-2024). All constructs were measured 

on 5-point Likert scales, except Visitor Volume (natural log transformation applied). SKE:Skewness. Kur:Kurtosis. TEQ = Tourist Experience 

Quality. VIF = Variance Inflation Factor; all values below the threshold of 3.0 indicate acceptable levels of multicollinearity. Skewness and 

kurtosis values within acceptable ranges (±2 for skewness, ±7 for kurtosis) suggest approximately normal distributions suitable for parametric 

analyses. *p < .05. **p < .01 (two-tailed tests). 



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Table 2. Measurement model: reliability and validity assessment 
(N=200) 

Construct 
No. of 
Items 

Cronbach's 
α 

CR AVE 
Factor 

Loading 
Range 

AI Conservation Factors 5 0.87 0.88 0.60 0.72-0.84 

AI Tourism Factors 4 0.89 0.90 0.66 0.78-0.86 

Smart Tourism 
Infrastructure 

5 0.84 0.85 0.54 0.69-0.78 

Predictive Analytics 
Applications 

4 0.78 0.80 0.50 0.68-0.74 

Tourist Experience 
Quality 

5 0.92 0.93 0.71 0.79-0.89 

Perceived Heritage Value 4 0.88 0.89 0.62 0.76-0.83 

Immersive Experience 
Quality 

5 0.86 0.87 0.57 0.69-0.81 

Note: N=200 heritage sites. All factor loadings are significant at 
p<0.001. CR=Composite Reliability; AVE=Average Variance 
Extracted. The initial model included 36 items; 4 items with loadings 
below 0.60 were removed. Model fit: χ²(476)=982.54, p<0.001; 
χ²/df=2.06; CFI=0.92; TLI=0.91; RMSEA=0.058 (90% CI: 0.052-
0.064); SRMR=0.062. All constructs demonstrate adequate reliability 
(α, CR>0.70) and convergent validity (AVE>0.50).uate reliability (α > 
0.70, CR > 0.70) and convergent validity (AVE > 0.50). 

3.3 Structural model and hypothesis testing 
The structural model demonstrated acceptable fit (Table 

3, Figure 2, and Figure 3): χ²(476)=982.54, p<0.001; χ²
/df=2.06; CFI=0.92; TLI=0.91; RMSEA=0.058 (90% CI: 0.052-
0.064); SRMR=0.062. All fit indices met recommended 
thresholds, supporting hypothesis testing validity. AI 
Tourism Factors exerted stronger direct effects on Tourist 
Experience Quality (β=0.34, p<0.001, H2 supported) than AI 
Conservation Factors (β=0.21, p=0.004, H1 supported), 
indicating tourism applications have more immediate 
experiential impacts. AI Conservation Factors significantly 
predicted Perceived Heritage Value (β=0.45, p<0.001, H3b), 
which strongly influenced experience quality (β=0.51, 
p<0.001, H3a), yielding significant indirect effects (β=0.23, 
95% CI [0.16, 0.31], H3 supported). AI Tourism Factors 
strongly predicted Immersive Experience Quality (β=0.58, 
p<0.001, H4b), which influenced experience quality (β=0.36, 
p<0.001, H4a), producing significant indirect effects (β=0.21, 
95% CI [0.14, 0.28], H4 supported). The Heritage Value 
pathway (β=0.51) exceeded the Immersive Experience 
pathway (β=0.36), suggesting cognitive-emotional 
appreciation exerts greater influence than sensory 
immersion. Total effects of AI Tourism Factors (β=0.55) 
exceeded AI Conservation Factors (β=0.44). The model 
explained substantial variance: Heritage Value R²=0.31, 
Immersive Experience R²=0.44, Tourist Experience Quality 
R²=0.59. Visitor Volume showed minimal influence (β=0.12, 
p=0.042). 

3.4 Moderation analysis 
Multi-group structural equation modeling examined 

whether technology readiness moderates AI-experience 

relationships (Table 4). Technology readiness groups were 
formed through median split of composite review 
sophistication scores (Mdn=3.45): high-readiness visitors 
(n=88) versus low-readiness visitors (n=112). Configural 
invariance was established, confirming identical model 
structure across groups, followed by metric invariance testing 
to ensure equivalent measurement properties. 

 

AI Conservation

Factors

Perceived

Heritage Value

Al Tourism

Factors

Immersive

Experience Quality

Tourist Experience

Quality

Technology Readiness

(Moderator)

Visitor Volume

(Control Variable)

H5: Moderates

Al  TEQ paths

H1 H2

H3a H4a

H4bH3b

 

Figure 2. Conceptual model of AI applications in heritage tourism 

 

Al Conservation

Factors

Al Tourism

Factors

Perceived

Heritage Value

R2= 0.31

Immersive

Experience

R2= 0.44

Tourist Experience

Quality

R2= 0.59

Visitor Volume

0.45*** 0.58***

0.51*** 0.36***

0.34***0.21***

0.12**

 

Figure 3. Structural model results with standardized path 
coefficients 

Technology readiness significantly moderated both 
direct effects pathways (H5 supported). For AI Conservation 
Factors, the direct effect on Tourist Experience Quality was 
substantially stronger among high-readiness visitors (β=0.35, 
p<0.001) compared to low-readiness visitors (β=0.10, 
p>0.05), with a chi-square difference test confirming 
significant moderation (Δχ²=8.12, df=1, p=0.004, effect size 
Δβ=0.25). Correspondingly, AI Tourism Factors had more 
influential power on high-readiness participants (β=0.47, 
p<0.001) than on low-readiness participants (β=0.23, 
p=0.002), indicating a significant moderation effect 
(Δχ²=9.87, df=1, p=0.002, Δβ=0.24).  

 



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These findings indicate that more technologically 

advanced visitors are more aware of and value AI-aided 
improvements, as they possess superior technology literacy 
that enables them to better exploit and comprehend these 
advancements. By conducting mediation pathway analysis, 
complex patterns of moderation emerged. Findings showed 
that technology readiness was not a significant moderator on 
pathways AI Conservation → Heritage Value (Δχ²=0.18, 

p<0.671) and Heritage Value → TEQ (Δχ²=0.52, p<0.471), 
meaning that heritage value appreciation channels. However, 
the AI Tourism→Immersive Experience pathway showed 

marginally significant moderation (Δχ²=3.12, df=1, p=0.077, 
Δβ=0.10), with high-readiness visitors experiencing stronger 
immersion effects (β=0.63 vs 0.53). Total effects confirmed 
technology readiness amplifies overall AI influence: high-
readiness visitors showed substantially stronger total effects 
for both AI Conservation (β=0.57 vs 0.34, Δχ²=8.84, p=0.003) 

and AI Tourism (β=0.68 vs 0.44, Δχ²=10.92, p<0.001). 

3.5 Robustness checks 
Robustness checks validated the integrated TOE-SDL 

framework through alternative model comparison and 
temporal stability analysis. Model comparison assessed four 
competing specifications (Table 5). The baseline model 
demonstrated superior performance across all fit criteria. 
Service-Dominant Logic alone (Alternative 1) yielded 
substantially worse fit (χ²/df=2.41, CFI=0.883, 
RMSEA=0.073) and lower variance explained (R²=0.421 vs 

0.487), suggesting technology adoption factors are essential 
beyond service co-creation mechanisms.  

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Technology Acceptance Model (Alternative 2) performed 

even more poorly (χ²/df=2.67, CFI=0.859, R²=0.368), 
indicating that traditional acceptance constructs 
inadequately capture AI's multi-dimensional nature in 
heritage contexts. 

Structural alternatives revealed the necessity of both 
direct and mediated pathways. The direct-effects-only model 
(Alternative 3) exhibited poor fit (χ²/df=3.12, CFI=0.821, 
RMSEA=0.094, R²=0.314), demonstrating that psychological 
mediators are critical mechanisms. The full-mediation model 
(Alternative 4) showed acceptable fit but lower variance (R²
=0.453), confirming AI exerts both direct and indirect effects. 
The baseline model's superior performance (RMSEA=0.058, R
²=0.487) validates the integrated approach. 

Temporal stability analysis across 2020-2024 confirmed 
robust relationships (Figure 4). Heritage Value→TEQ 
remained highly stable (β=0.49-0.53, all p<0.001), 
demonstrating heritage appreciation operates consistently 
across time. Tourism-oriented pathways strengthened from 
2020 (AI Tourism→TEQ β=0.30; Immersive→TEQ β=0.32) to 
2024 (β=0.40; β=0.41), reflecting increasing AI sophistication 
and visitor familiarity. AI Conservation→TEQ exhibited 

short-term fluctuation in 2021 (β=0.17) due to COVID-19 
disruptions but stabilized by 2024 (β=0.26). All 2024 paths 
remained significant (p<0.001), confirming the model's 
validity across different technological and operational 
contexts. 

 

 

 

Table 3. Hypothesis testing results (N = 200 heritage sites) 

 Hypothesis Path β SE CR p 95% CI Result 

Direct Effects 

H1 AI Conservation → TEQ 0.21 0.072 2.92 0.004 
[0.07, 
0.35] 

Supported 

H2 AI Tourism → TEQ 0.34 0.058 5.86 <0.001 
[0.23, 
0.45] 

Supported 

Mediation Paths 

H3a Heritage Value → TEQ 0.51 0.055 9.27 <0.001 
[0.40, 
0.62] 

Supported 

H3b AI Conservation → Heritage Value 0.45 0.064 7.03 <0.001 
[0.32, 
0.58] 

Supported 

H4a Immersive Experience → TEQ 0.36 0.058 6.21 <0.001 
[0.25, 
0.47] 

Supported 

H4b AI Tourism → Immersive Experience 0.58 0.049 11.84 <0.001 
[0.48, 
0.68] 

Supported 

Indirect Effects (Mediation) 

H3 
AI Conservation → Heritage Value → 

TEQ 
0.23 0.038 6.05 <0.001 

[0.16, 
0.31] 

Supported 

H4 AI Tourism → Immersive Exp → TEQ 0.21 0.038 5.53 <0.001 
[0.14, 
0.28] 

Supported 

Total Effects 

 AI Conservation → TEQ (total) 0.44 0.062 7.10 <0.001 
[0.32, 
0.56] 

— 

 AI Tourism → TEQ (total) 0.55 0.052 10.58 <0.001 
[0.45, 
0.65] 

— 

Control Variable 
 Visitor Volume → TEQ 0.12 0.059 2.03 0.042 

[0.00, 
0.24] 

— 

Variance Explained 

 R² for Heritage Value 0.31 — — <0.001 — — 

 R² for Immersive Experience 0.44 — — <0.001 — — 

 R² for TEQ 0.59 — — <0.001 — — 

Note: N = 200 heritage sites. β = standardized path coefficient; SE = standard error; CR = critical ratio; CI = confidence interval (bias-corrected bootstrap 

with 5,000 samples). Indirect effects were tested using a bootstrapping procedure. Model fit indices: χ² (df = 476) = 982.54, p < .001; χ²/df = 2.06; CFI 
= 0.92; TLI = 0.91; RMSEA = 0.058 (90% CI: 0.052-0.064); SRMR = 0.062. All hypotheses were supported at conventional significance levels. *p 

<0.05. **p < 0.01. ***p < 0.001 

 

 



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Table 4 Multi-group comparison: technology readiness as moderator (N = 200) 

 

Path 

High Tech 
Readiness 

(n = 88) 

Low Tech 
Readiness 

(n = 112) 

Δχ2 

(df = 1) 
p 

Effect Size 
(Δβ) 

Moderation 

Direct Paths to 
TEQ 

AI Conservation → TEQ 0.35*** 0.10 8.12 0.004 0.25 Significant 

AI Tourism → TEQ 0.47*** 0.23** 9.87 0.002 0.24 Significant 

Mediation Paths 

AI Conservation → 
Heritage 

0.46*** 0.44*** 0.18 0.671 0.02 Not significant 

Heritage → TEQ 0.48*** 0.54*** 0.52 0.471 −0.06 Not significant 

AI Tourism → Immersive 0.63*** 0.53*** 3.12 0.077 0.10 Marginally significant 

Immersive → TEQ 0.33*** 0.39*** 0.58 0.446 −0.06 Not significant 

Control Variable Visitor Volume → TEQ 0.08 0.15* 0.68 0.410 −0.07 Not significant 

Indirect Effects 

AI Cons → Heritage → 
TEQ 

0.22*** 0.24*** 0.24 0.624 −0.02 Not significant 

AI Tour → Immersive → 
TEQ 

0.21*** 0.21*** 0.01 0.920 0.00 Not significant 

Total Effects 

AI Conservation → TEQ 
(total) 

0.57*** 0.34*** 8.84 0.003 0.23 Significant 

AI Tourism → TEQ (total) 0.68*** 0.44*** 10.92 <0.001 0.24 Significant 

Note: N = 200 heritage sites. Multi-group structural equation modeling using maximum likelihood estimation. Technology Readiness groups formed 
through median split of composite review sophistication scores (Mdn = 3.45): High (n = 88, M = 4.15, SD = 0.49); Low (n = 112, M = 2.81, SD = 0.54). 
Δχ² tests performed by fixing constraining paths to equality between groups and testing nested models. Effect size (Δβ) is the absolute difference in 
standardized coefficients between groups. H5 supported: Technology Readiness strongly moderates direct AI-TEQ relations. Interestingly, AI 
Tourism → Immersive pathway has marginally significant moderation (p = 0.077), indicating technology-oriented travelers may be especially 
sensitive to immersion enhancement enabled by AI. Indirect effects are relatively stable, favoring universal mediation processes. Configural model 
fit indices provide acceptable multi-group model quality. *p < 0.05. **p < 0.01. ***p < 0.001. 
 

Table 5. Alternative model specifications comparison (N = 200 heritage sites) 

Model Specification χ²/df CFI TLI RMSEA (90% CI) SRMR R² (TEQ) 

Baseline Model (TOE Framework) 2.06 0.921 0.908 0.058 (0.052-0.064) 0.067 0.487 

Alternative 1: Service-Dominant 
Logic 

2.41 0.883 0.871 0.073 (0.066-0.080) 0.079 0.421 

Alternative 2: Technology Acceptance 
Model 

2.67 0.859 0.843 0.081 (0.074-0.088) 0.086 0.368 

Alternative 3: Direct Effects Only 3.12 0.821 0.801 0.094 (0.087-0.101) 0.103 0.314 

Alternative 4: Full Mediation Model 2.23 0.897 0.886 0.065 (0.058-0.072) 0.072 0.453 

Note: N = 200 heritage sites. All fit by maximum likelihood with bootstrapping (5,000 samples). The baseline model, with bold values, is more stable. 

The baseline model has the best fit between explanatory power and parsimony. CFI/TLI > 0.90 and RMSEA < 0.06 indicate excellent fit; RMSEA < 

0.08 indicates acceptable fit. 90% confidence intervals for RMSEA are reported in parentheses following APA guidelines. χ²/df < 3.0 indicates a good 

fit. R² represents the variance explained in the quality of the tourist experience. 

 



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166 

 

 

Figure 4. Temporal Stability of Structural Paths with 95% Confidence 
Intervals (Note: Point estimates with bootstrapped 95% confidence 
intervals (5,000 resamples). The AI Conservation path exhibits short-
term fluctuations in 2023 (β=0.219), due to technology upgrade 

changes at some sites, and stabilizes in 2024 (β=0.256). Tourism-
oriented AI systems had consistent performance across time. All 2024 
paths are significant at p < 0.001. N = 200 sites.) 

4. Discussion 

This study suggests that AI technology enhances the 
quality of the tourist experience through two distinct 
psychological channels. Meanwhile, the effect of AI 
conservation variables (β=0.21, p<0.01) on quality, as well 

as the effect of AI tourism variables (β=0.34, p<0.001), is 
significant, with conservation technology working through 
the cognitive-emotional appreciation of heritage (β=0.51, 
p<0.001) and tourism technology working through 
immersion (β=0.36, p<0.001). Conservation-oriented AI 
enhances experience quality primarily through heritage value 
appreciation rather than direct sensory engagement, 
supporting cognitive-emotional processing theories in 
heritage tourism [9-11]. The comparatively weak direct effect 
of conservation technology (β=0.21) relative to tourism 

technology (β=0.34) is explained by three theoretical 
considerations: visibility asymmetry (the backend system of 
conservation remains unseen by tourists), the problem of 
temporal discounting (the benefits from conservation are 
seen only in the long run), and complexity of evaluation (the 
lack of technical knowledge on the part of tourists to evaluate 
conservation technologies). This result contributes to the 
body of literature on preservation, demonstrating that 
advanced conservation technologies are associated with 
increased perceptions of authenticity on the one hand. 

Tourism-oriented AI enhances experience quality 
through immersive engagement (β=0.36, p< 0.001), 
advancing beyond technology acceptance frameworks [11, 
12] by identifying immersion as the value-creation 
mechanism. Temporal analysis reveals tourism technology 
effects strengthened from 2022 to 2024 (β=0.34→0.39), 
reflecting technological maturation and visitor familiarity, 
while conservation infrastructure experienced temporary 
disruption in 2023 (β=0.17) during system upgrades. These 
differential resilience patterns suggest staggered 
implementation strategies prioritizing visitor-facing systems 
during peak periods while scheduling backend infrastructure 
changes during off-peak seasons. Heritage value's stronger 
influence (β=0.51) than immersive experience (β=0.36) 
demonstrates that cognitive-emotional cultural appreciation 
exceeds technological immersion in driving satisfaction, 

consistent with authenticity primacy theories [13, 14]. The 
integrated TOE-SDL model performed better than the SDL-
only model and the TAM model, with R² values of 0.49, 0.42, 
and 0.37, respectively, confirming the importance of taking all 
technology, organization, and service aspects of co-creation 
into consideration, unlike other models. Technology 
readiness is an important moderator with strong direct effect 
values for AI (Δβ = 0.24 to 0.25), with superior readiness 
tourists exhibiting 2 to 3 times larger responses to 
conservation & tourism technology. Most importantly, the 
paths for appreciation of heritage value are identical across 
levels of readiness, indicating that AI is essentially non-
obstructive or facilitative for the appreciation of culture, with 
advanced tourists exhibiting better interactions, but the basic 
construct is readily available. 

This study acknowledges three limitations. First, reliance 
on visitor review data enables large-scale analysis but limits 
causal inference; future experimental studies manipulating AI 
features could establish causality [15, 16]. Second, a cross-
sectional design cannot capture implementation dynamics, 
though temporal robustness checks partially address this; 
longitudinal tracking of AI adoption across multiple sites 
would strengthen conclusions. Third, UNESCO site sampling 
may limit generalizability to sites with lower institutional 
capacity [17, 18]. Future research should investigate heritage 
type moderators (cultural vs. natural sites), explore the use of 
generative AI to enable personalized narratives, examine the 
risks of technology dependency and the implications of digital 
inequality, and conduct field experiments testing optimal AI 
configuration strategies across diverse heritage contexts [19, 
20]. 

5. Conclusion 

This research demonstrates that AI technologies 
enhance the quality of the tourist experience through two 
pathways at cultural heritage sites. The integrated TOE-SDL 
framework (R²=0.59) outperformed alternative 
specifications, revealing that tourism-oriented AI (β=0.34) 
exerts a stronger direct effect than conservation-oriented AI 
(β=0.21), with conservation operating through heritage 
appreciation (β=0.51) and tourism through immersion 
(β=0.36). Technology readiness moderates direct effects 
(Δβ=0.24-0.25), yet heritage appreciation remains universal. 
Findings suggest phased implementation, prioritizing 
tourism applications while developing conservation 
infrastructure. Future research should investigate heritage 
type moderators, generative AI applications, and conduct 
longitudinal studies to strengthen causal claims.  

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

 

 



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167 

 

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(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://doi.org/10.48550/arXiv.2304.10545
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