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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
Rethinking Smart Tourism: Why Sustainability and Mobility Matter
More Than Technology in Tourist Satisfaction
Haslinda Musa
1
, Sitinor Wardatulaina Mohd Yusof
1
, Nurulizwa Abdul Rashid
1
, Samer Ali Hussein Al-
Shami
1
, Mohd Nasar Othman
2
,
Nur Zafirah A Khadar
3
1
Universiti Teknikal Malaysia Melaka
2
NMS Legacy Sdn Bhd. Terengganu
3
ERADA Solutions Sdn Bhd. Ayer Keroh Melaka
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.923MIC3ST250031
Received: 12 September 2025; Accepted: 20 September 2025; Published: 24 October 2025
ABSTRACT
In an era of global smart cities, digital infrastructure, sustainability and mobility is becoming an important part
of tourism planning in order to improve visitor experiences. Yet, empirical evidence is ambivalent, which
elements underlie tourist destination satisfaction to the greatest extent. This study explores the influence that
Technology Integration, Sustainability Practices, and Mobility & Transportation Solutions have directly and
indirectly via Experience Quality on Tourist Satisfaction, moderated by Cultural Orientation. Applying Partial
Least Squares Structural Equation Modelling (PLS-SEM) on survey responses from 346 tourists who have
experienced smart city services, we find that while Sustainability Practices and Mobility Solutions positively
contribute to satisfaction, Technology Integration and Experience Quality do not contribute to satisfaction.
Additionally, EQ is not a mediator, whereas CO does not moderate any of the analyzed relationships. These
results question traditional models of technology-led tourism and point to a shift in preferences and satisfaction
drivers for urban travel based on use and sustainability. The results of the study are applicable to policymakers,
tourism planners and urban developers who want to create traveler-friendly smart city spaces.
Keywords: Smart Tourism, Smart Cities, Tourist Satisfaction, Sustainability Practices, Mobility Solutions
INTRODUCTION
This emerging concept is a product of the increasing integration of digital innovation, sustainable urban
development and experiential consumerism, bringing together the paradigm of smart city infrastructure and the
practice of tourism experience design: smart tourism. With the rapid process of globalization, urbanization, and
municipalities throughout the world spending more on technology to control the flow of tourists, the model of
“smart cities” has shifted from a techno-freak-dream to an integrated system that uses ICT (Information and
Communication Technologies), sustainability, mobility, and cultural inclusivity, not just as tools to improve both
urban life and tourist satisfaction (Gretzel et al., 2022, p. 4).
During this transition, all the same, tourist satisfaction is vital to the success of smart tourism and shapes
destination loyalty, social media advocacy, and economic benefits (Xu & Gursoy, 2022). However, theoretical
understanding of how different elements of smart cities affect tourist satisfaction is fragmentary and conflicting,
in a single framework, in particular. Although earlier research es has emphasized the importance of technology
integration to enrich tourist experiences (Neuhofer et al., 2015; Shin et al., 2022), recent evidence has signalled
the saturation effect, wherein improved digital elements are perceived as a hygiene factor rather than an attractive
feature (Kang et al., 2021). Meanwhile, the infrastructure-driven factors like mobility and sustainability could
have a growing influence over the perception and satisfaction (Choe et al., 2021; Yoo et al., 2021).
Problem Statement
Despite the increasing expenditure towards smart city projects worldwide, there is little understanding of which
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elements of the smart city could most effectively benefit tourist satisfaction. Most of the current literature takes
a technology-centric approach that views digital transformation as patently improving tourism experience.
However, there is evidence that tourists have started to take digital tools for granted, making them less satisfying
and even dissatisfying if a tool is not deemed very useful, personalized, or part of a broader experience of the
city (Shin et al., 2022). This ‘mismatch’ between technological input and perceived value represents a strategic
challenge for municipality planner and tourism stakeholders.
Furthermore, the experiential dimension, which has traditionally mediated the relationship between services and
satisfaction, may be undergoing a paradigm shift. In modern urban environments especially, those branded as
"smart" tourists may increasingly value efficiency, safety, and sustainability over immersive or emotionally
engaging experiences (Xu & Gursoy, 2022). However, this theoretical shift remains empirically underexplored,
particularly in models that include both direct and mediated pathways.
In the broader context, this knowledge gap complicates the ability of policymakers to allocate resources
effectively. Should investments prioritize mobility infrastructure, green initiatives, or digital services? Without
clear empirical direction, cities risk deploying costly smart solutions that fail to meaningfully enhance
satisfaction. Additionally, the role of cultural orientation as a moderating factor though widely acknowledged in
tourism has rarely been tested within the context of smart tourism models, leaving further ambiguity about how
demographic and cultural differences shape satisfaction outcomes.
Geographically and methodologically, there is also a lack of quantitative, model-based research that
simultaneously tests multiple constructs from smart city theory using advanced SEM techniques, especially with
youth-dominated samples who represent tech-savvy but increasingly eco-conscious cohorts (Choe et al., 2021).
These tourists may redefine the dimensions of satisfaction in ways not yet captured in legacy models.
Therefore, the significance of this problem lies in its strategic implications for urban governance, tourism
planning, and resource prioritization. Understanding how smart city components influence tourist satisfaction
directly, indirectly, or not at all can shape future investment decisions, policy alignment with UN Sustainable
Development Goals, and the design of inclusive, visitor-centric urban environments.
Research Objectives
In response to the outlined problem, this study aims to provide a comprehensive empirical assessment of how
smart city innovations influence tourist satisfaction, using a Partial Least Squares Structural Equation Modelling
(PLS-SEM) approach. Specifically, it seeks to:
1)
Examine the direct impact of three key smart city components Technology Integration, Sustainability
Practices, and Mobility & Transportation Solutions on Tourist Satisfaction.
2)
Investigate the role of Experience Quality as a mediating variable between smart city components and
satisfaction.
3)
Test the moderating role of Cultural Orientation in influencing the strength of relationships between smart
city components and satisfaction outcomes.
4)
Identify which factors most strongly predict satisfaction, offering strategic insights into where cities should
concentrate their smart tourism investments.
By addressing these objectives, the study aims to fill theoretical gaps, challenge outdated assumptions, and
inform practical policy in the design of future-ready, traveler-centric smart urban environments
LITERATURE REVIEW
The dynamic and competitive nature of the e-commerce industry demands that digital platforms offer not only
transactional efficiency but also highly optimized user experiences. Contemporary research in e-commerce and
information systems has begun shifting from adoption-focused models to investigations into post-adoption user
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behavior, satisfaction, and platform perception. This study contributes to that shift by investigating how key
platform characteristics loading time difference, features of e-commerce, dynamic pricing strategies, and website
usability affect user satisfaction and, subsequently, platform popularity, with device type introduced as a
moderator. Each construct is reviewed below considering relevant literature.
Smart Tourism and Smart City Integration
Smart tourism has emerged as a strategic evolution of urban development, integrating smart city infrastructure
with tourism experience design to enhance personalization, accessibility, and sustainability (Gretzel et al., 2022).
Smart cities provide the digital backbone through ICT, IoT, and big data that enables responsive and efficient
tourism services (Buhalis & Amaranggana, 2015). However, the success of smart tourism depends not only on
technology adoption but also on tourists' perceptions of sustainability, mobility, cultural richness, and service
experience.
Smart tourism frameworks such as the Smart Tourism Ecosystem (STE) model emphasize the interplay of
technological, socio-cultural, and experiential elements in shaping tourist behavior (Sigala, 2019). Yet, while
earlier studies highlighted the central role of technology, recent evidence suggests that infrastructure
functionality and environmental performance may now exert a stronger influence on satisfaction (Shin et al.,
2022).
Technology Integration and Tourist Satisfaction
Technology Integration in tourism involves tools such as mobile apps, AR/VR, smart kiosks, and automated
services that enable efficient travel planning, navigation, and service access (Neuhofer et al., 2015). Past studies
show that when perceived as useful and innovative, technology enhances both experience quality and satisfaction
(Zhang et al., 2017).
However, more recent research cautions that as these digital tools become ubiquitous, their influence on
satisfaction diminishes unless highly personalized or problem-solving (Kang et al., 2021). Tourists may now
expect digital features as standard infrastructure, thereby reducing their novelty effect (Shin et al., 2022).
Sustainability Practices and Tourist Satisfaction
Sustainability in smart tourism is manifested through eco-transportation, waste reduction, energy efficiency, and
green certifications. It aligns with increasing traveler demand for ethical and low-impact experiences (Choe et
al., 2021). Prior studies confirm that visible sustainability practices can positively influence satisfaction and
brand loyalty, especially among younger and environmentally conscious tourists (Lee et al., 2020).
Sustainable practices also enhance destination image and trust, contributing indirectly to experience quality and
satisfaction (Sigala, 2019; Kock et al., 2020). Smart cities that embed green values into urban services may create
psychological comfort and moral satisfaction among visitors.
Mobility & Transportation Solutions
Urban mobility is a cornerstone of smart city infrastructure and a critical determinant of the tourist experience.
Reliable, clean, and accessible public transportation systems reduce stress, improve accessibility to attractions,
and shape perceived convenience and city livability (Cohen & Gössling, 2015).
Recent findings emphasize that smart mobility solutions (e.g., real-time public transport info, shared micro-
mobility, autonomous shuttles) directly affect perceived ease of movement, which contributes to overall
satisfaction (Yoo et al., 2021). Tourists often value freedom of movement and time-saving benefits over
immersive experiences in unfamiliar urban settings.
Experience Quality and Its Role in Mediation
Experience Quality refers to a visitor's overall cognitive and emotional evaluation of service performance,
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engagement, and cultural immersion (Kang et al., 2021). While traditionally positioned as a key predictor and
mediator of satisfaction, some scholars now argue that tourists especially urban millennial travelers may
prioritize functionality and outcome quality over emotional engagement (Xu & Gursoy, 2022).
This potential decline in mediating power may reflect a shift in satisfaction drivers, from affective to pragmatic
domains, particularly in hyper-digitalized, utility-driven travel contexts.
Cultural Orientation and Moderation
Cultural Orientation is the degree to which a destination promotes authentic, inclusive cultural experiences. It is
often seen as a contextual enhancer of experience quality and satisfaction, especially in multicultural or heritage-
rich settings (Gretzel et al., 2022). However, findings are mixed on whether cultural context moderates the
effectiveness of other smart tourism constructs (Kock et al., 2020). Some studies suggest that cultural exposure
may be less impactful in urban, globally standardized environments, where tourists exhibit cultural
homogenization or digital cultural convergence.
To enhance conceptual clarity and illustrate the hypothesized relationships between the constructs, the proposed
research framework is presented (e.g. Fig. 1). This model integrates three key smart city components Technology
Integration, Sustainability Practices, and Mobility & Transportation Solutions as independent variables expected
to influence Tourist Satisfaction, either directly or indirectly through Experience Quality. Furthermore, Cultural
Orientation is modelled as a moderating variable that may shape the strength of these relationships. The
framework aligns with prior literature on smart tourism ecosystems and aims to test both direct, mediated, and
moderated pathways within a unified PLS-SEM model.
Fig 1. Proposed Research Framework: The Influence of Smart City Components on Tourist Satisfaction.
Hypotheses Development
Based on the above literature, the following hypotheses are proposed.
Direct Effects on Tourist Satisfaction
H1: Technology Integration positively influences Tourist Satisfaction (Neuhofer et al., 2015; Shin et al., 2022).
H2: Sustainability Practices positively influence Tourist Satisfaction (Choe et al., 2021; Lee et al., 2020).
H3: Mobility & Transportation Solutions positively influence Tourist Satisfaction (Yoo et al., 2021; Cohen &
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Gössling, 2015).
Direct Effects on Experience Quality
H4: Technology Integration positively influences Experience Quality (Zhang et al., 2017; Kang et al., 2021).
H5: Sustainability Practices positively influence Experience Quality (Sigala, 2019; Kock et al., 2020).
H6: Mobility & Transportation Solutions positively influence Experience Quality (Yoo et al., 2021; Xu &
Gursoy, 2022).
Mediating Effects of Experience Quality
H7: Experience Quality positively influences Tourist Satisfaction (Füller & Matzler, 2008; Kang et al., 2021).
H8: Experience Quality mediates the relationship between Technology Integration and Tourist Satisfaction.
H9: Experience Quality mediates the relationship between Sustainability Practices and Tourist Satisfaction.
H10: Experience Quality mediates the relationship between Mobility & Transportation Solutions and Tourist
Satisfaction (Sigala, 2019; Xu & Gursoy, 2022).
Moderating Effects of Cultural Orientation
H11: Cultural Orientation moderates the relationship between Experience Quality and Tourist Satisfaction.
H12a: Cultural Orientation moderates the relationship between Technology Integration and Tourist Satisfaction.
H12b: Cultural Orientation moderates the relationship between Sustainability Practices and Tourist Satisfaction.
H12c: Cultural Orientation moderates the relationship between Mobility & Transportation Solutions and Tourist
Satisfaction (Kock et al., 2020; Gretzel et al., 2022).
METHODOLOGY
Research Design
This study proposes a quantitative cross-sectional study with the goals of analyzing the interrelations of key
aspects of smart cities that include Technology Integration, Sustainability Practices, Mobility & Transportation
Solutions, Experience Quality, Cultural Orientation, and Tourist Satisfaction. The use of Partial Least Squares
Structural Equation Modeling (PLS-SEM) was considered the most appropriate methodology given that it is
particularly well-suited as a predictive, explorative approach and can model complex constructs with multiple
paths using reflective (and formative) indicators (Hair et al., 2021).
Sample and Data Collection
Data were collected using an online self-administered survey of both domestic and international tourists who
had recently visited one or more smart city destinations. an instrument in which to have used a smart city digital
services, sustainable transportation or culture experience service in the past year.
The sample was drawn using a non-probabilistic convenience sampling approach, as its appropriateness for
exploratory SEM research with latent constructs is widely acknowledged, especially when probabilistic sampling
is not feasible (Sarstedt et al., 2022). A valid sample of 346 responses was collected. The demographic profile
demonstrated an equal representation of male to female (54.3% female, 45.7% male), with most participants
being 18–24 years old (80.9%), consistent with previous studies suggesting that younger cohorts are more
familiar with and responsive to ST services (Choe et al., 2021).
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Tourists indicated different travel frequency where 63.9% have travelled at least once in six months; thus, the
respondents were mostly moderates in terms of travel frequency and have experience on smart tourism.
Measurement Instrument
All constructs were assessed utilizing multi-item scales with the aid of established literature and were endorsed
on a 5-point Likert-type scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Operationalization Each of
the latent constructs was defined as:
1)
Technology Infusion: Modified from Neuhofer et al. (2015) to ascertain visitors’ understanding of digital
information provision and connectivity (e.g. mobile applications, smart terminals).
2)
Sustainability: By the initiatives of Kock et al. (2020), and eco-supportive interventions (e.g., recycling
programs and green transport).
3)
Mobility & Transportation Solutions: Products related to mobility, public transport and smart navigation
(Cohen & Gössling, 2015).
4)
Service Quality: Assessed by service delivery, cultural enrichment and value for money (Kang et al., 2021).
5)
Culture Orientation: Determined the emphasis given by the destination for culture immersion (Gretzel et al.,
2022). Tourist Satisfaction: Comprised of satisfaction indicators such as expectation confirmation, and revisit
intention (Xu & Gursoy, 2022).
There were four items to measure each construct and pre-test with 20 samples solved that the instrument was
clear and with internal consistency.
Data Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM)
The data were analyzed using SmartPLS 4.0, following a two-step procedure: measurement model assessment
and structural model evaluation (Hair et al., 2021).
Measurement Model Assessment
Reliability and validity of constructs were tested using:
Cronbach’s Alpha and Composite Reliability (CR): All constructs exceeded the minimum 0.6 threshold (range:
0.684–0.864), ensuring acceptable internal consistency.
Average Variance Extracted (AVE): All values were above 0.5, confirming convergent validity.
Outer Loadings: Most indicators had loadings above 0.7, with a minimum of 0.601, aligning with recommended
thresholds (Sarstedt et al., 2022).
Discriminant Validity: Assessed via the Fornell–Larcker criterion, confirming that each construct was
empirically distinct from the others.
Structural Model Evaluation
The structural model’s explanatory power was validated using R² and Q² values, which were:
R² = 0.806 for Experience Quality
R² = 0.819 for Tourist Satisfaction
These indicate strong predictive relevance. The Standardized Root Mean Square Residual (SRMR) = 0.174,
although exceeding the ideal threshold (≤ 0.08), is considered tolerable in exploratory research with complex
models (Henseler et al., 2016).
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Bootstrapping (5,000 resamples) was applied to test hypotheses. Significant paths (p < 0.05) were found from
Sustainability Practices and Mobility & Transportation Solutions to Tourist Satisfaction, while Technology
Integration and Experience Quality were not significant, aligning with recent claims that digital features alone
may not predict satisfaction unless perceived as useful or personalized (Shin et al., 2022).
RESULTS
Respondent Profile
Table I presents the demographic profile of the respondents (N = 346). The gender distribution shows a balanced
sample with 54.3% female and 45.7% male respondents. In terms of age, a significant majority (80.9%) were
between 18 and 24 years old, followed by 14.5% in the 25–30 age range, and 4.6% above 30 years. Regarding
travel frequency, 63.9% of respondents reported traveling once every six months, while 36.1% indicated
traveling at least once per year. This distribution suggests that the sample is largely composed of young and
moderately active travelers, aligning well with the research’s focus on smart city-enabled tourism experiences
Table I Profile Of Respondents
Variable
Category
Frequency (n)
Valid Percentage (%)
Gender
Male
158
45.66
Female
188
54.34
Age Group
18 - 24 years old
280
80.92
25 - 30 years old
50
14.45
Above 30 years old
16
4.62
Travel Frequency
Very frequently (more than 3 times a year)
142
41.04
Occasionally (2-3 times a year)
121
34.97
Rarely (once a year or less)
83
23.99
Table II provides the descriptive statistics for the main constructs used in the study, including their minimum
and maximum scores, mean values, and standard deviations. All constructs were measured on a five-point Likert
scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), and the total sample size was N = 346. Among
the constructs, Tourist Satisfaction reported the highest mean (M = 4.23), indicating that most respondents
expressed strong agreement with satisfaction-related items. Mobility & Transportation and Sustainability
Practices also showed relatively high mean scores (M = 4.16 and M = 4.10 respectively), suggesting favorable
perceptions of public transport and eco-friendly initiatives in smart cities. Technology Integration had the lowest
mean (M = 3.84), reflecting a moderate level of agreement, possibly due to varied exposure or expectations
regarding digital services. Meanwhile, Experience Quality and Cultural Orientation scored means of 4.02
and 3.95 respectively, showing that most respondents perceived these dimensions positively but with slightly
more variability. The standard deviations (SDs) for all constructs ranged from 0.59 to 0.83, indicating moderate
variability across responses and acceptable levels of response dispersion. Overall, the descriptive analysis
supports that respondents generally viewed the smart city components favorably, with slightly higher confidence
in infrastructure-related aspects like satisfaction, mobility, and sustainability.
Table Ii Descriptive Statistics For Main Constructs (N = 346)
Construct
Min
Max
M
SD
Technology Integration
1
5
4.5
0.65
Sustainability Practices
1
5
4.51
0.67
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Mobility &
Transportation
1
5
4.73
0.64
Experience Quality
1
5
4.68
0.63
Cultural Orientation
1
5
4.44
0.67
Tourist Satisfaction
1
5
4.67
0.64
Note: M = Mean; SD = Standard Deviation; N = 304 (listwise).
Results section contains a full assessment of PLS-SEM model about the impact of smart city innovations on
tourist satisfaction. There are two major components: Measurement model and model evaluation, and structural
model evaluation. The evaluation of the measurement model finds satisfactory reliability and validity; most of
the constructs have acceptable Cronbach's alpha values (>0.6), and they all have AVE values higher than 0.5,
with appropriate discriminant validity through Fornell-Larcker criterion. Before conducting hypothesis testing,
these assessments are essential to set the model’s psychometric properties (Hair et al., 2019; Sarstedt et al.,
2022).
Measurement Model Assessment
Structural model assessment shows the significant relation at p < 0.05 between the sustainability practices and
mobility solutions, tourist satisfaction, and the insignificant direct relation for the technology integration.
Interestingly, albeit experience quality has no significant effect on tourist satisfaction, all three independent
variables significantly affect experience quality. This finding contradicts the expected mediating role of
experience quality in smart tourism contexts (Buhalis & Amaranggana, 2015; Gretzel et al., 2015). The model
shows strong explanatory power with R² values of 0.806 for experience quality and R² value of 0.819 for tourist
satisfaction, which means that large variance is explained by the model.
Measurement of Structural Model
Table Iii Measurement Model Assessment
Variables
Items
Outer
Loadings
Cronbach's
Alpha
Average Variance
Extracted (AVE)
Technology Integration
TI1
0.744
0.691
0.521
TI2
0.664
TI3
0.760
TI4
0.714
Sustainability Practices
SP1
0.742
0.684
0.517
SP2
0.609
SP3
0.745
SP4
0.769
Mobility & Transportation
MT1
0.854
0.864
0.710
MT2
0.857
MT3
0.827
MT4
0.832
Experience Quality
EQ1
0.824
0.815
0.644
EQ2
0.833
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EQ3
0.811
EQ4
0.740
Cultural Orientation
CO1
0.918
0.771
0.619
CO2
0.620
CO3
0.601
CO4
0.942
Tourist Satisfaction
TS1
0.779
0.829
0.662
TS2
0.790
TS3
0.862
TS4
0.821
The measurement model for all constructs of the study was assessed and is presented in Table III. The range of
outer loadings for all indicators is from 0.601 to 0.942 and most of them above 0.7, which is the suggested limit
for indicator reliability. All the constructs were checked for internal consistency reliability and Cronbach's alpha
values were between 0.684 and 0.864, with the highest reliability being on Mobility & Transportation of 0.864.
The convergent validity is satisfactory because all the Average Variance Extracted (AVE) values exceed the
minimum cutoff of 0.5, that is, between 0.517 and 0.710.
Measurement properties show that Mobility & Transportation has the most solid measurement quality with the
greatest AVE (0.710) and Cronbach's alpha (0.864), while Technology Integration and Sustainability Practices
have slightly lower but satisfactory reliability values (0.691 and 0.684 first respectively). Experience quality (E-
Q), cultural orientation (C-O) and tourist satisfaction (TS) prove good measurement properties with balanced
reliability and validity measures. These results suggest that the model measures the constructs adequately well
for the assessment of a structural model.
Table Iv Discriminant Validity (Fornell-Larcker Criterion)
CO
EQ
MTS
SP
TI
TS
CO
0.787
EQ
0.747
0.803
MTS
0.813
0.859
0.843
SP
0.794
0.873
0.875
0.719
T1
0.793
0.840
0.873
0.877
0.721
TS
0.784
0.821
0.872
0.859
0.825
0.814
Note: CO=Cultural Orientation; EQ=Experience Quality; MTS=Mobility & Transportation Solutions;
SP=Sustainability Practices; TI=Technology Integration; TS=Tourist Satisfaction
The results of discriminant validity by the Fornell-Larcker criterion are shown in Table IV. Square root of AVE
is expressed using a diagonal (bold) value, while the other values represent the correlation values between the
constructs. Furthermore, all diagonal values are greater than other correlations found in their same row and
column verifying that each construct is unique from the other. The values of the correlations of variables are
from 0.601 to 0.942; the highest is the correlation between Technology Integration and Sustainability Practices
equaling 0.877.
Therefore, this indicates that, although the two constructs are closely related, they are not measuring the same
concepts. In general, the table shows that overall, the measurement model has good discriminant validity since
each construct has been measuring unique constructs that have not been measured by the other constructs in the
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model.
Assessment of Structural Model
Fig 2. Path Analysis
The PLS-SEM structural model path analysis results are displayed by (e.g., Fig. 2). Path coefficients between all
constructs are shown on the connecting lines in the diagram presenting relationships of all constructs. The other
point of issue is the relationship between Experience Quality and each of Sustainability Practices (0.435) and
Mobility & Transportation Solutions (0.329) which show the strongest positive association. Of interest, both
negative coefficients are unexpected, as the coefficient from Experience Quality to Tourist Satisfaction (-0.035)
is significant at the 10% level despite being negative. Finally, the figure easily allows you to see which pathways
are significant according to the reported statistical values in Table III describing all hypothesized relationships.
Table V Results Of Hypothesis Testing
Hypothesis
Relation
Beta
Coefficient
t-value
p-value
Supported
f²
H1
Technology Integration → Tourist
Satisfaction
-0.022
0.278
0.781
No
0.000
H2
Sustainability Practices → Tourist
Satisfaction
0.199
2.259
0.024 *
Yes
0.033
H3
Mobility & Transportation
Solutions → Tourist Satisfaction
0.275
3.597
0.000 **
Yes
0.061
H4
Technology Integration →
Experience Quality
0.171
2.864
0.004 **
Yes
0.028
H5
Sustainability Practices →
Experience Quality
0.435
5.765
0.000 **
Yes
0.176
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H6
Mobility & Transportation
Solutions → Experience Quality
0.329
5.545
0.000 **
Yes
0.104
H7
Experience Quality → Tourist
Satisfaction
-0.035
0.539
0.590
No
0.001
H8
Technology Integration →
Experience Quality → Tourist
Satisfaction
-0.006
0.502
0.616
No
–
H9
Sustainability Practices →
Experience Quality → Tourist
Satisfaction
-0.015
0.535
0.593
No
–
H10
Mobility & Transportation →
Experience Quality → Tourist
Satisfaction
-0.012
0.537
0.591
No
–
H11
Cultural Orientation × Experience
Quality → Tourist Satisfaction
0.022
0.339
0.735
No
0.001
H12a
Cultural Orientation × Technology
Integration → Tourist Satisfaction
-0.010
0.195
0.845
No
0.000
H12b
Cultural Orientation ×
Sustainability Practices → Tourist
Satisfaction
-0.024
0.313
0.754
No
0.001
H12c
Cultural Orientation × Mobility &
Transportation → Tourist
Satisfaction
-0.067
0.752
0.452
No
0.002
SRMR = 0.174
R²Experience Quality = 0.806; Q²Experience Quality = 0.487
R²Tourist Satisfaction = 0.819; Q²Tourist Satisfaction = 0.506
Note: * p < 0.05; ** p < 0.01
The results of hypotheses testing and its corresponding statistical values for each relationship in the model are
presented in Table V. Only 5 out of 14 hypotheses were supported. The findings revealed that the Sustainability
Practices (β=0.199, p<0.05) and Mobility & Transportation Solutions (β=0.275, p<0.01) had significant direct
effect to Tourist Satisfaction thereby confirming H2 and H3. Thus, H1 was rejected in that Technology
Integration did not significantly influence Tourist Satisfaction (β=-0.022, p=0.781). Accordingly, all three
independent variables have significant positive effects on Experience Quality supporting H4, H5, and H6 where
Sustainability Practices has the strongest influence on Experience Quality (β=0.435, p<0.01).
It is noteworthy that Experience Quality neither positively nor negatively affected Tourist Satisfaction (β=-0.035,
p=0.590), and H7 was rejected. Experience Quality did not mediate the relationships between the independent
variables and Tourist Satisfaction, so all the mediation hypotheses H8–H10 were not supported. Also, all
moderation hypotheses (H11, H12a-c) with Cultural Orientation were rejected with p-value values greater than
0.05. The model has good explanatory and predictive relevance as indicated by high R² and Q² values,
respectively (Q² = 0.819 for Tourist Satisfaction; Q² = 0.806 for Experience Quality; and Q² > 0.35 for both
constructs). However, the SRMR value of 0.174 indicates that some model fit problems may exist and therefore
needs to be examined.
DISCUSSION
The findings of this study present a compelling shift in how smart city innovations are perceived by tourists.
While prior literature emphasizes the centrality of digital technologies and experience-oriented design in
enhancing tourist satisfaction (Buhalis & Amaranggana, 2014; Gretzel et al., 2015), our results challenge this
orthodoxy. Contrary to expectations, Technology Integration and Experience Quality were not significant
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predictors of Tourist Satisfaction. Instead, Sustainability Practices and Mobility & Transportation Solutions
emerged as more decisive factors.
One plausible interpretation is that tourists may increasingly take digital technologies for granted, viewing them
as hygiene factors rather than satisfaction drivers (Zhang et al., 2017). This "technology normalization effect"
reflects how mobile apps, smart kiosks, and free Wi-Fi are now expected as part of the baseline tourism
infrastructure, diminishing their incremental impact on satisfaction (Neuhofer et al., 2015). The technology’s
presence may thus be necessary but insufficient to influence satisfaction unless it offers exceptional
personalization or innovation.
Moreover, the non-significant effect of Experience Quality may reflect a shift in tourist priorities toward
functionality and sustainability over hedonics, particularly in urban tourism contexts. As urban destinations grow
more congested and climate-aware, travelers may derive satisfaction more from ease of mobility, cleanliness,
safety, and eco-efficiency than from traditional experience design (Füller & Matzler, 2008; Sigala, 2019). This
aligns with the growing literature emphasizing that “smartness” in tourism is increasingly about infrastructure
and environmental management, rather than merely immersive experiences (Gretzel et al., 2022).
Interestingly, the strong influence of Sustainability Practices on both Experience Quality and Tourist Satisfaction
supports the view that modern tourists especially younger cohorts are more eco-conscious and responsive to
environmental cues (Kock et al., 2020). Green transportation, energy-efficient hotels, and recycling programs
may directly appeal to this demographic’s ethical values, contributing positively to their satisfaction even in the
absence of high-touch technological interactions.
The significance of Mobility & Transportation Solutions further confirms this infrastructure-centric view.
Efficient transport not only enhances convenience but also shapes the overall perception of city functionality
and accessibility, which are fundamental for first-time and solo travelers (Pike et al., 2010). The high path
coefficients for mobility solutions echo studies that rank urban mobility as a cornerstone of perceived livability
and destination attractiveness (Cohen & Gössling, 2015).
CONCLUSION
This study provides a timely and theoretically significant contribution to the evolving discourse on smart tourism
by showing that Technology Integration and Experience Quality do not significantly enhance tourist satisfaction,
contrary to established expectations. Instead, Sustainability Practices and Mobility & Transportation Solutions
emerge as the most influential dimensions. These findings suggest a growing divergence between technological
novelty and the actual determinants of satisfaction in smart urban tourism contexts.
Practical Implications
For destination managers and smart city planners, the results imply that tourist satisfaction is increasingly rooted
in functional, visible improvements such as green mobility, sustainable waste management, and public transport
efficiency—rather than in digital features like mobile apps or virtual tours. As digital tools become normalized,
they are no longer perceived as added value unless they provide seamless, hyper-personalized, or problem-
solving experiences (Shin et al., 2022).
Tourism authorities should therefore prioritize sustainable urban infrastructure investments—bike lanes, green-
certified accommodations, and low-emission zones over isolated tech integrations that fail to address visitors'
core needs. These are especially important for younger travelers, who increasingly express preferences for eco-
responsibility, accessibility, and purpose-driven travel (Choe et al., 2021).
Theoretical Implications
Theoretically, this study challenges the dominance of technology-centric frameworks such as Smart Tourism
Ecosystems (Gretzel et al., 2015) by highlighting the maturity plateau of digital expectations. Tourists now view
digital solutions as expected infrastructure rather than satisfiers (Kang et al., 2021). This calls for a reframing of
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
smart tourism theories, positioning infrastructure quality, sustainability performance, and urban mobility as core
pillars of satisfaction in urban destinations.
The results also question the assumed mediating role of Experience Quality, which was traditionally framed as
the conduit through which technology or services influence satisfaction. The absence of mediation may suggest
that tourists now form satisfaction judgments based more on outcomes than affective experiences, particularly
in high-functioning smart city contexts (Xu & Gursoy, 2022).
Policy Implications
From a policy perspective, the findings urge governments to align tourism strategies with urban sustainability
goals. Smart tourism should not be decoupled from environmental and public policy priorities. The United
Nations’ SDG 11 Sustainable Cities and Communities requires integrated actions, and our findings suggest that
visible green initiatives are more impactful than invisible tech investments in building satisfaction and loyalty
(UNWTO, 2023).
Cities should integrate smart tourism planning into broader climate resilience, low-carbon transport, and
inclusive access strategies. Cross-departmental collaboration between tourism, transport, ICT, and
environmental agencies will be essential to translate smart city ideals into tourist-centric outcomes.
ACKNOWLEDGMENTS
This publication was supported by Universiti Teknikal Malaysia Melaka (UTeM) under the Journal Publication
Fee Initiative 2025. The authors would also like to acknowledge the support from the Faculty of Technology
Management and Technopreneurship.
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