Unlocking the secrets of destination competitiveness: a new tool to measure tourist satisfaction at the destination level Open Access

The competitiveness of destinations is a relatively recent topic, gaining prominence in international literature only in the last 2 decades (Pechlaner and Laesser, 2003). From the study of tourist systems emerged the concept of destination management, emphasizing the relevance of tourism development for places, citizens, businesses, institutions and tourists, all involved in different ways in the destination development processes.

Territorial development actions are diverse and often complex. As acknowledged in scientific literature, a destination becomes such when a public-private system collaborates effectively to enhance the value of involved areas (Wu et al., 2021). There is no standard rule for making a destination attractive, as developments can proceed in various directions with diverse forms of exchange among public and private actors.

In this context, two key elements stand out in creating a functioning and competitive system: collaboration among local actors and a shared knowledge of the places that includes citizens. Tourists and citizens should have an active and proactive role in destination management, influencing tourist perceptions during their experience.

Many tourism actions originate from the perceptions of residents and extend to the needs and expectations of tourists, who should be central in destination management dynamics (Tuohino and Konu, 2014).

Over the last 2 decades, literature has highlighted sustainability as a fundamental component of destination competitiveness. Already in 2000, Hassan (2000) introduced sustainability among the competitive levers, defining it as “environmental commitment” alongside “comparative advantage”, “alignment to demand” and “industry structure”.

Sustainability is a component of tourist satisfaction, though not the only one. This work highlights key dimensions with direct or indirect impact on tourist satisfaction. The urban context also plays a crucial role: visitors form their opinions based not only on personal impressions but also on tangible elements like street conditions and public space quality. Cleanliness and organization significantly influence the tourist’s evaluation of the experience.

This complexity reflects a multi-level logic of perception, incorporating not only strictly tourist aspects but also broader territorial dimensions.

Accordingly, this study specifically addresses tourist satisfaction as a multidimensional construct, aiming to understand how the different components of the tourism offer, captured through the “six As” model (access, attractions, accommodation, amenities, ancillary services and assemblage), influence the perceived experience at the destination level.

More in detail, this study concentrates on the measurement of tourist satisfaction during the visit itself, thereby providing more authentic and timely insights into how the offer is perceived.

The relationship between offer and demand becomes evident in destination branding, but only when the offer system creates an empathetic, active connection with tourists, through unique experiences that foster satisfaction and loyalty.

Therefore, the main research problem addressed is the lack of a robust, validated and experience-based instrument capable of capturing overall tourist satisfaction at the destination level. This gap is particularly critical for tourist destinations undergoing transformation, such as Naples.

The paper provides a methodological contribution by developing and validating a tool that measures satisfaction of the tourism offer.

It starts from the concept of destination branding to explore how the supporting offer system shapes tourists’ perceptions during their stay. It aims to analyse how the structure and coherence of the offer influence destination reputation and tourist experience.

The aim is to validate a measurement model that synthesizes tourists’ lived experiences and satisfaction during their actual visit, providing decision makers with a diagnostic tool to evaluate the effectiveness of destination management.

The paper is organized as follows: Section 2 presents the literature review; Section 3 outlines the methodology. Section 4 discusses the results, followed by the discussion in Section 5. Finally, conclusions, limitations, implications and suggestions for future research are addressed in Section 6.

Destination branding is a pivotal element in the highly competitive and dynamic tourism industry (Haq et al., 2024). Effective branding is crucial for differentiating tourist destinations and gaining a significant competitive advantage (Sahaf and Fazili, 2024; Xu et al., 2025). Destination branding is a complex and multi-faceted process, often involving numerous intangible and tangible elements (Shizhen et al., 2025). More in depth, it refers to the responsibility of destination management organizations (DMOs) but requires an appropriate degree of inter-organizational coordination and collaboration among various stakeholders, including local residents, the private sector and tourists (Amani, 2024; Mandagi and Centeno, 2024; Sahaf and Fazili, 2024).

Central to the tourism experience and a significant outcome of destination management and branding efforts is tourist satisfaction (Della Corte et al., 2015; Zhao et al., 2024). It is a critical driver of positive behaviours, including customer retention, loyalty, revisit intention and favourable word-of-mouth (Della Corte et al., 2015; Rahman et al., 2023). Destination loyalty, reflected in intentions to revisit and recommend, is directly influenced by satisfaction with travel experiences (Li et al., 2021). Therefore, understanding and enhancing tourist satisfaction is crucial for destination marketers and managers to sustain destination attractiveness.

Tourist satisfaction is a complex emotional evaluation process influenced by a wide array of factors.

Indeed, it depends on the quality of the tourism offer, including both tangible products and intangible services (Della Corte et al., 2015; Xu et al., 2025). The satisfaction level is shaped during the service delivery phase (Choi et al., 2021). Various attributes of a destination contribute to tourist satisfaction, including the availability of diverse activities, the hospitality of local people and the organization of cultural events (Biswas et al., 2021). Conversely, factors such as cleanliness, public transportation, perceived security and the quality of infrastructure (like streets and road-signs) can significantly impact satisfaction, sometimes leading to dissatisfaction (Das and Maitra, 2024). The overall tourist satisfaction level is a result of these individual elements of “partial satisfaction” (Della Corte et al., 2015).

Recent literature also emphasizes that satisfaction is not limited to rational evaluation, but is deeply rooted in the tourist’s emotional engagement, personal expectations and perceived authenticity of the experience (Biswas et al., 2021; Jing and Loang, 2024). Elements such as sense of welcome, atmosphere, aesthetic pleasure and the emotional resonance of places are increasingly recognized as central components in shaping satisfaction (Nian et al., 2023).

In this light, satisfaction emerges as a dynamic and subjective construct, influenced not only by services received but also by how the destination is interpreted and emotionally lived by the visitor (Liu et al., 2025).

The Destination Customer Satisfaction Pyramid, as proposed by Della Corte (2000, 2020), offers a framework for conceptualizing the components of tourist satisfaction. This model emphasizes the centrality of the quality of the tourist products and services as they are provided in the destination. It highlights the importance of the organic level marketing, which involves the reality of the local supply, as an essential component for customer satisfaction and retention (Della Corte, 2020).

While existing studies mention various specific attributes contributing to satisfaction, they do not explicitly detail the layered structure or potential evolution of this pyramid model itself. In addition, satisfaction emerges from a complex process requiring the harmonious functioning and cooperation of each actor in the destination system.

The idea of an integrated system of offer is crucial, as destination management requires collaboration between various local actors and a shared knowledge system that includes both citizens and tourists (Buonincontri et al., 2024; Luongo et al., 2023, 2024). Consequently, tourists and citizens should be active participants in destination management processes. In this wake, the ability to create, innovate and maintain this system of offer is fundamental.

Destination image is another critical construct closely related to tourist satisfaction. The latter is conceived as the culmination of people’s opinions and impressions (Zhao et al., 2024).

Extant literature considers destination image as an antecedent of tourist satisfaction (Kani et al., 2017; Siregar et al., 2021). However, this perspective can be seen as reductive, as destination image can also be influenced by the tourist experience itself, positioning the modification of image because of satisfaction (Marques et al., 2021). Therefore, there is a clear, dynamic relationship between tourist satisfaction and destination image.

Maintaining a strong coherence between the presented brand image and the reality of the local supply is vital to avoid a boomerang effect and negative word-of-mouth. For instance, tourists’ perceptions of tourism product and service quality can shape positive perceptions that enhance destination brand equity through destination brand value, brand resonance and brand self-congruity (Xu et al., 2025).

Strengthening a destination’s reputation is intrinsically linked to its ability to deliver positive tourist experiences and generate satisfaction (Biswas et al., 2021). Reputation depends on the effective functioning of the entire offer system. A strong reputation can enhance customer loyalty, perceived quality and brand image (Sahaf and Fazili, 2024). It can even help buffer the negative impact of service failures.

In line with the “six A’s” framework adopted in this study, tourist satisfaction is interpreted as the cumulative result of the evaluation of six integrated components: access, attractions, accommodation, amenities, ancillary services and assemblage. These dimensions represent the structural, logistical and experiential touchpoints that collectively shape the tourist’s perception of value. Grounding the analysis in this model enables a comprehensive understanding of satisfaction as the result of interconnected elements within the destination system.

This model also allows for the integration of both objective and subjective perspectives: the six components not only describe the physical and managerial organization of the tourism offer, but also capture how that offer is perceived, interpreted and emotionally processed by the tourist.

In this regard, extant studies highlight the complexity of destination branding and management (Hanna et al., 2021). On this aspect, it is important to underline two main factors: first, reputation is a never-ending investment and once you gain a high reputation it becomes more and more difficult to increase it; at the opposite, it is extremely easy to lose reputation if only one for the firms “playing in the orchestra” is not aligned to the other instruments. Second, studies acknowledge that destinations are complex entities comprising multi-dimensional attributes (Shizhen et al., 2025). At the same time, evaluating overall satisfaction is difficult due to the variety of factors involved. As a matter of fact, research on destination branding, particularly its measurement and conceptualization, remains fragmented and limited (Guleria et al., 2024).

Furthermore, the dampening effect of destination brand on negative customer reactions to service failures weakens in the case of severe service failures (Sahaf and Fazili, 2024). This suggests limitations to the mitigating power of destination-level brand strength at the individual firm level when critical failures occur. Another critical point is that part of existing research relies on cross-sectional data, making it challenging to understand the long-term dynamics of factors like authenticity, emotions and image perception on destination outcomes (Zhao et al., 2024). Also, findings may also be context-specific (Sahaf and Fazili, 2024).

Therefore, this study, by focusing on the city of Naples, provides a relevant context to explore these complexities.

Previous research indicates that tourists visiting Naples are not completely satisfied. Naples is perceived with a conflicting image, appreciated for its food tradition, natural beauty and artistic heritage, but negatively perceived for cleanliness, perceived security and disorganization (Della Corte et al., 2015; Aria et al., 2023). This reflects the challenges in managing the tangible and service aspects of the destination offer.

The ability of a destination like Naples to strengthen its reputation and enhance tourist satisfaction depends on its capacity to improve its offer system, potentially through enhanced governance and collaboration among stakeholders.

Local institutions are working on strengthening the reputation of the city through governance systems and the constitution of a new DMO, “Napoli: a new city”. This aligns with the broader literature emphasizing the interdependence between larger stakeholders like DMOs and smaller firms within the tourism ecosystem to achieve effective destination brand management and profitability (Mandagi and Centeno, 2024; Pechlaner et al., 2025).

By investigating these relationships within the specific context of Naples, this study contributes to the understanding of how destination attributes, service quality, image and stakeholder collaboration interact to influence tourist satisfaction and destination reputation, addressing some of the gaps in the existing literature.

In this work we analysed a sample of 1,468 people (females = 55%; males = 45%; Italian = 81%; other nationalities = 19%) who were given a self-administered questionnaire between January 2023 and December 2024 (Figures 1–3). The data collection took place in strategic locations across the city of Naples, including major tourist information points and hotel facilities. All respondents were tourists who had already completed or were about to complete their visit. Participation was voluntary and anonymity was guaranteed. The questionnaire was administered digitally via Google Forms, allowing for easy access and immediate data collection. The questionnaire was structured into three main sections. The first referred to respondents’ socio-demographic data (age, gender, nationality, employment status); the second collected information on how they reached Naples (transportation mode, booking channel used and whether it was their first visit to the city); the third section focused on evaluating tourist satisfaction across the six dimensions of the “six A’s” model using 5-point Likert scales (1 = not at all satisfied to 5 = extremely satisfied). For each dimension, multiple items were included, along with one item assessing overall satisfaction with that dimension. Example items include: “Punctuality and cleanliness of urban public transport” (Access); “Variety and quality of entertainment” (Attractions); “Variety of hotel and extra-hotel accommodation” (Accommodation); “Craftsmanship and authenticity of local products” (Amenities); “Car rental services” (Ancillary services); “Availability of tours and excursions” (Assemblage). Finally, the questionnaire concluded with a question on behavioural intention: whether the respondent intended to revisit Naples in the future.

The respondents were aged up to 24 years (33%), between 24 and 35 years (24%), between 36 and 50 years (23%) or over 50 years (20%). Most of them were students (32%), employees in the public or private sector (31%) and self-employed (24%), while only a minority said they were not in the labour market (housewife/househusband) or were looking for a new job.

According to the six “As” model (Della Corte, 2020), we collected information on tourists’ perceived satisfaction in six areas (Figure 4).

To study the satisfaction of tourists visiting Naples we developed a self-administered questionnaire, designed to measure tourist satisfaction and monitor it over time. Our analysis was intended at validating our proposed questionnaire.

After examining internal consistency and reliability of the satisfaction items administered to a sample of 1,468 people, we explored data dimensionality.

Using SPSS software (IBM, 2023), items were first examined using Cronbach’s alpha, a coefficient that measures items’ internal consistency, and computed as shown by Cronbach in his paper published in 1951. It is used to assess scale reliability as it helps to determine whether the items in a scale are consistently measuring the same concept. A higher alpha value (typically above 0.7) indicates that the items have good internal consistency and are likely to produce reliable results.

Furthermore, although Cronbach’s alpha primarily measures reliability, it also indirectly supports the validity of the scale. A reliable scale is more likely to accurately measure the intended construct, thus contributing to the overall validity of the research findings.

In addition, we computed the point-biserial correlation coefficient (Royer, 1941), which complements the previous analysis. While Cronbach’s alpha assesses the internal consistency of a set of items, the item-total correlation provides insight into how individual items relate to the overall scores: high item-total correlations indicate that the item is consistent with the total scale.

After controlling for the characteristics of the item battery, we further explored the collected data by conducting an exploratory factorial analysis (EFA). The first objective is to identify underlying factors that explain the patterns of correlations between observed variables. Understanding the dimensional structure of the collected data can confirm (or disconfirm) that the items are grouped into coherent dimensions that reflect different aspects of satisfaction, thus providing empirical evidence on the adequacy of the set of selected items to accurately represent the constructs they are intended to measure.

We also report factor loadings, which are used to characterise each factor as they indicate the strength and direction of the relationship between each variable and the identified factors. High loadings (typically greater than 0.30) indicate that a variable is strongly associated with a particular factor (Fabrigar and Duane, 2011). In order to identify the number of latent factors, we considered: eigenvalues (greater than 1 indicate factors that explain a significant amount of variance (Kaiser criterion); and, the scree plot to determine the optimal number of factors by identifying the “elbow” point where the explained variance levels off. The total variance explained indicates how much of the total variability in the data is accounted for by the extracted factors. A higher total variance explained means that the factors capture more of the underlying structure. It can also be seen as a measure of model efficiency, as it helps to assess the effectiveness of the factor model.

Table 1, reports the reliability and internal consistency of the six item groups (italic columns), together with the same statistics calculated on all administered items, regardless of their group membership (non-italic columns).

The Cronbach’s alpha computed without grouping items according to the 6 As model is 0.974. The internal consistency and reliability calculated separately for each group of items are also very high. Cronbach’s alpha is sensitive to the number of items analysed (i.e. the higher the number of items, the higher the value of Cronbach’s alpha). Therefore, the value computed in each group of items separately is even more “significant” than that computed on the overall items’ battery. The items administered via our questionnaire are thus consistent both within each group and across groups.

In order to explore data dimensionality, we conducted an EFA. Both the KMO (>0.5) and Bartlet’s (<0.05) tests confirm the suitability of our data set for factor analysis.

The scree plot (Figure 5) is crucial as it provides a visual representation of the eigenvalues associated with each factor and thus the number of factors to retain in the model.

According to the scree plot, there is one clear first factor (that explain 56% of the total variance) followed by three factors that altogether explain 19% of the total variance: the variance explained by the first factor is thus six time bigger than that explained by the second factor.

Results shown in Figure 1 indicate a predominantly unidimensional data structure, that is further confirmed by the qualitative interpretation of the other three factors extracted. To characterise them, we examined the factor loadings (in Table 4), i.e. the loadings of each variable on the extracted factor: the higher the factor loading associated to an item, the more important the role of that item in characterising the factor.

Results from the analysis (Table 2) show high and positive loadings of each variable on the first extracted factor. When all variables load positively on a single factor, while only some variables load (positively or negatively) on the other factors, we can interpret the first factor as a common factor (i.e. a general dimension) that represents a characteristic shared by all the variables. In our study this factor can be seen as a Tourist Global Satisfaction (TGS), to which all the variables contribute.

The other factors, on which only some variables have a positive or negative impact, represent aspects of the general concept. If a variable loads negatively on a factor, then high values of the variable are associated with low levels of tourist satisfaction.

Regarding the second factor, the results may indicate two aspects of tourist satisfaction that are inversely related. Positive loadings relating to practical and logistical aspects of the trip indicate that the tourists who value these aspects may be likely to prioritise comfort, infrastructure and environmental sustainability(i.e. that they are concerned with the efficiency and reliability of services and facilities that make their travel experience smooth and hassle-free), while negative factor loadings associated with cultural and experiential aspects of travel suggest that as, the factor increases, the associated variable decreases.

The factor is negatively associated with cultural and experiential aspects, capturing a dimension that contrasts with these elements, such as practical or logistical aspects of travel. However, since the second factor explain 6 times less that the variance explained the first one, this factor could be able to capture just some specific aspects if the TGS rather than a proper dimension.

To better explore this result, we performed a regression analysis of the score associated with the second factor against tourists’ socio-demographic characteristics, alongside a variable (“reservation”) indicating how tourists booked their trip, as specified in (1).

Results ( Appendix) indicate that respondents’ characteristics are not associated with these preferences, with just one exception, i.e. gender, that is statistically significant: women can be defined as “Efficiency-Oriented Tourists” to express their preference for comfort, infrastructure, environmental sustainability, and the smooth, hassle-free nature of their travel experience. However, the regression coefficient associated with gender is small in magnitude (0.066) and thus negligible.

The third factor is positively loaded only by the first four variables (there is just one variable that negatively loads it), thus partially overlapping with the second factor, and only 6% of the total variance (i.e. nine times less than that explained by the first factor).

Finally, for the fourth factor, there is only a single loading greater than |0.30| (related to car rental). Consequently, such a factor be characterised. Moreover, as it accounts for less than 4% of the total variance: the fourth factor is a residual factor mirroring data noise rather than an actual dimension.

In summary, results from the data analysis (Table 3) indicate unidimensional data structure.

After conducting a factor analysis to investigate the dimensionality of tourists’ satisfaction, factor scores were computed for each respondent. They are composite measures that represent respondents’ positions on the underlying factors identified in the factor analysis. In other words, they encapsulate the latent constructs derived from the observed variables, providing a synthesized measure of satisfaction. Therefore, we used them as dependent variables in a regression analysis.

To better understand the phenomenon under investigation, in addition to personal characteristics, we also included information on how the tourists organised their visit (through a travel agency, on their own or with the help of colleagues, friends and/or relatives) and their nationality.

The results of the regression analysis show no statistically significant relationship between tourist satisfaction and the independent variables, as indicated by both the goodness of fit statistics (R and R-squared) and the analysis of variance (ANOVA). Based on our data set, R and R-squared are 0.059 and 0.004 respectively, indicating that the strength of the linear relationship between the dependent and independent variables is almost zero and that only 0.4% of the variance in the dependent variable can be explained by the independent variables.

Such a result is further confirmed by the ANOVA: The F-statistic was not significant (p-value > 0.05), indicating that the model does not explain a significant amount of variance in the dependent variable ( Appendix).

All regression coefficients (which indicate the change in the dependent variable for a one-unit change in the predictor variable, holding other variables constant) were not statistically significant (Table 4). Results suggest that tourist satisfaction is not significantly influenced by the available personal characteristics of the respondents. Instead, satisfaction appears to be more closely related to the experience itself. In line with previous studies (Iqbal et al., 2023) that emphasise the importance of destination characteristics and environment in influencing tourist satisfaction, factors such as service quality, tourist attractions and interactions with local staff play a crucial role in determining the overall satisfaction.

The findings of this study underscore the complex and multifaceted nature of tourist satisfaction, particularly within the broader framework of destination competitiveness.

While previous literature has often emphasized the role of demographic characteristics or physical infrastructure as core determinants of satisfaction (Chen et al., 2013; Das and Maitra, 2024), our analysis reveals that the experiential and emotional dimensions of tourism play a significantly more influential role. This shift from static, attribute-based models to dynamic, experience-oriented interpretations reflects a broader evolution in tourism studies, demanding a reconfiguration of how satisfaction is conceptualized, measured and managed.

At the core of our results is the observation that tourist satisfaction is predominantly shaped by the quality and depth of experiences offered at a destination. This extends beyond mere service delivery or infrastructural adequacy, emphasizing instead the emotional engagement, personal interactions and meaning-making processes that tourists undergo during their journeys. This perspective resonates with the concept of emotional involvement as claimed by Biswas et al. (2021), which frames satisfaction as a function of the affective ties tourists develop with places.

Emotional narratives, constructed through interactions with local culture, service providers and co-travellers, appear to mediate the tourist experience, influencing not only immediate satisfaction but also long-term behavioural intentions, such as destination loyalty and advocacy (Li et al., 2021). This insight challenges the traditional paradigm that treats demographic segmentation as the primary lever for improving tourist satisfaction.

While demographic characteristics can offer valuable insights into tourist preferences, our findings suggest that their predictive capacity is limited when compared to experiential variables.

Notably, our analysis identified gender as a statistically significant factor in shaping certain satisfaction outcomes; however, other demographic variables such as age, education level and income exhibited no consistent influence. This diverges from earlier studies that heavily emphasized demographic profiling (Chen et al., 2013), prompting a critical reassessment of its utility in contemporary satisfaction modelling.

Instead, our results point toward the need for more holistic and psychologically grounded approaches that recognize the interplay between tourists’ emotional states and the contextual realities of the destination. As Zhao et al. (2024) and Iqbal et al. (2023) suggest, satisfaction emerges from a confluence of cognitive evaluations and emotional responses to various destination stimuli, including aesthetics, cultural immersion and service interaction quality. In this regard, tourist satisfaction can be better understood as a subjective, situational construct that evolves over time and is influenced by both pre-trip expectations and post-trip reflections.

Another critical dimension emerging from this study is the reciprocal relationship between destination image and tourist satisfaction. While traditional models have conceptualized destination image as an antecedent to satisfaction (Kani et al., 2017), our findings indicate a more dynamic, bi-directional relationship. Satisfied tourists contribute to the reinforcement and dissemination of positive destination images through word-of-mouth, online reviews and social media narratives. This aligns with the theoretical propositions of destination branding literature, where consumer experiences are central to co-creating destination meaning and brand equity (Zhao et al., 2024).

The rise of social media platforms and peer-to-peer communication environments has intensified this process, allowing tourists not only to share their impressions, but to actively shape the public discourse about destinations. These platforms act as amplifiers of satisfaction or dissatisfaction, turning individual experiences into socially validated narratives that influence others’ expectations.

The immediacy of digital communication platforms amplifies this effect, enabling tourists to influence destination perceptions in real-time and, in turn, affect the expectations of prospective travellers.

In this regard, satisfaction becomes both a private evaluation and a public performance, filtered through the logic of likes, shares and ratings. This opens up an important avenue for future research, particularly into how satisfaction is negotiated and expressed within digitally mediated tourism ecosystems.

This feedback loop introduces both opportunities and vulnerabilities for DMOs. On one hand, it creates a powerful mechanism for organic promotion; on the other, it places substantial pressure on destinations to consistently deliver high-quality, emotionally resonant experiences. The volatility of user-generated content means that a few negative experiences, if amplified online, can disproportionately affect the destination’s image. Therefore, brand management strategies must now incorporate real-time monitoring and response capabilities, along with proactive efforts to curate positive tourist narratives through authentic engagement and storytelling.

Future studies might explore how DMOs can engage with tourists as content creators and experience co-designers, rather than passive consumers. This would enable a deeper understanding of satisfaction as a socially interactive and co-produced construct.

Together with emotional engagement and destination image, emerging technologies such as virtual and augmented reality are reshaping the tourist satisfaction landscape. These tools offer immersive pre-trip experiences that influence tourist expectations and planning behaviour. However, they also introduce a new challenge: the potential dissonance between digitally curated experiences and real-world conditions. If a destination fails to live up to the expectations set by its digital simulations, tourists may experience dissatisfaction despite objective service quality. Conversely, if used strategically, these technologies can enhance satisfaction by reducing uncertainty, fostering anticipation and enriching on-site experiences through interactive guides or historical overlays.

This technological dimension demands a more nuanced understanding of the tourist journey, one that integrates virtual and physical touchpoints. Satisfaction is no longer a linear outcome of the travel experience, but a complex, iterative process shaped before, during and after the trip. As such, future research should explore how digital and real-world experiences coalesce to form a unified perception of destination quality and value.

In particular, there is a need to investigate how the integration of technological tools, digital storytelling and social interaction platforms shapes satisfaction trajectories and long-term destination attachment.

Cultural diversity represents another salient theme in the discourse on tourist satisfaction (Xu et al., 2025). As globalization intensifies cross-cultural encounters in tourism spaces, destinations must navigate increasingly heterogeneous tourist expectations and values. Tourists from different cultural backgrounds interpret service encounters, social norms and experiential quality through distinct cultural lenses (Choi et al., 2021). This creates potential misalignments between service delivery and tourist expectations, thereby complicating the delivery of a uniformly satisfying experience.

Our study, while focused on a specific tourist destination, highlights the implications of cultural nuances in satisfaction evaluation. For example, perceptions of hospitality or time management may vary significantly between Western and Asian tourists, influencing their overall evaluation of the same service interaction (Walters et al., 2021). To address this, destination stakeholders must adopt culturally adaptive service models that are both inclusive and responsive to diverse tourist needs. Embracing cultural diversity not as a challenge but as a strategic asset can facilitate more personalized and meaningful experiences, ultimately enhancing global competitiveness.

Moreover, sustainability has emerged as a critical lens through which tourists increasingly evaluate their satisfaction. With heightened awareness of environmental degradation and ethical consumption, tourists today often seek destinations that align with their values. Our findings support the growing consensus that sustainable practices, ranging from waste management and conservation efforts to community engagement, can significantly influence satisfaction. Tourists not only appreciate visible sustainability initiatives but also respond positively to destinations that communicate their values transparently and authentically.

This study explores the dimensionality of tourist satisfaction through factor analysis, which revealed a dominant latent factor and a secondary dimension, though the latter was less straightforward to interpret. To further examine this structure, a principal component analysis of residuals was conducted using Winstep (Linacre, 2002), which did not support the hypothesis of unidimensionality. This suggests the need for more nuanced models, such as Multidimensional Item Response Theory (MIRT), which can model multiple latent traits simultaneously. Given the complex and multifaceted nature of tourist satisfaction, including elements like service quality, attractions and overall experience, MIRT could yield more precise estimations and a deeper understanding of item performance across dimensions.

Future investigations should also explore how satisfaction can be strategically improved. While this study offered a tool for measuring satisfaction and assessing the contribution of different variables, more sophisticated modelling approaches, such as Partial Least Squares Path Modelling (PLS-PM), are recommended to identify which variables most significantly influence tourist satisfaction. Within this context, the Importance-Performance Map Analysis (IPMA) is particularly valuable, as it simultaneously assesses the impact and performance of model constructs. Constructs with high importance but low performance should be prioritized for improvement, offering data-driven guidance for resource allocation and strategic decision-making.

Our findings offer initial insights into the formation of tourist satisfaction, focused on selected dimensions and respondent groups. However, satisfaction is a complex, multi-level phenomenon shaped by both tangible and intangible elements, ranging from urban services and infrastructure to emotional and perceptual responses to a destination. Tourists’ experiences are often shaped by pre-visit expectations, which may not align with the on-site reality. Thus, bridging the gap between expectation and experience requires a comparative and layered perspective.

Moreover, sociodemographic variables play a significant role in shaping tourists’ consumption patterns and perceptions. Experiences such as cultural events or recreational services do not inherently yield high or low satisfaction; instead, value is constructed progressively through the interaction with services and the quality of delivery. Increasingly, these perceptions are influenced by digital tools, which serve both as pre-visit evaluative mechanisms and post-visit feedback platforms. The digital representation of a destination must therefore be realistic and reflective of actual offerings, as the digital image can influence both tourist expectations and ultimate satisfaction.

Technology plays a dual role: it shapes initial perceptions and expectations through online content and facilitates real-time interaction with the destination during the visit. Post-visit, digital platforms become avenues for tourists to share experiences and shape collective perceptions. In this context, the pre-visit image of a destination emerges as a foundational element in the formation of satisfaction. Therefore, destination marketing must be grounded in authenticity and supported by on-the-ground enhancements to tourist infrastructure and experience.

Fundamental urban services, such as public transportation, accommodations and information points, are critical for both tourists and residents. The provision of cultural events adds further value to a destination, enhancing its appeal and enabling differentiation from competitors. As tourism becomes increasingly competitive, especially among emerging destinations, it is essential to strategically manage both tangible infrastructure and intangible elements such as perception, emotional connection, and destination branding.

Conducting this analysis during tourists’ actual visits enabled real-time assessments of lived experiences. For instance, in the case of Naples, a city undergoing a significant image transformation, the study benefitted from in-person interaction, which revealed widespread enthusiasm and appreciation. This suggests that Naples’ evolving governance and enhanced collaboration between public and private actors have significantly improved tourist satisfaction. Nevertheless, to generalize findings, similar studies should be extended to other competitive destinations to account for context-specific dynamics.

The methodological approach used in this study is resource-intensive and time-consuming, limiting immediate applicability across multiple destinations. However, its thoroughness makes it a valuable model for future research. Future work should aim to replicate and refine this methodology across various contexts to test its robustness and adaptability.

Theoretically, this study contributes to the understanding of tourist satisfaction by framing it as a dynamic construct influenced not only by destination attributes but also by emotional engagement, personal interactions and evolving brand perceptions. Traditional models that focus solely on static service quality or destination features may overlook these critical experiential and psychological dimensions. Identifying emotional involvement as a key determinant opens new avenues for research into the psychological underpinnings of satisfaction, particularly in cross-cultural contexts.

Furthermore, the study suggests that the relationship between destination image and satisfaction operates as a feedback loop. This highlights the need for research into how branding evolves through tourist narratives and how these narratives impact destination competitiveness. Integrating social media analytics into this discourse could enrich the understanding of brand equity and tourist behaviour.

From a managerial perspective, the findings underscore the need to craft emotionally resonant, culturally rich and service-efficient tourist experiences. Beyond infrastructural improvements, managers should invest in staff training, authentic engagement strategies and storytelling approaches that align tourists with local culture and history. These efforts can enhance emotional connection and satisfaction.

Sustainability also emerged as a relevant consideration. Aligning service delivery with sustainable practices can not only satisfy environmentally conscious tourists but also enhance the destination’s reputation. This alignment should be positioned as a core strategic advantage, helping destinations differentiate themselves in a saturated market.

Cultural diversity must be considered in both marketing and service design. Managers should seek to understand the diverse backgrounds of tourists and tailor offerings accordingly. Promoting local cultural exchanges and inclusive services can foster loyalty and long-term satisfaction.

In summary, by integrating theoretical frameworks with actionable strategies, this study offers a roadmap for tourism stakeholders to better navigate the complexities of tourist expectations and experiences. Future research should build on these foundations to further examine the interplay between perception, emotion, and satisfaction in tourism.

The Kaiser criterion (REF) was used to interpret the results. The Kaiser criterion suggests retaining factors with eigenvalues greater than 1. An eigenvalue greater than 1 indicates that the factor explains more variance than a single observed variable. This method provides a simple rule for deciding the number of factors to retain, thus simplifying the decision-making process. In addition, by retaining only factors with significant eigenvalues, the Kaiser method helps to balance the complexity of the model with the need for simplicity and interpretability. Both the total variance explained and the Kaiser method are essential in ensuring that the factor analysis model is both robust and interpretable.

A 4-factor model is therefore robust. Nevertheless, the presence of a predominant factor is confirmed by the analysis of communalities (Table 3), which represent the proportion of the variance of each variable that can be explained by the common factors in the model. In essence, they indicate how much of the variability of each observed variable is shared with the other variables by the underlying factors. Therefore, a high value (close to 1) means that a large proportion of the variable’s variance is explained by the common factors, while a low value (close to 0) suggests that the variable’s variance is mostly unique and not well explained by the common factors. The common factors are crucial for understanding the effectiveness of the factor model in capturing the underlying structure of the data.

To better interpret these factors, we used the factor scores calculated, in SPSS, for each respondent using the regression method.

The factor scores represent the position of each respondent along the dimensions identified by the factor analysis. Factor scores can be used to compare respondents, allowing researchers to determine who has the highest or lowest scores on a particular factor, thereby facilitating the identification of groups with similar characteristics. In our study, the factors resulting from the analysis represent Tourists’ Global Satisfaction (TGS) or some aspects of TGS. Therefore, a high score on these factors indicates that the respondent has a high level of satisfaction.

A regression analysis of factor scores against tourists’ sociodemographic characteristics showed no statistically significant relationships between individual factor scores associated with the first factors. Conversely, gender emerged as a significant predictor of individual factor scores across both the second and third factors. Specifically, male respondents exhibited higher individual scores on the second and third factors. Furthermore, older people tend to show higher factor scores on the third factors compared to younger respondents. The remaining variables exhibited no statistically significant coefficients.