What Influences Tourists’ Choice of Protected Natural Areas? The Role of Ecosystem Services

Abstract

The issue of tourism in protected natural areas is becoming central to defining new patterns of use, so managers, policy makers, and local businesses have an interest in improving visitor experiences and the promotion of sustainable tourism. This study analysed the factors influencing tourists’ choices regarding the role of ecosystem services provided by protected natural areas by studying the behaviour of 400 visitors to the Gran Paradiso National Park (Italy). The results identified the key motivations driving tourists’ choices and behaviour, categorising them into distinct visit patterns in relation to the role air quality, consumption of local products, and biodiversity. Furthermore, certification systems were found to be central in defining the level of appreciation of local products among visitors. These results can provide valuable insights into improving visitor experiences and promoting sustainable tourism and highlights the potential of ecosystem services as a key driver for conservation-oriented tourism strategies.

1. Introduction and Theoretical Background

Protected areas were originally established to conserve iconic landscapes, protect biodiversity and manage natural resources [1]. They often set a benchmark for the implementation of sustainability principles by mitigating anthropogenic impacts on particularly vulnerable and ecologically significant areas [2]. However, these areas often face considerable challenges due to climate change, increasing human activities, and unsustainable land management practices [3]. Despite these challenges, protected areas provide substantial ecological and socio-economic benefits by promoting sustainable economic activities, facilitating scientific research, and ensuring environmental conservation [4]. In addition, these areas offer significant opportunities for local development, particularly through tourism interest [5].

Italy has established an extensive network of protected areas covering various ecosystems and levels of protection. Specifically, the country has 871 parks with varying degrees of protection, covering more than 6.5 million hectares of land and more than 4 million hectares of marine area [6]. Among these, the 24 national parks form the backbone of the national ecological network, covering almost 1.5 million hectares of land area and 71,000 hectares of marine areas. This network plays a crucial role in conserving biodiversity while supporting local economic development by balancing the constraints and opportunities within these fragile but invaluable contexts [7]. The vast extent of these protected areas should also be recognised for the wide range of ecosystem services they provide, which are essential for human well-being and ecosystem sustainability [8]. These services fall into four primary categories: provisioning, regulating, cultural, and supporting [9]. Provisioning services include tangible goods such as food, freshwater, timber, and fibres; regulating services encompass climate regulation, erosion control, and water purification; cultural services provide intangible benefits such as recreation, aesthetics, and spiritual inspiration; and supporting services, such as soil formation, nutrient cycling, and pollination, underpin the production of all other ecosystem services [9,10].

From an economic perspective, Italian protected areas have the potential to serve as a model for integrating conservation objectives with socio-economic opportunities outlined in recent policies [11]. Notably, the implementation of sustainable tourism, along with the development of management guidelines, financing frameworks, and business models within ecologically relevant areas, has been prioritised in the United Nations’ Agenda 2030 [12] and endorsed by the United Nations World Tourism Organization [13].

In this optic, ecotourism, as a sustainable form of tourism that emphasises conservation and community engagement, can generate substantial economic benefits at both local and national levels [14]. Engaging local communities and stakeholders in managing natural resources not only fosters environmental awareness among visitors but also encourages public participation in decision-making processes [15]. According to recent data, Italian parks attract approximately 30 million tourists annually [16], who contribute, either directly or indirectly, to the success of over 800,000 businesses operating within these areas [17]. To better foster both sustainable economic development and tourism practices, numerous protected areas have adopted a range of certification systems [18]. For this purpose, for example, the European Charter for Sustainable Tourism (ECST) was established to unite several regions under a framework of voluntary international commitments. Additionally, smaller-scale certifications, such as the Italian “Quality Label” of the Gran Paradiso National Park and the “Produced in the Adamello Brenta Natural Park” label, were created to promote products and services provided by businesses, farms, and hospitality establishments that maintain a strong connection to the local area.

The existing literature has explored the role of tourism in protected natural areas, highlighting various strategies—-including the implementation of certification schemes—that can foster collaboration among local communities, park authorities, and stakeholders [18,19,20]. Nonetheless, the successful valorisation of local-quality certification schemes requires a comprehensive understanding of visitors’ habits, expectations, and perceptions [21,22]. To enhance tourist attractions, develop targeted educational programs, and promote environmental awareness, previous studies have analysed the impact of the social dimensions of ecotourism, alongside their ecological and economic repercussions, based on stakeholder opinions [23,24,25]. However, tourists’ attitudes and interests must also be evaluated to improve the management strategies of protected natural areas and influence destination choices, since, as suggested by Pearce and Schänzel [26], the selection is influenced primarily by infrastructures, visitor information, and product development, while personal motivations and expectations serve as secondary drivers. In the case of green accommodation (environmentally friendly lodging), attitudes, personal norms, and pro-environmental behaviour are significant predictors of tourists’ choices [27]. Hinlayagan et al. [28] found that accommodation quality, accessibility, and cultural attractions are central factors in the decision-making process for cultural destinations. Additionally, according to a literature review by Ortaleza and Mangali [29], overnight stays enhance overall visitor experiences, although high costs may deter some tourists. Moreover, past experiences and online reviews significantly influence decision-making [30,31].

From a theoretical perspective, this study adopts the ecosystem services’ framework as an integrative concept linking ecological functions with human well-being showing their relations with tourism development. ESs represent the multiple benefits humans derive from natural ecosystems, playing at the same time an essential role in land-use management and the promotion of sustainable tourism [32,33]. Therefore, in the context of protected areas, ecosystem services are not only environmental assets but can also be key determinants of visitors’ experiences and destination choices being related with human well-being [24]. In particular, cultural services—such as recreation, aesthetic appreciation, and educational opportunities—can be operationalized as measurable variables including visitor satisfaction, perceived natural beauty, and opportunities for outdoor activities. Provisioning and regulating services (e.g., biodiversity conservation, clean water, and landscape maintenance) indirectly influence tourists’ preferences by shaping the perceived quality and sustainability of their experiences and can have a role in parks management decisions [3]. Based on these considerations, this study adopted ESs as perceived attributes of the natural and cultural environment that influence tourists’ evaluations, behavioural intentions and ultimately their destination choices.

Previous studies already showed that the perceived value of these services positively affect pro-environmental behaviour and intention to visit [34]. Comparative studies in other protected mountain areas have also highlighted the importance of ecosystem services in shaping visitors’ experiences and attitudes. For instance, research in the Swiss Alps and the Pyrenees demonstrated that cultural and regulating services significantly influence tourists’ satisfaction and their willingness to contribute to conservation initiatives [35,36]. Similar findings were reported in the Austrian Alpine National Parks and the French Vanoise National Park, where the perceived aesthetic and recreational value of landscapes emerged as a key determinant of destination choice [37,38]. These examples reinforce the relevance of adopting the ESs framework for analysing visitor motivations within mountain ecosystems.

Building on this theoretical foundation, the present study conducted an exploratory analysis within the Gran Paradiso National Park to assess how visitors’ perceptions and feedback can inform management strategies aimed at integrating ecosystem values into tourism planning and decision-making [39,40].

Despite the existing literature, however, a gap remains in understanding the factors influencing tourists’ choice of protected natural areas in Italy. The novelty of this work lies in examining ecosystem services as drivers of tourists’ decisions to visit protected natural areas, categorising these drivers based on visitor characteristics. The finding can support the development and innovation of governance approaches through evaluation and monitoring tools that incorporate tourist feedback, ultimately enhancing conservation efforts and sustainable land management strategies.

The primary objective of this study is to explore the motivations driving tourists to visit protected natural areas, including the importance of ecosystem services and visitor characteristics such as socio-demographics and habits in the analysis. To this end, we selected the Gran Paradiso National Park in Italy as an ideal case study due to its unique characteristics and relevance to the topic.

To investigate visitors’ motivations and the role of ecosystem services and tourist characteristics in visiting the park, four research questions were formulated as follows:

(1)

What ecosystem services are most relevant to visitors of a national park?

(2)

Are there patterns in visitor behaviour related to services provided by a natural park?

(3)

Do the sociodemographic characteristics of visitors influence their visiting patterns?

(4)

How do certification awareness and visitor habits affect visiting patterns?

2. Study Area

Established in 1922, the Gran Paradiso is the oldest national park and a pioneering model of nature conservation in Europe. Located in the Western Alps, it spans over 71,000 hectares across the Valle d’Aosta and Piedmont regions, encompassing five valleys and thirteen municipalities [41].

The park is renowned for its remarkable biodiversity and serves as a crucial refuge for iconic alpine species such as the ibex, chamois, marmot, and golden eagle. Its diverse landscapes, including glaciers, alpine meadows, and larch and spruce forests, offer a unique setting for conservation efforts and sustainable tourism [42]. In addition to its ecological significance, the park features important cultural elements, such as historic alpine villages and traditional grazing areas. With a well-developed network of trails and a strong emphasis on ecotourism practices, the park provides ample opportunities for outdoor recreation and educational activities, fostering environmental awareness [43]. The park is a model for Italian parks, thanks to its pioneering role in mountain biodiversity conservation, its rich biodiversity, scientific research, sustainable management, and historical-cultural value [44].

3. Materials and Methods

The study relies on survey data collected between August and September 2024 in the Gran Paradiso National Park. Trained interviewers conducted face-to-face interviews using a random walk recruitment method collecting 400 valid responses. To ensure a diverse sample, interviews took place on different days of the week, from Monday to Sunday, and at various times of the day. To enhance the representativeness of the sample, both Italian and international tourists, given the park’s international appeal, were considered suitable as surveying respondents by means of an interviewer-completed questionnaire.

The required sample size for statistical analyses, ensuring representativeness and robustness, was assessed using the free online software “Conjointly” [45]. The analysis applied the following parameters [46]: population based on the most recent available tourist flows (237,000 visitors per year) [47], confidence interval (95%), margin of error (5%), and sample proportion (0.5). The results indicated a minimum required sample of 384 valid responses.

A pilot survey of 20 respondents helped refine the questionnaire. This preliminary phase allowed to identify unclear questions and understand whether the questionnaire required excessive cognitive effort. After conducting these interviews, minor adjustments were implemented to improve clarity and comprehensibility of the questionnaire and, consequently, enhanced response reliability.

The final questionnaire, structured to identify factors driving tourists to visit protected natural areas, comprised four sections adapted from Sherrouse et al. [23] to fit the Italian context. The first section examined visiting habits, including visit frequency and duration. The second section assessed self-perceived satisfaction with visits and awareness of certifications used to promote local products. The third section explored interest in various ecosystem services provided by protected areas and adapted by the current classification [48]. The final section collected respondents’ sociodemographic characteristics to analyse potential links with visiting patterns. The survey included binary questions (yes/no), open-ended questions, and Likert-scale items (1–5), where 1 indicated low importance and 5 represented high importance attached to the assessed item.

Following data collection, we conducted a data cleaning process, resulting in 400 valid responses for statistical analysis. In particular, incomplete sections of the questionnaire were excluded from the analysis, as their inclusion would have significantly reduced the sample size. Moreover, the data cleaning process was essential to convert respondents’ answers into numerical codes compatible with the statistical software. Coding was specifically required for categorical and dummy sociodemographic variables.

Table 1. Descriptive socio-demographic statistics and visiting habits of the sample (n = 400).

Variables	Categories	Codes	Frequency	Percent
Age groups	18–35	1	164	41.00
	36–50	2	136	34.00
	Higher than 50	3	100	25.00
Certification awareness	No	0	177	44.25
	Yes	1	223	55.75
Children	No	0	237	59.25
	Yes	1	163	40.75
Family	1 member	1	60	15.00
	2 members	2	152	38.00
	3–4 members	3	148	37.00
	More than 4 members	4	40	10.00
Income	Up to 1000 EUR/month	1	77	19.25
	1001–2000 EUR/month	2	196	49.00
	2001–4000 EUR/month	3	103	25.75
	More than 4000 EUR/month	4	24	6.00
International visitors	No	0	283	70.75
	Yes	1	101	25.25
	Prefer not to answer	Missing	16	4.00
Intellectual professions	No	0	349	87.25
	Yes	1	51	12.75
Provenience	Urban area	0	238	59.50
	Rural area	1	162	40.50
Overnight guests	No	0	133	33.25
	Yes	1	267	66.75
Study degree	Elementary and middle school	1	25	6.25
	High school	2	136	34.00
	University degree	3	216	54.00
	Higher education	4	23	5.75
Gender	Male	0	221	55.25
	Female	1	179	44.75

presents the sample socio-demographic characteristics, visiting habits and the codes adopted for the analysis. The final sample size aligns with the preliminary assessment of optimal survey requirements.

The data analysis followed multiple steps, combining descriptive statistics, multivariate exploratory analyses, and inferential tests to address the research questions. Each research question required a specific analytical approach.

To address the first research question concerning the importance of ecosystem services, descriptive statistical analysis was combined with the Wilcoxon matched-pairs signed-rank test [49] to identify which aspects tourists consider most relevant when visiting protected natural areas. The analysis included 14 items: 11 related to interest in ecosystem services and 3 assessing self-perceived satisfaction with the visiting experience. The Wilcoxon test, a useful tool for comparing variables, evaluates whether the median difference between pairs is zero. Since this test does not require distributional assumptions, it allows for the use of categorical data [50]. Pairwise comparisons were performed for all item combinations, applying a significance threshold of 0.1 (p-value). This approach highlighted significant differences among variables and facilitated their grouping based on similar responses when the null hypothesis was not rejected.

The second research question, which focused on visiting patterns, required the reduction in the dimensionality of the original items through Exploratory Factor Analysis (EFA). This multivariate statistical technique reduces the number of original variables into a smaller set of factors while preserving as much variability as possible [51]. The EFA employed the principal component method, generating new orthogonal variables or vectors [52]. Reducing the initial number of items was essential for simplifying interpretation, and grouping intercorrelated items into new factorial dimensions provided a clearer understanding of visitor patterns [53]. The generation of orthogonal variables derived from highly intercorrelated items facilitated the identification of variable patterns [54]. Factorial dimensions (q) are related to the intercorrelated variables (p) adopted in the EFA. Specifically, the dimension q is used to linearly obtain the original variables p, as indicated in Equation (1) [54].

y_ij = z_i1 b_1j + z_i2 b_2j + ⋯ + z_iq b_qj + e_ij

(1)

where y_ij is the value of the ith respondent on the jth variable in the original subsets. The right-hand side of the equation includes the coefficients b_kj, also known as factor loadings, which serve to interpret the factorial dimensions, thereby revealing patterns among respondents [55]. Factor loadings were obtained using varimax rotation, with only those exceeding 0.4 considered to enhance interpretability and model reliability [50,54]. Additionally, only vectors with eigenvalues greater than 1 were retained to ensure that the model explained over 60% of the total variance [56].

It is important to note that the equation has an infinite number of solutions since all parameters on the right-hand side must be estimated [57]. The model also includes an error term (e_ij) to account for variability unexplained by common factors. Furthermore, it enables the calculation of factor scores (z_ik) for each respondent (i), which can be used in subsequent analyses, such as regressions, clustering, or inferential tests, to gain deeper insights into consumer behaviour [58].

The preliminary assessment of the minimum sample size alone may not be sufficient; therefore, it was also necessary to evaluate the goodness-of-fit of the EFA. For this purpose, two tests were conducted: the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test [59]. The KMO test assesses whether common factors adequately explain correlations, producing an index that should exceed 0.6 for acceptable data suitability [60]. Bartlett’s test examines whether the correlation matrix significantly differs from an identity matrix (where no correlation exists). A significant result confirms the presence of correlations, validating the use of factor analysis [61].

Once the model’s reliability was confirmed, an additional evaluation was conducted to assess the internal consistency of the factorial dimensions derived from the EFA. As is common in consumer studies, Cronbach’s alpha was used for this purpose [62]. Only factors with a value of 0.6 or higher were retained for inferential analysis.

The third and fourth research questions required inferential tests to examine whether factorial scores varied significantly based on visitors’ socio-demographic and certification awareness. To achieve this, nonparametric tests were performed using these variables as grouping factors. When the grouping variable was binary, the Mann–Whitney U-test was applied. This test compares the rank sums of two groups to determine whether their distributions differ significantly, testing the null hypothesis (H0) that both populations share the same distribution [63]. When the grouping variables had multiple categories, the Kruskal–Wallis test was used. Also known as nonparametric ANOVA, this test extends the Mann–Whitney U-test to multiple groups and evaluates whether rank sums differ significantly across categories [64].

All analyses were conducted using Stata 15 software.

4. Results and Discussion

4.1. Wilcoxon Matched-Pairs Signed-Rank Test Results

Table 2. Summary statistics of the explorative items and Wilcoxon signed-rank test results.

Variables	Type	Median	Mean	SD	Groups
Clean air	Interest in ecosystem services	5	4.81	0.42	a
Local food consumption	Interest in ecosystem services	5	4.64	0.62	bc
Interest in ibex protection	Interest in ecosystem services	5	4.56	0.72	bcd
Local fauna interest	Interest in ecosystem services	5	4.51	0.68	d
Psychophysical well-being	Interest in ecosystem services	5	4.50	0.77	cd
Local water consumption	Interest in ecosystem services	5	4.45	0.74	d
Local flora interest	Interest in ecosystem services	4	4.39	0.69	e
Recreational activities	Interest in ecosystem services	5	4.31	0.94	e
Feeling safe	Interest in ecosystem services	4	4.20	0.74	f
Satisfied by hospitality	Self-perceived satisfaction	4	4.17	0.82	f
Satisfaction with trails	Self-perceived satisfaction	4	4.01	0.89	g
Local products purchase	Interest in ecosystem services	4	3.86	1.02	h
Satisfaction with reception	Self-perceived satisfaction	4	3.82	0.67	h
Educational activities	Interest in ecosystem services	4	3.64	1.11	i
Spiritual aspect	Interest in ecosystem services	3	3.05	1.36	l

presents the descriptive statistics for the original items included in the EFA, along with the inferential results from the Wilcoxon matched-pairs signed-rank test. The table allows for the identification of the original scores of the items and highlights significant differences related to the importance of the ecosystem services and satisfaction for tourists. Indeed, this analysis addressed the first research question by identifying the relative importance of different items based on visitors’ perceptions. Understanding which services provided by protected natural parks are most valued by the sample of tourists can help enhance these areas, their activities, and the local products they promote. Indeed, discussing these results has a role in managing protected areas to make them more attractive to tourists and to understand what landscape values can be important [65]. The table provides key insights into what visitors consider important and which elements can be leveraged to attract tourism. Furthermore, based on the differences observed between the sum of ranks, it was possible to separate statistically different variables using a p-value threshold of 0.1. Consequently, the letters represent groups of variables that are similar to each other with a p-value greater than 0.1. Variables that show different letters mean that they have been separated by the nonparametric test and are therefore statistically different from each other.

The highest-rated item is “Clean air”, indicating that visitors greatly appreciate this factor and may choose to visit protected natural areas primarily for this reason. This finding aligns with existing literature, which identifies air quality as a key determinant in tourists’ decisions [66] and suggests that travel choices are influenced by air quality concerns [67]. The second highest-rated item is “Local food consumption”, highlighting the importance of hospitality services in natural parks. Local food production plays a crucial role in rural development by supporting niche markets and fostering a sense of community [68]. Additionally, sustainable agricultural practices in marginal areas contribute to ecosystem conservation, further enhancing the area’s attractiveness [69]. These results emphasise the role of local products as a key driver of visitors’ choices [70]. The third factor of greatest interest is the protection of the ibex, which scored very similarly to the interest in wildlife conservation. This consideration is very important because it can be helpful to managers of protected natural areas, as the interest of native species is very important to visitors and can be helpful for park management and valorisation of the park’s identity. Furthermore, ensuring the protection of these animals can enhance the value of the area and meet the favour of tourists [71]. Moreover, the ibex protection can be particularly important since some species are considered by tourists as particularly charismatic and attractive [72] Another variable that scored highly is psychophysical well-being, which can be considered an aspect derived from the park’s fruition, allowing tourists to relax thanks to the natural capital and the services provided and improving the revisit intention [73].

Some variables obtained medium–high scores, indicating their importance for the enjoyment of protected natural parks. Among these, interest in local flora is important but the results suggest that tourists are more interested in animal species, even though both are important components for tourists [74]. Feeling safe in parks can be an important consideration for visitors, who can be reassured by the quality of the infrastructure and services provided in parks. In fact, risk management in parks is a factor linked to a higher probability of visits [75]. Hospitality and trails are also of interest and can be considered when choosing a destination, ensuring a good visitor experience for tourists [76].

Among the items with mid-to-low perceived importance, “Spiritual aspects” (e.g., spiritual peace and mindfulness, connection with nature, personal growth and renewal) received the lowest score, indicating that only a niche segment of respondents values this dimension. While the literature acknowledges spiritual tourism as a meaningful experience extending beyond religious connotations [77], it also suggests that spiritual aspects can be a primary motivator for some visitors [78]. However, differences in study contexts mean that this result does not contradict existing literature, as visitors still perceive spiritual value positively. The second lowest-ranked item was “Educational activities”, suggesting that while respondents show some interest in this aspect, they prioritise other services, limiting educational engagement to specific moments and locations. This finding partially aligns with the literature, which recognises the importance of cultural factors in shaping tourist behaviour, though educational aspects alone may not fully explain motivation and satisfaction [79]. Another variable that received a low-to-medium score was satisfaction with reception services. This result could indicate that although accommodation facilities are a fundamental element in the enjoyment of protected natural areas and can be important for the revisit intention [80], respondents are more focused on naturalistic aspects to ensure a good visiting experience. With regard to the purchase of local products, this variable suggests that the promotion of the Park’s local trade can be an opportunity for consumers to purchase typical products and for the valorisation of the areas [81] although it is not one of the main drivers for visiting the park.

4.2. Exploratory Factor Analysis Results

An EFA was conducted to identify latent vectors within the original set of variables and simplify the analytical framework by detecting visitation patterns. The results are presented in

Table 3. Exploratory factor analysis.

Items	Natural-Based Fruition	Local Products Consumption	Perception and Well-Being	Hospitality Oriented	Discovery Pattern	Spiritual and Intellectual Value
Local products purchase		0.711
Local food consumption		0.861
Local water consumption		0.674
Clean air			0.730
Feeling safe			0.671
Local fauna interest	0.840
Interest in ibex protection	0.801
Local flora interest	0.817
Recreational activities					0.798
Educational activities						0.744
Psychophysical well-being			0.551
Spiritual aspect						0.836
Satisfied by hospitality				0.896
Satisfaction with reception				0.696
Satisfaction with trails					0.723
Explained variance	14.74	12.27	10.01	9.79	9.67	9.64
Alpha	0.64	0.78	0.43	0.60	0.46	0.50
Alpha: overall model	0.67
KMO test	0.66
Bartlett’s Test	0.00 ***

*** = Significant p-value at 0.01.

.

Regarding goodness-of-fit assessment, the KMO test yielded a value above 0.60, confirming the dataset’s adequacy for factor analysis being this value acceptable. Indeed, values lower than 0.5 are considered unacceptable for factor analysis [60,82]. Moreover, Bartlett’s test of sphericity was significant, indicating that the correlation matrix differs significantly from the identity matrix and that strong correlations exist among the original items. The analysis identified six factors with eigenvalues greater than 1, collectively explaining over 60% of the variance. These factors are discussed about visitation patterns. However, only factors with a Cronbach’s alpha coefficient of 0.60 or higher were retained for inferential testing while factors with lower coefficients were used only for descriptive purposes. Thus, inferential estimates were developed with robust factorial dimensions. Overall, the model demonstrated acceptable internal consistency, with a reliability coefficient exceeding 0.60 suggesting a good association between the variables chosen to develop the factor analysis being the value in line with literature using factorial analysis [83,84].

The first factor, accounting for 14.74% of the variance, was named ‘Natural-based fruition’. It includes items positively related to Local fauna interest (0.840), Interest in ibex protection (0.801), and Local flora interest (0.817), suggesting a visitation pattern centred on the naturalistic aspects, where some respondents visit parks primarily to observe local flora and fauna. This factor demonstrated high internal consistency, justifying its inclusion in inferential tests. Such a pattern is particularly relevant for species conservation and visiting choices, as interest in local biodiversity is recognised as a key determinant of tourists’ preferences [85]. Moreover, this factor is also in line with the “nature” dimension suggesting that natural aspects can be particularly important for tourism decisions [86].

The second factor, explaining 12.27% of the variance, was named ‘Local products consumption’. It includes local products purchase (0.711), local food consumption (0.861), and Local water consumption (0.674). This dimension exhibited the highest internal consistency (alpha = 0.74), justifying its inclusion in the final analysis. The results suggest that local food consumption plays a significant role in tourists’ decisions, aligning with studies that identify it as a key driver of visitor choices [87]. This dimension closely resembles the Consumption activities factor obtained by Ghazvini et al. [88]. This factor is of considerable interest due to the importance that tourism aimed at the consumption of local products can have on the economy of natural areas [89].

The third factor, explaining 10.01% of the variance, was named ‘Perception and well-being’. It relates to the role of ecosystems in enhancing psychological well-being (0.551) and sense of security (0.671). Additionally, clean air (0.730) is associated with this aspect, suggesting its contribution to psychological well-being and perceived safety. These findings indicate that natural areas influence visitors’ personal perceptions. The relationship between the sense of security and other variables aligns partially with the literature, which highlights how safety perception is shaped by local community characteristics and tourism dynamics [89]. However, as the alpha value was below the threshold, this factorial dimension was retained only for descriptive analysis.

The fourth factor, accounting for 9.79% of the variance was identified as hospitality-oriented. It includes the items related to satisfied by hospitality (0.896) and satisfaction with reception (0.696), both being positively related. This factor underscores the importance of hospitality services in facilitating visitor enjoyment of the ecosystem services. Given the acceptable internal consistency, this vector was included in both descriptive and inferential analyses. The results align with literature recognising hospitality as a component in tourists’ selection of natural destinations [88,90]. Moreover, a similar factor where consumers valued the quality of the hospitality services were found indicating the importance of these aspects for tourists’ overall satisfaction [76].

The fifth factor, explaining 9.67% of the variance, was named ‘Discovery pattern’. It combines interest in recreational activities (0.798) and satisfaction with trails (0.723), which are positively correlated. This suggests that the enjoyment of recreational activities is closely linked to the availability of accessible trails for nature exploration. Also, this finding is consistent with previous research highlighting the importance of recreational ecosystem services in enhancing visitor experiences [91]. However, as the alpha was below 0.60, this factor was excluded from the inferential analysis.

The final factor, explaining 9.64% of the variance, was labelled ‘Spiritual and intellectual value’. This dimension includes educational activities (0.744) and spiritual aspects (0.836), which are positively correlated. The results suggest that educational components are associated with the spiritual experience evoked by ecosystem services. This interpretation aligns with the literature examining the link between environmental education and spirituality [92]. Due to its low internal consistency, it was retained only for descriptive purposes.

4.3. Inferential Tests Analysis

We conducted nonparametric tests, combining the latent variables obtained through EFA with visitor characteristics as grouping variables. Specifically,

Table 4. Mann–Whitney U test applied to factorial analysis.

Variables	Categories	Natural-Based Fruition	Local Products Consumption	Hospitality Oriented
Certification awareness	No	−0.044	0.030	0.167
	Yes	0.397	0.513	0.152
Z-value		−1.671 *	−2.464 **	−0.468
Children	No	0.120	0.003	−0.002
	Yes	0.160	0.513	0.230
Z-value		−0.518	−2.717 ***	−1.762 *
Intellectual professions	No	0.295	0.194	0.151
	Yes	−0.155	0.428	0.203
Z-value		2.788 ***	−1.468	−0.371
International visitors	No	0.234	0.375	0.101
	Yes	0.134	0.080	0.436
Z-value		−0.485	2.737 ***	−3.005 ***
Provenience	Urban	0.295	0.148	0.192
	Rural	−0.008	0.294	−0.014
Z-value		1.074	−0.311	2.203 **
Overnight guests	No	0.028	−0.099	−0.135
	Yes	0.300	0.418	0.222
Z-value		−1.645 *	−3.433 ***	−2.555 **
Gender	Male	0.060	0.152	0.165
	Female	0.295	0.261	0.165
Z-value		−0.495	−1.005	0.108

***, **, * = Significant p-value at 0.01; 0.05; 0.1, respectively.

presents the results of the Mann–Whitney U test for dummy variables, while

Table 5. Nonparametric ANOVA applied to factorial analysis.

Variables	Categories	Natural-Based Fruition	Local Products Consumption	Hospitality Oriented
Age groups	18–35	0.228	−0.006	0.109
	36–50	0.060	0.423	0.186
	Higher than 50	0.295	0.401	0.183
Chi-square		1.181	6.055 **	0.977
Family	1 member	−0.164	−0.283	−0.027
	2 members	−0.016	0.157	0.177
	3–4 members	0.531	0.285	0.186
	More than 4 members	0.274	0.418	−0.081
Chi-square		15.461 ***	6.449 *	3.011
Income	Up to 1000 EUR/month	0.586	0.123	0.109
	1001–2000 EUR/month	−0.010	0.123	0.222
	2001–4000 EUR/month	0.133	0.362	−0.068
	More than 4000 EUR/month	0.609	0.571	0.159
Chi-square		4.092	3.219	4.321
Study degree	Elementary and middle school	−0.033	0.011	0.209
	High school	0.295	0.085	0.187
	University degree	0.295	0.258	0.159
	Higher education	−0.151	0.539	−0.253
Chi-square		3.305	5.152	2.501

***, **, * = Significant p-value at 0.01; 0.05; 0.1, respectively.

reports the results of the nonparametric ANOVA, used to assess non-causal relationships when categorical variables with at least three categories were considered as grouping factors. These tests are necessary to answer research questions 3 and 4 and thus identify differences in the visit patterns described by the factors previously identified based on respondents’ characteristics.

Certification awareness significantly influences the first and second factor, with respondents aware of Natural Park Certifications exhibiting a stronger interest in natural-based experiences and local product consumption. This suggests that certification awareness is positively linked to the consumption and purchase of local products [83]. Consumers familiar with certifications may better understand the attributes of local food products, leading to greater appreciation and interest in their consumption [93]. This result also confirms the important role of knowledge in shaping respondents’ decisions [94]. Additionally, certification awareness appears associated with greater sensitivity towards local fauna and flora [95].

The respondents travelling with children show significantly higher values for the second factor, indicating a stronger interest in local product consumption. This may be attributed to children’s influence on household purchasing decisions [96]. Moreover, local products are often perceived as high quality [93], and information on their origin can shape consumer perceptions and willingness to pay [97]. Parents, particularly those with young children, may prioritise quality and nutritional value when selecting food [96,98]. Furthermore, these results are consistent with the literature suggesting that families with children may be more interested in short supply chains, partly because of the educational factor this could have on children [99]. Finally, greater interest in the quality of recreational facilities is an essential factor for the comfort of families with children during trips [100].

Visitors engaged in intellectual professions show significantly lower values for nature-based experiences, indicating less interest in local flora and fauna. This group, often associated with higher income and economic stability, may be more inclined towards educational and adventure activities rather than biodiversity conservation [101].

International visitors display notable differences about local products perception and hospitality. Specifically, Italian tourists show a particularly strong interest in local products, consistent with previous literature highlighting the interest in regional products [102]. In contrast, foreign visitors show greater interest in hospitality service quality, which is a key determinant in international tourist choices due to infrastructure needs and language barriers [103].

Urban respondents report significantly higher satisfaction with hospitality services, suggesting they may have different expectations compared to rural tourists. This aligns with findings that urban visitors tend to appreciate natural areas more [104].

Overnight guests exhibit significantly higher interest in nature-based fruition and greater satisfaction with hospitality services. This suggests that respondents choosing overnight stays have a deeper appreciation of nature and heightened awareness of the parks’ natural aspects [104]. Longer stays are also linked to increased hospitality satisfaction, as extended exposure enhances the overall visitor experience. These findings align with research indicating that tourists staying in eco-certified accommodations are more likely to support sustainable tourism practices [105]. Moreover, consumption of local product can move tourists to extend their permanence, explaining the association between overnight guests and local food consumption [106].

Several relationships emerged when categorical characteristics were tested in combination with the factorial dimensions. Age significantly influences the second factor, with respondents aged 36–50 and over 50 showing greater interest in local product consumption. Older consumers tend to favour local food [105] due to their emphasis on supporting regional producers [93] and a stronger sense of local identity [107]. This may also be linked to higher income levels of older generations [108].

Family size significantly influences nature-based experiences and local production consumption. Families with three or four members exhibit a stronger interest in biodiversity conservation, while those with more than four members show greater consumption of local products. Large households may engage more with nature, and their higher local product consumption may stem from viewing local food as a healthier, higher-quality option [109,110]. In addition, local food consumption can be associated with families for their interest in the short food supply chain and for educative purposes [99].

5. Conclusions

5.1. Main Outcomes

This study enhances our understanding of the motivations influencing visitors’ choices of protected natural areas as tourist destinations, highlighting differences based on their socio-demographics characteristics and visiting habits. Regarding the first research question, we found that air quality and local product consumption play a crucial role in shaping respondents’ decisions. Other factors, such as the spiritual value of protected areas and educational activities, received mid-to-high scores but were considered less important.

For the second research question, we identified six visitation patterns, three of which demonstrated high internal consistency and were used for inferential tests. These patterns include an interest in local fauna and flora, local product consumption, and satisfaction with hospitality services. Addressing the third research question, we observed that socio-demographic characteristics influence visitation patterns. Families with children and older visitors showed a stronger interest in local product consumption, while urban respondents were more attracted to local flora and fauna. Additionally, other tourist characteristics also affect visitation patterns. In particular, focusing on the fourth research question related to the role of awareness and habits, certification awareness increases interest in purchasing local products, while overnight stays heighten the importance placed on natural biodiversity, local food products, and hospitality services.

5.2. Implications

This study provides valuable insights for local producers, hospitality managers, policymakers, and academics. For local producers and hoteliers, improving the quality of overnight experiences and expanding the availability of local products can improve visitors’ overall experience, thereby benefiting the local economy. Certification schemes can also help reduce asymmetric information, add value to local products, and potentially increase income.

For policymakers, identifying visitation patterns offers guidance for targeted investments that can be important to value protected natural areas and the connected ecosystem services. Indeed, developing services and enhancing ecosystem services within an integrated approach to conservation and local development can improve accessibility and the overall visitor experience. Furthermore, greater awareness can help managers of protected natural areas to promote these services and support them in dealing with the eventual introduction of a price for their use. Such considerations can be important both for the management of natural capital and ecosystem services, and for hospitality services. This possibility may be in accordance with current regulations and policies promoted by local authorities in accordance with the principle of subsidiarity. Finally, for academics, this study contributes novel insights into the factors influencing tourists’ choices regarding protected natural areas, addressing gaps in the literature on the relationships between ecosystem services and tourist behaviour.

5.3. Limitations and Future Research

While this study provides valuable clues about tourists’ behaviour and attitudes towards protected natural areas, it has certain limitations. The multivariate statistical approach employed is exploratory, as we used an EFA based on the principal component method. Although such an approach helps to identify novel aspects, its replicability is lower than that of confirmatory studies. Confirmatory analysis, such as structural equation modelling using validated scales, may provide a more robust assessment of variability in socio-economic experiments.

Moreover, while Gran Paradiso National Park represents a highly significant and emblematic case among the 24 Italian national parks, it may not fully capture the diversity of tourism experiences across all protected areas. Future research should, therefore, not only pursue cross-country comparisons but also extend investigations to other Italian natural areas. These works could include other aspects related to landscape values in their various expressions: ecological, economic, and social. This would allow for a more comprehensive evaluation of visitor behaviours and characteristics, thereby testing the representativeness and robustness of the findings.

Additionally, stated preference methods, such as choice experiments incorporating ecosystem service-related covariates, could be employed to assess tourists’ willingness to pay. This would offer valuable insights into the economic value of ecosystem services within protected natural areas supporting stakeholders in managing these areas.

Finally, since a convenience sample was used rather than a probability-sampling, generalisation to the tourist population may be limited. However, this choice is still a valid approach and consistent with the methodology used for the exploratory purposes of the study.