New Challenges to Managing Cultural Routes: The Visitor Perspective

Abstract

In recent decades, cultural routes have become increasingly important in the tourism offerings of rural destinations. It is vital to identify the factors that visitors consider important to their satisfaction with a cultural route and its overall impact on a tourist destination. This paper employs Principal Components Analysis (PCA) to investigate the dimensions of visitor satisfaction on cultural routes. A survey assessed satisfaction with 37 specific aspects of a cultural route in a tourist destination, revealing three key dimensions: satisfaction with destination characteristics, tourism infrastructure, and the cultural roots offerings. The findings highlight the need to move beyond traditional models of tourist satisfaction that focus on specific experiences and instead explore overall factors contributing to tourist satisfaction with a destination. The study examines cultural routes in rural regions of Primorje-Gorski Kotar County in Croatia and neighboring Bosnia and Herzegovina. The scientific findings have significant implications for cultural route managers, aiding in the innovation and creation of offerings that will enhance tourist and visitor satisfaction.

1. Introduction

Cultural tourism is an increasingly popular and significant form of tourism, as more travelers seek immersive experiences that allow them to engage directly with the rich history, cherished traditions, art, and vibrant local culture of their chosen destinations in rural regions. Providing consistently satisfying and memorable experiences for these cultural tourists is crucial for the long-term success and sustainability of cultural tourism destinations and attractions, including cultural routes, trails, and roads.

While tourist satisfaction has been widely studied across various tourism contexts, the specific factors underlying the experiences and perceptions of cultural tourists remain relatively underexplored in the academic literature. This study aims to delve deeper into identifying the key dimensions of satisfaction for tourists visiting a cultural route or heritage trail. By employing principal components analysis, the research seeks to reduce the large set of 37 discrete satisfaction attributes into a smaller, more manageable set of coherent and interpretable satisfaction components.

As noted in previous studies, developing a nuanced understanding of the multidimensional nature of tourist satisfaction can greatly assist destination managers and tourism planners in prioritizing strategic improvements and allocating resources more effectively to enhance the overall tourist experience and increase visitor satisfaction. This is particularly important for cultural tourism, where the unique and immersive nature of the experiences can lead to diverse satisfaction factors that may not be as prominent in other tourism contexts.

Cultural routes in tourism have been the subject of scientific studies since the 1990s when [1] studied the importance of Medieval pilgrimage routes in developing cultural tourism. In later years, cultural routes were researched from a number of aspects, with studies focusing on motivations to visit cultural routes [2], the combination of wine tourism and cultural routes [3,4], creative tourism on cultural routes [5], industrial tourism and cultural routes [6], accessible tourism on cultural routes [7], responsible tourism [8], and the economic aspect of the impact of activities and attractions along cultural routes that foster cooperation and partnerships between local regions [9]. Cultural routes are especially important for rural areas and in connecting and presenting heritage within rural tourist destinations. Cultural routes are acknowledged for their wide-ranging effects, encompassing social, environmental, and cultural dimensions. The social impact involves greater community engagement and heightened awareness of cultural heritage among locals, while environmentally, these routes promote sustainable tourism through responsible heritage use, and culturally, they strengthen connections with historical sites, encourage innovative heritage valuation, and raise local awareness of the importance of intercultural dialogue [7,8,9].

With the promotion of growing numbers of cultural routes for tourism purposes, recent studies have focused on how to carry out tourism activities on cultural routes, in particular with regard to sports and recreational activities. Trono (2014) [10] sees recreation on cultural routes as an innovative form of tourism offering, while Schirru’s study (2019) [11] identifies cycling as an element of tourism offering innovation to foster the sustainable tourism development of cultural routes. Vistad et al. (2020) [12] in their study explore motivations for hiking on cultural routes. Fan et al. (2023) [13] found that a positive perception of visitors to cultural routes depends on their satisfaction with the routes’ offerings, in which recreational offerings are especially important. In the literature, however, there are still gaps to be filled by studying the offerings of cultural routes (in particular with regard to outdoor activities that are often characteristic of cultural routes) as well as the perceived satisfaction of visitors to cultural routes, indirectly leading to new directions for developing cultural routes [14]. The literature also lacks an in-depth exploration of the infrastructure requirements necessary to support outdoor activities on cultural routes, as well as a comparative analysis of cultural routes across different geographic and cultural contexts.

This paper explores cultural routes through outdoor activities and examines the relationship between price and quality. Outdoor activities are prevalent on cultural routes and thus require the necessary infrastructure. The following research questions are posed:

RQ1: What factors positively influence the fulfillment of tourists’ expectations on cultural routes?

RQ2: How does satisfaction with various aspects of a tourist destination affect the perceived price-quality ratio of the offerings on a cultural route?

This study contributes to the development of cultural routes in rural regions by analyzing the satisfaction of visitors and tourists, a key factor in improving the offerings of cultural routes. Given the ever-greater increase of outdoor activities in tourism [15], the development of cultural routes is viewed within the context of outdoor recreation which makes cultural routes particularly adaptable to a variety of tourist activities, as well as to various forms of the offering. Providing tourist experiences is vital to the long-term success of cultural route destinations, and although the tourist experience has been extensively studied, previous studies have not fully explained the specific factors shaping the cultural tourist experience in particular on cultural routes [16]. Hence, this study aims to identify the key factors affecting the overall traveler experience of individuals involved in cultural route tourism.

2. Importance of Cultural Routes and Hypotheses Development

Strategic planning and the promotion of specific regions, with a focus on developing an authentic tourism offering, is crucial for the development of any tourism product, including cultural routes. This includes providing credible cultural experiences and ensuring high-quality infrastructure and excellent service to encourage visitation. The development of cultural routes should be based on the historical and cultural assets present in a given region [17].

Tourism policymakers, destination management organizations, and service providers need to work together to provide top-quality experiences to visitors that are cost-effective for destination stakeholders while ensuring that cultural routes are not threatened in terms of their environmental, social, and cultural integrity [18,19].

Ntassiou (2024) [20] underscores infrastructure as a key factor in cultural route development. In any tourist destination, the condition of tourism infrastructure has an immense impact on the development of tourism and the level of tourist satisfaction. Many authors have theoretically studied the firm link between tourism development and infrastructure. Singh, Saini, and Majumdar (2015) [21] argue that tourism infrastructure encompasses typical tourism infrastructure (accommodation facilities, arrival servicing facilities, tourist information, and routes) and atypical tourism infrastructure (transportation facilities, roads, transportation points, local facilities, communal and public transport, shopping and service facilities, hospitality facilities, and related facilities such as sports, recreation, and entertainment). Tourism infrastructure can have a huge impact not only on reinforcing a destination’s image but on changing it as well [22]. According to Mwankunda (2023) [23], balanced tourism development can be achieved through genuine cooperation between tourism infrastructure developers and conservationists by devising the best strategies for building the most environmentally friendly infrastructure.

In recent decades, cultural routes have become increasingly important tourism infrastructures for the development of tourist destinations, particularly in rural regions, and are regarded as enablers of communication and interpretation between a region’s natural and cultural assets. A cultural route should not be viewed narrowly as a mere nature hiking trail, but rather more broadly as a network of places or geographical regions of special interest [8]. A cultural route implies a collection of points of interest (POI) encompassing a given geographical region and sharing comparable attributes, a central theme, and distinctive architectural or historical features, together with facilities, natural landscapes, and buildings. Modern cultural routes strive to build a link between the past and the present of a given region, town, or settlement [24].

In their article on route tourism, Vada, Dupre, and Zhang (2023) [25] claim that tourist itineraries, mapped along existing routes that connect cultural attractions, help to stimulate social, economic, and cultural development. Malaperdas (2022) [26] points out that, taking into account the principle of sustainability, cultural routes provide numerous opportunities to tourists for gathering information, entertainment, and relaxation. According to Graf (2019) [27], cultural routes are of immeasurable value for local communities and have huge potential for reinforcing social cohesion and developing sustainable cultural tourism. Various authors have studied cultural routes as tourism products (for example, Graf, Popesku, 2016 [28]; Cojocariu, 2015 [29]; Dayoub, 2020 [30]), and with regard to their cultural and historical heritage, role in regional development [31,32], their impact on the local population [33] and with regard to sustainability [34,35,36,37,38].

As outdoor activities are increasingly being linked to learning about the local culture [39], Pröbstl-Haider, et al. (2023) [40], describe outdoor recreation as a form of activity that involves engaging with natural and cultural heritage. The outdoor recreation offering is typically a component of a cultural route; hence, the quality of the outdoor offering’s elements will determine the quality of a cultural route.

The concept of cultural routes has attracted much attention in recent years, as communities and policymakers begin to recognize the value of these interconnected routes featuring the unique heritage, traditions, and artistic expressions of regions [41]. Considering that tourists are looking for immersive and authentic experiences, understanding the factors that contribute to tourist satisfaction with the cultural route offering has become crucial for destinations wanting to improve their appeal and competitiveness [42]. The previous literature suggests that the diverse personality traits and motivations of tourists can have a significant effect on their satisfaction with cultural tourism services. Furthermore, sources of satisfaction, in either tangible or intangible cultural elements, may also have varying implications for loyalty to a destination [43], which is an important implication for managers [16].

The quality of a tourist destination exceeds the quality of its attributes and includes the quality of tourism services and the perceived behavior of service providers [44]. The price and quality of cultural facilities and services along a route are also factors affecting satisfaction. The perceived value and the cost of a cultural tourism experience are also key factors impacting the decision-making and satisfaction of visitors. Traveling a cultural route or visiting cultural heritage is connected with economic factors, primarily the influence of the price and costs of traveling, and with the tourism offerings and attractiveness [45,46]. Service quality is shown to have a significant positive effect on tourist satisfaction, with previous experience moderating this relationship [47]. Image destination, which is influenced by factors such as service quality, has a positive effect on perceived quality, perceived value, and tourist satisfaction [48]. Accordingly, the experience value of tourism, consisting of the benefits of return on investment for consumers, the excellence of services, aesthetics, etc., as a driver of tourist satisfaction and tourist behavior, is receiving more and more attention [49]. When the environment, service, and other aspects match the guests’ expectations, tourists feel great psychological pleasure in spending their time and money, in turn leading to greater tourist satisfaction and a greater probability of their making repeat visits to or recommending a destination [50]. Marketing efforts in tourism focused on measuring and enhancing tourist satisfaction are crucial in generating value, encouraging destination development, improving the offering, and boosting visitor loyalty.

Given the growing emphasis on cultural routes in the literature and their increasing importance in tourism, understanding the factors that drive tourist satisfaction has become essential. This study addresses key hypotheses aimed at exploring how expectations and perceived quality influence visitor satisfaction, particularly in the context of cultural routes and their associated outdoor activities.

The hypothesis directly aligns with RQ1 and RQ2 by proposing that tourists’ satisfaction with specific aspects of the destination (e.g., accommodations, attractions, customer service) will positively influence whether their expectations for engaging in outdoor activities are fulfilled. The rationale is that when tourists are satisfied with various aspects of their visit, they are more likely to feel that their overall expectations, including those related to outdoor activities, have been met or exceeded (RQ1). By testing the hypothesis, which aspects of the cultural route contribute most significantly to fulfilling tourists’ expectations can be identified. Therefore, two hypotheses are formulated:

H1. 

Satisfaction with aspects of the tourist destination is positively related to tourists’ fulfilled expectations regarding engaging in outdoor activities on the cultural route.

H2. 

Satisfaction with aspects of the tourist destination is positively related to perceived value for money and the perceived quality of the offering on the cultural route.

The hypotheses are designed to address RQ2 by examining how satisfaction with different components of the destination impacts tourists’ perceptions of value for money and the quality of the cultural route. Additionally, the hypotheses posit that higher satisfaction with aspects such as accommodations, local attractions, and customer service will lead to higher perceived value and quality. This relationship is vital for understanding how overall satisfaction influences tourists’ evaluation of their experience, particularly in terms of cost-effectiveness and quality assessment.

3. Methodology

Research was conducted in two countries—Croatia and Bosnia and Herzegovina—to study tourist satisfaction and the pursuit of outdoor activities on cultural routes. These two regions were selected for their distinctive and diverse cultural and historical heritages, which exemplify the broader spectrum of cultural tourism in the area. By encompassing a variety of historical periods, cultural themes, and outdoor activities, this research seeks to draw broader conclusions about the factors influencing tourist satisfaction across different types of cultural routes. The survey was carried out from March to December 2023. The research instrument was a questionnaire, written in Croatian and English, and distributed in a virtual setting as well as in physical form on-site. The study covered The Routes of the Frankopans and the Mythical-Historical Trail Trebišća-Perun, two different cultural routes in Primorje-Gorski Kotar County in Croatia, and the Ćiro Cycling Trail in Bosnia and Herzegovina. The studied cultural routes boast diverse cultural and historical heritages, tied together by networks of trails and paths. The connecting theme of the cultural route The Routes of Frankopans is the heritage left by the Frankopan family (castles, fortresses), together with Interpretation Centres, while the central theme of the Mythical-Historical Trail through Učka Nature Park is Old Slavic mythology. The cultural Ćiro Cycling Trail has revitalized the resources and networks of narrow-gauge railways dating from the end of the 19th and the beginning of the 20th century. The questionnaire was distributed to 986 respondents: 138 questionnaires in paper form and 848 in virtual form. A total of 362 questionnaires were returned, of which 95 were deemed invalid due to missing responses. The IBM SPSS Statistics 23 software was used for data analysis.

Before the main study, a pilot study was conducted to test the research instruments and verify the feasibility of the proposed analytical methods. The survey included 37 items assessing satisfaction on a Likert scale across various aspects of the destination and the cultural route itself. The primary objectives of the pilot study were to identify key variables that influence tourist satisfaction and expectations on cultural routes, to test and refine the survey questionnaire for clarity, relevance, and completeness, and to assess the feasibility of applying Principal Component Analysis (PCA) to reduce the dimensionality of satisfaction attributes into a more manageable set of underlying dimensions for hypothesis testing. PCA identifies the main dimensions, or principal components, that account for the maximum variance in the data while retaining underlying trends and patterns [51]. The Varimax rotation method was applied to the PCA to enhance the interpretability of the factors by maximizing the variance of squared loadings across the factors, thereby making it easier to identify the most significant variables within each factor.

The initial phase involved surveying a sample of 80 respondents to examine factors that positively influence the fulfillment of tourists’ expectations according to the research questions and hypotheses. PCA revealed a coherent structure in the data, identifying key components that encapsulate the major satisfaction attributes. This reduction facilitated easier interpretation and subsequent analysis in the main study. The PCA transformed the original satisfaction variables into a new set of principal components, which were then ranked based on the amount of variance they explained in the data. Principal Component 1 (PC1) captures the maximum variance in the data, with each subsequent component, orthogonal to the previous ones, capturing the remaining variance. After accounting for the variance explained by PC1, Principal Component 2 (PC2) was identified as the next orthogonal component, capturing the second highest amount of remaining variance and representing a different, uncorrelated pattern in the data. Similarly, Principal Component 3 (PC3) captures the next level of variance, also uncorrelated with the first two components, thereby revealing yet another distinct pattern within the dataset. This results in a reduced number of variables, facilitating further data analysis and hypothesis testing. Similar studies have employed PCA to gain insights into the dimensionality of tourist satisfaction. One study used PCA to identify six key dimensions of package tour satisfaction, including tour operator’s destination services and accommodation services, which were found to be the most important factors in explaining the success of the vacation experience [52]. Another study conceptualized a model of tourist satisfaction at the destination level, identifying eight latent constructs that serve as antecedents and consequences of satisfaction [53].

After performing PCA, hypothesis testing was carried out using multiple linear regression. This method analyzed the effects and relationships between several independent variables—ratings of satisfaction with three aspects of the tourism offer—and a dependent variable. This analysis helped to understand the influence of different factors on tourist satisfaction and the perceived price-quality ratio of the offerings on the cultural routes.

4. Results and Discussion

The pilot study in the first stage provided valuable insights that significantly refined the main study’s methodology. Based on the feedback received, several questions were rephrased for enhanced clarity, and additional questions were included to capture more nuanced aspects of tourist experiences. For instance, the section on gastronomy offers was expanded to include ratings for the general culinary offer, the offer of beverages, and the offer of local gastronomy. Additionally, the item that inquired whether respondents had visited cultural routes in other regions was adapted to also capture the intention to visit these routes in the future.

In PCA, the first principal component captures the maximum variance in the dataset, while each subsequent component, orthogonal to the previous ones, captures the remaining variance. This orthogonality ensures that each component adds unique information, resulting in a reduced number of variables and simplifying data analysis. Based on the PCA results from the 37 initial components, three key dimensions of tourist satisfaction were identified. Each of these dimensions represents specific aspects of the tourist experience, providing deeper insights into the factors impacting visitor satisfaction (Table 1).

Prior to analysis, eight respondents were removed, who gave the same ratings to each of the statements, i.e., whose intra-individual variance was 0 or close to 0 (less than 0.2).

The results of satisfaction with each component were calculated as the average satisfaction with all statements comprising a specific component (composite variables of satisfaction with individual components of the tourist destination). A total of six statements, whose loadings on the components were low (less than 0.5) were removed from the computation.

Table 2 shows the average values, standard deviation, and Cronbach’s alpha of the satisfaction evaluation scale of each component of satisfaction.

In Table 2 the components of satisfaction are synthesized into three categories:

PC1: Satisfaction with the destination’s characteristics—includes aspects referring to the beauty of the landscape, the preservation of the environment, the gastronomic offering, and similar characteristics of a destination that contribute to forming a visitor’s overall impression. The arithmetic mean is 5.7, indicating a high level of satisfaction among tourists. The standard deviation (0.948) indicates that the variability in satisfaction scores is relatively low, while Cronbach’s alpha of 0.900 suggests that the scale has high internal consistency and reliability.

PC2: Satisfaction with the tourism infrastructure in general—includes aspects referring to the general tourism infrastructure, including promenades, traffic accessibility, tourism signage, and other elements that help to make the tourist’s visit easier. The arithmetic mean of 5.5 suggests a high level of satisfaction. The standard deviation (0.934) again points to low variability in scores. Cronbach’s alpha of 0.893 indicates the scale has high internal consistency.

PC3: Satisfaction with the characteristics of the cultural route—focuses on certain aspects of cultural routes, including folklore events, mobile apps for cultural routes, souvenirs, and other similar features. The arithmetic mean of 4.5 suggests a slightly lower level of satisfaction relative to the two previous components. The standard deviation (1.499) indicates greater variability in satisfaction scores, while Cronbach’s alpha of 0.902 indicates the scale has high internal consistency and reliability.

All three components (PC1, PC2, and PC3) have Cronbach’s alpha values above 0.8, indicating high reliability and internal consistency of the scale. This suggests that the items within each principal component are highly reliable and effectively measure their respective dimensions.

These coefficients are high enough for the scales to be used in further analyses, such as hypotheses testing. Analysis shows that all three components of satisfaction with destination characteristics, tourism infrastructure, and the specific features of the cultural route are central to understanding overall tourist satisfaction. The composite variables of satisfaction are acceptable for further use as a result of the high Cronbach’s alpha coefficients.

The next step involved testing the hypotheses relating to these components to gain a better understanding of the specific factors contributing to tourist satisfaction and to identify potential areas for improvement.

4.1. Linear Regression—Scores × Fulfilled Expectations

The results of linear regression provide detailed insight into the relationship between scores (dependent variable) and fulfilled expectations (independent variable). The model was found to be statistically significant, providing useful insight into key factors affecting scores (Table 3).

An R² value of 0.216 indicates that fulfilled expectations explain 21.6% of the variance in scores. Although this model does not explain the remaining 78.4% of variance, the significant value of R² suggests that fulfilled expectations play a vital role in assigning a score. The adjusted R² of 0.201 confirms that when the number of predictors is taken into consideration, the model continues to maintain significant predictive power. The Root Mean Square Error (RMSE) value (1.062) in the alternative model is lower than in the null model (1.188), meaning that the model with fulfilled expectations as a predictor is better at predicting scores and reduces the average prediction error. The tourism infrastructure score is shown to be a statistically significant predictor, with B = 0.362 and p = 0.004, suggesting that improvements to tourism infrastructure contribute significantly to respondents giving higher scores.

Although the score for destination characteristics has a positive contribution, with B = 0.198, it is not statistically significant (p = 0.107). This suggests that despite destination characteristics having a positive effect on scores, their influence is not strong enough to be statistically significant in this model. A high F-value (14.583) (a higher F change value indicates that the new predictors significantly enhance the model’s explanatory power), and a very low p-value (1.945 × 10⁻⁸) strongly suggest that the model as a whole has significant predictive power. This means that the probability these results were obtained by chance is very small, thus confirming the significance of the relationship between scores and fulfilled expectations.

The score for cultural routes has a less positive contribution, with B = 0.086, and also is not statistically significant (p = 0.165), indicating that while the characteristics of cultural routes may have a positive effect, their contribution to the score is not statistically significant (Table 4).

Based on these results, investment in tourism infrastructure can be said to have the greatest impact on improving scores. Improvements to the characteristics of destinations and cultural routes may also contribute to better scores although their effects are not statistically significant according to this model.

The conclusion of the conducted analysis is that the regression is statistically significant and explains 21.6% of the variance (R = 0.465, R² = 0.216) in responses to the question regarding fulfilled expectations. The relationship between satisfaction scores and fulfilled expectations shows there is a relationship between tourism infrastructure scores and fulfilled expectations, while a statistically significant relationship was not found between the other two aspects, that is, satisfaction with those aspects and fulfilled expectations.

4.2. Linear Regression: Scores × Value for Money

The results of linear regression provide detailed insight into the relationship between scores (dependent variable) and perceived value for money (independent variable). The model was found to be statistically significant, providing useful insight into key factors affecting scores (Table 5).

An R² value of 0.331 indicates that the perceived value for money explains 33.1% of variations in scores. Although this model does not explain the remaining 66.9% of the variance, the significant value of R² suggests that perceived value for money plays a vital role in assigning a score. The adjusted R² of 0.318 confirms that when the number of predictors is taken into consideration, the model continues to maintain significant predictive power.

The RMSE value (1.028) in the alternative model is lower than the RMSE value in the null model (1.245), meaning that the model with perceived value for money as a predictor is better at predicting scores and reduces the average prediction error. The tourism infrastructure score is shown to be a statistically significant predictor, with B = 0.702 and p = 2.268 × 10⁻⁸. This suggests that improvements to tourism infrastructure contribute significantly to higher scores.

The score for destination characteristics has a positive contribution, with B = 0.198, but is not statistically significant (p = 0.213). This suggests that despite destination characteristics having a positive effect on scores, their influence is not strong enough to be statistically significant in this model.

The score for cultural routes has a negative contribution, with B = −0.086, and also is not statistically significant (p = 0.150), suggesting that while the characteristics of cultural routes may have a positive effect, their contribution to the score is not statistically significant (Table 6).

A high F-value (26.190) and a very low p-value (8.045 × 10⁻¹⁴) strongly suggest that the model as a whole has significant predictive power. This means that the probability these results were obtained by chance is very small, thus confirming the significance of the relationship between scores and perceived value for money.

Based on these results, investment in tourism infrastructure can be said to have the greatest impact on improving scores referring to perceived value for money. Improvements to the characteristics of destinations and cultural routes may also contribute to a better perception of value for money, although their effects are not statistically significant according to this model.

The conclusion of the conducted analysis is that the regression is statistically significant and explains 33.1% of the variance (R = 0.575, R² = 0.331) in responses to the question regarding received value for money.

The relationship between satisfaction scores and value for money shows there is a relationship between tourism infrastructure scores and value for money, while a statistically significant relationship was not found between the other two aspects, that is, satisfaction with those aspects and value for money.

4.3. Linear Regression: Scores × Integration of Local Entrepreneurs in the Offering along the Cultural Route

The results of linear regression provide detailed insight into the relationship between scores (dependent variable) and the integration of local entrepreneurs in the offering on the cultural route (independent variable). The model was found to be statistically significant, providing useful insight into key factors affecting scores (Table 7).

An R² value of 0.404 indicates that the integration of local entrepreneurs explains 40.4% of the variance in scores. Although this model does not explain the remaining 59.6% of variance, the significant value of R² suggests that the integration of local entrepreneurs plays a vital role in assigning a score. The adjusted R² of 0.393 confirms that when the number of predictors is taken into consideration, the model continues to maintain significant predictive power

The tourism infrastructure score is shown to be a statistically significant predictor, with B = 0.582B and p = 8.516 × 10⁻¹⁸, suggesting that improvements to tourism infrastructure contribute significantly to higher scores.

The score for cultural routes has a positive contribution, with B = 0.086B, which is also statistically significant. This suggests that the integration of local entrepreneurs in the offering of the cultural route contributes to higher scores (Table 8).

A high F-value (35.962) and a very low p-value (8.516 × 10⁻¹⁸) strongly suggest that the model as a whole has significant predictive power. This means that the probability these results were obtained by chance is very small, thus confirming the significance of the relationship between scores and the integration of local entrepreneurs.

Based on these results, investment into tourism infrastructure through entrepreneur integration can be said to have the greatest impact on improving scores. Improvements to the characteristics of destinations and cultural routes may also significantly contribute to better scores.

The conclusion of the conducted analysis is that the regression is statistically significant and explains 40.4% of the variance (R = 0.636) in responses to the question regarding the integration of entrepreneurs on the cultural route. The relationship between satisfaction scores and the perceived integration of local entrepreneurs in the cultural route shows there is a relationship between the satisfaction scores for all characteristics and the perceived integration of local entrepreneurs in the offering.

4.4. Linear Regression: Scores × Integration of Local Population in the Offering on the Cultural Route

The results of linear regression provide detailed insight into the relationship between scores (dependent variable) and the integration of the local population in the offering on the cultural route (independent variable). The model was found to be statistically significant, providing useful insight into key factors affecting scores (Table 9).

An R² value of 0.286 indicates that the integration of the local population explains 28.6% of the variance in the scores. Although this model does not explain the remaining 71.4% of variance, the significant value of R² suggests that the integration of the local population plays a vital role in assigning scores. The adjusted R² of 0.272 confirms that when the number of predictors is taken into consideration, the model continues to maintain significant predictive power. The RMSE value (1.208) in the alternative model is lower than the RMSE value in the null model (1.415), meaning that the model with local population integration as a predictor is better at predicting scores and reduces the average prediction error.

The tourism infrastructure score is shown to be a statistically significant predictor, with B = 0.455, and p = 0.001, suggesting that improvements to tourism infrastructure contribute significantly to higher scores.

The score for destination characteristics has a positive contribution with B = 0.454, B = 0.454, p = 0.001, which is also statistically significant. This suggests that improvements to destination characteristics have a significant effect on higher scores.

The score for cultural routes has a slight negative contribution, with B = −0.048, and is not statistically significant (p = 0.497), indicating that while the characteristics of cultural routes may have some effect, their contribution to the scores is not statistically significant (Table 10).

A high F-value (21.196) and a very low p-value (1.331 × 10⁻¹¹) strongly suggest that the model as a whole has significant predictive power. This means that the probability these results were obtained by chance is very small, thus confirming the significance of the relationship between scores and the integration of the local population.

Based on these results, investment in tourism infrastructure can be said to have the greatest impact on improving scores. Improvements to the characteristics of destinations may also significantly contribute to better scores, while improvements to cultural routes are not statistically significant according to this model.

The conclusion of the conducted analysis is that the regression is statistically significant and explains 28.6% of the variance (R = 0.534) in responses to the question regarding the integration of the local population into the cultural route. The relationship between satisfaction scores and perceived integration of the local population in the cultural route shows there is a relationship with the satisfaction scores for destination characteristics and tourism infrastructure, while there is no statistically significant relationship between satisfaction with aspects of the cultural route itself and the perceived integration of local population in the cultural route.

5. Hypotheses Testing

The results of multiple linear regression, which analyzes the effects and relationships between multiple independent variables—scores for satisfaction with three aspects of the tourism offering—and one dependent variable show that hypothesis H1 has been accepted. The regression is statistically significant (p < 0.001) and explains 21.6% of the variance (R = 0.465, R² = 0.216) in responses to questions regarding fulfilled expectations.

The relationship between satisfaction scores and fulfilled expectations is statistically significant (p < 0.05) with regards to satisfaction with tourism infrastructure (Beta = 0.284), while there is no statistically significant relationship between satisfaction with the two other aspects and fulfilled expectations (satisfaction with destination characteristics Beta = 0.158; satisfaction with cultural routes Beta = 0.108).

It follows, therefore, that tourism infrastructure (promenades, parks, signage, accessibility, etc.) has the greatest effect on fulfilling tourists’ expectations regarding their engagement in outdoor activities, whereas the two other aspects have no significant effect. It should be noted, however, that these data need to be interpreted with caution because it does not necessarily mean that the other two aspects are not important, despite not being significantly related to fulfilled expectations. It is possible that no relationship was established because foreign tourists may perceive certain aspects as being crucial preconditions to undertaking activities and visiting (so-called hygiene factors).

The hypothesis is accepted as it shows that satisfaction with tourism infrastructure has a direct effect on scores for fulfilled expectations with regard to engaging in outdoor activities.

Similar to the previous hypothesis, a statistically significant regression was found in Hypothesis 2. The coefficient R is 0.575 and the coefficient of determination R² is 0.331, meaning that the regression model explains 33.1% of response variance.

The relationships between satisfaction scores and perceived value for money and perceived quality of the offering is statistically significant (p < 0.05) with regards to satisfaction with tourism infrastructure (Beta = 0.527), while there is no statistically significant relationship between satisfaction with the two other aspects and fulfilled expectations (satisfaction with destination characteristics Beta = 0.113; satisfaction with cultural routes Beta = 0.104).

The hypothesis is accepted as it shows that satisfaction with tourism infrastructure has a direct effect on scores for perceived value for money and the quality of the offering on the cultural route.

These results suggest that satisfaction with tourism infrastructure is a key factor that positively affects perceived value for money and the quality of the offering on the cultural route. Although satisfaction with the characteristics of the destination and satisfaction with the cultural route is not significantly related to perceived value for money and quality, these aspects continue to play a vital role in the overall tourist experience.

It can be concluded from the hypotheses testing, based on the results of regression analyses, that satisfaction with tourism infrastructure is the most important factor affecting the fulfillment of expectations regarding outdoor activities, as well as the perceived value for money and quality of the offering on a cultural route. Improvements to tourism infrastructure, therefore, could contribute significantly to enhancing tourist satisfaction and improving the overall tourist experience. Although satisfaction with destination characteristics and satisfaction with the cultural route was not found to have a significant effect in these models, these aspects still continue to be important for overall tourist satisfaction and should be taken into consideration when planning and managing tourist destinations.

6. Conclusions

The conducted research aimed to study tourist satisfaction and engagement in outdoor activities on cultural routes. The main data analysis was based on PCA, making it possible to reduce the number of variables and identify the key dimensions of tourist satisfaction. The analysis resulted in identifying 3 principal components: satisfaction with destination characteristics, satisfaction with tourism infrastructure, and satisfaction with specific aspects of the cultural route. All three components were shown to have high internal consistency.

The results of the analysis provide additional insight into the key factors affecting satisfaction scores. The results show that fulfilled expectations and perceived value for money contribute significantly to overall scores, while tourism infrastructure was identified as the most important factor in explaining variance in scores.

This study shows that the satisfaction of tourists on cultural routes depends on a series of factors, the key factors being tourism infrastructure elements, destination characteristics, and the specific features of the cultural routes. Improvements in these areas could significantly enhance tourist satisfaction, while further analyses and hypotheses testing could help to better understand the specific factors impacting the tourist experience and tourist satisfaction.

Despite the obtained results, this study has some limitations, referring mostly to geographical limitations as the study was conducted only on selected cultural routes in Croatia and Bosnia and Herzegovina, thus limiting the generalization of results to other destinations. Incorporating qualitative methods, such as interviews or focus groups, into a mixed-methods approach can enrich quantitative analysis by providing deeper insights into visitor experiences. Although the study has its limitations, it offers valuable recommendations for enhancing visitor satisfaction and makes a significant contribution to cultural tourism research. To build on these findings, future research should address the identified weaknesses and expand the scope to include a more diverse range of cultural routes across various regions, thereby improving the generalizability and identifying opportunities for innovation in cultural route offerings.

Author Contributions

Conceptualization, E.R.; methodology, E.R. and V.Z.; software, V.Z.; validation, E.R., D.M. and V.Z.; formal analysis, V.Z.; investigation, E.R., D.M. and V.Z.; resources, E.R., D.M. and V.Z.; data curation, E.R. and V.Z.; writing—original draft preparation, E.R., D.M. and V.Z.; writing—review and editing, E.R., D.M. and V.Z.; visualization, D.M.; supervision, E.R.; project administration, E.R.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The IRB statement was waived for this study as the questionnaire incorporated a consent form confirming voluntary participation, with all participants being adults.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Principal components analysis—reducing the scores for cultural routes into a smaller number of dimensions.

Components	PC1	PC2	PC3	Uniqueness
Gastronomic offering, in general (food)	0.736			0.316
Offering of native wines and other beverages of domestic production	0.708			0.363
Being in touch with nature	0.694			0.421
Local gastronomic offering	0.678			0.381
Preserved environment	0.676			0.345
Feeling of safety and security	0.672			0.384
Beautiful landscapes	0.659			0.427
Cleanliness	0.631			0.396
Parking areas	0.625			0.489
Beverage offering	0.617			0.408
Working hours of hospitality facilities	0.547			0.450
Accommodation facilities	0.498			0.598
Promenades		0.584		0.414
Parks and green areas		0.605		0.380
Working hours of commercial facilities(shops, etc.)	0.492	0.483		0.500
Climate		0.529		0.500
Signage for cultural monuments		0.603		0.452
Outdoor activity offering			0.575	0.526
Tourism signage		0.605		0.511
Appeal of cultural and historical heritage (monuments, attractions)		0.428		0.682
Tourist information prior to travelling to the cultural route		0.655		0.468
Culture-related tourist events			0.634	0.467
Local transportation		0.749		0.387
Traffic accessibility		0.742		0.394
Design of cultural routes (artistic compositions, etc.)		0.661		0.471
Employees on the cultural route possess foreign language skills		0.635		0.516
Signage for outdoor tourist activities		0.503		0.511
Sports equipment rental offering		0.463	0.463	0.571
Friendliness of local population		0.461		0.604
Friendliness of employees in tourism		0.455		0.630
Learning about and watching wildlife			0.808	0.324
Using novel technologies for sight-seeing or undertaking activities (virtual and augmented reality—VR glasses, etc.)			0.789	0.377
Folklore events, etc.			0.777	0.362
Tourist events relating to outdoor activities			0.775	0.367
Using mobile apps of cultural routes			0.775	0.392
Learning about and looking for flora			0.760	0.403
Traditional souvenirs			0.622	0.468

Table 2

Principal components dimensions.

Component Dimensions	Arithmetic Mean	Std. Deviation	Cronbach’sAlpha
PC1 Scores for destination characteristics	5.7	0.948	0.900
PC2 Scores for tourism infrastructure	5.5	0.934	0.893
PC3 Scores for the cultural route	4.5	1.499	0.902

Table 3

Model Summary—Expectations.

Model	R	R2	Adjusted R2	RMSE	R2 Change	F Change	df1	df2	p
H0	0.000	0000	0000	1.188	0.000		0	1.62
H1	0.465	0.216	0.201	1.062	0.216	14.583	3	1.59	1.945 × 10−8

Table 4

Model coefficients—scores × fulfilled expectations.

Model	Scores	Unstandardized	Standard Error	Standardized	t	p
H0	Intercept	5.276	0.093		56.708	9.082 × 10−109
H1	Intercept	1.746	0.553		3.157	0.002
	Scores for destination characteristics	0.198	0.122	0.158	1.622	0.107
	Scores for tourism infrastructure	0.362	0.123	0.284	2.935	0.004
	Scores for cultural routes	0.086	0.061	0.108	1.396	0.165

Table 5

Model Summary—Value for Money.

Model	R	R2	Adjusted R2	RMSE	R2 Change	F Change	df1	df2	p
H0	0.000	0.000	0.000	1.245	0.000		0	162
H1	0.575	0.331	0.318	1.028	0.331	26.190	3	159	8.045 × 10−14

Table 6

Model coefficients—scores × value for money.

Model	Scores	Unstandardized	Standard Error	Standardized	t	p
H0	Intercept	5.828	0.098		59.757	2.763 × 10−112
H1	Intercept	1.466	0.536		2.737	0.007
	Scores for destination characteristics	0.148	0.118	0.113	1.251	0.213
	Scores for tourism infrastructure	0.702	0.119	0.527	5.886	2.268 × 10−8
	Scores for cultural routes	−0.086	0.060	0.104	1.445	0.150

Table 7

Model Summary—Integration of local entrepreneurs in the offering on the cultural route.

Model	R	R2	Adjusted R2	RMSE	R2 Change	F Change	df1	df2	p
H0	0.000	0.000	0.000	1.362	0.000		0	162
H1	0.636	0.404	0.393	1.061	0.404	35.962	3	159	8.516 × 10−18

Table 8

Model coefficients—scores × integration of local entrepreneurs in the offering along the cultural route.

Model	Scores	Unstandardized	Standard Error	Standardized	t	p
H0	Intercept	5.466	0.107		51.236	5.282 × 10−102
H1	Intercept	0.054	0.553		0.097	0.923
	Scores for destination characteristics	0.511	0.122	0.356	4.185	4.706 × 10−5
	Scores for tourism infrastructure	0.582	0.123	0.399	4.726	5.007 × 10−6
	Scores for cultural routes	0.168	0.061	0.185	2.741	0.007

Table 9

Model Summary—Integration of local population in the offering on the cultural route.

Model	R	R2	Adjusted R2	RMSE	R2 Change	F Change	df1	df2	p
H0	0.000	0.000	0.000	1.415	0.000		0	162
H1	0.534	0.286	0.272	1.208	0.286	21.196	3	159	1.331 × 10−11

Table 10

Model coefficients—scores × integration of local population in the offering on the cultural route.

Model	Scores	Unstandardized	Standard Error	Standardized	t	p
H0	Intercept	5.466	0.111		49.305	1.834 × 10−99
H1	Intercept	0.544	0.629		0.864	0.389
	Scores for destination characteristics	0.454	0.139	0.304	3.269	0.001
	Scores for tourism infrastructure	0.455	0.140	0.300	3.248	0.001
	Scores for cultural routes	−0.048	0.070	0.050	0.680	0.497

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