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Article

Fostering Sustainable Rural Tourism Post-COVID-19: Determinants of Revisit Intention Among Costa Rican Tourists

by
Marlen Treviño-Villalobos
1,
Luis Felipe Sancho-Jiménez
2,
Mauricio Carvache-Franco
3,
Ana Gabriela Víquez-Paniagua
4,
Orly Carvache-Franco
5 and
Wilmer Carvache-Franco
6,7,*
1
Escuela de Ingeniería en Computación, Instituto Tecnológico de Costa Rica, Campus San Carlos, San Carlos 223-21001, Costa Rica
2
Escuela de Idiomas y Ciencias Sociales, Instituto Tecnológico de Costa Rica, Campus San Carlos, San Carlos 223-21001, Costa Rica
3
Universidad Bolivariana del Ecuador, Campus Durán Km. 5.5 Vía Durán Yaguachi, Durán 092405, Ecuador
4
Escuela de Administración de Empresas, Instituto Tecnológico de Costa Rica, Campus San Carlos, San Carlos 223-21001, Costa Rica
5
Universidad Espíritu Santo, Km. 2.5 Vía a Samborondón, Samborondón 092301, Ecuador
6
Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km 30.5 Vía Perimetral, Guayaquil 090902, Ecuador
7
Facultat de Turisme i Geografia, Universitat Rovira I Virgili, Carrer Joanot Martorell, 15, 43480 Vila-seca, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5231; https://doi.org/10.3390/su17125231
Submission received: 29 April 2025 / Revised: 30 May 2025 / Accepted: 3 June 2025 / Published: 6 June 2025
(This article belongs to the Special Issue Sustainable Tourism Development in Natural and Rural Areas: Stakeholders, Tourists and Destination Management)
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Abstract

:
This study aims to identify the factors that influence the behavior of Costa Rican tourists visiting rural destinations after the COVID-19 pandemic, thereby contributing to sustainable rural tourism development. The study applies data analysis using the partial least squares (PLS) regression technique to evaluate a reflective measurement model, based on data collected via a questionnaire. The evidence indicates that for the analyzed destination, the most significant factors influencing the behavior of Costa Rican tourists visiting rural destinations after the COVID-19 pandemic are learning, ICTs, and, in particular, relaxation. Although biosecurity and social influence do not affect the intention to return, these findings highlight the crucial role of learning and ICTs in the tourist experience and loyalty to the destination. This study contributes novel empirical insights to the still limited post-pandemic research on rural tourism, by providing current information on changes in tourist behavior in a specific post-pandemic context. Additionally, it focuses on a popular rural tourist destination in Costa Rica, offering a deeper understanding of a less explored tourism segment, as most previous studies have examined urban or international tourism. Specifically, this research addresses the gap regarding domestic tourist behavior in rural areas using a quantitative approach (PLS), revealing key drivers of return intention. The findings may also be relevant for rural destinations facing similar post-pandemic challenges in other countries.

1. Introduction

Before the last century, it was believed that only economists could study consumers and that consumers were rational beings [1]. It was thought that consumer tastes did not change over time, so advertising was always the same, and the only concern was price [2]. However, according to reports, 80% of purchases are impulsive, driven by the need or desire to feel relief [3]. This led to the recognition of multiple variables beyond price, such as cultural, social, personal, psychological, and technological factors [4]. As consumer behavior evolves, so does its study. Therefore, understanding consumer tastes and preferences helps to correctly segment the market and develop appropriate strategies, products, and services [3]. Hence, it is crucial to understand the variables that affect the consumer’s buying process, both externally and internally [5].
Specifically, the literature has identified that, in the field of tourism, health and safety concerns, as well as travel uncertainty, have become significant barriers for tourists after the pandemic [5]. Studies indicate that post-pandemic tourist behavior has shifted, prioritizing comfort and factors such as social distancing and safety measures [4]. Moreover, online reviews have gained importance, helping customers make informed decisions about their purchases.
Understanding consumer behavior is essential for business success. According to [6], companies exist to meet consumer needs, and this can only be achieved if marketing specialists deeply understand the people and organizations who will use their products and services. Data on consumers helps define the market and identify threats and opportunities, especially considering the changes brought about by COVID-19 [7].
Following the COVID-19 pandemic, the economic factor has become established as a fundamental, decisive, and even critical factor in travel choices around the world. Post-COVID-19 economic instability, marked by inflation and changes in purchasing power, has increased price sensitivity driving the search for more affordable options, and shorter or closer trips [8,9].
The cognitive value of these studies lies in the fact that, beyond the obvious economic influence, they offer detailed empirical understanding. They identify pricing strategy as key and quantify specific impacts such as inflation on tourists and businesses, illustrating with data how economic factors reshape travel choices and sector resilience globally [8,9].
New consumption behaviors span all aspects of life, from work to shopping and entertainment. For example, Ref. [10] notes that many trends are accelerations of past behaviors, especially in digital adoption. The pandemic has also driven an increase in environmentally conscious consumption behaviors, presenting opportunities for hospitality and tourism companies to build resilience [5]. Although consumers are spending more, the beneficiaries tend to be providers of discretionary items such as travel and automobiles, suggesting a higher demand for tourism products [11].
A study conducted in Latin America by Boston Consulting Group [12] indicates that 49% of millennial tourists surveyed will not return to their old consumption habits, and 38% of Generation Z expressed the same sentiment. Identified trends include a greater emphasis on wellness and health, enjoyment of digital convenience, and a preference for engaging in activities from home. Despite this, Ref. [7] highlights the limited research on consumer health concerns and their effects on consumption behaviors in the tourism industry. Across much of Latin America and the Caribbean, the pandemic highlighted regional discontents amid internal tensions, external impacts, and economic fragility. This crisis forced a reconsideration and strengthening of the State’s role in protecting the population from pre-existing structural challenges and deep inequalities. However, the magnitude of the pandemic surpassed the capacity to guarantee rights and democratic progress. This destination largely depends on adventure tourism linked to nature, which is why the pandemic unleashed a scenario of uncertainty, making it difficult to predict the follow-up of the Sustainable Development Goals, with the implications emerging in social, economic, and environmental terms [13].
Therefore, studying adventure tourism is crucial due to its role and growth both continentally and globally, attracting a diverse segment of tourists seeking unique and thrilling experiences in varied natural and cultural settings [14,15]. With its biodiversity and cultural elements, Latin America has become an attractive destination for many tourists. It is essential to analyze the behavior, motivations, and expectations of these visitors, especially in the realm of adventure tourism, to enhance their experiences and promote sustainable tourism development [16].
Recent studies indicate that tourists’ perceptions of risk and their intentions to travel again are influenced by various factors, such as risk communication, resilience, impulsivity, personality traits, destination image, and risk perception [17,18]. The complex interaction of psychological, social, and experiential factors shaping adventure tourist behavior in the post-pandemic era has also been highlighted [19]. The pandemic led to unprecedented mobility restrictions, resulting in the adoption of “hyperlocal” adventure approaches [20], and the perception of safety played a mediating role in post-pandemic travel intentions [21]. Previous literature has studied how adventure tourism is driven by motivations such as the desire for relaxation, learning, social interaction, and skill mastery [22].
Recent studies have begun to explore post-pandemic tourist behavior, focusing on aspects like safety and online trends. However, there remains a notable gap in the literature, particularly concerning the factors influencing the intention to return among domestic tourists in specific rural destinations in developing countries like Costa Rica. While general trends are discussed, the unique dynamics of localized rural tourism post-COVID-19, considering factors like biosecurity perceptions after initial crisis phases, specific learning opportunities, the role of ICT in rural contexts, and nuanced social influences, are not yet fully understood through rigorous quantitative modeling. This study aims to bridge this gap by specifically investigating these factors in the context of La Fortuna, a prominent rural tourist destination in Costa Rica, using a robust PLS-SEM approach. By doing so, this research offers a unique academic contribution by providing empirical evidence from a less studied population and context, expanding the understanding of post-pandemic tourist behavior beyond urban or international settings, and offering a quantitative model validated in a rural Latin American environment.
Based on the aforementioned research gap and the need to understand post-pandemic tourist behavior in specific rural contexts, this study seeks to answer the following research questions: RQ1—Does biosecurity significantly influence the intention to return among Costa Rican tourists visiting the rural destination of La Fortuna after the COVID-19 pandemic? RQ2—Do learning experiences significantly influence the intention to return among Costa Rican tourists visiting the rural destination of La Fortuna after the COVID-19 pandemic? RQ3—Does the relaxation factor significantly influence the intention to return among Costa Rican tourists visiting the rural destination of La Fortuna after the COVID-19 pandemic? RQ4—Does social influence significantly influence the intention to return among Costa Rican tourists visiting the rural destination of La Fortuna after the COVID-19 pandemic? RQ5—Do ICTs significantly influence the intention to return among Costa Rican tourists visiting the rural destination of La Fortuna after the COVID-19 pandemic?
This document is structured as follows. Section 2 presents the arguments supporting the study and the proposed hypotheses. Section 3 describes the empirical method used. Section 4 presents the results and discussion, and the final section synthesizes and discusses the main findings, outlining their practical implications and future research directions.

2. Literature Review

2.1. Consumer Behavior

The decision to embark on a tourism adventure is not fortuitous; it responds to a range of needs and desires that are the fundamental driving force for selecting and enjoying a destination [23]. To [24], these motivations are dynamic, transforming as they integrate cognitive and affective components that act as mediators between the individual and their environment. As [25] pointed out, similar to Maslow’s hierarchy of needs, once a basic need is satisfied, others emerge.
On the other hand, the literature notes that COVID-19 has accelerated changes in consumer behavior, such as digitalization, forcing companies to reinvent themselves to adapt to new trends and consumption habits [26]. COVID-19 has significantly impacted daily life and altered consumer purchasing habits [27].
Studies highlight that income, age, and occupation sectors are essential elements in new purchasing patterns [27]. Specifically, Ref. [28] identifies three reasons for studying consumer behavior post-pandemic: to improve responses to future crises, to assist affected retailers, and to facilitate the transition to new consumer services. On the other hand, Ref. [5] indicates that older consumers are more price-sensitive, while younger consumers prefer responsible consumption. Finally, new trends show a greater emphasis on safety [29,30], the pursuit of authentic experiences [31], and a preference for natural, rural, and less crowded destinations, as well as outdoor activities [31,32].

2.2. Post-COVID-19 Tourist Profile

The COVID-19 pandemic, as noted by [5], caused significant global changes that forced society to adapt to a new interaction landscape. This scenario transformed economic, social, and environmental dynamics, notably affecting tourism. According to [33], the tourism ecosystem, which includes tourists, businesses, and destinations, underwent a profound transformation. Tourism studies have focused on the new demands of the post-COVID-19 tourist, who is characterized by being more experienced, independent, culturally diverse, flexible, and spontaneous. These changes highlight the increasing importance of service quality over traditional tourism products [34].
The pandemic has caused a significant shift in the tourist profile, influencing their motivations at various levels, including the pursuit of relaxation, safety, interpersonal relationships, self-esteem, personal development, updating, and fulfillment [35].
In this context, the redefinition of the tourist profile in the post-COVID-19 era has garnered increasing academic interest [36], as various studies emphasize the heterogeneity of changes in tourist habits and behaviors [37]. Historically, it has been observed that the motivation to explore other cultures is linked to formal education [38]. However, in the new tourist profile, economic factors also play a crucial role, as international travel tends to be more frequent among individuals with higher incomes [9].

2.3. Customer Loyalty and Intention to Return to the Destination

In the tourism sector, Ref. [39] reveals that awareness of monetary savings increases the intention to travel, while awareness of ecological consequences decreases it. Additionally, Ref. [36] shows that a destination’s social responsibility positively influences tourists’ intention to revisit. According to [27], merchants who effectively adapt to changes in purchasing patterns will emerge as the winners of this crisis.
The literature on loyalty in adventure tourism emphasizes that it is driven by various motivations, such as relaxation, novelty, and social interaction [22,40]. Likewise, intangible factors such as participation and cultural identification play a significant role in tourist loyalty [41]. Similarly, destination image, familiarity, and motivation to travel influence destination loyalty [42].

2.4. Driving Factors of Adventure Tourism Motivation

The importance of biosecurity in tourism has increased considerably, particularly in the context of the COVID-19 pandemic [43]. Research has shown that tourists’ values, attitudes, and behaviors toward biosecurity are shaped by various factors, with age, gender, and travel frequency serving as moderating variables [44]. Biosecurity practices have also been linked to enhanced tourist loyalty and the generation of positive feedback [22]. Additionally, a study conducted in Gauteng (South Africa) highlighted that adventure tourists are primarily motivated by fun, excitement, and the exploration of new destinations, which may include biosecurity elements as part of the overall experience [45]. More recent literature [46] has emphasized that the perception of biosecurity is a key factor influencing tourists’ motivation when choosing adventure tourism destinations. A higher perception of risk, including biosecurity concerns, can decrease the intention to visit certain destinations, while a positive perception of safety can increase the motivation and choice of those locations.
Based on this, the first hypothesis is proposed:
Hypothesis 1.
Biosecurity positively influences the tourists’ intention to return to the rural tourist destination of La Fortuna.
Learning is a key factor in motivation for tourism and enhances the experience, as learning experiences enrich adventure and promote sustainability [22,47]. The literature mentions that this factor enriches the adventure experience and contributes to sustainability and the development of adventure destinations, making them more attractive to tourists and fostering loyalty through repeat visits, recommendations, and positive reviews [22]. New studies emphasize that deep interaction in authentic activities enhances the transformative experience, and, consequently, tourist loyalty [48,49]. This is because the authentic and distinctive elements of a rural destination, such as its local culture and the hospitality of its inhabitants, strengthen the relationship between a traveler’s positive emotions and their loyalty [48].
Based on this, the second hypothesis of this study is formulated:
Hypothesis 2.
Learning experiences positively influence the intention to return to the rural tourist destination of La Fortuna.
The relaxation factor considerably influences adventure tourists’ behavior, affecting their participation in the trip, satisfaction levels, and overall experience. Research shows that the motivation to relax directly increases tourists’ involvement in their travels, leading to more enjoyable behaviors and a greater search for information [50]. At the same time, recreational satisfaction, linked to tourists’ intention to return, is related to the benefits of leisure, highlighting the importance of offering places and services that allow tourists to relax and disconnect from daily pressures [51]. Thus, the concept of “slow adventure” is proposed as a means to enhance tourists’ well-being by allowing them to engage in deep, immersive, and meaningful experiences during their outdoor travels, ultimately restoring their mental and physical balance [52]. Recent literature also indicates that the perceived quality of services, a relaxing atmosphere, and the emotional and social value of the experience enhance tourist satisfaction [53,54,55,56,57]. Furthermore, this positively impacts return intentions, as positive emotions and attachment to the destination, generated by relaxation and well-being experiences, are determining factors in the intention to return [56,58,59].
Based on the above, the third hypothesis is proposed:
Hypothesis 3.
The relaxation factor positively influences the tourists’ intention to return to the rural tourist destination of La Fortuna.
The social aspect is crucial in adventure tourists’ satisfaction, impacting their intentions to return and recommend destinations [60,61]. This aspect is highly relevant; recent studies have, for example, found that positive contact with residents increases immersion and attachment to the destination, leading to greater recommendation and revisit intention [49,62]. Similarly, the quality of interaction among tourists satisfies basic psychological needs and strengthens loyalty, especially in sociable individuals [63].
Based on this, the fourth hypothesis is derived:
Hypothesis 4.
The social factor positively influences the tourists’ intention to return to the rural tourist destination of La Fortuna.
Technology influences the behavior and experiences of adventure tourists by facilitating social interactions and transforming outdoor activities [64].
Additionally, according to [65,66], internet-based social networking technologies allow tourists to share their travel experiences, influencing the decisions and motivations of other travelers. Furthermore, Ref. [67] demonstrates how digital technologies transform outdoor activities through digital media and smart devices, altering perceptions and practices in adventure tourism.
Smart technologies facilitate the co-creation of experiences and enhance the perceived authenticity of the destination, a key factor for satisfaction and repeat visits, especially in rural and adventure tourism [68]. Furthermore, recent studies have shown how digital platforms enable personalized experiences and maintain a continuous connection with tourists, increasing their engagement and loyalty [69].
Based on this aspect, the fifth and final hypothesis is proposed:
Hypothesis 5.
The technological factor positively influences the tourists’ intention to return to the rural tourist destination of La Fortuna.

3. Sample, Definition of Variables, and Method

3.1. Research Approach and Methods

The present study received ethical approval from the Costa Rican Technological Research and Extension Council of the country Costa Rica with code CIE-243-2023. Informed consent was obtained from the respondents in writing at the beginning of the questionnaire.
This study uses a quantitative approach, as it is based on numerical measurement and statistical analysis [70]. The research has an empirical orientation, as a series of hypotheses were tested [71]. Additionally, it is descriptive, with a cross-sectional time dimension because “data collection occurs at a single point in time” [70]. The sample collection took place from 18 to 31 March 2024. The data collection date in March was strategically chosen to leverage the peak tourism season in La Fortuna, coinciding with summer in Costa Rica. This period is characterized by a large influx of tourists, which allowed for a broader and more diverse sample of participants for the study.

3.2. Sample

The participants in this research were visitors to the La Fortuna Tourist Development Center in Costa Rica. The visitor population is considered infinite due to its continuous growth, the diversity of attractions, the recovery and expansion of tourism post-pandemic, and the ability of the tourism infrastructure to handle a constant and diverse influx of visitors. Survey participants, who were surveyed on-site and personally, had to be over 18 years old and have participated in adventure tourism activities at the tourist destination studied. After reviewing and removing outliers, missing values, and invalid questionnaires, 326 valid questionnaires out of a total of 349 respondents were used for the final analysis. An infinite population was considered to determine the ideal sample size. A margin of error of ±5%, a confidence level of 95%, and a variation of 50% were established.
The sample of interviewed individuals consists of 53.68% women and 46.32% men. The most represented age group was young adults aged 21 to 30 years (35.89%), followed by individuals aged 31 to 40 years (23.93%), those over 61 years (10.74%), and individuals under 20 years (5.83%). The most common educational level was postgraduate (32.52%), followed by those with a university degree (27.30%). Geographically, 31.60% of the respondents are from North America and 30.98% are from South America, with a small percentage from Europe (0.61%) among others. Income levels show that 34.05% of individuals earn less than USD 500 monthly, and 28.53% earn between USD 500 and USD 999. Of the respondents, 53.37% are single, while 42.94% are married.

3.3. Variables

The data collection instrument was a structured questionnaire designed to capture information about tourists’ sociodemographic characteristics, their motivations for visiting rural destinations post-pandemic, and their intention to return to the studied destination. The questionnaire was divided into the three main sections mentioned. Questions related to motivations (learning, relaxation, biosecurity, social influence, ICTs) and intention to return were measured using a 5-point Likert scale, where 1 meant “Strongly disagree” and 5 meant “Strongly agree”. Questions about sociodemographic characteristics (gender, age, education level, origin, income, marital status) used categorical or predefined range response formats.
Following the methodological approach proposed by [22], the questionnaire was organized into three main sections. The first section captured sociodemographic characteristics, based on the instruments used in previous studies [22,72]. The second section addressed motivations for adventure tourism, grounded in the conceptual frameworks developed by [22,73]. Items measuring tourist loyalty—such as intention to return, recommendation, and positive word-of-mouth—were drawn from the work [74]. The exogenous (independent) variables considered in the model include learning, relaxation, biosecurity, information and communication technologies (ICT), and social influence. The endogenous (dependent) variable is the intention to return to the destination, while the control variables include gender, age, education level, place of origin, income, and marital status.

3.4. Data Processing

The method chosen for data analysis was Partial Least Squares (PLS) regression versión 4.0 (SmartPLS GmbH, Oststeinbek, Germany), as this method is suited for predictive and exploratory models [75,76]. Additionally, it allows for the use of exogenous constructs (predictors) with indicators measured at any level of measurement (nominal, ordinal, interval, or ratio) [77]. PLS involves evaluating the reflective and formative measurement models, assessing the structural model, and analyzing mediation.
In this case, only the reflective measurement model was evaluated, as only reflective items were included. This evaluation is carried out using several measures. (1) Internal Consistency: Assessed using factor loadings, Cronbach’s alpha, and composite reliability. (2) Convergent Validity: Determined by indicator reliability and Average Variance Extracted (AVE). (3) Discriminant Validity: Evaluated using the Fornell–Larcker criterion, cross-loadings between indicators and latent variables, and the Heterotrait–Monotrait Ratio (HTMT) [78].
Internal consistency indicates the construct’s reliability, while convergent validity shows whether a set of indicators, items, or measures represents a single underlying construct. Discriminant validity, on the other hand, indicates how distinct a given construct is from other constructs [79].
For each item’s factor loadings, a threshold value of 0.7 or higher is considered reliable, and items with loadings below this range are suggested to be removed [80]. Cronbach’s alpha and composite reliability values should equal or exceed 0.7, while AVE values should exceed 0.5.
The Fornell–Larcker criterion states that the square root of the Average Variance Extracted (AVE) for each latent variable must be greater than this variable’s correlations with other variables in the model. In other words, to achieve discriminant validity, the square root of the AVE for a construct must be greater than the correlations that the construct has with any other construct in the model. This ensures that the latent variable is capturing more variance from its indicators than it shares with other latent variables, strengthening the evidence that the construct is unique and different from the others in the model.
For cross-loadings, it is important that the loading of each indicator on its respective construct is higher than its loadings on other constructs. Additionally, for HTMT, a value lower than 0.9 should be confirmed. If any of these criteria indicate a lack of discriminant validity, there are various ways to address it. One strategy could be to reduce the HTMT by increasing the average of the correlations in the monotrait–heterotrait ratio of a construct. This can be achieved by removing items with low correlations with other items measuring the same construct [78].
Before conducting the analysis, data were screened and cleaned. Missing values were handled using listwise deletion. Outliers were identified based on values exceeding three standard deviations from the mean and subsequently removed from the dataset. After reviewing and removing outliers, missing values, and invalid questionnaires, a total of 326 valid responses out of 349 collected were retained for the final analysis.
Subsequently, once the validity and reliability of the reflective model are confirmed, the structural model is evaluated, focusing on five aspects: (1) assessment of collinearity; (2) evaluation of the algebraic sign, magnitude, and statistical significance of the path coefficients; (3) assessment of R2; (4) evaluation of effect sizes (f2) and (5) evaluation of predictive relevance (Q2) and effect sizes for Q2 [78].
Regarding collinearity assessment, multicollinearity is typically indicated when the Variance Inflation Factor (VIF) exceeds 5 and the tolerance level is below 0.20. In this study, the VIF analysis was performed following these criteria. Additionally, based on the recommendation by [81], the VIF values were also used as a post hoc test to assess common method bias (CMB), where VIF values below 3.3 suggest an absence of CMB.
For standardized path coefficients, significance is considered when values exceed 0.3. Additionally, Student’s t-statistic should be greater than 1.965, and the significance level (p-value) should be below 0.05. These values are obtained using the non-parametric bootstrapping technique, which involves resampling with replacement with 5000 iterations.
The R2 value represents a measure of predictive value, indicating the amount of variance in a construct explained by the predictor variables of the endogenous construct. Its values range between zero and one, with higher R2 values indicating greater predictive capability of the model. An R2 value should be equal to or greater than 0.19, with recommended interpretations of R2 as 0.75 (substantial), 0.50 (moderate), and 0.25 (weak) [78].
It is also necessary to assess the change in R2 when a particular exogenous construct is omitted from the model. In this case, f2 can be used to evaluate whether the omitted construct has a substantive impact on the endogenous constructs. The following values are used to evaluate f2: an effect of 0.02 is considered small, an effect of 0.15 is considered medium, and an effect of 0.35 is considered large. These values provide useful guidance for interpreting the magnitude of the impact of an exogenous construct on endogenous constructs [82].
To measure the predictive validity of the model’s dependent constructs, the Stone–Geisser procedure or the Q2 parameter (Cross-Validated Redundancy) was used. This parameter is calculated using the blindfolding technique. For the construct to have predictive validity, the Q2 parameter must be greater than or equal to 0.
The effect size Q2 allows for evaluating how an exogenous construct contributes to an endogenous latent construct Q2 as a measure of predictive relevance. Q2 values can be categorized as small (0.02), medium (0.15), or large (0.35).
The Standardized Root Mean Square Residual (SRMR) is the criterion used for the overall model fit. A model is considered to have a good fit when SRMR values are less than 0.12. Therefore, an SRMR value of 0 indicates a perfect fit [83].

4. Results

This section presents a descriptive analysis of the study results. This is followed by an inferential statistical analysis using the PLS technique, including an evaluation of the reflective and structural models.

4.1. Descriptive Statistics

Figure 1 presents a descriptive analysis of the six factors obtained from the survey responses conducted before analyzing the proposed hypotheses. This analysis shows that the intention to return is the factor with the highest rating, while biosecurity received the lowest score. All factors are above a rating of 3.3, intending to return being rated at 4.05. This indicates that, overall, all constructs received a favorable evaluation.

4.2. Evaluation of the Reflective Measurement Model

As shown in Table 1, items P18.16, P18.17, P18.20, and P18.27 did not meet the minimum threshold for factor loadings (i.e., ≥0.70) and were therefore removed from the model to improve its quality. The remaining items exhibit satisfactory psychometric properties, with all constructs meeting the established criteria for Cronbach’s alpha, composite reliability, and average variance extracted (AVE). These results confirm the internal consistency and convergent validity of the reflective measurement model.
Furthermore, the analysis confirmed that the constructs comply with the Fornell–Larcker criterion and demonstrate adequate discriminant validity through cross-loadings. Lastly, all HTMT (Heterotrait–Monotrait ratio) values were below the threshold of 0.90, providing additional support for discriminant validity across the model constructs.

4.3. Evaluation of the Structured Model

To assess the structural model, several key indicators were examined, including collinearity, path coefficients, and hypothesis testing. Collinearity was evaluated using the VIF, with all predictor constructs showing VIF values below the conservative threshold of 3.3. This indicates that multicollinearity is not a concern, and the model estimates can be considered stable and reliable.
Additionally, the VIF analysis was applied as a post hoc test to detect potential common method bias (CMB), in line with the recommendations of [81]. Since all inner VIF values were below the critical value of 3.3, the results provide sufficient assurance that CMB did not compromise the validity of the model.
With regard to hypothesis testing, Table 2 reports the standardized path coefficients, T-statistics, and p-values obtained through the bootstrapping procedure. A significance level of 0.05 was adopted to assess statistical support for each hypothesis.
Out of the five proposed hypotheses, three were supported by the data: H2 (Learning → Intention to Return), H3 (Relaxation → Intention to Return), and H5 (ICT → Intention to Return). These findings suggest that learning experiences, relaxation, and the use of ICT have a statistically significant and positive influence on tourists’ intention to return.
In contrast, hypotheses H1 and H4 were not supported. Specifically, the paths for H1 (Biosafety → Intention to Return) and H4 (Social Influence → Intention to Return) exhibited low coefficients and non-significant p-values (e.g., H4: β = 0.008, p = 0.902), indicating that, in this context, neither biosafety nor social influence exerts a direct, significant effect on tourists’ intention to revisit the destination.
Based on the analysis of the path coefficients in the model (see Figure 2 and Figure 3), relaxation emerges as the most influential factor in tourists’ intention to return, with a standardized coefficient of 0.450, indicating a moderate to strong effect. This suggests that when tourists perceive rural destinations as places to rest, disconnect, and reduce stress, they are considerably more likely to express a desire to revisit [84]. This is further supported by the medium effect size (f2) reported in Table 3, which confirms the substantive contribution of relaxation to the model’s explanatory power.
In addition to relaxation, learning and information and communication technologies (ICTs) also show positive and statistically significant effects, albeit of smaller magnitude. While specific coefficient values for these two constructs were not as high as relaxation, their significant contribution indicates that tourists value acquiring knowledge (e.g., about local culture, nature, or traditions) and having access to reliable technological infrastructure (e.g., connectivity, information access, booking systems) during their rural tourism experiences. These findings align with the growing importance of experiential learning and digital support tools in enhancing tourist satisfaction and loyalty in post-pandemic contexts [85].
In contrast, biosecurity and social influence were not found to have significant effects on the intention to return. These findings suggest that, in the context of rural tourism in Costa Rica, concerns related to health safety measures and the influence of others may not play a decisive role in shaping tourists’ behavioral intentions. One plausible interpretation is that these factors may have limited variability or salience among respondents in the post-pandemic period, reducing their explanatory power within the model. This is consistent with previous studies in which biosecurity measures were found to be more relevant during initial pandemic phases but less influential once they became standardized or expected by travelers [43].
These interpretations are supported by the R2 value of 0.563 for the “intention to return”, which indicates that the model explains 56.3% of the variance in the dependent variable—a moderate and meaningful level of explanatory power in behavioral studies. The Q2 value of 0.541, being well above zero, further confirms the predictive relevance of the model, reinforcing its robustness in forecasting tourist return intentions. According to the f2 values (Table 3), relaxation has a medium effect, while the other constructs have low effects.
Finally, the SRMR value of 0.064 confirms an acceptable overall model fit. Therefore, hypotheses H2 (learning), H3 (ICT), and H5 (relaxation) are supported, while hypotheses H1 (biosecurity) and H4 (social influence) are rejected.
In summary, for the analyzed destination, the most significant factors influencing the behavior of Costa Rican tourists visiting rural destinations after the COVID-19 pandemic are learning, ICTs, and especially relaxation. In contrast, biosecurity and social influence do not appear to play a meaningful role in shaping tourists’ intention to return.

5. Discussion

The findings align with existing literature, which highlights the importance of certain factors in tourist motivations and experiences. For instance, previous research has established that learning is crucial for enhancing tourist experiences, enriching adventure, and promoting sustainability [22,47]. This factor also fosters loyalty and the intention to return to a destination [22]. More recent studies emphasize that deep interaction with the local community and participation in authentic activities enhance the transformative experience, and, consequently, tourist loyalty [48,86]. This is because the authentic and distinctive elements of a rural destination, such as its local culture and the hospitality of its inhabitants, strengthen the relationship between a traveler’s positive emotions and their loyalty [48].
Additionally, the technological factor is emphasized in the literature for its impact on tourist behavior and experiences, as technology facilitates sharing travel experiences and influences the decisions and motivations of other travelers [64,65,66]. Smart technologies facilitate the co-creation of experiences and enhance the perceived authenticity of the destination, a key factor for satisfaction and repeat visits, especially in rural and adventure tourism [68,87].
Moreover, the study reinforces the significance of relaxation, which emerged as the most influential factor. Previous research has shown that relaxation directly increases tourists’ engagement in their travels [50] and their intention to return [51]. This suggests that relaxation is critical in visitors’ decisions to revisit a destination. Recent literature indicates that the perceived quality of services, a relaxing atmosphere, and the emotional and social value of the experience enhance tourist satisfaction [53,54,55,56,57]. Furthermore, this has an impact on return intentions, given that positive emotions and attachment to the destination, generated by relaxation and well-being experiences, are determining factors in the intention to return [56,58,59].
Although biosecurity and social influence are important factors in tourism in general [22,43,60,61], the specific context of this study suggests that these factors are not decisive in the intention of tourists to return to La Fortuna de San Carlos. In the post-pandemic context, tourists visiting this destination may assume that basic biosecurity measures have already been implemented and might not perceive significant differences between destinations. This could be related to the literature on the heterogeneity in changes in habits and behaviors [88], which may reduce the relative importance of biosecurity in their decision to return compared to earlier periods when the pandemic was more acute. More recent literature [46] has emphasized that the perception of biosecurity is a key factor influencing tourists’ motivation when choosing adventure tourism destinations. A higher perception of risk, including biosecurity concerns, can decrease the intention to visit certain destinations, while a positive perception of safety can increase motivation and the choice of those locations.
Furthermore, previous research indicates that the importance of biosecurity may diminish once travelers feel secure and standard practices are established. For instance, studies by [89] on post-pandemic tourism suggest that once safety measures are in place, emotional and experiential factors such as relaxation and personal enjoyment become more relevant.
Similarly, social influence can vary significantly in its impact depending on the type of trip and the tourist’s motivation. In nature or adventure destinations, tourists may be more focused on individual and personal experiences and less influenced by the opinions and behaviors of others [90].

6. Conclusions

This study reveals that, for Costa Rican tourists visiting rural destinations post-COVID-19 pandemic, the most influential factors are learning, information and communication technologies (ICTs), and, in particular, relaxation. These findings corroborate existing literature that highlights the crucial role of learning in tourism motivation as well as enhancing experiences, promoting sustainability, and loyalty to the destination. Additionally, ICTs play a significant role by influencing tourist behavior and decisions, allowing them to share experiences and motivations with other travelers. In contrast, factors such as biosecurity and social influence were not decisive in the tourists’ intention to return to La Fortuna de San Carlos. This suggests that, in the post-pandemic context, visitors might perceive these measures as standard and not differentiate between destinations.
The results of this study have important theoretical implications. First, researchers can further explore endogenous latent variables to better understand the factors influencing tourists’ intentions. Additionally, to understand why biosecurity has not been as decisive, further investigation into how safety perceptions affect tourists’ decisions to return could be beneficial. Continuous exposure to biosecurity messages may have led to informational saturation, where tourists no longer perceive this factor as a significant differentiator. Similarly, social influence may be diluted if tourists rely more on their experiences and personal expectations when returning to a destination.
This study highlights crucial practical implications for Costa Rican rural tourism, indicating that relaxation is key to revisiting intention. This means that tourism strategies should focus on designing specific relaxation experiences, leveraging the unique natural and cultural assets of rural Costa Rica, beyond general promotion.
To achieve this, concrete operational actions are proposed: diversifying relaxation offerings (e.g., quiet zones, forest bathing), integrating them with local culture (e.g., herbal remedy workshops), and marketing that emphasizes these opportunities for tranquility. It is also vital that institutions prioritize support for rural SMEs investing in these amenities, aligning with community-based tourism policies.
Implementing these context-aware strategies will enable Costa Rica’s rural destinations to enhance their appeal and foster repeat visitation, significantly contributing to their long-term economic viability and social resilience, thereby strengthening their sustainable development.
Among the limitations of this study is that it was conducted at a specific time (March 2024), which may influence the results due to temporal factors such as economic, political, or health conditions at that moment. Tourist behaviors and perceptions can vary significantly across different periods. A limitation inherent to this study is the potential influence of seasonality; as data was collected in March, during La Fortuna’s peak tourist season, the responses may reflect motivations and perceptions specific to this high-influx period. Therefore, the findings should be interpreted considering this seasonal factor, which suggests an opportunity for future research exploring the evolution of tourist experiences throughout the year.
Additionally, the sample was collected exclusively at a single rural destination, La Fortuna. While a prominent site, findings may not be fully generalizable to all rural tourism destinations in Costa Rica or other countries, which may have different visitor profiles, attractions, and pandemic recovery trajectories.
Other important factors influencing the intention to return may have been omitted, such as service quality, overall satisfaction with the tourism experience, climate, and specific activities available at the destination. Moreover, the results are based on tourists visiting a specific destination (La Fortuna de San Carlos) and may not apply to other tourist destinations with different characteristics. Generalizations to other contexts should be made with caution.
Lastly, a potential avenue for future research is the exploration of emerging factors that may influence the intention to return, such as sustainability, responsible tourism, and eco-friendly practices, given the growing interest in sustainable tourism. It would also be valuable to compare the findings from La Fortuna de San Carlos with other similar and different tourist destinations within and outside Costa Rica to better understand the specificities and generalities in tourist behavior. Additionally, it would be intriguing to investigate the role of advanced technologies such as artificial intelligence, augmented reality, and virtual reality in the tourist experience and their impact on the intention to return, particularly in the context of adventure tourism and ecotourism. These studies could help expand knowledge on tourist behavior and provide practical insights for the tourism industry, enhancing visitor experiences and fostering loyalty towards tourist destinations.

Author Contributions

Conceptualization, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F.; methodology, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F.; software, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F.; investigation, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F.; writing—original draft preparation, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F.; writing—review and editing, M.T.-V., L.F.S.-J., M.C.-F., A.G.V.-P., O.C.-F. and W.C.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Research and Extension Council of the Costa Rican Institute of Technology, Code: CIE-243-2023. Approval date: 7 November 2023.

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 conflicts of interest.

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Figure 1. Average responses by construct. (Source: research data; generated using PLS).
Figure 1. Average responses by construct. (Source: research data; generated using PLS).
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Figure 2. Structural model results (Source: research data; generated using PLS).
Figure 2. Structural model results (Source: research data; generated using PLS).
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Figure 3. Path coefficients by hypothesis (Source: research data; generated using PLS).
Figure 3. Path coefficients by hypothesis (Source: research data; generated using PLS).
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Table 1. Results of internal consistency and convergent validity.
Table 1. Results of internal consistency and convergent validity.
ConstructItem CodeFactor LoadingCronbach’s AlphaComposite ReliabilityAverage Variance Extracted (AVE)
Biosafety FactorP18.10.8240.9290.9430.703
P18.20.879
P18.30.86
P18.40.886
P18.50.852
P18.60.853
P18.70.703
Learning FactorP18.80.8030.9330.9440.679
P18.90.837
P18.100.864
P18.110.784
P18.120.848
P18.130.832
P18.140.814
P18.150.806
Relaxation FactorP18.280.8570.9250.9420.73
P18.290.866
P18.300.872
P18.310.881
P18.320.883
P18.330.759
Social FactorP18.160.6520.9350.9440.588
P18.170.693
P18.180.755
P18.190.775
P18.200.66
P18.210.817
P18.220.75
P18.230.823
P18.240.85
P18.250.847
P18.260.877
P18.270.66
ICTP250.9320.7740.8960.812
P260.87
Intention to returnP20.10.9230.9240.9520.868
P20.20.937
P20.30.934
Source: research data; generated using PLS.
Table 2. Path coefficients (standardized regression coefficients).
Table 2. Path coefficients (standardized regression coefficients).
HypothesisPathPath Coefficients (Standardized β)T-Statistic Student (Bootstrapping)p-ValueObservations
H1Biosafety → Intention to Return0.0641.1550.248Rejected
H2Learning → Intention to Return0.1482.3340.020Accepted
H3Relaxation → Intention to Return0.4506.8920.000Accepted
H4Social Influence → Intention to Return0.0080.1230.902Rejected
H5ICT →Intention to Return0.2554.4910.000Accepted
Source: research data; generated using PLS.
Table 3. Effect sizes f2.
Table 3. Effect sizes f2.
f2
Biosafety → Intention to Return0.005
Learning → Intention to Return0.027
Relaxation → Intention to Return0.223
Social Influence → Intention to Return0.000
ICT → Intention to Return0.106
Source: research data; generated using PLS.
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Treviño-Villalobos, M.; Sancho-Jiménez, L.F.; Carvache-Franco, M.; Víquez-Paniagua, A.G.; Carvache-Franco, O.; Carvache-Franco, W. Fostering Sustainable Rural Tourism Post-COVID-19: Determinants of Revisit Intention Among Costa Rican Tourists. Sustainability 2025, 17, 5231. https://doi.org/10.3390/su17125231

AMA Style

Treviño-Villalobos M, Sancho-Jiménez LF, Carvache-Franco M, Víquez-Paniagua AG, Carvache-Franco O, Carvache-Franco W. Fostering Sustainable Rural Tourism Post-COVID-19: Determinants of Revisit Intention Among Costa Rican Tourists. Sustainability. 2025; 17(12):5231. https://doi.org/10.3390/su17125231

Chicago/Turabian Style

Treviño-Villalobos, Marlen, Luis Felipe Sancho-Jiménez, Mauricio Carvache-Franco, Ana Gabriela Víquez-Paniagua, Orly Carvache-Franco, and Wilmer Carvache-Franco. 2025. "Fostering Sustainable Rural Tourism Post-COVID-19: Determinants of Revisit Intention Among Costa Rican Tourists" Sustainability 17, no. 12: 5231. https://doi.org/10.3390/su17125231

APA Style

Treviño-Villalobos, M., Sancho-Jiménez, L. F., Carvache-Franco, M., Víquez-Paniagua, A. G., Carvache-Franco, O., & Carvache-Franco, W. (2025). Fostering Sustainable Rural Tourism Post-COVID-19: Determinants of Revisit Intention Among Costa Rican Tourists. Sustainability, 17(12), 5231. https://doi.org/10.3390/su17125231

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