rural tourism thesis

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• Published: 31 March 2023

The benefits of tourism for rural community development

• Yung-Lun Liu 1 ,
• Jui-Te Chiang 2 &
• Pen-Fa Ko 2  

Humanities and Social Sciences Communications volume  10 , Article number:  137 ( 2023 ) Cite this article

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• Business and management
• Development studies

While the main benefits of rural tourism have been studied extensively, most of these studies have focused on the development of sustainable rural tourism. The role of tourism contributions to rural community development remains unexplored. Little is known about what tourism contribution dimensions are available for policy-makers and how these dimensions affect rural tourism contributions. Without a clear picture and indication of what benefits rural tourism can provide for rural communities, policy-makers might not invest limited resources in such projects. The objectives of this study are threefold. First, we outline a rural tourism contribution model that policy-makers can use to support tourism-based rural community development. Second, we address several methodological limitations that undermine current sustainability model development and recommend feasible methodological solutions. Third, we propose a six-step theoretical procedure as a guideline for constructing a valid contribution model. We find four primary attributes of rural tourism contributions to rural community development; economic, sociocultural, environmental, and leisure and educational, and 32 subattributes. Ultimately, we confirm that economic benefits are the most significant contribution. Our findings have several practical and methodological implications and could be used as policy-making guidelines for rural community development.

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Introduction.

In many countries, rural areas are less developed than urban areas. They are often perceived as having many problems, such as low productivity, low education, and low income. Other issues include population shifts from rural to urban areas, low economic growth, declining employment opportunities, the loss of farms, impacts on historical and cultural heritage, sharp demographic changes, and low quality of life. These issues indicate that maintaining agricultural activities without change might create deeper social problems in rural regions. Li et al. ( 2019 ) analyzed why some rural areas decline while others do not. They emphasized that it is necessary to improve rural communities’ resilience by developing new tourism activities in response to potential urban demands. In addition, to overcome the inevitability of rural decline, Markey et al. ( 2008 ) pointed out that reversing rural recession requires investment orientation and policy support reform, for example, regarding tourism. Therefore, adopting rural tourism as an alternative development approach has become a preferred strategy in efforts to balance economic, social, cultural, and environmental regeneration.

Why should rural regions devote themselves to tourism-based development? What benefits can rural tourism bring to a rural community, particularly during and after the COVID pandemic? Without a clear picture and answers to these questions, policy-makers might not invest limited resources in such projects. Understanding the contributions of rural tourism to rural community development is critical for helping government and community planners realize whether rural tourism development is beneficial. Policy-makers are aware that reducing rural vulnerability and enhancing rural resilience is a necessary but challenging task; therefore, it is important to consider the equilibrium between rural development and potential negative impacts. For example, economic growth may improve the quality of life and enhance the well-being index. However, it may worsen income inequality, increase the demand for green landscapes, and intensify environmental pollution, and these changes may impede natural preservation in rural regions and make local residents’ lives more stressful. This might lead policy-makers to question whether they should support tourism-based rural development. Thus, the provision of specific information on the contributions of rural tourism is crucial for policy-makers.

Recently, most research has focused on rural sustainable tourism development (Asmelash and Kumar, 2019 ; Polukhina et al., 2021 ), and few studies have considered the contributions of rural tourism. Sustainability refers to the ability of a destination to maintain production over time in the face of long-term constraints and pressures (Altieri et al., 2018 ). In this study, we focus on rural tourism contributions, meaning what rural tourism contributes or does to help produce something or make it better or more successful. More specifically, we focus on rural tourism’s contributions, not its sustainability, as these goals and directions differ. Today, rural tourism has responded to the new demand trends of short-term tourists, directly providing visitors with unique services and opportunities to contact other business channels. The impact on the countryside is multifaceted, but many potential factors have not been explored (Arroyo et al., 2013 ; Tew and Barbieri, 2012 ). For example, the demand for remote nature-based destinations has increased due to the fear of COVID-19 infection, the perceived risk of crowding, and a desire for low tourist density. Juschten and Hössinger ( 2020 ) showed that the impact of COVID-19 led to a surge in demand for natural parks, forests, and rural areas. Vaishar and Šťastná ( 2022 ) demonstrated that the countryside is gaining more domestic tourists due to natural, gastronomic, and local attractions. Thus, they contended that the COVID-19 pandemic created rural tourism opportunities.

Following this change in tourism demand, rural regions are no longer associated merely with agricultural commodity production. Instead, they are seen as fruitful locations for stimulating new socioeconomic activities and mitigating public mental health issues (Kabadayi et al., 2020 ). Despite such new opportunities in rural areas, there is still a lack of research that provides policy-makers with information about tourism development in rural communities (Petrovi’c et al., 2018 ; Vaishar and Šťastná, 2022 ). Although there are many novel benefits that tourism can bring to rural communities, these have not been considered in the rural community development literature. For example, Ram et al. ( 2022 ) showed that the presence of people with mental health issues, such as nonclinical depression, is negatively correlated with domestic tourism, such as rural tourism. Yang et al. ( 2021 ) found that the contribution of rural tourism to employment is significant; they indicated that the proportion of nonagricultural jobs had increased by 99.57%, and tourism in rural communities had become the leading industry at their research site in China, with a value ten times higher than that of agricultural output. Therefore, rural tourism is vital in counteracting public mental health issues and can potentially advance regional resilience, identity, and well-being (López-Sanz et al., 2021 ).

Since the government plays a critical role in rural tourism development, providing valuable insights, perspectives, and recommendations to policy-makers to foster sustainable policies and practices in rural destinations is essential (Liu et al., 2020 ). Despite the variables developed over time to address particular aspects of rural tourism development, there is still a lack of specific variables and an overall measurement framework for understanding the contributions of rural tourism. Therefore, more evidence is needed to understand how rural tourism influences rural communities from various structural perspectives and to prompt policy-makers to accept rural tourism as an effective development policy or strategy for rural community development. In this paper, we aim to fill this gap.

The remainder of this paper is organized as follows: the section “Literature review” presents the literature review. Our methodology is described in the section “Methodology”, and our results are presented in the section “Results”. Our discussion in the section “Discussion/implications” places our findings in perspective by describing their theoretical and practical implications, and we provide concluding remarks in the section “Conclusion”.

Literature review

The role of rural tourism.

The UNWTO ( 2021 ) defined rural tourism as a type of tourism in which a visitor’s experience is related to a wide range of products generally linked to nature-based activity, agriculture, rural lifestyle/culture, angling, and sightseeing. Rural tourism has been used as a valid developmental strategy in rural areas in many developed and developing countries. This developmental strategy aims to enable a rural community to grow while preserving its traditional culture (Kaptan et al., 2020 ). In rural areas, ongoing encounters and interactions between humans and nature occur, as well as mutual transformations. These phenomena take place across a wide range of practices that are spatially and temporally bound, including agriculture, forestry, fishing, hunting, farm tourism, cultural heritage preservation, and country life (Hegarty and Przezbórska, 2005 ). To date, rural tourism in many places has become an important new element of the regional rural economy; it is increasing in importance as both a strategic sector and a way to boost the development of rural regions (Polukhina et al., 2021 ). Urban visitors’ demand for short-term leisure activities has increased because of the COVID-19 pandemic (Slater, 2020 ). Furthermore, as tourists shifted their preferences from exotic to local rural tourism amid COVID-19, Marques et al. ( 2022 ) suggested that this trend is a new opportunity that should be seized, as rural development no longer relies on agriculture alone. Instead, other practices, such as rural tourism, have become opportunities for rural areas. Ironically, urbanization has both caused severe problems in rural areas and stimulated rural tourism development as an alternative means of economic revitalization (Lewis and Delisle, 2004 ). Rural tourism provides many unique events and activities that people who live in urban areas are interested in, such as agricultural festivals, crafts, historical buildings, natural preservation, nostalgia, cuisine, and opportunities for family togetherness and relaxation (Christou, 2020 ; Getz, 2008 ). As rural tourism provides visitors from urban areas with various kinds of psychological, educational, social, esthetic, and physical satisfaction, it has brought unprecedented numbers of tourists to rural communities, stimulated economic growth, improved the viability of these communities, and enhanced their living standards (Nicholson and Pearce, 2001 ). For example, rural tourism practitioners have obtained significant economic effects, including more income, more direct sales, better profit margins, and more opportunities to sell agricultural products or craft items (Everett and Slocum, 2013 ). Local residents can participate in the development of rural tourism, and it does not necessarily depend on external resources. Hence, it provides entrepreneurial opportunities (Lee et al., 2006 ). From an environmental perspective, rural tourism is rooted in a contemporary theoretical shift from cherishing local agricultural resources to restoring the balance between people and ecosystems. Thus, rural land is preserved, natural landscapes are maintained, and green consumerism drives farmers to focus on organic products, green chemistry, and value-added products, such as land ethics (Higham and Ritchie, 2001 ). Therefore, the potential contributions of rural tourism are significant and profound (Marques, 2006 ; Phillip et al., 2010 ). Understanding its contributions to rural community development could encourage greater policy-maker investment and resident support (Yang et al., 2010 ).

Contributions of rural tourism to rural community development

Maintaining active local communities while preventing the depopulation and degradation of rural areas requires a holistic approach and processes that support sustainability. What can rural tourism contribute to rural development? In the literature, rural tourism has been shown to bring benefits such as stimulating economic growth (Oh, 2005 ), strengthening rural and regional economies (Lankford, 1994 ), alleviating poverty (Zhao et al., 2007 ), and improving living standards in local communities (Uysal et al., 2016 ). In addition to these economic contributions, what other elements have not been identified and discussed (Su et al., 2020 )? To answer these questions, additional evidence is a prerequisite. Thus, this study examines the following four aspects. (1) The economic perspective: The clustering of activities offered by rural tourism stimulates cooperation and partnerships between local communities and serves as a vehicle for creating various economic benefits. For example, rural tourism improves employment opportunities and stability, local residents’ income, investment, entrepreneurial opportunities, agricultural production value-added, capital formation, economic resilience, business viability, and local tax revenue (Atun et al., 2019 ; Cheng and Zhang, 2020 ; Choi and Sirakaya, 2006 ; Chong and Balasingam, 2019 ; Cunha et al., 2020 ). (2) The sociocultural perspective: Rural tourism no longer refers solely to the benefits of agricultural production; through economic improvement, it represents a greater diversity of activities. It is important to take advantage of the novel social and cultural alternatives offered by rural tourism, which contribute to the countryside. For example, rural tourism can be a vehicle for introducing farmers to potential new markets through more interactions with consumers and other value chain members. Under such circumstances, the sociocultural benefits of rural tourism are multifaceted. These include improved rural area depopulation prevention (López-Sanz et al., 2021 ), cultural and heritage preservation, and enhanced social stability compared to farms that do not engage in the tourism business (Ma et al., 2021 ; Yang et al., 2021 ). Additional benefits are improved quality of life; revitalization of local crafts, customs, and cultures; restoration of historical buildings and community identities; and increased opportunities for social contact and exchange, which enhance community visibility, pride, and cultural integrity (Kelliher et al., 2018 ; López-Sanz et al., 2021 ; Ryu et al., 2020 ; Silva and Leal, 2015 ). (3) The environmental perspective: Many farms in rural areas have been rendered noncompetitive due to a shortage of labor, poor managerial skills, and a lack of financial support (Coria and Calfucura, 2012 ). Although there can be immense pressure to maintain a farm in a family and to continue using land for agriculture, these problems could cause families to sell or abandon their farms or lands (Tew and Barbieri, 2012 ). In addition, unless new income pours into rural areas, farm owners cannot preserve their land and its natural aspects; thus, they tend to allow their land to become derelict or sell it. In the improved economic conditions after farms diversify into rural tourism, rural communities have more money to provide environmental care for their natural scenic areas, pastoral resources, forests, wetlands, biodiversity, pesticide mitigation, and unique landscapes (Theodori, 2001 ; Vail and Hultkrantz, 2000 ). Ultimately, the entire image of a rural community is affected; the community is imbued with vitality, and farms that participate in rural tourism instill more togetherness among families and rural communities. In this study, the environmental benefits induced by rural tourism led to improved natural environmental conservation, biodiversity, environmental awareness, infrastructure, green chemistry, unspoiled land, and family land (Di and Laura, 2021 ; Lane, 1994 ; Ryu et al., 2020 ; Yang et al., 2021 ). (4) The leisure and educational perspective: Rural tourism is a diverse strategy associated with an ongoing flow of development models that commercialize a wide range of farming practices for residents and visitors. Rural territories often present a rich set of unique resources that, if well managed, allow multiple appealing, authentic, and memorable tourist experiences. Tourists frequently comment that the rural tourism experience positively contrasts with the stress and other negatively perceived conditions of daily urban life. This is reflected in opposing, compelling images of home and a visited rural destination (Kastenholz et al., 2012 ). In other words, tourists’ positive experiences result from the attractions and activities of rural tourism destinations that may be deemed sensorially, symbolically, or socially opposed to urban life (Kastenholz et al. 2018 ). These experiences are associated with the “search for authenticity” in the context of the tension between the nostalgic images of an idealized past and the demands of stressful modern times. Although visitors search for the psychological fulfillment of hedonic, self-actualization, challenge, accomplishment, exploration, and discovery goals, some authors have uncovered the effects of rural tourism in a different context. For example, Otto and Ritchie ( 1996 ) revealed that the quality of a rural tourism service provides a tourist experience in four dimensions—hedonic, peace of mind, involvement, and recognition. Quadri-Felitti and Fiore ( 2013 ) identified the relevant impact of education, particularly esthetics, versus memory on satisfaction in wine tourism. At present, an increasing number of people and families are seeking esthetic places for relaxation and family reunions, particularly amid COVID-19. Rural tourism possesses such functions; it remains a novel phenomenon for visitors who live in urban areas and provides leisure and educational benefits when visitors to a rural site contemplate the landscape or participate in an agricultural process for leisure purposes (WTO, 2020 ). Tourists can obtain leisure and educational benefits, including ecological knowledge, information about green consumerism, leisure and recreational opportunities, health and food security, reduced mental health issues, and nostalgia nurturing (Alford and Jones, 2020 ; Ambelu et al., 2018 ; Christou, 2020 ; Lane, 1994 ; Li et al., 2021 ). These four perspectives possess a potential synergy, and their effects could strengthen the relationship between rural families and rural areas and stimulate new regional resilience. Therefore, rural tourism should be understood as an enabler of rural community development that will eventually attract policy-makers and stakeholders to invest more money in developing or advancing it.

Methodology

The literature on rural tourism provides no generally accepted method for measuring its contributions or sustainability intensity. Although many statistical methods are available, several limitations remain, particularly in terms of the item generation stage and common method bias (CMB). For example, Marzo-Navar et al. ( 2015 ) used the mean and SD values to obtain their items. However, the use of the mean has been criticized because it is susceptible to extreme values or outliers. In addition, they did not examine omitted variables and CMB. Asmelash and Kumar ( 2019 ) used the Delphi method with a mean value for deleting items. Although they asked experts to suggest the inclusion of any missed variables, they did not discuss these results. Moreover, they did not assess CMB. Islam et al. ( 2021 ) used a sixteen-step process to formulate sustainability indicators but did not consider omitted variables, a source of endogeneity bias. They also did not designate a priority for each indicator. Although a methodologically sound systematic review is commonly used, little attention has been given to reporting interexpert reliability when multiple experts are used to making decisions at various points in the screening and data extraction stages (Belur et al., 2021 ). Due to the limitations of the current methods for assessing sustainable tourism development, we aim to provide new methodological insights. Specifically, we suggest a six-stage procedure, as shown in Fig. 1 .

Steps required in developing the model for analysis after obtaining the data.

Many sources of data collection can be used, including literature reviews, inferences about the theoretical definition of the construct, previous theoretical and empirical research on the focal construct, advice from experts in the field, interviews, and focus groups. In this study, the first step was to retrieve data from a critical literature review. The second step was the assessment of omitted variables to produce items that fully captured all essential aspects of the focal construct domain. In this case, researchers must not omit a necessary measure or fail to include all of the critical dimensions of the construct. In addition, the stimuli of CMB, for example, double-barreled items, items containing ambiguous or unfamiliar terms, and items with a complicated syntax, should be simplified and made specific and concise. That is, researchers should delete items contaminated by CMB. The third step was the examination of construct-irrelevant variance to retain the variances relevant to the construct of interest and minimize the extent to which the items tapped concepts outside the focal construct domain. Variances irrelevant to the targeted construct should be deleted. The fourth step was to examine intergroup consistency to ensure that there was no outlier impact underlying the ratings. The fifth step was to examine interexpert reliability to ensure rating conformity. Finally, we prioritized the importance of each variable with the fuzzy analytic hierarchy process (AHP), which is a multicriteria decision-making approach. All methods used in this study are expert-based approaches.

Selection of experts

Because this study explores the contributions of rural tourism to rural community development, it involves phenomena in the postdevelopment stage; therefore, a few characteristics are essential for determining the choice of experts. The elements used to identify the experts in this study were (1) the number of experts, (2) expertise, (3) knowledge, (4) diversity, (5) years working in this field, and 5) commitment to participation. Regarding the number of experts, Murphy-Black et al. ( 1998 ) suggested that the more participants there are, the better, as a higher number reduces the effects of expert attrition and rater bias. Taylor-Powell ( 2002 ) pointed out that the number of participants in an expert-based study depends not only on the purpose of the research but also on the diversity of the target population. Okoli and Pawlowski ( 2004 ) recommended a target number of 10–18 experts for such a purpose. Therefore, we recruited a group of 18 experts based on their stated interest in the topic and asked them to comment on our rationale concerning the rating priorities among the items. We asked them to express a degree of agreement or disagreement with each item we provided. We adopted a heterogeneous and anonymous arrangement to ensure that rater bias did not affect this study. The 18 experts had different backgrounds, which might have made it easier for them to reach a consensus objectively. We divided the eighteen experts into three subgroups: (1) at least six top managers from rural tourism businesses, all of whom had been in the rural tourism business for over 10 years; (2) at least six academics who taught subjects related to tourism at three different universities in Taiwan; and (3) at least six government officials involved in rural development issues in Taiwan.

Generating items to represent the construct

Step 1: data collection.

Data collection provides evidence for investigation and reflects the construct of interest. While there is a need to know what rural tourism contributes, previous studies have provided no evidence for policy-makers to establish a rural community strategy; thus, it is essential to use a second source to achieve this aim. We used a literature review for specific topics; the data we used were based on the findings being presented in papers on rural tourism indexed in the SSCI (Social Sciences Citation Index) and SCIE (Science Citation Index Expanded). In this study, we intended to explore the role of rural tourism and its contributions to rural development. Therefore, we explored the secondary literature on the state of the questions of rural development, sustainable development, sustainability indicators, regional resilience, farm tourism, rural tourism, COVID-19, tourist preferences, and ecotourism using terms such as land ethics, ecology, biodiversity, green consumerism, environmentalism, green chemistry, community identity, community integration, community visibility, and development goals in an ad hoc review of previous studies via Google Scholar. Based on the outcomes of this first data collection step, we generated thirty-three subattributes and classified them into four domains.

Step 2: Examine the face validity of omitted variables and CMB

Face validity is defined as assessing whether a measurement scale or questionnaire includes all the necessary items (Dempsey and Dempsey, 1992 ). Based on the first step, we generated data subattributes from our literature review. However, there might have been other valuable attributes or subattributes that were not considered or excluded. Therefore, our purposes for examining face validity were twofold. First, we assessed the omitted variables, defined as the occurrence of crucial aspects or facets that were omitted (Messick, 1995 ). These comprise a threat to construct validity that, if ignored by researchers, might result in unreliable findings. In other words, face validity is used to distinguish whether the researchers have adequately captured the full dimensions of the construct of interest. If not, the evaluation instrument or model is deficient. However, the authors found that most rural tourism studies have not assessed the issue of omitted variables (An and Alarcon, 2020 ; Lin, 2022 ). Second, we mitigated the CMB effect. In a self-report survey, it is necessary to provide a questionnaire without CMB to the targeted respondents, as CMB affects respondent comprehension. Therefore, we assessed item characteristic effects, item context effects, and question response process effects. These three effects are related to the respondents’ understanding, retrieval, mood, affectivity, motivation, judgment, response selection, and response reporting (Podsakoff et al., 2003 ). Specifically, items containing flaws from these three groups in a questionnaire can seriously influence an empirical investigation and potentially result in misleading conclusions. We assessed face validity by asking all the experts to scrutinize the content items that we collected from the literature review and the questionnaire that we drafted. The experts could then add any attribute or subattribute they thought was essential that had been omitted. They could also revise the questionnaire if CMB were embedded. We added the new attributes or subattributes identified by the experts to those collected from the literature review.

Step 3: Examine interexpert consensus for construct-irrelevant variances

After examining face validity, we needed to rule out items irrelevant to the construct of interest; otherwise, the findings would be invalid. We examined the interexpert consensus to achieve this aim. The purpose was to estimate the experts’ ratings of each item. In other words, interexpert consensus assesses the extent to which experts make the same ratings (Kozlowski and Hattrup, 1992 ; Northcote et al., 2008 ). In prior studies, descriptive statistics have often been used to capture the variability among individual characteristics, responses, or contributions to the subject group (Landeta, 2006 ; Roberson et al., 2007 ). Many expert-based studies have applied descriptive statistics to determine consensus and quantify its degree (Paraskevas and Saunders, 2012 ; Stewart et al., 2016 ). Two main groups of descriptive statistics, central tendencies (mode, mean, and median) and level of dispersion (standard deviation, interquartile, and coefficient of variation), are commonly used when determining consensus (Mukherjee et al., 2015 ). Choosing the cutoff point of interexpert consensus was critical because we used it as a yardstick for item retention and its value can also be altered by a number on the Likert scale (Förster and von der Gracht, 2014 ). In the case of a 5-point Likert scale, the coefficient of variation (CV) is used to measure interexpert consensus. Hence, CV ≤ 0.3 indicated high consensus (Zinn et al., 2001 ). In addition, based on the feedback obtained from the expert panel, we used standard deviation (SD) as another measurement to assess the variation in our population. Henning and Jordaan ( 2016 ) indicate that SD ≤ 1 represents a high level of consensus, meaning that it can act as a guideline for cutoff points. In addition, following Vergani et al. ( 2022 ), we used the percentage agreement (% AGR) to examine interexpert consensus. If the responses reached ≧ 70% 4 and 5 in the case of a 5-point Likert scale, it indicated that the item had interexpert consensus; thus, we could retain it. Moreover, to avoid the impact of outliers, we used the median instead of the mean as another measurement. Items had a high consensus if their median value was ≥4.00 (Rice, 2009 ). Considering these points, we adopted % AGR, median, SD, and CV to examine interexpert consensus.

Step 4: Examine intergroup consistency

In this expert-based study, the sample size was small. Any rater bias could have caused inconsistency among the subgroups of experts; therefore, we needed to examine the effect of rater bias on intergroup consistency. When the intergroup ratings showed substantially different distributions, the aggregated data were groundless. Dajani et al. ( 1979 ) remarked that interexpert consensus is meaningless if the consistency of responses in a study is not reached, as it means that any rater bias could distort the median, SD, or CV. Most studies have used one-way ANOVA to determine whether there is a significant difference between the expected and observed frequency in three or more categories. However, this method is based on large sample size and normal distribution. In the case of expert-based studies, the expert sample size is small, and the assessment distribution tends to be skewed. Thus, we used the nonparametric test instead of one-way ANOVA for consistency measurement (Potvin and Roff, 1993 ). We used the Kruskal‒Wallis test (K–W) to test the intergroup consistency among the three subgroups of experts. The purpose of the K–W test is to determine whether there are significant differences among three or more subgroups regarding the ratings of the domains (Huck, 2004 ). The judgment criteria in the K-W test depended on the level of significance, and we set the significance level at p  < 0.05 (Love and Irani, 2004 ), with no significant differences among groups set at p  > 0.05 (Loftus et al., 2000 ; Rice, 2009 ). We used SPSS to conduct the K–W test to assess intergroup consistency in this study.

Step 5: Examine interexpert reliability

Interexpert reliability, on the one hand, is usually defined as the proportion of systematic variance to the total variance in ratings (James et al., 1984 ). On the other hand, interexpert reliability estimation is not concerned with the exact or absolute value of ratings. Rather, it measures the relative ordering or ranking of rated objects. Thus, interexpert reliability estimation concerns the consistency of ratings (Tinsley and Weiss, 1975 ). If an expert-based study did not achieve interexpert reliability, we could not trust its analysis (Singletary, 1994 ). Thus, we examined interexpert reliability in this expert-based study. Many methods are available in the literature for measuring interexpert reliability, but there seems to be little consensus on a standard method. We used Kendall’s W to assess the reliability among the experts for each sample group (Goetz et al., 1994 ) because it was available for any sample size or ordinal number. If W was 1, all the experts were unanimous, and each had assigned the same order to the list of objects or concerns. As Spector et al. ( 2002 ) and Schilling ( 2002 ) suggested, reliabilities well above the recommended value of .70 indicate sufficient internal reliability. In this study, there was a strong consensus when W  > 0.7. W  > 0.5 represented a moderate consensus; and W  < 0.3 indicated weak interexpert agreement (Schmidt et al., 2001 ). To measure Kendall’s W , we used SPSS 23 to assess interexpert reliability.

Step 6: Examine the fuzzy analytic hierarchy process

After examining face validity, interexpert consensus, intergroup consistency, and interexpert reliability, we found that the aggregated items were relevant, authentic, and reliable in relation to the construct of interest. To provide policy-makers with a clear direction regarding which contributions are more or less important, we scored each attribute and subattribute using a multicriteria decision-making technique. Fuzzy AHP is a well-known decision-making tool for modeling unstructured problems. It enables decision-makers to model a complex issue in a hierarchical structure that indicates the relationships between the goal, criteria, and subcriteria on the basis of scores (Park and Yoon, 2011 ). The fuzzy AHP method tolerates vagueness and ambiguity (Mikhailov and Tsvetinov, 2004 ). In other words, fuzzy AHP can capture a human’s appraisal of ambiguity when considering complex, multicriteria decision-making problems (Erensal et al., 2006 ). In this study, we used Power Choice 2.5 software to run fuzzy AHP, determine weights, and develop the impact structure of rural tourism on sustainable rural development.

Face validity

To determine whether we had omitted variables, we asked all 18 experts to scrutinize our list of four attributes and 33 subattributes for omitted variables and determine whether the questionnaire contained any underlying CMB. We explained the meaning of omitted variables, the stimuli of CMB, and the two purposes of examining face validity to all the experts. In their feedback, the eighteen experts added one item as an omitted variable: business viability. The experts suggested no revisions to the questionnaire we had drafted. These results indicated that one omitted variable was revealed and that our prepared questionnaire was clear, straightforward, and understandable. The initially pooled 34 subattributes represented the construct of interest, and all questionnaires used for measurement were defendable in terms of CMB. The biasing effects of method variance did not exist, indicating that the threat of CMB was minor.

Interexpert consensus

In this step, we rejected any items irrelevant to the construct of interest. Consensus measurement played an essential role in aggregating the experts’ judgments. This study measured the AGR, median, SD, and CV. Two items, strategic alliance (AGR = 50%) and carbon neutrality (AGR = 56%) were rated < 70%, and we rejected them accordingly. These results are shown in Table 1 . The AGR, median, SD, and CV values were all greater than the cutoff points, thus indicating that the majority of experts in this study consistently recognized high values and reached a consensus for the rest of the 32 subattributes. Consequently, the four attributes and 32 subattributes remained and were initially identified as determinants for further analysis.

Intergroup consistency and interexpert reliability

In this study, with scores based on a 5-point Likert scale, we conducted the K–W test to assess intergroup differences for each subattribute. Based on the outcomes, the K–W test yielded significant results for all 32 subattributes; all three groups of experts reached consistency at p  > 0.05. This result indicated that no outlier or extreme value underlay the ratings, and therefore, intergroup consistency was reached. Finally, we measured interexpert reliability with Kendall’s W . The economic perspective was W  = 0.73, the sociocultural perspective was W  = 0.71, the environmental perspective was W  = 0.71, and the leisure and educational perspective was W  = 0.72. These four groups of W were all ≧ 0.7, indicating high reliability for the ranking order and convergence judged by all subgroup experts. These results are shown in Table 2 .

The hierarchical framework

The results of this study indicate that rural tourism contributions to rural community development comprise four attributes and thirty-two subattributes. The economic perspective encompasses nine subattributes and is weighted at w  = 0.387. In addition, rural tourism has long been considered a possible means of sociocultural development and regeneration of rural areas, particularly those affected by the decline in traditional rural

activities, agricultural festivals, and historical buildings. According to the desired benefits, the sociocultural perspective encompasses nine subattributes and is weighted at w  = 0.183. Moreover, as rural tourism can develop on farms and locally, its contribution to maintaining and enhancing environmental regeneration and protection is significant. Therefore, an environmental perspective can determine rural tourism’s impact on pursuing environmental objectives. Our results indicate that the environmental perspective encompasses seven subattributes and that its weight is w  = 0.237. Furthermore, the leisure and educational perspective indicates the attractiveness of rural tourism from visitors’ viewpoint and their perception of a destination’s value and contributions. These results show that this perspective encompasses seven subattributes and is weighted at w  = 0.193. This specific contribution model demonstrates a 3-level hierarchical structure, as shown in Fig. 2 . The scores for each criterion could indicate each attribute’s importance and explain the priority order of the groups. Briefly, the critical sequence of each measure in the model at Level 2 is as follows: economic perspective > environmental perspective > leisure and educational perspective > sociocultural perspective. Since scoring and ranking were provided by 18 experts from three different backgrounds and calculated using fuzzy AHP, our rural tourism contribution model is established. It can provide policy-makers with information on the long-term benefits and advantages following the completion of excellent community development in rural areas.

The priority index of each attribute and sub-attribute.

Discussion/Implications

In the era of sustainable rural development, it is vital to consider the role of rural tourism and how research in this area shapes access to knowledge on rural community development. This study provides four findings based on the increasing tendency of policy-makers to use such information to shape their policy-making priorities. It first shows that the demand for rural tourism has soared, particularly during COVID-19. Second, it lists four significant perspectives regarding the specific contributions of rural tourism to rural community development and delineates how these four perspectives affect rural tourism development. Our findings are consistent with those of prior studies. For example, geography has been particularly important in the rural or peripheral tourism literature (Carson, 2018 ). In terms of the local geographical context, two contributions could be made by rural tourism. The first stems from the environmental perspective. When a rural community develops rural tourism, environmental protection awareness is increased, and the responsible utilization of natural resources is promoted. This finding aligns with Lee and Jan ( 2019 ). The second stems from the leisure and educational perspective. The geographical context of a rural community, which provides tourists with geographical uniqueness, advances naturally calming, sensory-rich, and emotion-generating experiences for tourists. These results suggest that rural tourism will likely positively impact tourists’ experience. This finding is consistent with Kastenhoz et al. ( 2020 ). Third, although expert-based approaches have considerable benefits in developing and testing underlying phenomena, evidence derived from interexpert consensus, intergroup consistency, and interexpert reliability has been sparse. This study provides such evidence. Fourth, this research shows that rural tourism makes four main contributions, economic, sociocultural, environmental, leisure, and educational, to rural community development. Our results show four key indicators at Level 2. The economic perspective is strongly regarded as the most important indicator, followed by the environmental perspective, leisure and educational perspective, and sociocultural perspective, which is weighted as the least important. The secondary determinants of contributions have 32 subindicators at Level 3: each was identified and assigned a different weight. These results imply that the attributes or subattributes with high weights have more essential roles in understanding the contributions of rural tourism to rural community development. Policy-makers can use these 32 subindicators to formulate rural tourism development policies or strategies.

This study offers the following five practical implications for policymakers and rural communities:

First, we argue that developing rural tourism within a rural community is an excellent strategy for revitalization and countering the effects of urbanization, depopulation, deforestation, and unemployment.

Second, our analytical results indicate that rural tourism’s postdevelopment contribution is significant from the economic, sociocultural, environmental, leisure, and educational perspectives, which is consistent with Lee and Jan ( 2019 ).

Third, there is an excellent opportunity to build or invest more in rural tourism during COVID-19, not only because of the functions of rural tourism but also because of its timing. Many prior studies have echoed this recommendation. For example, Yang et al. ( 2021 ) defined rural tourism as the leading industry in rural areas, offering an output value ten times higher than that of agriculture in China. In addition, rural tourism has become more attractive to urban tourists amid COVID-19. Vaishar and Šťastná ( 2022 ) suggested that the COVID-19 pandemic created a strong demand for rural tourism, which can mitigate threats to public mental health, such as anxiety, depression, loneliness, isolation, and insomnia. Marques et al. ( 2022 ) showed that tourists’ preference for tourism in rural areas increased substantially during COVID-19.

Fourth, the contributions of this study to policy development are substantial. The more focused rural tourism in rural areas is, the more effective revitalization becomes. This finding highlights the importance of such features in developing rural tourism to enhance rural community development from multiple perspectives. This finding echoes Zawadka et al. ( 2022 ); i.e., policy-makers should develop rural tourism to provide tourists with a safe and relaxed environment and should not ignore the value of this model for rural tourism.

Fifth, our developed model could drive emerging policy issues from a supporting perspective and provide policy-makers with a more comprehensive overview of the development of the rural tourism sector, thus enabling them to create better policies and programs as needed. For example, amid COVID-19, rural tourism created a safe environment for tourists, mainly by reducing their fears of contamination (Dennis et al., 2021 ). This novel contribution that rural tourism destinations can provide to residents and visitors from other places should be considered and built into any rural community development policy.

This study also has the following four methodological implications for researchers:

First, it addresses methodological limitations that still impede tourism sustainability model development. Specifically, we suggest a six-stage procedure as the guideline; it is imperative that rural tourism researchers or model developers follow this procedure. If they do not, their findings tend to be flawed.

Second, to ensure that collected data are without extraneous interference or differences via subgroups of experts, the assessment of intergroup consistency with the K–W test instead of one-way ANOVA is proposed, especially in small samples and distribution-free studies.

Third, providing interexpert reliability evidence within expert-based research is critical; we used Kendall’s W to assess the reliability among experts for each sample group because it applies to any sample size and ordinal number.

Finally, we recommend using fuzzy AHP to establish a model with appropriate indicators for decision-making or selection. This study offers novel methodological insights by estimating a theoretically grounded and empirically validated rural tourism contribution model.

There are two limitations to this study. First, we examine all subattributes by interexpert consensus to delete construct-irrelevant variances that might receive criticism for their lack of statistical rigor. Future studies can use other rigorous methods, such as AD M( j ) or rWG ( j ) , interexpert agreement indices to assess and eliminate construct-irrelevant variances. Second, we recommend maximizing rural tourism contributions to rural community development by using the general population as a sample to identify any differences. More specifically, we recommend using Cronbach’s alpha, confirmatory factor analysis (CFA), and structural equation modeling (SEM) to test the overall reliability and validity of the data and results. It is also necessary to provide results for goodness-of-fit measures—e.g., the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), normed fit index (NFI), Tucker–Lewis Index (TLI), standardized root mean square residual (SRMR), or root mean square error of approximation (RMSEA).

Numerous empirical studies have illustrated how rural tourism can positively and negatively affect the contexts in rural areas where it is present. This study reveals the positive contributions of rural tourism to rural community development. The findings show that using rural tourism as a revitalization strategy is beneficial to nonurban communities in terms of their economic, sociocultural, environmental, and leisure and educational development. The contribution from the economic perspective is particularly important. These findings suggest that national, regional, and local governments or community developers should make tourism a strategic pillar in their policies for rural development and implement tourism-related development projects to gain 32 benefits, as indicated in Fig. 2 . More importantly, rural tourism was advocated and proved effective for tourists and residents to reduce anxiety, depression, or insomnia during the COVID-19 pandemic. With this emerging contribution, rural tourism is becoming more critical to tourists from urban areas and residents involved in rural community development. With this model, policy-makers should not hesitate to develop or invest more in rural communities to create additional tourism-based activities and facilities. As they could simultaneously advance rural community development and public mental health, policy-makers should include these activities among their regional resilience considerations and treat them as enablers of sustainable rural development. We conclude that amid COVID-19, developing rural tourism is an excellent strategy for promoting rural community development and an excellent alternative that could counteract the negative impacts of urbanization and provide stakeholders with more positive interests. The proposed rural tourism contribution model also suggests an unfolding research plan.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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We declare all authors involved in the work. The division of labor is stated as follows; Conceptualization: J-TC; Supervision: J-TC; Methodology: Y-LL; Investigation: Y-LL; Data collection, analysis, and curation: J-TC, Y-LL, P-FK; Original draft preparation: J-TC, Y-LL; Review: P-FK; Interpretation and editing: P-FK; Validation: J-TC, Y-LL, P-FK.

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Liu, YL., Chiang, JT. & Ko, PF. The benefits of tourism for rural community development. Humanit Soc Sci Commun 10 , 137 (2023). https://doi.org/10.1057/s41599-023-01610-4

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DOI : https://doi.org/10.1057/s41599-023-01610-4

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ORIGINAL RESEARCH article

Rural tourism and the sustainable development goals. a study of the variables that most influence the behavior of the tourist.

• 1 Economics and Business Management Department, Faculty of Economics, Business and Tourism, Universidad de Alcalá, Alcalá de Henares, Spain
• 2 Department of Business Administration, Faculty of Economic and Management Sciences, Universidad de León, León, Spain

Tourism is an activity that contributes directly and indirectly to the development of rural areas. But this development needs to be sustainable. To do this, appropriate policies that positively influence these areas from an economic, social and cultural point of view must be implemented. All this in accordance with the Sustainable Development Goals. This study will analyze the contribution of rural tourism to develop and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products. The variables that most influence the tourist behavior, motivation, the destination image, and the satisfaction obtained by the tourist will be analyzed. After an exhaustive review of the literature, an empirical investigation was carried out with 1,658 valid surveys among rural tourists in Soria, a Spanish province with one of the highest levels of depopulation. A structural equation model was drawn up to discover the relationships between the variables. The results demonstrated the importance of the motivation in the formation of the destination image, as well as satisfaction with the trip. In the same way, we will verify which component of the image of the destination (affective or cognitive) has the most influence on their formation, and how the image of the destination, like motivation, influences tourist satisfaction. The proposed model could be used in many studies that analyze the different variables that influence consumer behavior since its reliability and predictive capacity have been proven. The results of the study can also be used by the authorities to design or modify the most appropriate strategies that influence rural tourism, specially promoting the destination image as a variable that positively influences tourist satisfaction.

Introduction

This study is an original investigation of the rural tourists' behavior, attending to the most important variables that help to understand this behavior. It is analyzed how policies focused on rural tourism should be in line with Sustainable Development Goals defined by the UN in 2017, especially with objective 8 “Decent Work and Economic Growth,” to promote sustainable tourism, which creates jobs and promotes culture and local products, as can be seen in the goal 8.9 of that goal number 8.

Rural tourism has gained broad acceptance in Spain. The wide range of accommodation and activities included in the definition of “Rural Tourism” makes it a very attractive option to consumers. In Spain, it is now an important alternative to sun and beach tourism, which has traditionally been a very popular choice of vacationers.

As a consequence, for depopulated and depressed areas in Spain, this kind of tourism has become an additional economic activity, so they no longer depend exclusively on primary activities such as agriculture and livestock. There are extensive opportunities for agrotourism, combining tourism with agriculture-related activities, which indicate the potential synergies between them. The local authorities managing rural tourism must therefore implement policies to promote its development. For Polo (2010) , the development of the rural tourist activity is very suitable for improving the development of the rural areas, likewise Marzo-Navarro (2017) stated that rural tourism promotes the development and economic growth of the destination areas, for which it is a priority to achieve the objectives of economic, sociocultural, and environmental sustainability. The World Tourism Organization (UNWTO) (2021) has recognized that “tourism is one of the driving forces of global economic growth and is currently responsible for the creation of 1 in 11 jobs. By giving access to decent work opportunities in the tourism sector, society, in particular, young people and women, can benefit from improved skills and professional development. The sector's contribution to job creation is recognized in target 8.9: by 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products.” To this end, it is thus very important to analyze a range of variables and components that may influence rural tourism behavior.

Among the most influential variables, satisfaction is a key factor that indicates what the trip has meant to the tourist. Many studies have demonstrated the importance of perceived value and satisfaction in tourist behavior ( Barsky and Labagh, 1992 ; Tam, 2000 ; Choi and Chu, 2001 ; Tian-Cole and Cromption, 2003 ; Petrick, 2004 ; Yoon and Uysal, 2005 ; Hutchinson et al., 2009 ; Kim et al., 2009 ; Jin et al., 2013 ; Asgarnezhad et al., 2018 ; Chin et al., 2019 ; Penelas-Leguía et al., 2019 ; Castro et al., 2020 ). Several studies considered “word-of-mouth” a very important factor to explain the future behavior and it is the link between satisfaction and loyalty ( Hutchinson et al., 2009 ; Kim et al., 2009 ; García, 2011 ; Lai et al., 2018 ; Xu et al., 2020 ). It is, however, essential to discover how the tourist's image of their destination, and their other motivations, drive them to choose that destination. To Tasci and Gartner (2007) , destination image is a key factor in successful tourism development. To Ejarque (2016) , this image has a vital importance in tourists' selection processes. And a tourist's motivation has an important impact on destination image formation, as Li et al. (2010) and Sancho and Álvarez (2010) explained in their studies. It is, however, interesting to investigate the influence motivation has on overall visitor satisfaction, as per Albayrak and Caber (2018) .

Conceptual Framework and Hypothesis

Research framework.

In this research, we reviewed the literature on the variables that affect tourist behavior (motivation, image and satisfaction). We then used the results of this review to lay the foundations of a behavior model using Structural Equations, with Partial Least Squares (PLS) as the chosen method, as you can see in Figure 1 . This will indicate the links between those variables and the strength of these relationships. Thus, the main objective of the research is to analyze the links between tourist motivation, destination image and vacation satisfaction. And as secondary objectives, which complement the analysis, we expose:

- To research how motivations influence destination image formation.

- To analyze the link between destination image and satisfaction with rural tourism

- To research the importance of the affective and cognitive components of the image in forming the overall destination image.

Figure 1 . Proposed theorical model.

Research Hypothesis

Motivation has been widely studied by various authors and in different areas, from psychology to sociology and marketing. Motivation is the driving force of the process. A consumer can have a positive attitude to the purchasing process, an excellent image of the product or service, but if they aren't strongly motivated, the process doesn't begin. A motive, as Santesmases (2012 , p. 261) explains, is “the reason why the consumer purchases the product.” Consumers buy something because they get a benefit, and those benefits satisfy needs. Motivation is therefore, according to Santesmases (p. 261), “a general disposition that leads to the behavior aimed at obtaining what the consumer wants.” Kotler (2016 , p. 199) defines motive as “a need that is sufficiently pressing to drive the person to act.”

From the tourism point of view, motivation is one of the most important and most extensively studied variables. Wong et al. (2018) , point out the influence that motivations have on the tourism process, especially on the tourist. One of the early studies was by Dann (1977) . He attempted to explain the reasons why people travel, as well as their choice of destination. This was the first-time push and pull factors were discussed.

One of the most relevant and important studies of this topic is by Crompton (1979) . This author found nine key motives for a tourist's choice, seven of which were categorized as socio-psychological (escape from a perceived mundane environment, exploration and evaluation of self, relaxation, prestige, regression, enhancement of kinship relationships and facilitation of social interaction), and two cultural motives (novelty and education). The socio-psychological motive, also referred to as “push” motives, explain the wish to take holidays, while cultural motives, also called “pull” motives, explains the choice of the destination or the kind of destination. In addition to this author, Crandall (1980) , based on Crompton's work, continues the explanation of the value of motivation in tourist behavior, and list seventeen personal motives. These are, clearly, an extension to Crompton's nine motives.

Other authors, such as Line et al. (2018) , focus on the importance of motivations in tourist behavior. They explain the importance of motivation, with a special link between motivation and sustainability programs. González and Vallejo (2021) , they also explained that importance. Polo et al. (2016) evaluate the motivations with influence in the rural tourist in Spain, their behavior and the different strategies, and Prebensen et al. (2010) study tourist behavior, in this case, the sun and sand tourist and the link between motivations to travel, tourist satisfaction and intentions to communicate with others by word-of-mouth.

Regarding the practice of rural tourism, Penelas-Leguía et al. (2019) classified the different motivational factors into which tourist motivation is divided. These factors were natural and cultural motivations, social motivations, personal motivations, novelty motivations and escape motivations, reaching the conclusion that natural and cultural and social motivations are the ones that have the most influence on the formation of tourist motivation. Buffa (2015) , also focused on the study of cultural and natural motivations in rural tourism practice, concluding that tourists, especially the youngest, feel motivated when traveling to discover new cultures, new natural spaces, contemplate the natural and artistic heritage, be in contact with the local population and contact with nature. Han et al. (2017) , continues in this line, on the importance of nature and natural heritage in tourism decision-making. Luo and Deng (2007) , exposed the environment and nature as one of the main reasons that move tourists to visit a tourist area, while Gnoth and Zins (2010) and Kim and Prideaux (2005) , considered that motivation cultural and knowing the cultural heritage of the area, were the main reason that moves the tourist. Regarding social motivations, several studies point out this type as the main factor when making decisions by tourists. Van der Merwe et al. (2011) exposed the great importance of these motivations, after an exhaustive review of the literature. Lee et al. (2004) and Park et al. (2008) , focused their studies on the key importance of social motivations in the tourist's behavior. Moreno et al. (2012) , exposed the three main types of motivations that move tourists, highlighting cultural motivations and social motivations, as well as those of “self-expression.”

Therefore, we observe the importance of natural and cultural and social motivations in tourist decision-making, so we propose the following hypotheses:

Hypothesis 1.1 (H11): “Cultural and natural motivation is the main dimension of the tourist motivation.”

Hypothesis 1.2 (H12): “Social motivation has an important relevance in the formation of tourist motivation.”

About the link between tourist motivation and destination image, several authors have studied this influence. For Li et al. (2010) , destination image is an essential component of tourist destination success, because if the place has a recognizable image, it will be more likely to be chosen by tourists as a place for recreation and leisure. In this study, they recognize three motivational factors: intellectual, belonging and escape. Each of them has a direct effect on the cognitive component of the image, but for the escape dimension of the motivation, this effect is negative. For the affective component of the image, the relationship is direct if we focus on the escape motivation as well as on the cognitive component.

Sancho and Álvarez (2010) point out the importance of motivation in the decision-making process of going on a trip and determining where to go. They consider five main motivations: past experiences, physical, cultural, interpersonal, social and prestige. They concluded that interpersonal and social motivations have a direct effect on the cognitive component of the image and on the overall image. They also found that the cognitive component has a direct effect on the affective component, which in turn affects the overall image. Madden et al. (2016) also analyzed this link, carrying out an exhaustive analysis of the literature, as did Dagustani et al. (2018) , Pereira and Hussain (2019) and Santoso (2019) , who presented a behavior model studying the relationship between motivations, destination image and tourist satisfaction. In addition to these authors, many others have studied the close relationship between motivations and destination image, and we would highlight the studies by Mayo and Jarvis (1981) , Michie (1986) and Gong and Sun Tung (2017) . It is also worth highlighting the study of Hwang et al. (2020) , who study the relationship between the destination image and the tourist motivations, but inversely, how the destination image influences the formation of the tourist motivations.

We therefore conclude there is an important link between tourist motivations and destination image formation. Thus, we define the following as hypothesis:

Hypothesis 2 (H2): “Tourist motivation significantly positive influences destination image formation.”

Image is a key factor when tourists are choosing their destinations, and crucial when planning a trip ( Marine-Roig and Ferrer-Rosell, 2018 ). As Beerli and Martín (2002) point out, the image has an important impact on tourist behavior, and varies from person to person. In the same way, Foroudi et al. (2018 , p. 97) explain that “a positive image is much more likely to be taken into consideration and probably chosen in the decision process.” But this image has to be protected, because it can turn into a negative variable, as Bachiller et al. (2005) explain when they state the problem that overcrowding causes in the destination image. Additionally, Alrawadieh et al. (2019) , point out that this feeling of overcrowding doesn't influence the image, but does influence intentions to visit the place again.

What does “destination image” mean? Many authors have contributed their own definitions. To Crompton (1979) , destination image is “the sum of all beliefs, ideas and impressions that people associate with a destination.” In 1993, Echtner and Ritchie (1993 , p. 3) defined it as “perceptions of individual destination attributes, as well as, total, holistic impressions.” Baloglu and McCleary (1999 , p. 870) considered destination image to be “an individual's mental representation of knowledge (beliefs), feelings and global impression about an object or destination.” Sanz (2008) , p. 98 explains to us that destination image is “the global perception of a destination, in other words, the representation in the tourist's mind of what he or she feels and knows about it.” And López-Sanz et al. (2021b) defined destination image as the overall mental impressions each person has of a place or destination formed by knowledge as well as by the feelings the destination produces in them.

All these definitions have a common link. Destination image is made up of two components: the cognitive and affective components. Baloglu and McCleary (1999 , p. 870) defined both. For them, the cognitive component “refers to beliefs or knowledge about a destination's attributes,” whereas the affective component “refers to feelings about a destination, or attachment to it.” Many other authors, however, have written about the difference between the cognitive and affective components. Beerli et al. (2003) explain that the affective component is “individuals' feelings toward a destination or as an emotional response of individuals to a place,” while the cognitive component “is knowledge about a destination.” To Lee et al. (2008 , p. 814), the cognitive component “derives from factual information,” while the affective component “can be viewed as one's diffuse feelings about a specific tourism destination.” Other authors, such as Zhang and Zhang (2020) , emphasize this division of destination image. We can therefore state that destination image is formed by the link between two components: cognitive, related to beliefs and knowledge acquired from external information sources or experience; and the affective component, related to feelings. These are strongly linked, in such a way if that the cognitive component changes after the first vacation, the affective response will also be affected. The overall image is made up of these two components. A destination choice depends on the overall image, and when we refer to destination image, we mean the overall image.

We have analyzed the components into which the overall destination image is divided. It is now necessary to focus on the elements that influence the tourist in forming that image. Several authors have discussed these variables. For Baloglu and McCleary (1999) , the variety and type of information sources, and the tourist's age, education and motivation all influence destination image formation. For Beerli and Martín (2002) , the perceived image of a place is formed by the interaction of several factors, such as the tourist's motivations, previous experience, preferences and other personal characteristics (sex, age, etc.); other psychological factors such as values, personality, lifestyle, etc. also have an influence. To Sirakaya et al. (2001) , consumers' choice processes are influenced by their motivations, attitudes, beliefs and values, as well as other types of factors, such as time. Gunn (1993) states that destination image undergoes a constant process of modification. For this author, there are several steps in image formation. First of all, a destination image is generated from previous information (documentaries, acquaintances' experiences, etc.). Later, due to promotional information such as brochures, an induced image is generated. Nowadays, for those referred to as “2.0 tourists,” the importance of “on-line reputation” is increasing, so innovation is essential to building an initial image of destinations, especially the more traditional ones. For some places, destination image may be reinforced by heritage-related cultural events that are publicized over social networks ( Campillo-Alhama and Martínez-Sala, 2019 ). This image may help individuals choose a destination, depending on their motivations. After the vacation and the tourist's personal experience, a final image is generated. For Um and Crompton (1990) and Ugarte (2007) , the perceived image of a place will be formed by the interaction of the projected image (the destination image the promotional information projects) and the individual's needs, motivations, experience, preferences and personal characteristics, and this image is very resistant to change, even in times of economic crisis ( Gkritzali et al., 2018 ). Thus, we propose the following hypotheses:

Hypothesis 3.1 (H31): “Affective destination image has a positive influence on destination image formation.”

Hypothesis 3.2 (H32): “Cognitive destination image has a positive influence on destination image formation.”

Overall satisfaction with the vacation is a very interesting variable, because, depending on the level of satisfaction or dissatisfaction, the degree of tourist loyalty to both the geographical area and the accommodation can be calculated. For Serra (2011 , p. 122), after the vacation the tourist, through introspection, evaluates the experience and a feeling of satisfaction or dissatisfaction is created. As a result, a post-trip attitude is generated. This modifies several factors, such as the tourist's perception of the destination and attitude toward it, and these in turn influence the destination image for the tourist and his or her relatives and friends. The development of a more digitalized and responsible economy is also highlighted from the point of the view of the influence on other consumers, as explained by Moreno-Izquierdo et al. (2018) , in which the collaboration between citizens and tourists is the frame of reference. Sevilla and Rodríguez (2019) emphasize the emotion caused by viewing the landscape during the journey and stay, which produces a satisfactory or unsatisfactory response to the tourist's expectations. Park et al. (2018) concluded that providing additional information before each visit can improve tourist satisfaction. Fernández-Herrero et al. (2018) , state that tourist “autonomy improves overall satisfaction with the destination,” while Rojas-De-Gracia and Alarcón-Urbistondo (2019) explain the link between satisfaction and the decision-making process.

In studying tourist satisfaction, it is very important to perform multilevel analysis. This enables us to see the “big picture” of the factors affecting overall tourist satisfaction ( Radojevic et al., 2017 ). The link between destination image and satisfaction has been widely researched. The study by Olague de la Cruz et al. (2017) focuses on the link between tourist motivation, destination image and satisfaction. They explain how motivations influence both cognitive and affective image, and both of this influence tourist satisfaction. For Rajesh (2013) , destination image has a direct influence on both overall satisfaction and destination loyalty. Additionally, tourist satisfaction influences destination image—in other words, the new destination image a tourist generates after the vacation depends on the level of satisfaction. It's important to review the research by Martín et al. (2016) , into the influence of destination image on satisfaction, and of satisfaction on loyalty. Herle (2018) , Cruz et al. (2018) , Machado et al. (2009) , Huete and López (2020) and López-Sanz et al. (2021a) also researched this relationship. And we wish to highlight the study of Nysveen et al. (2018) , who found a link between “green destination image” and tourist satisfaction. The expectancy disconfirmation theory will be used to explain the relationship between variables. This theory is very popular in consumer satisfaction research ( Elkhani and Bakri, 2012 ; Kim et al., 2014 ). Positive disconformation happens when the final result is higher than initially expected, while negative disconformation happens when product performance and the final result is lower than expected at the beginning. Thus, we define the following hypothesis:

Hypothesis 4 (H4): “Destination image has a positive influence on overall trip satisfaction.”

Correia et al. (2013) , explain that there is a relationship between the motivational “push” and “pull” variables and overall tourist satisfaction. Battour et al. (2012) , who concluded that tourist motivation positively influences vacation satisfaction, should also be reviewed. For their part, Hidalgo-Fernández et al. (2019) also conclude in their study that there is a relationship between the motivations or interests of the tourist and satisfaction with the trip, turning this satisfaction into recommendation of the destination. This relationship is also found in their study Forteza et al. (2017) and He and Ming (2020) .

The decision to choose the Spanish province of Soria was taken because of several factors. First, this is Spain's least populous province [a population of 88,658 in 2020 ( Instituto Nacional de Estadística (INE), 2021 )], and this area is suffering a worrying level of depopulation. And, on the other hand, it is a province with great potential from the touristic point of view, because it has a wide variety of natural and cultural resources. The province includes many very different areas: the highlands, with a special landscape and similar weather to the Scottish Highlands (hence its name); cities with an important cultural heritage, such as El Burgo de Osma and Soria itself; very interesting archeological areas including Numancia, La Dehesa's Roman Villa and the ancient village of Tiermes; and attractive natural sites such as La Laguna Negra, the Lobos River Canyon and the Fuentona sinkhole.

This province therefore can and must leverage the Rural Tourism boom in Spain and implement rural development based on the service sector, not only in terms of the increasing amount of accommodation available, but also through all the related activities. This includes promoting tourist routes, both cultural through the province's many heritage sites, and natural routes, that can in turn link with adventure and sports tourism. The province can also promote “experience-based tourism,” as explained by Mazarrasa (2016) . This kind of tourism offers some activities which are relatively passive, such as visiting a winery to observe the steps in wine production. There are also, however, activities for which the tourist can actively participate in the experience.

The significance of this study lies in the fact that it can be a starting point for the right marketing actions to improve Rural Tourism in the area and prevent depopulation to the extent possible. To be successful, these actions must be supported by the national, local, provincial and regional authorities.

Survey Design

This research is based on a descriptive study using primary data from a questionnaire used on a representative sample of tourist over 18 years old who visited the province of Soria (Spain) and stayed in a rural tourism establishment. The primary selection of the different items of constructs was based on a review of the literature. Previously, the items had been carefully chosen, and before sending out the survey, preceding qualitative research was carried out through a focus group, which included five professors who are experts in tourism and consumer behavior. As a result of this qualitative research, the final questionnaire was achieved, consisting of four constructs with a total of 16 items: five for cognitive image ( Baloglu and McCleary, 1999 ); two for affective image ( Baloglu and McCleary, 1999 ); seven for tourist motivation ( Crompton, 1979 ) and two for tourist satisfaction ( Lee, 2009 ). In order to obtain data to analyze, 1,658 valid questionnaires were completed by adult tourists who stayed in a rural tourism establishment in the province, between January 2016 to January 2017, which implies a sampling error of ± 2.45% (with a confidence interval of 95.5% and p = q = 0.5) (see Table 1 ).

Table 1 . Technical details of the study.

The data was collected through personal surveys. All items of the questionnaire used the same 4-point Likert-type scale, where 4 = a lot and 1 = little bit, except affective image and satisfaction items, where the scale was a 5-point Likert scale where 5 = strongly agree and 1 = strongly disagree (see Table 2 ).

Table 2 . Scales of the model's constructors.

A pretest of this questionnaire was performed on 50 people who had visited the province and stayed in a rural tourism establishment, to evaluate if the scales were well-constructed and the multiple questions on the questionnaire were understood. After checking that everything was correct, the data were collected personally in the tourist areas of Soria province.

Sample Size and Composition

The total sample consisted of 1,658 valid questionnaires of visitor over the age of 18 who were staying in a rural tourism establishment in the province of Soria (see Table 3 ).

Table 3 . Sample information.

The purpose of analyzing the information collected is to transform it into relevant information that assists the decision-making process. Several statistical techniques were applied to the data collected in the research, including Principal Component Analysis (PCA), and a model was prepared using Partial Least Squares Structural Equation Modeling (PLS-SEM). The programs used were IBM SPSS Statistic, DYANE 4 ( Santesmases, 2009 ) and SmartPLS 3.2.28 ( Ringle et al., 2015 ). Hair et al. (2011 ; p. 144) recommend selecting PLS-SEM if the research is exploratory or an extension of an existing structural theory. Hair et al. (2014) also recommend using PLS-SEM when formatively measured constructs are part of the structural model, the structural model is complex (many constructs and many indicators) and the data are non-normally distributed. It is possible to find these issues in this model, including a very complex structural model that was presented in the first moment.

Factor Analysis of Variables

To facilitate the analysis of some of the variables studied, a factor analysis was performed. The chosen technique was Principal Component Analysis (PCA), a factor analysis technique that reveals the underlying dimensions or factors in the relationships between the values of the variables analyzed ( Harman, 1976 ). It is a statistical method used to summarize and structure the information of a data matrix to reduce the number of variables ( Lozares and López, 1991 ). The aim of this method is to reduce the number of dimensions by obtaining linear combinations with maximum variance that are uncorrelated to the original variables ( Aguilera et al., 1996 ). In this study, we have used this technique to reduce the number of variables for the destination image and motivations constructs, due to their high number of variables. After our analysis, the cognitive destination image, which started with 31 variables, had just five factors, “variety of natural attractions vs. situational elements,” “cultural interest,” “entertainment and luxury,” “restful and attractive environment,” and “attractive accommodation.” Regarding affective image, we started with four variables that were reduced to two factors, “internal affective image” and “external affective image.” Finally, for motivations, the initial 23 variables were reduced to five factors, “cultural and natural,” “social,” “personal,” “novelty,” and “escape.”

Having retained the relevant information in the factors, as mentioned above, this research aims to find possible links between motivations, rural tourism destination image and tourist satisfaction for Spain's Soria province. The research focuses on studying the direct and indirect relationships between the variables. To analyze the cause-effect relationships between latent constructs ( Hair et al., 2011 ) the Partial Least Squares (PLS) technique, which enables researchers to examine the structural component of a model ( Gefen et al., 2000 ), was chosen. PLS-SEM has advantages over other SEM tools, such as LISREL, because PLS can be applied to explore the underlying theoretical model ( Gefen et al., 2000 ). PLS-SEM doesn't require restrictive distributional assumptions about the data ( Compeau et al., 1999 ), and the use of consistent PLS (PLSc) corrects the behavior of relationship coefficients between latent variables in reflective constructs. If, as in our study, the results are very similar, it is not necessary to apply this algorithm, but the deviations of the model's path coefficients are minimized ( Dijkstra and Henseler, 2012 ).

Behavior Model

The research studies the links between seven measured variables ( Figure 1 ). This required a selection to be made.

For tourist motivations, we started with five factors ( Table 4 ), but only cultural and natural motivations, and social motivations, have a loading of at least 0.3. The other ones (personal, novelty and escape), don't reach the required level. The valid items of every motivation factor are shown in Table 4 .

Table 4 . Rotated components matrix (Varimax method).

The destination image variable may be composed of the factors of the cognitive and affective images ( Table 4 ). Of the seven factors obtained for the cognitive and affective images, only variety of natural attractions vs. situational elements image, for cognitive image, and internal affective image, for affective image, have a loading of at least 0.3 or more on their constructs, resulting in seven valid items ( Table 2 ). To measure the satisfaction, tourist perception was used, based on the abovementioned theoretical discussion, with two items: destination satisfaction and satisfaction in terms of expectations.

Using all these factors, we presented a theoretical model, as seen in Figure 1 . The abovementioned link, between motivation, image and satisfaction is shown, as well as the factors that affect them most strongly.

The questionnaire was designed to measure seven different latent constructs: motivation (a second order construct with two dimensions), destination image (a second order construct with two dimensions) and satisfaction. The factor analysis was run to separately validate the measurement of those constructs. The Varimax rotation was used to assist in understanding the initial factor model. The factorial loads (see Table 4 ) provide evidence for the factorial validation of the scales.

The PLS measurement model is evaluated in terms of the inter-construct correlations, the correlations between items, Cronbach's Alpha, the reliability and the average variance extracted for every construct (AVE). In this case, the seven latent variables (two of which are second order constructs) are made up of scales with reflective items. The reliability, internal consistency and discriminant validity of every component in this study are assessed below.

The reliability assessment examines how each item is linked to the latent construct ( Table 4 ). In this respect, the most generally accepted and widely used empirical rule is the one proposed by Carmines and Zeller (1979) , who state that, to accept an indicator as part of a construct, it must have a loading ≥0.707. In this case, only one of the 16 indicators used ( Table 2 ) doesn't reach this acceptable reliability level. However, as Chin (1998) and Barclay et al. (1995) explain, a loading of at least 0.5 can be acceptable if other indicators that measure the construct have higher assessed reliability. Furthermore, Falk and Miller (1992) propose a loading of 0.55—in other words, 30% of the variance of the manifest variable is related to the construct. The loading-−0.64—that didn't exceed the first condition did exceed these latter proposed levels and has a higher loading in its construct than in any other. These results strongly support the reliability of the reflective measurements (see Table 5 ).

Table 5 . Model cross loading.

Finally, motivation and image are valued as second-order reflective constructs for a molar model ( Chin, 2010 ). The above discussion provides a basis for supporting the quality of the measurements of the components of these second order variables. The loadings of the dimensions of these constructs are also of interest. The statistics for all the dimensions were as expected, except for affective image, whose loading as a second order variable of image is 0.587 and therefore doesn't reach the AVE level of 0.707, although it exceeds the value of 0.55 (see Table 6 ).

Table 6 . Internal consistency and AVE.

With respect to internal consistency, two measurements are taken into consideration, Rho value (rho_A) and Composite Reliability (see Table 6 ). Nunnally and Bernstein (1994) suggests 0.7 as a level indicating “modest” reliability which is suitable for the early stages of research, and a stricter one of 0.8 for basic research. As shown in Table 6 , both indicators exceed the 0.8 value (except affective image, for which composite reliability is > 0.7 and Rho value is under 0.3).

Absolute fit indices determine how well a priori model fits the sample data ( McDonald and Ho, 2002 ). These measures provide the most fundamental indication of how well the proposed theory fits the data. Included in this category is the Standardized Root Mean Square Residual (SRMR). The SRMR is an absolute measure of fit and is defined as the standardized difference between the observed correlation and the predicted correlation. Thus, it allows assessing the average magnitude of the discrepancies between observed and expected correlations as an absolute measure of (model) fit criterion. A value < 0.10 or of 0.08 are considered a good fit ( Hu and Bentler, 1999 ). For this research model SRMR is 0.069 (below 0.08). Incremental fit indices, also known as comparative ( Miles and Shevlin, 2007 ) or relative fit indices ( McDonald and Ho, 2002 ), are a group of indices that do not use the chi-square in its raw form but compare the chi-square value to a baseline model. One of these indices is the Normed Fit Index (NFI). This statistic assesses the model by comparing the chi-square value of the model to the chi-square of the null model and values > 0.95 are recommended ( Hu and Bentler, 1999 ) for a good fit. After the analysis it was found a NFI of 0.987 indicating a good fit.

The discriminant validity is obtained in two ways. First, the Average Variance Extracted (AVE) is examined, which indicates the amount of variance captured by the construct in relation to the variance due to measurement error. The value must exceed 0.50 ( Fornell and Lacker, 1981 ). As shown in Table 6 , all the AVE values exceed that value, except for image construct, which is close to it (0.492). Secondly, the square root of AVE (in the diagonal of Table 10 ) is compared to the other constructs (below the diagonal in Table 7 ). These statistics suggest that every construct is stronger in its own measurement than in the measurements of other constructs.

Table 7 . Correlation and square root of AVE for first order latent variables.

Collectively, these results support the quality of the measurements. Specifically, the statistics suggest that the components of our measurements are reliable, internally consistent and they have discriminant validity.

Results of SEM

A PLS estimated model allows us to establish the variance of the explained endogenous variables by the constructs that predict them. Falk and Miller (1992) suggest that the explained variance of the endogenous variables ( R 2 ) should be ≥0.1. Related to this model, the indexes (see Table 8 ) explain the large variance of the second order variables, because the R 2 values of the dimensions (both image and motivation) exceed 0.5 (except in the case of the affective image, which exceeds 0.3). The R 2 value for satisfaction also exceeds 0.3. Stone-Geisser's Q 2 value must exceed 0, and this suggests a predictive relevance related to the endogenous construct model ( Chin, 1998 ). In this case, all the variables exceed that value (the lowest is satisfaction with a value of 0.2).

Table 8 . R square and stone-geisser.

To obtain indications of external validity, image and tourist satisfaction need to be significantly linked with motivation, as the theory explains ( Bagozzi, 1994 ). Based on this literature, a model in which motivation is a precedent and has a positive relationship with destination image was estimated, and this is also a precedent of satisfaction (see Figure 2 ).

Figure 2 . Results.

Table 9 shows that the path coefficients are significant ( p < 0.001) since there aren't any non-significant coefficients. The significance of the coefficients was estimated using PLS bootstrapping with 500 samples, an appropriate quantity to obtain reasonable estimations of standard error ( Chin, 2010 ).

Table 9 . Significance of the coefficients.

And since one of our hypotheses focuses on the indirect effect of the motivation with satisfaction variable, we can observe the existing relationship (0.47) through the results of Table 10 .

Table 10 . Direct and indirect effects of the coefficients.

In summary, in the model there is a direct and strong link between motivation and destination image (0.853). Motivation thus seems to be an important element influencing destination image. We have therefore proven that our hypothesis 2, “Tourist motivation significantly positive influences destination image formation” is correct (see Table 11 ).

Table 11 . Summary of hypothesis verification.

Regarding the hypotheses 1.1 and 1.2, “Cultural and natural motivation is the main dimension of the tourist motivation” and “Social motivation has an important relevance in the formation of tourist motivation,” the dimension of cultural and natural motivation is the one that reflects motivation (0.837) better than the other dimension of motivation, social motivation (0.251). It is possible that the type of motivation that is most influential will vary depending on the characteristics of the destination. In this case, motivations related to nature and culture are the most significant (see Table 11 ).

It is the cognitive image dimension that best reflects destination image (0.905) and there are some problems in considering the affective image to be a good reflection of destination image. The hypothesis 3.1, “Affective destination image has a positive influence on destination image formation” is therefore incorrect, while hypothesis 3.2, “Cognitive destination image has a positive influence on destination image formation” is correct. There is also a positive and direct link between destination image and satisfaction (0.556), and as a result we can accept the hypothesis 4, “Destination image has a positive influence on overall trip satisfaction” (see Table 11 ).

On the other hand, and indirectly, a relatively important link (0.470) between motivation and satisfaction has been found (see Table 10 ), especially if we consider the current difficulty in influencing satisfaction. This is a consequence of a strong link, which is direct and positive, between motivation and destination image. This relationship between tourist motivations and satisfaction was studied by Correia et al. (2013) , explained that there is a relationship between the motivational “push” and “pull” variables and overall tourist satisfaction. Battour et al. (2012) , who concluded that tourist motivation positively influences vacation satisfaction, should also be reviewed. For their part, Hidalgo-Fernández et al. (2019) also conclude in their study that there is a relationship between the motivations or interests of the tourist and satisfaction with the trip, turning this satisfaction into recommendation of the destination. This relationship is also found in their study Forteza et al. (2017) and He and Ming (2020) . Thus, we can check that motivation seems to be an important element in influencing both destination image and satisfaction, which has significant entrepreneurial consequences.

Discussion and Conclusions

Theorical discussions.

This study aims to analyze how rural tourism, in line with the Sustainable Development Goal number 8 of the UNWTO ( World Tourism Organization (UNWTO), 2021 ), can serve to sustainably develop the most depopulated rural areas ( Marzo-Navarro, 2017 ). We must focus on the social and economic sustainability of this type of tourism, which should translate into improving the quality of life of the indigenous population of the area ( Pérez de la Heras, 2004 ), and culturally and socially enriching the local population ( Rytkönen and Tunón, 2020 ). The social well-being of local economies is linked to tourism in those areas ( Tasci, 2017 ) and increases the sustainability of the local population.

The analysis of rural tourism has been carried out through the relationship that exists between the motivations that move the tourist ( Dann, 1977 ; Wong et al., 2018 ), which is one of the most important variables for decision-making in tourism ( Prebensen et al., 2010 ; Polo et al., 2016 ; Line et al., 2018 ; González and Vallejo, 2021 ); the image of the tourist destination, a key factor when tourists are choosing their destinations, and crucial when planning a trip ( Marine-Roig and Ferrer-Rosell, 2018 ); and satisfaction with the trip, a relationship studied by Forteza et al. (2017) , Hidalgo-Fernández et al. (2019) and He and Ming (2020) . This relationship has served to study the behavior of rural tourists related to sustainable development goals, especially goal number 8 “decent work and economic growth.”

From an academic point of view, the proposed Structural Equation Model could be used in many studies researching the links between the three variables studied (tourist motivation, destination image and trip satisfaction), because its reliability and predictive capacity have been proven, as shown by the results obtained. It is not only useful for research into rural tourism, but also for general tourism research, as well as for research into other kinds of rural tourism that have recently become popular, such as adventure tourism, sport tourism, cultural tourism and, in countries with a traditional wine industry, wine tourism.

Summary, we have demonstrated the importance of these three variables in the study of the rural tourism behavior and, thanks to this study, real and effective measures can be taken for the sustainable development of the rural area and thus be able to meet the objective number 8 of the UN Sustainable Development Goals.

Managerial Discussions

From a managerial point of view, this research can assist all those authorities that influence rural tourism policies in Spain's Soria province and the rest of Spain, when making policies to promote this kind of tourism, specially promoting the cognitive image that each of us have of a tourist area. We have seen the importance to these rural areas, the country's most depopulated, of tourism ( Flores and Barroso, 2012 ) as a complement to their more traditional activities (principally agriculture and livestock). Depopulation in these areas is a critical problem ( del Romero, 2018 ), since in some places, including some that offer rural, cultural, and natural attractions, the population has almost completely disappeared. This also leads to a loss of heritage for the province and for the country in general.

The results obtained demonstrate the importance of studying the variables used, especially the image of the tourist destination ( Beerli et al., 2003 ), for the promotion of the tourist area. This promotion seems very important, as explained by Baloglu and McCleary (1999) and Zhang et al. (2018) . And as we have verified, this image is formed especially as a result of the knowledge we obtain about the destination ( Sanz, 2008 ), much more than from the feelings that the destination causes in us.

It is also important, as Prebensen et al. (2010) , Polo et al. (2016) , Line et al. (2018) , and González and Vallejo (2021) explained, to analyze the motivations that drive tourists. Sancho and Álvarez (2010) point out the importance of motivation in the decision-making process. Therefore, the different administrations involved in tourism policies, as well as the owners of rural establishments, should consider the different motivations that influence decision-making ( Wong et al., 2018 ), as well as the formation of the community destination image ( Mayo and Jarvis, 1981 ; Michie, 1986 ; Gong and Sun Tung, 2017 ). In addition, due to the indirect but strong link between tourist motivations and satisfaction with the trip ( Fernández-Herrero et al., 2018 ), the need to cover these motivations must be considered, especially cultural, natural and social motivations ( Penelas-Leguía et al., 2019 ), so that the tourist has a satisfactory trip, which will positively influence loyalty with the destination ( López-Sanz et al., 2021b ) and will have an impact on better business results for tourist establishments of the area ( Moliner et al., 2009 ).

From the point of view of the Sustainable Development Goals, especially Goal 8, “Decent Work and Economic Growth,” the development of rural tourism can directly help to achieve this SDG ( Alcivar, 2020 ), as well as to avoid depopulation that threatens these regions of Spain so much ( Maroto and Pinos, 2020 ), promoting quality employment and avoiding exodus to the city and to other richer areas.

Limitations and Future Research

The main limitation of this study is that we have focused on a Spanish province. It would be convenient to apply this methodology to a complete study, focusing on the Autonomous Community of Castilla y León, to which Soria belongs, or even the entire Spanish state. A comparative study could also be made with other provinces with similar levels of depopulation in Spain, to compare both the strategies that are carried out in each of them, as well as the differences in the motivations that move tourists to those other provinces like the image that each one projects.

Another future line of research would be to extend the study to other different motivational factors, not only natural and cultural and social, to obtain other conclusions about tourist behavior. In addition, due to the discovery of the strong indirect effect that tourist motivations have on satisfaction, the study could be extended toward loyalty with the destination, and check if this indirect effect also applies between the tourist motivations and loyalty with the destination.

Finally, a similar study could be carried out by directing the questionnaire to tourists who focus on nature tourism, to discover any differences between them and rural tourists.

Conclusions

Therefore, if we look at in the principal and secondary objectives, the proposed model ( Figure 2 ) below, shows the direct link between the motivations that drive a tourist and his or her perceived destination image, as well as between image and overall tourist satisfaction with the trip. A link between motivations and satisfaction has been demonstrated, although it is indirect. These relationships demonstrate the importance of these three variables in the rural tourist behavior.

This study is important to be able to make decisions, especially from the point of view of local, regional and national tourism policies, to promote sustainable rural development and economic growth in the area, promoting job creation, to meet the Goal number 8 of Sustainable Development. With this economic development, a sustainable social development is directly achieved that is one of the pillars for the eradication of inequalities and poverty in rural areas.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

All authors contributed to conception and design of the study, organized the database, performed the statistical analysis, wrote the first draft of the manuscript, wrote all the sections of the manuscript, contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: motivation, destination imagen, satisfaction, rural tourism, SDG

Citation: López-Sanz JM, Penelas-Leguía A, Gutiérrez-Rodríguez P and Cuesta-Valiño P (2021) Rural Tourism and the Sustainable Development Goals. A Study of the Variables That Most Influence the Behavior of the Tourist. Front. Psychol. 12:722973. doi: 10.3389/fpsyg.2021.722973

Received: 09 June 2021; Accepted: 23 June 2021; Published: 23 July 2021.

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Copyright © 2021 López-Sanz, Penelas-Leguía, Gutiérrez-Rodríguez and Cuesta-Valiño. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: José María López-Sanz, jm.lopez@uah.es ; Pedro Cuesta-Valiño, pedro.cuesta@uah.es

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International Symposium on Advancement of Construction Management and Real Estate

CRIOCM 2019: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate pp 2089–2103 Cite as

The Promotion of Rural Tourism to Rural Revitalization—A Case Study of Longmen County

• Ting Yuan 4 ,
• Shuailin Wu 5 ,
• Chenpeng Song 6 ,
• Zijin Liao 6 &
• Zhigang Wu 6  
• Conference paper
• First Online: 08 June 2021

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Rural tourism plays an active role in optimizing industrial structure, improving rural environment, promoting the inheritance of local culture, and enhancing employment and income. Rural tourism is an important driving force for rural revitalization. Taking Longmen County of Huizhou City as an example, this paper used semi-structured interviews and questionnaires to investigate and analyze the current situation of rural tourism development, the distribution and development of rural tourism resources, residents’ participation and tourism talents in Longmen County. The study found that rural tourism in Longmen County is in the early stage of development with uneven distribution and development of tourism resources, low participation of residents, serious homogenization of characteristic tourism souvenirs, nonstandard service standards in tourism employee and lack of high-end employees. On this basis, some suggestions are put forward in the following: first, developing tourism industry as the core to comprehensively promote sustainable rural revitalization and enhancing the beauty of the countryside; second, digging deep into the cultural connotation and making scientific planning; third, perfecting tourism facilities and improving the participation of farmers; last, establishing a benefit sharing mechanism.

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Acknowledgements

We would like to express our gratitude to all those who have helped us during the writing of this thesis. We gratefully acknowledge the help of tourism administration of Longmen County, includes local government staff and villagers. They provided a lot of valuable information and make great cooperation during investigation. Thank you for all the support.

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Center for Urban and Regional Development Studies, Hong Kong, China

Shuailin Wu

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Chenpeng Song, Zijin Liao & Zhigang Wu

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Yuan, T., Wu, S., Song, C., Liao, Z., Wu, Z. (2021). The Promotion of Rural Tourism to Rural Revitalization—A Case Study of Longmen County. In: Ye, G., Yuan, H., Zuo, J. (eds) Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate. CRIOCM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-8892-1_146

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