The benefits of tourism for rural community development

Rural areas often face lower productivity, education, income, depopulation, declining employment, loss of farms, heritage impacts, demographic shifts, and reduced quality of life. Prior work suggests enhancing rural resilience and reforming investment and policy (e.g., via tourism) to counter rural decline. Policymakers lack clear information on what benefits rural tourism brings to communities, especially during/after COVID-19, making investment decisions difficult. This study shifts focus from sustainability to concrete contributions—what rural tourism does to aid community development—amid changing tourist demand favoring nature-based, low-density destinations post-COVID. Emerging evidence highlights rural tourism’s roles in employment growth, mental health mitigation, resilience, identity, and well-being. Given government’s critical role and the absence of a comprehensive measurement framework for contributions, this study aims to: (1) develop a rural tourism contribution model to inform policy; (2) address methodological limitations in existing sustainability modeling; and (3) provide a six-step procedure to construct a valid contribution model.

Rural tourism, as defined by UNWTO, encompasses experiences linked to nature, agriculture, rural lifestyles/culture, angling, and sightseeing. It is used to foster development while preserving traditional culture and has grown as part of regional rural economies. COVID-19 shifted preferences from exotic to local rural destinations, offering opportunities beyond agriculture alone. Rural tourism appeals to urban visitors through festivals, crafts, heritage, natural preservation, nostalgia, cuisine, and family-oriented leisure, delivering psychological, educational, social, esthetic, and physical satisfaction while stimulating economic growth and community viability. Documented benefits include increased income and direct sales, entrepreneurship opportunities, and environmental stewardship (e.g., preservation of land and landscapes, organic production, green chemistry). The literature identifies four contribution perspectives: (1) Economic—enhanced employment opportunities and stability, resident income, investment, entrepreneurship, value-added production, capital formation, resilience, business viability, and tax revenue; (2) Sociocultural—depopulation prevention, cultural/heritage preservation, social stability, improved quality of life, revitalization of crafts/customs, restoration of historical buildings and identities, increased social contact, visibility, pride, and cultural integrity; (3) Environmental—tourism-derived income enabling conservation, biodiversity protection, infrastructure, environmental awareness, green chemistry, maintaining unspoiled/family land; (4) Leisure and educational—experiences contrasting urban life, fulfilling hedonic and self-actualization needs, education and esthetics, opportunities for relaxation/family reunion, ecological knowledge, green consumerism, recreation, health and food security, reduced mental health issues, and nurturing nostalgia. These perspectives can synergize to strengthen family–place bonds and regional resilience, making rural tourism an enabler of community development and a target for investment.

The study addresses gaps in measuring rural tourism’s contributions by proposing a six-stage expert-based procedure to generate and validate indicators while mitigating common method bias (CMB) and omitted-variable risks: (1) Data collection via a critical literature review (SSCI/SCIE indexed works; search terms spanning rural development, sustainability, resilience, farm/rural/ecotourism, COVID-19, and concepts like land ethics, biodiversity, green consumerism, environmentalism, community identity). This generated 33 subattributes in four domains. (2) Face validity assessment focused on omitted variables and CMB (simplifying ambiguous/double-barreled items and complex syntax). Experts could add omitted attributes and suggest revisions. One omitted variable (business viability) was added; no CMB-related revisions were suggested, yielding 34 subattributes. (3) Interexpert consensus to remove construct-irrelevant variance, using descriptive statistics suitable for a 5-point Likert scale: percentage agreement (%AGR; ≥70% of responses in 4–5), median (≥4.0), standard deviation (SD ≤1), and coefficient of variation (CV ≤0.3). Items failing thresholds were dropped. (4) Intergroup consistency assessed via the nonparametric Kruskal–Wallis (K–W) test (p>0.05 threshold), appropriate for small, non-normal expert samples, to ensure subgroup rating distributions did not differ significantly. (5) Interexpert reliability assessed using Kendall’s W; W≥0.7 indicates strong consensus, 0.5–0.7 moderate, <0.3 weak. (6) Attribute weighting using fuzzy Analytic Hierarchy Process (AHP) with Power Choice 2.5, tolerating ambiguity in complex multicriteria judgments, to derive weights and build the hierarchical contribution model. Expert panel: 18 heterogeneous, anonymous experts from Taiwan (≥6 rural tourism top managers with >10 years’ experience, ≥6 tourism academics from three universities, ≥6 government officials in rural development), selected for expertise, knowledge, diversity, experience, and commitment. Experts rated items and contributed to face validity and consensus stages.

• Face validity: Experts added one omitted subattribute (business viability) to the initial list; no CMB-driven revisions were needed. Total subattributes considered reached 34.
• Interexpert consensus (5-point Likert thresholds: %AGR≥70%, median≥4, SD≤1, CV≤0.3): Two subattributes failed and were removed—Strategic alliance (AGR=50%) and Carbon neutrality (AGR=56%). The remaining 32 subattributes met all criteria (see Table 1 in the paper for detailed metrics).
• Intergroup consistency: Kruskal–Wallis tests for all 32 retained subattributes showed no significant differences among the three expert subgroups (all p>0.05), indicating consistent ratings across managers, academics, and officials.
• Interexpert reliability: Kendall’s W indicated strong consensus within each domain—Economic W=0.73, Sociocultural W=0.71, Environmental W=0.71, Leisure and educational W=0.72.
• Hierarchical model: Four Level-2 attributes and 32 Level-3 subattributes were validated and weighted using fuzzy AHP. • Level-2 weights (priority): Economic (w=0.387) > Environmental (w=0.237) > Leisure and educational (w=0.193) > Sociocultural (w=0.183). Economic benefits are the most significant contribution. • Selected Level-3 subattribute weights within domains (examples from Fig. 2):
  • Economic: Employment stability (0.206), Investment opportunities (0.167), Employment opportunities (0.157), Increased income (0.136), Economic resilience (0.098), Entrepreneurial opportunities (0.082), Product diversification (0.064), Tax revenue (0.054), Business viability (0.036).
  • Sociocultural: Social stability (0.188), Depopulation (0.164), Community identity (0.147), Culture & heritage preservation (0.135), Quality of life & well-being (0.133), Restoration of historical buildings (0.096), Community visibility (0.063), Community pride (0.046), Cultural integrity (0.028).
  • Environmental: Kept land in family (0.247), Infrastructure (0.194), Biodiversity (0.167), Green chemistry (0.115), Environmental awareness (0.106), Natural environmental conservation (0.097), Kept land unspoiled (0.074).
  • Leisure & educational: Leisure & recreational opportunities (0.303), Reducing mental health problems (0.233), Technology skills & capabilities (0.178), Nurturing nostalgia (0.105), Ecological knowledge (0.085), Health & food security (0.054), Green consumerism (0.042).
• Overall, the model confirms the breadth of rural tourism benefits across economic, sociocultural, environmental, and leisure/educational dimensions, with robust expert consensus and clear priority structure for policy use.

The study directly addresses policymakers’ need for clear, structured evidence of rural tourism’s contributions to community development. Findings confirm a surge in rural tourism demand during COVID-19 and validate a four-perspective contribution framework. The economic perspective emerges as most critical, aligning with literature on tourism’s role in growth and resilience. Environmental contributions include heightened protection awareness and resource stewardship tied to tourism development. The leisure and educational perspective leverages rural geographical uniqueness to enhance calming, sensory-rich, and emotionally engaging experiences, potentially improving tourist satisfaction and well-being. Methodologically, the study contributes rare evidence of interexpert consensus, intergroup consistency, and reliability in expert-based model building, and demonstrates the utility of fuzzy AHP in prioritizing multidimensional contributions. Practically, the weighted indicators guide policy prioritization, suggesting targeted investments that maximize economic impacts while supporting environmental stewardship and social/cultural vitality. The model underscores rural tourism’s role in mitigating mental health challenges exacerbated by the pandemic and bolstering regional resilience, identity, and community well-being.

Rural tourism provides substantial positive contributions to rural community development across economic, sociocultural, environmental, and leisure/educational dimensions, with economic contributions ranked most important. The validated model offers 32 actionable subindicators with priorities to inform policy and strategic investment, particularly relevant during and after COVID-19 when rural destinations can support public mental health. Policymakers at national, regional, and local levels should position tourism as a strategic pillar of rural development, implementing projects that leverage these contributions to advance resilience and well-being. Future research should extend validation with broader populations and rigorous psychometric and structural modeling, and further refine agreement metrics to enhance generalizability.

Two main limitations: (1) Construct-irrelevant variance was addressed via interexpert consensus, which may be criticized for limited statistical rigor; future work should employ agreement indices such as ADM() or rWG(). (2) The expert-based approach limits generalizability; future studies should use general population samples and apply reliability and validity testing (e.g., Cronbach’s alpha, confirmatory factor analysis, structural equation modeling), reporting fit indices (GFI, AGFI, CFI, NFI, TLI, SRMR, RMSEA).