A B S T R A C T
Contemporary rural development theories emphasize minimizing external intervention while strengthening endogenous processes and supporting the local economy. Tourism presents both positive and negative impacts on the economic prosperity of rural communities. Tourism farms have the potential to stimulate socio-economic development while mitigating negative environmental effects. This study aims to investigate the role of tourism farms, in conjunction with livelihood housing, within a structural equation modeling (SEM) framework of business spaces in endogenous villages characterized by limited development and support. The study employed correlation analysis and structural equation modeling to examine the variables and their structural relationships. The statistical population comprised rural and tourism producers, with a sample size of 180. Data were collected using a closed-ended questionnaire with a 4-point Likert scale, and path analysis was conducted using SPSS 20 and AMOS 24 software. The implementation steps included: (1) developing the index model and converting it into the target model; (2) constructing hypothetical models of intrinsic tourism architecture; (3) evaluating indicators to prioritize two-, three-, and four-factor models; and (4) discussing and interpreting the results within the SEM framework. Removing the exogenous factor of village development and support enhanced the evaluation indicators of the proposed models. Generally, reducing the number of factors and variables improves model fit, with two-factor models often achieving near-zero error. The most complex endogenous structure consists of four factors: tourism farm, livelihood housing, village-creative farm, and non-residential architecture. For three-factor structural planning, non-residential architecture is removed, while for two-factor planning, the village-creative farm is excluded.
Extended Abstract
Introduction
Given the growth of rural populations and the imperative to enhance villagers’ income and welfare, the development of rural businesses has become essential. Contemporary rural development theories primarily emphasize minimizing external intervention, fostering endogenous processes, and focusing on local economies, production activities, and business spaces, including marketplaces. Business space is conceptualized as comprising four elements: (1) livelihood housing, (2) indigenous tourism farms, (3) village-creative farms, and (4) non-residential workspaces.
Scholars have offered diverse perspectives on the relationship between tourism and the economic prosperity of rural communities. This relationship can be understood through positive, negative, and dual dimensions: negative impacts arise from extensive and uncontrolled development, whereas positive outcomes result from dispersed and integrated development in the context of sustainable tourism. The dual effects are context-dependent, varying with time, location, and planning strategies.
Tourism farms, also referred to as agricultural tourism, can stimulate socio-economic development while mitigating negative environmental impacts. Consequently, they can be categorized as a form of sustainable tourism. However, research on the architecture of rural business spaces remains limited. This study aims to examine the role of tourism farms, in combination with livelihood housing, within a SEM framework of business spaces in endogenous villages characterized by limited development and support.
 
Methodology
To examine the variables and their structural relationships, the study employed correlation analysis and SEM. The study population comprised rural and tourism business owners and producers. A sample of 180 participants was surveyed using a closed-ended questionnaire with a 4-point Likert scale. Path analysis was conducted using SPSS 20 and AMOS 24 software.
The implementation steps included: (1) developing the indicator model, entering it into the software, and converting it into the target model; (2) constructing hypothetical models of intrinsic tourism architecture; (3) evaluating indicators to prioritize two-, three-, and four-factor models; and (4) discussing and interpreting the results within the SEM framework.
Factor analysis was performed twice in SPSS to identify the underlying factors. Initially, 32 factors were extracted. Due to the large number of factors and resulting modeling options, the variables of each factor were aggregated into composite functions. A second round of factor analysis in SPSS produced seven second-order factors and 21 first-order factors. Subsequently, these factors were incorporated into a conceptual model and refined in AMOS according to standard model fit indicators, ultimately yielding five second-order factors and twelve first-order factors.
Regarding relationships among components, the maximum standardized regression weight between the tourism farm and livelihood housing was 1.684. Among the components, indigenous tourism exhibited the strongest association, with a standardized weight of 0.611 in the second stage of livelihood breeding (silkworm production), decreasing to 0.61 and 0.31 in subsequent business stages.
 
Results and Discussion
This study examined the indicators of seven structural relationship models with two, three, and four factors. A summary of the results and their interpretation is presented in the table below.
 
 
Table 1. The recommended number of factors involved in business architecture in the village of intrinsic tourism




Priority according to the number of factors


Impressionable


Mediator


Influential


Number of factors


Total rank




First 2 factors-attention to the relationship between these two factors in simple planning


Livelihood housing


-


Tourism farm


2


1




Second 2 factors-the development of non-residential buildings is not the first priority.


Non-residential business


-


Tourism farm


2


2




Third 2 factors-not recommended.


Creative farm


-


Tourism farm


2


3




First 3 factors-tourism and creative farms can develop livelihood housing.


Livelihood housing


Creative farm


Tourism farm


3


4




Second 3 factors - not recommended.


Non-residential business


Creative farm


Tourism farm


3


5




This model can be used in planning on the scale of the rural area where these factors exist.


Non and residential business


Creative farm


Tourism farm


4


6




Support development unit to serve rural business development as a initiator, driver and facilitator.


Non-residential business


3other factors


Support development


5


7




 
 
Conclusion
In conclusion, the study demonstrated that removing the exogenous factor of village development and support improved the evaluation indicators across all hypothetical models. Therefore, in planning and designing rural business spaces, it is recommended to focus on the minimal number of factors necessary. The simplest and most effective structure highlights two factors: tourism farms and livelihood housing.
The most complex model incorporates four factors—tourism farms, livelihood housing, village-creative farms, and non-residential architecture—but produced the weakest outcomes among the proposed models. The optimal configuration is a three-factor model, comprising tourism farms, livelihood housing, and village-creative farms.
The elimination of the exogenous factor, combined with the evaluation, validation, and interpretation of the tourism farm business models, demonstrates the effectiveness of removing exogenous influences while preserving the endogenous role of the tourism farm. These findings support the theoretical framework of second-generation rural development, which emphasizes endogenous models.
 
Funding
There was no funding support.
 
 
Authors’ Contribution
First author prepared the questinarie, collected the data, and drafted the article. The second author supervised the research, checked and controled the results, and corrected, revised, and finalized the article (correspond). The third author interpretated the information and supervised the research. The fourth author participated in research, method design, calculation and analysis of information.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this research.