The spread of the coronavirus disease in 2019 (COVID-19) in Thailand has led to a lack of liquidity and income for entrepreneurs, increasing the variety of distribution channels compared to store sales. This will be a solution for businesses struggling and creating value to raise the income levels of community enterprises in Thailand. This was an integrated and participatory action research using qualitative techniques through observation, interviews, recordings, analysis, and interpretation of the operational characteristics of community enterprises from field visits for consultation. This study aimed to examine the problems and obstacles of online selling by community enterprise entrepreneurs and to find guidelines for advising lead entrepreneurs in the Digital Market. These 25 community enterprise entrepreneurs produced community herbal products in Thailand. The research findings were analyzed using grounded theory according to the research objectives. From the research results, it is possible to summarize the problems and obstacles faced by entrepreneurs in selling products online among community enterprise entrepreneurs owing to the lack of knowledgeable administrators and the decline in demand for products affected by the COVID-19 pandemic. Furthermore, barriers to laws, regulations requirements related to cannabis products included legal controls only for cultivation and the production process until the product was sold, and production capacity could not be produced to meet the demand when there was a large volume of orders. Solutions were as follows: increasing skills and knowledge for entrepreneurs, especially in the potential; finding a way to pass on the business to the new generation to continue the business; using strategies to create cooperation with other enterprise networks and government agencies; creating online selling channels through various platforms; increasing funding to develop production processes; and using technology to create competitive advantages and marketing planning and delivery to make online sales an essential channel.

Purpose: To reveal the impact mechanism of rural museum intervention on the construction of local identity of rural community residents, and provide practical reference for the protection and utilization of rural cultural identity. Methods: This study takes the Weijiapo Rural Museum in Luoyang, China as the research object, uses participatory observation and in-depth interview methods, and explains the specific characteristics of rural community resident identity construction through identity process theory (IPT). Results: (1) The impact of the intervention of rural museums on rural areas is reflected in four aspects: local spatial reconstruction, transformation of livelihood methods, reconstruction of social relationships, and evolution of cultural customs; (2) under the influence of rural museum construction, the representation of community residents’ identity has shown complex characteristics, with both positive and negative impacts coexisting; (3) the local identity of community residents affects their perception and attitude towards the construction of rural museums.

The government’s land registration program aims to protect communities from future land disputes. However, lack of community support presents challenges to its process and implementation. Utilizing a qualitative case study approach, this article examines these challenges from the community’s perspective, focusing on land registration, community participation, and implementation dynamics. It suggests that learning from these dynamics can enhance the program’s effectiveness, highlighting the need for a systematic approach to community involvement.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.