This research was conducted from February to May 2025 in Konda District, South Konawe Regency, Indonesia. This study used a quantitative approach with an exploratory survey design to analyze the relationship between farmer capacity, the use of Cyber Extension, and rural community empowerment in rice production canters in Konda District, South Konawe Regency. The research population included all paddy rice farmers active in the area, totaling 215 people. The sample frame was compiled based on a list of active farmer group members obtained from field agricultural extension workers and local agricultural agencies in 2024, so that all population units had an equal chance of being selected as respondents in the study.

The sample size was determined using the Slovin formula with a margin of error of 5%, resulting in 140 respondents. Respondents were selected through proportional random sampling, in which the number of samples in each farmer group was determined proportionally based on the size of the group, and then respondents were randomly selected from the list of active farmer group members. Data collection was carried out through field observations, structured surveys using questionnaires, and documentation studies. All questionnaires distributed were returned and deemed suitable for analysis, resulting in a response rate of 100 percent. However, this study has the potential for sampling bias because it only covers farmers who are members of active farmer groups, so the results of the study are exploratory and need to be interpreted contextually.

The variables in this study consist of three parts, namely farmer capacity, utilization of Cyber Extension, and community empowerment. The farmer capacity variable (X1) includes: (X1.1) farmers’ ability to manage their farms, (X1.2) Decision-making ability, (X1.3) Ability to optimize available resources, (X1.4) farmers’ practical knowledge in running their farms, (X1.5) practical skills in running farming businesses, (X1.6) Ability to interact and cooperate, (X1.7) Ability to build social networks, (X1.8) Ability to adapt to the environment, (X1.9) Ability to solve problems together, and (X1.10) Ability to communicate effectively. Cyber Extension utilization variables (X2) which include: (X2.1) Media for the dissemination of agricultural information, (X2.2) Facilitating farmers’ access to agricultural information, (X2.3) Learning tools for farmers, (X2.4) Tools for agricultural development facilitation activities, (X2.5) Information exchange media, and (X2.6) Media for documenting agricultural information at the local level. Then for the rural community empowerment variable (Y), which includes: (Y1) Farming business development, (Y2) Improving access to capital for farmers, (Y3) Developing local potential, (Y4) Improving the capacity of individual farmers, (Y5) Improving welfare through job creation, (Y6) Training and mentoring activities, (Y7) Strengthening village institutions, (Y8) Management of accountable, open, and transparent institutions, (Y9) Development of basic regional infrastructure, (Y10) Increased access to information and technology, and (Y11) Easy access to resources in a fair manner. Variable measurement was carried out using a Likert scale (Harpe, 2015; Robinson, 2023), namely: very high/good (score = 5), high/good (score = 4), moderate/fair (3), low/poor (score = 2), and very low/poor (score = 1).

Data and information were described and interpreted according to logical flow through the application of descriptive and inferential statistics using a systems approach and analysis. Descriptive analysis was used to describe and explain in detail and depth the conditions of farmer capacity, the use of Cyber Extension, and rural community empowerment. Descriptive analysis was conducted using class interval formulas to categorize data into three groups, namely low, medium, and high. Thus, the categories found were low/poor (values 1–2.3), medium/fair (2.4–3.6), and high/good (3.7–5).

Further analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with several methodological considerations. First, PLS-SEM is appropriate for exploratory research and model development, especially when the relationship between constructs still requires empirical testing in a specific local context. Second, this method is effective for medium sample sizes and does not require strict assumptions of normal distribution, making it relevant to the characteristics of survey data on rural farming communities. Third, PLS-SEM allows for the simultaneous testing of direct and indirect relationships (mediating effects) between farmer capacity, Cyber Extension utilization, and rural community empowerment. In addition, this approach provides the advantage of evaluating measurement models and structural models simultaneously, thereby capturing the complexity of latent relationships in the process of digital extension-based empowerment.

Inferential analysis refers to Structural Equation Modeling–Partial Least Squares (SEM-PLS) analysis conducted using the SmartPLS 3.0 program. SEM analysis is an equation model of PLS using a variance-based or component-based structural equation modeling approach. This analysis aims to build or develop theory (Henseler, 2018; Hair et al., 2019). SEM-PLS analysis itself consists of four stages, namely measurement model design, outer model, inner model, and hypothesis testing.

In evaluating the relationship between indicators and latent variables to be observed, the role of the outer model is very important in a standard research framework. Several criteria are set for a model to be valid. These criteria are convergent validity, discriminant validity, composite reliability, and Cronbach’s alpha. A factor loading value >0.50 is the standard value for measuring convergent validity (Cheung et al., 2024; Guenther et al., 2023). In formulating a research model, an Average Variance Extracted (AVE) value >0.50 is a general requirement. A Heterotrait-Monotrait Ratio (HTMT) value <0.90 is a general prerequisite. The AVE and HTMT values represent Discriminant Validity. In addition, composite reliability and Cronbach’s alpha values >0.70 are requirements that must be met. The collinearity test was conducted using the Variance Inflation Factor (VIF) with a VIF value requirement of <5.0 for the model to be accepted.

The next stage is the inner model, which aims to describe the relationship between data and latent variables in the theory to be developed. The tests consist of Path Coefficient analysis, R-square (R²), Goodness of Fit (GoF), and Q-square (Q²). Path Coefficient shows the direction (positive or negative) of the influence between latent variables. A positive relationship is indicated by a Path Coefficient value close to +1, and vice versa (Sarstedt et al., 2021). The R-square (R²) test aims to measure the extent to which exogenous variables explain endogenous variables. The Goodness of Fit (GoF) analysis aims to see the extent to which the observed data is consistent with the proposed model. Goodness of Fit formula = √(AVE × R²) (Tenenhaus et al., 2004). Q-square analysis assesses the model in terms of predictive relevance. A higher Q-square value (>0) indicates better predictive relevance of the proposed model, and vice versa (Chin, 1998).

The hypotheses were tested using the Bootstrapping method in the SmartPLS 3.0 program. The hypotheses proposed in this study are:

Farmer capacity is one of the main factors in achieving successful agricultural development, especially in rural community empowerment programs. According to Chambers and Conway (1992); Uphoff and Dazzo (2016), farmer capacity is the ability of farmers to manage and utilize resources, apply agricultural innovations, and make appropriate decisions to increase the productivity of their farms while maintaining sustainability. Farmer capacity is not only measured by technical ability, but also by the managerial and social abilities of farmers, which support each other in forming the ability to adapt to change. In this study, the capacity of rice farmers in Konda District can be seen in Table 1.

Table 1 above shows that the capacity level of rice farmers in Konda Subdistrict is in the moderate category with an average score of 3.58. This means that rice farmers have sufficient capacity to run and manage their farms. In addition, this situation shows that there is still potential for developing or increasing farmer capacity. Based on the farmer capacity indicators, out of the 10 indicators, only three are in the high category, while the other seven are in the moderate category. The farmer capacity indicators in the high category are farmers’ ability to manage their farms (3.79), their ability to adapt to the environment (4.39), and their ability to solve problems together (3.82). Meanwhile, the farmer capacity indicators in the moderate category are decision-making ability (3.33), the ability to optimize available resources (3.22), practical knowledge of farming (3.55), practical farming skills (3.38), ability to interact and cooperate (3.29), ability to build social networks (3.46), and ability to communicate effectively (3.59).

In general, the capacity of rice farmers in Konda Subdistrict has demonstrated that farmers have a fairly good basic ability to manage their businesses. Therefore, strategic actions are needed to jointly strengthen managerial, technical, and social aspects. Strengthening farmer capacity aims to enhance the competitiveness of the agricultural sector and accelerate the adoption of appropriate innovations in line with farmers’ needs (Tambo and Wünscher, 2018). Additionally, it will have an impact on the economic independence of farming households in terms of sustainability. Vrabcová and Urbancová (2023) state that managerial skills, technical skills, and market access are the main factors in enhancing the competitiveness of sustainable farming through agricultural innovation.

Cyber Extension is an agricultural extension system based on information and communication technology that aims to expand the coverage of extension services to target audiences or farmers. In addition, Cyber Extension can be defined as a medium for information, communication, and education that utilizes the internet as a medium of interaction between farmers, extension workers, the government, researchers, and the private sector (Akinboye et al., 2024). By using this system, farmers are able to access various agricultural information, ranging from technological innovations, market prices, government policies, and others. The use of Cyber Extension has a positive impact on farmers (Zhang et al., 2016). Farmers are able to easily access and search for agricultural information according to their needs. In this study, the level of Cyber Extension utilization in Konda District can be seen in Table 2.

Table 2 shows that the level of Cyber Extension utilization by rice farmers in Konda Subdistrict is in the moderate category with an average score of 3.34. This result indicates that farmers are aware of and have begun to utilize Cyber Extension as a means of extension and community empowerment, although it has not been utilized to its full potential. The integration of Cyber Extension into farmers’ daily activities has not yet become a habit. Therefore, extension workers play a role as intermediaries for farmers in obtaining the agricultural information they need. This can be seen from the indicators of Cyber Extension utilization, which are all in the moderate category. These indicators are the media for the dissemination of agricultural information (3.36), facilitating farmers’ access to agricultural information (3.51), learning tools for farmers (3.35), tools for development facilitation activities (3.21), information exchange media (3.24), and media for documenting agricultural information at the local level (3.38).

The use of Cyber Extension is still in its early stages among rice farmers in Konda District in their journey toward digital transformation in the agricultural sector. Many factors hinder this digital transformation process, ranging from inadequate internet networks, lack of knowledge in using technology, and limited assistance in digital literacy. This situation has resulted in most rice farmers still not being accustomed to using the Cyber Extension system to increase the productivity of their farms. Therefore, the use of Cyber Extension through strengthening the digital capacity of farmers and extension workers needs to be carried out (Afzal et al., 2016). The government and related institutions need to take action to support the integration of the Cyber Extension system. Mushi et al. (2024) found that the integration of digital agricultural systems through platforms or applications provides potential benefits and convenience for farmers in accessing technical information, markets, and capital services. Active efforts will encourage farmers to play an active role, not just as recipients of information and benefits, in accelerating community empowerment activities. Arnés et al. (2018), found that the learning process for farmers through appropriate digital agricultural systems will encourage community participation, increase confidence, and improve the ability to identify, test, and apply new innovations, thereby impacting community empowerment as seen through increased participation, capacity, and resource management.

Rural community empowerment is a process of improving the capabilities and independence of communities through active participation in managing their potential to achieve prosperity. Gutierrez (2023) states that participation in development activities does not always result in empowerment. Therefore, appropriate and sustainable strategies are needed to achieve the goals of empowerment. In empowerment activities, communities are not only limited to being objects of development, but also subjects in development who play a role in the planning, implementation, and supervision of activities (Haldane et al., 2019; Luisi and Hämel, 2021). In this study, rural community empowerment is divided into four dimensions, namely economic, social, institutional, and infrastructure and community access. From these four dimensions, rural community empowerment is then measured based on eleven indicators. The results of the study related to rural community empowerment in Konda District can be seen in Table 3.

Table 3 shows that rural community empowerment in Konda Subdistrict is in the moderate category with an average score of 3.48. This result means that efforts in community empowerment activities have been quite successful, but in terms of achieving independence and welfare for the farming community, they have not been maximized. Of all the indicators measuring rural community empowerment, only 4 are in the high category, while the other 6 are in the moderate category. The indicators in the high category are agricultural development (4.17), strengthening village institutions (3.69), accountable and transparent management of institutions (3.79), and development of basic regional infrastructure (4.29). The indicators in the moderate category are increasing access to capital for farmers (2.87), development of local potential (3.49), improvement of individual capacity (3.41), improvement of welfare through increased employment (3.01), training and mentoring activities (3.31), improvement of access to information and technology (2.81), and ease of access to resources in a fair manner (3.41). Most indicators are still classified as moderate, indicating that there is still potential for further improvement.

Community empowerment activities in Konda Subdistrict are still limited to strengthening basic and institutional capacities, and have not yet fully led to farmer independence. Empowerment activities should be aimed at strengthening human resource capacity, adopting agricultural technology (Shikuku, 2019), and developing farmers’ entrepreneurial skills in order to encourage the community to adapt to existing changes and challenges and to be able to identify and take advantage of market opportunities (Takahashi et al., 2020). In addition, a partnership network needs to be established between farmers, extension workers, the government, and capital institutions to develop farmers’ access to information, business capital, and innovation in the agricultural sector. Rural community empowerment strategies need to be developed based on the principles of participation and sustainability to improve economic welfare and strengthen social independence and community capacity in independently managing village potential for development (Berg et al., 2020). A study by Dushkova and Ivlieva (2024) found that empowerment programs designed based on the principles of participation and sustainability will increase the capacity of communities to manage local resources and minimize risks.

Farmer capacity is one of the main factors in achieving successful agricultural development, especially in rural community empowerment programs. Farmer capacity can be improved through various activities such as training, mentoring, and agricultural extension (Norton and Alwang, 2020). However, conventional methods still face major problems, especially in terms of space and time limitations. In today’s digital era, there is a need for an approach based on information and communication technology so that the process of increasing farmer capacity can continue and be sustainable (Baul et al., 2024). Extension and training activities as well as information dissemination can be carried out by utilizing the Cyber Extension system. Cyber Extension itself is a medium for information, communication, and education that utilizes the internet as a medium of interaction between farmers, extension workers, the government, researchers, and the private sector (Akinboye et al., 2024). Proper utilization of Cyber Extension will increase farmers’ capacity, empowering them to independently achieve prosperity, which will indirectly impact the empowerment of rural communities.

The influence of farmer capacity through the use of Cyber Extension on rural community empowerment in this study was analyzed using SmartPLS 3.0 software. The results of the analysis of the influence of farmer capacity through the use of Cyber Extension on rural community empowerment were measured from the Bootsrepping analysis output in SmartPLS, which is presented in Table 4.

In formulating a model for rural community empowerment through farmer capacity building based on the use of Cyber Extension, the SmartPLS 3.0 program was used. The Partial Least Squares–Structural Equation Modeling (PLS–SEM) approach was used to analyze the research data. This analysis is a technique used to predict or refine an existing theory (Garson, 2016). The approach using this analysis consists of four stages, namely the formulation of a theory for the research model, testing the outer model, testing the inner model, and testing the hypothesis (Hair et al., 2014).