Amid the declining number of young Indonesian farmers over the last 10 years [1], advances in information and communication technology (ICT) have played a strategic role in overcoming the challenges of sustainable agricultural development through increased knowledge and more effective farming practices. As an agricultural country, Indonesia’s agricultural sector plays an important role in maintaining food security, the economy, poverty, and community welfare [2,3,4]. However, the transformation of the agricultural sector into an era of digital disruption and the threat of climate change requires a new approach that integrates digital technology with human resources to deal with change.
Referring to Bunyasiri et al. [5], the incorporation of the younger generation into agricultural activities and the utilization of labor-saving machines emerge as pivotal adaptive strategies. This perspective is echoed by few other researchers as well [6,7], the main factors for the failure of the agricultural sector empowerment program were a low level of farmer participation.
Increasing the participation of millennial farmers in Indonesia is designed through an agricultural human resource empowerment program called Youth Entrepreneur and Employment Support Services (YESS), which is expected to be able to produce strong and high-quality millennial entrepreneurs [8]. In this context, millennial farmers have been identified as key actors in the transformation of Indonesia’s agricultural sector. When they tend to be more open to technology, they have a great potential to adopt technology and carry out sustainable agricultural practices. However, the success of this transformation is highly dependent on their level of participation and ability to adapt quickly to changes in the technology, markets, and environmental conditions in which they work [9]. Adaptability is one of the requirements for the sustainability of agricultural work systems [5], particularly when dealing with technological disruptions and climate change.
Farmers often use digital communication technology for social communication, contact intermediaries with market products, and obtain real-time advice regarding agriculture from experts [10]. The provision of relevant knowledge and information through digital communication not only reduces barriers to accessing resources but also facilitates productive interactions between farmers and farmers, customers, or markets, as well as with the government [11]. Online participation in the form of communication interactions and knowledge sharing with extension workers and fellow farmers through ICT can encourage the formation of a more informed agricultural society (information society) and be better prepared to adopt better and sustainable agricultural practices, especially in developing countries with a majority of small-scale enterprises [12].
The low level of online participation, especially knowledge sharing among farmers, is a challenge for many developing countries, among the reasons is because of what is mentioned [13] that realizing knowledge sharing among farmers requires alignment between farmers’ personal expectations and the expectations of their communities, in addition to the low level of digital literacy and experience among farmers [14].
Several studies have argued that the ability to access agricultural information, capital, production facilities, and the promotion of agricultural production products has a positive impact on responding to market demand more productively and on improving farmers’ practical skills [15,16,17]. Other research on young farmers has examined aspects of technology adoption [18,19,20], agricultural production technology [21,22], smart farming, and the Internet of Things [23,24] as well as the competence and performance of extension workers [25,26,27].
Recent studies are yet to confirm a positive link between digital communication competence, online participation, and the adaptability of millennial farmers. This finding indicates that digital access alone is insufficient to achieve successful adaptive power among millennial farmers. The absence of robust evidence supporting a direct link between digital skills and enhanced adaptability among millennial farmers underscores the need for this study. Similarly, previous research did not explain the internal mechanism of digital communication competencies from the perspective of millennial farmers to increase their adaptability of millennial farmers in the face of climate change, technological disruption, and market demand. Therefore, this study seeks to fill this gap by analyzing the success of millennial farmers in improving adaptive performance and digital transformation through online participation.
This study differs from previous research in several aspects. The study’s objectives are twofold: First, to dissect the mechanisms through which digital communication competence influences the adaptability of small-scale millennial farmers amidst digital disruptions; and second, to explore the mediating role of online participation in this process. This is to trace the linkages of millennials, who are often identified as highly skilled individuals and rely on various digital technology devices, including smartphones, social media, the Internet, e-marketing, mobile banking, and other technologies, with online participation [28] through communication and knowledge-sharing on their adaptability.
A quantitative approach was adopted and data were collected through a survey conducted in Bogor, Indonesia. This study focused on millennial farmers who are actively participating in the Millennial Farmer Empowerment Program and the YESS Program, and Bogor Regency was chosen for these two main reasons. The study population consisted of millennial farmers aged 19–39 years who had used digital ICT devices. Based on data obtained from district agriculture, the population of millennial farmers under the intervention of the service is 1,763 people. Based on Slovin’s calculation with a margin of error of 5%, the sample size in this study was 326. After screening the incoming responses, 345 samples were found suitable for analysis, exceeding the minimum sample size requirements and deemed adequate for the purpose of the study. The demographic profiles of the respondents are presented in Table 1.
Demographics of millennial farmers
| Descriptor | Category | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Male | 166 | 48.12 |
| Female | 179 | 51.88 | |
| Age | 17–25 years old | 116 | 33.62 |
| 26–35 years old | 177 | 51.30 | |
| 36–45 years old | 52 | 15.07 | |
| Education | Elementary school | 36 | 10.43 |
| Junior high school | 51 | 14.78 | |
| Senior high school | 202 | 58.55 | |
| University | 56 | 16.23 | |
| Land ownership | <5,000 m2 | 299 | 86.67 |
| 5,000–10,000 m2 | 26 | 7.54 | |
| >10,000 m2 | 20 | 5.80 | |
| ICT ownership | Smartphone | 338 | 97.97 |
| Tab/tablet | 1 | 0.29 | |
| Laptop | 4 | 1.16 | |
| Computer/PC | 2 | 0.58 | |
| Internet access | <1 h/day | 86 | 24.93 |
| 1–4 h/day | 114 | 33.04 | |
| >4 h/day | 145 | 42.03 |
A questionnaire was used as a research instrument. The research questionnaire consisted of four sections: Demographics, digital communication competencies, online participation, and adaptive performance(1). To develop the questionnaire, operationalized latent variables were adapted based on a comprehensive review of the relevant literature. This study considered three variables of digital communication competence: Digital motivation, digital knowledge, and digital skills [29,30,31]; online participation, [32,33]; and farmers’ individual adaptiveness [34,35].
All variables were measured utilizing a five-point Likert scale, wherein 1 denoted “strongly disagree,” 2 denoted “disagree,” 3 denoted “neutral,” 4 denoted “agree,” and 5 denoted “strongly agree.” Table A1 presents the final revised questionnaire and references.
Given that the data were collected through self-assessment, there exists a potential for respondent subjectivity bias, including social desirability bias, recall bias, or unobservable economic factors [36]; to mitigate these concerns, the study ensured respondent anonymity and utilized validated instruments. For the pilot testing of the survey instruments, data were gathered from 30 millennial farmers in the Sukabumi Regency. To assess the internal consistency and reliability of the survey items, researchers employed Cronbach’s alpha (α) coefficient analysis. Factor analysis was used to evaluate the instrument’s validity. The cut-off values for the validity and reliability tests, along with their outcomes, are presented in Table 2. The 36 items included in the final questionnaire were considered highly reliable, as the Cronbach’s α coefficients of all five constructs exceeded the recommended threshold of 0.8 [37].
Results of the validity and reliability tests
| Construct validity | Reliability | |||
|---|---|---|---|---|
| Variables | Kaiser-Meyer-Olkin (KMO) | Bartlett’s test of sphericity (sig.) | Factor loading | Cronbach’s α |
| Cut-off values [37,38,39] | ≥0.5 | ≤0.5 | ≥0.5 | ≥0.6 |
| Digital motivation | 0.685 | 0.001 | 0.599−0.932 | 0.820 |
| Digital knowledge | 0.818 | 0.001 | 0.790−0.924 | 0.907 |
| Digital skills | 0.690 | 0.001 | 0.554−0.917 | 0.785 |
| Online participation | 0.771 | 0.001 | 0.786−0.953 | 0.848 |
| Adaptive performance | 0.740 | 0.001 | 0.597−0.989 | 0.934 |
Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships in the research model. PLS focuses on explaining the variance of the latent variables and indicators being measured. PLS can be used to confirm theories, identify existing or non-existent relationships, and provide a basis for further testing [39]. The PLS analysis, including significance tests for path coefficients, was performed using SmartPLS Version 3.2.9.
The choice of PLS is guided by three considerations [39,40]: (1) the ability of PLS to model latent constructions, both formative and reflective; (2) the ability of PLS to assess measurement models in the context of mediated models; and (3) achieving high levels of statistical power with small sample sizes. The research model was analyzed in the context of measurement and structural models. The measurement model was used to confirm that the construction had sufficient validity and reliability, whereas structural model measurements were used to assess the relationships proposed in this research model.
Digital communication competencies are important to increase farmers’ capacity for sustainable social, economic, and agricultural development. Digital communication competence is a manifestation of a person’s motivation, knowledge, and digital skills and is in line with technological advances [29,30,31,41,42]. Digital motivation in this study is defined as the desire of millennial farmers to communicate effectively and appropriately through digital devices, whereas digital knowledge is defined as the capacity of millennial farmers to comprehend the requirements for effective communication through digital devices [43]. Digital skills pertain to the ability of farmers to generate effective communication behavior through the utilization of digital devices such as computers, mobile phones, and the Internet to retrieve, filter, create, evaluate, and disseminate knowledge.
Digital communication competencies can affect the use of ICT to access agricultural information, communication, knowledge-sharing, production, and marketing [44,45,46,47]. With advanced digital skills, farmers can easily obtain the latest information on agricultural production, supply, and market demand [48]. On the other hand, research [46,49,50] has shown that a lack of digital skills causes non-optimal use of information and communication technology to increase its capacity to support agricultural business activities. In addition to research at the level of access and productivity, several studies on the use of ICT among farmers have begun to focus on the impact of initiatives and collaborations [51], the adoption of innovations [17,47,52,53,54,55], and the improvement of knowledge [56,57] and skills [49].
Previous research has shown that the new generation of digital technologies not only increases the likelihood that farmers will engage in agricultural entrepreneurship but also significantly improves their individual performance. The existing literature provides a strong theoretical foundation for this research; however, this is not yet complete. First, previous research mainly focused on the second and fourth levels of the digital divide [58,59] but did not pay attention to the influence of digital communication competencies in compensating for the gap in the use of digital technology among small-scale millennial farmers. Second, the existing research has not explained how digital communication competence affects the adaptive performance of small-scale millennial farmers in rural areas. This study aims to address this gap by examining how digital communication competencies affect the adaptive performance of individual small-scale millennial farmers in rural areas through online participation.
Competence in digital communication begins with motivation as an important prerequisite [60]. With negative motivation, the knowledge and skills possessed by a person cannot be used adequately. Furthermore, the need or desire to use technical systems that support digital communication and the belief that such activities produce positive or desirable outputs affects how often the system is used. This means that the more motivated millennial farmers are to use digital communication technology, the greater their knowledge and skills. As a result, the proportion of farmers achieving desired results increases.
This study hypothesizes that there is a significant causal relationship between motivation, knowledge, digital skills, online participation, and the adaptive performance of millennial farmers. In addition, based on previous studies, it can be expected that the higher the motivation for digital communication, the higher the knowledge and digital communication skills of millennial farmers, and the higher their online participation and adaptive performance of millennial farmers [29,30,41]. Based on this premise, this study proposed the hypotheses presented in Figure 1.
Hypothetical framework.
The measurement model includes three tests: reliability, convergent validity, and discriminant validity. Reliability refers to the extent to which a set of indicators consistently measures aggregate constructs [39]. This study used Composite Reliability (CR) and Cronbach’s α coefficients to verify the internal consistency of the indicators within each construct and showed that both tests were considered acceptable if they exceeded 0.70 [39].
Convergence validity refers to the degree of convergence of indicators that measure the same construct. This study used two methods to verify the validity of convergence: the first was represented by the indicator’s standard loading factor on each construct, which must exceed the threshold of 0.70, and the second was represented by the extracted mean of variance (AVE) which must exceed 0.50 as the threshold [39,61].
Based on Table 3, there were some indicator items that had an outer loadings below 0.70; therefore, they were excluded from the model. The final results showed that the Cronbach’s α values ranged from 0.823 to 0.947, and the CR values ranged from 0.876 to 0.954, indicating strong internal reliability. The AVE and item loading of 0.50 or greater, indicates convergent validity. As shown in Table 3, the loading factor ranged from 0.709 to 0.898, and the average extracted variance ranged from 0.585 to 0.717, which were greater than the recommended threshold. Therefore, convergent validity of the measurement instruments was confirmed in this study.
Convergent reliability and validity
| Latent variable | Loading factor | Cronbach’s α | Composite reliability | AVE |
|---|---|---|---|---|
| Cut-off value | >0.7 | >0.7 | >0.7 | >0.5 |
| Motivation | 0.721–0.783 | 0.823 | 0.876 | 0.585 |
| Knowledge | 0.717–0.898 | 0.899 | 0.926 | 0.717 |
| Skills | 0.739–0.817 | 0.841 | 0.887 | 0.612 |
| Online participation | 0.723–0.844 | 0.917 | 0.930 | 0.624 |
| Adaptive | 0.709–0.847 | 0.947 | 0.954 | 0.676 |
Discriminant validity was assessed according to the Fornell−Larcker criterion and the heterotrait–monotrait (HTMT) ratio. As shown in Table 4, the square root of the mean variance extracted from each construct (in bold) was greater than its correlation with the other constructs, confirming that discriminant validity has been achieved. Meanwhile, the HTMT calculates the average ratio of indicator correlations between constructs divided by the correlation of indicators in the same construct. The maximum threshold was 0.9, and Table 4 shows the HTMT matrix with a value lower than 0.9, indicating satisfactory discriminant validity. All constructs are below the threshold and thus demonstrate the acceptable of discriminant validity.
Measurement model-discriminant validity
| Fornell−Larcker criterion | HTMT ratio | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| ADV | KNW | MOV | OLP | SKL | ADV | KNW | MOV | OLP | SKL | |
| ADV | 0.822 | |||||||||
| KNW | 0.382 | 0.847 | 0.416 | |||||||
| MOV | 0.420 | 0.733 | 0.765 | 0.474 | 0.852 | |||||
| OLP | 0.515 | 0.404 | 0.403 | 0.790 | 0.513 | 0.418 | 0.435 | |||
| SKL | 0.497 | 0.754 | 0.684 | 0.428 | 0.782 | 0.550 | 0.867 | 0.814 | 0.457 | |
Each bold value (diagonal values) should be greater than all off-diagonal values (correlations between constructs) in its row and column.
Structural analysis was performed to test the hypotheses. Table 5 presents the relationships between the independent and bound variables. Motivation was found to influence millennial farmers’ digital skills (β = 0.283, t = 3.558, p < 0.05), thus supporting H1. Motivation also influenced millennial farmers’ knowledge (β = 0.733, t = 19.648, p < 0.05), thus supporting H2. Table 5 shows that digital knowledge had a significant influence on digital skills (β = 0.547, t = 5.712, p < 0.05), supporting H3. Digital skills have a significant influence on the adaptive power of millennial farmers (β = 0.338, t = 5.129, p < 0.05), supporting H4. Additionally, digital skills had a significant influence on millennial farmers’ online participation (β = 0.428, t = 8.615, p < 0.05), supporting H5. Finally, online participation had a direct and significant influence on the adaptive power of millennial farmers (β = 0.370, t = 7.796, p < 0.05), supporting H6.
Path coefficient and hypotheses testing
| Hypotheses | Path | Path coefficient | SD | t-value | Hypotheses support |
|---|---|---|---|---|---|
| H1 | Motivation → Skills | 0.283 | 0.079 | 3.558 | Yes |
| H2 | Motivation → Knowledge | 0.733 | 0.037 | 19.648 | Yes |
| H3 | Knowledge → Skills | 0.547 | 0.096 | 5.712 | Yes |
| H4 | Skills → Adaptive | 0.338 | 0.066 | 5.129 | Yes |
| H5 | Skills → Online participation | 0.428 | 0.050 | 8.615 | Yes |
| H6 | Online participation → Adaptive | 0.370 | 0.048 | 7.796 | Yes |
Tables 5 and 6 show that digital skills had a positive impact on online participation rates. This shows that digital skills increase the online participation of millennial farmers and that increasing online participation significantly increases their performance possibilities of millennial farmers. Therefore, the mediating effect of online participation was significant, as shown in Table 6. Thus, the online participation mechanism follows the path of digital skills → for online participation in → millennial farmers’ adaptability. The higher level of online participation through active communication and knowledge sharing among millennial farmers not only show their attitude toward altruism, but also show their skill in building agricultural information networks through contemporary technology, both of which are beneficial for the adaptability of millennial farmers in the face of change. This adaptive ability is necessary for every individual, especially in the agricultural sector, when facing the challenges of climate change, market demand, and technological disruption.
Summary mediation effect
| Indirect effect | Coeff. | 95% Conf. Int. | t-value | Sig. |
|---|---|---|---|---|
| Motivation → Knowledge → Skills | 0.401 | [0.247, 0.549] | 5.180 | Sig |
| Skills → Online participation → Adaptive | 0.158 | [0.112–0.211] | 6.255 | Sig |
Digital motivation has a positive impact on millennial farmers’ digital knowledge. Furthermore, digital motivation and knowledge had positive coefficients at the 5% significance level. This shows that digital motivation is able to increase the digital knowledge of millennial farmers, and the increase in digital knowledge significantly improves the digital skills of millennial farmers. Therefore, the mediation effect of digital knowledge was significant. In this study, the digital knowledge mechanism followed the paths of digital motivation →, knowledge →, and skills. This model shows that motivation plays an important role in paving the way for the mastery of digital knowledge, which ultimately facilitates the development of digital skills that are essential to adapt and succeed in the digital age. The relationship between motivation, knowledge, and skills is not purely linear but rather interdependent. As individuals develop digital skills, they receive positive feedback, which increases their digital motivation to continue learning and deepens their knowledge, thereby creating a continuous development cycle. This process forms a cycle of reinforcement, where digital skills gained from previous knowledge can trigger increased motivation, which encourages individuals to continue learning and developing new skills. The more individuals practice digital skills, the greater the knowledge they accumulate, which ultimately strengthens their motivation to master digital technologies.
The aim of this study was to examine the influence of digital communication competence on millennial farmers’ behavior in sharing online knowledge and individual adaptive performance. The results of the study show that the digital communication competence of millennial farmers is formed by digital motivation which positively and significantly affects digital knowledge and skills, and digital knowledge positively and significantly affects digital skills. This means that millennial farmers who are motivated both internally and externally, are interested or happy to participate in communication using digital technology, are more likely to seek out and engage in digital training to improve digital knowledge and skills. This motivation involves the interest and willingness of millennial farmers to interact through digital platforms as well as perceptions of the importance of digital communication. In the context of agriculture in the digital era, this motivation can be influenced by millennial farmers’ awareness of the benefits of digital technology in increasing their adaptability to environmental changes and digital disruptions.
This corroborates previous research, which states that these internal and external motivations can increase farmers’ willingness to learn new digital skills, such as using smartphones to obtain agricultural information [62,63,64,65,66], adopting digital marketing [46,63,67,68,69,70], and utilizing apps for weather forecasting and aquaculture [52,71,72]. The results of this study strengthen the theoretical model of computer-mediated communication competence [29] and the study by Morreale et al. [41] forms its theoretical basis. The proposed model includes three key interrelated elements: motivation, knowledge, and skills. Each element plays a crucial role in determining how effectively a person can communicate using digital technologies. Without adequate motivation, digital communication knowledge and skills may not be optimally utilized. Motivation acts as a driver to take advantage of communication technologies, learn new platforms, and adapt to technological changes to meet agricultural information access needs. This finding corroborates the research by Bubaš [60], competence in digital communication begins with motivation as an important prerequisite, with negative motivation, the knowledge and skills possessed by a person cannot be used adequately.
Hypothesis testing revealed that digital communication competence through digital skills had a positive and significant effect on millennial farmers’ online participation. This is because these skills can increase the confidence of millennial farmers in using technology to communicate with extension workers as well as with fellow farmers to share agricultural information. This participation strengthens their ability to compile and distribute knowledge and expands their communication reach. Thus, the better a person’s digital skills, the greater is their tendency to engage in active and effective online participation. This confirms that the use of digital communication has a positive influence on social communication to contact intermediaries and market products and to obtain real-time advice on agriculture from experts [10]. Not only in the agricultural sector, but the research by Martzoukou et al. [73] reports that students’ prior everyday participation as digital citizens was connected to important academic skills, including digital learning and development, their digital abilities to complete academic work, their information literacy skills, and their skills around managing their digital wellbeing and identity.
Millennial farmers have a form of active commitment that leads to a desire to participate in creating intellectual capital with their group. Therefore, the use of digital ICT, such as social media as an interactive media oriented to communication, as well as the willingness of millennial farmers to actively share (contribute their own intellectual capital) and collect (consult with the intellectual capital of others) knowledge is a form of this commitment.
The results further show that digital communication competence through digital skills had a positive and significant effect on millennial farmers’ adaptability. In this study, the adaptability of individual farmers is defined as the result of the efforts of farmers to adapt to the work context in a digital agricultural environment. High adaptability is required for farmers in the transition to a future agricultural landscape based on data and the timely adoption of automated and advanced technologies, including drones, precision equipment, wireless sensor networks, and AI-based systems [74]. This is in line with the research results Anbananthen et al. [75], which states that high adaptability, scalability, and support for cutting-edge technology of The Open Group Architecture Framework are viewed as capable of enabling the digital transformation of Malaysian agricultural enterprises.
Research in the education sector reveals that digital communication skills are crucial for success in higher education and modern workplaces, where learning and collaboration increasingly occur online through virtual environments and tools [76]. These skills enable individuals to effectively navigate and participate in digital spaces, thereby enhancing their ability to adapt to changing technological landscapes. They enable individuals to effectively engage in virtual collaboration, solve digital problems, and demonstrate reflective judgments in interconnected digital environments [77]. As technology continues to evolve, those with strong digital communication skills are better positioned to adapt to new challenges and opportunities in both educational and professional settings.
Digital skills allow millennial farmers to access relevant information, use agricultural applications, and utilize technology to improve their work efficiency. A study involving the use of rocoto red and green chili detector devices in Ecuador showed an accuracy rate exceeding 99% [78]. Through these detectors, small-scale chili farmers can more easily detect chili types early, thereby increasing access to relevant information and potentially increasing their work efficiency. On the other hand, research [46,49,50] shows that lack of skills leads to the non-optimal use of ICT to increase its capacity to support agricultural business activities, especially in the digital era, and to increasingly uncertain climate change that requires human capital that is adaptive to change.
The results of the causal relationship analysis in the structural model show that online participation has a positive and statistically significant effect on millennial farmers’ adaptability. This means that the adaptability of individual millennial farmers can increase with an increase in communication activities and knowledge sharing through digital media. This is because millennial farmers gain access to other farmers’ experiences through dialogue and discussion, which are positively reflected in their cultivation practices.
This study has explained several findings related to digital communication competency and online participation among millennial farmers. These findings have theoretical, managerial, and policy implications. However, this study has several limitations that need to be considered. First, the scope of this study was geographically limited. Second, self-reported data may be potentially biased. Third, external factors can influence digital motivation and adaptability. Consequently, further research covering a wider geographical area, external factors such as economic incentives, peer influence, and government support, and contextual factors such as economic constraints, infrastructure availability, and traditional farming practices are needed to determine the factors that influence millennial farmers’ motivation and adaptability.
This study investigated how digital communication competencies affect millennial farmers’ online participation and adaptability. The findings revealed that digital skills are shaped by farmers’ motivation and digital knowledge. Furthermore, millennial farmers’ online participation is directly influenced by their digital communication competencies, which in turn are driven by digital skills. The study also showed that online participation mediates the relationship between digital skills and millennial farmers’ adaptability. In other words, the more competent millennial farmers are in digital communication and the more actively they participate online in communicating and sharing knowledge with their peers, the more adaptive they will be to change.
By offering an in-depth analysis of the factors that shape digital communication competency, this study makes a significant contribution to the digital communication literature in the agricultural context, particularly for millennial farmers. This study presents a new understanding of the relationship between digital motivation, digital knowledge, and digital skills, and how they influence farmers’ online participation and adaptability to technological change. The model developed in this study provides further insight into how digital communication competency can improve millennial farmers’ performance and resilience when facing the challenges of digitalization and work system changes. Consequently, we propose several interventions to enhance the adaptive capacity of millennial farmers in addressing various changes, particularly technological disruptions. First, it is imperative to establish a structured digital training program that emphasizes digital motivation, fundamental technological comprehension, and communication skills, by utilizing blended learning methodologies for optimal outcomes. Additionally, it is crucial for millennial farmers to cultivate an online community to exchange knowledge and experiences through webinars and virtual discussions, thereby promoting online engagement. We also suggest the need for a digital mentorship program involving experienced farmers as mentors to support digital skills development. The finding of this study will significantly contribute to the current efforts in formulating strategies focused on addressing the decline in young farmers in Indonesia, specifically in adapting to the digital era and climate change.
In this study, respondent participation was voluntary. Before completing the questionnaire, each respondent was given clear information about the purpose of the study, the procedures involved, and their rights, including the right to withdraw from the study at any time without consequence. All respondents were asked to provide informed consent after reading and understanding the information provided. The data collected were kept confidential and were used only for the purposes of this study. This approach protected the privacy and confidentiality of the participants. None of the questions were associated with controversial issues that could cause mental or physical harm to the respondents.
Informed consent: Informed consent was obtained from all millennial farmers who participated in this study.
Ethical approval: This research has obtained ethical clearance from the Ethics Committee of Social and Humanities Studies, National Research and Innovation Agency (NRIA) – Indonesia. The approval reference number is 981/KE.01/SK/12/2024.