Digital Readiness Map of Mango Farmers in Indonesia

Digital readiness is widely recognized as the preparedness of individuals or communities to adopt and effectively use digital technologies. According to Eshet-Alkalai [22], digital literacy is a multidimensional competence involving technical, cognitive, and socio-emotional skills required in the digital era. In agriculture, readiness not only refers to access to devices or connectivity but also the ability to interpret and apply digital information for decision-making. Lioutas and Charatsari [23] argued that without adequate literacy, infrastructure alone cannot ensure meaningful adoption.

Theoretical frameworks provide important foundations for analyzing adoption in agriculture. The Technology Acceptance Model (TAM) (Davis, 1989) in Akpe et al.’s [24] study emphasizes the role of perceived usefulness and perceived ease of use in shaping technology adoption. Similarly, the Diffusion of Innovation theory [41] highlights innovation attributes, communication channels, and social systems. Both theories help explain why adoption varies across farmer groups and generations. However, their use in commodity-specific readiness mapping has been limited, suggesting opportunities for further application.

Globally, Agriculture 4.0 has received growing attention. Wolfert et al. [13] identified the potential of Big Data in smart farming, showing how digitalization could enhance efficiency and sustainability. More recent studies provide nuanced evidence of both opportunities and barriers. A systematic review in Malaysia identified costs, infrastructure, and digital literacy as decisive factors for precision agriculture adoption [35]. In Ghana, smallholder engagement with digital technologies was found to depend strongly on education, age, and access [42]. In Tanzania, digital platforms improved farmers’ access to markets and information but infrastructural barriers persisted [6].

Evidence from South Africa further reveals systemic constraints. Von Maltitz et al. [43] found that even agricultural extension professionals lack sufficient readiness, underscoring that barriers are not confined to farmers but extend across institutions. These studies collectively show that readiness is context-dependent, shaped by socio-economic and institutional environments, and cannot be generalized across regions. In Indonesia, research on agricultural digitalization has begun to explore smart farming ecosystems, IoT, and blockchain-enabled value chains. Scholars have emphasized that digital transformation is a socio-technical process shaped by infrastructure, institutions, and farmer capacity [21, 44]. These works highlight important innovations but remain broad, focusing largely on sectoral potential rather than readiness assessments.

Commodity-specific research is still rare. For example, Susandi [20] examined blockchain for traceability and transparency, while other researchers analyzed IoT applications in mango farming[21, 45]. Both provide valuable insights but stop short of systematically evaluating how prepared farmers are to adopt these innovations. Thus, there remains a lack of empirical evidence that maps readiness across different dimensions such as access, literacy, and generational gaps. From this body of literature, it becomes evident that global studies document readiness challenges and contextual variations, while Indonesian research has mostly explored technological potential without systematic readiness mapping. This gap is critical because readiness determines whether innovations can be effectively adopted at the farmer level. Mango farming, as a strategic horticultural commodity with high domestic and export value, has not been studied in this regard. This study therefore positions itself as the first systematic attempt to map the digital readiness of Indonesian mango farmers, contributing to theory by linking readiness with adoption models and to practice by informing targeted interventions in agricultural policy and capacity building. Therefore, understanding readiness at the commodity level becomes essential before large-scale digital agriculture policies are implemented.

4.1 Digital readiness dimensions of mango farmers

This subsection presents descriptive results illustrating the current profile of digital readiness among mango farmers. Based on a comprehensive survey of 403 mango farmers, this unique and seminal research examines three key aspects: access to technology, data availability, and digital training. The technology access mean score (3.7) in this study suggests that farmers had a substantial exposure to technologies. In contrast, the average for data access is 3.6, indicating that, despite some bare sufficiency, there are opportunities to improve data retrieval. Digital literacy – here referring to the ability to make good use of digital tools and technologies – was in fact rated as the least valued element (2.9) among all the producers examined, signaling substantial unease or a gap when operating between cultural and digital environments. This imbalance highlights the need for literacy and infrastructure-oriented responses. Therefore, although there is growing infrastructure to support them, many farmers still face difficulties adopting digital technologies for farm management and decision-making (Table 1). These descriptive findings indicate that digital readiness disparities are not merely technological but also behavioral and generational in nature.

Table 1. Mean scores of digital readiness dimensions

The disparities between the dimensions are displayed in a spider chart (Figure 1). The chart makes clear the gap between infrastructure and literacy, showing that while access and data are moderately good, digital literacy is remarkably low. Though technology and data are relatively balanced in their representation on the chart, the axis shrinks to the point of literacy, suggesting an area for intervention: one where a structural vulnerability becomes clear. In other developing-country settings, the 'ability of farmers to turn access into active use appears to be limited by poor skills'.

The dominance of technology access as the strongest predictor suggests that digital transformation in smallholder agriculture remains infrastructure-driven rather than capability-driven. In developing-country contexts, access functions as an enabling condition that determines whether farmers can even enter the digital ecosystem. However, consistent with socio-technical transition theory, access alone does not guarantee effective adoption unless supported by cognitive readiness and institutional facilitation.

4.2 Classification of farmer readiness levels

To learn more about these findings, farmers were classified into four readiness categories. Figure 2 presents this stratification as a pyramid, indicating that most farmers remain in the lower tiers. The chart distribution shows that 14% of respondents were in the "high readiness" group, 32% in the "moderate readiness" group, 41% in the "low readiness" group, and 13% in the "critical readiness gap" group. Beyond aggregate scores, readiness varies substantially across farmer groups, requiring classification analysis. Targeted interventions and different strategies are necessary to build digital capacity.

The digital readiness map for mango farmers and SMEs in Indonesia shows a tiered adoption system. Few young farmers and SMEs possess digital processing skills and high readiness; most have medium or low assurance, indicating they have access to digital tools but do not use them. A significant gap exists between older farmers and traditional SMEs, both of which lack access to digital resources and the necessary literacy skills. This mapping provides evidence that agriculture digital readiness in Indonesia is uneven, generationally stratified, and based on infrastructure, which warrants targeted policy responses tailored to each level of readiness.

The use of the pyramid structure emphasizes the necessity of differentiated strategies for each of these groups. That map shows that readiness is not homogeneous but rather layered. High-readiness farmers can be early adopters who might become digital champions within their communities. Farmers with moderate readiness have access to digital tools but lack the skills to use them effectively. Farmers with low readiness face challenges related to literacy and infrastructure, while those in the critical gap require intensive support, including access to basic connectivity and individualized literacy support.

There are twofold implications; first, readiness requires infrastructure and well-designed programs to achieve greater efficiency. Second, policy measures should leverage the potential of young farmers as digital champions, ensuring older farmers remain included. Tailored capacity-building efforts that address a range of readiness states, along with strengthened extension and open data services, are necessary to tackle this demand. Combining evidence from statistics, descriptive findings, and theoretical underpinnings, the Digital Readiness Map contributes to readiness theory in academia and guides the promotion of inclusive digital transformation in Indonesian agriculture.

4.3 Determinants of digital readiness: Regression analysis

To further explain these disparities, regression analysis was conducted to identify the relative contribution of each readiness dimension. Regression analysis was performed to explore the correlates of digital readiness. Table 2 shows that all three — access to technology, data availability, and digital literacy — impact readiness. Technology access was the strongest determinant (ß = 0.501, p < 0.001), followed by data availability (ß = 0.343, p < 0.001) and digital literacy (ß = 0.291, p < 0.01), indicating that improved technological access substantially increases farmers readiness to engage in digital agriculture practices. The model explains 62.4% of the variance in readiness, suggesting potent explanatory ability. These results confirm that readiness is not due to a single factor, but instead requires a balance or integration of infrastructure, data resources, and data literacy.

Table 2. Regression results: Determinants of digital readiness

While technological access emerges as the strongest predictor, the qualitative evidence demonstrates that access alone does not guarantee meaningful utilization, highlighting the mediating role of digital literacy. The dominance of technology access as the strongest predictor suggests that digital transformation in smallholder agriculture remains infrastructure-driven. Access functions as an enabling condition allowing farmers to enter digital ecosystems; however, consistent with socio-technical adoption theory, access alone does not ensure effective utilization without sufficient digital capability and institutional support.

4.4 Generational dynamics and digital intermediaries

The regression analysis verifies that access remains the primary pathway to readiness, but qualitative interviewing reminds us that access is not equivalent to utilization alone. Younger farmers (aged 35 or younger) were also significantly higher on all dimensions than older comparison farmers (aged 50 or older). The remarks showed that smartphones, when owned, were primarily used for communication rather than for agricultural decision-making. Older farmers distrusted online information and had trouble using mobile phone apps. This situation means the practical benefits of digitalization was eroded by a general lack of confidence in interpreting data from weather apps or online market platforms, often resulting in farmers going back to square one, doing what they have been doing for centuries.

Younger farmers often served as "digital intermediaries" in their communities, helping older members with mobile apps for weather reports and price information, as well as WhatsApp trading platforms. This underscores the importance of digital literacy, which correlates less strongly with access but is an enabling factor for the successful use of Information and Communication Technology (ICT). Extension officers noted that the uptake patterns largely hinged on youth leadership in farmer groups, emphasizing generational readiness. Farmers from remote villages also shared problems with unreliable Internet access and expensive data plans, in line with the finding that just 62% of rural households in West Java can access the Internet reliably. This lack of infrastructure impedes the success of access measures and underscores the need for increased investment in the digital infrastructure of the countryside. The coexistence of infrastructure and literacy challenges explains why readiness scores vary, even as access to technology grows.

The emergence of younger farmers as digital intermediaries indicates that digital adoption follows a socially mediated pathway rather than an individual adoption process. Younger farmers act as knowledge brokers who translate digital information into locally applicable practices, accelerating diffusion through trust-based social networks. This finding extends diffusion theory by highlighting intergenerational mediation as a critical mechanism in smallholder digital transformation.

Low digital literacy affects decision-making by increasing perceived complexity and uncertainty associated with digital tools. Older farmers often rely on experiential knowledge accumulated through long-term farming practices, leading to lower perceived usefulness and ease of use—two central constructs of the TAM, which emphasize education and access as influential factors in determining technology adoption across contexts. Farmers in younger age categories who find mobile apps and online platforms more valuable report higher readiness, whereas older farmers encounter barriers to perceived ease of use. This finding aligns with the TAM, where perceived usefulness and perceived ease of use shape adoption behavior. Older farmers hesitation reflects lower perceived ease of use, whereas younger farmers demonstrate higher perceived usefulness through practical experimentation with digital tools.

These findings are also in line with reflecting global patterns, such as: in Malaysia, cost and literacy are barriers; while the key factors influencing adoption in Ghana and Tanzania are education and access; likewise, institutional preparedness among extension officers is still low in South Africa. However, the Indonesian mango business is unique in that it has an extreme generational gap, driven by a very cultural adherence to heritage processes. This can offset the projected gains from agricultural innovations. Adoption of technology should not be limited to tools; it is needed in activities to ensure the availability of all threats. As a result, digital information is frequently viewed as supplementary rather than authoritative, causing farmers to revert to conventional decision-making despite technological availability.

4.5 Policy implications and digital agriculture roadmap

Combining the quantitative and qualitative results emphasizes that digital readiness is multifaceted. It might look like infrastructure and access are enough; they are not if there are no literacy and behavioral adaptation. Initiatives that only provide devices or Internet access are at risk of having a limited effect unless they are accompanied by proper training and support. Adult literacy-focused interventions for older farmers can be effective, particularly when they involve younger generations as agents of change. Institutional interventions, such as open data initiatives and improved extension services, are also needed to narrow the readiness gap. Typically, the findings (Table 3) emphasize that, even as access to technology and data coverage has improved, digital literacy is lagging. This study, by offering an initial structured readiness map for mango farmers, demonstrates that effective digital transformation in agriculture requires addressing infrastructure, skills, and socio-cultural aspects alike. The readiness map, therefore, not only helps to inform theoretical debates but also provides practical relevance for policymakers, extension services and agribusiness actors to develop specific strategies for inclusive agricultural digitalization in Indonesia.

The implication is that readiness is not something you can buy by simply throwing money at infrastructure. Moreover, access without capability development leads to unsustainable adoption outcomes; if its comfort is not established correctly, or its use is nonexistent, the reins will eventually slip. Policy interventions should be implemented through coordinated efforts involving local agricultural extension services, farmer cooperatives, and government agencies, particularly by developing youth-led digital training programs and subsidizing rural Internet infrastructure.

Table 3. Findings to implications map of digital readiness among mango farmers

The findings contribute theoretically by integrating digital readiness assessment with technology adoption theories, demonstrating that readiness in smallholder agriculture is shaped not only by technological availability but also by intergenerational social dynamics. Practically, the study provides an evidence-based framework for designing differentiated digitalization policies tailored to heterogeneous farmer readiness levels.

Based on these findings, the paper develops a policy roadmap that explains to both scholars and practitioners which structural, cultural, financial, and regulatory strategies are required to improve digital readiness. This roadmap outlines pathways to translate the evidence into specific channels, ensuring contributions from academia are squarely focused on feasible interventions for farmers, SMEs, and decision-makers (Figure 3). Policy Roadmap for Inclusive Digital Agriculture outlines four areas of intervention (structural, cultural, financial and regulatory), to strengthen digital readiness and uptake among farmers. The roadmap outlines ways to address these issues, thereby improving product quality, SMEs performance, and inclusive digital transformation. It also provides a general blueprint for a range of structural, cultural, financial, and normative interventions tailored to address the identified awareness gaps and stimulate acceptance among mango growers and SMEs. By framing its recommendations in this way, the roadmap supports translating evidence into timely, relevant interventions for policymakers, extension agencies, and agribusiness interests.

Structurally, investing in rural broadband infrastructure, subsidizing Internet access, and fostering public-private partnerships are necessary to achieve proper inclusive connectivity. These interventions are the strategic responses to address the access divides identified by the survey, especially in areas where producers from remote communities suffer irregular connectivity. Cultural aspects are just as important, particularly given the generation gap identified in this research. The potential of young farmers, both as digital intermediaries and in mechanisms for avoiding the marginalization of older farmers, e.g., intergenerational digital schools, youth-to-elder mentorship programs, or farmer cooperatives for capacity building, can be mobilized by such interventions.

Regulatory frameworks are needed to build trust and enable interoperability. To succeed, it will require adhering to international open data guidelines such as AGRIS, complying with Food and Agriculture Organization (FAO)-provided international quality certification processes, and creating robust privacy and data governance instruments. Without these components, digital tools might not take off or be used by farmers. The expected path will contribute to enhanced product quality, increased performance of participating SMEs, and prompt an inclusive digital transformation that directly aligns with SDGs 2 (Zero Hunger), 8 (Decent Work & Economic Growth), and 9 (Industry, Innovation & Infrastructure). These results highlight the academic importance of this research and the pressing need for change in Indonesian agricultural policy.

Implementation of the proposed roadmap requires multi-level collaboration involving local agricultural extension agencies, regional governments, farmer cooperatives, and private agriculture technology providers. Extension officers may act as facilitators of literacy programs, while younger farmers can function as peer-based digital intermediaries supporting knowledge diffusion within farmer communities. Overall, the findings demonstrate that digital readiness is a multidimensional socio-technical condition shaped by infrastructure, literacy, and intergenerational dynamics, providing both theoretical advancement and practical guidance for inclusive agricultural digital transformation.

This work was supported by the Directorate of Research and Community Service, Directorate General of Research and Development, Ministry of Higher Education, Science, and Technology, Republic of Indonesia, through the 2025 fiscal year research grant scheme. The authors gratefully acknowledge the ministry for its support in promoting agricultural digitalization and smallholder empowerment in Indonesia.