DOI: https://doi.org/10.5513/JCEA01/26.2.4668
Original scientific paper
E-government in agriculture: assessment of Serbian farmers' ability to use the digital platform eAgrar
2025, 26 (2) p. 528-541
Vesna PARAUŠIĆ, Nataša KLJAJIĆ, Bojana BEKIĆ ŠARIĆ
Abstract
Digitalization of public administration services (e-government) is increasingly becoming prevalent in the agriculture sector in many countries, bringing numerous benefits to farmers and government authorities. However, the sustainability of these digital solutions is questionable, primarily due to the digital divide in rural areas. Using the example of Serbia and the implementation of the electronic information system (application) eAgrar, the authors examine the factors determining farmers’ ability to use this application independently vs. requiring assistance. An online survey was conducted from April to December 2023 using a semi-structured questionnaire, which involved 510 farmers. Binary logistic regression was used for statistical inference. Results show that the following predictors have a statistically significant impact on respondents’ ability to use eAgrar independently: the economic development of the region where the farm is situated, farmers’ age and education, farm size, type of farming on the farm, and the application’s adaptability to the local context, i.e., target users, both regarding the complexity of the software solution, and the availability of different types of IT support for farmers. The last predictor had the greatest impact on the studied dependent variable (Exp (B)=9.64; 95% CI 5.42, 17.14; P=0.000). The results indicate to government authorities that the sustainability of e-services in the agriculture sector requires an institutionally organized and extensive network of intermediaries for the digital training of farmers, as well as the software design of e-services that aligns with farmers’ socio-economic characteristics and digital literacy.
Keywords
digitalisation of public services, farmers’ digital literacy, farmers' attitudes, binary logistic regression, survey
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