Abstract
While several studies have focused on the actual adoption of agricultural apps and the relevance of the apps’ content, very few studies have focused on drivers of the farmer’s intention and initial decision to adopt. Based on a survey of 394 smallholder farmers in 2019, this study investigated willingness to adopt an agricultural advice app in Guanajuato, Mexico. A structural equation modeling approach, based on the unified theory of acceptance and use of technology (UTAUT), was applied. To understand the farmers’ adoption decisions, extended constructs were studied (e.g., mastery-approach goals) along with the farmers’ age and participation in an innovation hub. Results showed that the intention to adopt the app is predicted by how farmers appraise the technical infrastructure and acquire new knowledge by using an app. The multi-group analysis revealed that performance expectancy is a relevant predictor of the intention to adopt, whereas the mastery-approach goal is relevant only for younger farmers and farmers not connected to the innovation hub. This study provides valuable insights about the innovation hubs’ role in the intention to adopt apps, offering precision agriculture advice in developing countries. The findings are useful for practitioners and app developers designing digital-decision support tools.
Highlights
There is an increasing interest in literature that identifies the use of emerging technologies in precision agriculture to efficiently increase production while reducing its environmental impacts [1]
The results provide insights on ways to encourage adoption of noncommAerscimalo(sfrtefea)rmagerriscudlotunraoltahpapvsethexapt eprrioevnicdeeinafftohreduabseleopfrtehceissieontyapgersicoufltmuorebisleer-vpihcoesne,thwaet choympoptlhemeseiznet tthheatwthorekcoofnexnteecntsioionnwagitehntthseininthneovfiaetlido.nAhdudbitbiornokalelrye, dthbeyreCsuIMltsMeYluTciidnaGteutahneasjmuaatlolh(oSledcetrios’na3d.o1)ptmioondderirvaeterss othf eapefpfsectthsaot fpUroTvAidUeTcocmonmstorduicttysparnidceafdodreitciaosntsalacnodnfistnrauncctsiadl ubeentcohtmheardkeivnegl,owpmhileent enocfoaucroangdinugcitvheementvoircoonnmtriebnuttfeowr iinthnoinv-astitiuoninafnodrmdaetciiosnioanb-omuatksionigl .management and yield data [1F8i]n.ally, the mastery-approach goals (MAG) is based on the goal orientation theory, which articulates that the
The results showed that the effect of performance expectancy on behavioral intention was significantly higher for non-connected farmers (Table 7)
Summary
There is an increasing interest in literature that identifies the use of emerging technologies in precision agriculture to efficiently increase production while reducing its environmental impacts [1]. Precision agriculture is a management strategy that uses electronic information and other technologies to gather, process, and analyze spatial and temporal data for the purpose of guiding targeted actions that improve site-specific crop management, productivity, and sustainability of agricultural operations [2,3]. Smartphone apps play an increasing role in this [5]. Based on these technologies, it has become possible to process and access real-time data about the conditions of the soil, crops, and weather, along with other relevant services such as crop and fruit supply chains, food safety, and animal grazing. Precision agriculture has shown an uneven success, with greater adoption in developed countries and among large-scale farms [2,6]. While GIS and remote sensors have been the key transformational driver on large farms, mobile phones and their ubiquity are predicted to bring similar transformational potential to small-scale farmers [7,8]
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