4.6 Article

Does the farmer's social information network matter? Explaining adoption behavior for disaster risk reduction measures using the theory of planned behavior

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DOI: 10.1016/j.ijdrr.2023.103721

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Theory of planned behavior; Disaster risk reduction measures; Structural equation modeling; Adoption intention; Landslides; Floods

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Smallholder farmers in Sub-Saharan Africa are becoming more vulnerable to climate-related disasters due to land-use changes and poor adoption behavior for Disaster Risk Reduction (DRR) measures. Limited access to information leads to poor beliefs about DRR measures, and traditional agricultural extension workers are less trusted. The Theory of Planned Behavior (TPB) can explain adoption behavior, and Social Information Networks (SIN) also play a role in predicting adoption intentions. The study emphasizes the need to enhance the technical capacity of extension staff and informal networks in sharing DRR information with farmers.
Smallholder farmers' vulnerability to climate-related disasters in Sub-Saharan Africa is increasing, partly due to land-use changes and poor adoption behavior for Disaster Risk Reduction (DRR) measures. Such behavior can be explained by poor beliefs about DRR measures due to limited access to information. Agricultural extension workers are increasingly less trusted because they tend to transfer information less targeted to DRR and have limited spatial coverage. We apply the Theory of Planned Behavior (TPB) to explain the adoption behavior for DRR measures and further investigate whether Social Information Networks (SIN) explain the behavior beyond the TPB determinants. Cross-sectional data were collected from 602 randomly selected households from Rwenzori and Ankole in Western Uganda, the sub-regions that are prone to landslides and floods. Results from the Structural Equation Modeling (SEM) demonstrate that TPB explains the adoption behavior with Perceived Behavioral Control (PBC) as a stronger driver of intentions than subjective norms and attitudes. The intention to apply DRR measures is significantly associated with actual adoption. Farmers' behavior to control landslides and floods is correlated because the same location is at risk of such interacting disasters. Moreover, SIN also significantly predicts adoption intentions through the three theoretical predictors by playing a moderating role. PBC and professional networks being the main drivers of adoption intentions suggests that the role of extension services cannot be substituted by informal social networks but the two should be complementary. Thus, the study shows the need to build the technical capacity of extension staff and informal networks in DRR measures to share information with farmers.

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