| Sumario: | Many studies on adoption of new technologies assume a linear movement from introduction of the technologies to
their eventual uptake by targeted stakeholders. However, human behavior follows a gradual and cumulative path
from information acquisition, developing an interest, forming a desire and eventually taking action to use new
technologies. This study provides analytical results on how a farmer moves from being aware, getting interested,
forming a purchase desire and actually purchasing improved sweetpotato seed in Uganda. The study uses primary
data from both a baseline and follow up survey of 1192 sweetpotato farmers in Amuria district of Uganda. Principal
component analysis and partial least square structural equation model were applied to analyze the empirical
linkages between four key constructs; awareness, interest, desire and action (commonly referred to as AIDA model)
in tracing the farmer’s journey towards eventual purchase and use/planting of the improved sweetpotato vines.
Results show that contrary to theoretical expectation of a linear relationship and exact prediction of action from
awareness, interest and desire, the baseline data only linked 47.6% of farmer behavior towards purchase of the
improved sweetpotato vines to the sequential movement from awareness, interest and desire. Two distinct
categories of farmers were also established; keen customers who pay attention to product details and
environmentally-conscious customers who care more about product costs and adaptability to drought and low water
stress. The follow up survey plus inclusion of contextual factors improved the explained variance from 48% in the
baseline data to 60%. This implies that farmers’ behavior towards adoption of improved sweetpotato requires
multiple rounds of data spread over a longer time period to correctly predict. Moreover, contextual factors such as
farmers’ resource endowment situation, gender roles and culture add value to the standard AIDA model constructs
and should be incorporated in such models to improve the precision of analysis and make the findings more relevant
to farmers’ environment, thus resulting to realistic interventions.
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