Leveraging climate resilience through AI chatbot farming adoption among rural Zimbabwean women

Leveraging climate resilience through AI chatbot farming adoption among rural Zimbabwean women

2331-1886

Cogent Social Sciences
21.8.25

English

16 pp.

631 Mw08

The present study sought to establish the factors which drive the adoption of chatbot artificial intelligence and to examine the relationship between chatbot artificial intelligence and climate change resilience. The study targeted 206 women in two rural communities in Zimbabwe. Data was collected in a cross-sectional survey using a stratified sampling procedure. Structural Equation Modelling (SEM) was performed to analyse data using Analysis of Moment Structures (AMOS). The study confirmed that the simplicity driver (β=0.257, t=5.315, p<0.001), infrastructural driver (β=0.421, t=8.275, p<0.001) and utility driver (β=0.326, t=6.306, p<0.001) positively influenced AI chatbot adoption in Zimbabwean rural communal farmers. The effects of social and intrinsic drivers on farmbot adoption were not statistically significant (p>0.05). Further, the relationship between AI Chatbot adoption and climate change resilience was also positive and statistically significant (β=0.747, t=14.037, p<0.001). The study validates the farmbot artificial intelligence innovation model as it significantly augments climate change resilience. To mitigate the gendered impacts of climate change and enhance food sustainability, the study recommends the adoption of farming chatbots to promote free flow of climate change-adaptive agriculture information especially among marginalised farming populations, such as rural communal women.The present study sought to establish the factors which drive the adoption of chatbot artificial intelligence and to examine the relationship between chatbot artificial intelligence and climate change resilience. The study targeted 206 women in two rural communities in Zimbabwe. Data was collected in a cross-sectional survey using a stratified sampling procedure. Structural Equation Modelling (SEM) was performed to analyse data using Analysis of Moment Structures (AMOS). The study confirmed that the simplicity driver (β=0.257, t=5.315, p<0.001), infrastructural driver (β=0.421, t=8.275, p<0.001) and utility driver (β=0.326, t=6.306, p<0.001) positively influenced AI chatbot adoption in Zimbabwean rural communal farmers. The effects of social and intrinsic drivers on farmbot adoption were not statistically significant (p>0.05). Further, the relationship between AI Chatbot adoption and climate change resilience was also positive and statistically significant (β=0.747, t=14.037, p<0.001). The study validates the farmbot artificial intelligence innovation model as it significantly augments climate change resilience. To mitigate the gendered impacts of climate change and enhance food sustainability, the study recommends the adoption of farming chatbots to promote free flow of climate change-adaptive agriculture information especially among marginalised farming populations, such as rural communal women

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