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Socioeconomic and Behavioural Factors Influencing Electrical Appliance Adoption Among Rural Farming Households: An Endogeneity Switching Regression Analysis

Article scientifique 2025 Autre

Résumé

In rural areas, household agricultural income has a significant influence on the adoption of new technologies, including electrical appliances. Despite technological advancements, adoption rates remain inconsistent due to socioeconomic disparities and behavioural patterns. This study examines the impact of socioeconomic and behavioural factors, as well as agricultural income, on the adoption of electrical appliances among farming households in South Africa’s Eastern Cape Province. Data were collected from 224 households using structured questionnaires and analysed through descriptive statistics and Endogeneity Switching Regression (ESR). The results show that variables such as age (p<0.01), years of schooling (p<0.05), occupation (p<0.01), family size (p<0.05), access to extension services (p<0.05), off-farm income activities (p<0.05), and distance to markets (p<0.01) significantly influence the likelihood of adopting electrical appliances. The ESR model confirms a statistically significant and positive relationship between adoption and household agricultural income. The Average Treatment Effect on the Treated (ATT) indicates an income gain of ZAR2,757 (p<0.01), while the Average Treatment Effect on the Untreated (ATU) suggests potential gains of ZAR1,765 (p<0.01) if non-adopters were to adopt. The study recommends targeted interventions, including expanded extension services, adult education programs, and gender-sensitive strategies to empower female-headed households. Strengthening rural electrification and promoting affordable, energy-efficient technologies are also crucial for enhancing energy security and improving productivity in rural farming systems.

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Ntonjane, P., Mdletshe, S., Nontu, Y. (2025). Socioeconomic and Behavioural Factors Influencing Electrical Appliance Adoption Among Rural Farming Households: An Endogeneity Switching Regression Analysis. https://doi.org/10.17159/2413-3221/2025/v53n6a21866

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