A tobit regression model for the timing of smartphone adoption in agriculture

Abstract

Smartphones are excellent tools well-suited for applications in agriculture because of their mobility, high data processing power, access to agricultural apps, and compatibility with precision agriculture technologies. Although smartphone adoption and the use of agricultural apps are well-studied, variables influencing the timing of smartphone adoption in agriculture have not yet been closely examined. Comprehending both the timing of when a certain technology is adopted and identifying the specific characteristics of early and late adopters aids in the anticipation and thereby the fostering of the diffusion process. This study’s objective is therefore to analyse the timing of smartphone adoption for agricultural purposes by applying a tobit regression model to a data set of 207 German farmers, which was collected in 2019. The results indicate that significant factors influencing the timing of smartphone adoption in agriculture include farmers' gender, risk attitude, age, size and location of their farm, among other factors. These results may be interesting to several stakeholders in agriculture such as extension services, policymakers and researchers as well as smartphone providers and sellers.