The explanatory variables analysed are shown in Table 1. In addition, Figure 2 shows the causal loop diagram of the variables analysed. The data were organised and studied using the Statistical Package for Social Sciences (SPSS version 28). First, descriptive statistics were extracted and checked for significant differences between adopters and non-adopters for the variables analysed. For dichotomous variables, the chi-square test was used, and for continuous variables, the t-test was used. Before performing binary logistic regression, the variables were subjected to a multicollinearity test by calculating the correlation matrix. Values greater than 0.7 are considered critical values for multicollinearity (Rogério-Foguesatto & Dessimon-Machado 2022). To corroborate the viability of the regression, several statistics of the model were used for verification. To determine if the model fits the data correctly, the Hosmer and Lemeshow fit test was used. According to this test, the significance must be greater than 0.05, indicating that there are differences between the observed and expected values, so that the model fits the data well. Nagelkerke's R² is an estimated coefficient of determination for models with categorical response variables. This coefficient can range between 0 and 1, indicating the amount of variance explained by the model (Aydogdu 2016). The overall percentage of cases that were correctly classified was also taken into account, with values greater than 60% being considered adequate (Muriu-Ng'ang'a et al. 2017).