Furthermore, in terms of dominant adopter category, Figure 2 shows that the late majority is the most prevalent category (36.6%) in the study region. This suggests that a number of smallholder farmers in Ghana, especially in the Ketu North District, are more sceptical about new technologies and may want to see pictures or videos of people using them before making adoption decisions or probably wait for about half of the population adopting them before they also decide to use them. Also, by comparison, the percentages obtained for each adopter category in the present study vary from the percentages of Roger adopter categories (see Figure 1). In the present study, we observed a trend of downwards reduction in innovativeness to an upwards increase in less innovativeness. For instance, innovators are 1.4% in the current study as against 2.5% in Rogers and Laggards are 17.9% presently as against 16% in Rogers. Therefore, the obtained result suggests that a result from a macro level study (used by Rogers) may not show the same result at the micro-level (present study). i.e., Rogers' work was developed for the market (macro-scale), whereas the current study was developed for a category of farmers (micro-scale). Table 5 presents the logistic regression analysis results that test the four hypotheses on the effect of the predictor variables on the response variable. The main variables adapted in the model to predict the innovativeness of the respondents were educational level, age, gender, and income. The likelihood of the model to predict the outcome correctly was 89.7%, which indicates that the **data** fit the model.