## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Variable** & **B.** & **S.E.** & **Wald** & **Significance** & **Exp(B)** \\ \hline \multirow{3}{*}{Farmers’} & Perceived Usefulness & 0.367 & 0.219 & 2.800 & 0.094 & 1.443 \\ \cline{2-7}  & Trust & 0.548 & 0.241 & 5.181 & 0.023 & 1.731 \\ \cline{2-7}  & Motivation & 1.660 & 0.335 & 24.531 & 0.000 & 5.262 \\ \hline Direct and & Awareness & 0.563 & 0.131 & 18.459 & 0.000 & 1.756 \\ \cline{2-7}   wider social & Info & 0.367 & 0.137 & 7.206 & 0.007 & 1.443 \\ \cline{2-7}  & Income & 0.719 & 0.267 & 7.268 & 0.007 & 2.052 \\ \cline{2-7}  & F-T Farmer & \(-1.431\) & 0.531 & 7.278 & 0.007 & 0.239 \\ Farmers’ & Residence & \(-1.040\) & 0.648 & 2.573 & 0.109 & 0.354 \\ Ability & Education & \(-0.276\) & 0.306 & 0.812 & 0.368 & 0.759 \\ \cline{2-7}  & Gender & 2.226 & 0.821 & 7.352 & 0.007 & 9.260 \\ \cline{2-7}  & Age & 0.006 & 0.016 & 0.150 & 0.698 & 1.006 \\ \hline  & Constant & \(-5.852\) & 1.543 & 14.376 & 0.000 & 0.003 \\ \hline  & Tests & X\({}^{2}\) & df & Sign. & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Omnibus Tests of \\ Model Coefficients \\ \end{tabular} } & \multirow{3}{*}{101.042} & \multirow{3}{*}{11} & \multirow{3}{*}{0.000} & \multirow{3}{*}{} \\  & [PERSON] and & & & & \\ \cline{1-1} \cline{4-5}  & [PERSON] & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the binomial logistic regression model. As for the variables of the farmers' ability category, the estimated coefficients vary heavily in terms of signs and statistical significance. The estimated coefficient for the _Income_ variable is positive and statistically significant at the <0.01 level. This finding clearly indicates that farmers with higher earnings can more easily accommodate their participation in a PRP. In addition, the _F-T Farmer_ coefficient is negative and statistically significant at the <0.01 level, too, denoting that part time farmers more easily engage in PRP than their full-time colleagues. These estimations show that it is not only the amount of income but also the source of income that affects the decisions of farmer regarding their participation in PRPs. The _Residence_ estimated coefficient value was not significant. As for the _Education_ variable, surprisingly, the model cannot confirm any statistically significant relationship between farmers' educational levels and PRP engagement. In addition, the estimation for the _Gender_ variable is positive and statistically significant at the <0.01 level, demonstrating that women are more likely to engage in PRP than their male colleagues. It is noteworthy that according to the exp(B) estimations women are nine times more likely to take part in a PRP than men. Finally, the coefficient for the _Age_ variable lacks statistical significance, thus showing that the life cycle of farmers is not related to their decisions for engaging in PRP. Finally, in Figure 1 the results of the Shapley-Owen decomposition analysis and the respective contribution of variables and factors on the determination of the Mc-Fadden Pseudo R\({}^{2}\) coefficient are presented. As can be seen, the most important factor category is this of Farmers' Willingness as it explains 41.36% of the model's variability. Among the variables of this category, _Motivation_ is the most important driving force with a relative share of over 30%, followed by _Trust_ and _PU_. It should be noted that _Motivation_ is the most important variable not only among the variables of farmers' willingness, but also among all the variables of the model. Due to the subjectivity of the above terms, further research is needed to clarify how these are being perceived by farmers. Therefore, the results obtained must be handled with caution. Moreover, the Social Influences category accounts for a large proportion of Pseudo-R\({}^{2}\) coefficient, which exceeds 39%. Between the two variables of this category, _Avareness_ seems to have a more significant role than _Info_ in explaining farmers' choices to participate in PRPs.