## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{4}{c}{**Factor**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline PU 1 & 0.671 & & \\ \hline PU 2 & 0.846 & & \\ \hline PU 3 & 0.853 & & \\ \hline Trust 1 & & 0.851 & \\ \hline Trust 2 & & 0.741 & \\ \hline Trust 3 & & 0.683 & \\ \hline Mot 1 & & & 0.799 \\ \hline Mot 2 & & & 0.894 \\ \hline Eigen & 2.028 & 1.858 & 1.671 \\ \hline \% of Variance & 25.352 & 23.231 & 20.889 \\ \hline Cumulative \% & 25.352 & 48.583 & 69.472 \\ \hline Cronbach’s Alpha & 0.748 & 0.66 & 0.700 \\ \hline \multicolumn{4}{c}{Tests and Goodness of Fit Indices} \\ \hline Bartlett’s Test & 676.772 & Sig. & 0.000 \\ \hline Kaiser–Meyer–Olkin & 0.660 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Results of the factor analysis for the constructs of the ‘farmers’ willingness’ factor category. As can be seen from Table 3, the analysis returned three factors whose eigenvalues exceeded the score of 1 and explain about 70% of the total variance. The items loaded well in the factors as expected, as loadings lie well above 0.60 and no double loadings were observed (the highest second loading was estimated at 0.296). The Cronbach's Alpha for the whole sample is estimated at 0.7 whilst the internal reliability of the three constructs exceeds 0.7 except for the 2 nd factor for which Cronbach Alpha lies just a little below the 0.7 threshold. Considering that a Cronbach's Alpha of over 0.7 denotes an acceptable consistency, a value between 0.6 and 0.7 a questionable one, and only when Alpha is found below 0.6 could be considered as a poor fitting, it can be assumed that the general reliability of the items is satisfactory, in order to use the constructs for the succeeding analysis [43]. The quite satisfactory outcomes of the factor analysis are further confirmed from the results of the two tests. More precisely, the [PERSON]'s test returned a statistically significant estimation which indicates that the items are related and therefore suitable for building the expected structures, while the Kaiser-Meyer-Olkin statistic lies far above the critical value of 0.5 thus indicating that the sample is adequate for factor analysis [42]. After estimating the factor loadings, the scores of each factor are obtained, based on the method of least squares regression analysis [42,44] in order to be incorporated in the succeeding binomial regression model. Finally, before analyzing the results of the regression analysis, the descriptive statistics of the variables of the other two categories of factors are presented in Table 4. The average value of the _Awareness_ variable is 2.23. This tally shows that the average farmer has less than medium knowledge regarding the PRP being implemented in the nearby areas. In addition, the _Info_ average (2.75) reveals that farmers in the area usually do not use more than three out of seven total sources of information. Considering the _Income_ variable, for which the estimated average value is 3.10, it is concluded that the average farmer earns more than 20K \(\boldsymbol{\upepsilon}\) per annum, which is quite satisfactory for the Greek context. The mean value of the _F-T Farmer_ variable (0.68) and _Residence_ (0.14) variables verify that about two to three respondents are full-time farmers, while only the 14% of them live in a relatively high populated area. The mean of the _Education_ variable is estimated at 2.96, showing that the average farmer has finished High School. The 0.07 estimated mean value of the _Gender_ variable denotes that only 7% of the sample are female farmers, and finally the average farmer's _Age_ is estimated at 47 years with the minimum age at 18 and the maximum at 83. The above numbers verify the representativeness of the sample, because they follow the structure of the general population of the last census of the country [45].