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Sathish Kumar Murugan, Ganeshkumar D.Rede, Teena Lakshmi Baskaran, Prity Kumari,
Alina Cristina Nuta: Determinants of Solar Water Pumping System Adoption Among
Farmers: A Factor
Analysis Approach

Table 3 Influencing factors to adopt the solar water pumping system communalities

Description Initial Extraction
Availability of after-sales service influences adoption decisions. 1.000 .572
Banks and cooperatives offer credit facilities for the installation of 1.000 .598
solar pumps.
Solar pumps reduce the recurring cost of diesel or electricity for 1.000 .693
irrigation.
Farmers perceive solar pumps as a financially sustainable solution. 1.000 .479
Solar pumps are easy to operate and require less technical 1.000 .738
knowledge.
Government schemes and subsidies make solar pumps affordable. 1.000 .669

In the presence of multiple variables with low communalities (less than 0.5) it has
been found that disproportionately more factors are identified when these variables
are combined together. Interestingly, no variables had a communality score of less
than 0.5 in Table 3. Therefore, even in the most ideal circumstances, these variables
could be identified.

As a rule of thumb, factor eigenvalues are calculated by adding up the squared
loadings of each factor. The strength of eigenvalues is often used as a means of
selecting the number of factors to be extracted from an analysis, a standard method
for determining the optimal number of factors. When the value of the eigenvalue (I)
is greater than or equal to 1, significant results can be obtained. To extract factors, the
varimax rotation method has been employed.

Table 4: An eigenvalue-based factor analysis to extract the principal components

Total Variance Explained
Extraction Sums of Squared Rotation Sums of Squared
Initial Eigenvalues Loadings Loadings
% of Cumulative % of Cumulative % of Cumulative
Component Total Variance % Total Variance % Total Variance %
1 1.509 25.158 25.158 1.509 25.158 25.158 1.347 22.443 22.443
2 1.185 19.745 44.902 1.185 19.745 44.902 1.244 20.734 43.177
3 1.055 17.580 62.483 1.055 17.580 62.483 1.158 19.306 62.483
4 .827 13.777 76.259
5 .740 12.332 88.592
6 .684 11.408 100.00
Extraction Method: Principal Component Analysis.

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