Figure 2. Factor loadings related to Y (overall customer satisfaction
with counter services)
The correlation coefficients between each of the question items in
groups A to C and ZY1 are displayed in Table 2. The question items
underlined were selected and will be used in the following principal
components analysis for each group. Figure 3 shows the results of the
principal component analysis for each question in category A. The
principal component scores for the first and second components were
added to the dataset as variables ZA1 and ZA2. As shown, AQ2 (Q3_2) and
AQ3 (Q3_3) had significant effects on the first principal component,
which we interpreted as ‘office equipment inside the building’. We can
then interpret component 2 as the ‘branch office location’ if focusing
on the position of AQ1 (Q3_1).
Figure 4 shows the results of the principal component analysis for each
question in category B. As shown, the principal component scores for the
first and second components were added to the dataset as variables ZB1
and ZB2 variables. Figure 4 indicates that BQ2 (Q3_8) to BQ5 (Q3_11)
had significant effects on component 1 (ZB1), which was interpreted as
‘how officers deal with and explain issues to customers at the counter’.
We can then interpret component 2 (ZB2) as ‘waiting time at the branch
office’ if focusing on the position of BQ1 (Q3_7).
Figure 5 shows the results of the principal component analysis for each
question in category C. As indicated, the principal component scores for
the first and second components were added to the dataset as variables
ZC1 and ZC2. Also shown in Figure 5, we interpreted component 1 as
‘solving customer problems and concerns’. We can then interpret
component 2 as ‘solving customer problems based on feelings and
emotions’ if focusing on the position of AQ7 (Q4_3).