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).