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Future Business Journal

Table 11 Rotated component matrix

From: Cognition and affect in consumer decision making: conceptualization and validation of added constructs in modified instrument

Item

Component/factors

Items

Component/factors

1

2

3

4

5

6

7

8

9

1

2

3

4

5

6

7

8

9

A1

  

.758

      

A27

      

.874

  

A2

   

.962

     

A28

      

.843

  

A3

       

.856

 

A29

      

.655

  

A4

    

.937

    

A30

  

.783

      

A5

       

.691

 

A31

 

.787

       

A6

     

.902

   

A32

 

.919

       

A7

 

.747

       

A33

  

.785

      

A8

    

.899

    

A34

 

.777

       

A9

    

.906

    

A35

 

.835

       

A10

 

.927

       

A36

  

.673

      

A11

   

.741

     

A37

  

.599

      

A12

   

.941

     

A38

     

.715

   

A13

       

.453

.541

A39

      

.876

  

A14

    

.829

    

A40

      

.911

  

A15

   

.962

     

A41

  

.820

      

A16

   

.810

     

A42

 

.712

       

A17

  

.780

      

A43

     

.746

   

A18

     

.597

   

A44

     

.902

   

A19

     

.793

   

B1

.918

        

A20

    

.760

    

B2

.639

        

A21

       

.713

 

B3

.860

        

A22

       

.654

 

B4

.890

        

A23

       

.599

− .435

B5

.830

        

A24

       

.862

 

B6

.885

        

A25

  

.721

      

B7

.869

        

A26

  

.637

      

B8

.880

        
  1. Extraction method: principal component analysis
  2. Rotation method: Varimax with Kaiser normalization
  3. (a) Rotation converged in 6 iterations
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