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Table 10 Component matrix

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

Items

Component/factors

Component/factors

1

2

3

4

5

6

7

8

9

Items

1

2

3

4

5

6

7

8

9

A1

 

− .529

       

A27

   

.437

  

.417

.496

 

A2

.403

  

− .429

.534

    

A28

   

.525

     

A3

   

.422

   

− .490

 

A29

   

.430

     

A4

.553

 

.441

   

− .496

  

A30

 

− .577

       

A5

         

A31

.654

        

A6

.440

    

.476

.458

  

A32

.680

   

− .417

    

A7

.426

        

A33

 

− .571

       

A8

.503

 

.462

   

− .465

  

A34

.583

        

A9

.531

 

.418

   

− .483

  

A35

.665

        

A10

.658

   

− .416

    

A36

 

− .423

       

A11

    

.410

    

A37

         

A12

.429

  

− .422

.501

    

A38

.436

        

A13

        

.530

A39

   

.585

     

A14

  

.406

   

− .473

  

A40

   

.573

     

A15

.405

  

− .428

.534

    

A41

 

− .590

       

A16

    

.482

    

A42

.626

        

A17

 

− .592

       

A43

     

.437

   

A18

         

A44

.479

    

.519

.436

  

A19

     

.444

.420

  

B1

.568

.574

− .402

      

A20

.448

        

B2

.465

.423

       

A21

   

.402

   

− .430

 

B3

.487

.586

       

A22

         

B4

.583

.542

       

A23

   

.407

    

− .444

B5

.442

.561

− .401

      

A24

   

.438

   

− .484

 

B6

.535

.571

       

A25

 

− .495

       

B7

.530

.569

       

A26

 

− .455

       

B8

.521

.554

− .424

      
  1. Extraction method: principal component analysis
  2. (a) 9 components extracted