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Agricultural and Food Economics

Table 5 Structure of rotated component matrix for the rice supply chain (N = 215)

From: The impact of environmental uncertainty on the performance of the rice supply chain in the Ayeyarwaddy Region, Myanmar

Types of uncertainty

Code

Component

1

2

3

4

5

6

7

Climate uncertainty (CLU)

CLU3

0.890

      

CLU2

0.889

      

CLU4

0.887

      

CLU1

0.830

      

Planning and control uncertainty (PCU)

PCU2

 

0.953

     

PCU3

 

0.950

     

PCU1

 

0.833

     

Competitor uncertainty (CU)

CU2

  

0.952

    

CU3

  

0.944

    

CU1

  

0.668

    

Government policy uncertainty (GU)

GU1

   

0.825

   

GU3

   

0.800

   

GU2

   

0.758

   

Process uncertainty (PU)

PU2

    

0.824

  

PU1

    

0.771

  

PU3

    

0.714

  

Supply uncertainty (SU)

SU1

     

0.840

 

SU2

     

0.808

 

SU3

     

0.678

 

Demand uncertainty (DU)

DU2

      

0.832

DU3

      

0.727

DU1

      

0.700

Eigen value

3.270

2.706

2.393

2.085

2.004

1.989

1.978

% of variance

14.864

12.298

10.879

9.478

9.108

9.039

8.989

Cumulative % of variance

14.864

27.161

38.041

47.519

56.627

65.667

74.656

  1. Extraction method: principal component analysis
  2. Before we conduct a principal component analysis or factor analysis, we must verify if the necessary conditions are fulfilled:
  3. To measure the scale reliability, we calculate the correlation matrix of the 22 uncertainty factors and the determinant. Since the determinant is different from zero, the factor analysis may be completed. Moreover, in order to measure scale reliability of the questionnaire, Cronbach’s alpha is used (Bryman 2003; Haire et al. 1995). The value of Cronbach’s alpha is accepted for an exploratory study if it exceeds 0.7 (Nunnally 1967). The Cronbach’s alpha of these scales ranges from 0.710 to 0.922. No items are deleted in the analysis
  4. The scale validity is measured by the Kaiser-Meyer-Olkin Measure (KMO) and Bartlett’s Test. The result for the Kaiser-Meyer-Olkin Measure (KMO) is acceptable since it is larger than 0.6 (Kaiser 1974), and Bartlett’s Test is highly significant at p < 0.000. Scale validity indicates the construct is able to measure accurately the concept under study (Haire et al. 1995)
  5. The construct validity is measured by explanatory factory analysis (EFA) (Haire et al. 1995). All components have Eigenvalues larger than 1, which confirms the construct validity
  6. Rotation method: varimax with Kaiser normalization
  7. Source: own data (2017) and SPSS
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