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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