Results

Analysis of the study is divided into two major parts. Part one was based on an outer model assessment in which reliability and validity were examined. Whereas, second part was focused on an inner model assessment in which, hypotheses were tested. First part was mandatory to proceed for inner model assessment. In the first part, convergent validity and discriminant validity was examined. Convergent validity was examined through composite reliability, factor loadings and Average Variance Extracted (AVE). According to the literature, the value of factor loading for each item should be more than 0.4, composite reliability should be more than 0.7 and Average Variance Extracted (AVE) should not be less than 0.5.

Results of the inner model assessment are shown in Figure 2 & Table 2. According to these results, factor loading is above 0.7 for all items, Average Variance Extracted (AVE) is more than 0.5 and composite reliability is also more than 0.7. To achieve the satisfactory level of validity, few items with factor loading below than 0.7 were deleted.

Figure 2: Outer Model Assessment

Table 2
Outer Model Results
Construct Indicators Loadings Cronbach's Alpha Composite Reliability AVE
Supply chain Sustainability (SCS) SCS2
SCS3
SCS5
SCS7
SCS9		 0.785
0.901
0.752
0.812
0.797		 0.902 0.907 0.710
Green Supply Chain Integration
(GSCI)		 GCMI1
GCMI2
GCMI3
GCMI4
GCMI5
GCMI6		 0.873
0.753
0.900
0.860
0.830
0.806		 0.899 0.801 0.688
Inventory (INV) INV2
INV3
INV4
INV6		 0.796
0.736
0.897
0.814		 0.815 0.807 0.664
Sourcing (SRC) SRC1
SRC2
SRC6
SRC7
SRC8		 0.897
0.821
0.641
0.560
0.646		 0.825 0.888 0.780
Facility (FCL) FCL1
FCL2
FCL3
FCL4
FCL5		 0.740
0.748
0.824
0.827
0.754		 0.899 0.901 0.688
Transport (TRANS) TR1
TR2
TR3
TR7		 0.755
0.719
0.744
0.707		 0.799 0.801 0.588
Information (INF) INF1
INF2
INF3
INF4		 0.828
0.792
0.757
0.767		 0.879 0.751 0.678
Pricing (PRC) PR1
PR2
PR4
PR6		 0.752
0.854
0.837
0.767		 0.889 0.731 0.778

Discriminant validity is shown in Table 3. It was examined by the square root of Average Variance Extracted (AVE). Measurement of discriminant validity through Average Variance Extracted (AVE) was suggested by Fornell-Larcker.

Table 3
The Square Root Of Ave
SCS GSCI INV SRC FCL TRANS INF PRC
SCS 0.785      
GSCI 0.440 0.869      
INV 0.424 0.401 0.770      
SRC 0.429 0.430 0.622 0.726      
FCL 0.430 0.466 0.524 0.424 0.635      
TRANS 0.520 0.536 0.450 0.764 0.535 0.869    
INF 0.450 0.436 0.523 0.524 0.615 0.401 0.770  
PRC 0.520 0.516 0.503 0.637 0.515 0.430 0.622 0.726

After assessment of the outer model, the inner model was examined to check the relationship between dependent and independent variables. This is the second part of the analysis. In this part, both direct, as well as an indirect hypothesis with a mediating variable was examined.

To test the direct hypothesis, t-value was examined, where the value of 1.96 levels was considered as the minimum level to accept the hypothesis. According to the results, the entire direct hypothesis has t-value more than 1.96 which is the evidence to accept the entire direct hypothesis.

In Table 4 shows, the first set of hypotheses (H1-H7) was about the impact of Green Supply Chain Integration (GSCI), Inventory (INV), Sourcing (SRC), Facility (FCL), Transport (TRANS), Information (INF), and Pricing (PRC) on Supply Chain Sustainability (SCS). The results of the study have revealed the fact that Transport (TRANS), Information (INF), and Pricing (PRC) on Supply Chain Sustainability (SCS have a significant positive relationship Supply Chain Sustainability (SCS). Whereas the second set of hypotheses which was hypothesizing the relationship between Inventory (INV), Sourcing (SRC), Facility (FCL), Transport (TRANS), Information (INF), and Pricing (PRC) on Supply Chain Sustainability (SCS) and Green Supply Chain Integration (GSCI) were also approved significantly.

Table 4
Direct Effect Results
Hypotheses Relationship (β) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Decisions
H1 INV-> SCS 0.211 0.075 2.912 0.004 Supported
H2 SRC-> SCS 0.168 0.076 2.161 0.031 Supported
H3 FCL-> SCS 0.260 0.066 3.833 0.000 Supported
H4 TRANS-> SCS 0.538 0.063 8.518 0.000 Supported
H5 INF-> SCS 0.147 0.074 2.008 0.045 Supported
H6 PRC-> SCS 0.206 0.093 2.145 0.032 Supported
H7 GSCI -> SCS 0.362 0.069 5.295 0.000 Supported
H8 INV-> GSCI 0.111 0.035 3.161 0.002 Supported
H9 SRC-> GSCI 0.207 0.043 4.810 0.000 Supported
H10 FCL-> GSCI 0.447 0.109 3.999 0.025 Supported
H11 TRANS-> GSCI 0.332 0.108 3.051 0.003 Supported
H12 INF-> GSCI 0.151 0.013 11.580 0.000 Supported
H13 PRC-> GSCI 0.113 0.022 5.119 0.000 Supported

The indirect effect is shown in Table 5. According to the indirect effect, the mediation effect Green Supply Chain Integration (GSCI) between Inventory (INV), Sourcing (SRC), Facility (FCL), Transport (TRANS), Information (INF), and Pricing (PRC) and Supply Chain Sustainability (SCS) was significant.

Table 5
Indirect Effect
Hypotheses Relationship (β) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Decisions
H14 INV-> GSCI-> SCS 0.133 0.046 2.908 0.004 Supported
H15 SRC-> GSCI-> SCS 0.108 0.041 2.690 0.007 Supported
H16 FCL-> GSCI-> SCS 0.109 0.017 6.399 0.000 Supported
H17 TRANS-> GSCI-> SCS 0.217 0.105 2.031 0.035 Supported
H18 INF-> GSCI-> SCS 0.325 0.111 2.909 0.003 Supported
H19 PRC-> GSCI-> SCS 0.231 0.021 2.809 0.023 Supported

Additionally, the Table 6 shows the variance explained. It shows that Green Supply Chain Integration (GSCI) Inventory (INV), Sourcing (SRC), Facility (FCL), Transport (TRANS), Information (INF), and Pricing (PRC) were collectively explained 69.7% variance in Supply Chain Sustainability (SCS).

Table 6
Variance Explained
Variance explained
SCS 0.697