Moderation Causal Effect

In this model, adaptive capability is assumed as moderator, disruption vulnerabil- ity is the moderated variable, and resilience is the outcome variable. The interac-

tion terms in the model are resulted as product of the moderating and moderated variables233_{. Following the interaction term for manufacturing moderation.}

Figure 40 Interaction Term for Manufacturing

The interaction term in the model of supply chain resilience is the product of adaptive capability (moderating variable) and disruption vulnerability (moderated variable). The interaction term for manufacturing process is the product of all

indicator of manufacturing adaptive capability and manufacturing disruption vul- nerability in supply chain resilience model. Manufacturing adaptive capability has four indicators and manufacturing disruption vulnerability has also four indi- cators. The manufacturing interaction term would calculate sixteen product vari- able as presented in Figure 40.

The interaction term for transportation process is the product of all indicator of transportation adaptive capability and transportation disruption vulnerability. Transportation adaptive capability has three indicators and transportation disrup- tion vulnerability has four indicators. The transportation interaction term would calculate twelve product variables as presented in Figure 41.

Figure 41 Interaction Term for Transportation

The effects of adaptive capability, disruption vulnerability and interaction terms are estimated and examined for significance. The indicators for constructs and interaction terms are huge in number and therefore not shown in Figure 42.

Figure 42 Interaction Moderation Effect

The mediation effect for manufacturing process is proposed as:

H6a. Manufacturing adaptive capability dampens the negative relationship be- tween manufacturing disruption vulnerability and manufacturing resilience. The effect between moderating variable of manufacturing adaptive capability and manufacturing resilience is 0.31 (Figure 42) with test statistics 3.95 (Figure 43) that is significance at 5 percent confidence level. The interaction terms for manu- facturing process in the model has effect of 0.24 (Figure 42) on dependent varia- ble of manufacturing resilience. The effect is tested for significance with boot- strapping procedure that calculate test statistics for the effect is 3.08 (Figure 43) that is significance at 5 percent confidence level. The relationship between mod- erated variable of manufacturing disruption vulnerability and manufacturing re- silience is -0.29 with test statistics 3.13 with 5 percent confidence level.

Figure 43 Significance of Interaction Moderation Effect

The mediation effect for manufacturing process is proposed as:

H6b. Transportation adaptive capability dampens the negative relationship be- tween transportation disruption vulnerability and transportation resilience.

The effect between moderating variable of transportation adaptive capability and transportation resilience is 0.31 (Figure 42) with test statistics 4.13 (Figure 43) that is significance at 1 percent confidence level. The interaction terms for trans- portation process in the model has effect of 0.32 (Figure 42) on dependent varia- ble of manufacturing resilience. The test statistics for the effect is 5.29 (Figure 43) that is significance at 1 percent confidence level. The relationship between moderated variable of manufacturing disruption vulnerability and manufacturing resilience is -0.25 (Figure 42) with test statistics 2.92 (Figure 43) with 5 percent confidence level.

So there is empirical evidence of the presence of moderating variable, cause change in the relationship of independent and dependent variable. In order to find whether the presence of adaptive capability negatively affect the relationship be-

tween disruption vulnerability and resilience, the interaction effect is represented graphically. For this purpose two way interactions are plotted, one for effect of disruption vulnerability on resilience without adaptive capability as moderator and the second effect in the presence of moderator.

Figure 44 Interaction Moderation Plots

In Figure 44, the graph line with diamond heads represent the relationship be- tween disruption vulnerability and resilience of supply chain processes of manu- facturing and transportation, in the absence of adaptive capability. The line with square heads represents the relationship between disruption vulnerability and resilience of supply chain processes in the presence of adaptive capability. The graph indicates that resilience is high as the vulnerability disruption remain low but the resilience drops as disruption vulnerability increases in the absence of adaptive capability. However, in the presence of high adaptive capability, the resilience stays high against disruption vulnerability. This indicates that adaptive capability dampens the negative effect of disruption vulnerability on resilience of supply chain process.

As suggested during the evaluation criteria of heterogeneity, the model is test for data groups for different countries. The multi-group moderation for Germany, Pakistan and Turkey is tested. The moderation is examined for manufacturing process that is carried out in Pakistan and Turkey. Whereas, the moderation is examined for transportation process that is carried out across Pakistan, Turkey and Germany. As it has been established that adaptive capability moderates the

negative effect of disruption vulnerability on resilience, the purpose of multi- group moderation is to assess whether the changing effect is invariably true across different population. However it is assumed that the intensity of effects would be different because of the difference between the locational conditions and situation of supply chain entity. The results are presented in Figure 45.

Mediated Relationsh

ip

Data Group Sample Si

ze Effect Standard Er ror Test Statis tics Significance MDV->MR Pakistan 91 -0.42 0.09 1.22 0.23 Turkey 40 -0.13 0.30 TDV->TR Pakistan 91 -0.36 0.11 1.79 0.07 Turkey 40 -0.69 0.12 Pakistan 91 -0.36 0.11 0.45 0.66 Germany 28 -0.45 0.18 Turkey 40 -0.69 0.12 1.14 0.26 Germany 28 -0.45 0.19

Figure 45 Significance Multi-group Moderation

For the purpose of multi-group moderation the difference in the significance of path coefficients for groups are tested. Test statistics for the data groups are cal- culated by using the path coefficients, standard errors, and sample sizes of the groups. The test statistics are used to calculate the significance level234_{. In Smart} PLS, bootstrapping is used to estimate the effects and standard errors. The results are given in Figure 1.

The effects of disruption vulnerability on resilience for data groups changes for groups however the difference are not statistically significant with 0.23 (Figure 45) for manufacturing processes. The results suggests that relationship between adaptive capability and resilience is not significantly different in case of manu- facturing for data groups of Pakistan, and Turkey. Similarly, the group difference for moderation in transportation process in Pakistan and Turkey, Pakistan and Germany, and Turkey and Germany are not significant.

234 _{Chin, 2000, p. PLS FAQ}

The non-significant differences among data groups for moderation shows that the model is true across population located at different geographical locations with unique conditions. The reason for non-significance is the fact that adaptive capa- bility moderates and reduces the occurrence of disruption vulnerability and hence its effect on resilience. In case of inability of a supply chain entity to utilize adap- tive capability when required, the data group for supply chain processes in vola- tile conditions would have different value. This suggests that adaptive capability enables supply chain entities to respond to disruption vulnerability, reduce the occurrence of disruption vulnerability, and supply chain process to function in a required manner. Volatile conditions of a location is not the only determinant of supply chain processes resilience. The complex relationship of adaptive capabil- ity has empirically tested and found through mediation and moderation.

Supply chain resilience has been studied in the context of international garments supply chain with partners located in Germany, Pakistan, and Turkey. Supply chain resilience is influenced by country specific conditions beside the condition of supply chain entity. It is in this context that assumptions were summarized in section 5.9. In order to analyze the assumptions, descriptive statistics (referred to as descriptives) are discussed in the following section.