Mediation Causal Effect

Mediation model requires variables in the roles of predictor, mediator, and out- come variables. Supply chain resilience model suggests inherently that resilience is determined by disruption vulnerability and adaptive capability. Adaptive capa- bility comes into play in the advent of disruption. Disruption vulnerability as- sumes the role of predictor variable, adaptive capability functions as mediator

and resilience takes the role of outcome variable. The mediation assumes that the effect of disruptive vulnerability is supposed to cause changes in resilience after the contribution of adaptive capability.

For manufacturing process, the hypothesis is stated as:

H5a. Manufacturing adaptive capability negatively mediates the negative rela- tionship between manufacturing disruption vulnerability and manufacturing resil- ience.

In order to test the role of manufacturing adaptive capability as mediating varia- ble, the required empirical conditions are examined. For manufacturing, the di- rect relationship between disruption vulnerability and resilience is tested first for significance in the absence and then in the presence of mediator to assess media- tion as shown in Figure 36.

Figure 36 Mediation Effect for Manufacturing

This relationship is required to be significant. Bootstrapping is used for estima- tion of t value and the values of 2.58, 1.96, and 1.65 are significant at 1 percent, 5 percent, and 10 percent confidence level respectively230_{. The next step is to test} the significance of direct relationship between disruption vulnerability and resili- ence variable, this time in the presence of mediator. The decision is made accord-

ing to the criteria as stated above for empirical conditions. The relationship be- tween independent variable and dependent variable is estimated first without and then with mediator. The relationship between independent variable and mediator and mediator and dependent variable are also assessed for identifying the type of mediation that is affected by mediating variable. The path coefficients, test statis- tics and significance are reported for the relationship among the constructs. The direct effect between independent variable of manufacturing disruption vul- nerability and dependent variable of manufacturing resilience is noted and tested for significance. The path coefficient between manufacturing disruption vulnera- bility and manufacturing resilience is -0.39 (Figure 36) in the absence of media- tor. With introduction of mediation variable of manufacturing adaptive capabil- ity, the direct effect between manufacturing disruption vulnerability and manu- facturing resilience drops to -0.25 (Figure 36).

The test statistics, estimated through bootstrapping, is 4.72 (Figure 37) that is significant with confidence level of 1 percent231_{. The direct effect is still signifi-} cant with introduction of mediator for which the test value is 2.23 (Figure 37) that is significant with confidence level of 5 percent. The relationship between manufacturing disruption vulnerability and manufacturing resilience is statistical- ly significant both in the presence and absence of manufacturing adaptive capa- bility. This means that manufacturing adaptive capability is having mediation effect and the effect of manufacturing disruption vulnerability passes through manufacturing adaptive capability.

231 _{Hair, Ringle, & Sarstedt, 2011, p. 145}

Figure 37 Significance of Mediation Effect for Manufacturing

The mediation is further analyzed for partial or no mediation. For this purpose the path coefficients between independent variable and mediator and mediator and dependent variable are to be assessed for significance. The relationship be- tween independent variable and mediator i.e. manufacturing disruption vulnera- bility and manufacturing adaptive capability is -0.49 (Figure 36). The path coef- ficient is significant with test statistics of 6.18 (Figure 37) with 1 percent confi- dence level. As a last step, the paths from mediator to dependent variable are ex- amined for significance. The path coefficient between manufacturing adaptive capability and manufacturing resilience is 0.24 (Figure 36). The test statistics, estimated through bootstrapping, is 2.05 (Figure 37) that is significant with con- fidence level of 5 percent.

All the relationships between manufacturing disruption vulnerability, manufac- turing adaptive capability and manufacturing resilience are significant suggesting that H5a has empirical evidence to show partial. The sign for relationship be- tween the manufacturing disruption vulnerability and manufacturing resilience remains the same as negative. This suggests that the mediation is also not incon- sistent. The relationship between manufacturing disruption vulnerability and manufacturing resilience remains significant after the introduction of mediator so this does not suggest full mediation. With all the relationships significant, partial mediation is suggested. The implications are that manufacturing adaptive capa- bility is used judiciously to respond to disruptions and contribute to manufactur- ing resilience. For mediation in transportation process, the hypothesis is stated as:

H5b. Transportation adaptive capability negatively mediates the negative rela- tionship between transportation disruption vulnerability and transportation resili- ence.

For transportation, the direct relationship between disruption vulnerability and resilience is tested first for significance in the absence and then in the presence of mediator to assess mediation as shown in Figure 38.

Figure 38 Mediation Effect for Transportation

The direct effect between transportation disruption vulnerability and transporta- tion resilience is -0.41 (Figure 38). The test statistics, estimated through boot- strapping, is 6.36 (Figure 39) is significant with confidence level of 1 percent232_. With introduction of mediation variable of transportation adaptive capability the path coefficients between transportation disruption vulnerability and transporta- tion resilience drops for transportation process to -0.34 (Figure 38). However the relationships is still significant with critical t values as 3.56 (Figure 39) that is significant with 1 percent confidence level. Partial or no mediation for transpor- tation process is analyzed. For this purpose, the relationship between independent variable and mediator i.e. transportation disruption vulnerability and transporta- tion adaptive capability is estimated as -0.55 (Figure 38). The direct effect is sig- nificant with test statistics 9.03 (Figure 39) with 1 percent confidence level. As a final step, the path from mediator to dependent variable i.e. manufacturing adap-

tive capability to manufacturing resilience are analyzed. The path coefficient be- tween mediator and dependent variable is 0.25 (Figure 38) for transportation pro- cess. The test statistics, estimated through bootstrapping, is 2.89 (Figure 39) that is significant with confidence level of 1 percent.

Figure 39 Significance of Mediation Effect for Transportation

All the relationships between transportation disruption vulnerability, transporta- tion adaptive capability and transportation resilience are significant suggesting that H5b has empirical evidence to show partial mediation. The sign for relation- ship between the transportation disruption vulnerability and transportation resili- ence remains the same as negative. This suggests that the mediation is also not inconsistent. The relationship between transportation disruption vulnerability and transportation resilience remains significant after the introduction of mediator so this does not suggest full mediation. With all the relationships significant, partial mediation is suggested. The implications are that transportation adaptive capabil- ity is used judiciously to respond to disruptions and contribute to transportation resilience.