Table 1
Aligning CAS and DC Theories with Military SCRES.
| CONCEPT | DESCRIPTION | SCRES IN MILITARY OPERATIONS | PRACTICAL EXAMPLE |
|---|---|---|---|
| CAS Theory | Complex systems where multiple actors interact, adapting to their dynamic environment. | Managing the dynamic and unpredictable nature of military supply chains in operations. | Sustainment of operations with a variety of mobility resources and organizational units while facing disruptions. |
| DC Theory | The organization’s ability to reconfigure and adapt its operations and strategies to meet changing demands and conditions. | How C2 plans, responds to disruptions, recovers, and adjusts strategies/tactics dynamically to overcome disruptions. | Planning for and executing rapid reallocation of resources and supply lines according to changes in mission requirements. |
Figure 1
Research Model and Instrument Development.
Table 2
Antecedents of supply chain resilience, including vulnerabilities.
| DIMENSION/CATEGORY | HAN ET AL. (2020) – SCRES CAPABILITIES | PETTIT ET AL. (2010) – CAPABILITY & VULNERABILITY FACTORS |
|---|---|---|
| Readiness | Situation awareness, visibility, redundancy, security | Anticipation, adaptability, flexibility in sourcing, visibility, security |
| Response | Agility, flexibility, collaboration, leadership | Collaboration, capacity, efficiency, dispersion, organization |
| Recovery | Contingency planning, market position, knowledge management | Recovery, financial strength, market position |
| Vulnerabilities | – | Turbulence, deliberate threats, external pressures, resource limits, sensitivity, connectivity, supplier/customer disruptions |
Figure 2
Research Model.
Table 3
R-squared Values.
| R-SQUARE | |
|---|---|
| CAP (capacity) | 0.280 |
| REC (recovery) | 0.468 |
| RESP (responsiveness) | 0.744 |
| SCRES (dependent variable supply chain resilience) | 0.727 |
Table 4
Descriptive Statistics.
| ITEM | STD | MEAN | LOADING | CRONBACH’S ALPHA | COMPOSITE RELIABILITY (RHO_A) | AVERAGE VARIANCE EXTRACTED (AVE) |
|---|---|---|---|---|---|---|
| COOP1 | 1.306 | 4.500 | 0.841 | |||
| COOP2 | 1.260 | 4.689 | 0.828 | |||
| COOP3 | 1.448 | 3.986 | 0.759 | |||
| COOP4 | 1.398 | 3.865 | 0.805 | |||
| COOP5 | 1.438 | 4.649 | 0.801 | 0.867 | 0.879 | 0.652 |
| SCRES1 | 1.685 | 4.811 | 0.912 | |||
| SCRES2 | 1.531 | 5.284 | 0.892 | |||
| SCRES3 | 1.244 | 4.716 | 0.859 | |||
| SCRES4 | 1.240 | 4.432 | 0.835 | |||
| SCRES5 | 1.526 | 4.000 | 0.883 | |||
| SCRES6 | 1.488 | 4.243 | 0.787 | 0.931 | 0.935 | 0.744 |
| DISR1 | 1.004 | 6.081 | 0.762 | |||
| DISR2 | 0.986 | 5.986 | 0.782 | |||
| DISR3 | 1.073 | 5.162 | 0.741 | |||
| DISR4 | 1.191 | 4.919 | 0.722 | 0.748 | 0.761 | 0.565 |
| PLAN1 | 1.471 | 3.973 | 0.703 | |||
| PLAN2 | 1.443 | 3.838 | 0.716 | |||
| PLAN3 | 1.274 | 4.527 | 0.722 | |||
| PLAN4 | 1.338 | 4.176 | 0.897 | |||
| PLAN5 | 1.341 | 4.189 | 0.840 | 0.837 | 0.852 | 0.608 |
| REC1 | 1.361 | 5.108 | 0.814 | |||
| REC2 | 1.498 | 4.595 | 0.888 | |||
| REC3 | 1.618 | 4.770 | 0.844 | |||
| REC4 | 1.621 | 4.878 | 0.783 | |||
| REC5 | 1.622 | 4.432 | 0.898 | |||
| REC6 | 1.338 | 4.865 | 0.850 | |||
| REC7 | 1.631 | 4.446 | 0.849 | 0.934 | 0.940 | 0.718 |
| CAP1 | 1.668 | 3.284 | 0.900 | |||
| CAP2 | 1.747 | 3.959 | 0.847 | |||
| CAP3 | 1.576 | 3.149 | 0.903 | |||
| CAP4 | 1.427 | 3.865 | 0.719 | |||
| CAP5 | 1.465 | 3.068 | 0.914 | |||
| CAP6 | 1.510 | 4.095 | 0.830 | 0.925 | 0.935 | 0.731 |
| RESP1 | 1.665 | 3.905 | 0.801 | |||
| RESP2 | 1.671 | 3.405 | 0.861 | |||
| RESP3 | 1.577 | 3.243 | 0.868 | |||
| RESP4 | 1.624 | 3.514 | 0.878 | |||
| RESP5 | 1.474 | 4.338 | 0.870 | |||
| RESP6 | 1.779 | 4.986 | 0.783 | 0.919 | 0.921 | 0.713 |
| VIS1 | 1.941 | 4.284 | 0.864 | |||
| VIS2 | 2.024 | 4.230 | 0.933 | |||
| VIS3 | 2.016 | 3.824 | 0.940 | |||
| VIS4 | 1.507 | 3.689 | 0.822 | |||
| VIS5 | 1.651 | 4.041 | 0.842 | |||
| VIS6 | 1.310 | 3.811 | 0.821 | |||
| VIS7 | 1.781 | 4.081 | 0.904 | 0.949 | 0.958 | 0.768 |
Table 5
Heterotrait-Monotrait Ratio (HTMT).
| HETEROTRAIT-MONOTRAIT RATIO (HTMT) | |
|---|---|
| COOP <-> CAP | 0.589 |
| DISR <-> CAP | 0.396 |
| DISR <-> COOP | 0.238 |
| PLAN <-> CAP | 0.581 |
| PLAN <-> COOP | 0.469 |
| PLAN <-> DISR | 0.351 |
| REC <-> CAP | 0.656 |
| REC <-> COOP | 0.577 |
| REC <-> DISR | 0.270 |
| REC <-> PLAN | 0.748 |
| RESP <-> CAP | 0.898 |
| RESP <-> COOP | 0.654 |
| RESP <-> DISR | 0.369 |
| RESP <-> PLAN | 0.579 |
| RESP <-> REC | 0.725 |
| SCRES <-> CAP | 0.854 |
| SCRES <-> COOP | 0.663 |
| SCRES <-> DISR | 0.401 |
| SCRES <-> PLAN | 0.701 |
| SCRES <-> REC | 0.783 |
| SCRES <-> RESP | 0.867 |
| VIS <-> CAP | 0.748 |
| VIS <-> COOP | 0.673 |
| VIS <-> DISR | 0.338 |
| VIS<-> PLAN | 0.581 |
| VIS <-> REC | 0.768 |
| VIS <-> RESP | 0.760 |
| VIS <-> SCRES | 0.791 |
Table 6
Fornell-Larcker Criterion.
| CAP | COOP | DISR | PLAN | REC | RESP | SCRES | VIS | |
|---|---|---|---|---|---|---|---|---|
| CAP | 0.855 | |||||||
| COOP | 0.552 | 0.807 | ||||||
| DISR | –0.338 | –0.169 | 0.752 | |||||
| PLAN | 0.529 | 0.404 | –0.271 | 0.780 | ||||
| REC | 0.618 | 0.534 | –0.225 | 0.668 | 0.848 | |||
| RESP | 0.835 | 0.604 | –0.332 | 0.528 | 0.684 | 0.844 | ||
| SCRES | 0.795 | 0.611 | –0.347 | 0.635 | 0.740 | 0.809 | 0.862 | |
| VIS | 0.713 | 0.609 | –0.301 | 0.531 | 0.728 | 0.722 | 0.750 | 0.876 |
[i] Note. Bold values are square root of AVE, which should be greater than correlation values.
Table 7
VIF Values.
| RELATIONSHIP | VIF |
|---|---|
| CAP -> RESP | 2.140 |
| COOP -> RESP | 1.761 |
| DISR -> SCRES | 1.124 |
| PLAN -> CAP | 1.000 |
| REC -> SCRES | 1.879 |
| RESP -> REC | 1.000 |
| RESP -> SCRES | 2.005 |
| VIS -> RESP | 2.427 |
| VIS × CAP -> RESP | 1.072 |
Table 8
Q2 Predict, PLS-SEM vs. LM.
| Q2 PREDICT | PLS RMSE | LM RMSE | PLS MAE | LM MAE | |
|---|---|---|---|---|---|
| CAP | 0.169 | 1.441 | 1.641 | 1.181 | 1.269 |
| REC | 0.318 | 1.271 | 1.505 | 1.021 | 1.175 |
| RESP | 0.321 | 1.358 | 1.593 | 1.101 | 1.224 |
| SCRES | 0.376 | 1.162 | 1.449 | 0.962 | 1.135 |
Table 9
Significance and NCA.
| ORIGINAL SAMPLE (O) | T STATISTICS (|0/STDEV|) | p VALUES | ORIGINAL EFFECT SIZE | PERMUTATION P VALUE | INTERPRETATION | ||
|---|---|---|---|---|---|---|---|
| CAP -> SCRES | 0.478 | 5.701 | 0.000 | LV scores – CAP | 0.225 | 0.000 | significant and necessary |
| COOP -> SCRES | 0.103 | 1.763 | 0.078 | LV scores – COOP | 0.348 | 0.000 | nonsignificant but necessary |
| DISR -> SCRES | –0.089 | 1.430 | 0.153 | LV scores – DISR | 0.404 | 0.605 | nonsignificant and not necessary |
| PLAN -> SCRES | 0.253 | 4.169 | 0.000 | LV scores – PLAN | 0.268 | 0.000 | significant and necessary |
| REC -> SCRES | 0.352 | 3.245 | 0.001 | LV scores – REC | 0.365 | 0.000 | significant and necessary |
| RESP -> SCRES | 0.779 | 17.624 | 0.000 | LV scores – RESP | 0.286 | 0.000 | significant and necessary |
| VIS -> SCRES | 0.159 | 1.777 | 0.076 | LV scores – VIS | 0.191 | 0.000 | nonsignificant but necessary |
Table 10
NCA Bottleneck.
| LV SCORES – SCRES | LV SCORES – CAP | LV SCORES – COOP | LV SCORES – PLAN | LV SCORES – REC | LV SCORES – RESP | LV SCORES – VIS | |
|---|---|---|---|---|---|---|---|
| 0.000% | 1.000 | 1.160 | 1.884 | NN | 1.298 | 1.324 | 1.150 |
| 10.000% | 1.600 | 1.160 | 1.884 | NN | 1.298 | 1.324 | 1.150 |
| 20.000% | 2.200 | 1.160 | 1.884 | 1.570 | 1.298 | 1.324 | 1.150 |
| 30.000% | 2.800 | 1.160 | 1.884 | 1.570 | 1.748 | 1.324 | 1.150 |
| 40.000% | 3.400 | 1.160 | 2.156 | 1.570 | 1.748 | 1.725 | 1.150 |
| 50.000% | 4.000 | 1.160 | 2.156 | 1.570 | 1.748 | 1.725 | 1.515 |
| 60.000% | 4.600 | 1.160 | 2.985 | 2.674 | 3.191 | 2.592 | 1.828 |
| 70.000% | 5.200 | 3.481 | 3.880 | 3.989 | 4.759 | 3.870 | 1.828 |
| 80.000% | 5.800 | 3.481 | 4.075 | 4.536 | 4.759 | 3.870 | 1.828 |
| 90.000% | 6.400 | 5.294 | 5.304 | 4.536 | 6.391 | 5.506 | 5.657 |
| 100.000% | 7.000 | 6.125 | 6.649 | 5.029 | 6.768 | 5.943 | 6.335 |
[i] Note. Latent variable scores needed to achieve corresponding SCRES score. NN means “not necessary” condition.
Figure 3
Path Coefficients and (t-values) of the Structural Model, Including R2 of Variables.
Figure 4
Importance-Performance Map.
Note. Table with variable effect on SCRES inserted.