TABLE 5.
Polynomial regression with response surface analysis (the multiplicative approach).
| Variables | Coefficients | SE |
| Constant (b0) | 16.658∗∗∗ | 4.224 |
| Age | –0.375 | 0.752 |
| Education | –0.525 | 0.739 |
| Employee | −0.650† | 0.357 |
| Asset | 1.883 | 1.334 |
| Loss orientation coping (LOC) (b1) | 0.355 | 0.610 |
| Restoration orientation coping (ROC) (b2) | 1.391∗ | 0.691 |
| LOC squared (b3) | 1.429∗∗ | 0.455 |
| LOC × ROC (b4) | –0.382 | 0.611 |
| ROC squared (b5) | 0.626 | 0.645 |
| a1 | 1.746∗ | 0.744 |
| a2 | 1.673∗ | 0.761 |
| a3 | –1.036 | 1.070 |
| a4 | 2.437† | 1.280 |
Dependent variable: innovation ambidexterity; significance level: †p < 0.10; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001 (two-tail tests, sample size = 106); a1 = b1 + b2, a2 = b3 + b4 + b5, a3 = b1 – b2, and a4 = b3 – b4 + b5, where b1 is the coefficient for LOC, b2 is the coefficient for ROC, b3 is the coefficient for LOC squared, b4 is the coefficient for LOC × ROC, b5 is the coefficient for LOC squared.