Measurement model

Table 4-5 Results of regression analysis (Technological diversification on

dependent variable: ROA, Tobin‟s q, MVA, and EVA)

Independent

Table 4-5 displays the results of regression analyses regarding the effects of technological diversification on firm performance that using four type of indicator:

ROA, Tobin‟s q, MVA, and EVA. Each of Performance indicators has four model:

Respectively, Model 1 is the base model that includes two control variables: Firm size and R&D intensity. Models 2 try to capture the direct effect of technological diversification on the dependent variable.

Table 4-5 displays the results of the effects of technological diversification and on four types of performance indicator. For ROA, E(1) indicates that the

combination of control variables does not have significant impact on the dependent

variable (F =0.32, =0.00). It show that the control variable don‟t have directly

effect on ROA. E(2) does not have significant and can explain an additional 0.2% of variance over what the control variables alone explain. The coefficient of

technological diversification is negative and not significant. For Tobin‟s q term, E(5) shows that the combination of control variables has significant impact on the

dependent variable (F =4.31, P<0.05, =0.03). It show that the control variable have directly effect on Tobin‟s q. E(6) and have significant and can explain an

additional 1% (F =3.83, P<0.01) of variance over what the control variables alone explain. The coefficient of technological diversification is negative (0.1, P<0.1) and significant. The finding indicate that technological diversification has an negative relationship with firm's performance, the results in Tobin‟s q terms support Hypothesis

1. For MVA terms, E(9) indicates that the combination of control variables have significant impact on the dependent variable (F =323.01, P<0.01 =0.70). It show

that the control variable have directly effect on MVA. E(10) have significant and can explain an additional 1.5% (F =230.81, P<0.01) of variance over what the control variables alone explain. The coefficient of technological diversification is negative

(-0.12, P<0.01) and significant. The finding indicate that technological diversification has an negative relationship with firm performance, the results was support

Hypothesis 1. For EVA term, E(13) indicates that the combination of control variables does not have significant impact on the dependent variable (F =0.09, =0.001). It show that the control variable don‟t have directly effect on EVA. E(14) does not have significant and can explain equal to what the control variables alone explain. The coefficient of technological diversification is negative and not significant.

Followed Table 4-5, the second part, this study examines the contingent role of organizational slack between technological diversification and firm performance.

Model 4 adds the two dimensions of organizational slack: absorbed slack and unabsorbed slack and their two interaction terms with the technological diversification dimension. All the results was shown from table 4-6 to 4-9.

Table 4-6: Results of regression analysis (Dependent variable: ROA) Independent variable Dependent variable: ROA

In the term with the dependent variable of ROA, table 4-6 displays the results of the effects of technological diversification and organizational slack on ROA.

The E(4) is not significant (F =0.64, =0.02) and explains an additional 1.6 percent of variance over what the control variables alone explain. The coefficient of the

interaction term between technological diversification and absorbed slack in Model 4 is negatively signed and not significant. The absorbed interaction term was inconsistent with the predict positive signed. The coefficient of the interaction term

between technological diversification and unabsorbed slack is negatively signed and not significant.

Table 4-7: Results of regression analysis (Dependent variable: Tobin’s q)

Independent variable Dependent variable: Tobin‟s q Predict

In the term with the dependent variable of Tobin‟s q, table 4-7 displays the results of the effects of technological diversification and organizational slack on Tobin‟s q. The E(8) is significant (F =2.24, P<0.05, =0.06) and explains an

additional 3.2 percent of variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed

slack in E(8) is positively signed and not significant. The coefficient of the interaction

term between technological diversification and unabsorbed slack is negatively signed and not significant. Although E(8) with two dimensions neither significant, but the

sign are consistent with the original predict as Hypothesis 2 and Hypothesis3.

Table 4-8: Results of regression analysis (Dependent variable: MVA)

Independent Variable Dependent variable: MVA

In the term with the dependent variable of MVA, table 4-8 displays the results of the effects of technological diversification and organizational slack on MVA.

The E(12) is significant (F =279.52, =0.89) and explains an additional 19.3 percent of variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed slack in E(12) is positively signed (0.12, P<0.01) and significant. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed (-0.21, P<0.01) and significant. These findings support Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance.

Table 4-9: Results of regression analysis (Dependent variable: EVA) Independent Variable Dependent variable: EVA

In the term with the dependent variable of EVA, table 4-9 displays the results of the effects of technological diversification and organizational slack on EVA. The

E(16) is significant (F =5.47, =0.14) and explains an additional 13.9 percent of variance over what the control variables alone explain. The coefficient of the

interaction term between technological diversification and absorbed slack in E(16)is positively signed (0.59, P<0.001) and significant. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed (-0.46, P<0.001) and significant. These findings support Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance.

Table 4-10: Result of hypothesis testing:

Dependent variable

Hypothesis Coefficient t-value Conclusion

ROA H1 -0.00 -0.04 Insignificant

All the results was organized into a simple form as table 4-10. Overall, four performance indicators including ROA, Tobin's q, MVA, and EVA. In addition to traditional accounting indicators ROA, the technological diversification and organization slack dimension to the new economy has a certain degree of reflection.

The first step in testing Hypothesis 1: Technology Diversification has a negative impact on performance. Tobin's q and the MVA is negative and significant. Though other two performance indicator ROA and EVA was not significant, but also negative signed to the same direction, generally in support of hypothesis 1.

The second step was testing the moderating role of organization slack between technological diversification and firm performance. This study using Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance. Except to ROA and Tobin's q is not significant, the absorbed slack and unabsorbed slack of MVA and EVA were positive and negative as significant.

Although the ROA and Tobin's q was not significant, but except to the unabsorbed ROA interaction is inconsistent with the predict negative signed, the other signed was consistent with previous predict. The result was general support of the Hypothesis 2 and 3.

Table 4-11 Results of regression analysis (Technological diversification squared term on dependent variable: ROA, Tobin‟s q, MVA, and EVA)

Independent

The additional step in testing technological diversification and firm performance linear relationship, does there exist nonlinear relationship by including the linear term of technological diversification in Model 2 and adding the squared term in Model 3 to alternate the maximum of technological diversification. The result shows in table 4-11. Although the four performance indicators in the technological diversification squared term was not significant, but in addition to MVA was positive signed. ROA, Tobin's q , and EVA are respectively positive, with a U-shaped trend, may also be used as a research reference.