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112 CHAPTER 4. OPPORTUNISM MITIGATION EFFECTIVENESS<br />
measure, and the original independent variables (information asymmetry, goal ambiguity,<br />
measurability of outputs, decentralization of decision rights, and the control variables).<br />
The second regression is also significant (ANOVA F-statistic is 4.647). The model explains<br />
13.8% of the variance in RFMO (Adjusted R 2 = 10.5%).<br />
Finally, I conduct the Hausman test for endogeneity to assess the existence of an endogeneity<br />
problem. I follow Larcker & Rusticus’ (2010) explanation, which argues that if the<br />
coefficient for the predicted RPE-Use is significant, the Hausman test rejects the null of<br />
no endogeneity problem. In my second regression, as shown in table 4.15, the predicted<br />
RPE-Use is insignificant (p = 0.244). This finding implies that the model does not suffer<br />
from endogeneity problems 14 . This finding reinforces the results of the previous analyses.<br />
Table 4.15: Results of Second-Stage Regression Analysis (RPE-Use)<br />
Coefficient Std. Error t-Stat. Prob.<br />
Constant 2.651 0.895 2.963 0.00<br />
Unstandardized Predicted<br />
-0.216 0.185 -1.168 0.24<br />
Value<br />
RPE use 0.134 0.064 2.089 0.04<br />
Information Asymm. 0.107 0.073 1.474 0.14<br />
Goal Ambiguity 0.417 0.130 3.202 0.00<br />
Measurability of Outputs -0.318 0.118 -2.702 0.01<br />
Decentralization 0.076 0.057 1.318 0.19<br />
BU Size -0.008 0.031 -0.248 0.80<br />
Firm Size -0.007 0.029 -0.244 0.81<br />
Dummy production 0.232 0.165 1.406 0.16<br />
Dummy financial services -0.086 0.168 -0.513 0.61<br />
Dummy not-for-profit -0.169 0.174 -0.966 0.34<br />
R 2 = 0.115 F-statistic = 2.844<br />
Adjusted R 2 = 0.074 Prob(F-statistic) = 0.001<br />
Included observations: 276<br />
4.4.3 Summary of the Findings<br />
Similar to the overall findings in chapter 2, the analyses in the current chapter do not support<br />
the claim that RPE mitigates opportunism. Based on Murphy (2001), this chapter<br />
14 The absence of the endogeneity problem does not mean that RPE use and noise in the performance<br />
evaluation are not determined simultaneously. The Hausman test results do not imply that the dependent<br />
and independent variables are not interrelated; the test only suggests that the model estimation is unbiased<br />
(Abernethy et al. 2004).