Path coefficient

The similarity is assumed to be caused by the latent genetic factor shown in the circle (G). The causal paths are labeled with path coefficients. Again the rules of path analysis tell us that the correlation is made up of the sum of the multiplication of the terms of each path that connect the trait. Thus the equation ... 

Requirenents for good activity (ranked according the absolute value of the corresponding path coefficients given in brackets) ... 

The partial r value is still the highest for J/ir and MRjjj, the total lipophilicity alone explains over 60% of the total variance and it is also visible that it has a particular Importance in the para position. Since has a negative coefficient, electron donating substituents are preferred in the meta position. MR has almost the same coefficients for positions II, III and V, the latest, however, has the lowest absolute path coefficient [0.138]. Therefore, compounds 3,4-dlsubstituted with lipophilic substituents are expected to be highly active. 

It Is Interesting that In the Individual substituent positions molar volume (MR) was found to be the relevant parameter rather than ff. Eg or the STERIMOL descriptors. This fact and the positive coefficients for MR suggest that the enzyme-Inhibitor Interaction proceeds via London dispersion forces (31, ) and the binding to the enzyme Is favored If the bulky substituents are In meta or para positions, which Is In accordance with the earlier results (11). The parameter H-DO In position I seems to be Important In the regression. It systematically appears In each equation. Its path coefficient and partial r value, however. Is relatively low compared to those of Jit and MRjjj. In addition, the H-DOj variable Is not very useful because these Indicator parameters are the most poorly defined and the number of proton donor substituents (H-DO-1) Is rather small. 

Fig. 1 Standardized solution for the chemistry self-efficacy model. Note The two path coefficients are significant at the 0.001 level...

Vallerand (1997) recommended that Structural Equation Modeling (SEM), a statistical technique allowing one to analyze all variables simultaneously and to test complex causal models, be used. In this research SEM, via the EQS 6.1 software package, was used to test for the existence of the relationships hypothesized in the diagram above, and then to compute values for the coefficients, called path coefficients, in the linear functions described above. Further, SEM was used to test... 

Between these two models tested, the data better supports the proposed model in Fig. 7.1. The findings for the proposed structural model are summarized in Table 7.17. Eight out of nine hypothesized relationships are strongly supported with the significant, direct positive effects at the 0.01 level. These hypotheses include H2 (lOS appropriation to supply chain collaboration), H3a (collaborative culture to lOS appropriation), Hsb (collaborative culture to supply chain collaboration), H4a (trust to lOS appropriation), H4b (trust to supply chain collaboration), H5 (supply chain collaboration to collaborative advantage), Hg (supply chain collaboration to firm performance) and H7 (collaborative advantage to firm performance). The path coefficients and t-values for these hypotheses are respectively 0.37(3.26), 0.30(2.94), 0.24(3.17), 0.44(4.54), 0.41(4.95), 0.63(8.92), 0.36(5.09), and 0.49(6.41). Hi (IT resources to lOS appropriation) is supported with the significant, direct positive effect (path coefficient = 0.26, t-value = 2.07) at the 0.05 level. 

Collaborative culture also has an indirect positive effect on supply chain collaboration (path coefficient = 0.11 and t-value = 2.26, significant at the 0.05 level), resulting in a total effect of 0.35. This indirect effect is mediated by lOS appropriation. Collaborative culture facilitates the extent of lOS use among the supply chain partners, which further intensifies the level of collaborations among partners. 

It was postulated that trust has a significant positive relationship with supply chain collaboration (H4b). From the results, H4b is supported with the significant, direct positive effect (path coefficient = 0.41, t-value = 4.95) at the 0.01 level. The indirect effect of trust on supply chain collaboration (path coefficient = 0.16, t-value = 2.74) is also significant at the 0.01 level. This indirect effect is through lOS appropriation, which further amplifies the level of collaboration among supply chain partners. It confirms that trust has significant positive effect on supply chain collaboration both directly and indirectly. 

Moderation Effect of Firm Size Table 8.2 Path coefficients and t-values by firm size... 

Path coefficient t- value Path coefficient t- value Path coefficient t- value ... 

Fig. 8.1 Plot of Standardized Path Coefficients of SCC->CA, CA->FP, SCC->FP Across Firm...

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