4. Analysis and Results
To provide evidence on the efficacy of the proposed model presented in Figure 1, five structured equation
models were tested using AMOS 18. The first four equations tested each of the proposed hypotheses
separately. The fifth equation tested the proposed research model (Figure 1) with the four CRM dimensions
as independent variables and performance as the dependent variable. The influence of industry sector on
performance was controlled for in the five equations. The independent variables were entered as observed
variables, while the dependent variable was entered as a latent variable reflecting its three observed
indicators: customer satisfaction, customer retention, and sales growth. Table 3 provides evidence on the
models goodness of fit and Table 4 presents the standardized regression weight for the relationship paths.
The first equation tests for the impact of CRM technology on performance. CRM technology showed a
significant positive impact on performance with a path coefficient 0.253 significant at the 0.01 level. Service
industry sector also showed a positive influence on performance with a path coefficient of 0.32 significant at
the 0.001 level. Both variables explained 21% of the variance in performance. The path model linking CRM
to technology exhibited good fit levels as shown in Table 3.
The second path model illustrates that CRM processes have a positive influence on performance with a path
coefficient of 0.38 significant at the 0.00l level. Service industry also showed a positive impact on
performance with a coefficient of 0.30 significant at the 0.001 level. Both variables explained 28% variance
in performance. Table 3 shows that the model fits the data well.
The third equation, linking CRM orientation to performance, indicates a significant positive influence of
customer orientation on performance with a path coefficient of 0.39 significant at the 0.001 level. The
results also confirm the positive influence of service industry sector on performance with a path coefficient
of 0.25 significant at the 0.01 level. Both variables explain 28% of performance. The model shows
reasonable fit with the data.
The forth equation measures the impact of CRM organization on performance. It shows that CRM
organization positively influences performance with a path coefficient of 0.57 significant at the 0.001 level.
However, the effect of the service industry on performance appeared insignificant. CRM organizing alone
explains 41% of the variance in performance. As seen in Table 3, the fit indices are good.