Regression analysis
To determine the effect of E- service quality, ease of use, usability and enjoyment on 
CRM performance?” regression analysis was undertaken on the antecedent factors and 
CRM performance. The major assumptions take in our consideration are sample size, 
Multicollinearity and singularity, Outliers, Normality, linearity, homoscedasticity. All these 
assumptions have been tested to make this data suitable for regression analysis.
Table 3 below provides evidence on the influence of the antecedent factors on CRM
performance. With the F-statistic of 66.327 and Sig 0.000(a) provides evidence that the 
relationship between the independents and dependent variables is significant (R2 =.555; 
Sig =.000(a)). The R2 obtained indicates that the antecedent factors account for 55.5 
percent of the variation in CRM performance. Of all the variables included in the 
regression equation, three variables emerged as significant predictors of CRM
performance. These are E- service quality and ease of use
 
Regression analysis
To determine the effect of E- service quality, ease of use, usability and enjoyment on 
CRM performance?” regression analysis was undertaken on the antecedent factors and 
CRM performance. The major assumptions take in our consideration are sample size, 
Multicollinearity and singularity, Outliers, Normality, linearity, homoscedasticity. All these 
assumptions have been tested to make this data suitable for regression analysis.
Table 3 below provides evidence on the influence of the antecedent factors on CRM
performance. With the F-statistic of 66.327 and Sig 0.000(a) provides evidence that the 
relationship between the independents and dependent variables is significant (R2 =.555; 
Sig =.000(a)). The R2 obtained indicates that the antecedent factors account for 55.5 
percent of the variation in CRM performance. Of all the variables included in the 
regression equation, three variables emerged as significant predictors of CRM
performance. These are E- service quality and ease of use
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