Evaluation of Outliers. Outliers is the condition of observation of the data that Estimated Parameter Values After analysis confirmatory and modeling process, then

The 2015 International Conference of Management Sciences ICoMS 2015, April 23, UMY, Indonesia | 42 Measurement model was tested by using AMOS. All items construct the measurement model shows the loading factor above 0.6. All items are used to measure this construct can be known significant of convergen validity: t-value of 0.4. The model in this study is good enough can be indicated from RMSEA = 0.049; NFI = 0.99; and 55 in the path of this research model is significant. No modification indices high value, so that it can indicate that the overall model of the hypothesis. Table 2 presents the complete standardized coefficient estimates and t-values for each lane contained in this hypothesis. These items are not significantly removed in the model, then reprocessed only for the items eligible for analysis. Probability = 0,00; Standard RMSEA = 0.048; NNFI = 0.99. The ability of listening skills can influence the performance of the sales force by 81; adaptive selling 23, = 40 customer orientation, learning orientation = 30. Based on the results of this analysis can be explained that the general structure of the model accurately hypothesis can explain the relationship between variables. 5.3 Validity and Reliability Validity is the ability of a measuring instrument questionnaire to measure what should be measured. Reliability is the consistency of measuring instruments used. Said to be reliable because the tool produces almost the same results even if used in different times or by different respondents. Test confirmatory factor analysis done to eliminate grains invalid. Ferdinand, 2013; Faith Widodo, 2011; Singgih Santoso, 2011. 5.4 Assessment of normality Multivariate test data as a condition of assumptions that must be met by maximum likelihood. Here pieces AMOS output. Table 1 Normality evaluation is done by using the criteria skewnes critical ratio value equal to ± 2.58. Based on the critical value of skewness skewness for all variables are critical ratio between the limits specified, at a significance level of 1, and DAPT is said that the data is the data in this study are multivariate normal distribution.

5.5 Evaluation of Outliers. Outliers is the condition of observation of the data that

has unique characteristics that looks very different from other observations and appeared in the form of extreme value, either for a single variable or to a combination or variables. Mulitivariate outlier detection can be done by considering the value of Mahalanobis Distance Ferdinand, 2013; Faith Widodo, 2011; Singgih Santoso, 2011. Criteria are based on the Chi-Square at 10 degrees of freedom, namely the number of indicator variables at a significance level α = 5, the value of Mahalanobis Distance χ² = 23.98. All the cases had Mahalanobis distance is more than 23.98 is an outlier. As seen in Figure 2 below: Picure 2 5.6 Multicolinearity Evaluation Multicollinearity can be seen through determinan covariance matrix. Determinant value is very small, indicates the presence of multicollinearity or singularity problem, so that the data can not be used for research. Table 3 below presents the AMOS output: Table-3 Source: print out the output amos The 2015 International Conference of Management Sciences ICoMS 2015, April 23, UMY, Indonesia | 43 Amos output results it appears that the value of the determinant of the sample covariance matrix = 0.000. It can be said that there is no multicollinearity and singularity problems in the data analyzed.

5.7 Estimated Parameter Values After analysis confirmatory and modeling process, then

performed a full estimation of structural models. Figure 1 below presents the full structural models. Figure-1 Full Structural Model Pictured above can be explained that the model has good goodness of fit with the indication Chi-Square 37.943 with probability 0.061 significant. So this model can be said to be in accordance with the data of empirical. Another criterion CFI = 0.983; AGFI = 0.925; GFI = 0.964; RMSEA = 0.048; TLI = 0.970. So it can be said that the model meets the criteria of goodness of fit. Testing the hypothesis proposed can be seen from the results of the standardized regression coefficients. Pieces of the output estimation results can be seen in Table 1, and Table 2 below: Table 1 Sales adaptive did not affect the quality of the sales relationship with the standardized coefficient of 0.679; probability value of 0.800 Hypothesis 1. Oriented sales force to customers significant positive effect on the quality of the customer relationship with the standardized coefficient of -0.029; probability value of 0.000 Hipotesis3. Salespeople are oriented to learning significant positive effect on the quality of the customer relationship with the standardized coefficient of 0.331, 0.000 probability value Hypothesis 4. Salespeople who can easily adapt to the environment in which he was in charge, does not affect the performance of the sales force with the standardized coefficient of -0.036; probability value of 0.812 Hipotesis3. Oriented sales force to customers significant positive effect on the performance of the sales force, with a standardized coefficient of 0.287; probability value of 0.014 Hypothesis 5. Salespeople are oriented to learning significant positive effect on the performance of the sales force, with a standardized coefficient of 1.025; probability value of 0.000 Hypothesis 6.

5.8 Discussion