Regression analysis
回归分析
Identifying the key measurements of customer service is only fulfilling the first research goal. To find the relationship between identified key measurements with the overall customer satisfaction, the impact of the measurements on overall customer satisfaction should be evaluated. The 4 extracted factors were entered as independent variables and the overall customer satisfaction was entered as dependent variable. The regression models for overall customer satisfaction were statistically significant at p ＜ 0.001 (see Tables 8). This regression equation explained 85.9 percent of the variance of the dependent variable.
确定客户服务的关键测量只完成第一个研究目标。要找到确定的关键测量的整体客户满意度之间的关系，整体客户满意度的测量的影响进行评估。提取的因素作为自变量，输入客户的整体满意度为因变量输入。整体客户满意度的回归模型有统计学意义（P <0.001）（见表8）。这个回归方程解释因变量的方差的85.9％。
Table 8: Model fit for key measurements and the overall customer satisfaction
The beta coefficients obtained from the regression analysis imply the degree of impact of key measurements against the overall satisfaction. Based on beta coefficients summarized in Table 9, beta value of two measurements, “Sales & Promotion” and “Order & Delivery Efficiency” are higher than that of others. Beta value of 0.645 implies that if “Sales & Promotion” increases by one unit, the overall customer satisfaction will also increased by 0.645 or 64.5% on its own measurement unit. Thus “Sales & Promotion” and “Order & Delivery Efficiency” were significantly and positively associated with overall customer satisfaction. The other two measurements, such as “Post-Sales Support” and “Product” were statistically insignificant in this regression equation. However, it does not imply that “Post-sales support” and “Product” are not important. They just have little impact on customer satisfaction.
从回归分析得到的β系数意味着对整体满意度的关键测量的影响程度。基于β系数表9总结了两个测量，“销售及推广”和“网上订货及送货效率比别人高，β值。
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Table 9: Regression analysis results between key measurements and overall customer satisfaction
表9：关键测量和整体客户满意度之间的回归分析结果
Figure 8 displays the impact of 4 key measurements on the overall customer satisfaction.
图8显示了4个关键测量影响的整体客户满意度。
Figure 8：Retailer customer service measurements impact (formed from data collected)
图8：零售商客户服务测量的影响（形成了从收集的数据）
Measurement-level Impact-Satisfaction analysis
测量水平影响满意度分析
To classify key measurements into Impact-Satisfaction model, level of satisfaction to measurements is needed. Simply using mean of items satisfaction as key measurements satisfaction loses sight of different weight of items impact on the loaded key measurements. Johnson and Gustafsson (2000) suggest calculating key measurements satisfaction by using a weighted average of all items included in a key measurement. Component score coefficient matrix got from previous Principal Component Analysis provided the items weights constructing the principle component (see Table 10).
影响满意度模型，水平的满意度测量的主要测量到分类是必要的。简单地使用项目满意度的意思，关键测量满意忽视加载的关键测量项目影响不同重量。约翰逊和古斯塔夫森（2000）建议使用一键测量中包含所有项目的加权平均计算的关键测量满意。成分得分系数矩阵得到了从以前的主成分分析的项目权重构建组成原理（见表10）。
Table 10: Summary of component score coefficient matrix
Based on Johnson and Gustafsson (2000)’s method, items weights, standard deviation, and mean of items satisfaction were used to calculate key measurements satisfaction. Calculate results are presented in Table 11.
基于约翰逊和古斯塔夫森（2000）的方法，项目的权重，标准偏差和平均满意度来计算关键测量满意的项目。计算结果列于表11。
Table 11: Key measurements satisfaction
To have a good understanding of key measurement impacts and level of satisfaction, an Impact-Satisfaction chart was formed by plotting 4 key measurements against its satisfaction score showed in Table 11 and impact score got from previous regression analysis (see Figure 9).
有一个良好的关键测量影响的理解和满意程度，影响满意度的图表绘制4个关键测量针对其满意度得分由显示在表11和碰撞成绩得到了从先前回归分析（见图9）。
Figure 9: Measurement-level Impact-Satisfaction Chart (formed from data collected)
According to the Impact-Satisfaction chart, “Sales & Promotion” has an impact of 0.645 and a satisfaction level of 3.599; “Order & Delivery Efficiency” has an impact of 0.336 and a satisfaction level of 3.111; “Post-sales Support” has an impact of 0.049 and a satisfaction level of 3.502; “Product” has an impact of 0.002 and a satisfaction level of 4.202. Evaluating these values separately is not particularly meaningful as there are no standard criteria for impact scores. High impact and low impact are depended on comparison of 4 key measurements. Impact-Satisfaction chart helps to classify key measurements into high impact category and low impact category. Associating the level of satisfaction, the 4 key measurements were put in Impact-Satisfaction model present in Figure 11.
根据影响满意图表，“销售及推广”有一定的影响，0.645和3.599满意度“网上订货及送货效率”有一定的影响为0.336，3.111和满意度，“售后技术支持”影响0.049和3.502;“产品”，满意度有一定的影响为0.002和4.202，满意度。单独评估这些值是不是特别有意义的影响分数，因为没有标准。高冲击和低冲击取决于4个关键测量比较。影响满意度的图表有助于分类的关键测量到高冲击类和低冲击类。关联的满意程度，在影响满意度模型目前在图11的4个关键测量。
Figure 10: Measurement-level Impact-Satisfaction Model (formed from data collected)
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